ytzi/the-stack-dedup-python-filtered-docstrings

a23c631e3dd2c756a6d94518dd902a850aaee56b

5,005

py

Python

data/dataset_implementations/rnn/single_sequence_dataset.py

pdeubel/world-models-testing

36f2baf79898452e677fe141f11ba434f92e9218

[ "MIT" ]

null

data/dataset_implementations/rnn/single_sequence_dataset.py

pdeubel/world-models-testing

36f2baf79898452e677fe141f11ba434f92e9218

[ "MIT" ]

null

data/dataset_implementations/rnn/single_sequence_dataset.py

pdeubel/world-models-testing

36f2baf79898452e677fe141f11ba434f92e9218

[ "MIT" ]

null

import os import h5py import numpy as np import torch from torch.utils.data import Dataset

46.342593

119

0.710689

import os import h5py import numpy as np import torch from torch.utils.data import Dataset class GUISingleSequenceDataset(Dataset): def __init__(self, root_dir: str, sequence_length: int, vae_preprocessed_data_path: str, hdf5_data_group_path: str, actions_transformation_function=None, rewards_transformation_function=None): self.root_dir = root_dir self.sequence_length = sequence_length self.vae_preprocessed_data_path = vae_preprocessed_data_path self.hdf5_data_group_path = hdf5_data_group_path self.actions_transformation_function = actions_transformation_function self.rewards_transformation_function = rewards_transformation_function with np.load(os.path.join(self.root_dir, "data.npz")) as data: self.rewards: torch.Tensor = torch.from_numpy(data["rewards"]).unsqueeze(-1) self.actions: torch.Tensor = torch.from_numpy(data["actions"]) self.vae_preprocessed_data = h5py.File(vae_preprocessed_data_path, "r") self.mus = self.vae_preprocessed_data[f"{self.hdf5_data_group_path}/mus"] self.log_vars = self.vae_preprocessed_data[f"{self.hdf5_data_group_path}/log_vars"] self.dataset_length = self.rewards.size(0) - self.sequence_length assert self.rewards.size(0) == self.actions.size(0) == (self.mus.shape[0] - 1) == (self.log_vars.shape[0] - 1) assert self.__len__() > 0, ("Dataset length is 0 or negative, probably too large sequence length or too few " "data samples") def __len__(self): return self.dataset_length def __getitem__(self, index): sub_sequence_mus = self.mus[index:index + self.sequence_length + 1] sub_sequence_log_vars = self.log_vars[index:index + self.sequence_length + 1] mus = sub_sequence_mus[:-1] next_mus = sub_sequence_mus[1:] log_vars = sub_sequence_log_vars[:-1] next_log_vars = sub_sequence_log_vars[1:] rewards = self.rewards[index:index + self.sequence_length] actions = self.actions[index:index + self.sequence_length] if self.rewards_transformation_function is not None: rewards = self.rewards_transformation_function(rewards) if self.actions_transformation_function is not None: actions = self.actions_transformation_function(actions) return mus, next_mus, log_vars, next_log_vars, rewards, actions class GUISingleSequenceShiftedDataset(Dataset): def __init__(self, root_dir: str, sequence_length: int, vae_preprocessed_data_path: str, hdf5_data_group_path: str, actions_transformation_function=None, rewards_transformation_function=None): self.root_dir = root_dir self.sequence_length = sequence_length self.vae_preprocessed_data_path = vae_preprocessed_data_path self.hdf5_data_group_path = hdf5_data_group_path self.actions_transformation_function = actions_transformation_function self.rewards_transformation_function = rewards_transformation_function with np.load(os.path.join(self.root_dir, "data.npz")) as data: self.rewards: torch.Tensor = torch.from_numpy(data["rewards"]).unsqueeze(-1) self.actions: torch.Tensor = torch.from_numpy(data["actions"]) self.vae_preprocessed_data = h5py.File(vae_preprocessed_data_path, "r") self.mus = self.vae_preprocessed_data[f"{self.hdf5_data_group_path}/mus"] self.log_vars = self.vae_preprocessed_data[f"{self.hdf5_data_group_path}/log_vars"] self.dataset_length = self.rewards.size(0) // self.sequence_length assert self.rewards.size(0) == self.actions.size(0) == (self.mus.shape[0] - 1) == (self.log_vars.shape[0] - 1) assert self.__len__() > 0, ("Dataset length is 0 or negative, probably too large sequence length or too few " "data samples") def __len__(self): return self.dataset_length def __getitem__(self, index): index_start_point = index * self.sequence_length sub_sequence_mus = self.mus[index_start_point:index_start_point + self.sequence_length + 1] sub_sequence_log_vars = self.log_vars[index_start_point:index_start_point + self.sequence_length + 1] mus = sub_sequence_mus[:-1] next_mus = sub_sequence_mus[1:] log_vars = sub_sequence_log_vars[:-1] next_log_vars = sub_sequence_log_vars[1:] rewards = self.rewards[index_start_point:index_start_point + self.sequence_length] actions = self.actions[index_start_point:index_start_point + self.sequence_length] if self.rewards_transformation_function is not None: rewards = self.rewards_transformation_function(rewards) if self.actions_transformation_function is not None: actions = self.actions_transformation_function(actions) return mus, next_mus, log_vars, next_log_vars, rewards, actions

4,658

45

208

fa3a643a8c4d5d570be2001b96c00037782267da

1,110

py

Python

release/stubs.min/System/__init___parts/DivideByZeroException.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

release/stubs.min/System/__init___parts/DivideByZeroException.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

release/stubs.min/System/__init___parts/DivideByZeroException.py

tranconbv/ironpython-stubs

a601759e6c6819beff8e6b639d18a24b7e351851

[ "MIT" ]

null

class DivideByZeroException(ArithmeticException): """ The exception that is thrown when there is an attempt to divide an integral or decimal value by zero. DivideByZeroException() DivideByZeroException(message: str) DivideByZeroException(message: str,innerException: Exception) """ def ZZZ(self): """hardcoded/mock instance of the class""" return DivideByZeroException() instance=ZZZ() """hardcoded/returns an instance of the class""" def __init__(self,*args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self,message=None,innerException=None): """ __new__(cls: type) __new__(cls: type,message: str) __new__(cls: type,message: str,innerException: Exception) __new__(cls: type,info: SerializationInfo,context: StreamingContext) """ pass SerializeObjectState=None

34.6875

215

0.726126

class DivideByZeroException(ArithmeticException): """ The exception that is thrown when there is an attempt to divide an integral or decimal value by zero. DivideByZeroException() DivideByZeroException(message: str) DivideByZeroException(message: str,innerException: Exception) """ def ZZZ(self): """hardcoded/mock instance of the class""" return DivideByZeroException() instance=ZZZ() """hardcoded/returns an instance of the class""" def __init__(self,*args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass @staticmethod def __new__(self,message=None,innerException=None): """ __new__(cls: type) __new__(cls: type,message: str) __new__(cls: type,message: str,innerException: Exception) __new__(cls: type,info: SerializationInfo,context: StreamingContext) """ pass def __reduce_ex__(self,*args): pass def __str__(self,*args): pass SerializeObjectState=None

28

0

48

3d85f4be772c1a036395de1aa5c734416e429682

11,772

py

Python

core/tenhou/log.py

SakuraSa/TenhouLoggerX

7d6bcfb7e22d631673c61321f3af1c05ec011db5

[ "MIT" ]

2

2016-09-19T16:33:29.000Z

2017-12-09T01:02:39.000Z

core/tenhou/log.py

SakuraSa/TenhouLoggerX

7d6bcfb7e22d631673c61321f3af1c05ec011db5

[ "MIT" ]

null

core/tenhou/log.py

SakuraSa/TenhouLoggerX

7d6bcfb7e22d631673c61321f3af1c05ec011db5

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 """ core.tenhou.log """ __author__ = 'Rnd495' import os import json import datetime import urllib from core.configs import Configs configs = Configs.instance() class Log(object): """ Log """ @property @property @property @property @property @property @property @property @property @property @property @property @property @property @staticmethod @staticmethod @staticmethod class StatisticForSubLog(object): """ StatisticForSubLog """ @property @property @property @property @property @property @property @property @property @property @property @property @property @property def get_results(ref_list, player_name): """ do statistics on given refs for given player result dict format (example value is avg value on data set 2015/05/15) : { fulu_chong : 0.3940, dama : 0.1165, win_time : 11.50, chong : 0.1347, win : 0.2484, win_point : 6690, ends_listening : 0.5170, fulu : 0.3717, after_richi : 0.0288, now_line_days : 3.71, max_line_days : 16.67, first_richi : 0.1597, count : 1000, } :param ref_list: ref list :param player_name: player name :return: result dict """ counter = {} adder = {} game_date_text_set = set() ref_counter = 0 for ref in ref_list: ref_counter += 1 log = Log(ref) game_date_text_set.add(log.time.strftime("%Y%m%d")) player_index = log.get_player_index(player_name) if player_index < 0: # should not be here continue for sub_log in log.sub_log: statistics = StatisticForSubLog(sub_log) results = statistics.get_result(player_index) for key, value in results.iteritems(): if value is not None: counter[key] = counter.get(key, 0) + 1 adder[key] = adder.get(key, 0) + value results = {} for key, value in counter.iteritems(): results[key] = (adder[key] / float(value)) if value else 0 max_line_days = now_line_days = 0 last_date = None for date_text in sorted(game_date_text_set): now_date = datetime.datetime.strptime(date_text, "%Y%m%d") if last_date: if int((now_date - last_date).days) <= 1: now_line_days += 1 max_line_days = max(max_line_days, now_line_days) else: now_line_days = 1 last_date = now_date results['max_line_days'] = max_line_days results['now_line_days'] = now_line_days results['count'] = ref_counter return results if __name__ == '__main__': import time from sqlalchemy import func, desc from core.models import get_new_session, PlayerLog session = get_new_session() counter = func.count(PlayerLog.name) query = session.query(PlayerLog.name).filter((PlayerLog.lobby == '0000') & (PlayerLog.name != 'NoName')) \ .group_by(PlayerLog.name).having(counter >= 50).order_by(desc(counter)) results = {} for name in (row[0] for row in query): start_time = time.time() query = session.query(PlayerLog.ref).filter((PlayerLog.name == name) & (PlayerLog.lobby == '0000')) refs = [row[0] for row in query] results[name] = get_results(refs, name) size = len(refs) time_cost = time.time() - start_time hz = size / time_cost print '%6d' % size, '%.2fs' % time_cost, '%.2fHz' % hz, name session.close() data_lists = {} for row in results.itervalues(): for key, value in row.iteritems(): data_lists.setdefault(key, []).append(value) print '' print '%20s' % 'type', '%6s' % 'avg', '%6s' % 'max', '%6s' % 'min', '%6s' % 'mu' # import numpy as np from scipy.stats import norm # import matplotlib.pyplot as plt for key, data_list in data_lists.iteritems(): avg = sum(data_list) / float(len(data_list)) mu, std = norm.fit(data_list) print '%20s' % key, format_data(avg), format_data(max(data_list)), format_data(min(data_list)), format_data( mu), std # # # Plot the histogram. # plt.hist(data_list, bins=25, normed=True, alpha=0.6, color='g') # # # Plot the PDF. # xmin, xmax = plt.xlim() # x = np.linspace(xmin, xmax, 100) # p = norm.pdf(x, mu, std) # plt.plot(x, p, 'k', linewidth=2) # title = "%s fit results: mu = %.2f, std = %.2f" % (key, mu, std) # plt.title(title) # # plt.show()

29.802532

116

0.569147

#!/usr/bin/env python # coding=utf-8 """ core.tenhou.log """ __author__ = 'Rnd495' import os import json import datetime import urllib from core.configs import Configs configs = Configs.instance() class Log(object): """ Log """ def __init__(self, ref): with open(Log.get_file_name(ref), 'rb') as file_handle: self.json = json.load(file_handle) # cache self._scores = None self._rankings = None @property def size(self): return len(self.names) @property def sub_log(self): return self.json['log'] @property def ref(self): return self.json['ref'] @property def rule(self): return self.json['rule']['disp'] @property def lobby(self): return self.ref.split('-')[2] @property def rule_code(self): return self.ref.split('-')[1] @property def dans(self): return self.json['dan'] @property def names(self): return self.json['name'] @property def scores(self): if not self._scores: g = iter(self.json['sc']) self._scores = zip(g, g) return self._scores @property def time(self): return datetime.datetime.strptime(self.ref[:10], '%Y%m%d%H') @property def points(self): return [point[0] for point in self.scores] @property def pts(self): return [point[1] for point in self.scores] @property def rankings(self): if not self._rankings: point_sorted = sorted(((point, index) for index, point in enumerate(self.points)), reverse=True) self._rankings = [None] * len(point_sorted) for ranking, (_, index) in enumerate(point_sorted): self._rankings[index] = ranking return self._rankings @property def rates(self): return self.json['rate'] @staticmethod def check_exists(ref): return os.path.exists(Log.get_file_name(ref)) @staticmethod def get_file_name(ref): return os.path.join(configs.tenhou_log_dir, '%s.json' % ref) @staticmethod def iter_all(): for root, dirs, files in os.walk(configs.tenhou_log_dir): for file_name in files: ref = os.path.splitext(file_name)[0] yield Log(ref) def get_player_index(self, name): try: return self.names.index(name) except ValueError: return None def get_tenhou_link(self, tw_name=None): base = "/watch/log?" params = {'ref': self.ref} for i, name in enumerate(self.names): if isinstance(name, unicode): name = name.encode("utf-8") params['UN%d' % i] = name tw = None if tw_name: tw = self.get_player_index(tw_name) if tw is not None: params['tw'] = tw return base + urllib.urlencode(params) class StatisticForSubLog(object): """ StatisticForSubLog """ def __init__(self, sub_log): self.sub_log = sub_log self._richi_list = None self._fulu_list = None @property def game_size(self): return len(self.point_starts) @property def game_index(self): return self.sub_log[0] @property def dora_pointers_out(self): return self.sub_log[2] @property def dora_pointers_in(self): return self.sub_log[3] @property def start_cards(self): return self.sub_log[4:4 + 3 * self.game_size:3] @property def cards_ins(self): return self.sub_log[5:5 + 3 * self.game_size:3] @property def cards_outs(self): return self.sub_log[6:6 + 3 * self.game_size:3] @property def result_list(self): return self.sub_log[16] @property def is_agari(self): return self.result_description == u'和了' @property def result_description(self): return self.result_list[0] @property def point_starts(self): return self.sub_log[1] @property def point_changes(self): return self.result_list[1::2] @property def richi_list(self): if self._richi_list is None: self._get_player_details() return self._richi_list @property def is_fulu_list(self): if self._fulu_list is None: self._get_player_details() return self._fulu_list def _get_player_details(self): self._richi_list = [None] * self.game_size self._fulu_list = [False] * self.game_size # scan card outs for player_index, card_out in enumerate(self.cards_outs): for time_index, action in enumerate(card_out): if self._richi_list[player_index] is not None: break if self._fulu_list[player_index]: break if not isinstance(action, int): if action.startswith('r'): self._richi_list[player_index] = (time_index, action) else: self._fulu_list[player_index] = True # scan card ins for player_index, card_in in enumerate(self.cards_ins): for time_index, action in enumerate(card_in): if self._richi_list[player_index] is not None: richi_time, richi_action = self._richi_list[player_index] if time_index >= richi_time: break if self._fulu_list[player_index]: break elif not isinstance(action, int): self._fulu_list[player_index] = True def get_result(self, player_index): # attack point_change = sum(sc[player_index] for sc in self.point_changes) win = self.is_agari and point_change > 0 win_point = point_change if win else None # speed first_richi = self.richi_list[player_index] if first_richi: for richi in self.richi_list: if richi is not None and richi[0] < first_richi[0]: first_richi = False break first_richi = bool(first_richi) win_time = None if win: win_time = len(self.cards_ins[player_index]) # int dama = None if win: dama = not self.is_fulu_list[player_index] and not self.richi_list[player_index] ends_listening = None if self.result_description == u'全員聴牌': ends_listening = True elif self.result_description == u'流局': ends_listening = point_change > 0 # def someone_chong = self.result_description == u'和了' and \ len(self.point_changes) == 1 and \ sum(p < 0 for p in self.point_changes[0]) == 1 chong = someone_chong and point_change < 0 fulu_chong = None if chong: fulu_chong = self.is_fulu_list[player_index] # brave after_richi = not first_richi and bool(self.richi_list[player_index]) fulu = self.is_fulu_list[player_index] return dict( win=win, win_point=win_point, first_richi=first_richi, win_time=win_time, dama=dama, ends_listening=ends_listening, chong=chong, fulu_chong=fulu_chong, after_richi=after_richi, fulu=fulu ) def get_results(ref_list, player_name): """ do statistics on given refs for given player result dict format (example value is avg value on data set 2015/05/15) : { fulu_chong : 0.3940, dama : 0.1165, win_time : 11.50, chong : 0.1347, win : 0.2484, win_point : 6690, ends_listening : 0.5170, fulu : 0.3717, after_richi : 0.0288, now_line_days : 3.71, max_line_days : 16.67, first_richi : 0.1597, count : 1000, } :param ref_list: ref list :param player_name: player name :return: result dict """ counter = {} adder = {} game_date_text_set = set() ref_counter = 0 for ref in ref_list: ref_counter += 1 log = Log(ref) game_date_text_set.add(log.time.strftime("%Y%m%d")) player_index = log.get_player_index(player_name) if player_index < 0: # should not be here continue for sub_log in log.sub_log: statistics = StatisticForSubLog(sub_log) results = statistics.get_result(player_index) for key, value in results.iteritems(): if value is not None: counter[key] = counter.get(key, 0) + 1 adder[key] = adder.get(key, 0) + value results = {} for key, value in counter.iteritems(): results[key] = (adder[key] / float(value)) if value else 0 max_line_days = now_line_days = 0 last_date = None for date_text in sorted(game_date_text_set): now_date = datetime.datetime.strptime(date_text, "%Y%m%d") if last_date: if int((now_date - last_date).days) <= 1: now_line_days += 1 max_line_days = max(max_line_days, now_line_days) else: now_line_days = 1 last_date = now_date results['max_line_days'] = max_line_days results['now_line_days'] = now_line_days results['count'] = ref_counter return results if __name__ == '__main__': import time from sqlalchemy import func, desc from core.models import get_new_session, PlayerLog session = get_new_session() counter = func.count(PlayerLog.name) query = session.query(PlayerLog.name).filter((PlayerLog.lobby == '0000') & (PlayerLog.name != 'NoName')) \ .group_by(PlayerLog.name).having(counter >= 50).order_by(desc(counter)) results = {} for name in (row[0] for row in query): start_time = time.time() query = session.query(PlayerLog.ref).filter((PlayerLog.name == name) & (PlayerLog.lobby == '0000')) refs = [row[0] for row in query] results[name] = get_results(refs, name) size = len(refs) time_cost = time.time() - start_time hz = size / time_cost print '%6d' % size, '%.2fs' % time_cost, '%.2fHz' % hz, name session.close() data_lists = {} for row in results.itervalues(): for key, value in row.iteritems(): data_lists.setdefault(key, []).append(value) def format_data(d): if d < 1: return '%6s' % ('%.2f%%' % (d * 100)) elif abs(d) < 100: return '%6s' % ('%.2f' % d) else: return '%6s' % ('%d' % d) print '' print '%20s' % 'type', '%6s' % 'avg', '%6s' % 'max', '%6s' % 'min', '%6s' % 'mu' # import numpy as np from scipy.stats import norm # import matplotlib.pyplot as plt for key, data_list in data_lists.iteritems(): avg = sum(data_list) / float(len(data_list)) mu, std = norm.fit(data_list) print '%20s' % key, format_data(avg), format_data(max(data_list)), format_data(min(data_list)), format_data( mu), std # # # Plot the histogram. # plt.hist(data_list, bins=25, normed=True, alpha=0.6, color='g') # # # Plot the PDF. # xmin, xmax = plt.xlim() # x = np.linspace(xmin, xmax, 100) # p = norm.pdf(x, mu, std) # plt.plot(x, p, 'k', linewidth=2) # title = "%s fit results: mu = %.2f, std = %.2f" % (key, mu, std) # plt.title(title) # # plt.show()

5,939

0

994

b97e642f766dedecd2b8dc7fbaf1c4aba0a274fb

5,267

py

Python

tests/parser/features/test_assert.py

upgradvisor/vyper

642884ea938a25793c1b2fac866e8458e63a7b49

[ "Apache-2.0" ]

1

2021-12-20T16:19:47.000Z

tests/parser/features/test_assert.py

upgradvisor/vyper

642884ea938a25793c1b2fac866e8458e63a7b49

[ "Apache-2.0" ]

1

2022-03-19T00:45:47.000Z

tests/parser/features/test_assert.py

upgradvisor/vyper

642884ea938a25793c1b2fac866e8458e63a7b49

[ "Apache-2.0" ]

null

import pytest from eth_abi import decode_single from eth_tester.exceptions import TransactionFailed # web3 returns f"execution reverted: {err_str}" # TODO move exception string parsing logic into assert_tx_failed invalid_code = [ """ @external def test(a: int128) -> int128: assert a > 1, "" return 1 + a """, """ @external def test(a: int128) -> int128: raise "" """, """ @external def test(): assert create_forwarder_to(self) """, ] @pytest.mark.parametrize("code", invalid_code) valid_code = [ """ @external def mint(_to: address, _value: uint256): raise """, """ @internal def ret1() -> int128: return 1 @external def test(): assert self.ret1() == 1 """, """ @internal def valid_address(sender: address) -> bool: selfdestruct(sender) @external def test(): assert self.valid_address(msg.sender) """, """ @external def test(): assert raw_call(msg.sender, b'', max_outsize=1, gas=10, value=1000*1000) == b'' """, """ @external def test(): assert create_forwarder_to(self) == self """, ] @pytest.mark.parametrize("code", valid_code)

25.444444

94

0.65806

import pytest from eth_abi import decode_single from eth_tester.exceptions import TransactionFailed # web3 returns f"execution reverted: {err_str}" # TODO move exception string parsing logic into assert_tx_failed def _fixup_err_str(s): return s.replace("execution reverted: ", "") def test_assert_refund(w3, get_contract_with_gas_estimation, assert_tx_failed): code = """ @external def foo(): assert 1 == 2 """ c = get_contract_with_gas_estimation(code) a0 = w3.eth.accounts[0] gas_sent = 10 ** 6 tx_hash = c.foo(transact={"from": a0, "gas": gas_sent, "gasPrice": 10}) # More info on receipt status: # https://github.com/ethereum/EIPs/blob/master/EIPS/eip-658.md#specification. tx_receipt = w3.eth.getTransactionReceipt(tx_hash) assert tx_receipt["status"] == 0 # Checks for gas refund from revert assert tx_receipt["gasUsed"] < gas_sent def test_assert_reason(w3, get_contract_with_gas_estimation, assert_tx_failed, memory_mocker): code = """ @external def test(a: int128) -> int128: assert a > 1, "larger than one please" return 1 + a @external def test2(a: int128, b: int128, extra_reason: String[32]) -> int128: c: int128 = 11 assert a > 1, "a is not large enough" assert b == 1, concat("b may only be 1", extra_reason) return a + b + c @external def test3(reason_str: String[32]): raise reason_str """ c = get_contract_with_gas_estimation(code) assert c.test(2) == 3 with pytest.raises(TransactionFailed) as e_info: c.test(0) assert _fixup_err_str(e_info.value.args[0]) == "larger than one please" # a = 0, b = 1 with pytest.raises(TransactionFailed) as e_info: c.test2(0, 1, "") assert _fixup_err_str(e_info.value.args[0]) == "a is not large enough" # a = 1, b = 0 with pytest.raises(TransactionFailed) as e_info: c.test2(2, 2, " because I said so") assert _fixup_err_str(e_info.value.args[0]) == "b may only be 1" + " because I said so" # return correct value assert c.test2(5, 1, "") == 17 with pytest.raises(TransactionFailed) as e_info: c.test3("An exception") assert _fixup_err_str(e_info.value.args[0]) == "An exception" invalid_code = [ """ @external def test(a: int128) -> int128: assert a > 1, "" return 1 + a """, """ @external def test(a: int128) -> int128: raise "" """, """ @external def test(): assert create_forwarder_to(self) """, ] @pytest.mark.parametrize("code", invalid_code) def test_invalid_assertions(get_contract, assert_compile_failed, code): assert_compile_failed(lambda: get_contract(code)) valid_code = [ """ @external def mint(_to: address, _value: uint256): raise """, """ @internal def ret1() -> int128: return 1 @external def test(): assert self.ret1() == 1 """, """ @internal def valid_address(sender: address) -> bool: selfdestruct(sender) @external def test(): assert self.valid_address(msg.sender) """, """ @external def test(): assert raw_call(msg.sender, b'', max_outsize=1, gas=10, value=1000*1000) == b'' """, """ @external def test(): assert create_forwarder_to(self) == self """, ] @pytest.mark.parametrize("code", valid_code) def test_valid_assertions(get_contract, code): get_contract(code) def test_assert_staticcall(get_contract, assert_tx_failed, memory_mocker): foreign_code = """ state: uint256 @external def not_really_constant() -> uint256: self.state += 1 return self.state """ code = """ interface ForeignContract: def not_really_constant() -> uint256: view @external def test(): assert ForeignContract(msg.sender).not_really_constant() == 1 """ c1 = get_contract(foreign_code) c2 = get_contract(code, *[c1.address]) # static call prohibits state change assert_tx_failed(lambda: c2.test()) def test_assert_in_for_loop(get_contract, assert_tx_failed, memory_mocker): code = """ @external def test(x: uint256[3]) -> bool: for i in range(3): assert x[i] < 5 return True """ c = get_contract(code) c.test([1, 2, 3]) assert_tx_failed(lambda: c.test([5, 1, 3])) assert_tx_failed(lambda: c.test([1, 5, 3])) assert_tx_failed(lambda: c.test([1, 3, 5])) def test_assert_with_reason_in_for_loop(get_contract, assert_tx_failed, memory_mocker): code = """ @external def test(x: uint256[3]) -> bool: for i in range(3): assert x[i] < 5, "because reasons" return True """ c = get_contract(code) c.test([1, 2, 3]) assert_tx_failed(lambda: c.test([5, 1, 3])) assert_tx_failed(lambda: c.test([1, 5, 3])) assert_tx_failed(lambda: c.test([1, 3, 5])) def test_assert_reason_revert_length(w3, get_contract, memory_mocker): code = """ @external def test() -> int128: assert 1 == 2, "oops" return 1 """ c = get_contract(code) w3.manager.provider.ethereum_tester.backend.is_eip838_error = lambda err: False with pytest.raises(TransactionFailed) as e_info: c.test() error_bytes = eval(_fixup_err_str(e_info.value.args[0])) assert len(error_bytes) == 100 msg = decode_single("string", error_bytes[36:]) assert msg == "oops"

3,906

0

204

3f29ebd88cd6558019edc58f99d89c4d08dc0aae

8,654

py

Python

netl2api/l2api/__init__.py

locaweb/netl2api

f84c0362d1676c8771015b7cc48461e44a21c34d

[ "Apache-2.0" ]

3

2015-04-08T18:50:02.000Z

2019-06-05T22:40:45.000Z

netl2api/l2api/__init__.py

locaweb/netl2api

f84c0362d1676c8771015b7cc48461e44a21c34d

[ "Apache-2.0" ]

null

netl2api/l2api/__init__.py

locaweb/netl2api

f84c0362d1676c8771015b7cc48461e44a21c34d

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8; -*- # # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @author: Eduardo S. Scarpellini # @author: Luiz Ozaki __copyright__ = "Copyright 2012, Locaweb IDC" from netl2api.l2api.exceptions import * from netl2api.l2api.autocache import L2APIAutoCache from netl2api.l2api.transport import SysSSHTransport #, TransportManager __all__ = ["L2API"] class L2API(L2APIAutoCache): """ Base class for L2 operations. Vendor-specific classes should extend this, declare 'self.__VENDOR__' (vendor str), 'self.__HWTYPE__' (hardware type str), 'self.prompt_mark', 'self.error_mark' and 'self.config_term_cmd' (see transport classes for understand these three last parameters). Ex.: class ExampleVendorAPI(L2API): def __init__(self, *args, **kwargs): self.__VENDOR__ = "ExampleVendor" self.__HWTYPE__ = "stackable_switch" self.prompt_mark = "#" self.error_mark = "% Error:" self.config_term_cmd = "terminal length 0" super(ExampleVendorAPI, self).__init__(*args, **kwargs) ... def show_version(self): ... def show_interfaces(self): .... """ # def __del__(self): # if self.transport is not None: # try: # self.transport.close() # except Exception: # pass

40.064815

122

0.605616

#!/usr/bin/python # -*- coding: utf-8; -*- # # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @author: Eduardo S. Scarpellini # @author: Luiz Ozaki __copyright__ = "Copyright 2012, Locaweb IDC" from netl2api.l2api.exceptions import * from netl2api.l2api.autocache import L2APIAutoCache from netl2api.l2api.transport import SysSSHTransport #, TransportManager __all__ = ["L2API"] class L2API(L2APIAutoCache): """ Base class for L2 operations. Vendor-specific classes should extend this, declare 'self.__VENDOR__' (vendor str), 'self.__HWTYPE__' (hardware type str), 'self.prompt_mark', 'self.error_mark' and 'self.config_term_cmd' (see transport classes for understand these three last parameters). Ex.: class ExampleVendorAPI(L2API): def __init__(self, *args, **kwargs): self.__VENDOR__ = "ExampleVendor" self.__HWTYPE__ = "stackable_switch" self.prompt_mark = "#" self.error_mark = "% Error:" self.config_term_cmd = "terminal length 0" super(ExampleVendorAPI, self).__init__(*args, **kwargs) ... def show_version(self): ... def show_interfaces(self): .... """ def __init__(self, host=None, port=None, username=None, passwd=None, transport=None): super(L2API, self).__init__() if not hasattr(self, "__VENDOR__"): raise InvalidParameter("'self.__VENDOR__' is not defined (class '%s')" % self.__class__.__name__) if not hasattr(self, "__HWTYPE__"): raise InvalidParameter("'self.__HWTYPE__' is not defined (class '%s')" % self.__class__.__name__) if not host or type(host) not in (str, unicode): raise InvalidParameter("'host' parameter is not defined or invalid") if not username or type(username) not in (str, unicode): raise InvalidParameter("'username' parameter is not defined or invalid") if not passwd or type(passwd) not in (str, unicode): raise InvalidParameter("'passwd' parameter is not defined or invalid") if not hasattr(self, "prompt_mark"): self.prompt_mark = "#" if not hasattr(self, "error_mark"): self.error_mark = None if not hasattr(self, "config_term_cmd"): self.config_term_cmd = None if not transport: transport = SysSSHTransport.SysSSH self.use_cache = True self.cache_config = { "show_system": { "ttl": 600, "clear_on": [] }, "show_hostname": { "ttl": 600, "clear_on": [] }, "show_version": { "ttl": 600, "clear_on": [] }, "show_interfaces": { "ttl": 120, "clear_on": ["enable_interface", "disable_interface", "change_interface_description"] }, "show_lldp": { "ttl": 180, "clear_on": [] }, "show_arp": { "ttl": 180, "clear_on": [] }, "show_uplinks": { "ttl": 180, "clear_on": [] }, "show_vlans": { "ttl": 180, "clear_on": ["create_vlan", "destroy_vlan", "enable_vlan", "disable_vlan", "change_vlan_description", "interface_attach_vlan", "interface_detach_vlan", "lag_attach_vlan", "lag_detach_vlan"] }, "show_lags": { "ttl": 180, "clear_on": ["create_lag", "destroy_lag", "enable_lag", "disable_lag", "change_lag_description", "lag_attach_interface", "lag_detach_interface"] }, } #self.transport = TransportManager.TransportPool(transport=transport, max_connections=2, host=host, port=port, # username=username, passwd=passwd, prompt_mark=self.prompt_mark, # error_mark=self.error_mark, config_term_cmd=self.config_term_cmd) self.transport = transport(host=host, port=port, username=username, passwd=passwd, prompt_mark=self.prompt_mark, error_mark=self.error_mark, config_term_cmd=self.config_term_cmd) def dump_config(self): raise NotImplementedError("Not implemented") def save_config(self): raise NotImplementedError("Not implemented") def show_system(self): raise NotImplementedError("Not implemented") def show_hostname(self): raise NotImplementedError("Not implemented") def show_version(self): raise NotImplementedError("Not implemented") def show_interfaces(self, interface_id=None): raise NotImplementedError("Not implemented") def show_lldp(self, interface_id=None): raise NotImplementedError("Not implemented") def show_arp(self, interface_id=None): raise NotImplementedError("Not implemented") def show_uplinks(self): raise NotImplementedError("Not implemented") def show_vlans(self, vlan_id=None): raise NotImplementedError("Not implemented") def show_lags(self, lag_id=None): raise NotImplementedError("Not implemented") def create_vlan(self, vlan_id=None, vlan_description=None): raise NotImplementedError("Not implemented") def create_lag(self, lag_id=None, lag_description=None): raise NotImplementedError("Not implemented") def enable_interface(self, interface_id=None): raise NotImplementedError("Not implemented") def enable_vlan(self, vlan_id=None): raise NotImplementedError("Not implemented") def enable_lag(self, lag_id=None): raise NotImplementedError("Not implemented") def disable_interface(self, interface_id=None): raise NotImplementedError("Not implemented") def disable_vlan(self, vlan_id=None): raise NotImplementedError("Not implemented") def disable_lag(self, lag_id=None): raise NotImplementedError("Not implemented") def change_interface_description(self, interface_id=None, interface_description=None): raise NotImplementedError("Not implemented") def change_vlan_description(self, vlan_id=None, vlan_description=None): raise NotImplementedError("Not implemented") def change_lag_description(self, lag_id=None, lag_description=None): raise NotImplementedError("Not implemented") def destroy_vlan(self, vlan_id=None): raise NotImplementedError("Not implemented") def destroy_lag(self, lag_id=None): raise NotImplementedError("Not implemented") def interface_attach_vlan(self, interface_id=None, vlan_id=None, tagged=True): raise NotImplementedError("Not implemented") def interface_detach_vlan(self, interface_id=None, vlan_id=None, tagged=True): raise NotImplementedError("Not implemented") def lag_attach_vlan(self, lag_id=None, vlan_id=None, tagged=True): raise NotImplementedError("Not implemented") def lag_detach_vlan(self, lag_id=None, vlan_id=None, tagged=True): raise NotImplementedError("Not implemented") def lag_attach_interface(self, lag_id=None, interface_id=None): raise NotImplementedError("Not implemented") def lag_detach_interface(self, lag_id=None, interface_id=None): raise NotImplementedError("Not implemented") # def __del__(self): # if self.transport is not None: # try: # self.transport.close() # except Exception: # pass

5,832

0

837

53e3d50dfd8819a70c242ed90be54300221236ee

12,531

py

Python

pymeasure/instruments/agilent/agilent8257D.py

dphaas/pymeasure

580c33bf5f1e409bb575c46bbd1df682bf27cfe1

[ "MIT" ]

null

pymeasure/instruments/agilent/agilent8257D.py

dphaas/pymeasure

580c33bf5f1e409bb575c46bbd1df682bf27cfe1

[ "MIT" ]

null

pymeasure/instruments/agilent/agilent8257D.py

dphaas/pymeasure

580c33bf5f1e409bb575c46bbd1df682bf27cfe1

[ "MIT" ]

null

# # This file is part of the PyMeasure package. # # Copyright (c) 2013-2022 PyMeasure Developers # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # from pymeasure.instruments import Instrument from pymeasure.instruments.validators import truncated_range, strict_discrete_set class Agilent8257D(Instrument): """Represents the Agilent 8257D Signal Generator and provides a high-level interface for interacting with the instrument. .. code-block:: python generator = Agilent8257D("GPIB::1") generator.power = 0 # Sets the output power to 0 dBm generator.frequency = 5 # Sets the output frequency to 5 GHz generator.enable() # Enables the output """ power = Instrument.control( ":POW?;", ":POW %g dBm;", """ A floating point property that represents the output power in dBm. This property can be set. """ ) frequency = Instrument.control( ":FREQ?;", ":FREQ %e Hz;", """ A floating point property that represents the output frequency in Hz. This property can be set. """ ) start_frequency = Instrument.control( ":SOUR:FREQ:STAR?", ":SOUR:FREQ:STAR %e Hz", """ A floating point property that represents the start frequency in Hz. This property can be set. """ ) center_frequency = Instrument.control( ":SOUR:FREQ:CENT?", ":SOUR:FREQ:CENT %e Hz;", """ A floating point property that represents the center frequency in Hz. This property can be set. """ ) stop_frequency = Instrument.control( ":SOUR:FREQ:STOP?", ":SOUR:FREQ:STOP %e Hz", """ A floating point property that represents the stop frequency in Hz. This property can be set. """ ) start_power = Instrument.control( ":SOUR:POW:STAR?", ":SOUR:POW:STAR %e dBm", """ A floating point property that represents the start power in dBm. This property can be set. """ ) stop_power = Instrument.control( ":SOUR:POW:STOP?", ":SOUR:POW:STOP %e dBm", """ A floating point property that represents the stop power in dBm. This property can be set. """ ) dwell_time = Instrument.control( ":SOUR:SWE:DWEL1?", ":SOUR:SWE:DWEL1 %.3f", """ A floating point property that represents the settling time in seconds at the current frequency or power setting. This property can be set. """ ) step_points = Instrument.control( ":SOUR:SWE:POIN?", ":SOUR:SWE:POIN %d", """ An integer number of points in a step sweep. This property can be set. """ ) is_enabled = Instrument.measurement( ":OUTPUT?", """ Reads a boolean value that is True if the output is on. """, cast=bool ) has_modulation = Instrument.measurement( ":OUTPUT:MOD?", """ Reads a boolean value that is True if the modulation is enabled. """, cast=bool ) ######################## # Amplitude modulation # ######################## has_amplitude_modulation = Instrument.measurement( ":SOUR:AM:STAT?", """ Reads a boolean value that is True if the amplitude modulation is enabled. """, cast=bool ) amplitude_depth = Instrument.control( ":SOUR:AM:DEPT?", ":SOUR:AM:DEPT %g", """ A floating point property that controls the amplitude modulation in precent, which can take values from 0 to 100 %. """, validator=truncated_range, values=[0, 100] ) AMPLITUDE_SOURCES = { 'internal': 'INT', 'internal 2': 'INT2', 'external': 'EXT', 'external 2': 'EXT2' } amplitude_source = Instrument.control( ":SOUR:AM:SOUR?", ":SOUR:AM:SOUR %s", """ A string property that controls the source of the amplitude modulation signal, which can take the values: 'internal', 'internal 2', 'external', and 'external 2'. """, validator=strict_discrete_set, values=AMPLITUDE_SOURCES, map_values=True ) #################### # Pulse modulation # #################### has_pulse_modulation = Instrument.measurement( ":SOUR:PULM:STAT?", """ Reads a boolean value that is True if the pulse modulation is enabled. """, cast=bool ) PULSE_SOURCES = { 'internal': 'INT', 'external': 'EXT', 'scalar': 'SCAL' } pulse_source = Instrument.control( ":SOUR:PULM:SOUR?", ":SOUR:PULM:SOUR %s", """ A string property that controls the source of the pulse modulation signal, which can take the values: 'internal', 'external', and 'scalar'. """, validator=strict_discrete_set, values=PULSE_SOURCES, map_values=True ) PULSE_INPUTS = { 'square': 'SQU', 'free-run': 'FRUN', 'triggered': 'TRIG', 'doublet': 'DOUB', 'gated': 'GATE' } pulse_input = Instrument.control( ":SOUR:PULM:SOUR:INT?", ":SOUR:PULM:SOUR:INT %s", """ A string property that controls the internally generated modulation input for the pulse modulation, which can take the values: 'square', 'free-run', 'triggered', 'doublet', and 'gated'. """, validator=strict_discrete_set, values=PULSE_INPUTS, map_values=True ) pulse_frequency = Instrument.control( ":SOUR:PULM:INT:FREQ?", ":SOUR:PULM:INT:FREQ %g", """ A floating point property that controls the pulse rate frequency in Hertz, which can take values from 0.1 Hz to 10 MHz. """, validator=truncated_range, values=[0.1, 10e6] ) ######################## # Low-Frequency Output # ######################## low_freq_out_amplitude = Instrument.control( ":SOUR:LFO:AMPL? ", ":SOUR:LFO:AMPL %g VP", """A floating point property that controls the peak voltage (amplitude) of the low frequency output in volts, which can take values from 0-3.5V""", validator=truncated_range, values=[0, 3.5] ) LOW_FREQUENCY_SOURCES = { 'internal': 'INT', 'internal 2': 'INT2', 'function': 'FUNC', 'function 2': 'FUNC2' } low_freq_out_source = Instrument.control( ":SOUR:LFO:SOUR?", ":SOUR:LFO:SOUR %s", """A string property which controls the source of the low frequency output, which can take the values 'internal [2]' for the inernal source, or 'function [2]' for an internal function generator which can be configured.""", validator=strict_discrete_set, values=LOW_FREQUENCY_SOURCES, map_values=True ) def enable_low_freq_out(self): """Enables low frequency output""" self.write(":SOUR:LFO:STAT ON") def disable_low_freq_out(self): """Disables low frequency output""" self.write(":SOUR:LFO:STAT OFF") def config_low_freq_out(self, source='internal', amplitude=3): """ Configures the low-frequency output signal. :param source: The source for the low-frequency output signal. :param amplitude: Amplitude of the low-frequency output """ self.enable_low_freq_out() self.low_freq_out_source = source self.low_freq_out_amplitude = amplitude ####################### # Internal Oscillator # ####################### internal_frequency = Instrument.control( ":SOUR:AM:INT:FREQ?", ":SOUR:AM:INT:FREQ %g", """ A floating point property that controls the frequency of the internal oscillator in Hertz, which can take values from 0.5 Hz to 1 MHz. """, validator=truncated_range, values=[0.5, 1e6] ) INTERNAL_SHAPES = { 'sine': 'SINE', 'triangle': 'TRI', 'square': 'SQU', 'ramp': 'RAMP', 'noise': 'NOIS', 'dual-sine': 'DUAL', 'swept-sine': 'SWEP' } internal_shape = Instrument.control( ":SOUR:AM:INT:FUNC:SHAP?", ":SOUR:AM:INT:FUNC:SHAP %s", """ A string property that controls the shape of the internal oscillations, which can take the values: 'sine', 'triangle', 'square', 'ramp', 'noise', 'dual-sine', and 'swept-sine'. """, validator=strict_discrete_set, values=INTERNAL_SHAPES, map_values=True ) def enable(self): """ Enables the output of the signal. """ self.write(":OUTPUT ON;") def disable(self): """ Disables the output of the signal. """ self.write(":OUTPUT OFF;") def disable_modulation(self): """ Disables the signal modulation. """ self.write(":OUTPUT:MOD OFF;") self.write(":lfo:stat off;") def config_amplitude_modulation(self, frequency=1e3, depth=100.0, shape='sine'): """ Configures the amplitude modulation of the output signal. :param frequency: A modulation frequency for the internal oscillator :param depth: A linear depth precentage :param shape: A string that describes the shape for the internal oscillator """ self.enable_amplitude_modulation() self.amplitude_source = 'internal' self.internal_frequency = frequency self.internal_shape = shape self.amplitude_depth = depth def enable_amplitude_modulation(self): """ Enables amplitude modulation of the output signal. """ self.write(":SOUR:AM:STAT ON") def disable_amplitude_modulation(self): """ Disables amplitude modulation of the output signal. """ self.write(":SOUR:AM:STAT OFF") def config_pulse_modulation(self, frequency=1e3, input='square'): """ Configures the pulse modulation of the output signal. :param frequency: A pulse rate frequency in Hertz :param input: A string that describes the internal pulse input """ self.enable_pulse_modulation() self.pulse_source = 'internal' self.pulse_input = input self.pulse_frequency = frequency def enable_pulse_modulation(self): """ Enables pulse modulation of the output signal. """ self.write(":SOUR:PULM:STAT ON") def disable_pulse_modulation(self): """ Disables pulse modulation of the output signal. """ self.write(":SOUR:PULM:STAT OFF") def config_step_sweep(self): """ Configures a step sweep through frequency """ self.write(":SOUR:FREQ:MODE SWE;" ":SOUR:SWE:GEN STEP;" ":SOUR:SWE:MODE AUTO;") def start_step_sweep(self): """ Starts a step sweep. """ self.write(":SOUR:SWE:CONT:STAT ON") def stop_step_sweep(self): """ Stops a step sweep. """ self.write(":SOUR:SWE:CONT:STAT OFF") def shutdown(self): """ Shuts down the instrument by disabling any modulation and the output signal. """ self.disable_modulation() self.disable()

36.533528

100

0.615194

# # This file is part of the PyMeasure package. # # Copyright (c) 2013-2022 PyMeasure Developers # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # from pymeasure.instruments import Instrument from pymeasure.instruments.validators import truncated_range, strict_discrete_set class Agilent8257D(Instrument): """Represents the Agilent 8257D Signal Generator and provides a high-level interface for interacting with the instrument. .. code-block:: python generator = Agilent8257D("GPIB::1") generator.power = 0 # Sets the output power to 0 dBm generator.frequency = 5 # Sets the output frequency to 5 GHz generator.enable() # Enables the output """ power = Instrument.control( ":POW?;", ":POW %g dBm;", """ A floating point property that represents the output power in dBm. This property can be set. """ ) frequency = Instrument.control( ":FREQ?;", ":FREQ %e Hz;", """ A floating point property that represents the output frequency in Hz. This property can be set. """ ) start_frequency = Instrument.control( ":SOUR:FREQ:STAR?", ":SOUR:FREQ:STAR %e Hz", """ A floating point property that represents the start frequency in Hz. This property can be set. """ ) center_frequency = Instrument.control( ":SOUR:FREQ:CENT?", ":SOUR:FREQ:CENT %e Hz;", """ A floating point property that represents the center frequency in Hz. This property can be set. """ ) stop_frequency = Instrument.control( ":SOUR:FREQ:STOP?", ":SOUR:FREQ:STOP %e Hz", """ A floating point property that represents the stop frequency in Hz. This property can be set. """ ) start_power = Instrument.control( ":SOUR:POW:STAR?", ":SOUR:POW:STAR %e dBm", """ A floating point property that represents the start power in dBm. This property can be set. """ ) stop_power = Instrument.control( ":SOUR:POW:STOP?", ":SOUR:POW:STOP %e dBm", """ A floating point property that represents the stop power in dBm. This property can be set. """ ) dwell_time = Instrument.control( ":SOUR:SWE:DWEL1?", ":SOUR:SWE:DWEL1 %.3f", """ A floating point property that represents the settling time in seconds at the current frequency or power setting. This property can be set. """ ) step_points = Instrument.control( ":SOUR:SWE:POIN?", ":SOUR:SWE:POIN %d", """ An integer number of points in a step sweep. This property can be set. """ ) is_enabled = Instrument.measurement( ":OUTPUT?", """ Reads a boolean value that is True if the output is on. """, cast=bool ) has_modulation = Instrument.measurement( ":OUTPUT:MOD?", """ Reads a boolean value that is True if the modulation is enabled. """, cast=bool ) ######################## # Amplitude modulation # ######################## has_amplitude_modulation = Instrument.measurement( ":SOUR:AM:STAT?", """ Reads a boolean value that is True if the amplitude modulation is enabled. """, cast=bool ) amplitude_depth = Instrument.control( ":SOUR:AM:DEPT?", ":SOUR:AM:DEPT %g", """ A floating point property that controls the amplitude modulation in precent, which can take values from 0 to 100 %. """, validator=truncated_range, values=[0, 100] ) AMPLITUDE_SOURCES = { 'internal': 'INT', 'internal 2': 'INT2', 'external': 'EXT', 'external 2': 'EXT2' } amplitude_source = Instrument.control( ":SOUR:AM:SOUR?", ":SOUR:AM:SOUR %s", """ A string property that controls the source of the amplitude modulation signal, which can take the values: 'internal', 'internal 2', 'external', and 'external 2'. """, validator=strict_discrete_set, values=AMPLITUDE_SOURCES, map_values=True ) #################### # Pulse modulation # #################### has_pulse_modulation = Instrument.measurement( ":SOUR:PULM:STAT?", """ Reads a boolean value that is True if the pulse modulation is enabled. """, cast=bool ) PULSE_SOURCES = { 'internal': 'INT', 'external': 'EXT', 'scalar': 'SCAL' } pulse_source = Instrument.control( ":SOUR:PULM:SOUR?", ":SOUR:PULM:SOUR %s", """ A string property that controls the source of the pulse modulation signal, which can take the values: 'internal', 'external', and 'scalar'. """, validator=strict_discrete_set, values=PULSE_SOURCES, map_values=True ) PULSE_INPUTS = { 'square': 'SQU', 'free-run': 'FRUN', 'triggered': 'TRIG', 'doublet': 'DOUB', 'gated': 'GATE' } pulse_input = Instrument.control( ":SOUR:PULM:SOUR:INT?", ":SOUR:PULM:SOUR:INT %s", """ A string property that controls the internally generated modulation input for the pulse modulation, which can take the values: 'square', 'free-run', 'triggered', 'doublet', and 'gated'. """, validator=strict_discrete_set, values=PULSE_INPUTS, map_values=True ) pulse_frequency = Instrument.control( ":SOUR:PULM:INT:FREQ?", ":SOUR:PULM:INT:FREQ %g", """ A floating point property that controls the pulse rate frequency in Hertz, which can take values from 0.1 Hz to 10 MHz. """, validator=truncated_range, values=[0.1, 10e6] ) ######################## # Low-Frequency Output # ######################## low_freq_out_amplitude = Instrument.control( ":SOUR:LFO:AMPL? ", ":SOUR:LFO:AMPL %g VP", """A floating point property that controls the peak voltage (amplitude) of the low frequency output in volts, which can take values from 0-3.5V""", validator=truncated_range, values=[0, 3.5] ) LOW_FREQUENCY_SOURCES = { 'internal': 'INT', 'internal 2': 'INT2', 'function': 'FUNC', 'function 2': 'FUNC2' } low_freq_out_source = Instrument.control( ":SOUR:LFO:SOUR?", ":SOUR:LFO:SOUR %s", """A string property which controls the source of the low frequency output, which can take the values 'internal [2]' for the inernal source, or 'function [2]' for an internal function generator which can be configured.""", validator=strict_discrete_set, values=LOW_FREQUENCY_SOURCES, map_values=True ) def enable_low_freq_out(self): """Enables low frequency output""" self.write(":SOUR:LFO:STAT ON") def disable_low_freq_out(self): """Disables low frequency output""" self.write(":SOUR:LFO:STAT OFF") def config_low_freq_out(self, source='internal', amplitude=3): """ Configures the low-frequency output signal. :param source: The source for the low-frequency output signal. :param amplitude: Amplitude of the low-frequency output """ self.enable_low_freq_out() self.low_freq_out_source = source self.low_freq_out_amplitude = amplitude ####################### # Internal Oscillator # ####################### internal_frequency = Instrument.control( ":SOUR:AM:INT:FREQ?", ":SOUR:AM:INT:FREQ %g", """ A floating point property that controls the frequency of the internal oscillator in Hertz, which can take values from 0.5 Hz to 1 MHz. """, validator=truncated_range, values=[0.5, 1e6] ) INTERNAL_SHAPES = { 'sine': 'SINE', 'triangle': 'TRI', 'square': 'SQU', 'ramp': 'RAMP', 'noise': 'NOIS', 'dual-sine': 'DUAL', 'swept-sine': 'SWEP' } internal_shape = Instrument.control( ":SOUR:AM:INT:FUNC:SHAP?", ":SOUR:AM:INT:FUNC:SHAP %s", """ A string property that controls the shape of the internal oscillations, which can take the values: 'sine', 'triangle', 'square', 'ramp', 'noise', 'dual-sine', and 'swept-sine'. """, validator=strict_discrete_set, values=INTERNAL_SHAPES, map_values=True ) def __init__(self, adapter, **kwargs): super().__init__( adapter, "Agilent 8257D RF Signal Generator", **kwargs ) def enable(self): """ Enables the output of the signal. """ self.write(":OUTPUT ON;") def disable(self): """ Disables the output of the signal. """ self.write(":OUTPUT OFF;") def enable_modulation(self): self.write(":OUTPUT:MOD ON;") self.write(":lfo:sour int; :lfo:ampl 2.0vp; :lfo:stat on;") def disable_modulation(self): """ Disables the signal modulation. """ self.write(":OUTPUT:MOD OFF;") self.write(":lfo:stat off;") def config_amplitude_modulation(self, frequency=1e3, depth=100.0, shape='sine'): """ Configures the amplitude modulation of the output signal. :param frequency: A modulation frequency for the internal oscillator :param depth: A linear depth precentage :param shape: A string that describes the shape for the internal oscillator """ self.enable_amplitude_modulation() self.amplitude_source = 'internal' self.internal_frequency = frequency self.internal_shape = shape self.amplitude_depth = depth def enable_amplitude_modulation(self): """ Enables amplitude modulation of the output signal. """ self.write(":SOUR:AM:STAT ON") def disable_amplitude_modulation(self): """ Disables amplitude modulation of the output signal. """ self.write(":SOUR:AM:STAT OFF") def config_pulse_modulation(self, frequency=1e3, input='square'): """ Configures the pulse modulation of the output signal. :param frequency: A pulse rate frequency in Hertz :param input: A string that describes the internal pulse input """ self.enable_pulse_modulation() self.pulse_source = 'internal' self.pulse_input = input self.pulse_frequency = frequency def enable_pulse_modulation(self): """ Enables pulse modulation of the output signal. """ self.write(":SOUR:PULM:STAT ON") def disable_pulse_modulation(self): """ Disables pulse modulation of the output signal. """ self.write(":SOUR:PULM:STAT OFF") def config_step_sweep(self): """ Configures a step sweep through frequency """ self.write(":SOUR:FREQ:MODE SWE;" ":SOUR:SWE:GEN STEP;" ":SOUR:SWE:MODE AUTO;") def enable_retrace(self): self.write(":SOUR:LIST:RETR 1") def disable_retrace(self): self.write(":SOUR:LIST:RETR 0") def single_sweep(self): self.write(":SOUR:TSW") def start_step_sweep(self): """ Starts a step sweep. """ self.write(":SOUR:SWE:CONT:STAT ON") def stop_step_sweep(self): """ Stops a step sweep. """ self.write(":SOUR:SWE:CONT:STAT OFF") def shutdown(self): """ Shuts down the instrument by disabling any modulation and the output signal. """ self.disable_modulation() self.disable()

356

0

135

dec7d6d3d15ea5d55e90e3e5423d903170fe436f

12,428

py

Python

lib/googlecloudsdk/command_lib/storage/resources/s3_resource_reference.py

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

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

2

2019-11-10T09:17:07.000Z

2019-12-18T13:44:08.000Z

lib/googlecloudsdk/command_lib/storage/resources/s3_resource_reference.py

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

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

null

lib/googlecloudsdk/command_lib/storage/resources/s3_resource_reference.py

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

2a48a04df14be46c8745050f98768e30474a1aac

[ "Apache-2.0" ]

1

2020-07-25T01:40:19.000Z

# -*- coding: utf-8 -*- # # Copyright 2020 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """S3 API-specific resource subclasses.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import collections from googlecloudsdk.api_lib.storage import errors from googlecloudsdk.command_lib.storage.resources import resource_reference from googlecloudsdk.command_lib.storage.resources import resource_util _INCOMPLETE_OBJECT_METADATA_WARNING = ( 'Use "-j", the JSON flag, to view additional S3 metadata.') def _json_dump_recursion_helper(metadata): """See _get_json_dump docstring.""" if isinstance(metadata, list): return [_json_dump_recursion_helper(item) for item in metadata] if not isinstance(metadata, dict): return resource_util.convert_to_json_parsable_type(metadata) # Sort by key to make sure dictionary always prints in correct order. formatted_dict = collections.OrderedDict(sorted(metadata.items())) for key, value in formatted_dict.items(): if isinstance(value, dict): # Recursively handle dictionaries. formatted_dict[key] = _json_dump_recursion_helper(value) elif isinstance(value, list): # Recursively handled lists, which may contain more dicts, like ACLs. formatted_list = [_json_dump_recursion_helper(item) for item in value] if formatted_list: # Ignore empty lists. formatted_dict[key] = formatted_list elif value or resource_util.should_preserve_falsy_metadata_value(value): formatted_dict[key] = resource_util.convert_to_json_parsable_type(value) return formatted_dict def _get_json_dump(resource): """Formats S3 resource metadata as JSON. Args: resource (S3BucketResource|S3ObjectResource): Resource object. Returns: Formatted JSON string. """ return resource_util.configured_json_dumps( collections.OrderedDict([ ('url', resource.storage_url.url_string), ('type', resource.TYPE_STRING), ('metadata', _json_dump_recursion_helper(resource.metadata)), ])) def _get_error_or_exists_string(value): """Returns error if value is error or existence string.""" if isinstance(value, errors.S3ApiError): return value else: return resource_util.get_exists_string(value) def _get_formatted_acl_section(acl_metadata): """Returns formatted ACLs, error, or formatted none value.""" if isinstance(acl_metadata, errors.S3ApiError): return resource_util.get_padded_metadata_key_value_line('ACL', acl_metadata) elif acl_metadata: return resource_util.get_metadata_json_section_string( 'ACL', acl_metadata, _json_dump_recursion_helper) else: return resource_util.get_padded_metadata_key_value_line('ACL', '[]') def _get_full_bucket_metadata_string(resource): """Formats S3 resource metadata as string with rows. Args: resource (S3BucketResource): Resource with metadata. Returns: Formatted multi-line string. """ # Hardcoded strings found in Boto docs: # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html logging_enabled_value = _get_error_or_exists_string( resource.metadata['LoggingEnabled']) website_value = _get_error_or_exists_string(resource.metadata['Website']) cors_value = _get_error_or_exists_string(resource.metadata['CORSRules']) encryption_value = _get_error_or_exists_string( resource.metadata['ServerSideEncryptionConfiguration']) lifecycle_configuration_value = _get_error_or_exists_string( resource.metadata['LifecycleConfiguration']) if isinstance(resource.metadata['Versioning'], errors.S3ApiError): versioning_enabled_value = resource.metadata['Versioning'] else: versioning_status = resource.metadata['Versioning'].get('Status') if versioning_status == 'Enabled': versioning_enabled_value = True elif versioning_status == 'Suspended': versioning_enabled_value = False else: versioning_enabled_value = None if isinstance(resource.metadata['Payer'], errors.S3ApiError): requester_pays_value = resource.metadata['Payer'] elif resource.metadata['Payer'] == 'Requester': requester_pays_value = True elif resource.metadata['Payer'] == 'BucketOwner': requester_pays_value = False else: requester_pays_value = None return ( '{bucket_url}:\n' '{location_constraint_line}' '{versioning_enabled_line}' '{logging_config_line}' '{website_config_line}' '{cors_config_line}' '{encryption_config_line}' '{lifecycle_config_line}' '{requester_pays_line}' '{acl_section}' ).format( bucket_url=resource.storage_url.versionless_url_string, location_constraint_line=resource_util.get_padded_metadata_key_value_line( 'Location Constraint', resource.metadata['LocationConstraint']), versioning_enabled_line=resource_util.get_padded_metadata_key_value_line( 'Versioning Enabled', versioning_enabled_value), logging_config_line=resource_util.get_padded_metadata_key_value_line( 'Logging Configuration', logging_enabled_value), website_config_line=resource_util.get_padded_metadata_key_value_line( 'Website Configuration', website_value), cors_config_line=resource_util.get_padded_metadata_key_value_line( 'CORS Configuration', cors_value), encryption_config_line=resource_util.get_padded_metadata_key_value_line( 'Encryption Configuration', encryption_value), lifecycle_config_line=resource_util.get_padded_metadata_key_value_line( 'Lifecycle Configuration', lifecycle_configuration_value), requester_pays_line=resource_util.get_padded_metadata_key_value_line( 'Requester Pays Enabled', requester_pays_value), # Remove ending newline character because this is the last list item. acl_section=_get_formatted_acl_section(resource.metadata['ACL'])[:-1]) def _get_full_object_metadata_string(resource): """Formats S3 resource metadata as string with rows. Args: resource (S3ObjectResource): Resource with metadata. Returns: Formatted multi-line string. """ # Hardcoded strings found in Boto docs: # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html if 'LastModified' in resource.metadata: optional_time_updated_line = resource_util.get_padded_metadata_time_line( 'Update Time', resource.metadata['LastModified']) else: optional_time_updated_line = '' if 'StorageClass' in resource.metadata: optional_storage_class_line = resource_util.get_padded_metadata_key_value_line( 'Storage Class', resource.metadata['StorageClass']) else: optional_storage_class_line = '' if 'CacheControl' in resource.metadata: optional_cache_control_line = resource_util.get_padded_metadata_key_value_line( 'Cache-Control', resource.metadata['CacheControl']) else: optional_cache_control_line = '' if 'CacheDisposition' in resource.metadata: optional_content_disposition_line = resource_util.get_padded_metadata_key_value_line( 'Cache-Disposition', resource.metadata['CacheDisposition']) else: optional_content_disposition_line = '' if 'ContentEncoding' in resource.metadata: optional_content_encoding_line = resource_util.get_padded_metadata_key_value_line( 'Content-Encoding', resource.metadata['ContentEncoding']) else: optional_content_encoding_line = '' if 'ContentLanguage' in resource.metadata: optional_content_language_line = resource_util.get_padded_metadata_key_value_line( 'Content-Language', resource.metadata['ContentLanguage']) else: optional_content_language_line = '' if 'PartsCount' in resource.metadata: optional_component_count_line = ( resource_util.get_padded_metadata_key_value_line( 'Component-Count', resource.metadata['PartsCount'])) else: optional_component_count_line = '' if resource.md5_hash is not None: optional_md5_line = resource_util.get_padded_metadata_key_value_line( 'Hash (MD5)', resource.md5_hash) elif 'SSECustomerAlgorithm' in resource.metadata: optional_md5_line = resource_util.get_padded_metadata_key_value_line( 'Hash (MD5)', 'Underlying data encrypted') else: optional_md5_line = '' if 'SSECustomerAlgorithm' in resource.metadata: optional_encryption_algorithm_line = ( resource_util.get_padded_metadata_key_value_line( 'Encryption Algorithm', resource.metadata['SSECustomerAlgorithm'])) else: optional_encryption_algorithm_line = '' if resource.generation: optional_generation_line = resource_util.get_padded_metadata_key_value_line( 'Generation', resource.generation) else: optional_generation_line = '' return ( '{object_url}:\n' '{optional_time_updated_line}' '{optional_storage_class_line}' '{optional_cache_control_line}' '{optional_content_disposition_line}' '{optional_content_encoding_line}' '{optional_content_language_line}' '{content_length_line}' '{content_type_line}' '{optional_component_count_line}' '{optional_md5_line}' '{optional_encryption_algorithm_line}' '{etag_line}' '{optional_generation_line}' '{acl_section}' ' {incomplete_warning}').format( object_url=resource.storage_url.versionless_url_string, optional_time_updated_line=optional_time_updated_line, optional_storage_class_line=optional_storage_class_line, optional_cache_control_line=optional_cache_control_line, optional_content_disposition_line=optional_content_disposition_line, optional_content_encoding_line=optional_content_encoding_line, optional_content_language_line=optional_content_language_line, content_length_line=resource_util.get_padded_metadata_key_value_line( 'Content-Length', resource.size), content_type_line=resource_util.get_padded_metadata_key_value_line( 'Content-Type', resource.metadata.get('ContentType')), optional_component_count_line=optional_component_count_line, optional_md5_line=optional_md5_line, optional_encryption_algorithm_line=optional_encryption_algorithm_line, etag_line=resource_util.get_padded_metadata_key_value_line( 'ETag', resource.etag), optional_generation_line=optional_generation_line, acl_section=_get_formatted_acl_section(resource.metadata.get('ACL')), incomplete_warning=_INCOMPLETE_OBJECT_METADATA_WARNING) class S3BucketResource(resource_reference.BucketResource): """API-specific subclass for handling metadata.""" class S3ObjectResource(resource_reference.ObjectResource): """API-specific subclass for handling metadata.""" def __init__(self, storage_url_object, content_type=None, creation_time=None, etag=None, crc32c_hash=None, md5_hash=None, metadata=None, metageneration=None, size=None): """Initializes resource. Args are a subset of attributes.""" super(S3ObjectResource, self).__init__( storage_url_object, content_type=content_type, creation_time=creation_time, etag=etag, crc32c_hash=None, md5_hash=md5_hash, metadata=metadata, metageneration=metageneration, size=size)

38.716511

89

0.745735

# -*- coding: utf-8 -*- # # Copyright 2020 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """S3 API-specific resource subclasses.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import collections from googlecloudsdk.api_lib.storage import errors from googlecloudsdk.command_lib.storage.resources import resource_reference from googlecloudsdk.command_lib.storage.resources import resource_util _INCOMPLETE_OBJECT_METADATA_WARNING = ( 'Use "-j", the JSON flag, to view additional S3 metadata.') def _json_dump_recursion_helper(metadata): """See _get_json_dump docstring.""" if isinstance(metadata, list): return [_json_dump_recursion_helper(item) for item in metadata] if not isinstance(metadata, dict): return resource_util.convert_to_json_parsable_type(metadata) # Sort by key to make sure dictionary always prints in correct order. formatted_dict = collections.OrderedDict(sorted(metadata.items())) for key, value in formatted_dict.items(): if isinstance(value, dict): # Recursively handle dictionaries. formatted_dict[key] = _json_dump_recursion_helper(value) elif isinstance(value, list): # Recursively handled lists, which may contain more dicts, like ACLs. formatted_list = [_json_dump_recursion_helper(item) for item in value] if formatted_list: # Ignore empty lists. formatted_dict[key] = formatted_list elif value or resource_util.should_preserve_falsy_metadata_value(value): formatted_dict[key] = resource_util.convert_to_json_parsable_type(value) return formatted_dict def _get_json_dump(resource): """Formats S3 resource metadata as JSON. Args: resource (S3BucketResource|S3ObjectResource): Resource object. Returns: Formatted JSON string. """ return resource_util.configured_json_dumps( collections.OrderedDict([ ('url', resource.storage_url.url_string), ('type', resource.TYPE_STRING), ('metadata', _json_dump_recursion_helper(resource.metadata)), ])) def _get_error_or_exists_string(value): """Returns error if value is error or existence string.""" if isinstance(value, errors.S3ApiError): return value else: return resource_util.get_exists_string(value) def _get_formatted_acl_section(acl_metadata): """Returns formatted ACLs, error, or formatted none value.""" if isinstance(acl_metadata, errors.S3ApiError): return resource_util.get_padded_metadata_key_value_line('ACL', acl_metadata) elif acl_metadata: return resource_util.get_metadata_json_section_string( 'ACL', acl_metadata, _json_dump_recursion_helper) else: return resource_util.get_padded_metadata_key_value_line('ACL', '[]') def _get_full_bucket_metadata_string(resource): """Formats S3 resource metadata as string with rows. Args: resource (S3BucketResource): Resource with metadata. Returns: Formatted multi-line string. """ # Hardcoded strings found in Boto docs: # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html logging_enabled_value = _get_error_or_exists_string( resource.metadata['LoggingEnabled']) website_value = _get_error_or_exists_string(resource.metadata['Website']) cors_value = _get_error_or_exists_string(resource.metadata['CORSRules']) encryption_value = _get_error_or_exists_string( resource.metadata['ServerSideEncryptionConfiguration']) lifecycle_configuration_value = _get_error_or_exists_string( resource.metadata['LifecycleConfiguration']) if isinstance(resource.metadata['Versioning'], errors.S3ApiError): versioning_enabled_value = resource.metadata['Versioning'] else: versioning_status = resource.metadata['Versioning'].get('Status') if versioning_status == 'Enabled': versioning_enabled_value = True elif versioning_status == 'Suspended': versioning_enabled_value = False else: versioning_enabled_value = None if isinstance(resource.metadata['Payer'], errors.S3ApiError): requester_pays_value = resource.metadata['Payer'] elif resource.metadata['Payer'] == 'Requester': requester_pays_value = True elif resource.metadata['Payer'] == 'BucketOwner': requester_pays_value = False else: requester_pays_value = None return ( '{bucket_url}:\n' '{location_constraint_line}' '{versioning_enabled_line}' '{logging_config_line}' '{website_config_line}' '{cors_config_line}' '{encryption_config_line}' '{lifecycle_config_line}' '{requester_pays_line}' '{acl_section}' ).format( bucket_url=resource.storage_url.versionless_url_string, location_constraint_line=resource_util.get_padded_metadata_key_value_line( 'Location Constraint', resource.metadata['LocationConstraint']), versioning_enabled_line=resource_util.get_padded_metadata_key_value_line( 'Versioning Enabled', versioning_enabled_value), logging_config_line=resource_util.get_padded_metadata_key_value_line( 'Logging Configuration', logging_enabled_value), website_config_line=resource_util.get_padded_metadata_key_value_line( 'Website Configuration', website_value), cors_config_line=resource_util.get_padded_metadata_key_value_line( 'CORS Configuration', cors_value), encryption_config_line=resource_util.get_padded_metadata_key_value_line( 'Encryption Configuration', encryption_value), lifecycle_config_line=resource_util.get_padded_metadata_key_value_line( 'Lifecycle Configuration', lifecycle_configuration_value), requester_pays_line=resource_util.get_padded_metadata_key_value_line( 'Requester Pays Enabled', requester_pays_value), # Remove ending newline character because this is the last list item. acl_section=_get_formatted_acl_section(resource.metadata['ACL'])[:-1]) def _get_full_object_metadata_string(resource): """Formats S3 resource metadata as string with rows. Args: resource (S3ObjectResource): Resource with metadata. Returns: Formatted multi-line string. """ # Hardcoded strings found in Boto docs: # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html if 'LastModified' in resource.metadata: optional_time_updated_line = resource_util.get_padded_metadata_time_line( 'Update Time', resource.metadata['LastModified']) else: optional_time_updated_line = '' if 'StorageClass' in resource.metadata: optional_storage_class_line = resource_util.get_padded_metadata_key_value_line( 'Storage Class', resource.metadata['StorageClass']) else: optional_storage_class_line = '' if 'CacheControl' in resource.metadata: optional_cache_control_line = resource_util.get_padded_metadata_key_value_line( 'Cache-Control', resource.metadata['CacheControl']) else: optional_cache_control_line = '' if 'CacheDisposition' in resource.metadata: optional_content_disposition_line = resource_util.get_padded_metadata_key_value_line( 'Cache-Disposition', resource.metadata['CacheDisposition']) else: optional_content_disposition_line = '' if 'ContentEncoding' in resource.metadata: optional_content_encoding_line = resource_util.get_padded_metadata_key_value_line( 'Content-Encoding', resource.metadata['ContentEncoding']) else: optional_content_encoding_line = '' if 'ContentLanguage' in resource.metadata: optional_content_language_line = resource_util.get_padded_metadata_key_value_line( 'Content-Language', resource.metadata['ContentLanguage']) else: optional_content_language_line = '' if 'PartsCount' in resource.metadata: optional_component_count_line = ( resource_util.get_padded_metadata_key_value_line( 'Component-Count', resource.metadata['PartsCount'])) else: optional_component_count_line = '' if resource.md5_hash is not None: optional_md5_line = resource_util.get_padded_metadata_key_value_line( 'Hash (MD5)', resource.md5_hash) elif 'SSECustomerAlgorithm' in resource.metadata: optional_md5_line = resource_util.get_padded_metadata_key_value_line( 'Hash (MD5)', 'Underlying data encrypted') else: optional_md5_line = '' if 'SSECustomerAlgorithm' in resource.metadata: optional_encryption_algorithm_line = ( resource_util.get_padded_metadata_key_value_line( 'Encryption Algorithm', resource.metadata['SSECustomerAlgorithm'])) else: optional_encryption_algorithm_line = '' if resource.generation: optional_generation_line = resource_util.get_padded_metadata_key_value_line( 'Generation', resource.generation) else: optional_generation_line = '' return ( '{object_url}:\n' '{optional_time_updated_line}' '{optional_storage_class_line}' '{optional_cache_control_line}' '{optional_content_disposition_line}' '{optional_content_encoding_line}' '{optional_content_language_line}' '{content_length_line}' '{content_type_line}' '{optional_component_count_line}' '{optional_md5_line}' '{optional_encryption_algorithm_line}' '{etag_line}' '{optional_generation_line}' '{acl_section}' ' {incomplete_warning}').format( object_url=resource.storage_url.versionless_url_string, optional_time_updated_line=optional_time_updated_line, optional_storage_class_line=optional_storage_class_line, optional_cache_control_line=optional_cache_control_line, optional_content_disposition_line=optional_content_disposition_line, optional_content_encoding_line=optional_content_encoding_line, optional_content_language_line=optional_content_language_line, content_length_line=resource_util.get_padded_metadata_key_value_line( 'Content-Length', resource.size), content_type_line=resource_util.get_padded_metadata_key_value_line( 'Content-Type', resource.metadata.get('ContentType')), optional_component_count_line=optional_component_count_line, optional_md5_line=optional_md5_line, optional_encryption_algorithm_line=optional_encryption_algorithm_line, etag_line=resource_util.get_padded_metadata_key_value_line( 'ETag', resource.etag), optional_generation_line=optional_generation_line, acl_section=_get_formatted_acl_section(resource.metadata.get('ACL')), incomplete_warning=_INCOMPLETE_OBJECT_METADATA_WARNING) class S3BucketResource(resource_reference.BucketResource): """API-specific subclass for handling metadata.""" def get_full_metadata_string(self): return _get_full_bucket_metadata_string(self) def get_json_dump(self): return _get_json_dump(self) class S3ObjectResource(resource_reference.ObjectResource): """API-specific subclass for handling metadata.""" def __init__(self, storage_url_object, content_type=None, creation_time=None, etag=None, crc32c_hash=None, md5_hash=None, metadata=None, metageneration=None, size=None): """Initializes resource. Args are a subset of attributes.""" super(S3ObjectResource, self).__init__( storage_url_object, content_type=content_type, creation_time=creation_time, etag=etag, crc32c_hash=None, md5_hash=md5_hash, metadata=metadata, metageneration=metageneration, size=size) def get_full_metadata_string(self): return _get_full_object_metadata_string(self) def get_json_dump(self): return _get_json_dump(self)

198

0

100

d66df52d49ff61c5175e83fab8cd02546b04169c

1,982

py

Python

apistar_sentry.py

LeadPages/apistar_sentry

f718784b256399ae04f4e8bf82b177f9cc3b1008

[ "MIT" ]

2

2018-06-10T14:37:04.000Z

2018-06-16T22:33:46.000Z

apistar_sentry.py

LeadPages/apistar_sentry

f718784b256399ae04f4e8bf82b177f9cc3b1008

[ "MIT" ]

3

2020-03-24T17:19:55.000Z

2021-02-02T22:08:44.000Z

apistar_sentry.py

LeadPages/apistar_sentry

f718784b256399ae04f4e8bf82b177f9cc3b1008

[ "MIT" ]

1

2018-04-16T18:44:33.000Z

import typing from apistar import Settings from apistar.interfaces import Auth from apistar.types import ReturnValue from raven import Client __version__ = "0.2.0"

26.426667

72

0.590817

import typing from apistar import Settings from apistar.interfaces import Auth from apistar.types import ReturnValue from raven import Client __version__ = "0.2.0" class Sentry: def __init__(self, settings: Settings) -> None: self.client = Client( settings["SENTRY_DSN"], environment=settings["ENVIRONMENT"], release=settings["VERSION"], ) @classmethod def setup(cls, settings: Settings) -> typing.Optional["Sentry"]: if settings.get("SENTRY_DSN"): return cls(settings) return None @classmethod def setup_celery(cls, settings: Settings) -> None: from raven.contrib import celery as raven_celery sentry = cls(settings) raven_celery.register_logger_signal(sentry.client) raven_celery.register_signal(sentry.client) def track(self, auth: Auth) -> None: self.client.context.activate() if auth is not None: self.client.context.merge({ "user": { "id": auth.get_user_id(), "name": auth.get_display_name(), "authenticated": auth.is_authenticated(), } }) def clear(self) -> None: self.client.context.clear() def capture_exception(self) -> None: self.client.captureException() class SentryMixin: def exception_handler(self, exc: Exception, sentry: Sentry) -> None: try: return super().exception_handler(exc) except Exception: if sentry is not None: try: sentry.capture_exception() finally: sentry.clear() raise def before_request(auth: Auth, sentry: Sentry) -> None: if sentry is not None: sentry.track(auth) def after_request(sentry: Sentry, ret: ReturnValue) -> ReturnValue: if sentry is not None: sentry.clear() return ret

1,510

184

118

460ed8df205faa3ecff6b37fb600ecfce371a297

7,121

py

Python

app.py

tanasijevich/project3

cd4870727e31bad47868625a59a565f4b96d80a5

[ "MIT" ]

null

app.py

tanasijevich/project3

cd4870727e31bad47868625a59a565f4b96d80a5

[ "MIT" ]

null

app.py

tanasijevich/project3

cd4870727e31bad47868625a59a565f4b96d80a5

[ "MIT" ]

null

# import necessary libraries # from models import create_classes import pandas as pd import os import sqlalchemy from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func from sqlite3 import connect import json from flask import ( Flask, render_template, jsonify, request, redirect, jsonify) # Read data from csv #csv_file = "data/Chicago Health Atlas.csv" #df = pd.read_csv(csv_file) #df.head() #df.rename(columns={"VRDIBR_2015-2019":"VRDIBR_2015_2019","VRDIAR_2015-2018":"VRDIAR_2015_2018","VRDTH_2015-2019":"VRDTH_2015_2019","VRCAR_2015-2019":"VRCAR_2015_2019","VRADR_2015-2019":"VRADR_2015_2019","HDX_2015-2019":"HDX_2015_2019"},inplace=True) #creating sqlite engine to create database #engine = create_engine('sqlite:///data/Chicago_Health_database.db') #engine = create_engine('sqlite:///C:/Users/doyel/Desktop/project3_flask_ex1/data/mydatabase.db') #Table name : Chicago_Health_Atlas #df.to_sql('Chicago_Health_Atlas',con=engine,if_exists='replace') ##################################################################### engine = create_engine("sqlite:///data/mydatabase.db") # reflect an existing database into a new model Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Save reference to the table print(Base.classes.keys()) Healthatlas = Base.classes.healthatlas #Actors = Base.classes.actors ################################################# # Flask Setup ################################################# app = Flask(__name__) # --------------------------------------------------------- # Web site @app.route("/") @app.route("/data.html") @app.route("/templates/map.html") @app.route("/templates/d3_chart.html") # --------------------------------------------------------- # API to call "when data.html" page is loading with community information table @app.route("/api/community") # API to call when a disease is selectd from list by user in "data.html" page @app.route("/api/deceases/<decease>") @app.route("/api/geojson") @app.route('/api/d3_chart/<field_x>/<field_y>') if __name__ == "__main__": app.run()

42.136095

576

0.72869

# import necessary libraries # from models import create_classes import pandas as pd import os import sqlalchemy from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func from sqlite3 import connect import json from flask import ( Flask, render_template, jsonify, request, redirect, jsonify) # Read data from csv #csv_file = "data/Chicago Health Atlas.csv" #df = pd.read_csv(csv_file) #df.head() #df.rename(columns={"VRDIBR_2015-2019":"VRDIBR_2015_2019","VRDIAR_2015-2018":"VRDIAR_2015_2018","VRDTH_2015-2019":"VRDTH_2015_2019","VRCAR_2015-2019":"VRCAR_2015_2019","VRADR_2015-2019":"VRADR_2015_2019","HDX_2015-2019":"HDX_2015_2019"},inplace=True) #creating sqlite engine to create database #engine = create_engine('sqlite:///data/Chicago_Health_database.db') #engine = create_engine('sqlite:///C:/Users/doyel/Desktop/project3_flask_ex1/data/mydatabase.db') #Table name : Chicago_Health_Atlas #df.to_sql('Chicago_Health_Atlas',con=engine,if_exists='replace') ##################################################################### engine = create_engine("sqlite:///data/mydatabase.db") # reflect an existing database into a new model Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Save reference to the table print(Base.classes.keys()) Healthatlas = Base.classes.healthatlas #Actors = Base.classes.actors ################################################# # Flask Setup ################################################# app = Flask(__name__) # --------------------------------------------------------- # Web site @app.route("/") def home(): return render_template("index.html") @app.route("/data.html") def data(): return render_template("data.html") @app.route("/templates/map.html") def map(): return render_template("map.html") @app.route("/templates/d3_chart.html") def d3_chart(): return render_template("d3_chart.html") # --------------------------------------------------------- # API to call "when data.html" page is loading with community information table @app.route("/api/community") def community_grid(): session = Session(engine) results = session.query(Healthatlas.Name,Healthatlas.Median_Household_Income,Healthatlas.Poverty_Rate,Healthatlas.Receiving_Food_Stamps,Healthatlas.Public_Assistance_Income,Healthatlas.High_School_Grad_Rate, Healthatlas.College_Grad_Rate,Healthatlas.Non_Hispanic_White,Healthatlas.Non_Hispanic_Black,Healthatlas.Asian_Pacific_Islander,Healthatlas.Hispanic_or_Latino,Healthatlas.Population_All,Healthatlas.Population_Infants,Healthatlas.Population_Juveniles,Healthatlas.Population_Young_Adults,Healthatlas.Population_Middle_Aged_Adults,Healthatlas.Population_Seniors).all() #results = session.query(Healthatlas.Name,Healthatlas.GEOID, Healthatlas.Population,Healthatlas.Longitude, Healthatlas.Latitude).all() #results = pd.read_sql('SELECT Name,GEOID,Population,Longitude,Latitude FROM Chicago_Health_Atlas', engine) #results = engine.execute("SELECT Name,GEOID,Population,Longitude,Latitude FROM Chicago_Health_Atlas").fetchall() #session.query(Movies.title, Movies.director, Movies.year, Movies.rating, Movies.imdb_votes, Movies.imdb_score).all() results = [list(r) for r in results] table_results = { "table": results } session.close() return jsonify(table_results) # API to call when a disease is selectd from list by user in "data.html" page @app.route("/api/deceases/<decease>") def deceases(decease): session = Session(engine) if decease == "diabetes": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRDIAR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() elif decease == "diabetes_related": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRDIBR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018 ).all() elif decease == "alzheimer": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRADR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() elif decease == "cancer": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRCAR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() elif decease == "hypertension": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.HCSHYTP_2016_2018, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() elif decease == "adult_obesity": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.HCSOBP_2016_2018, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() elif decease == "coronary_heart_disease": results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRCHDR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() #elif decease == "all" : # results = session.query(Healthatlas.Name, Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRDTH_2015_2019, Healthatlas.HDX_2015_2019).all() else: results = session.query(Healthatlas.Name,Healthatlas.Population_All, Healthatlas.Longitude, Healthatlas.Latitude, Healthatlas.VRADR_2015_2019, Healthatlas.HDX_2015_2019, Healthatlas.HCSSP_2016_2018, Healthatlas.HCSSMKP_2016_2018).all() results = [list(r) for r in results] name = [result[4] for result in results] hardship = [result[5] for result in results] soda = [result[6] for result in results] smoke = [result[7] for result in results] decease_results = { "decease_name": name, "hd_index": hardship, "soda_con":soda, "smoking":smoke, } session.close() return jsonify(decease_results) @app.route("/api/geojson") def map_data(): with open('data/geo.json', 'r') as file: your_data = json.loads(file.read()) # print(your_data) return jsonify(your_data) @app.route('/api/d3_chart/<field_x>/<field_y>') def d3_chart_api(field_x, field_y): session = Session(engine) x_column = getattr(Healthatlas, field_x) y_column = getattr(Healthatlas, field_y) results = session.query(x_column, y_column).all() results = [list(r) for r in results] session.close() return jsonify(results) if __name__ == "__main__": app.run()

4,772

0

176

e202b3b1b0dd6517b189261d661038ca7be2cad9

2,377

py

Python

lambda/extract_yelp/extract.py

Rdbaker/barfinder

63c75dc99f2371371aa8072078175558d1917864

[ "BSD-3-Clause" ]

null

lambda/extract_yelp/extract.py

Rdbaker/barfinder

63c75dc99f2371371aa8072078175558d1917864

[ "BSD-3-Clause" ]

null

lambda/extract_yelp/extract.py

Rdbaker/barfinder

63c75dc99f2371371aa8072078175558d1917864

[ "BSD-3-Clause" ]

null

import logging from models import tag as Tag, business as Business, business_tag_join_table logging.getLogger().setLevel(logging.INFO) def business_exists(yelp_id, conn): """Return True if the business exists.""" return conn.execute(Business.select().where(Business.c.yelp_id == yelp_id))\ .first() is not None def delete_business(yelp_id, conn): """Delete the business with the given yelp id.""" return conn.execute(Business.delete().where(Business.c.yelp_id == yelp_id))

32.561644

80

0.640303

import logging from models import tag as Tag, business as Business, business_tag_join_table logging.getLogger().setLevel(logging.INFO) def parse_yelp_business(business): return { 'source': 'yelp', 'raw_yelp_data': business, 'yelp_id': business.get('id'), 'name': business.get('name', 'UNKNOWN'), 'price': len(business.get('price', '')), 'latitude': business.get('coordinates', {}).get('latitude'), 'longitude': business.get('coordinates', {}).get('longitude'), 'phone': business.get('phone'), } def business_exists(yelp_id, conn): """Return True if the business exists.""" return conn.execute(Business.select().where(Business.c.yelp_id == yelp_id))\ .first() is not None def delete_business(yelp_id, conn): """Delete the business with the given yelp id.""" return conn.execute(Business.delete().where(Business.c.yelp_id == yelp_id)) def tag_exists(alias, conn): return conn.execute(Tag.select().where(Tag.c.alias == alias))\ .first() is not None def create_tag(tag, conn): conn.execute(Tag.insert().values(**tag)) def get_or_create_tags(tags, conn): names = [] for tag in tags: if not tag_exists(tag['alias'], conn): create_tag(tag, conn) names.append(tag['alias']) return conn.execute(Tag.select().where(Tag.c.alias.in_(names))).fetchall() def create_business(business, conn): conn.execute(Business.insert().values(**business)) return conn.execute(Business.select().where(Business.c.yelp_id == business['yelp_id'])).first() def link_business_to_tags(business, tags, conn): for tag in tags: conn.execute( business_tag_join_table.insert().values(tag_id=tag.id, business_id=business.id)) def extract_business(business_dict, engine): conn = engine.connect() if business_exists(business_dict['id'], conn): delete_business(business_dict['id'], conn) business = parse_yelp_business(business_dict) tags = get_or_create_tags(business_dict['categories'], conn) business = create_business(business, conn) link_business_to_tags(business, tags, conn) logging.info('successfully processed business: {}' .format(business_dict['id']))

1,706

0

161

5ddcfbd5c5a68beee52f20bf25f10cc164269d23

14,652

py

Python

doubling_agent/motility_functions.py

lkmartin90/doubling_agent

73a7f06aa43c5fa51ea1263b72ebe6f8319bf894

[ "MIT" ]

1

2020-12-03T15:47:24.000Z

doubling_agent/motility_functions.py

lkmartin90/doubling_agent

73a7f06aa43c5fa51ea1263b72ebe6f8319bf894

[ "MIT" ]

null

doubling_agent/motility_functions.py

lkmartin90/doubling_agent

73a7f06aa43c5fa51ea1263b72ebe6f8319bf894

[ "MIT" ]

null

from random import random from random import choice import numpy as np import plotly.express as px import struct import operator ### # Broadly the same as "basic_functions.py" but updated to include motility # intentionally trying to keep them separate so as not to slow down the basic version ###

36.35732

115

0.574597

from random import random from random import choice import numpy as np import plotly.express as px import struct import operator ### # Broadly the same as "basic_functions.py" but updated to include motility # intentionally trying to keep them separate so as not to slow down the basic version ### class MotilityParameters: def __init__(self, switch_to_m_rate, switch_to_p_rate, motility_rate): # 0 motility state is proliferating, 1 is moving self.m = switch_to_m_rate self.p = switch_to_p_rate self.rate = motility_rate self.dict = {0: switch_to_m_rate, 1: switch_to_p_rate + motility_rate} class ParametersBasic: def __init__(self, s_division_rate, epsilon, p_division_rate, apoptosis_rate): self.s = s_division_rate self.p = p_division_rate self.e = epsilon self.a = apoptosis_rate self.dict = {0: s_division_rate, 1: p_division_rate, 2: apoptosis_rate} self.death = {0: 0, 1: 0, 2: apoptosis_rate} class ParametersQuiescent: def __init__(self, k1, k2, k3, k4, k5, k6, k7, k8): # s>s+s :k1, s>s+p:k2, s>dead:k3, p>p+p:k4, p>dead:k5, p>Q:k6, D>dead:k7, Q>s:k8 self.k1 = k1 self.k2 = k2 self.k3 = k3 self.k4 = k4 self.k5 = k5 self.k6 = k6 self.k7 = k7 self.k8 = k8 # rate of something happening for each state # note the slight change in notation, 0 is stem cell, 1 progenitor, 2 differentiated and 3 quiescent self.dict = {0: k1+k2+k3, 1: k4+k5+k6, 2: k7, 3: k8} self.death = {0: k3, 1: k5, 2: k7, 3: 0} def cancer_seed_single(cells, switch_3d): # created initial cancer stem cell at [0,0] if switch_3d: cells.update({(0, 0, 0): [0, 0, 0]}) else: cells.update({(0,0): [0, 0, 0]}) def cancer_seed_single_quiescent(cells): # created initial cancer cell (differentiated) at [0,0] cells.update({(0,0): [3, 0, 0]}) def cancer_seed_single_progen(cells): # created initial cancer cell (differentiated) at [0,0] cells.update({(0,0): [1, 0, 0]}) def timing_update_all(cells, params, mot_params): # update second entry in dict to give a timing based on the first entry, the state # time is log(1/rand_no)/rate # Now want to account for fact that cells can either change motility state or move or divide # options: # motility state 1: can either move, change motility state, or die # motility state 0: can either change motility state or go though all division choices # including death (already written) for k in cells.keys(): state = cells.get(k)[0] mot = cells.get(k)[2] div = params.dict[state] # division or death rate for motility 0 m_or_c = mot_params.dict[mot] # move or change rate mot_death = params.death[state] # death rate for motility state 1 if mot == 0: rate = div+m_or_c else: rate = m_or_c + mot_death cells.update({k: [state, np.log(1/random())/rate, mot]}) def choose_new_pos(pos, cells): # Identifies a free position for a cell to divide or move into. In this function a 2d square grid is used # space is searched for in the surrounding area, by random number generator, if there is already a cell # occupying the space then that space is excluded from possible locations and a new random number is generated. i = pos[0] j = pos[1] neighbours = [(i+1, j), (i-1, j), (i, j-1), (i, j+1)] options = [0, 1, 2, 3] cont = 0 new_pos = 0 while cont == 0 and len(options) > 0: pick = choice(options) check = neighbours[pick] if check in cells: options.remove(pick) else: cont = 1 new_pos = check return new_pos def choose_new_pos_eq(pos, cells): # choses a new position by identifying all the free spaces first and then assigning them all equal probability i = pos[0] j = pos[1] neighbours = [(i+1, j), (i-1, j), (i, j-1), (i, j+1)] options = [0, 1, 2, 3] for n in range(len(neighbours)): if neighbours[n] in cells: options.remove(n) if len(options) > 0: new_pos = neighbours[choice(options)] else: new_pos = 0 return new_pos def choose_new_pos_3d(pos, cells): # 3d version of "choose_new_pos", the same method is used i = pos[0] j = pos[1] k = pos[2] # this currently assumes only square transitions on the cubic grid, may want to alter neighbours = [(i + 1, j, k), (i - 1, j, k), (i, j + 1, k), (i, j - 1, k), (i, j, k + 1), (i, j, k - 1)] options = [0, 1, 2, 3, 4, 5] cont = 0 new_pos = 0 while cont == 0 and len(options) > 0: pick = choice(options) check = neighbours[pick] if check in cells: options.remove(pick) else: cont = 1 new_pos = check return new_pos def choose_new_pos_3d_eq(pos, cells): # 3d version of "choose_new_pos", the same method is used i = pos[0] j = pos[1] k = pos[2] # this currently assumes only square transitions on the cubic grid, may want to alter neighbours = [(i + 1, j, k), (i - 1, j, k), (i, j + 1, k), (i, j - 1, k), (i, j, k + 1), (i, j, k - 1)] options = [0, 1, 2, 3, 4, 5] cont = 0 new_pos = 0 while cont == 0 and len(options) > 0: pick = choice(options) check = neighbours[pick] if check in cells: options.remove(pick) else: cont = 1 new_pos = check return new_pos def move_cell(cells, pos, state, switch_3d): # moves the cell if switch_3d: new_location = choose_new_pos_3d(pos, cells) else: new_location = choose_new_pos(pos, cells) if new_location != 0: del cells[pos] cells.update({new_location: [state, 0, 1]}) def update_cell_basic(cells, pos, params, switch_3d, mot_params): # updates a given cell based on the current state of that cell # pos is string describing position # time is from random number generator giving time of interaction # cells is dict describing all cells in the tumour state = cells.get(pos)[0] mot = cells.get(pos)[2] # Once motility is included first thing is to make a decision on whether the cell moves, divides, or switches # to a different motility state. Need to check that an appropriate time step is being used still. mot_check = random() if mot == 1: # Can move, cell can either switch motility state or move or die if mot_check < mot_params.p/(mot_params.dict.get(mot) + params.death.get(state)): # then the motilty state changes cells.update({pos: [state, 0, abs(mot-1)]}) elif mot_check < mot_params.dict.get(mot)/(mot_params.dict.get(mot) + params.death.get(state)): # The cell moves move_cell(cells, pos, state, switch_3d) else: # cell death del cells[pos] else: # No motility, can either switch state or go to division decisions if mot_check < mot_params.m/(mot_params.dict.get(mot) + params.dict.get(state)): # then the motilty state changes cells.update({pos: [state, 0, abs(mot - 1)]}) # The cell divides or dies, can ust move on to that section as we have already conditioned on the # probability of it happening else: if switch_3d: daughter = choose_new_pos_3d(pos, cells) else: daughter = choose_new_pos(pos, cells) if state == 0: # if it's a stem cell there are 2 possibilities, S > S + S, S > S + P # generate random number to determine fate, compare to epsilon r_num = random() if r_num < params.e: # divide > S + S if daughter != 0: cells.update({daughter: [0, 0, 0]}) else: # divide > S + P if daughter != 0: cells.update({daughter: [1, 0, 0]}) elif state == 1: # if it's a progentior cell there are 2 possibilities, P > P + P, P > D # generate random number to determine fate, start by assuming each happens with equal chance r_num = random() if r_num < 0.5: # P > P + P if daughter != 0: cells.update({daughter: [1, 0, 0]}) else: # P > D cells.update({pos: [2, 0, 0]}) else: # If it is differentiated cell the only possible state change is death del cells[pos] def update_cell_quiescent(cells, pos, params, switch_3d, mot_params): # updates a given cell based on the current state of that cell # pos is string describing position # time is from random number generator giving time of interaction # cells is dict describing all cells in the tumour state = cells.get(pos)[0] mot = cells.get(pos)[2] # Once motility is included first thing is to make a decision on whether the cell moves, divides, or switches # to a different motility state. Need to check that an appropriate time step is being used still. mot_check = random() if mot == 1: # Can move, cell can either switch motility state or move or die if mot_check < mot_params.p / (mot_params.dict.get(mot) + params.death.get(state)): # then the motilty state changes cells.update({pos: [state, 0, abs(mot - 1)]}) elif mot_check < mot_params.dict.get(mot) / (mot_params.dict.get(mot) + params.death.get(state)): # The cell moves move_cell(cells, pos, state, switch_3d) else: # cell death del cells[pos] else: # No motility, can either switch state or go to division decisions if mot_check < mot_params.m / (mot_params.dict.get(mot) + params.dict.get(state)): # then the motilty state changes cells.update({pos: [state, 0, abs(mot - 1)]}) else: if switch_3d: daughter = choose_new_pos_3d(pos, cells) else: daughter = choose_new_pos(pos, cells) if state == 0: # if it's a stem cell there are 3 possibilities, S > S + S, S > S + P and S > dead # generate random number to determine fate r_num = random() if r_num < params.k1/params.dict.get(0): # divide > S + S if daughter != 0: cells.update({daughter: [0, 0, 0]}) elif r_num < (params.k1+params.k2)/params.dict.get(0): # divide > S + P if daughter != 0: cells.update({daughter: [1, 0, 0]}) else: # die del cells[pos] elif state == 1: # if it's a progentior cell there are 3 possibilities, P > P + P, P > D, P > Q # generate random number to determine fate r_num = random() if r_num < params.k4/params.dict.get(1): # P > P + P if daughter != 0: cells.update({daughter: [1, 0, 0]}) elif r_num < (params.k4+params.k5)/params.dict.get(1): # P > D cells.update({pos: [2, 0, 0]}) else: # P > Q cells.update({pos: [3, 0, 0]}) elif state == 2: # If it is differentiated cell the only possible state change is death del cells[pos] else: # If its Quiescent the only possible fate is to return to a stem cell cells.update({pos: [0, 0, 0]}) def animate(animation_df, r, name): # animate the simulations using plotly and save as a .html animation_df['coord'] = animation_df[['x', 'y']].values.tolist() animation_df['coord'] = animation_df['coord'].apply(lambda x: np.array(x)) #print(animation_df) if len(animation_df['coord'].values[0]) > 2: print("currently cannot animate for 3d") raise ValueError() mapping = {0: 'stem cell', 1: 'progenitor cell', 2: 'differentiated cell', 3: 'quiescent cell'} animation_df = animation_df.replace({'state': mapping}) animation_df = animation_df.append( {'state': 'differentiated cell', 'count': 0, 'coord': 0, 'x': 10000, 'y': 10000}, ignore_index=True) animation_df = animation_df.append( {'state': 'progenitor cell', 'count': 0, 'coord': 0, 'x': 10000, 'y': 10000}, ignore_index=True) animation_df = animation_df.append( {'state': 'quiescent cell', 'count': 0, 'coord': 0, 'x': 10000, 'y': 10000}, ignore_index=True) fig = px.scatter(animation_df, x="x", y="y", animation_frame="count", color='state', size_max=55, range_x=[-50, 50], range_y=[-50, 50]) fig.update_traces(marker=dict(size=12)) fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 20 fig.show() fig.write_html(name + '/ani_' + str(r) + '.html') def read_from_file(file_name, switch_3d): # read data from binary file in the form: time step, x, y, state, motility if switch_3d: struct_fmt = '=iiiiii' # 6 ints else: struct_fmt = '=iiiii' # 5 ints struct_len = struct.calcsize(struct_fmt) struct_unpack = struct.Struct(struct_fmt).unpack_from results = [] with open(file_name, "rb") as f: while True: data = f.read(struct_len) if not data: break s = struct_unpack(data) results.append(s) return results def calculate_timestep(params, mot_params): # calculates timestep based on the probability of 2 or more events happening in a timestep (<0.01) max_rate = max(params.dict.items(), key=operator.itemgetter(1))[1] + \ max(mot_params.dict.items(), key=operator.itemgetter(1))[1] # playing it safe by summing max of each lambert = 0.135157 step = lambert/max_rate print('exact timestep from calculation', step) if step > 0.1: return step // 0.1 * 0.1 elif step > 0.01: return step // 0.01 * 0.01 else: return step // 0.001 * 0.001

13,857

10

469

4f5568285363d98aa73edfd78a49dd8dcefd4487

2,616

py

Python

DATA/Ejercicio Pandas/MisMacrosPy.py

JeisonAlarcon/Data_Mining

265f1ffc202ca1f8b9f6223e95d56bb72e4c8ff3

[ "MIT" ]

null

DATA/Ejercicio Pandas/MisMacrosPy.py

JeisonAlarcon/Data_Mining

265f1ffc202ca1f8b9f6223e95d56bb72e4c8ff3

[ "MIT" ]

null

DATA/Ejercicio Pandas/MisMacrosPy.py

JeisonAlarcon/Data_Mining

265f1ffc202ca1f8b9f6223e95d56bb72e4c8ff3

[ "MIT" ]

null

import pandas as pd

51.294118

147

0.619266

import pandas as pd def onewayfreq(rows,data,weight=None,cum=True,ord="level",subset=None): if weight is None: weight = 1 else: weight = data[weight] if subset != None: data = data.query(subset,engine="python") out = (pd.crosstab(index=data[rows],values=weight,aggfunc="sum",columns="sum").reset_index() .rename(columns={"sum":"Frequency"}) .eval("Percent=100*Frequency/Frequency.sum()",engine="python") ) if ord == "freq": out = out.sort_values(by=["Frequency"],ascending=False).reset_index(drop=True) if ord == "-freq": out = out.sort_values(by=["Frequency"],ascending=True).reset_index(drop=True) if cum == True: out = (out.eval("CumulativeFrequency=Frequency.cumsum()",engine="python") .eval("CumulativePercent=Percent.cumsum()",engine="python")) out.columns.name="" return(out) def twowayfreq(rows,columns,data,weight=None,subset=None,ord="level",percent=False,rowpercent=False,colpercent=False): if weight is None: weight = 1 else: weight = data[weight] if subset != None: data = data.query(subset,engine="python") out = pd.crosstab(index=data[rows],values=weight,aggfunc="sum",columns=data[columns],margins=True,margins_name="Total").reset_index().fillna(0) p1 = out.head(n=(len(out.index)-1)) if ord == "freq": p1 = p1.sort_values(by=["Total"],ascending=False) if ord == "-freq": p1 = p1.sort_values(by=["Total"],ascending=True) out = p1.append(out.tail(n=1)).reset_index(drop=True) ids = out.select_dtypes("number").columns out2 = out.copy().assign(Ind = "2") out3 = out.copy().assign(Ind = "3") out4 = out.copy().assign(Ind = "4") out = out.assign(Ind = "1") def pct(x): return(200*x/x.sum()) out2[ids] = 200*out2[ids]/out2["Total"].sum() out3[ids] = out._get_numeric_data().apply(pct,axis=1) out4[ids] = out._get_numeric_data().apply(pct,axis=0) out = out.append(out2).append(out3).append(out4).rename_axis('MyIdx').sort_values(by=["MyIdx","Ind"]) out.loc[out["Ind"] != "1",rows] = "" print("Frequency") if percent == False: out = out.query("Ind != '2'") else: print("Percent") if rowpercent == False: out = out.query("Ind != '3'") else: print("Row percent") if colpercent == False: out = out.query("Ind != '4'") else: print("Col percent") out = out.drop(columns="Ind").reset_index(drop=True) out.columns.name="" out.index.names=[""] out = out.rename(columns={rows: rows + " / " + columns}) print(" ") return(out)

2,551

0

46

7c713c20032eec8a8f8dbf77f8cd9a9bca904c31

1,454

py

Python

TSP.py

ccfelius/TravelingSalesMan

ebc3b960859590623c0eb301545cd093c41d157a

[ "MIT" ]

1

2020-12-10T17:36:39.000Z

TSP.py

ccfelius/TravelingSalesMan

ebc3b960859590623c0eb301545cd093c41d157a

[ "MIT" ]

null

TSP.py

ccfelius/TravelingSalesMan

ebc3b960859590623c0eb301545cd093c41d157a

[ "MIT" ]

1

2021-01-05T13:08:07.000Z

""" TSP SIMULATED ANNEALING """ # Imports import matplotlib.pyplot as plt import pandas as pd import numpy as np # read data from file filename = "eil51.tsp" f = open(f"TSP-configurations/{filename}.txt", "r") network = f.readlines()[6:-1] # create dictionary to store coordinates nodes = dict() # split data and put in dict for node in network: node = [int(x) for x in node.rstrip().split(' ')] nodes[node[0]] = node[1:] x = [x[0] for x in nodes.values()] y = [y[1] for y in nodes.values()] # load in data of optimal path data = pd.read_csv("data/eil51.tsp.tsp-batch-20.txt", sep="\t") colname = "428.87" z = list(map(float,list(data[f'{colname}-19']))) # optimum so far (costs = 428.87175639203394) # r= [1.0, 32, 11, 38, 5, 37, 17, 4, 18, 47, 12, 46, 51.0, 27, 6, 48, 23, 7, 43, 24, 14, 25, 13, 41, 40, 19, 42, 44, 15, 45, 33, 39, 10, 49, 9, 30, 34, 21, 50, 16, 2, 29, 20, 35, 36, 3, 28, 31, 26, 8, 22, 1.0] temp = [] # get coordinates of each point for item in z: temp.append(nodes[item]) temp = np.array(temp) # path = [temp[i:i+2] for i in range(len(temp)-2+1)] # print(path) # Plot the nodes and coordinates fig, ax = plt.subplots() ax.scatter(x, y, color="deeppink") for i, txt in enumerate(nodes.keys()): ax.annotate(txt, (x[i], y[i])) ax.plot(*temp.T, color="deeppink", alpha=0.5) ax.set_title(f"Shortest Route: {filename}, costs: {colname}", fontsize=16) # plt.savefig("plots/eil51-opt-route-3.png") plt.show()

25.508772

209

0.636176

""" TSP SIMULATED ANNEALING """ # Imports import matplotlib.pyplot as plt import pandas as pd import numpy as np # read data from file filename = "eil51.tsp" f = open(f"TSP-configurations/{filename}.txt", "r") network = f.readlines()[6:-1] # create dictionary to store coordinates nodes = dict() # split data and put in dict for node in network: node = [int(x) for x in node.rstrip().split(' ')] nodes[node[0]] = node[1:] x = [x[0] for x in nodes.values()] y = [y[1] for y in nodes.values()] # load in data of optimal path data = pd.read_csv("data/eil51.tsp.tsp-batch-20.txt", sep="\t") colname = "428.87" z = list(map(float,list(data[f'{colname}-19']))) # optimum so far (costs = 428.87175639203394) # r= [1.0, 32, 11, 38, 5, 37, 17, 4, 18, 47, 12, 46, 51.0, 27, 6, 48, 23, 7, 43, 24, 14, 25, 13, 41, 40, 19, 42, 44, 15, 45, 33, 39, 10, 49, 9, 30, 34, 21, 50, 16, 2, 29, 20, 35, 36, 3, 28, 31, 26, 8, 22, 1.0] temp = [] # get coordinates of each point for item in z: temp.append(nodes[item]) temp = np.array(temp) # path = [temp[i:i+2] for i in range(len(temp)-2+1)] # print(path) # Plot the nodes and coordinates fig, ax = plt.subplots() ax.scatter(x, y, color="deeppink") for i, txt in enumerate(nodes.keys()): ax.annotate(txt, (x[i], y[i])) ax.plot(*temp.T, color="deeppink", alpha=0.5) ax.set_title(f"Shortest Route: {filename}, costs: {colname}", fontsize=16) # plt.savefig("plots/eil51-opt-route-3.png") plt.show()

0

161e27c9bae1210d9e0d2d5bf83676988c609bbf

90,220

py

Python

tests/Lopez12CPL/test_Lopez12CPL.py

jrlivesey/vplanet

4384221baa78e81d46b0c0fcb7de2f5a5de2e83c

[ "MIT" ]

null

tests/Lopez12CPL/test_Lopez12CPL.py

jrlivesey/vplanet

4384221baa78e81d46b0c0fcb7de2f5a5de2e83c

[ "MIT" ]

null

tests/Lopez12CPL/test_Lopez12CPL.py

jrlivesey/vplanet

4384221baa78e81d46b0c0fcb7de2f5a5de2e83c

[ "MIT" ]

null

import astropy.units as u import pytest from benchmark import Benchmark, benchmark @benchmark( { "log.initial.system.Age": {"value": 3.155760e13, "unit": u.sec}, "log.initial.system.Time": {"value": 0.000000, "unit": u.sec}, "log.initial.system.TotAngMom": { "value": 6.747268e40, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.system.TotEnergy": {"value": -2.482441e43, "unit": u.Joule}, "log.initial.system.PotEnergy": {"value": -2.482440e43, "unit": u.Joule}, "log.initial.system.KinEnergy": {"value": 5.347271e34, "unit": u.Joule}, "log.initial.system.DeltaTime": {"value": 0.000000, "unit": u.sec}, "log.initial.star.Mass": {"value": 1.988416e30, "unit": u.kg}, "log.initial.star.Obliquity": {"value": 0.000000, "unit": u.rad}, "log.initial.star.PrecA": {"value": 0.000000, "unit": u.rad}, "log.initial.star.Xobl": {"value": 0.000000}, "log.initial.star.Yobl": {"value": 0.000000}, "log.initial.star.Zobl": {"value": 1.000000}, "log.initial.star.Radius": {"value": 6.378100e06, "unit": u.m}, "log.initial.star.RadGyra": {"value": 0.500000}, "log.initial.star.RotAngMom": { "value": 1.470605e39, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.star.RotKinEnergy": {"value": 5.347271e34, "unit": u.Joule}, "log.initial.star.RotVel": {"value": 463.828521, "unit": u.m / u.sec}, "log.initial.star.BodyType": {"value": 0.000000}, "log.initial.star.RotRate": {"value": 7.272205e-05, "unit": 1 / u.sec}, "log.initial.star.RotPer": {"value": 8.640000e04, "unit": u.sec}, "log.initial.star.Density": {"value": 1.829552e09, "unit": u.kg / u.m ** 3}, "log.initial.star.SurfEnFluxTotal": { "value": 4.474499e-12, "unit": u.kg / u.sec ** 3, }, "log.initial.star.TidalQ": {"value": 1.000000e06}, "log.initial.star.ImK2": {"value": -5.000000e-07}, "log.initial.star.K2": {"value": 0.500000}, "log.initial.star.K2Man": {"value": 0.010000}, "log.initial.star.Imk2Man": {"value": 0.000000}, "log.initial.star.TidalQMantle": {"value": 100.000000}, "log.initial.star.HEcc": {"value": 0.000000}, 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"log.final.rr.OrbPotEnergy": { "value": -1.096077e35, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.LostEnergy": { "value": 2.725921e33, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.TidalRadius": {"value": 2.095926e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.DsemiDtEqtide": { "value": -4.999833e-06, "unit": u.m / u.sec, "rtol": 1e-4, }, "log.final.rr.DeccDtEqtide": { "value": -2.532564e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DMeanMotionDtEqtide": { "value": 3.437522e-21, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.rr.DOrbPerDtEqtide": {"value": -4.918370e-10, "rtol": 1e-4}, "log.final.rr.EccTimeEqtide": { "value": 2.695885e13, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.SemiTimeEqtide": { "value": 2.891664e15, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.DHEccDtEqtide": { "value": -0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DKEccDtEqtide": { "value": -2.532564e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DXoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DYoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DZoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.LockTime": {"value": 1.711407e11, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.BodyDsemiDtEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.BodyDeccDt": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.DOblDtEqtide": { "value": 0.000000, "unit": u.rad / u.sec, "rtol": 1e-4, }, "log.final.rr.DRotPerDtEqtide": {"value": -7.959031e-298, "rtol": 1e-4}, "log.final.rr.DRotRateDtEqtide": { "value": 5.562685e-309, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.rr.EqRotRateDiscrete": { "value": 6.626773e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPerDiscrete": { "value": 9.481516e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotRateCont": { "value": 6.920233e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPerCont": { "value": 9.079442e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPer": {"value": 9.481516e05, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.EqTidePower": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.GammaRot": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.GammaOrb": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.OceanK2": {"value": 0.010000, "rtol": 1e-4}, "log.final.rr.EnvTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.OceanTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.TideLock": {"value": 1.000000, "rtol": 1e-4}, "log.final.rr.RotTimeEqtide": { "value": 1.191290e303, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EnvK2": {"value": 0.500000, "rtol": 1e-4}, "log.final.rr.OblTimeEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.PowerEqtide": {"value": 1.895236e19, "unit": u.W, "rtol": 1e-4}, "log.final.rr.SurfEnFluxEqtide": { "value": 34.332187, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.rr.SurfWaterMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.rr.EnvelopeMass": { "value": 0.999399, "unit": u.Mearth, "rtol": 1e-4, }, "log.final.rr.OxygenMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.rr.RGLimit": {"value": 3.127270e09, "unit": u.m, "rtol": 1e-4}, "log.final.rr.XO": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.EtaO": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.PlanetRadius": { "value": 32.861293, "unit": u.Rearth, "rtol": 1e-4, }, "log.final.rr.OxygenMantleMass": { "value": 0.000000, "unit": u.kg, "rtol": 1e-4, }, "log.final.rr.RadXUV": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.RadSolid": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.PresXUV": {"value": 5.000000, "rtol": 1e-4}, "log.final.rr.ScaleHeight": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.ThermTemp": {"value": 400.000000, "unit": u.K, "rtol": 1e-4}, "log.final.rr.AtmGasConst": {"value": 4124.000000, "rtol": 1e-4}, "log.final.rr.PresSurf": {"value": -1.000000, "unit": u.Pa, "rtol": 1e-4}, "log.final.rr.DEnvMassDt": { "value": -1.147322e08, "unit": u.kg / u.sec, "rtol": 1e-4, }, "log.final.rr.FXUV": {"value": 0.073380, "unit": u.W / u.m ** 2, "rtol": 1e-4}, "log.final.rr.AtmXAbsEffH2O": {"value": 0.300000, "rtol": 1e-4}, "log.final.rr.RocheRadius": {"value": 1.822097e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.BondiRadius": {"value": 8.033012e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.HEscapeRegime": {"value": 6.000000, "rtol": 1e-4}, "log.final.rr.RRCriticalFlux": { "value": 0.000139, "unit": u.W / u.m ** 2, "rtol": 1e-4, }, "log.final.rr.KTide": {"value": 1.000000, "rtol": 1e-4}, "log.final.rr.RGDuration": {"value": 1.00000e06, "unit": u.yr, "rtol": 1e-4}, } )

45.200401

88

0.496675

import astropy.units as u import pytest from benchmark import Benchmark, benchmark @benchmark( { "log.initial.system.Age": {"value": 3.155760e13, "unit": u.sec}, "log.initial.system.Time": {"value": 0.000000, "unit": u.sec}, "log.initial.system.TotAngMom": { "value": 6.747268e40, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.system.TotEnergy": {"value": -2.482441e43, "unit": u.Joule}, "log.initial.system.PotEnergy": {"value": -2.482440e43, "unit": u.Joule}, "log.initial.system.KinEnergy": {"value": 5.347271e34, "unit": u.Joule}, "log.initial.system.DeltaTime": {"value": 0.000000, "unit": u.sec}, "log.initial.star.Mass": {"value": 1.988416e30, "unit": u.kg}, "log.initial.star.Obliquity": {"value": 0.000000, "unit": u.rad}, "log.initial.star.PrecA": {"value": 0.000000, "unit": u.rad}, "log.initial.star.Xobl": {"value": 0.000000}, "log.initial.star.Yobl": {"value": 0.000000}, "log.initial.star.Zobl": {"value": 1.000000}, "log.initial.star.Radius": {"value": 6.378100e06, "unit": u.m}, "log.initial.star.RadGyra": {"value": 0.500000}, "log.initial.star.RotAngMom": { "value": 1.470605e39, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.star.RotKinEnergy": {"value": 5.347271e34, "unit": u.Joule}, "log.initial.star.RotVel": {"value": 463.828521, "unit": u.m / u.sec}, "log.initial.star.BodyType": {"value": 0.000000}, "log.initial.star.RotRate": {"value": 7.272205e-05, "unit": 1 / u.sec}, "log.initial.star.RotPer": {"value": 8.640000e04, "unit": u.sec}, "log.initial.star.Density": {"value": 1.829552e09, "unit": u.kg / u.m ** 3}, "log.initial.star.SurfEnFluxTotal": { "value": 4.474499e-12, "unit": u.kg / u.sec ** 3, }, "log.initial.star.TidalQ": {"value": 1.000000e06}, "log.initial.star.ImK2": {"value": -5.000000e-07}, "log.initial.star.K2": {"value": 0.500000}, "log.initial.star.K2Man": {"value": 0.010000}, "log.initial.star.Imk2Man": {"value": 0.000000}, "log.initial.star.TidalQMantle": {"value": 100.000000}, "log.initial.star.HEcc": {"value": 0.000000}, "log.initial.star.HZLimitDryRunaway": {"value": 3.036202e09, "unit": u.m}, "log.initial.star.HZLimRecVenus": {"value": 2.502002e09, "unit": u.m}, "log.initial.star.HZLimRunaway": {"value": 3.267138e09, "unit": u.m}, "log.initial.star.HZLimMoistGreenhouse": {"value": 3.310536e09, "unit": u.m}, "log.initial.star.HZLimMaxGreenhouse": {"value": 5.611497e09, "unit": u.m}, "log.initial.star.HZLimEarlyMars": {"value": 6.122597e09, "unit": u.m}, "log.initial.star.Instellation": { "value": -1.000000, "unit": u.kg / u.sec ** 3, }, "log.initial.star.KEcc": {"value": 0.000000}, "log.initial.star.Eccentricity": {"value": -1.000000}, "log.initial.star.OrbEnergy": {"value": 0.000000, "unit": u.Joule}, "log.initial.star.MeanMotion": {"value": -1.000000, "unit": 1 / u.sec}, "log.initial.star.OrbPeriod": {"value": -1.000000, "unit": u.sec}, "log.initial.star.SemiMajorAxis": {"value": -1.000000, "unit": u.m}, "log.initial.star.CriticalSemiMajorAxis": {"value": -1.000000, "unit": u.m}, "log.initial.star.COPP": {"value": 0.000000}, "log.initial.star.OrbAngMom": { "value": 0.000000, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.star.LongP": {"value": 0.000000, "unit": u.rad}, "log.initial.star.LXUVTot": {"value": 1.923000e20, "unit": u.kg / u.sec ** 3}, "log.initial.star.TotOrbEnergy": {"value": -2.119237e35, "unit": u.Joule}, "log.initial.star.OrbPotEnergy": {"value": -1.000000, "unit": u.Joule}, "log.initial.star.LostEnergy": {"value": 5.562685e-309, "unit": u.Joule}, "log.initial.star.LostAngMom": { "value": 5.562685e-309, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.star.LockTime": {"value": -1.000000, "unit": u.sec}, "log.initial.star.BodyDsemiDtEqtide": {"value": -1.000000}, "log.initial.star.BodyDeccDt": {"value": -1.000000}, "log.initial.star.DOblDtEqtide": {"value": 0.000000, "unit": u.rad / u.sec}, "log.initial.star.DRotPerDtEqtide": {"value": 2.054554e-27}, "log.initial.star.DRotRateDtEqtide": { "value": -1.729298e-36, "unit": 1 / u.sec ** 2, }, "log.initial.star.EqRotRateDiscrete": { "value": 6.296062e-06, "unit": 1 / u.sec, }, "log.initial.star.EqRotPerDiscrete": {"value": 9.979547e05, "unit": u.sec}, "log.initial.star.EqRotRateCont": {"value": 8.688566e-06, "unit": 1 / u.sec}, "log.initial.star.EqRotPerCont": {"value": 7.231556e05, "unit": u.sec}, "log.initial.star.EqRotPer": {"value": 9.979547e05, "unit": u.sec}, "log.initial.star.EqTidePower": {"value": 0.000000, "unit": 1 / u.sec}, "log.initial.star.GammaRot": {"value": -1.000000, "unit": u.sec}, "log.initial.star.GammaOrb": {"value": -1.000000, "unit": u.sec}, "log.initial.star.OceanK2": {"value": 0.010000}, "log.initial.star.EnvTidalQ": {"value": -1.000000}, "log.initial.star.OceanTidalQ": {"value": -1.000000}, "log.initial.star.TideLock": {"value": 0.000000}, "log.initial.star.RotTimeEqtide": {"value": 0.000000, "unit": u.sec}, "log.initial.star.EnvK2": {"value": 0.010000}, "log.initial.star.OblTimeEqtide": {"value": -1.000000}, "log.initial.star.PowerEqtide": {"value": 2287.372458, "unit": u.W}, "log.initial.star.SurfEnFluxEqtide": { "value": 4.474499e-12, "unit": u.kg / u.sec ** 3, }, "log.initial.star.Luminosity": {"value": 1.923000e23, "unit": u.W}, "log.initial.star.LXUVStellar": {"value": 1.923000e20, "unit": u.W}, "log.initial.star.Temperature": {"value": 5778.000000, "unit": u.K}, "log.initial.star.LXUVFrac": {"value": 0.001000}, "log.initial.star.RossbyNumber": {"value": 0.078260}, "log.initial.star.DRotPerDtStellar": {"value": 6.530034e-18}, "log.initial.auto.Mass": {"value": 2.000000, "unit": u.Mearth}, "log.initial.auto.Obliquity": {"value": 0.785398, "unit": u.rad}, "log.initial.auto.PrecA": {"value": 0.000000, "unit": u.rad}, "log.initial.auto.Xobl": {"value": 0.707107}, "log.initial.auto.Yobl": {"value": 0.000000}, "log.initial.auto.Zobl": {"value": 0.707107}, "log.initial.auto.Radius": {"value": 2.096446e08, "unit": u.m}, "log.initial.auto.RadGyra": {"value": 0.400000}, "log.initial.auto.RotAngMom": { "value": 1.221650e37, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.auto.RotKinEnergy": {"value": 8.884088e32, "unit": u.Joule}, "log.initial.auto.RotVel": {"value": 3.049157e04, "unit": u.m / u.sec}, "log.initial.auto.BodyType": {"value": 0.000000}, "log.initial.auto.RotRate": {"value": 0.000145, "unit": 1 / u.sec}, "log.initial.auto.RotPer": {"value": 0.500000, "unit": u.day}, "log.initial.auto.Density": {"value": 0.309474, "unit": u.kg / u.m ** 3}, "log.initial.auto.SurfEnFluxTotal": { "value": 2.324795e04, "unit": u.kg / u.sec ** 3, }, "log.initial.auto.TidalQ": {"value": -1.000000e05}, "log.initial.auto.ImK2": {"value": -5.000000e-06}, "log.initial.auto.K2": {"value": 0.500000}, "log.initial.auto.K2Man": {"value": 0.300000}, "log.initial.auto.Imk2Man": {"value": -0.003000}, "log.initial.auto.TidalQMantle": {"value": 100.000000}, "log.initial.auto.HEcc": {"value": 0.000000}, "log.initial.auto.HZLimitDryRunaway": {"value": 3.098811e09, "unit": u.m}, "log.initial.auto.HZLimRecVenus": {"value": 2.502002e09, "unit": u.m}, "log.initial.auto.HZLimRunaway": {"value": 3.267138e09, "unit": u.m}, "log.initial.auto.HZLimMoistGreenhouse": {"value": 3.310536e09, "unit": u.m}, "log.initial.auto.HZLimMaxGreenhouse": {"value": 5.611497e09, "unit": u.m}, "log.initial.auto.HZLimEarlyMars": {"value": 6.122597e09, "unit": u.m}, "log.initial.auto.Instellation": { "value": 69.788358, "unit": u.kg / u.sec ** 3, }, "log.initial.auto.KEcc": {"value": 0.200000}, "log.initial.auto.Eccentricity": {"value": 0.200000}, "log.initial.auto.OrbEnergy": {"value": -5.298093e34, "unit": u.Joule}, "log.initial.auto.MeanMotion": {"value": 6.296062e-06, "unit": 1 / u.sec}, "log.initial.auto.OrbPeriod": {"value": 9.979547e05, "unit": u.sec}, "log.initial.auto.SemiMajorAxis": {"value": 0.100000, "unit": u.au}, "log.initial.auto.CriticalSemiMajorAxis": {"value": -1.000000, "unit": u.m}, "log.initial.auto.COPP": {"value": 0.000000}, "log.initial.auto.OrbAngMom": { "value": 1.648983e40, "unit": (u.kg * u.m ** 2) / u.sec, }, "log.initial.auto.LongP": {"value": 0.000000, "unit": u.rad}, "log.initial.auto.LXUVTot": {"value": -1.000000, "unit": u.kg / u.sec ** 3}, "log.initial.auto.TotOrbEnergy": {"value": -2.119237e35, "unit": u.Joule}, "log.initial.auto.OrbPotEnergy": {"value": -1.059619e35, "unit": u.Joule}, "log.initial.auto.LostEnergy": {"value": 5.562685e-309, "unit": u.Joule}, "log.initial.auto.TidalRadius": {"value": 2.096446e08, "unit": u.m}, "log.initial.auto.DsemiDtEqtide": {"value": 0.000192, "unit": u.m / u.sec}, "log.initial.auto.DeccDtEqtide": {"value": 4.036352e-15, "unit": 1 / u.sec}, "log.initial.auto.DMeanMotionDtEqtide": { "value": -1.211805e-19, "unit": 1 / u.sec ** 2, }, "log.initial.auto.DOrbPerDtEqtide": {"value": 1.920766e-08}, "log.initial.auto.EccTimeEqtide": {"value": 4.954969e13, "unit": u.sec}, "log.initial.auto.SemiTimeEqtide": {"value": 7.793412e13, "unit": u.sec}, "log.initial.auto.DHEccDtEqtide": {"value": 0.000000, "unit": 1 / u.sec}, 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1e-4, }, "log.final.el.LostEnergy": { "value": 2.085924e33, "unit": u.Joule, "rtol": 1e-4, }, "log.final.el.TidalRadius": {"value": 2.084572e08, "unit": u.m, "rtol": 1e-4}, "log.final.el.DsemiDtEqtide": { "value": -4.938887e-06, "unit": u.m / u.sec, "rtol": 1e-4, }, "log.final.el.DeccDtEqtide": { "value": -2.496493e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.DMeanMotionDtEqtide": { "value": 3.400372e-21, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.el.DOrbPerDtEqtide": {"value": -4.857058e-10, "rtol": 1e-4}, "log.final.el.EccTimeEqtide": { "value": 2.742067e13, "unit": u.sec, "rtol": 1e-4, }, "log.final.el.SemiTimeEqtide": { "value": 2.925710e15, "unit": u.sec, "rtol": 1e-4, }, "log.final.el.DHEccDtEqtide": { "value": -0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.DKEccDtEqtide": { "value": -2.496493e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.DXoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.DYoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.DZoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.LockTime": {"value": 8.193554e10, "unit": u.sec, "rtol": 1e-4}, "log.final.el.BodyDsemiDtEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.el.BodyDeccDt": {"value": -1.000000, "rtol": 1e-4}, "log.final.el.DOblDtEqtide": { "value": 0.000000, "unit": u.rad / u.sec, "rtol": 1e-4, }, "log.final.el.DRotPerDtEqtide": {"value": -7.945685e-298, "rtol": 1e-4}, "log.final.el.DRotRateDtEqtide": { "value": 5.562685e-309, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.el.EqRotRateDiscrete": { "value": 6.632335e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.EqRotPerDiscrete": { "value": 9.473564e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.el.EqRotRateCont": { "value": 6.927597e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.EqRotPerCont": { "value": 9.069791e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.el.EqRotPer": {"value": 9.473564e05, "unit": u.sec, "rtol": 1e-4}, "log.final.el.EqTidePower": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.el.GammaRot": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.el.GammaOrb": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.el.OceanK2": {"value": 0.010000, "rtol": 1e-4}, "log.final.el.EnvTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.el.OceanTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.el.TideLock": {"value": 1.000000, "rtol": 1e-4}, "log.final.el.RotTimeEqtide": { "value": 1.192290e303, "unit": u.sec, "rtol": 1e-4, }, "log.final.el.EnvK2": {"value": 0.500000, "rtol": 1e-4}, "log.final.el.OblTimeEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.el.PowerEqtide": {"value": 1.862016e19, "unit": u.W, "rtol": 1e-4}, "log.final.el.SurfEnFluxEqtide": { "value": 34.098849, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.el.SurfWaterMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.el.EnvelopeMass": { "value": 0.986370, "unit": u.Mearth, "rtol": 1e-4, }, "log.final.el.OxygenMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.el.RGLimit": {"value": 3.127704e09, "unit": u.m, "rtol": 1e-4}, "log.final.el.XO": {"value": 0.000000, "rtol": 1e-4}, "log.final.el.EtaO": {"value": 0.000000, "rtol": 1e-4}, "log.final.el.PlanetRadius": { "value": 32.683276, "unit": u.Rearth, "rtol": 1e-4, }, "log.final.el.OxygenMantleMass": { "value": 0.000000, "unit": u.kg, "rtol": 1e-4, }, "log.final.el.RadXUV": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.el.RadSolid": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.el.PresXUV": {"value": 5.000000, "rtol": 1e-4}, "log.final.el.ScaleHeight": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.el.ThermTemp": {"value": 400.000000, "unit": u.K, "rtol": 1e-4}, "log.final.el.AtmGasConst": {"value": 4124.000000, "rtol": 1e-4}, "log.final.el.PresSurf": {"value": -1.000000, "unit": u.Pa, "rtol": 1e-4}, "log.final.el.DEnvMassDt": { "value": -2.614005e09, "unit": u.kg / u.sec, "rtol": 1e-4, }, "log.final.el.FXUV": {"value": 0.073463, "unit": u.W / u.m ** 2, "rtol": 1e-4}, "log.final.el.AtmXAbsEffH2O": {"value": 0.300000, "rtol": 1e-4}, "log.final.el.RocheRadius": {"value": 1.817114e08, "unit": u.m, "rtol": 1e-4}, "log.final.el.BondiRadius": {"value": 7.982897e08, "unit": u.m, "rtol": 1e-4}, "log.final.el.HEscapeRegime": {"value": 3.000000, "rtol": 1e-4}, "log.final.el.RRCriticalFlux": { "value": 0.000139, "unit": u.W / u.m ** 2, "rtol": 1e-4, }, "log.final.el.KTide": {"value": 1.000000, "rtol": 1e-4}, "log.final.el.RGDuration": {"value": 1.00000e06, "unit": u.yr, "rtol": 1e-4}, "log.final.rr.Mass": {"value": 1.999399, "unit": u.Mearth, "rtol": 1e-4}, "log.final.rr.Obliquity": {"value": 0.000000, "unit": u.rad, "rtol": 1e-4}, "log.final.rr.PrecA": {"value": 0.000000, "unit": u.rad, "rtol": 1e-4}, "log.final.rr.Xobl": {"value": 1.563665e-162, "rtol": 1e-4}, "log.final.rr.Yobl": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.Zobl": {"value": 1.000000, "rtol": 1e-4}, "log.final.rr.Radius": {"value": 2.095926e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.RadGyra": {"value": 0.400000, "rtol": 1e-4}, "log.final.rr.RotAngMom": { "value": 5.561691e35, "unit": (u.kg * u.m ** 2) / u.sec, "rtol": 1e-4, }, "log.final.rr.RotKinEnergy": { "value": 1.842803e30, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.RotVel": { "value": 1388.922578, "unit": u.m / u.sec, "rtol": 1e-4, }, "log.final.rr.BodyType": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.RotRate": { "value": 6.626773e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.RotPer": {"value": 10.973977, "unit": u.day, "rtol": 1e-4}, "log.final.rr.Density": { "value": 0.309611, "unit": u.kg / u.m ** 3, "rtol": 1e-4, }, "log.final.rr.SurfEnFluxTotal": { "value": 33.332187, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.rr.TidalQ": {"value": -1.000000e05, "rtol": 1e-4}, "log.final.rr.ImK2": {"value": -5.000000e-06, "rtol": 1e-4}, "log.final.rr.K2": {"value": 0.500000, "rtol": 1e-4}, "log.final.rr.K2Man": {"value": 0.300000, "rtol": 1e-4}, "log.final.rr.Imk2Man": {"value": -0.003000, "rtol": 1e-4}, "log.final.rr.TidalQMantle": {"value": 100.000000, "rtol": 1e-4}, "log.final.rr.HEcc": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.HZLimitDryRunaway": { "value": 3.043303e09, "unit": u.m, "rtol": 1e-4, }, "log.final.rr.HZLimRecVenus": {"value": 2.502002e09, "unit": u.m, "rtol": 1e-4}, "log.final.rr.HZLimRunaway": {"value": 3.267138e09, "unit": u.m, "rtol": 1e-4}, "log.final.rr.HZLimMoistGreenhouse": { "value": 3.310536e09, "unit": u.m, "rtol": 1e-4, }, "log.final.rr.HZLimMaxGreenhouse": { "value": 5.611497e09, "unit": u.m, "rtol": 1e-4, }, "log.final.rr.HZLimEarlyMars": { "value": 6.122597e09, "unit": u.m, "rtol": 1e-4, }, "log.final.rr.Instellation": { "value": 73.379922, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.rr.KEcc": {"value": 0.068275, "rtol": 1e-4}, "log.final.rr.Eccentricity": {"value": 0.068275, "rtol": 1e-4}, "log.final.rr.OrbEnergy": { "value": -5.480386e34, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.MeanMotion": { "value": 6.626773e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.OrbPeriod": {"value": 9.481516e05, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.SemiMajorAxis": {"value": 0.096645, "unit": u.au, "rtol": 1e-4}, "log.final.rr.CriticalSemiMajorAxis": { "value": -1.000000, "unit": u.m, "rtol": 1e-4, }, "log.final.rr.COPP": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.OrbAngMom": { "value": 1.650154e40, "unit": (u.kg * u.m ** 2) / u.sec, "rtol": 1e-4, }, "log.final.rr.LongP": {"value": 0.000000, "unit": u.rad, "rtol": 1e-4}, "log.final.rr.LXUVTot": { "value": -1.000000, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.rr.TotOrbEnergy": { "value": -1.740032e35, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.OrbPotEnergy": { "value": -1.096077e35, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.LostEnergy": { "value": 2.725921e33, "unit": u.Joule, "rtol": 1e-4, }, "log.final.rr.TidalRadius": {"value": 2.095926e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.DsemiDtEqtide": { "value": -4.999833e-06, "unit": u.m / u.sec, "rtol": 1e-4, }, "log.final.rr.DeccDtEqtide": { "value": -2.532564e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DMeanMotionDtEqtide": { "value": 3.437522e-21, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.rr.DOrbPerDtEqtide": {"value": -4.918370e-10, "rtol": 1e-4}, "log.final.rr.EccTimeEqtide": { "value": 2.695885e13, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.SemiTimeEqtide": { "value": 2.891664e15, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.DHEccDtEqtide": { "value": -0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DKEccDtEqtide": { "value": -2.532564e-15, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DXoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DYoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.DZoblDtEqtide": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.LockTime": {"value": 1.711407e11, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.BodyDsemiDtEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.BodyDeccDt": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.DOblDtEqtide": { "value": 0.000000, "unit": u.rad / u.sec, "rtol": 1e-4, }, "log.final.rr.DRotPerDtEqtide": {"value": -7.959031e-298, "rtol": 1e-4}, "log.final.rr.DRotRateDtEqtide": { "value": 5.562685e-309, "unit": 1 / u.sec ** 2, "rtol": 1e-4, }, "log.final.rr.EqRotRateDiscrete": { "value": 6.626773e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPerDiscrete": { "value": 9.481516e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotRateCont": { "value": 6.920233e-06, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPerCont": { "value": 9.079442e05, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EqRotPer": {"value": 9.481516e05, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.EqTidePower": { "value": 0.000000, "unit": 1 / u.sec, "rtol": 1e-4, }, "log.final.rr.GammaRot": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.GammaOrb": {"value": -1.000000, "unit": u.sec, "rtol": 1e-4}, "log.final.rr.OceanK2": {"value": 0.010000, "rtol": 1e-4}, "log.final.rr.EnvTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.OceanTidalQ": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.TideLock": {"value": 1.000000, "rtol": 1e-4}, "log.final.rr.RotTimeEqtide": { "value": 1.191290e303, "unit": u.sec, "rtol": 1e-4, }, "log.final.rr.EnvK2": {"value": 0.500000, "rtol": 1e-4}, "log.final.rr.OblTimeEqtide": {"value": -1.000000, "rtol": 1e-4}, "log.final.rr.PowerEqtide": {"value": 1.895236e19, "unit": u.W, "rtol": 1e-4}, "log.final.rr.SurfEnFluxEqtide": { "value": 34.332187, "unit": u.kg / u.sec ** 3, "rtol": 1e-4, }, "log.final.rr.SurfWaterMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.rr.EnvelopeMass": { "value": 0.999399, "unit": u.Mearth, "rtol": 1e-4, }, "log.final.rr.OxygenMass": {"value": 0.000000, "unit": u.kg, "rtol": 1e-4}, "log.final.rr.RGLimit": {"value": 3.127270e09, "unit": u.m, "rtol": 1e-4}, "log.final.rr.XO": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.EtaO": {"value": 0.000000, "rtol": 1e-4}, "log.final.rr.PlanetRadius": { "value": 32.861293, "unit": u.Rearth, "rtol": 1e-4, }, "log.final.rr.OxygenMantleMass": { "value": 0.000000, "unit": u.kg, "rtol": 1e-4, }, "log.final.rr.RadXUV": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.RadSolid": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.PresXUV": {"value": 5.000000, "rtol": 1e-4}, "log.final.rr.ScaleHeight": {"value": -1.000000, "unit": u.m, "rtol": 1e-4}, "log.final.rr.ThermTemp": {"value": 400.000000, "unit": u.K, "rtol": 1e-4}, "log.final.rr.AtmGasConst": {"value": 4124.000000, "rtol": 1e-4}, "log.final.rr.PresSurf": {"value": -1.000000, "unit": u.Pa, "rtol": 1e-4}, "log.final.rr.DEnvMassDt": { "value": -1.147322e08, "unit": u.kg / u.sec, "rtol": 1e-4, }, "log.final.rr.FXUV": {"value": 0.073380, "unit": u.W / u.m ** 2, "rtol": 1e-4}, "log.final.rr.AtmXAbsEffH2O": {"value": 0.300000, "rtol": 1e-4}, "log.final.rr.RocheRadius": {"value": 1.822097e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.BondiRadius": {"value": 8.033012e08, "unit": u.m, "rtol": 1e-4}, "log.final.rr.HEscapeRegime": {"value": 6.000000, "rtol": 1e-4}, "log.final.rr.RRCriticalFlux": { "value": 0.000139, "unit": u.W / u.m ** 2, "rtol": 1e-4, }, "log.final.rr.KTide": {"value": 1.000000, "rtol": 1e-4}, "log.final.rr.RGDuration": {"value": 1.00000e06, "unit": u.yr, "rtol": 1e-4}, } ) class TestLopez12CPL(Benchmark): pass

0

20

22

35b4a123604f3ad39f518c8cd7cd58c05193a395

7,579

py

Python

common-python/rest_wrappers/oc/oc/upload_storage_object.py

LaudateCorpus1/atg-commerce-iaas

f1ae31657fc0111a5c019d46a28a3c81aae1acb2

[ "MIT" ]

28

2016-11-07T14:03:25.000Z

2022-02-01T08:46:52.000Z

common-python/rest_wrappers/oc/oc/upload_storage_object.py

LaudateCorpus1/atg-commerce-iaas

f1ae31657fc0111a5c019d46a28a3c81aae1acb2

[ "MIT" ]

3

2016-11-09T13:23:03.000Z

2018-04-05T15:49:22.000Z

common-python/rest_wrappers/oc/oc/upload_storage_object.py

LaudateCorpus1/atg-commerce-iaas

f1ae31657fc0111a5c019d46a28a3c81aae1acb2

[ "MIT" ]

13

2016-10-27T17:59:38.000Z

2022-02-18T04:38:38.000Z

#!/usr/bin/python # Copyright (c) 2013, 2014-2017 Oracle and/or its affiliates. All rights reserved. """Provide Module Description """ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# __author__ = "Andrew Hopkinson (Oracle Cloud Solutions A-Team)" __copyright__ = "Copyright (c) 2013, 2014-2017 Oracle and/or its affiliates. All rights reserved." __ekitversion__ = "@VERSION@" __ekitrelease__ = "@RELEASE@" __version__ = "1.0.0.0" __date__ = "@BUILDDATE@" __status__ = "Development" __module__ = "upload_storage_object" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# import datetime import getopt import hashlib import json import locale import logging import multiprocessing import operator import os import requests import shutil import subprocess import sys import tempfile from contextlib import closing # Import utility methods from oscsutils import callRESTApi from oscsutils import getPassword from oscsutils import printJSON from authenticate_oscs import authenticate from oc_exceptions import REST401Exception # Define methods # Read Module Arguments # Main processing function # Main function to kick off processing if __name__ == "__main__": main(sys.argv[1:])

35.919431

197

0.613933

#!/usr/bin/python # Copyright (c) 2013, 2014-2017 Oracle and/or its affiliates. All rights reserved. """Provide Module Description """ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# __author__ = "Andrew Hopkinson (Oracle Cloud Solutions A-Team)" __copyright__ = "Copyright (c) 2013, 2014-2017 Oracle and/or its affiliates. All rights reserved." __ekitversion__ = "@VERSION@" __ekitrelease__ = "@RELEASE@" __version__ = "1.0.0.0" __date__ = "@BUILDDATE@" __status__ = "Development" __module__ = "upload_storage_object" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# import datetime import getopt import hashlib import json import locale import logging import multiprocessing import operator import os import requests import shutil import subprocess import sys import tempfile from contextlib import closing # Import utility methods from oscsutils import callRESTApi from oscsutils import getPassword from oscsutils import printJSON from authenticate_oscs import authenticate from oc_exceptions import REST401Exception # Define methods def md5(fname, readbuf=104857600, **kwargs): hash_md5 = hashlib.md5() cnt = 1 with open(fname, "rb") as f: for chunk in iter(lambda: f.read(readbuf), b""): hash_md5.update(chunk) #print('Chunk: '+str(cnt)) cnt +=1 return hash_md5.hexdigest() def getsplitprefix(filename): return os.path.split(filename)[-1] + '-' def getsplitdir(filename): return filename + '.split' def splitfile(filename, size='5GB', **kwargs): files = [] if filename is not None: splitdir = getsplitdir(filename) os.makedirs(splitdir) prefix = os.path.join(splitdir, getsplitprefix(filename)) cmd = ['split', '-b', size, filename, prefix] cmdEnv = dict(os.environ) outputLines = [] with closing(tempfile.TemporaryFile()) as fout: try: outputLines = subprocess.check_output(cmd, env=cmdEnv, stderr=fout).splitlines() except subprocess.CalledProcessError as e: fout.flush() fout.seek(0) print(fout.read()) print('\n'.join(outputLines)) raise e return [os.path.join(splitdir, fn) for fn in os.listdir(splitdir)] def uploadfile((endpoint, basepath, authtoken, filename, authendpoint, user, password, headers, params)): print('Uploading : ' + filename) files = None resourcename = os.path.split(filename)[-1] try: with closing(open(filename, 'rb')) as f: response = callRESTApi(endpoint, basepath, resourcename, method='PUT', authtoken=authtoken, headers=headers, params=params, data=f, files=files) except REST401Exception as e: # Reauthenticate and retry if authendpoint is not None and user is not None and password is not None: authtoken, endpoint = authenticate(authendpoint, user, password) with closing(open(filename, 'rb')) as f: response = callRESTApi(endpoint, basepath, resourcename, method='PUT', authtoken=authtoken, headers=headers, params=params, data=f, files=files) else: raise print('Uploaded : ' + filename) return def uploadStorageObject(endpoint, container='compute_images', authtoken=None, filename=None, splitsize=4000, poolsize=4, authendpoint=None, user=None, password=None, extractarchive=None, **kwargs): basepath = container imgbasepath = basepath splitbasepath = basepath + '_segments' headers = None params = None if extractarchive is not None: if params is None: params = {} params['extract-archive'] = extractarchive data = None files = None jsonResponse = '' if filename is not None and os.path.exists(filename): #md5hash = md5(filename) filesize = os.path.getsize(filename) filesize /= (1024 * 1024) if filesize > splitsize: print('Splitting : ' + filename) filelist = splitfile(filename, str(splitsize) + 'MB') print('Into ' + str(len(filelist)) + ' segments') basepath = splitbasepath + '/' + os.path.split(filename)[-1] + '/_segment_' pool = multiprocessing.Pool(poolsize) # Build tupal list workerdata = [] for fn in filelist: workerdata.append([endpoint, basepath, authtoken, fn, authendpoint, user, password, headers, params]) #print(workerdata) # Start processes pool.map(uploadfile, workerdata) # Upload manifest file to point to parts manifest = basepath + '/' + getsplitprefix(filename) resourcename = os.path.split(filename)[-1] headers = {'Content-Length': "0", 'X-Object-Manifest': manifest} printJSON(headers) data = None basepath = imgbasepath try: response = callRESTApi(endpoint, basepath, resourcename, method='PUT', authtoken=authtoken, headers=headers, params=params, data=data, files=files) except REST401Exception as e: # Reauthenticate and retry if authendpoint is not None and user is not None and password is not None: authtoken, endpoint = authenticate(authendpoint, user, password) response = callRESTApi(endpoint, basepath, resourcename, method='PUT', authtoken=authtoken, headers=headers, params=params, data=data, files=files) else: raise # Remove splitfiles splitdir = getsplitdir(filename) shutil.rmtree(splitdir) else: # Simple single file upload basepath = imgbasepath # Upload file print('Uploading : ' + filename) resourcename = os.path.split(filename)[-1] with closing(open(filename, 'rb')) as f: response = callRESTApi(endpoint, basepath, resourcename, method='PUT', authtoken=authtoken, headers=headers, params=params, data=f, files=files) print('Uploaded : ' + filename) jsonResponse = response.text return jsonResponse # Read Module Arguments def readModuleArgs(opts, args): moduleArgs = {} moduleArgs['endpoint'] = None moduleArgs['user'] = None moduleArgs['password'] = None moduleArgs['pwdfile'] = None # Read Module Command Line Arguments. for opt, arg in opts: if opt in ("-e", "--endpoint"): moduleArgs['endpoint'] = arg elif opt in ("-u", "--user"): moduleArgs['user'] = arg elif opt in ("-p", "--password"): moduleArgs['password'] = arg elif opt in ("-P", "--pwdfile"): moduleArgs['pwdfile'] = arg return moduleArgs # Main processing function def main(argv): # Configure Parameters and Options options = 'e:u:p:P:' longOptions = ['endpoint=', 'user=', 'password=', 'pwdfile='] # Get Options & Arguments try: opts, args = getopt.getopt(argv, options, longOptions) # Read Module Arguments moduleArgs = readModuleArgs(opts, args) except getopt.GetoptError: usage() except Exception as e: print('Unknown Exception please check log file') logging.exception(e) sys.exit(1) return # Main function to kick off processing if __name__ == "__main__": main(sys.argv[1:])

6,118

0

181

70aa394b1e7534f0761f177159418f6363ceeb78

14,891

py

Python

snpy/get_osc.py

emirkmo/snpy

2a0153c84477ba8a30310d7dbca3d5a8f24de3c6

[ "MIT" ]

6

2019-01-14T19:40:45.000Z

2021-06-05T12:19:39.000Z

snpy/get_osc.py

emirkmo/snpy

2a0153c84477ba8a30310d7dbca3d5a8f24de3c6

[ "MIT" ]

3

2017-04-25T20:06:22.000Z

2021-06-09T20:46:41.000Z

snpy/get_osc.py

emirkmo/snpy

2a0153c84477ba8a30310d7dbca3d5a8f24de3c6

[ "MIT" ]

8

2017-04-25T19:57:57.000Z

2021-11-12T11:54:19.000Z

''' Module for SNooPy to download/parse data from the Open Supernova Catalog. ''' from __future__ import print_function import six import json if six.PY3: import urllib.request as urllib else: import urllib from astropy.coordinates import Angle from snpy import sn,lc,fset from numpy import array,log10 import astropy.units as u from snpy.filters import spectrum from snpy.specobj import timespec # Some well-known publications and their mappings: pubs = { '1999AJ....117..707R': # Riess et al. (1999) Standard Photometry CfAbands, '2006AJ....131..527J': # Jha et al. (2006) Standard Photometry CfAbands, '2009ApJ...700..331H': # Hicken et al. (2009) CfA3 Natural Photometry CfAbands, '2012ApJS..200...12H': # Hicken et al. (2012) CfA4 Natural Photometry CfAbands } # telescope,band --> SNooPy filter database # We do this by matching (band,system,telescope,observatory) info from the # database to SNooPy filters. ftrans = {} ftrans_standard = {} standard_warnings = {} for band in ['u','g','r','i','B','V','Y','J','H','K']: ftrans[(band,"CSP",'',"LCO")] = band for band in ['U','B','V','R','I']: ftrans[(band,'','kait2','')] = band+'kait' for band in ['U','B','V','R','I']: ftrans[(band,'','kait3','')] = band+'kait' for band in ['J','H','Ks']: ftrans[(band,'','PAIRITEL','')] = band+'2m' for band in ['B','V','R','I']: ftrans[(band,'','kait4', '')] = band+'kait' for band in ['U','V','B']: ftrans[(band, 'Vega','Swift','')] = band+"_UVOT" for band in ['UVW1','UVW2','UVM2']: ftrans[(band, 'Vega','Swift','')] = band for band in ['g','r','i','z']: ftrans[(band, '', 'PS1','')] = "ps1_"+band # These are for data in (what I'm assuming) would be standard filters. # We will issue a warning, though. for band in ['U','B','V','R','I']: ftrans_standard[(band,'','','')] = band+"s" standard_warnings[band] = "Johnson/Kron/Cousins " for band in ['u','g','r','i','z']: ftrans_standard[(band,'','','')] = band+"_40" standard_warnings[band] = "Sloan (APO) " for band in ["u'","g'","r'","i'","z'"]: ftrans_standard[(band,'','','')] = band[0]+"_40" standard_warnings[band] = "Sloan (USNO-40) " for band in ["J","H","Ks"]: ftrans_standard[(band[0],'','','')] = band+"2m" standard_warnings[band[0]] = "2MASS " # Our own photometric systems: def CSP_systems(filt, MJD): '''Given a filter name and MJD date, output the correct telescope and system information.''' if filt == "V": if MJD < 53748.0: return (dict(telescope='Swope',instrument='Site2',band='V-3014', zeropoint="{:.4f}".format(fset['V0'].zp))) elif MJD < 53759.0: return (dict(telescope='Swope',instrument='Site2',band='V-3009', zeropoint="{:.4f}".format(fset['V1'].zp))) elif MJD < 56566.0: return (dict(telescope='Swope',instrument='Site2',band='V-9844', zeropoint="{:.4f}".format(fset['V'].zp))) else: return (dict(telescope='Swope',instrument='e2v',band='V-9844', zeropoint="{:.4f}".format(fset['V2'].zp))) if filt == "Jrc2": return (dict(telescope='Swope',instrument='RetroCam',band='J', zeropoint="{:.4f}".format(fset[filt].zp))) if filt in ['u','g','r','i','B']: if MJD < 56566.0: return (dict(telescope='Swope',instrument='Site2',band=filt, zeropoint="{:.4f}".format(fset[filt].zp))) else: return (dict(telescope='Swope',instrument='e2v',band=filt, zeropoint="{:.4f}".format(fset[filt+'2'].zp))) if filt in ['Y','J','H']: if MJD < 55743.0: return (dict(telescope='Swope',instrument='RetroCam',band=filt, zeropoint="{:.4f}".format(fset[filt].zp))) else: return (dict(telescope='DuPont',instrument='RetroCam',band=filt, zeropoint="{:.4f}".format(fset[filt+'d'].zp))) return({}) MJD_offsets = { 'MJD':0, 'JD':-2400000.5 } warning_message = { 'upperlims_noerr':'Warning: Data lacking errorbars or with upper-limits not imported', 'upperlims':'Warning: Data with upper-limits not imported', } OSC_template = '''https://sne.space/astrocats/astrocats/supernovae/output/json/{}.json''' def get_obj(url, full_data=True, allow_no_errors=False, missing_error=0.01): '''Attempt to build a SNooPy object from a Open Supernova Catalog server URL.''' if url.find('osc:') == 0: # Try to construct a url based only on a name. url = OSC_template.format(url.split(':')[1]) try: uf = urllib.urlopen(url) except: return None,"Invalid URL" try: d = json.load(uf) except: uf.close() if full_data: return None,"Failed to decode JSON",None return None,"Failed to decode JSON" else: uf.close() # We now have the JSON data. Get the info we need d = list(d.values())[0] name = d['name'] if 'redshift' not in d or 'ra' not in d or 'dec' not in d: return None,"No redshift, RA, or DEC found" zhel = float(d['redshift'][0]['value']) ra = Angle(" ".join([d['ra'][0]['value'],d['ra'][0]['u_value']])).degree decl = Angle(" ".join([d['dec'][0]['value'],d['dec'][0]['u_value']])).degree snobj = sn(name, ra=ra, dec=decl, z=zhel) # All primary sources all_sources_dict = [item for item in d['sources'] \ if not item.get('secondary',False)] all_sources_dict2 = [item for item in d['sources'] \ if item.get('secondary',False)] all_sources = {} for source in all_sources_dict: all_sources[source['alias']] = (source.get('bibcode',''), source.get('reference','')) all_sources2 = {} for source in all_sources_dict2: all_sources2[source['alias']] = (source.get('bibcode',''), source.get('reference','')) # Next, the photometry. used_sources = [] MJD = {} mags = {} emags = {} sids = {} known_unknowns = [] unknown_unknowns = [] warnings = [] photometry = d.get('photometry', []) for p in photometry: if p.get('upperlimit',False): continue t = (p.get('band',''),p.get('system',''),p.get('telescope',''), p.get('observatory','')) # Deal with source of photometry ss = p.get('source').split(',') this_source = None for s in ss: if s in all_sources: this_source = all_sources[s] break if this_source is None: for s in ss: if s in all_sources2: this_source = all_sources2[s] if this_source is None: print("Warning: no primary source, skipping") continue bibcode = this_source[0] if bibcode in pubs: b = pubs[bibcode](t[0],float(p['time'])) elif t in ftrans: b = ftrans[t] elif t in ftrans_standard: b = ftrans_standard[t] if t not in known_unknowns: known_unknowns.append(t) print("Warning: no telescope/system info, assuming ", \ standard_warnings[b[0]], b[0]) elif (t[0],"","","") in ftrans_standard: b = ftrans_standard[(t[0],"","","")] if t not in known_unknowns: known_unknowns.append(t) print("Warning: telescope/system defined by %s/%s/%s not "\ "recognized, assuming %s %s" %\ (t[1],t[2],t[3],standard_warnings[t[0]],t[0])) else: # No idea if t not in unknown_unknowns: unknown_unknowns.append(t) print("Warning: telescope/system defined by %s/%s/%s not "\ "recognized and can't figure out the filter %s" % \ (t[1],t[2],t[3],t[0])) unknown_unknowns.append(t) continue if b not in MJD: MJD[b] = [] mags[b] = [] emags[b] = [] sids[b] = [] if 'time' in p and 'magnitude' in p: if not allow_no_errors and 'e_magnitude' not in p and\ 'e_lower_magnitude' not in p and 'e_upper_magnitude' not in p: if 'upperlims' not in warnings: warnings.append('upperlims') continue MJD[b].append(float(p['time'])) mags[b].append(float(p['magnitude'])) if 'e_magnitude' in p: emags[b].append(float(p['e_magnitude'])) elif 'e_lower_magnitude' in p and 'e_upper_magnitude' in p: emags[b].append((float(p['e_lower_magnitude']) +\ float(p['e_upper_magnitude']))/2) else: emags[b].append(missing_error) elif 'time' in p and 'countrate' in p and 'zeropoint' in p: if not allow_no_errors and 'e_countrate' not in p: if 'upperlims' not in warnings: warnings.append('upperlims') continue if float(p['countrate']) < 0: continue MJD[b].append(float(p['time'])) mags[b].append(-2.5*log10(float(p['countrate'])) + \ float(p['zeropoint'])) ec = p.get('e_countrate',None) if ec is not None: emags[b].append(1.087*float(p['e_countrate'])/float(p['countrate'])) else: emags[b].append(missing_error) else: if 'upperlims_noerr' not in warnings: warnings.append('upperlims_noerr') continue if this_source not in used_sources: used_sources.append(this_source) # At this point we're actually using the photometry, so find source sid = used_sources.index(this_source) sids[b].append(sid) for b in MJD: if len(MJD[b]) > 0: snobj.data[b] = lc(snobj, b, array(MJD[b]), array(mags[b]), array(emags[b]), sids=array(sids[b], dtype=int)) snobj.data[b].time_sort() snobj.sources = used_sources snobj.get_restbands() if len(unknown_unknowns) > 0: unknown_unknowns = list(set(unknown_unknowns)) print("Warning: the following photometry was not recognized by SNooPy") print("and was not imported:") for item in unknown_unknowns: print(item) if warnings: for warning in warnings: print(warning_message[warning]) # lastly, the spectroscopy if d.get('spectra',None) is not None: spectra = [] dates = [] sids = [] for s in d['spectra']: wu = s.get('u_wavelengths', 'Agnstrom') fu = s.get('u_fluxes', 'Uncalibrated') try: wu = u.Unit(wu) except ValueError: print("Warning: unrecognized unit for wavelength: {}".format(wu)) print(" assuming Angstroms") wu = u.Angstrom if fu == 'Uncalibrated': fluxed = False fu = u.dimensionless_unscaled else: try: fu = u.Unit(fu) fluxed = True except ValueError: print("Warning: unrecognized unit for flux: {}".format(fu)) fluxed = False fu = u.dimensionless_unscaled tu = s.get('u_time', 'MJD') t = float(s['time']) if tu not in MJD_offsets: print("Warning: unrecognized time unit: {}".format(tu)) if len(s['time'].split('.')[0]) == 7 and s['time'][0] == '2': print(" assuming JD") t = t - 2400000.5 elif len(s['time'].split('.')[0]) == 5 and s['time'][0] == '5': print(" assuming MJD") else: print(" skipping this spectrum.") continue w = array([float(item[0]) for item in s['data']])*wu f = array([float(item[1]) for item in s['data']])*fu dr = s.get('deredshifted', False) if dr: w = w*(1+zhel) # At this point, we should be able to convert to the units we want w = w.to('Angstrom').value if fluxed: f = f.to('erg / (s cm2 Angstrom)') f = f.value # source reference srcs = s.get('source','').split(',') this_source = None for src in srcs: if src in all_sources: this_source = all_sources[src] break if this_source is None: print("Warning: spectrum has no source") if this_source not in used_sources: used_sources.append(this_source) # At this point we're actually using the spectroscopy, so find source sid = used_sources.index(this_source) sids.append(sid) spectra.append(spectrum(wave=w, flux=f, fluxed=fluxed, name="Spectrum MJD={:.1f}".format(t))) dates.append(t) snobj.sdata = timespec(snobj, dates, spectra) snobj.sdata.sids = sids if full_data: # make a dictionary of the remaining OSC meta data and make it a member # variable snobj.osc_meta = {} for key in d.keys(): if key not in ['name','redshift','ra','dec','sources','photometry', 'spectra']: snobj.osc_meta[key] = d[key] return(snobj,'Success') def to_osc(s, ref=None, bibcode=None, source=1): '''Given a supernova object, s, output to JSON format suitable for upload to the OSC.''' data = {s.name:{"name":s.name}} if ref or bibcode: sources = [dict(bibcode=bibcode, name=ref, alias=str(source))] data['sources'] = sources phot = [] for filt in s.data: for i in range(len(s.data[filt].MJD)): datum = dict(survey='CSP', observatory='LCO') datum.update(CSP_systems(filt, s.data[filt].MJD[i])) datum['time'] = "{:.3f}".format(s.data[filt].MJD[i]) datum['u_time'] = "MJD" datum['magnitude'] = "{:.3f}".format(s.data[filt].mag[i]) flux,eflux = s.data[filt].flux[i],s.data[filt].e_flux[i] datum['flux'] = "{:.5f}".format(flux) datum['u_flux'] = "s^-1 cm^-2" datum['e_flux'] = "{:.5f}".format(eflux) datum['e_upper_magnitude'] = "{:.3f}".format( -2.5*log10((flux-eflux)/flux)) datum['e_lower_magnitude'] = "{:.3f}".format( -2.5*log10(flux/(flux+eflux))) datum['source'] = "{}".format(source) phot.append(datum) data['photometry'] = phot return json.dumps(data, indent=4)

36.05569

92

0.555906

''' Module for SNooPy to download/parse data from the Open Supernova Catalog. ''' from __future__ import print_function import six import json if six.PY3: import urllib.request as urllib else: import urllib from astropy.coordinates import Angle from snpy import sn,lc,fset from numpy import array,log10 import astropy.units as u from snpy.filters import spectrum from snpy.specobj import timespec def CfAbands(filt, MJD): if MJD < 51913.0: return filt[0]+'s' # standard photometry elif 51913.0 < MJD < 55058: if filt[0] == 'U': return 'U4sh' if filt[0] == 'I': return 'I4sh' if filt[0] == 'R': return 'R4sh' return filt[0]+'k1' # natural photometry CfA3 + CfA4 period 1 else: if filt[0] == 'U': return 'U4sh' if filt[0] == 'I': return 'I4sh' if filt[0] == 'R': return 'R4sh' return filt[0]+'k2' # natural photometry CfA4 period 2 # Some well-known publications and their mappings: pubs = { '1999AJ....117..707R': # Riess et al. (1999) Standard Photometry CfAbands, '2006AJ....131..527J': # Jha et al. (2006) Standard Photometry CfAbands, '2009ApJ...700..331H': # Hicken et al. (2009) CfA3 Natural Photometry CfAbands, '2012ApJS..200...12H': # Hicken et al. (2012) CfA4 Natural Photometry CfAbands } # telescope,band --> SNooPy filter database # We do this by matching (band,system,telescope,observatory) info from the # database to SNooPy filters. ftrans = {} ftrans_standard = {} standard_warnings = {} for band in ['u','g','r','i','B','V','Y','J','H','K']: ftrans[(band,"CSP",'',"LCO")] = band for band in ['U','B','V','R','I']: ftrans[(band,'','kait2','')] = band+'kait' for band in ['U','B','V','R','I']: ftrans[(band,'','kait3','')] = band+'kait' for band in ['J','H','Ks']: ftrans[(band,'','PAIRITEL','')] = band+'2m' for band in ['B','V','R','I']: ftrans[(band,'','kait4', '')] = band+'kait' for band in ['U','V','B']: ftrans[(band, 'Vega','Swift','')] = band+"_UVOT" for band in ['UVW1','UVW2','UVM2']: ftrans[(band, 'Vega','Swift','')] = band for band in ['g','r','i','z']: ftrans[(band, '', 'PS1','')] = "ps1_"+band # These are for data in (what I'm assuming) would be standard filters. # We will issue a warning, though. for band in ['U','B','V','R','I']: ftrans_standard[(band,'','','')] = band+"s" standard_warnings[band] = "Johnson/Kron/Cousins " for band in ['u','g','r','i','z']: ftrans_standard[(band,'','','')] = band+"_40" standard_warnings[band] = "Sloan (APO) " for band in ["u'","g'","r'","i'","z'"]: ftrans_standard[(band,'','','')] = band[0]+"_40" standard_warnings[band] = "Sloan (USNO-40) " for band in ["J","H","Ks"]: ftrans_standard[(band[0],'','','')] = band+"2m" standard_warnings[band[0]] = "2MASS " # Our own photometric systems: def CSP_systems(filt, MJD): '''Given a filter name and MJD date, output the correct telescope and system information.''' if filt == "V": if MJD < 53748.0: return (dict(telescope='Swope',instrument='Site2',band='V-3014', zeropoint="{:.4f}".format(fset['V0'].zp))) elif MJD < 53759.0: return (dict(telescope='Swope',instrument='Site2',band='V-3009', zeropoint="{:.4f}".format(fset['V1'].zp))) elif MJD < 56566.0: return (dict(telescope='Swope',instrument='Site2',band='V-9844', zeropoint="{:.4f}".format(fset['V'].zp))) else: return (dict(telescope='Swope',instrument='e2v',band='V-9844', zeropoint="{:.4f}".format(fset['V2'].zp))) if filt == "Jrc2": return (dict(telescope='Swope',instrument='RetroCam',band='J', zeropoint="{:.4f}".format(fset[filt].zp))) if filt in ['u','g','r','i','B']: if MJD < 56566.0: return (dict(telescope='Swope',instrument='Site2',band=filt, zeropoint="{:.4f}".format(fset[filt].zp))) else: return (dict(telescope='Swope',instrument='e2v',band=filt, zeropoint="{:.4f}".format(fset[filt+'2'].zp))) if filt in ['Y','J','H']: if MJD < 55743.0: return (dict(telescope='Swope',instrument='RetroCam',band=filt, zeropoint="{:.4f}".format(fset[filt].zp))) else: return (dict(telescope='DuPont',instrument='RetroCam',band=filt, zeropoint="{:.4f}".format(fset[filt+'d'].zp))) return({}) MJD_offsets = { 'MJD':0, 'JD':-2400000.5 } warning_message = { 'upperlims_noerr':'Warning: Data lacking errorbars or with upper-limits not imported', 'upperlims':'Warning: Data with upper-limits not imported', } OSC_template = '''https://sne.space/astrocats/astrocats/supernovae/output/json/{}.json''' def get_obj(url, full_data=True, allow_no_errors=False, missing_error=0.01): '''Attempt to build a SNooPy object from a Open Supernova Catalog server URL.''' if url.find('osc:') == 0: # Try to construct a url based only on a name. url = OSC_template.format(url.split(':')[1]) try: uf = urllib.urlopen(url) except: return None,"Invalid URL" try: d = json.load(uf) except: uf.close() if full_data: return None,"Failed to decode JSON",None return None,"Failed to decode JSON" else: uf.close() # We now have the JSON data. Get the info we need d = list(d.values())[0] name = d['name'] if 'redshift' not in d or 'ra' not in d or 'dec' not in d: return None,"No redshift, RA, or DEC found" zhel = float(d['redshift'][0]['value']) ra = Angle(" ".join([d['ra'][0]['value'],d['ra'][0]['u_value']])).degree decl = Angle(" ".join([d['dec'][0]['value'],d['dec'][0]['u_value']])).degree snobj = sn(name, ra=ra, dec=decl, z=zhel) # All primary sources all_sources_dict = [item for item in d['sources'] \ if not item.get('secondary',False)] all_sources_dict2 = [item for item in d['sources'] \ if item.get('secondary',False)] all_sources = {} for source in all_sources_dict: all_sources[source['alias']] = (source.get('bibcode',''), source.get('reference','')) all_sources2 = {} for source in all_sources_dict2: all_sources2[source['alias']] = (source.get('bibcode',''), source.get('reference','')) # Next, the photometry. used_sources = [] MJD = {} mags = {} emags = {} sids = {} known_unknowns = [] unknown_unknowns = [] warnings = [] photometry = d.get('photometry', []) for p in photometry: if p.get('upperlimit',False): continue t = (p.get('band',''),p.get('system',''),p.get('telescope',''), p.get('observatory','')) # Deal with source of photometry ss = p.get('source').split(',') this_source = None for s in ss: if s in all_sources: this_source = all_sources[s] break if this_source is None: for s in ss: if s in all_sources2: this_source = all_sources2[s] if this_source is None: print("Warning: no primary source, skipping") continue bibcode = this_source[0] if bibcode in pubs: b = pubs[bibcode](t[0],float(p['time'])) elif t in ftrans: b = ftrans[t] elif t in ftrans_standard: b = ftrans_standard[t] if t not in known_unknowns: known_unknowns.append(t) print("Warning: no telescope/system info, assuming ", \ standard_warnings[b[0]], b[0]) elif (t[0],"","","") in ftrans_standard: b = ftrans_standard[(t[0],"","","")] if t not in known_unknowns: known_unknowns.append(t) print("Warning: telescope/system defined by %s/%s/%s not "\ "recognized, assuming %s %s" %\ (t[1],t[2],t[3],standard_warnings[t[0]],t[0])) else: # No idea if t not in unknown_unknowns: unknown_unknowns.append(t) print("Warning: telescope/system defined by %s/%s/%s not "\ "recognized and can't figure out the filter %s" % \ (t[1],t[2],t[3],t[0])) unknown_unknowns.append(t) continue if b not in MJD: MJD[b] = [] mags[b] = [] emags[b] = [] sids[b] = [] if 'time' in p and 'magnitude' in p: if not allow_no_errors and 'e_magnitude' not in p and\ 'e_lower_magnitude' not in p and 'e_upper_magnitude' not in p: if 'upperlims' not in warnings: warnings.append('upperlims') continue MJD[b].append(float(p['time'])) mags[b].append(float(p['magnitude'])) if 'e_magnitude' in p: emags[b].append(float(p['e_magnitude'])) elif 'e_lower_magnitude' in p and 'e_upper_magnitude' in p: emags[b].append((float(p['e_lower_magnitude']) +\ float(p['e_upper_magnitude']))/2) else: emags[b].append(missing_error) elif 'time' in p and 'countrate' in p and 'zeropoint' in p: if not allow_no_errors and 'e_countrate' not in p: if 'upperlims' not in warnings: warnings.append('upperlims') continue if float(p['countrate']) < 0: continue MJD[b].append(float(p['time'])) mags[b].append(-2.5*log10(float(p['countrate'])) + \ float(p['zeropoint'])) ec = p.get('e_countrate',None) if ec is not None: emags[b].append(1.087*float(p['e_countrate'])/float(p['countrate'])) else: emags[b].append(missing_error) else: if 'upperlims_noerr' not in warnings: warnings.append('upperlims_noerr') continue if this_source not in used_sources: used_sources.append(this_source) # At this point we're actually using the photometry, so find source sid = used_sources.index(this_source) sids[b].append(sid) for b in MJD: if len(MJD[b]) > 0: snobj.data[b] = lc(snobj, b, array(MJD[b]), array(mags[b]), array(emags[b]), sids=array(sids[b], dtype=int)) snobj.data[b].time_sort() snobj.sources = used_sources snobj.get_restbands() if len(unknown_unknowns) > 0: unknown_unknowns = list(set(unknown_unknowns)) print("Warning: the following photometry was not recognized by SNooPy") print("and was not imported:") for item in unknown_unknowns: print(item) if warnings: for warning in warnings: print(warning_message[warning]) # lastly, the spectroscopy if d.get('spectra',None) is not None: spectra = [] dates = [] sids = [] for s in d['spectra']: wu = s.get('u_wavelengths', 'Agnstrom') fu = s.get('u_fluxes', 'Uncalibrated') try: wu = u.Unit(wu) except ValueError: print("Warning: unrecognized unit for wavelength: {}".format(wu)) print(" assuming Angstroms") wu = u.Angstrom if fu == 'Uncalibrated': fluxed = False fu = u.dimensionless_unscaled else: try: fu = u.Unit(fu) fluxed = True except ValueError: print("Warning: unrecognized unit for flux: {}".format(fu)) fluxed = False fu = u.dimensionless_unscaled tu = s.get('u_time', 'MJD') t = float(s['time']) if tu not in MJD_offsets: print("Warning: unrecognized time unit: {}".format(tu)) if len(s['time'].split('.')[0]) == 7 and s['time'][0] == '2': print(" assuming JD") t = t - 2400000.5 elif len(s['time'].split('.')[0]) == 5 and s['time'][0] == '5': print(" assuming MJD") else: print(" skipping this spectrum.") continue w = array([float(item[0]) for item in s['data']])*wu f = array([float(item[1]) for item in s['data']])*fu dr = s.get('deredshifted', False) if dr: w = w*(1+zhel) # At this point, we should be able to convert to the units we want w = w.to('Angstrom').value if fluxed: f = f.to('erg / (s cm2 Angstrom)') f = f.value # source reference srcs = s.get('source','').split(',') this_source = None for src in srcs: if src in all_sources: this_source = all_sources[src] break if this_source is None: print("Warning: spectrum has no source") if this_source not in used_sources: used_sources.append(this_source) # At this point we're actually using the spectroscopy, so find source sid = used_sources.index(this_source) sids.append(sid) spectra.append(spectrum(wave=w, flux=f, fluxed=fluxed, name="Spectrum MJD={:.1f}".format(t))) dates.append(t) snobj.sdata = timespec(snobj, dates, spectra) snobj.sdata.sids = sids if full_data: # make a dictionary of the remaining OSC meta data and make it a member # variable snobj.osc_meta = {} for key in d.keys(): if key not in ['name','redshift','ra','dec','sources','photometry', 'spectra']: snobj.osc_meta[key] = d[key] return(snobj,'Success') def to_osc(s, ref=None, bibcode=None, source=1): '''Given a supernova object, s, output to JSON format suitable for upload to the OSC.''' data = {s.name:{"name":s.name}} if ref or bibcode: sources = [dict(bibcode=bibcode, name=ref, alias=str(source))] data['sources'] = sources phot = [] for filt in s.data: for i in range(len(s.data[filt].MJD)): datum = dict(survey='CSP', observatory='LCO') datum.update(CSP_systems(filt, s.data[filt].MJD[i])) datum['time'] = "{:.3f}".format(s.data[filt].MJD[i]) datum['u_time'] = "MJD" datum['magnitude'] = "{:.3f}".format(s.data[filt].mag[i]) flux,eflux = s.data[filt].flux[i],s.data[filt].e_flux[i] datum['flux'] = "{:.5f}".format(flux) datum['u_flux'] = "s^-1 cm^-2" datum['e_flux'] = "{:.5f}".format(eflux) datum['e_upper_magnitude'] = "{:.3f}".format( -2.5*log10((flux-eflux)/flux)) datum['e_lower_magnitude'] = "{:.3f}".format( -2.5*log10(flux/(flux+eflux))) datum['source'] = "{}".format(source) phot.append(datum) data['photometry'] = phot return json.dumps(data, indent=4)

476

0

23

b838c3e4fd3bce1a2cc716eb2ba8a849168a9356

744

py

Python

Day 15 - OOP/main.py

secureterminal/100-Days-of-Code

04383ae541938d8a551b5aac9a0dad3348a6ef23

[ "MIT" ]

1

2022-01-28T13:55:39.000Z

Day 15 - OOP/main.py

secureterminal/100-Days-of-Code

04383ae541938d8a551b5aac9a0dad3348a6ef23

[ "MIT" ]

1

2022-02-02T00:13:18.000Z

2022-02-03T11:32:53.000Z

Day 15 - OOP/main.py

secureterminal/100-Days-of-Code

04383ae541938d8a551b5aac9a0dad3348a6ef23

[ "MIT" ]

2

2022-02-07T20:49:36.000Z

2022-02-19T21:22:15.000Z

from menu import Menu, MenuItem from coffee_maker import CoffeeMaker from money_machine import MoneyMachine money_machine = MoneyMachine() coffee_maker = CoffeeMaker() menu = Menu() coffee_maker.report() money_machine.report() coffee_machine_is_on = True while coffee_machine_is_on: choices = menu.get_items() user_order = input(f'Please choose a coffee: ({choices})>>> ') if user_order == 'off': coffee_machine_is_on = False elif user_order == 'report': coffee_maker.report() money_machine.report() else: drink = menu.find_drink(user_order) if coffee_maker.is_resource_sufficient(drink) and money_machine.make_payment(drink.cost): coffee_maker.make_coffee(drink)

24.8

97

0.717742

from menu import Menu, MenuItem from coffee_maker import CoffeeMaker from money_machine import MoneyMachine money_machine = MoneyMachine() coffee_maker = CoffeeMaker() menu = Menu() coffee_maker.report() money_machine.report() coffee_machine_is_on = True while coffee_machine_is_on: choices = menu.get_items() user_order = input(f'Please choose a coffee: ({choices})>>> ') if user_order == 'off': coffee_machine_is_on = False elif user_order == 'report': coffee_maker.report() money_machine.report() else: drink = menu.find_drink(user_order) if coffee_maker.is_resource_sufficient(drink) and money_machine.make_payment(drink.cost): coffee_maker.make_coffee(drink)

0

fc8a2c85d00bef3bd3bd075b7a046a93e1e9c68c

4,269

py

Python

intera_interface/src/intera_interface/digital_io.py

thinclab/intera_sdk

556de67a88049687404734404e16b147943cde3c

[ "Apache-2.0" ]

38

2017-01-20T15:44:22.000Z

2022-01-28T15:15:40.000Z

intera_interface/src/intera_interface/digital_io.py

thinclab/intera_sdk

556de67a88049687404734404e16b147943cde3c

[ "Apache-2.0" ]

47

2016-12-16T19:41:03.000Z

2022-03-21T14:04:04.000Z

intera_interface/src/intera_interface/digital_io.py

thinclab/intera_sdk

556de67a88049687404734404e16b147943cde3c

[ "Apache-2.0" ]

52

2017-02-03T13:26:23.000Z

2021-03-16T14:25:51.000Z

# Copyright (c) 2013-2018, Rethink Robotics Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import errno import rospy import intera_dataflow from intera_core_msgs.msg import ( DigitalIOState, DigitalOutputCommand, ) class DigitalIO(object): """ DEPRECATION WARNING: This interface will likely be removed in the future. Transition to using the IO Framework and the wrapper classes: gripper.py, cuff.py, camera.py Interface class for a simple Digital Input and/or Output on the Intera robots. Input - read input state Output - turn output On/Off - read current output state """ def __init__(self, component_id): """ Constructor. @param component_id: unique id of the digital component """ self._id = component_id self._component_type = 'digital_io' self._is_output = False self._state = None self.state_changed = intera_dataflow.Signal() type_ns = '/robot/' + self._component_type topic_base = type_ns + '/' + self._id self._sub_state = rospy.Subscriber( topic_base + '/state', DigitalIOState, self._on_io_state) intera_dataflow.wait_for( lambda: self._state != None, timeout=2.0, timeout_msg="Failed to get current digital_io state from %s" \ % (topic_base,), ) # check if output-capable before creating publisher if self._is_output: self._pub_output = rospy.Publisher( type_ns + '/command', DigitalOutputCommand, queue_size=10) def _on_io_state(self, msg): """ Updates the internally stored state of the Digital Input/Output. """ new_state = (msg.state == DigitalIOState.PRESSED) if self._state is None: self._is_output = not msg.isInputOnly old_state = self._state self._state = new_state # trigger signal if changed if old_state is not None and old_state != new_state: self.state_changed(new_state) @property def is_output(self): """ Accessor to check if IO is capable of output. """ return self._is_output @property def state(self): """ Current state of the Digital Input/Output. """ return self._state @state.setter def state(self, value): """ Control the state of the Digital Output. (is_output must be True) @type value: bool @param value: new state to output {True, False} """ self.set_output(value) def set_output(self, value, timeout=2.0): """ Control the state of the Digital Output. Use this function for finer control over the wait_for timeout. @type value: bool @param value: new state {True, False} of the Output. @type timeout: float @param timeout: Seconds to wait for the io to reflect command. If 0, just command once and return. [0] """ if not self._is_output: raise IOError(errno.EACCES, "Component is not an output [%s: %s]" % (self._component_type, self._id)) cmd = DigitalOutputCommand() cmd.name = self._id cmd.value = value self._pub_output.publish(cmd) if not timeout == 0: intera_dataflow.wait_for( test=lambda: self.state == value, timeout=timeout, rate=100, timeout_msg=("Failed to command digital io to: %r" % (value,)), body=lambda: self._pub_output.publish(cmd) )

29.853147

79

0.606699

# Copyright (c) 2013-2018, Rethink Robotics Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import errno import rospy import intera_dataflow from intera_core_msgs.msg import ( DigitalIOState, DigitalOutputCommand, ) class DigitalIO(object): """ DEPRECATION WARNING: This interface will likely be removed in the future. Transition to using the IO Framework and the wrapper classes: gripper.py, cuff.py, camera.py Interface class for a simple Digital Input and/or Output on the Intera robots. Input - read input state Output - turn output On/Off - read current output state """ def __init__(self, component_id): """ Constructor. @param component_id: unique id of the digital component """ self._id = component_id self._component_type = 'digital_io' self._is_output = False self._state = None self.state_changed = intera_dataflow.Signal() type_ns = '/robot/' + self._component_type topic_base = type_ns + '/' + self._id self._sub_state = rospy.Subscriber( topic_base + '/state', DigitalIOState, self._on_io_state) intera_dataflow.wait_for( lambda: self._state != None, timeout=2.0, timeout_msg="Failed to get current digital_io state from %s" \ % (topic_base,), ) # check if output-capable before creating publisher if self._is_output: self._pub_output = rospy.Publisher( type_ns + '/command', DigitalOutputCommand, queue_size=10) def _on_io_state(self, msg): """ Updates the internally stored state of the Digital Input/Output. """ new_state = (msg.state == DigitalIOState.PRESSED) if self._state is None: self._is_output = not msg.isInputOnly old_state = self._state self._state = new_state # trigger signal if changed if old_state is not None and old_state != new_state: self.state_changed(new_state) @property def is_output(self): """ Accessor to check if IO is capable of output. """ return self._is_output @property def state(self): """ Current state of the Digital Input/Output. """ return self._state @state.setter def state(self, value): """ Control the state of the Digital Output. (is_output must be True) @type value: bool @param value: new state to output {True, False} """ self.set_output(value) def set_output(self, value, timeout=2.0): """ Control the state of the Digital Output. Use this function for finer control over the wait_for timeout. @type value: bool @param value: new state {True, False} of the Output. @type timeout: float @param timeout: Seconds to wait for the io to reflect command. If 0, just command once and return. [0] """ if not self._is_output: raise IOError(errno.EACCES, "Component is not an output [%s: %s]" % (self._component_type, self._id)) cmd = DigitalOutputCommand() cmd.name = self._id cmd.value = value self._pub_output.publish(cmd) if not timeout == 0: intera_dataflow.wait_for( test=lambda: self.state == value, timeout=timeout, rate=100, timeout_msg=("Failed to command digital io to: %r" % (value,)), body=lambda: self._pub_output.publish(cmd) )

0

07a2a8bad2c82e238b18e385c8b1b2d9e1a12999

2,535

py

Python

tests/models/mysql_dumps_test.py

ywlianghang/mysql_streamer

7fc85efaca3db6a387ea4b791632c2df2d04cb3e

[ "Apache-2.0" ]

419

2016-11-17T18:41:47.000Z

2022-03-14T02:50:02.000Z

tests/models/mysql_dumps_test.py

ywlianghang/mysql_streamer

7fc85efaca3db6a387ea4b791632c2df2d04cb3e

[ "Apache-2.0" ]

19

2016-11-30T18:09:00.000Z

2019-04-02T06:20:02.000Z

tests/models/mysql_dumps_test.py

ywlianghang/mysql_streamer

7fc85efaca3db6a387ea4b791632c2df2d04cb3e

[ "Apache-2.0" ]

90

2016-11-23T06:26:20.000Z

2022-01-22T09:24:42.000Z

# -*- coding: utf-8 -*- # Copyright 2016 Yelp Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from __future__ import absolute_import from __future__ import unicode_literals import pytest from replication_handler.models.mysql_dumps import MySQLDumps @pytest.mark.itest @pytest.mark.itest_db

28.483146

77

0.672189

# -*- coding: utf-8 -*- # Copyright 2016 Yelp Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from __future__ import absolute_import from __future__ import unicode_literals import pytest from replication_handler.models.mysql_dumps import MySQLDumps @pytest.mark.itest @pytest.mark.itest_db class TestMySQLDumps(object): @pytest.fixture def cluster_name(self): return 'yelp_main' @pytest.fixture def test_dump(self): return 'This is a test dump' @pytest.yield_fixture def initialize_dump( self, sandbox_session, cluster_name, test_dump ): assert MySQLDumps.dump_exists(sandbox_session, cluster_name) is False test_mysql_dump = MySQLDumps.update_mysql_dump( session=sandbox_session, database_dump=test_dump, cluster_name=cluster_name ) sandbox_session.flush() assert MySQLDumps.dump_exists(sandbox_session, cluster_name) is True yield test_mysql_dump def test_get_latest_mysql_dump( self, initialize_dump, cluster_name, test_dump, sandbox_session ): new_dump = 'This is a new dump' retrieved_dump = MySQLDumps.get_latest_mysql_dump( session=sandbox_session, cluster_name=cluster_name ) assert retrieved_dump == test_dump MySQLDumps.update_mysql_dump( session=sandbox_session, database_dump=new_dump, cluster_name=cluster_name ) returned_new_dump = MySQLDumps.get_latest_mysql_dump( session=sandbox_session, cluster_name=cluster_name ) assert returned_new_dump == new_dump MySQLDumps.delete_mysql_dump( session=sandbox_session, cluster_name=cluster_name ) dump_exists = MySQLDumps.dump_exists( session=sandbox_session, cluster_name=cluster_name ) assert not dump_exists

1,533

182

22

0ec5fc82f6363d39869fe20305aa7077435f30d4

1,232

py

Python

WORK/working/crime_vis/crime.py

jessicagtz/WorkingFolder

4791618e1ec12b9cc38a6ceb1ff03bab1799b0bc

[ "MIT" ]

null

WORK/working/crime_vis/crime.py

jessicagtz/WorkingFolder

4791618e1ec12b9cc38a6ceb1ff03bab1799b0bc

[ "MIT" ]

null

WORK/working/crime_vis/crime.py

jessicagtz/WorkingFolder

4791618e1ec12b9cc38a6ceb1ff03bab1799b0bc

[ "MIT" ]

1

2018-12-06T21:33:44.000Z

# import dependencies from flask import Flask, jsonify, render_template, request, redirect from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func, inspect import pandas as pd import numpy as np import datetime as dt # database setup using automap engine = create_engine("sqlite:///chi_db.sqlite") Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Save references to the tables AllCrime = Base.classes.all_crime # Create our session (link) from Python to the DB session = Session(engine) # initialize Flask app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = "sqlite:///chi_db.sqlite" @app.route("/crimehistory") if __name__ == "__main__": app.run(debug=True)

27.377778

117

0.730519

# import dependencies from flask import Flask, jsonify, render_template, request, redirect from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func, inspect import pandas as pd import numpy as np import datetime as dt # database setup using automap engine = create_engine("sqlite:///chi_db.sqlite") Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Save references to the tables AllCrime = Base.classes.all_crime # Create our session (link) from Python to the DB session = Session(engine) # initialize Flask app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = "sqlite:///chi_db.sqlite" @app.route("/crimehistory") def crime_dict(crime): results = session.query.(AllCrime.id, AllCrime.crimeGroup, AllCrime.year,AllCrime.nunCrimes).filter(AllCrimes.id) dict=[] for result in results: crime_dict= {} crime_dict["year"] = result.year crime_dict["id"] = result.id crime_dict["crimeGroup"] = result.crimeGroup crime_dict["nunCrimes"] = result.nunCrimes dict.append(crime_dict) return jsonify(dict) if __name__ == "__main__": app.run(debug=True)

421

0

22

60d2134f1b978a5ccd35690d147a761894f25efe

19,494

py

Python

easyocr/easyocr.py

ghandic/EasyOCR

f96bea526e7208e4630a18698c18d0223e2a1168

[ "Apache-2.0" ]

1

2021-07-19T03:17:50.000Z

easyocr/easyocr.py

ghandic/EasyOCR

f96bea526e7208e4630a18698c18d0223e2a1168

[ "Apache-2.0" ]

null

easyocr/easyocr.py

ghandic/EasyOCR

f96bea526e7208e4630a18698c18d0223e2a1168

[ "Apache-2.0" ]

1

2020-10-24T11:40:29.000Z

# -*- coding: utf-8 -*- import os import sys from logging import getLogger from typing import Any, List, Tuple import cv2 import numpy as np import torch from bidi.algorithm import get_display from .detection import get_detector, get_textbox from .imgproc import loadImage from .recognition import get_recognizer, get_text from .settings import * from .utils import calculate_md5, download_and_unzip, get_image_list, get_paragraph, group_text_box if sys.version_info[0] == 2: from io import open from six.moves.urllib.request import urlretrieve from pathlib2 import Path else: from urllib.request import urlretrieve from pathlib import Path LOGGER = getLogger(__name__)

46.194313

123

0.601005

# -*- coding: utf-8 -*- import os import sys from logging import getLogger from typing import Any, List, Tuple import cv2 import numpy as np import torch from bidi.algorithm import get_display from .detection import get_detector, get_textbox from .imgproc import loadImage from .recognition import get_recognizer, get_text from .settings import * from .utils import calculate_md5, download_and_unzip, get_image_list, get_paragraph, group_text_box if sys.version_info[0] == 2: from io import open from six.moves.urllib.request import urlretrieve from pathlib2 import Path else: from urllib.request import urlretrieve from pathlib import Path LOGGER = getLogger(__name__) class Reader(object): def __init__( self, lang_list: List[str], gpu: bool = True, model_storage_directory: str = None, download_enabled: bool = True ): """Create an EasyOCR Reader. Args: lang_list (List[str]): Language codes (ISO 639) for languages to be recognized during analysis. gpu (bool, optional): Enable GPU support. Defaults to True. model_storage_directory (str, optional): Path to directory for model data. If not specified, models will be read from a directory as defined by the environment variable EASYOCR_MODULE_PATH (preferred), MODULE_PATH (if defined), or ~/.EasyOCR/. Defaults to None. download_enabled (bool, optional): Enabled downloading of model data via HTTP. Defaults to True. """ self._set_device(gpu) self._set_model_lang(lang_list) self._set_character_choices() self._set_lang_char(lang_list) # self.lang_list doesn't seem to be used self._set_model_paths(model_storage_directory) self._download_models(download_enabled) self.detector = get_detector(self._detector_path, self.device) self.recognizer, self.converter = get_recognizer( input_channel, output_channel, hidden_size, self.character, self.separator_list, self.dict_list, self._recognition_model_path, device=self.device, ) def readtext( self, image: Any, decoder: str = "greedy", beamWidth: int = 5, batch_size: int = 1, workers: int = 0, allowlist: List[str] = None, blocklist: List[str] = None, detail: int = 1, paragraph: bool = False, contrast_ths: float = 0.1, adjust_contrast: float = 0.5, filter_ths: float = 0.003, text_threshold: float = 0.7, low_text: float = 0.4, link_threshold: float = 0.4, canvas_size: int = 2560, mag_ratio: float = 1.0, slope_ths: float = 0.1, ycenter_ths: float = 0.5, height_ths: float = 0.5, width_ths: float = 0.5, add_margin: float = 0.1, ) -> List: # TODO: ghandic - unsure on output shape """[summary] # TODO Args: image (Any): [description] decoder (str, optional): [description]. Defaults to "greedy". beamWidth (int, optional): [description]. Defaults to 5. batch_size (int, optional): [description]. Defaults to 1. workers (int, optional): [description]. Defaults to 0. allowlist (List[str], optional): [description]. Defaults to None. blocklist (List[str], optional): [description]. Defaults to None. detail (int, optional): [description]. Defaults to 1. paragraph (bool, optional): [description]. Defaults to False. contrast_ths (float, optional): [description]. Defaults to 0.1. adjust_contrast (float, optional): [description]. Defaults to 0.5. filter_ths (float, optional): [description]. Defaults to 0.003. text_threshold (float, optional): [description]. Defaults to 0.7. low_text (float, optional): [description]. Defaults to 0.4. link_threshold (float, optional): [description]. Defaults to 0.4. canvas_size (int, optional): [description]. Defaults to 2560. mag_ratio (float, optional): [description]. Defaults to 1.0. slope_ths (float, optional): [description]. Defaults to 0.1. ycenter_ths (float, optional): [description]. Defaults to 0.5. height_ths (float, optional): [description]. Defaults to 0.5. width_ths (float, optional): [description]. Defaults to 0.5. add_margin (float, optional): [description]. Defaults to 0.1. Returns: List: [description] """ img, img_cv_grey = self._load_image(image) text_box = get_textbox( self.detector, img, canvas_size, mag_ratio, text_threshold, link_threshold, low_text, False, self.device ) horizontal_list, free_list = group_text_box(text_box, slope_ths, ycenter_ths, height_ths, width_ths, add_margin) # should add filter to screen small box out image_list, max_width = get_image_list(horizontal_list, free_list, img_cv_grey, model_height=imgH) if allowlist: ignore_char = "".join(set(self.character) - set(allowlist)) elif blocklist: ignore_char = "".join(set(blocklist)) else: ignore_char = "".join(set(self.character) - set(self.lang_char)) if self.model_lang in ["chinese_tra", "chinese_sim", "japanese", "korean"]: decoder = "greedy" result = get_text( self.character, imgH, int(max_width), self.recognizer, self.converter, image_list, ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths, workers, self.device, ) if self.model_lang == "arabic": direction_mode = "rtl" result = [list(item) for item in result] for item in result: item[1] = get_display(item[1]) else: direction_mode = "ltr" if paragraph: result = get_paragraph(result, mode=direction_mode) if detail == 0: return [item[1] for item in result] else: return result def _load_image(self, image: Any) -> Tuple[np.ndarray, np.ndarray]: if type(image) == str: if image.startswith("http://") or image.startswith("https://"): tmp, _ = urlretrieve(image) img_cv_grey = cv2.imread(tmp, cv2.IMREAD_GRAYSCALE) os.remove(tmp) else: img_cv_grey = cv2.imread(image, cv2.IMREAD_GRAYSCALE) image = os.path.expanduser(image) img = loadImage(image) # can accept URL elif type(image) == bytes: nparr = np.frombuffer(image, np.uint8) img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img_cv_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) elif type(image) == np.ndarray: if len(image.shape) == 2: # grayscale img_cv_grey = image img = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) elif len(image.shape) == 3: # BGRscale img = image img_cv_grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: raise TypeError("Could not load image") return img, img_cv_grey def _download_models(self, download_enabled): corrupt_msg = "MD5 hash mismatch, possible file corruption" if os.path.isfile(self._detector_path) == False: if not download_enabled: raise FileNotFoundError("Missing %s and downloads disabled" % self._detector_path) LOGGER.warning( "Downloading detection model, please wait. " "This may take several minutes depending upon your network connection." ) download_and_unzip(model_url["detector"][0], DETECTOR_FILENAME, self.model_storage_directory) assert calculate_md5(self._detector_path) == model_url["detector"][1], corrupt_msg LOGGER.info("Download complete") elif calculate_md5(self._detector_path) != model_url["detector"][1]: if not download_enabled: raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % self._detector_path) LOGGER.warning(corrupt_msg) os.remove(self._detector_path) LOGGER.warning( "Re-downloading the detection model, please wait. " "This may take several minutes depending upon your network connection." ) download_and_unzip(model_url["detector"][0], DETECTOR_FILENAME, self.model_storage_directory) assert calculate_md5(self._detector_path) == model_url["detector"][1], corrupt_msg # check model file if os.path.isfile(self._recognition_model_path) == False: if not download_enabled: raise FileNotFoundError("Missing %s and downloads disabled" % self._recognition_model_path) LOGGER.warning( "Downloading recognition model, please wait. " "This may take several minutes depending upon your network connection." ) download_and_unzip( model_url[self._recognition_model_file][0], self._recognition_model_file, self.model_storage_directory ) assert ( calculate_md5(self._recognition_model_path) == model_url[self._recognition_model_file][1] ), corrupt_msg LOGGER.info("Download complete.") elif calculate_md5(self._recognition_model_path) != model_url[self._recognition_model_file][1]: if not download_enabled: raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % self._recognition_model_path) LOGGER.warning(corrupt_msg) os.remove(self._recognition_model_path) LOGGER.warning( "Re-downloading the recognition model, please wait. " "This may take several minutes depending upon your network connection." ) download_and_unzip( model_url[self._recognition_model_file][0], self._recognition_model_file, self.model_storage_directory ) assert ( calculate_md5(self._recognition_model_path) == model_url[self._recognition_model_file][1] ), corrupt_msg LOGGER.info("Download complete") def _set_lang_char(self, lang_list: List[str]): self.dict_list = {} for lang in lang_list: self.dict_list[lang] = os.path.join(BASE_PATH, "dict", lang + ".txt") self.lang_char = [] for lang in lang_list: char_file = os.path.join(BASE_PATH, "character", lang + "_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: char_list = input_file.read().splitlines() self.lang_char += char_list self.lang_char = set(self.lang_char).union(set(number + symbol)) self.lang_char = "".join(self.lang_char) def _set_model_lang(self, lang_list: List[str]): # check available languages unknown_lang = set(lang_list) - set(all_lang_list) if unknown_lang != set(): raise ValueError(unknown_lang, "is not supported") # choose model if "th" in lang_list: self.model_lang = "thai" if set(lang_list) - set(["th", "en"]) != set(): raise ValueError('Thai is only compatible with English, try lang_list=["th","en"]') elif "ch_tra" in lang_list: self.model_lang = "chinese_tra" if set(lang_list) - set(["ch_tra", "en"]) != set(): raise ValueError('Chinese is only compatible with English, try lang_list=["ch_tra","en"]') elif "ch_sim" in lang_list: self.model_lang = "chinese_sim" if set(lang_list) - set(["ch_sim", "en"]) != set(): raise ValueError('Chinese is only compatible with English, try lang_list=["ch_sim","en"]') elif "ja" in lang_list: self.model_lang = "japanese" if set(lang_list) - set(["ja", "en"]) != set(): raise ValueError('Japanese is only compatible with English, try lang_list=["ja","en"]') elif "ko" in lang_list: self.model_lang = "korean" if set(lang_list) - set(["ko", "en"]) != set(): raise ValueError('Korean is only compatible with English, try lang_list=["ko","en"]') elif "ta" in lang_list: self.model_lang = "tamil" if set(lang_list) - set(["ta", "en"]) != set(): raise ValueError('Tamil is only compatible with English, try lang_list=["ta","en"]') elif set(lang_list) & set(arabic_lang_list): self.model_lang = "arabic" if set(lang_list) - set(arabic_lang_list + ["en"]) != set(): raise ValueError('Arabic is only compatible with English, try lang_list=["ar","fa","ur","ug","en"]') elif set(lang_list) & set(devanagari_lang_list): self.model_lang = "devanagari" if set(lang_list) - set(devanagari_lang_list + ["en"]) != set(): raise ValueError('Devanagari is only compatible with English, try lang_list=["hi","mr","ne","en"]') elif set(lang_list) & set(cyrillic_lang_list): self.model_lang = "cyrillic" if set(lang_list) - set(cyrillic_lang_list + ["en"]) != set(): raise ValueError( 'Cyrillic is only compatible with English, try lang_list=["ru","rs_cyrillic","be","bg","uk","mn","en"]' ) else: self.model_lang = "latin" def _set_character_choices(self): self.separator_list = {} if self.model_lang == "latin": all_char = ( "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" + "ÀÁÂÃÄÅÆÇÈÉÊËÍÎÑÒÓÔÕÖØÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿąęĮįıŁłŒœŠšųŽž" ) self.character = number + symbol + all_char self._recognition_model_file = "latin.pth" elif self.model_lang == "arabic": ar_number = "٠١٢٣٤٥٦٧٨٩" ar_symbol = "«»؟،؛" ar_char = "ءآأؤإئااًبةتثجحخدذرزسشصضطظعغفقكلمنهوىيًٌٍَُِّْٰٓٔٱٹپچڈڑژکڭگںھۀہۂۃۆۇۈۋیېےۓە" self.character = number + symbol + en_char + ar_number + ar_symbol + ar_char self._recognition_model_file = "arabic.pth" elif self.model_lang == "cyrillic": cyrillic_char = ( "ЁЂЄІЇЈЉЊЋЎЏАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяёђєіїјљњћўџҐґҮүө" ) self.character = number + symbol + en_char + cyrillic_char self._recognition_model_file = "cyrillic.pth" elif self.model_lang == "devanagari": devanagari_char = ( ".ँंःअअंअःआइईउऊऋएऐऑओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळवशषसह़ािीुूृॅेैॉोौ्ॐ॒क़ख़ग़ज़ड़ढ़फ़ॠ।०१२३४५६७८९॰" ) self.character = number + symbol + en_char + devanagari_char self._recognition_model_file = "devanagari.pth" elif self.model_lang == "chinese_tra": char_file = os.path.join(BASE_PATH, "character", "ch_tra_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: ch_tra_list = input_file.read().splitlines() ch_tra_char = "".join(ch_tra_list) self.character = number + symbol + en_char + ch_tra_char self._recognition_model_file = "chinese.pth" elif self.model_lang == "chinese_sim": char_file = os.path.join(BASE_PATH, "character", "ch_sim_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: ch_sim_list = input_file.read().splitlines() ch_sim_char = "".join(ch_sim_list) self.character = number + symbol + en_char + ch_sim_char self._recognition_model_file = "chinese_sim.pth" elif self.model_lang == "japanese": char_file = os.path.join(BASE_PATH, "character", "ja_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: ja_list = input_file.read().splitlines() ja_char = "".join(ja_list) self.character = number + symbol + en_char + ja_char self._recognition_model_file = "japanese.pth" elif self.model_lang == "korean": char_file = os.path.join(BASE_PATH, "character", "ko_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: ko_list = input_file.read().splitlines() ko_char = "".join(ko_list) self.character = number + symbol + en_char + ko_char self._recognition_model_file = "korean.pth" elif self.model_lang == "tamil": char_file = os.path.join(BASE_PATH, "character", "ta_char.txt") with open(char_file, "r", encoding="utf-8-sig") as input_file: ta_list = input_file.read().splitlines() ta_char = "".join(ta_list) self.character = number + symbol + en_char + ta_char self._recognition_model_file = "tamil.pth" elif self.model_lang == "thai": self.separator_list = {"th": ["\xa2", "\xa3"], "en": ["\xa4", "\xa5"]} separator_char = [] for lang, sep in self.separator_list.items(): separator_char += sep special_c0 = "ุู" special_c1 = "ิีืึ" + "ั" special_c2 = "่้๊๋" special_c3 = "็์" special_c = special_c0 + special_c1 + special_c2 + special_c3 + "ำ" th_char = "กขคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรลวศษสหฬอฮฤ" + "เแโใไะา" + special_c + "ํฺ" + "ฯๆ" th_number = "0123456789๑๒๓๔๕๖๗๘๙" self.character = "".join(separator_char) + symbol + en_char + th_char + th_number self._recognition_model_file = "thai.pth" else: LOGGER.error("invalid language") raise NotImplementedError("invalid language") def _set_model_paths(self, dir: str): self.model_storage_directory = MODULE_PATH + "/model" if dir: self.model_storage_directory = dir Path(self.model_storage_directory).mkdir(parents=True, exist_ok=True) self._recognition_model_path = os.path.join(self.model_storage_directory, self._recognition_model_file) self._detector_path = os.path.join(self.model_storage_directory, DETECTOR_FILENAME) def _set_device(self, gpu: bool): if gpu is False: self.device = "cpu" LOGGER.warning("Using CPU. Note: This module is much faster with a GPU.") elif not torch.cuda.is_available(): self.device = "cpu" LOGGER.warning("CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.") elif gpu is True: self.device = "cuda" else: self.device = gpu

13,459

5,903

23

b9ade0befeaaf199c9e1afc1d7f76c7fb111996b

740

py

Python

src/proxies/images.py

otanadzetsotne/nn-image-similarity

8a00c30359e56c4a229942b4b2df6265fa2856a7

[ "MIT" ]

null

src/proxies/images.py

otanadzetsotne/nn-image-similarity

8a00c30359e56c4a229942b4b2df6265fa2856a7

[ "MIT" ]

null

src/proxies/images.py

otanadzetsotne/nn-image-similarity

8a00c30359e56c4a229942b4b2df6265fa2856a7

[ "MIT" ]

null

# local from src.utils.images import ImagesHelper from src.dtypes import ImagesInner

20.555556

50

0.601351

# local from src.utils.images import ImagesHelper from src.dtypes import ImagesInner class ProxyImages: @staticmethod def filter_correct( images: ImagesInner, ) -> ImagesInner: """ Filter images and return just corrects """ return ImagesHelper.filter_correct(images) @staticmethod def filter_error( images: ImagesInner, ) -> ImagesInner: """ Filter images and return just with errors """ return ImagesHelper.filter_error(images) @staticmethod def has_correct( images: ImagesInner, ) -> bool: """ Check ImagesInner object """ return ImagesHelper.has_correct(images)

0

631

23

22afb31fa0ba4539038dbf716afbd984f54b90ca

6,054

py

Python

code/src/main/python/misconceptions/rUtils/functions.py

DynamicCodeSearch/CodeSeer

ee985ece7691691585952eb88565f0e08bdc9113

[ "MIT" ]

5

2020-04-05T18:04:13.000Z

2021-04-13T20:34:19.000Z

code/src/main/python/misconceptions/rUtils/functions.py

DynamicCodeSearch/CodeSeer

ee985ece7691691585952eb88565f0e08bdc9113

[ "MIT" ]

1

2020-04-29T21:42:26.000Z

2020-05-01T23:45:45.000Z

code/src/main/python/misconceptions/rUtils/functions.py

DynamicCodeSearch/CodeSeer

ee985ece7691691585952eb88565f0e08bdc9113

[ "MIT" ]

3

2020-01-27T16:02:14.000Z

2021-02-08T13:25:15.000Z

import sys import os sys.path.append(os.path.abspath(".")) sys.dont_write_bytecode = True __author__ = "bigfatnoob" import copy import signal import time import re import rpy2 import rpy2.robjects as robjects from rpy2 import rinterface from rpy2.robjects import pandas2ri from rpy2.robjects.functions import SignatureTranslatedFunction from collections import OrderedDict from analysis.helpers import constants as a_consts from analysis import execute from misconceptions.common import datatypes from misconceptions.rUtils import generator, dataframer from utils import cache pandas2ri.activate() rinterface.set_writeconsole_warnerror(None) rinterface.set_writeconsole_regular(None) r_source = robjects.r['source'] R_GEN_PREFIX = "gen_func_r_" FUNC_BODY_REGEX = r'function\s*$.*?$\s*((.|\s)+)' FUNCTION_STORE = "/Users/panzer/Raise/ProgramRepair/CodeSeer/code/src/main/python/expt/r_functions.pkl"

28.422535

107

0.743971

import sys import os sys.path.append(os.path.abspath(".")) sys.dont_write_bytecode = True __author__ = "bigfatnoob" import copy import signal import time import re import rpy2 import rpy2.robjects as robjects from rpy2 import rinterface from rpy2.robjects import pandas2ri from rpy2.robjects.functions import SignatureTranslatedFunction from collections import OrderedDict from analysis.helpers import constants as a_consts from analysis import execute from misconceptions.common import datatypes from misconceptions.rUtils import generator, dataframer from utils import cache pandas2ri.activate() rinterface.set_writeconsole_warnerror(None) rinterface.set_writeconsole_regular(None) r_source = robjects.r['source'] R_GEN_PREFIX = "gen_func_r_" FUNC_BODY_REGEX = r'function\s*$.*?$\s*((.|\s)+)' FUNCTION_STORE = "/Users/panzer/Raise/ProgramRepair/CodeSeer/code/src/main/python/expt/r_functions.pkl" def get_R_error_message(exception): return exception.message.strip() def get_env_variables(r_file_path): try: robjects.r(''' source('%s') ''' % r_file_path) return robjects.globalenv except rinterface.RRuntimeError as e: print("Error while fetching environment variables.\n%s" % get_R_error_message(e)) return None def r_compile(r_file_path, del_compiled=True): try: robjects.r(''' library(compiler) cmpfile('%s') ''' % r_file_path) if del_compiled: compiled_file = r_file_path.rsplit(".", 1)[0] + ".Rc" cache.delete_file(compiled_file) return True except Exception as e: # print("Error while compilation.\n%s" % get_R_error_message(e)) # error_message = get_R_error_message(e) # return error_message and "import pandas" in error_message pass return False def get_r_function(r_file_path, func_name): env_variables = get_env_variables(r_file_path) if not env_variables: return None for name in env_variables.keys(): if name == func_name and isinstance(env_variables[name], SignatureTranslatedFunction): return env_variables[name] return None def get_r_functions(r_file_path): r_functions = {} env_variables = get_env_variables(r_file_path) if not env_variables: return None for name in env_variables.keys(): if isinstance(env_variables[name], SignatureTranslatedFunction): r_functions[name] = env_variables[name] return r_functions def get_function_arg_names(r_func): return list(r_func.formals().names) def get_function_body(r_func): func_str = str(r_func).strip() return re.match(FUNC_BODY_REGEX, func_str).group(1) def get_r_types(r_func): formal_args = r_func.formals() arg_names = get_function_arg_names(r_func) if formal_args is None or type(formal_args) == rpy2.rinterface.RNULLType: return None r_types = OrderedDict() for arg_name, formal_arg in zip(arg_names, formal_args): r_types[arg_name] = {"type": rpy2.robjects.vectors.DataFrame} return r_types def get_function_as_str(func_name, func): return ("%s <- %s" % (func_name, str(func))).strip() def convert_to_R_args(py_args): r_args = [] for py_arg in py_args: r_arg = datatypes.convert_py_object_to_r(py_arg) r_args.append(r_arg) return r_args def execute_R_function(r_func, arg): cloned = convert_to_R_args([copy.deepcopy(x) for x in arg]) prev_signal = signal.getsignal(signal.SIGALRM) signal.signal(signal.SIGALRM, execute.timeout_handler) signal.alarm(a_consts.METHOD_WAIT_TIMEOUT) duration = a_consts.METHOD_WAIT_TIMEOUT * 1000 ret_obj = {"return": None, "errorMessage": None} try: start = time.time() ret = r_func(*cloned) duration = (time.time() - start) * 1000 ret_obj["return"] = datatypes.convert_r_object_to_py(ret) except execute.TimeoutException: ret_obj["errorMessage"] = "Method timed out after %d seconds" % a_consts.METHOD_WAIT_TIMEOUT except rinterface.RRuntimeError as e: # print("Error while executing rUtils function %s. Error: %s" % (func_name, e.message)) ret_obj["errorMessage"] = e.message except Exception as e: ret_obj["errorMessage"] = e.message ret_obj["duration"] = duration signal.alarm(0) signal.signal(signal.SIGALRM, prev_signal) return ret_obj def process_R_function(file_path, func_name, r_func): print("Processing %s ... " % func_name) r_types = get_r_types(r_func) if r_types is None: return None args = generator.load_args(r_types) func_key = generator.make_key(r_types) results = execute_R_function_on_args(r_func, args) function_data = { "name": func_name, "filePath": file_path, "inputKey": func_key, "body": get_function_as_str(func_name, r_func) } if results: function_data["outputs"] = results return function_data def execute_R_function_on_args(r_func, args_set): results = [] is_valid = False for args in args_set: result = execute_R_function(r_func, args) if not is_valid and result.get("return", None) is not None: is_valid = True results.append(result) if not is_valid: print("Function is invalid") return None return results def save_function(func_data): saved_funcs = cache.load_pickle(FUNCTION_STORE) if not saved_funcs: saved_funcs = {} saved_funcs[func_data["name"]] = func_data cache.save_pickle(FUNCTION_STORE, saved_funcs) def extract_col_names(r_func): arg_names = get_function_arg_names(r_func) func_body = get_function_body(r_func) arg_cols = {} for arg_name in arg_names: df = dataframer.extract_col_names(arg_name, func_body) if df: arg_cols[arg_name] = df return arg_cols def parse_function_for_col_names(func_name, source_file): all_funcs = get_r_functions(source_file) r_func = all_funcs[func_name] return extract_col_names(r_func) def test_function(): file_path = '/Users/panzer/Raise/ProgramRepair/CodeSeer/projects/src/main/R/Example/PandasR/r_snippets.R' func_name = 'gen_func_r_drop' r_functions = get_r_functions(file_path) r_func = r_functions[func_name] process_R_function(file_path, func_name, r_func)

4,737

0

391

a65d3f0e19e9c311490bb7bc77d8eea9559cd262

339

py

Python

bot/plugins/joke.py

Preocts/twitch-chat-bot

50341c30d8eada4b50634c8f25a9eb0eed681735

[ "MIT" ]

62

2019-11-16T22:07:42.000Z

2022-03-08T20:50:01.000Z

bot/plugins/joke.py

Preocts/twitch-chat-bot

50341c30d8eada4b50634c8f25a9eb0eed681735

[ "MIT" ]

30

2019-03-19T15:05:55.000Z

2022-03-24T05:00:53.000Z

bot/plugins/joke.py

Preocts/twitch-chat-bot

50341c30d8eada4b50634c8f25a9eb0eed681735

[ "MIT" ]

56

2019-06-08T20:34:31.000Z

2022-02-21T20:10:38.000Z

from __future__ import annotations from typing import Match import pyjokes from bot.config import Config from bot.data import command from bot.data import esc from bot.data import format_msg @command('!joke', '!yoke')

21.1875

61

0.764012

from __future__ import annotations from typing import Match import pyjokes from bot.config import Config from bot.data import command from bot.data import esc from bot.data import format_msg @command('!joke', '!yoke') async def cmd_joke(config: Config, match: Match[str]) -> str: return format_msg(match, esc(pyjokes.get_joke()))

94

0

22

b10aa05fe838d0b0b31227f058840a4db0cf7599

11,594

py

Python

ngskit/trim_reads.py

kim-lab/NGSKit

62f609111ba59b9d7d87dc9979a9a2c57959e297

[ "MIT" ]

1

2021-12-10T22:23:50.000Z

ngskit/trim_reads.py

kimlaborg/NGSKit

62f609111ba59b9d7d87dc9979a9a2c57959e297

[ "MIT" ]

null

ngskit/trim_reads.py

kimlaborg/NGSKit

62f609111ba59b9d7d87dc9979a9a2c57959e297

[ "MIT" ]

null

#!/usr/bin/env python import os import sys import logging import argparse import time import ngskit.barcodes as barcodes from ngskit.utils import fasta_tools, fastq_tools #import barcodes #from utils import fasta_tools, fastq_tools def trimming(demultiplexed_fastq, barcode, quality_threshold, trgt_len, output_fmt, output_folder): """Extract seq from the FASTAQ demultiplexed files. Trim barcodes + Constant Parameters ---------- demultiplexed_fastq : str Path of the demultiplexed fastq file barcode : barcode.object Barcode object wiht info about barcode and constant regions quality_threshold : int reading quality Threshold, any sequence will be trimmed under that level trgt_len : int length in bases of the target sequences. output_fmt : str Output format, by default fasta working_folder : str Output folder to save files with trimmed sequences Returns ------- output format save fasta or fastq Notes ----- Result str, in Fasta format >FASTAQ_ID+ length + Quality ATGATGGTAGTAGTAGAAAGATAGATGATGATGAT it will be storage: /data_path/Sequences/Sample_id.fasta """ # Init the output format, retunr a function logger = logging.getLogger(__name__) create_folder(output_folder) # if output_fmt == 'fasta': save_seq = fasta_tools.write_fasta_sequence filehdl_output = open(output_folder+'/'+barcode.id+'.fasta','a') logger.info('Output file: %s' % (output_folder+'/'+barcode.id+'.fasta')) if output_fmt == 'fastq': save_seq = fastq_tools.write_fastq_sequence filehdl_output = open(output_folder+'/'+barcode.id+'.fastq','a') logger.info('Output file: %s' % (output_folder+'/'+barcode.id+'.fastq')) # check barcodes integrity, peplength, fastq # barcodes_list = barcodes.read(barcode_file) # Stats nseqs = 0 ntrimed = 0 # Open Fastq file with open(demultiplexed_fastq, 'r') as read1: for read1_id in read1: # Read 4 by 4 # ID lane info, seq info etc # Read seq and Quality info read1_seq, read1_strand, read1_qual = [next(read1) for _ in range(3)] #Translate the Quality to a list of Integers qual = [ord(c)-33 for c in read1_qual.strip()] target_sequence = read1_seq[barcode.b1_len+barcode.c1_len: barcode.b1_len+barcode.c1_len+trgt_len] #remove the quality of the barcode and the constant region target_qual = qual[barcode.b1_len+barcode.c1_len: barcode.b1_len+barcode.c1_len+trgt_len] nseqs += 1 # Control try: avg_quality = sum(target_qual)/float(len(target_qual)) except ZeroDivisionError: logger.error('Sequence with no lenght or no score', exc_info=True) logger.error(read1_seq,read1_qual,target_qual,target_qual,trgt_len) sys.exit() if len(target_sequence) == trgt_len and avg_quality >= quality_threshold: ntrimed += 1 # save output format # attach Qavgm and length origin to the id seq_id = '{}_Q:{:.2f}_F:{}'.format(read1_id.strip(), avg_quality, trgt_len) save_seq([seq_id, target_sequence, target_qual], file_output=filehdl_output) # save else: # Stats pass logger.info('Read %i Sequences' % (nseqs)) logger.info('Trimmed %i Sequences' % (ntrimed)) filehdl_output.close() def get_options(): """Get arguments from command line. Parameters ---------- Returns ------- """ parser = argparse.ArgumentParser(description=""" Trimming Fastq sequences tool Usage Trimming: %prog -d [demultiplexed Folder]-b [BarCode_file.inp] -q [Quality threshold]\ -m [method] --output_fmt fasta """) parser.add_argument('-d', '--input_folder', action="store", dest="input_folder", default=False, help='Folder \ contains demultiplexed folders and files', required=True) parser.add_argument('-b', '--barcode_file', action="store", dest="barcode_file", default=False, help='File that \ contains barcodes and cosntant regions', required=True) parser.add_argument('-o', '--out_folder', action="store", dest="out_folder", default='Sequences', help='Output folder, called \ Sequences by default') # optional Arguments parser.add_argument('-m', '--trimming_method', action="store", dest="trimming_method", default='standard', type=str, choices=['standard', 'dynamic'], help="""standard Trimm sequences according barcode file configuration, ignores float window output files\n dynamic Trimm sequences using file lenght label, or output of float window demultiplex """) # Default 1 parser.add_argument('-q', '--quality', action="store", dest="quality", default=30, type=int, help='Quality reading threshold \ (default 30)') parser.add_argument('--output_fmt', help='Output format, default fasta', dest='output_fmt', default='fasta', action='store') parser.add_argument('--force-lenght', help='force a lenght and ignore file label, overwrites dynamic option', dest='force_lenght', default=False, action='store') options = parser.parse_args() return options def main(): """Pipeline Control. Parameters ---------- opts """ opts = get_options() # init logging time_stamp = time.ctime() seconds_time = int(time.time()) logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%m-%d %H:%M', filename= opts.input_folder+ '/Logs/Trimming_'+opts.input_folder.rpartition('/')[-1]+'_'+opts.barcode_file+'_{}.log'.format(seconds_time), filemode='w') logger = logging.getLogger(__name__) logger.info('JOB START {4} {1} {2} {0} {3}'.format(*time_stamp.split())) # DEMULTIPLEX # Check inputs # Load Barcodes info # check barcodes integrity, peplength, fastq barcodes_list = barcodes.read(opts.barcode_file) # make output folder # Init Logging logger.info('#### TRIMMING ####') # incompatible logger.info('Method: {}'.format(opts.trimming_method)) logger.info('Quality threshold: {}'.format(opts.quality)) logger.info('Output format: {}'.format(opts.output_fmt)) # logger.info('Barcode file: {}'.format(opts.barcode_file)) logger.info('Input folder: {}'.format(opts.input_folder)) output_folder = opts.input_folder+'/'+opts.out_folder logger.info('Output folder: {}'.format(output_folder)) logger.info('Force target lenght: %s', opts.force_lenght) # foreach sample in barcodes for barcode in barcodes_list: logger.info('Triming Sample: {}'.format(barcode.id)) # folder must == sample id in the barcode # TODO: need to improve this line, it can be problematic working_folder = './'+opts.input_folder+'/'+barcode.id+'/' # get all fastq under the folder for demultiplexed_fastq in os.listdir(working_folder): # ToDO: only get fastq files #ToDo: only those I want (target lenthg) # if method is dynamic, get all the files in the folder if opts.trimming_method == 'dynamic': # To do # read lenght from the filename seq_length = get_length_label(demultiplexed_fastq) # modifiy target size # Skip empty vectors if seq_length: # modify output folder dir_emultiplexed_fastq = working_folder+demultiplexed_fastq # trim! trimming(dir_emultiplexed_fastq, barcode, quality_threshold= opts.quality, trgt_len= seq_length, output_fmt= opts.output_fmt, output_folder=output_folder+'_'+str(seq_length)) # raw_name = demultiplexed_file.replace('_F.fastq','') # read the length from the file elif opts.trimming_method == 'standard': # Trim time dir_emultiplexed_fastq = working_folder+demultiplexed_fastq # ignore files from dynamic target seq_length = get_length_label(demultiplexed_fastq) if seq_length != barcode.trgt_len: logger.info("file label and barcode lenght are different: %s SKIPPING FILE", demultiplexed_fastq) continue else: logger.info('Triming file: {}'.format(demultiplexed_fastq)) trimming(dir_emultiplexed_fastq, barcode, quality_threshold= opts.quality, trgt_len= barcode.trgt_len, output_fmt= opts.output_fmt, output_folder=output_folder) # add here, multilenghts trimmming elif opts.trimming_method == 'force': # Todo: this option can be useful in the future continue else: # unknow method pass # DONE time_stamp = time.ctime() logger.info('JOB ENDS {4} {1} {2} {0} {3}'.format(*time_stamp.split())) return # def main(): # # Read argtments # opts = get_options() # # init logging # time_stamp = time.ctime() # logging.basicConfig(level=logging.INFO, # format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', # datefmt='%m-%d %H:%M', # filename= 'Trimming_'+opts.input_folder+'_'+opts.barcode_file+'_{4}_{1}_{2}_{0}_{3}.log'.format(*time_stamp.split()), # filemode='w') # logger = logging.getLogger(__name__) # logger.info('JOB START {4} {1} {2} {0} {3}'.format(*time_stamp.split())) # # DEMULTIPLEX # workflow(opts) # # DONE # time_stamp = time.ctime() # logger.info('JOB ENDS {4} {1} {2} {0} {3}'.format(*time_stamp.split())) if __name__ == '__main__': main()

35.894737

163

0.582456

#!/usr/bin/env python import os import sys import logging import argparse import time import ngskit.barcodes as barcodes from ngskit.utils import fasta_tools, fastq_tools #import barcodes #from utils import fasta_tools, fastq_tools def create_folder(output_folder): # Create output folder logger = logging.getLogger(__name__) logger.info('Open folder %s', output_folder) try: # by default Sequences os.makedirs(output_folder) except OSError: _ = sys.exc_info() logger.warning('Warning, Folder %s already exist', output_folder) return def trimming(demultiplexed_fastq, barcode, quality_threshold, trgt_len, output_fmt, output_folder): """Extract seq from the FASTAQ demultiplexed files. Trim barcodes + Constant Parameters ---------- demultiplexed_fastq : str Path of the demultiplexed fastq file barcode : barcode.object Barcode object wiht info about barcode and constant regions quality_threshold : int reading quality Threshold, any sequence will be trimmed under that level trgt_len : int length in bases of the target sequences. output_fmt : str Output format, by default fasta working_folder : str Output folder to save files with trimmed sequences Returns ------- output format save fasta or fastq Notes ----- Result str, in Fasta format >FASTAQ_ID+ length + Quality ATGATGGTAGTAGTAGAAAGATAGATGATGATGAT it will be storage: /data_path/Sequences/Sample_id.fasta """ # Init the output format, retunr a function logger = logging.getLogger(__name__) create_folder(output_folder) # if output_fmt == 'fasta': save_seq = fasta_tools.write_fasta_sequence filehdl_output = open(output_folder+'/'+barcode.id+'.fasta','a') logger.info('Output file: %s' % (output_folder+'/'+barcode.id+'.fasta')) if output_fmt == 'fastq': save_seq = fastq_tools.write_fastq_sequence filehdl_output = open(output_folder+'/'+barcode.id+'.fastq','a') logger.info('Output file: %s' % (output_folder+'/'+barcode.id+'.fastq')) # check barcodes integrity, peplength, fastq # barcodes_list = barcodes.read(barcode_file) # Stats nseqs = 0 ntrimed = 0 # Open Fastq file with open(demultiplexed_fastq, 'r') as read1: for read1_id in read1: # Read 4 by 4 # ID lane info, seq info etc # Read seq and Quality info read1_seq, read1_strand, read1_qual = [next(read1) for _ in range(3)] #Translate the Quality to a list of Integers qual = [ord(c)-33 for c in read1_qual.strip()] target_sequence = read1_seq[barcode.b1_len+barcode.c1_len: barcode.b1_len+barcode.c1_len+trgt_len] #remove the quality of the barcode and the constant region target_qual = qual[barcode.b1_len+barcode.c1_len: barcode.b1_len+barcode.c1_len+trgt_len] nseqs += 1 # Control try: avg_quality = sum(target_qual)/float(len(target_qual)) except ZeroDivisionError: logger.error('Sequence with no lenght or no score', exc_info=True) logger.error(read1_seq,read1_qual,target_qual,target_qual,trgt_len) sys.exit() if len(target_sequence) == trgt_len and avg_quality >= quality_threshold: ntrimed += 1 # save output format # attach Qavgm and length origin to the id seq_id = '{}_Q:{:.2f}_F:{}'.format(read1_id.strip(), avg_quality, trgt_len) save_seq([seq_id, target_sequence, target_qual], file_output=filehdl_output) # save else: # Stats pass logger.info('Read %i Sequences' % (nseqs)) logger.info('Trimmed %i Sequences' % (ntrimed)) filehdl_output.close() def get_length_label(demultiplexed_fastq_file): logger = logging.getLogger(__name__) filename, _ = os.path.splitext(demultiplexed_fastq_file) seq_lenght = filename.split('_')[-2:-1] logger.info("Label lenght: %s", seq_lenght[0]) return int(seq_lenght[0]) def get_options(): """Get arguments from command line. Parameters ---------- Returns ------- """ parser = argparse.ArgumentParser(description=""" Trimming Fastq sequences tool Usage Trimming: %prog -d [demultiplexed Folder]-b [BarCode_file.inp] -q [Quality threshold]\ -m [method] --output_fmt fasta """) parser.add_argument('-d', '--input_folder', action="store", dest="input_folder", default=False, help='Folder \ contains demultiplexed folders and files', required=True) parser.add_argument('-b', '--barcode_file', action="store", dest="barcode_file", default=False, help='File that \ contains barcodes and cosntant regions', required=True) parser.add_argument('-o', '--out_folder', action="store", dest="out_folder", default='Sequences', help='Output folder, called \ Sequences by default') # optional Arguments parser.add_argument('-m', '--trimming_method', action="store", dest="trimming_method", default='standard', type=str, choices=['standard', 'dynamic'], help="""standard Trimm sequences according barcode file configuration, ignores float window output files\n dynamic Trimm sequences using file lenght label, or output of float window demultiplex """) # Default 1 parser.add_argument('-q', '--quality', action="store", dest="quality", default=30, type=int, help='Quality reading threshold \ (default 30)') parser.add_argument('--output_fmt', help='Output format, default fasta', dest='output_fmt', default='fasta', action='store') parser.add_argument('--force-lenght', help='force a lenght and ignore file label, overwrites dynamic option', dest='force_lenght', default=False, action='store') options = parser.parse_args() return options def main(): """Pipeline Control. Parameters ---------- opts """ opts = get_options() # init logging time_stamp = time.ctime() seconds_time = int(time.time()) logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%m-%d %H:%M', filename= opts.input_folder+ '/Logs/Trimming_'+opts.input_folder.rpartition('/')[-1]+'_'+opts.barcode_file+'_{}.log'.format(seconds_time), filemode='w') logger = logging.getLogger(__name__) logger.info('JOB START {4} {1} {2} {0} {3}'.format(*time_stamp.split())) # DEMULTIPLEX # Check inputs # Load Barcodes info # check barcodes integrity, peplength, fastq barcodes_list = barcodes.read(opts.barcode_file) # make output folder # Init Logging logger.info('#### TRIMMING ####') # incompatible logger.info('Method: {}'.format(opts.trimming_method)) logger.info('Quality threshold: {}'.format(opts.quality)) logger.info('Output format: {}'.format(opts.output_fmt)) # logger.info('Barcode file: {}'.format(opts.barcode_file)) logger.info('Input folder: {}'.format(opts.input_folder)) output_folder = opts.input_folder+'/'+opts.out_folder logger.info('Output folder: {}'.format(output_folder)) logger.info('Force target lenght: %s', opts.force_lenght) # foreach sample in barcodes for barcode in barcodes_list: logger.info('Triming Sample: {}'.format(barcode.id)) # folder must == sample id in the barcode # TODO: need to improve this line, it can be problematic working_folder = './'+opts.input_folder+'/'+barcode.id+'/' # get all fastq under the folder for demultiplexed_fastq in os.listdir(working_folder): # ToDO: only get fastq files #ToDo: only those I want (target lenthg) # if method is dynamic, get all the files in the folder if opts.trimming_method == 'dynamic': # To do # read lenght from the filename seq_length = get_length_label(demultiplexed_fastq) # modifiy target size # Skip empty vectors if seq_length: # modify output folder dir_emultiplexed_fastq = working_folder+demultiplexed_fastq # trim! trimming(dir_emultiplexed_fastq, barcode, quality_threshold= opts.quality, trgt_len= seq_length, output_fmt= opts.output_fmt, output_folder=output_folder+'_'+str(seq_length)) # raw_name = demultiplexed_file.replace('_F.fastq','') # read the length from the file elif opts.trimming_method == 'standard': # Trim time dir_emultiplexed_fastq = working_folder+demultiplexed_fastq # ignore files from dynamic target seq_length = get_length_label(demultiplexed_fastq) if seq_length != barcode.trgt_len: logger.info("file label and barcode lenght are different: %s SKIPPING FILE", demultiplexed_fastq) continue else: logger.info('Triming file: {}'.format(demultiplexed_fastq)) trimming(dir_emultiplexed_fastq, barcode, quality_threshold= opts.quality, trgt_len= barcode.trgt_len, output_fmt= opts.output_fmt, output_folder=output_folder) # add here, multilenghts trimmming elif opts.trimming_method == 'force': # Todo: this option can be useful in the future continue else: # unknow method pass # DONE time_stamp = time.ctime() logger.info('JOB ENDS {4} {1} {2} {0} {3}'.format(*time_stamp.split())) return # def main(): # # Read argtments # opts = get_options() # # init logging # time_stamp = time.ctime() # logging.basicConfig(level=logging.INFO, # format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', # datefmt='%m-%d %H:%M', # filename= 'Trimming_'+opts.input_folder+'_'+opts.barcode_file+'_{4}_{1}_{2}_{0}_{3}.log'.format(*time_stamp.split()), # filemode='w') # logger = logging.getLogger(__name__) # logger.info('JOB START {4} {1} {2} {0} {3}'.format(*time_stamp.split())) # # DEMULTIPLEX # workflow(opts) # # DONE # time_stamp = time.ctime() # logger.info('JOB ENDS {4} {1} {2} {0} {3}'.format(*time_stamp.split())) if __name__ == '__main__': main()

592

0

46

feade5496f17453a160a194c258f2778a56f8b61

75

py

Python

checkov/yaml_doc/registry.py

pmalkki/checkov

b6cdf386dd976fe27c16fed6d550756a678a5d7b

[ "Apache-2.0" ]

null

checkov/yaml_doc/registry.py

pmalkki/checkov

b6cdf386dd976fe27c16fed6d550756a678a5d7b

[ "Apache-2.0" ]

null

checkov/yaml_doc/registry.py

pmalkki/checkov

b6cdf386dd976fe27c16fed6d550756a678a5d7b

[ "Apache-2.0" ]

null

from checkov.yaml_doc.base_registry import Registry registry = Registry()

18.75

51

0.826667

from checkov.yaml_doc.base_registry import Registry registry = Registry()

0

223e2dd85d17fc5cef76030696a77e0b1f297257

497

py

Python

api/v1/internal.py

anthill-gaming/game_controller

849ea700263d7724d7a66907e0961956940e6c64

[ "MIT" ]

null

api/v1/internal.py

anthill-gaming/game_controller

849ea700263d7724d7a66907e0961956940e6c64

[ "MIT" ]

null

api/v1/internal.py

anthill-gaming/game_controller

849ea700263d7724d7a66907e0961956940e6c64

[ "MIT" ]

null

""" Internal api methods for current service. Example: from anthill.platform.api.internal import as_internal, InternalAPI @as_internal() async def your_internal_api_method(api: InternalAPI, *params, **options): # current_service = api.service ... """ from anthill.platform.api.internal import as_internal, InternalAPI @as_internal() @as_internal()

20.708333

77

0.714286

""" Internal api methods for current service. Example: from anthill.platform.api.internal import as_internal, InternalAPI @as_internal() async def your_internal_api_method(api: InternalAPI, *params, **options): # current_service = api.service ... """ from anthill.platform.api.internal import as_internal, InternalAPI @as_internal() async def spawn(api: InternalAPI, **options): pass @as_internal() async def terminate(api: InternalAPI, **options): pass

70

0

44

4842a357559df39c5885b5a0a2d27b724cb94ce7

12,748

py

Python

tf-model-manip.py

PeiqinSun/tf-tutorials

4d3a9560bce018989e62e9146d63e8fe16eaed91

[ "Apache-2.0" ]

184

2019-02-25T09:03:30.000Z

2020-05-20T12:30:00.000Z

tf-model-manip.py

megvii-research/tf-tutorials

4d3a9560bce018989e62e9146d63e8fe16eaed91

[ "Apache-2.0" ]

73

2019-02-28T02:51:14.000Z

2020-04-08T10:48:07.000Z

tf-model-manip.py

PeiqinSun/tf-tutorials

4d3a9560bce018989e62e9146d63e8fe16eaed91

[ "Apache-2.0" ]

103

2019-02-28T09:05:21.000Z

2020-05-18T13:22:10.000Z

#!/usr/bin/env mdl # -*- coding: utf-8 -*- # ======================================= # File Name : # Purpose : # Creation Date : # Last Modified : # Created By : sunpeiqin # ======================================= import os import sys import argparse import magic import keyword import importlib import collections import re import tabulate import numpy as np import tensorflow as tf def import_python_source_as_module(fpath, mod_name=None): """ import a python source as a module; its directory is added to ``sys.path`` during importing, and ``sys.path`` would be restored afterwards. Modules newly loaded in the same directory as *fpath* would have an attribute `__dynamic_loaded_by_spq__` set to 1, and fpath itself would have that value set to 2. :type fpath: str :param fpath: python source file path :type mod_name: str or None :param mod_name: target module name; if it exists in `sys.modules`, the corresponding module would be directly returned; otherwise it is added to ``sys.modules`` afterward. If it is None, module name would be derived from *fpath* by replacing '/' to '.' and special chars to '_' """ fpath = os.path.realpath(fpath) if mod_name is None: # automatically generate mod_name mod_name = [] for i in fpath.split(os.path.sep): v = '' for j in i: if not j.isidentifier() and not j.isdigit(): j = '_' v += j if not v.isidentifier() or keyword.iskeyword(v): v = '_' + v assert v.isidentifier() and not keyword.iskeyword(v), ( 'failed to convert to python identifier: in={} out={}'.format( i, v)) mod_name.append(v) mod_name = '_'.join(mod_name) if mod_name in sys.modules: return sys.modules[mod_name] old_path = sys.path[:] mod_dir = os.path.dirname(fpath) sys.path.append(mod_dir) old_mod_names = set(sys.modules.keys()) try: final_mod = importlib.machinery.SourceFileLoader( mod_name, fpath).load_module() finally: sys.path.remove(mod_dir) sys.modules[mod_name] = final_mod for name, mod in list(sys.modules.items()): if name in old_mod_names: continue try: fpath = getattr(mod, '__file__', None) except Exception as exc: print('caught exception {} while trying to get ' 'read __file__ attr from {}'.format(repr(exc), name)) continue if fpath is not None and ( os.path.dirname(os.path.realpath(fpath)).startswith(mod_dir)): try: mod.__dynamic_loaded_by_spq__ = 1 except Exception: pass try: final_mod.__dynamic_loaded_by_spq__ = 2 except Exception: pass return final_mod def load_network(network, get_kwargs={}): '''load a model defined by model.py''' network = os.path.realpath(network) mf = magic.from_file(network, mime=True) mf = mf.decode('utf-8') if isinstance(mf, bytes) else mf if mf.startswith('text'): return import_python_source_as_module(network).Model().build() else: print('Only supports a model.py which defines a network') exit(0) if __name__ == "__main__": actions = [InfoAction,] parser = argparse.ArgumentParser() parser.add_argument('network') subparsers = parser.add_subparsers(help='action') for i in actions: i.add_subparser(subparsers) args = parser.parse_args() # load network load_network(args.network) if hasattr(args, 'func'): args.func(args) else: print('no action given')

34.361186

122

0.547145

#!/usr/bin/env mdl # -*- coding: utf-8 -*- # ======================================= # File Name : # Purpose : # Creation Date : # Last Modified : # Created By : sunpeiqin # ======================================= import os import sys import argparse import magic import keyword import importlib import collections import re import tabulate import numpy as np import tensorflow as tf def sizeof_fmt(num, suffix='B'): for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']: if abs(num) < 1024.0: return "{:3.3f} {}{}".format(num, unit, suffix) num /= 1024.0 sign_str = '-' if num < 0 else '' return "{}{:.1f} {}{}".format(sign_str, num, 'Yi', suffix) def import_python_source_as_module(fpath, mod_name=None): """ import a python source as a module; its directory is added to ``sys.path`` during importing, and ``sys.path`` would be restored afterwards. Modules newly loaded in the same directory as *fpath* would have an attribute `__dynamic_loaded_by_spq__` set to 1, and fpath itself would have that value set to 2. :type fpath: str :param fpath: python source file path :type mod_name: str or None :param mod_name: target module name; if it exists in `sys.modules`, the corresponding module would be directly returned; otherwise it is added to ``sys.modules`` afterward. If it is None, module name would be derived from *fpath* by replacing '/' to '.' and special chars to '_' """ fpath = os.path.realpath(fpath) if mod_name is None: # automatically generate mod_name mod_name = [] for i in fpath.split(os.path.sep): v = '' for j in i: if not j.isidentifier() and not j.isdigit(): j = '_' v += j if not v.isidentifier() or keyword.iskeyword(v): v = '_' + v assert v.isidentifier() and not keyword.iskeyword(v), ( 'failed to convert to python identifier: in={} out={}'.format( i, v)) mod_name.append(v) mod_name = '_'.join(mod_name) if mod_name in sys.modules: return sys.modules[mod_name] old_path = sys.path[:] mod_dir = os.path.dirname(fpath) sys.path.append(mod_dir) old_mod_names = set(sys.modules.keys()) try: final_mod = importlib.machinery.SourceFileLoader( mod_name, fpath).load_module() finally: sys.path.remove(mod_dir) sys.modules[mod_name] = final_mod for name, mod in list(sys.modules.items()): if name in old_mod_names: continue try: fpath = getattr(mod, '__file__', None) except Exception as exc: print('caught exception {} while trying to get ' 'read __file__ attr from {}'.format(repr(exc), name)) continue if fpath is not None and ( os.path.dirname(os.path.realpath(fpath)).startswith(mod_dir)): try: mod.__dynamic_loaded_by_spq__ = 1 except Exception: pass try: final_mod.__dynamic_loaded_by_spq__ = 2 except Exception: pass return final_mod def load_network(network, get_kwargs={}): '''load a model defined by model.py''' network = os.path.realpath(network) mf = magic.from_file(network, mime=True) mf = mf.decode('utf-8') if isinstance(mf, bytes) else mf if mf.startswith('text'): return import_python_source_as_module(network).Model().build() else: print('Only supports a model.py which defines a network') exit(0) def compute_receptiveField_and_stride(nodes): stride_list = [] receptive_field_list = [] new_nodes = collections.OrderedDict() for k, v_dict in nodes.items(): data_format = v_dict.get('data_format', None) ksize = v_dict.get('ksize', []) shape = v_dict.get('shape', []) strides = v_dict.get('strides', []) if data_format == 'NHWC': h_stride, w_stride = strides[1], strides[2] if ksize: h_size, w_size = ksize[1], ksize[2] else: h_size, w_size = shape[0], shape[1] elif data_format == 'NCHW': h_stride, w_stride = strides[2], strides[3] if ksize: h_size, w_size = ksize[2], ksize[3] else: h_size, w_size = shape[0], shape[1] else: continue if not stride_list: receptive_field_list.append((h_size, w_size)) stride_list.append((h_stride, w_stride)) else: pre_s = stride_list[-1] pre_rf = receptive_field_list[-1] stride_list.append((h_stride * pre_s[0], w_stride * pre_s[1])) receptive_field_list.append((h_size * pre_s[0] + pre_rf[0] - pre_s[0], w_size * pre_s[1] + pre_rf[1] - pre_s[1])) nodes[k].update({ 'receptive_field': receptive_field_list[-1], 'g_stride': stride_list[-1], }) new_nodes.update({k:nodes[k]}) return new_nodes class InfoAction: @classmethod def add_subparser(cls, subparsers): parser = subparsers.add_parser( 'info', help='view some summary infomation in text') parser.set_defaults(func=cls.run) @classmethod def run(cls, args): sess = tf.Session() sess.run(tf.global_variables_initializer()) # must init graph cls._cache = collections.OrderedDict() cls.param_stats(sess) cls.flops_stats(sess) cls.summary(sess) @classmethod def summary(cls, sess): data = [['item', 'value']] data.extend(list(cls._cache.items())) print('\n'*2) print('summary\n' + tabulate.tabulate(data)) @classmethod def param_stats(cls, sess, bar_length_max=20): tot_param_dim, param_size_bit = 0, 0 data = [] for param in tf.trainable_variables(): value = sess.run(param) param_dim = np.prod(value.shape) tot_param_dim += int(param_dim) nbits = int(re.findall(r"\d+", str(param.dtype))[0]) param_size_bit += param_dim * nbits # fill data data.append(dict( name=param.name, shape=param.get_shape(), param_dim=param_dim, param_type=param.dtype, size=sizeof_fmt(param_dim * nbits / 8), size_cum=sizeof_fmt(tot_param_dim * nbits / 8), mean='{:.2g}'.format(value.mean()), std='{:.2g}'.format(value.std()), )) for d in data: ratio = d['param_dim'] / tot_param_dim d['ratio'] = ratio d['percentage'] = '{:.2f}%'.format(ratio * 100) # construct bar max_ratio = max([d['ratio'] for d in data]) for d in data: bar_length = int(d['ratio'] / max_ratio * bar_length_max) d['size_bar'] = '#' * bar_length param_size = sizeof_fmt(param_size_bit / 8) data.append(dict( name='total', param_dim=tot_param_dim, size=param_size, )) cls._cache['#params'] = len(data) cls._cache['tot_param_dim'] = tot_param_dim cls._cache['param_size'] = param_size cls._param_size = param_size_bit / 8 header = [ 'name', 'shape', 'mean', 'std', 'param_dim', 'size', 'size_cum', 'percentage', 'size_bar' ] # make a table print('\n'*2) print('param stats: \n' + tabulate.tabulate( cls._dict2table(data, header=header))) @classmethod def _dict2table(self, list_of_dict, header): table_data = [header] for d in list_of_dict: row = [] for h in header: v = '' if h in d: v = d[h] row.append(v) table_data.append(row) return table_data @classmethod def flops_stats(cls, sess, bar_length_max=20): nodes = [n for n in tf.get_default_graph().as_graph_def(add_shapes=True).node] cls._cache['#nodes'] = len(nodes) # get nodes which can affect recept filed and stride rf_nodes = collections.OrderedDict() for n in nodes: if n.op in ['Conv2D', 'VariableV2']: name_scope = '/'.join(n.name.split('/')[:-1]) if name_scope not in rf_nodes.keys(): rf_nodes[name_scope] = {} if 'shape' in n.attr.keys() and not rf_nodes[name_scope].get('shape', []): rf_nodes[name_scope].update(shape=[i.size for i in n.attr['shape'].shape.dim]) if 'strides' in n.attr.keys(): rf_nodes[name_scope].update(strides=list(n.attr['strides'].list.i)) rf_nodes[name_scope].update(data_format=n.attr['data_format'].s.decode('utf-8')) rf_nodes[name_scope].update(operator=n) if n.op in ['MaxPool', 'AvgPool']: rf_nodes[n.name] = { 'ksize': list(n.attr['ksize'].list.i), 'strides': list(n.attr['ksize'].list.i), 'data_format': n.attr['data_format'].s.decode('utf-8'), 'operator': n, } rf_nodes = compute_receptiveField_and_stride(rf_nodes) # find the input node (only data) for n in nodes: if n.op == 'Placeholder': input_shape = [i.size for i in n.attr['shape'].shape.dim][1:] break for k, v_dict in rf_nodes.items(): if v_dict['data_format'] == 'NHWC': v_dict['input_shape'] = input_shape v_dict['output_shape'] = [i.size for i in v_dict['operator'].attr['_output_shapes'].list.shape[0].dim][1:] elif v_dict['data_format'] == 'NCHW': pass if v_dict['operator'].op in ['Conv2D']: ic = v_dict['input_shape'][-1] v_dict['flops'] = np.prod(v_dict['output_shape']) * ic * np.prod(v_dict['shape'][:2]) elif v_dict['operator'].op in ['MaxPool', 'AvgPool']: v_dict['flops'] = 0 input_shape = v_dict['output_shape'] opr_info = [] total_flops = 0 for k, v_dict in rf_nodes.items(): total_flops += v_dict['flops'] opr_info.append({ 'opr_name': v_dict['operator'].name, 'opr_class': v_dict['operator'].op, 'input_shapes': v_dict['input_shape'], 'output_shapes': v_dict['output_shape'], 'flops_num': v_dict['flops'], 'flops_cum': total_flops, 'receptive_field': v_dict['receptive_field'], 'stride': v_dict['g_stride'] }) flops = [i['flops_num'] for i in opr_info] max_flops = max(flops + [0]) for i in opr_info: f = i['flops_num'] i['flops'] = sizeof_fmt(f, suffix='OPs') fc = i['flops_cum'] i['flops_cum'] = sizeof_fmt(fc, suffix='OPs') r = i['ratio'] = f / total_flops i['percentage'] = '{:.2f}%'.format(r * 100) bar_length = int(f / max_flops * bar_length_max) i['bar'] = '#' * bar_length header = ['opr_name', 'opr_class', 'input_shapes', 'output_shapes', 'receptive_field', 'stride', 'flops', 'flops_cum', 'percentage', 'bar'] total_flops_str = sizeof_fmt(total_flops, suffix='OPs') #total_var_size = sum(sum(s[1] for s in i['output_shapes']) for i in opr_info) opr_info.append(dict( opr_name='total', flops=total_flops_str, #output_shapes=total_var_size )) cls._cache['total_flops'] = total_flops_str cls._cache['flops/param_size'] = '{:.3g}'.format( total_flops / cls._param_size) print('\n'*2) print('flops stats: \n' + tabulate.tabulate( cls._dict2table(opr_info, header=header))) if __name__ == "__main__": actions = [InfoAction,] parser = argparse.ArgumentParser() parser.add_argument('network') subparsers = parser.add_subparsers(help='action') for i in actions: i.add_subparser(subparsers) args = parser.parse_args() # load network load_network(args.network) if hasattr(args, 'func'): args.func(args) else: print('no action given')

8,612

260

69

d607417a565fc6e36134e72eef7edfbbfe35876d

3,985

py

Python

preprocessing.py

pedrada88/rwe

a3462556a70bd4a51d2978cadc6101e22723356a

[ "BSD-Source-Code" ]

15

2019-06-05T21:24:42.000Z

2021-01-04T00:30:29.000Z

preprocessing.py

pedrada88/rwe

a3462556a70bd4a51d2978cadc6101e22723356a

[ "BSD-Source-Code" ]

null

preprocessing.py

pedrada88/rwe

a3462556a70bd4a51d2978cadc6101e22723356a

[ "BSD-Source-Code" ]

1

2022-01-29T16:23:03.000Z

# -*- coding: utf-8 -*- import numpy as np import random #Load embedding vocabulary #Load embedding vocabulary #Load embeddings filtered by pre-given vocabulary #Load embedding matrices input/output #Split training and development data

38.68932

108

0.673275

# -*- coding: utf-8 -*- import numpy as np import random #Load embedding vocabulary def load_vocab_embeddings(input_path): first_line=True vocab=set() input_file_relations=open(input_file_relations, 'r', encoding='utf-8') for line in input_file_relations: if first_line==True: first_line=False else: vocab.add(line.strip().split(" ")[0]) return vocab #Load embedding vocabulary def load_word_vocab_from_relation_vectors(input_path): pre_word_vocab=set() first_line=True final_word_vocab=set() input_file_relations=open(input_path, 'r', encoding='utf-8') for line in input_file_relations: linesplit=line.strip().split(" ") if first_line==True: first_line=False else: relation=linesplit[0] if "__" not in relation: sys.exit("ERROR: Pair '"+relation+"' does not contain underscore") relation_split=relation.rsplit("__",1) word1=relation_split[0] word2=relation_split[1] pre_word_vocab.add(word1) pre_word_vocab.add(word2) return pre_word_vocab #Load embeddings filtered by pre-given vocabulary def load_embeddings_filtered_byvocab(input_path,vocab): word2index={} index2word={} matrix_word_embeddings=[] first_line=True input_file_relations=open(input_path, 'r', encoding='utf-8') cont=0 for line in input_file_relations: linesplit=line.strip().split(" ") if first_line==True: dimensions=int(linesplit[1]) first_line=False else: word=linesplit[0] if word in vocab and word not in word2index: word2index[word]=cont index2word[cont]=word cont+=1 matrix_word_embeddings.append(np.asarray([float(dim) for dim in linesplit[1:dimensions+1]])) return matrix_word_embeddings,word2index,index2word,dimensions #Load embedding matrices input/output def load_training_data(input_path,matrix_word_embeddings,word2index): matrix_input=[] matrix_output=[] first_line=True input_file_relations=open(input_path, 'r', encoding='utf-8') for line in input_file_relations: linesplit=line.strip().split(" ") if first_line==True: dimensions=int(str(line.split(" ")[1])) first_line=False else: relation=linesplit[0] if "__" not in relation: sys.exit("ERROR: Pair '"+relation+"' does not contain underscore") relation_split=relation.rsplit("__",1) word1=relation_split[0] word2=relation_split[1] if word1 in word2index and word2 in word2index: matrix_input.append(np.asarray([word2index[word1],word2index[word2]])) matrix_output.append(np.asarray([float(dim) for dim in linesplit[1:dimensions+1]])) return matrix_input,matrix_output,dimensions #Split training and development data def split_training_data(matrix_input,matrix_output,devsize,batchsize): matrix_input_train=[] matrix_output_train=[] matrix_input_dev=[] matrix_output_dev=[] num_instances=int((len(matrix_input)//batchsize)*batchsize) final_size_dev=int(((num_instances*devsize)//batchsize)*batchsize) final_size_train=int(((num_instances-final_size_dev)//batchsize)*batchsize) print ("Size train set: "+str(final_size_train)) print ("Size dev set: "+str(final_size_dev)) all_instances=range(num_instances) list_index_dev=random.sample(all_instances,final_size_dev) for i in range(num_instances): if i in list_index_dev: matrix_input_dev.append(matrix_input[i]) matrix_output_dev.append(matrix_output[i]) else: matrix_input_train.append(matrix_input[i]) matrix_output_train.append(matrix_output[i]) return matrix_input_train,matrix_output_train,matrix_input_dev,matrix_output_dev

3,633

0

110

eb783e2a63d00549231dcb398c0fd227b6e984a0

1,671

py

Python

cogs/mod_watch.py

MarTCM/ryuko-ng

f28ae5924065096efd72ec5c0d9af895b4310293

[ "MIT" ]

11

2018-12-23T14:51:20.000Z

2019-03-03T21:13:26.000Z

cogs/mod_watch.py

MarTCM/ryuko-ng

f28ae5924065096efd72ec5c0d9af895b4310293

[ "MIT" ]

47

2019-03-11T18:32:05.000Z

2021-12-08T17:50:38.000Z

cogs/mod_watch.py

MarTCM/ryuko-ng

f28ae5924065096efd72ec5c0d9af895b4310293

[ "MIT" ]

26

2019-03-11T17:04:05.000Z

2022-03-08T09:35:38.000Z

import discord from discord.ext import commands from discord.ext.commands import Cog from helpers.checks import check_if_staff from helpers.userlogs import setwatch

35.553191

76

0.655895

import discord from discord.ext import commands from discord.ext.commands import Cog from helpers.checks import check_if_staff from helpers.userlogs import setwatch class ModWatch(Cog): def __init__(self, bot): self.bot = bot @commands.guild_only() @commands.check(check_if_staff) @commands.command() async def watch(self, ctx, target: discord.Member, *, note: str = ""): """Puts a user under watch, staff only.""" setwatch(target.id, ctx.author, True, target.name) await ctx.send(f"{ctx.author.mention}: user is now on watch.") @commands.guild_only() @commands.check(check_if_staff) @commands.command() async def watchid(self, ctx, target: int, *, note: str = ""): """Puts a user under watch by userid, staff only.""" setwatch(target, ctx.author, True, target.name) await ctx.send(f"{target.mention}: user is now on watch.") @commands.guild_only() @commands.check(check_if_staff) @commands.command() async def unwatch(self, ctx, target: discord.Member, *, note: str = ""): """Removes a user from watch, staff only.""" setwatch(target.id, ctx.author, False, target.name) await ctx.send(f"{ctx.author.mention}: user is now not on watch.") @commands.guild_only() @commands.check(check_if_staff) @commands.command() async def unwatchid(self, ctx, target: int, *, note: str = ""): """Removes a user from watch by userid, staff only.""" setwatch(target, ctx.author, False, target.name) await ctx.send(f"{target.mention}: user is now not on watch.") def setup(bot): bot.add_cog(ModWatch(bot))

51

1,407

46

89b0c8a21bc6d8dd82ceda3dad99a30e2b867960

4,219

py

Python

research/carls/candidate_sampling/candidate_sampler_config_builder_test.py

srihari-humbarwadi/neural-structured-learning

345b8d644dd7745179263bf6dc9aeb8a921528f4

[ "Apache-2.0" ]

939

2019-08-28T06:50:30.000Z

2022-03-30T02:37:07.000Z

research/carls/candidate_sampling/candidate_sampler_config_builder_test.py

srihari-humbarwadi/neural-structured-learning

345b8d644dd7745179263bf6dc9aeb8a921528f4

[ "Apache-2.0" ]

80

2019-09-01T19:47:30.000Z

2022-02-02T20:38:38.000Z

research/carls/candidate_sampling/candidate_sampler_config_builder_test.py

srihari-humbarwadi/neural-structured-learning

345b8d644dd7745179263bf6dc9aeb8a921528f4

[ "Apache-2.0" ]

196

2019-09-01T19:38:53.000Z

2022-02-08T01:25:57.000Z

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for candidate_sampler_config_builder.""" from research.carls.candidate_sampling import candidate_sampler_config_builder as cs_config_builder from research.carls.candidate_sampling import candidate_sampler_config_pb2 as cs_config_pb2 import tensorflow as tf if __name__ == '__main__': tf.test.main()

34.024194

99

0.71178

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for candidate_sampler_config_builder.""" from research.carls.candidate_sampling import candidate_sampler_config_builder as cs_config_builder from research.carls.candidate_sampling import candidate_sampler_config_pb2 as cs_config_pb2 import tensorflow as tf class CandidateSamplerConfigBuilderTest(tf.test.TestCase): def test_negative_sampler(self): self.assertProtoEquals( """ unique: true sampler: UNIFORM """, cs_config_builder.negative_sampler( True, cs_config_pb2.NegativeSamplerConfig.UNIFORM)) self.assertProtoEquals( """ unique: false sampler: UNIFORM """, cs_config_builder.negative_sampler( False, cs_config_pb2.NegativeSamplerConfig.UNIFORM)) self.assertProtoEquals( """ unique: true sampler: LOG_UNIFORM """, cs_config_builder.negative_sampler( True, cs_config_pb2.NegativeSamplerConfig.LOG_UNIFORM)) self.assertProtoEquals( """ unique: false sampler: LOG_UNIFORM """, cs_config_builder.negative_sampler( False, cs_config_pb2.NegativeSamplerConfig.LOG_UNIFORM)) self.assertProtoEquals( """ unique: false sampler: UNIFORM """, cs_config_builder.negative_sampler(False, 'UNIFORM')) self.assertProtoEquals( """ unique: true sampler: LOG_UNIFORM """, cs_config_builder.negative_sampler(True, 'LOG_UNIFORM')) def test_brute_force_topk_sampler_success(self): self.assertProtoEquals(""" similarity_type: COSINE """, cs_config_builder.brute_force_topk_sampler('COSINE')) self.assertProtoEquals( """ similarity_type: COSINE """, cs_config_builder.brute_force_topk_sampler(cs_config_pb2.COSINE)) self.assertProtoEquals( """ similarity_type: DOT_PRODUCT """, cs_config_builder.brute_force_topk_sampler('DOT_PRODUCT')) self.assertProtoEquals( """ similarity_type: DOT_PRODUCT """, cs_config_builder.brute_force_topk_sampler(cs_config_pb2.DOT_PRODUCT)) def test_brute_force_topk_sampler_failed(self): with self.assertRaises(ValueError): cs_config_builder.brute_force_topk_sampler(cs_config_pb2.UNKNOWN) with self.assertRaises(ValueError): cs_config_builder.brute_force_topk_sampler('Unknown type string') with self.assertRaises(ValueError): cs_config_builder.brute_force_topk_sampler(cs_config_pb2.SampleContext()) with self.assertRaises(ValueError): cs_config_builder.brute_force_topk_sampler(999) def test_build_candidate_sampler_config_success(self): self.assertProtoEquals( """ extension { [type.googleapis.com/carls.candidate_sampling.BruteForceTopkSamplerConfig] { similarity_type: COSINE } } """, cs_config_builder.build_candidate_sampler_config( cs_config_builder.brute_force_topk_sampler('COSINE'))) self.assertProtoEquals( """ extension { [type.googleapis.com/carls.candidate_sampling.NegativeSamplerConfig] { unique: true sampler: UNIFORM } } """, cs_config_builder.build_candidate_sampler_config( cs_config_builder.negative_sampler(True, 'UNIFORM'))) def test_build_candidate_sampler_config_failed(self): with self.assertRaises(ValueError): cs_config_builder.build_candidate_sampler_config(100) with self.assertRaises(ValueError): cs_config_builder.build_candidate_sampler_config('invalid') if __name__ == '__main__': tf.test.main()

3,144

37

148

3089c603282eb0dd2e940a59b5b3d380394bef44

4,460

py

Python

experiments/tuning/tune_came.py

antoineBarbez/Project

8fa42b5198d03b5b142f413e218b7d7a2d994fc9

[ "MIT" ]

4

2019-09-30T19:47:42.000Z

2020-02-13T18:46:32.000Z

experiments/tuning/tune_came.py

antoineBarbez/CAME

8fa42b5198d03b5b142f413e218b7d7a2d994fc9

[ "MIT" ]

null

experiments/tuning/tune_came.py

antoineBarbez/CAME

8fa42b5198d03b5b142f413e218b7d7a2d994fc9

[ "MIT" ]

null

from context import ROOT_DIR, nnUtils, train_came, came import tensorflow as tf import numpy as np import argparse import os import progressbar import random os.environ['TF_CPP_MIN_LOG_LEVEL']='2' if __name__ == "__main__": args = parse_args() data_x, data_y = train_came.build_dataset(train_came.training_systems, args.antipattern, args.history_length) data_x, data_y = nnUtils.shuffle(data_x, data_y) bar = progressbar.ProgressBar(maxval=args.n_test, \ widgets=['Performing cross validation: ' ,progressbar.Percentage()]) bar.start() output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'came_' + args.antipattern + '_' + str(args.history_length) + '.csv') params = [] perfs = [] for i in range(args.n_test): learning_rate, beta, gamma, nb_filters, kernel_sizes, pool_sizes, dense_sizes = generateRandomHyperParameters(args.history_length) params.append([learning_rate, beta, gamma, nb_filters, kernel_sizes, pool_sizes, dense_sizes]) predictions = np.empty(shape=[0, 1]) for j in range(args.n_fold): x_train, y_train, x_test, y_test = get_cross_validation_dataset(data_x, data_y, j, args.n_fold) # New graph tf.reset_default_graph() # Create model model = came.CAME( nb_metrics=x_train.shape[-1], history_length=args.history_length, filters=nb_filters, kernel_sizes=kernel_sizes, pool_sizes=pool_sizes, dense_sizes=dense_sizes) with tf.Session() as session: # Initialize the variables of the TensorFlow graph. session.run(tf.global_variables_initializer()) train( session=session, model=model, x_train=x_train, y_train=y_train, num_step=args.n_step, lr=learning_rate, beta=beta, gamma=gamma) predictions = np.concatenate((predictions, session.run(model.inference, feed_dict={model.input_x: x_test})), axis=0) perfs.append(nnUtils.f_measure(predictions, data_y)) indexes = np.argsort(np.array(perfs)) with open(output_file_path, 'w') as file: file.write("Learning rate;Beta;Gamma;Filters;Kernel;Pool;Dense;F-measure\n") for j in reversed(indexes): for k in range(len(params[j])): file.write(str(params[j][k]) + ';') file.write(str(perfs[j]) + '\n') bar.update(i+1) bar.finish()

33.533835

148

0.717937

from context import ROOT_DIR, nnUtils, train_came, came import tensorflow as tf import numpy as np import argparse import os import progressbar import random os.environ['TF_CPP_MIN_LOG_LEVEL']='2' def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("antipattern", help="either 'god_class' or 'feature_envy'") parser.add_argument("history_length", type=int) parser.add_argument("-n_fold", type=int, default=5) parser.add_argument("-n_step", type=int, default=100) parser.add_argument("-n_test", type=int, default=100) return parser.parse_args() def generateRandomHyperParameters(history_length): learning_rate = 10**-random.uniform(0.0, 2.5) beta = 10**-random.uniform(0.0, 2.5) gamma = random.randint(1, 10) nb_filters = [] kernel_sizes = [] pool_sizes = [] nb_conv_layer = 0 if history_length <= 1 else random.randint(0,1) if history_length <= 10 else random.randint(1,2) if history_length <= 100 else 2 for _ in range(nb_conv_layer): nb_filter = random.randint(10,60) kernel_size = random.randint(2,4) pool_size = random.choice([2, 5, 10]) if history_length <=100 else random.choice([5, 10, 15, 20]) nb_filters.append(nb_filter) kernel_sizes.append(kernel_size) pool_sizes.append(pool_size) minBound = 4 maxBound = 100 dense_sizes = [] nb_dense_layer = random.randint(1, 3) for _ in range(nb_dense_layer): dense_size = random.randint(minBound, maxBound) dense_sizes.append(dense_size) maxBound = dense_size return learning_rate, beta, gamma, nb_filters, kernel_sizes, pool_sizes, dense_sizes def get_cross_validation_dataset(X, Y, fold_index, n_fold): folds_x, folds_y = nnUtils.split(X, Y, n_fold) x_train = np.empty(shape=[0, X.shape[1], X.shape[2]]) y_train = np.empty(shape=[0, 1]) for i in range(n_fold): if i != fold_index: x_train = np.concatenate((x_train, folds_x[i]), axis=0) y_train = np.concatenate((y_train, folds_y[i]), axis=0) return x_train, y_train, folds_x[fold_index], folds_y[fold_index] def train(session, model, x_train, y_train, num_step, lr, beta, gamma): learning_rate = lr for step in range(num_step): feed_dict_train = { model.input_x: x_train, model.input_y: y_train, model.learning_rate:learning_rate, model.beta:beta, model.gamma:gamma} session.run(model.learning_step, feed_dict=feed_dict_train) if __name__ == "__main__": args = parse_args() data_x, data_y = train_came.build_dataset(train_came.training_systems, args.antipattern, args.history_length) data_x, data_y = nnUtils.shuffle(data_x, data_y) bar = progressbar.ProgressBar(maxval=args.n_test, \ widgets=['Performing cross validation: ' ,progressbar.Percentage()]) bar.start() output_file_path = os.path.join(ROOT_DIR, 'experiments', 'tuning', 'results', 'came_' + args.antipattern + '_' + str(args.history_length) + '.csv') params = [] perfs = [] for i in range(args.n_test): learning_rate, beta, gamma, nb_filters, kernel_sizes, pool_sizes, dense_sizes = generateRandomHyperParameters(args.history_length) params.append([learning_rate, beta, gamma, nb_filters, kernel_sizes, pool_sizes, dense_sizes]) predictions = np.empty(shape=[0, 1]) for j in range(args.n_fold): x_train, y_train, x_test, y_test = get_cross_validation_dataset(data_x, data_y, j, args.n_fold) # New graph tf.reset_default_graph() # Create model model = came.CAME( nb_metrics=x_train.shape[-1], history_length=args.history_length, filters=nb_filters, kernel_sizes=kernel_sizes, pool_sizes=pool_sizes, dense_sizes=dense_sizes) with tf.Session() as session: # Initialize the variables of the TensorFlow graph. session.run(tf.global_variables_initializer()) train( session=session, model=model, x_train=x_train, y_train=y_train, num_step=args.n_step, lr=learning_rate, beta=beta, gamma=gamma) predictions = np.concatenate((predictions, session.run(model.inference, feed_dict={model.input_x: x_test})), axis=0) perfs.append(nnUtils.f_measure(predictions, data_y)) indexes = np.argsort(np.array(perfs)) with open(output_file_path, 'w') as file: file.write("Learning rate;Beta;Gamma;Filters;Kernel;Pool;Dense;F-measure\n") for j in reversed(indexes): for k in range(len(params[j])): file.write(str(params[j][k]) + ';') file.write(str(perfs[j]) + '\n') bar.update(i+1) bar.finish()

2,067

0

92

183dfb78ccd3bcd30e74ce6de360e7e3b9981c39

2,304

py

Python

tests/test_paginators.py

thesadru/genshin.py

806b8d0dd059a06605e66dead917fdf550a552bc

[ "MIT" ]

63

2021-10-04T19:53:54.000Z

2022-03-30T07:21:03.000Z

tests/test_paginators.py

thesadru/genshin.py

806b8d0dd059a06605e66dead917fdf550a552bc

[ "MIT" ]

17

2021-11-16T20:42:52.000Z

2022-03-31T10:11:52.000Z

tests/test_paginators.py

thesadru/genshin.py

806b8d0dd059a06605e66dead917fdf550a552bc

[ "MIT" ]

10

2021-10-16T22:41:41.000Z

2022-02-19T17:55:23.000Z

import typing import pytest from genshin import paginators @pytest.fixture(name="counting_paginator")

28.097561

92

0.677951

import typing import pytest from genshin import paginators class CountingPaginator(paginators.Paginator[int]): _index = 0 async def __anext__(self) -> int: if self._index >= 5: self._complete() self._index += 1 return self._index @pytest.fixture(name="counting_paginator") def counting_paginator_fixture(): return CountingPaginator() async def test_paginator_iter(counting_paginator: paginators.Paginator[int]): async for value in counting_paginator: assert 1 <= value <= 5 async def test_paginator_flatten(): paginator = CountingPaginator() assert await paginator.flatten() == [1, 2, 3, 4, 5] paginator = CountingPaginator() assert await paginator == [1, 2, 3, 4, 5] async def test_paginator_next(counting_paginator: paginators.Paginator[int]): assert await counting_paginator.next() == 1 async def test_paginator_next_empty(): paginator = paginators.base.BasicPaginator(()) with pytest.raises(StopAsyncIteration): await paginator.__anext__() with pytest.raises(LookupError): await paginator.next() async def test_buffered_paginator(): class MockBufferedPaginator(paginators.BufferedPaginator[int]): async def next_page(self) -> typing.Sequence[int]: index = self._counter - 1 return list(range(index, index + 5)) paginator = MockBufferedPaginator(limit=12) assert not paginator.exhausted values = await paginator.flatten() assert values == list(range(0, 12)) assert paginator.exhausted async def test_merged_paginator(): # from heapq.merge doc sequences = [[1, 3, 5, 7], [0, 2, 4, 8], [5, 10, 15, 20], [], [25]] iterators = [paginators.base.aiterate(x) for x in sequences] paginator = paginators.MergedPaginator(iterators) assert await paginator.flatten() == [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25] async def test_merged_paginator_with_key(): # from heapq.merge doc sequences = [["dog", "horse"], [], ["cat", "fish", "kangaroo"], ["rhinoceros"]] iterators = [paginators.base.aiterate(x) for x in sequences] paginator = paginators.MergedPaginator(iterators, key=len, limit=5) assert await paginator.flatten(lazy=True) == ["dog", "cat", "fish", "horse", "kangaroo"]

1,912

72

206

9c0b38982301d37af313b38f7a230a9f3dcabbd3

5,190

py

Python

src/main/python/twitter/thermos/core/muxer.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

src/main/python/twitter/thermos/core/muxer.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

src/main/python/twitter/thermos/core/muxer.py

isomer/incubator-aurora

5f54d4de25413bb18acec16120eb18f3e08c6bf0

[ "Apache-2.0" ]

null

import os import errno from twitter.common import log from twitter.common.recordio import ThriftRecordReader from gen.twitter.thermos.ttypes import RunnerCkpt

36.293706

95

0.668015

import os import errno from twitter.common import log from twitter.common.recordio import ThriftRecordReader from gen.twitter.thermos.ttypes import RunnerCkpt class ProcessMuxer(object): class ProcessExists(Exception): pass class ProcessNotFound(Exception): pass class CorruptCheckpoint(Exception): pass def __init__(self, pathspec): self._processes = {} # process_name => fp self._watermarks = {} # process_name => sequence high watermark self._pathspec = pathspec def __del__(self): for fp in filter(None, self._processes.values()): fp.close() def register(self, process_name, watermark=0): log.debug('registering %s' % process_name) if process_name in self._processes: raise ProcessMuxer.ProcessExists("Process %s is already registered" % process_name) self._processes[process_name] = None self._watermarks[process_name] = watermark def _bind_processes(self): for process_name, fp in self._processes.items(): if fp is None: process_ckpt = self._pathspec.given(process=process_name).getpath('process_checkpoint') log.debug('ProcessMuxer binding %s => %s' % (process_name, process_ckpt)) try: self._processes[process_name] = open(process_ckpt, 'r') except IOError as e: if e.errno == errno.ENOENT: log.debug(' => bind failed, checkpoint not available yet.') continue else: log.error("Unexpected inability to open %s! %s" % (process_ckpt, e)) except Exception as e: log.error("Unexpected inability to open %s! %s" % (process_ckpt, e)) self._fast_forward_stream(process_name) def _fast_forward_stream(self, process_name): log.debug('Fast forwarding %s stream to seq=%s' % (process_name, self._watermarks[process_name])) assert self._processes.get(process_name) is not None fp = self._processes[process_name] rr = ThriftRecordReader(fp, RunnerCkpt) current_watermark = -1 records = 0 while current_watermark < self._watermarks[process_name]: last_pos = fp.tell() record = rr.try_read() if record is None: break new_watermark = record.process_status.seq if new_watermark > self._watermarks[process_name]: log.debug('Over-seeked %s [watermark = %s, high watermark = %s], rewinding.' % ( process_name, new_watermark, self._watermarks[process_name])) fp.seek(last_pos) break current_watermark = new_watermark records += 1 if current_watermark < self._watermarks[process_name]: log.warning('Only able to fast forward to %s@sequence=%s, high watermark is %s' % ( process_name, current_watermark, self._watermarks[process_name])) if records: log.debug('Fast forwarded %s %s record(s) to seq=%s.' % (process_name, records, current_watermark)) def unregister(self, process_name): log.debug('unregistering %s' % process_name) if process_name not in self._processes: raise ProcessMuxer.ProcessNotFound("No trace of process: %s" % process_name) else: self._watermarks.pop(process_name) fp = self._processes.pop(process_name) if fp is not None: fp.close() def has_data(self, process): """ Return true if we think that there are updates available from the supplied process. """ self._bind_processes() # TODO(wickman) Should this raise ProcessNotFound? if process not in self._processes: return False fp = self._processes[process] rr = ThriftRecordReader(fp, RunnerCkpt) old_pos = fp.tell() try: expected_new_pos = os.fstat(fp.fileno()).st_size except OSError as e: log.debug('ProcessMuxer could not fstat for process %s' % process) return False update = rr.try_read() if update: fp.seek(old_pos) return True return False def select(self): """ Read and multiplex checkpoint records from all the forked off process coordinators. Checkpoint records can come from one of two places: in-process: checkpoint records synthesized for FORKED and LOST events out-of-process: checkpoint records from from file descriptors of forked coordinators Returns a list of RunnerCkpt objects that were successfully read, or an empty list if none were read. """ self._bind_processes() updates = [] for handle in filter(None, self._processes.values()): try: fstat = os.fstat(handle.fileno()) except OSError as e: log.error('Unable to fstat %s!' % handle.name) continue if handle.tell() > fstat.st_size: log.error('Truncated checkpoint record detected on %s!' % handle.name) elif handle.tell() < fstat.st_size: rr = ThriftRecordReader(handle, RunnerCkpt) while True: process_update = rr.try_read() if process_update: updates.append(process_update) else: break if len(updates) > 0: log.debug('select() returning %s updates:' % len(updates)) for update in updates: log.debug(' = %s' % update) return updates

2,785

2,221

23

35765a3f52057a1d8c00d42bc632985e8ea22e07

4,364

py

Python

confluent_server/confluent/discovery/handlers/imm.py

brianfinley/confluent

6458eac93b1e3c6d45e26a7ddb434d692b5cdff2

[ "Apache-2.0" ]

27

2015-02-11T13:56:46.000Z

2021-12-28T14:17:20.000Z

confluent_server/confluent/discovery/handlers/imm.py

brianfinley/confluent

6458eac93b1e3c6d45e26a7ddb434d692b5cdff2

[ "Apache-2.0" ]

32

2015-09-23T13:19:04.000Z

2022-03-15T13:50:45.000Z

confluent_server/confluent/discovery/handlers/imm.py

brianfinley/confluent

6458eac93b1e3c6d45e26a7ddb434d692b5cdff2

[ "Apache-2.0" ]

24

2015-07-14T20:41:55.000Z

2021-07-15T04:18:51.000Z

# Copyright 2017 Lenovo # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import codecs import confluent.discovery.handlers.bmc as bmchandler import pyghmi.exceptions as pygexc import pyghmi.ipmi.private.util as pygutil import confluent.util as util import struct

38.964286

77

0.552704

# Copyright 2017 Lenovo # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import codecs import confluent.discovery.handlers.bmc as bmchandler import pyghmi.exceptions as pygexc import pyghmi.ipmi.private.util as pygutil import confluent.util as util import struct class NodeHandler(bmchandler.NodeHandler): devname = 'IMM' @classmethod def adequate(cls, info): # We can sometimes receive a partially initialized SLP packet # This is not adequate for being satisfied return bool(info.get('attributes', {})) def scan(self): slpattrs = self.info.get('attributes', {}) self.isdense = False try: ff = slpattrs.get('enclosure-form-factor', [''])[0] except IndexError: return wronguuid = slpattrs.get('node-uuid', [''])[0] if wronguuid: # we need to fix the first three portions of the uuid uuidprefix = wronguuid.split('-')[:3] uuidprefix = codecs.encode(struct.pack( '<IHH', *[int(x, 16) for x in uuidprefix]), 'hex') uuidprefix = util.stringify(uuidprefix) uuidprefix = uuidprefix[:8] + '-' + uuidprefix[8:12] + '-' + \ uuidprefix[12:16] self.info['uuid'] = uuidprefix + '-' + '-'.join( wronguuid.split('-')[3:]) self.info['uuid'] = self.info['uuid'].lower() room = slpattrs.get('room-id', [None])[0] if room: self.info['room'] = room rack = slpattrs.get('rack-id', [None])[0] if rack: self.info['rack'] = rack name = slpattrs.get('name', [None])[0] if name: self.info['hostname'] = name unumber = slpattrs.get('lowest-u', [None])[0] if unumber: self.info['u'] = unumber location = slpattrs.get('location', [None])[0] if location: self.info['location'] = location if ff not in ('dense-computing', 'BC2'): # do not probe unless it's a dense platform return self.isdense = True encuuid = slpattrs.get('chassis-uuid', [None])[0] if encuuid: self.info['enclosure.uuid'] = encuuid slot = int(slpattrs.get('slot', ['0'])[0]) if slot != 0: self.info['enclosure.bay'] = slot def probe(self): if self.info.get('enclosure.bay', 0) == 0: self.scan() if self.info.get('enclosure.bay', 0) != 0: # scan has already populated info return ff = self.info.get('attributes', {}).get('enclosure-form-factor', '') if ff != 'dense-computing': return try: # we are a dense platform, but the SLP data did not give us slot # attempt to probe using IPMI ipmicmd = self._get_ipmicmd() guiddata = ipmicmd.xraw_command(netfn=6, command=8) self.info['uuid'] = pygutil.decode_wireformat_uuid( guiddata['data']).lower() ipmicmd.oem_init() bayid = ipmicmd._oem.immhandler.get_property( '/v2/cmm/sp/7') if not bayid: return self.info['enclosure.bay'] = int(bayid) smmid = ipmicmd._oem.immhandler.get_property( '/v2/ibmc/smm/chassis/uuid') if not smmid: return smmid = smmid.lower().replace(' ', '') smmid = '{0}-{1}-{2}-{3}-{4}'.format(smmid[:8], smmid[8:12], smmid[12:16], smmid[16:20], smmid[20:]) self.info['enclosure.uuid'] = smmid self.info['enclosure.type'] = 'smm' except pygexc.IpmiException as ie: print(repr(ie)) raise

3,440

139

23

c2c0dc95899f6f8dad0a7096d7c04088b895f8b1

363

py

Python

alembic/versions/0367b739bb81_add_country_code_to_table.py

danieliheonu/bigfastapi

483554776195c9f38bb46ba719b613360eda1028

[ "MIT" ]

1

2022-03-20T21:46:05.000Z

alembic/versions/0367b739bb81_add_country_code_to_table.py

danieliheonu/bigfastapi

483554776195c9f38bb46ba719b613360eda1028

[ "MIT" ]

null

alembic/versions/0367b739bb81_add_country_code_to_table.py

danieliheonu/bigfastapi

483554776195c9f38bb46ba719b613360eda1028

[ "MIT" ]

null

"""add country code to table Revision ID: 0367b739bb81 Revises: 1e09924c1938 Create Date: 2022-01-27 16:10:57.297020 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '0367b739bb81' down_revision = '1e09924c1938' branch_labels = None depends_on = None

14.52

40

0.741047

"""add country code to table Revision ID: 0367b739bb81 Revises: 1e09924c1938 Create Date: 2022-01-27 16:10:57.297020 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '0367b739bb81' down_revision = '1e09924c1938' branch_labels = None depends_on = None def upgrade(): pass def downgrade(): pass

6

0

46

9c35a421d2475f3566cf629c7b74b3188447fc25

152

py

Python

scripts/visualize_dataset.py

birlrobotics/smach_based_introspection_framework

f16742339cddfc86effba4dbf6e5062304704b89

[ "BSD-3-Clause" ]

7

2018-02-23T13:02:13.000Z

2020-07-28T18:27:47.000Z

scripts/visualize_dataset.py

birlrobotics/smach_based_introspection_framework

f16742339cddfc86effba4dbf6e5062304704b89

[ "BSD-3-Clause" ]

null

scripts/visualize_dataset.py

birlrobotics/smach_based_introspection_framework

f16742339cddfc86effba4dbf6e5062304704b89

[ "BSD-3-Clause" ]

1

2019-06-24T09:20:06.000Z

#!/usr/bin/env python import os import smach_based_introspection_framework.offline_part.visualize_dataset as m if __name__ == "__main__": m.run()

19

78

0.776316

#!/usr/bin/env python import os import smach_based_introspection_framework.offline_part.visualize_dataset as m if __name__ == "__main__": m.run()

0

489726ea2da03626c5a2318798d31acaac09e9b1

12,261

py

Python

packages/weevely/modules/net/proxy.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/weevely/modules/net/proxy.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/weevely/modules/net/proxy.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

from core.loggers import log, dlog from core import messages from core.vectors import ModuleExec from core.module import Module from core.config import base_path from http.server import HTTPServer, BaseHTTPRequestHandler from tempfile import gettempdir from socketserver import ThreadingMixIn from urllib.parse import urlparse, urlunparse, ParseResult from io import StringIO from http.client import HTTPResponse import threading import re import os import sys import socket import ssl import select import http.client import urllib.parse import threading import time import json import re from http.server import HTTPServer, BaseHTTPRequestHandler from socketserver import ThreadingMixIn from io import BytesIO from subprocess import Popen, PIPE from html.parser import HTMLParser from tempfile import mkdtemp re_valid_ip = re.compile( "^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])$") re_valid_hostname = re.compile("^(([a-zA-Z0-9\-]+)\.)*([A-Za-z]|[A-Za-z][A-Za-z0-9\-]*[A-Za-z0-9])$") temp_certdir = mkdtemp() lock = threading.Lock() # Create path for the CA certificates and keys cert_folder = os.path.join(base_path, 'certs') try: os.makedirs(cert_folder) except: pass # # Most of the Proxy part has been taken from https://github.com/inaz2/proxy2 # class Proxy(Module): """Run local proxy to pivot HTTP/HTTPS browsing through the target."""

32.350923

119

0.575402

from core.loggers import log, dlog from core import messages from core.vectors import ModuleExec from core.module import Module from core.config import base_path from http.server import HTTPServer, BaseHTTPRequestHandler from tempfile import gettempdir from socketserver import ThreadingMixIn from urllib.parse import urlparse, urlunparse, ParseResult from io import StringIO from http.client import HTTPResponse import threading import re import os import sys import socket import ssl import select import http.client import urllib.parse import threading import time import json import re from http.server import HTTPServer, BaseHTTPRequestHandler from socketserver import ThreadingMixIn from io import BytesIO from subprocess import Popen, PIPE from html.parser import HTMLParser from tempfile import mkdtemp re_valid_ip = re.compile( "^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])$") re_valid_hostname = re.compile("^(([a-zA-Z0-9\-]+)\.)*([A-Za-z]|[A-Za-z][A-Za-z0-9\-]*[A-Za-z0-9])$") temp_certdir = mkdtemp() lock = threading.Lock() class FakeSocket(): def __init__(self, response_str): self._file = BytesIO(response_str) def makefile(self, *args, **kwargs): return self._file # Create path for the CA certificates and keys cert_folder = os.path.join(base_path, 'certs') try: os.makedirs(cert_folder) except: pass def get_cert_path(path): return os.path.join(cert_folder, path) def initialize_certificates(): cakey_path = get_cert_path("ca.key") cacrt_path = get_cert_path("ca.crt") certkey_path = get_cert_path("cert.key") if not os.path.isfile(cakey_path) or not os.path.isfile(cacrt_path) or not os.path.isfile(certkey_path): # openssl genrsa -out ca.key 2048 p1 = Popen(["openssl", "genrsa", "-out", cakey_path, "2048"]) p1.communicate() p1.wait() # openssl req -new -x509 -days 3650 -key ca.key -out ca.crt -subj "/CN=proxy2 CA" p2 = Popen(["openssl", "req", "-new", "-x509", "-days", "3650", "-key", cakey_path, "-out", cacrt_path, "-subj", "/CN=proxy2 CA"]) p2.communicate() p2.wait() # openssl genrsa -out cert.key 2048 p3 = Popen(["openssl", "genrsa", "-out", certkey_path, "2048"]) p3.communicate() p3.wait() # # Most of the Proxy part has been taken from https://github.com/inaz2/proxy2 # class ThreadingHTTPServer(ThreadingMixIn, HTTPServer): address_family = socket.AF_INET daemon_threads = True def handle_error(self, request, client_address): # surpress socket/ssl related errors cls, e = sys.exc_info()[:2] if cls is socket.error or cls is ssl.SSLError: pass else: return HTTPServer.handle_error(self, request, client_address) class ProxyRequestHandler(BaseHTTPRequestHandler): cakey = get_cert_path('ca.key') cacert = get_cert_path('ca.crt') certkey = get_cert_path('cert.key') certdir = temp_certdir timeout = 5 lock = threading.Lock() def __init__(self, *args, **kwargs): self.tls = threading.local() self.tls.conns = {} BaseHTTPRequestHandler.__init__(self, *args, **kwargs) def log_error(self, format, *args): # surpress "Request timed out: timeout('timed out',)" if isinstance(args[0], socket.timeout): return def do_CONNECT(self): self.connect_intercept() def connect_intercept(self): hostname = self.path.split(':')[0] certname = "%s.crt" % (hostname) certpath = os.path.join(self.certdir, certname) if not (re_valid_ip.match(hostname) or re_valid_hostname.match(hostname)): log.warning("CN name '%s' is not valid, using 'www.weevely.com'" % (hostname)) hostname = 'www.weevely.com' with self.lock: if not os.path.isfile(certpath): epoch = "%d" % (time.time() * 1000) p1 = Popen(["openssl", "req", "-new", "-key", self.certkey, "-subj", "/CN=%s" % hostname], stdout=PIPE) p2 = Popen(["openssl", "x509", "-req", "-days", "3650", "-CA", self.cacert, "-CAkey", self.cakey, "-set_serial", epoch, "-out", certpath], stdin=p1.stdout, stderr=PIPE) p2.communicate() self.send_response_only(200, 'Connection Established') self.end_headers() try: self.connection = ssl.wrap_socket(self.connection, keyfile=self.certkey, certfile=certpath, server_side=True) self.rfile = self.connection.makefile("rb", self.rbufsize) self.wfile = self.connection.makefile("wb", self.wbufsize) except Exception as e: log.debug(e) raise conntype = self.headers.get('Proxy-Connection', '') if self.protocol_version == "HTTP/1.1" and conntype.lower() != 'close': self.close_connection = 0 else: self.close_connection = 1 def connect_relay(self): address = self.path.split(':', 1) address[1] = int(address[1]) or 443 try: s = socket.create_connection(address, timeout=self.timeout) except Exception as e: self.send_error(502) return self.send_response(200, 'Connection Established') self.end_headers() conns = [self.connection, s] self.close_connection = 0 while not self.close_connection: rlist, wlist, xlist = select.select(conns, [], conns, self.timeout) if xlist or not rlist: break for r in rlist: other = conns[1] if r is conns[0] else conns[0] data = r.recv(8192) if not data: self.close_connection = 1 break other.sendall(data) def do_GET(self): if self.path == 'http://weevely/': self.send_cacert() return req = self content_length = int(req.headers.get('Content-Length', 0)) req_body = self.rfile.read(content_length) if content_length else '' if req.path[0] == '/': if isinstance(self.connection, ssl.SSLSocket): req.path = "https://%s%s" % (req.headers['Host'], req.path) else: req.path = "http://%s%s" % (req.headers['Host'], req.path) req.headers['Content-length'] = str(len(req_body)) u = urllib.parse.urlsplit(req.path) scheme, netloc, path = u.scheme, u.netloc, (u.path + '?' + u.query if u.query else u.path) assert scheme in ('http', 'https') if netloc: req.headers['Host'] = netloc setattr(req, 'headers', self.filter_headers(req.headers)) net_curl_args = [ '-X', self.command, '-i' ] net_curl_args.append(self.path) for h in req.headers: if h.title().lower() == 'host': host = self.headers[h] else: net_curl_args += ['-H', '%s: %s' % (h.title(), self.headers[h])] if self.command == 'POST': content_len = int(self.headers.get('content-length', 0)) net_curl_args += ['-d', req_body] lock.acquire() try: result, headers, saved = ModuleExec( 'net_curl', net_curl_args ).run() finally: lock.release() if not headers: log.debug('Error no headers') self.send_error(502) return log.debug( '> ' + '\r\n> '.join( ['%s: %s' % ( h.title(), self.headers[h] ) for h in self.headers ] ) ) log.debug('< ' + '\r\n< '.join([h.decode('utf-8', 'replace') for h in headers])) http_response_str = b'\r\n'.join(headers) + b'\r\n\r\n' + result source = FakeSocket(http_response_str) res = HTTPResponse(source) res.begin() version_table = {10: 'HTTP/1.0', 11: 'HTTP/1.1'} setattr(res, 'headers', res.msg) setattr(res, 'response_version', version_table[res.version]) # support streaming if not 'Content-Length' in res.headers and 'no-store' in res.headers.get('Cache-Control', ''): setattr(res, 'headers', self.filter_headers(res.headers)) self.relay_streaming(res) return try: res_body = res.read() except Exception as e: log.debug(e) self.send_error(500) return setattr(res, 'headers', self.filter_headers(res.headers)) respstring = "%s %d %s\r\n" % (self.protocol_version, res.status, res.reason) self.wfile.write(respstring.encode('utf-8')) self.wfile.write(res.headers.as_bytes()) self.wfile.write(res_body) self.wfile.flush() def relay_streaming(self, res): respstring = "%s %d %s\r\n" % (self.protocol_version, res.status, res.reason) self.wfile.write(respstring.encode('utf-8')) self.wfile.write(res.headers.as_bytes() + b"\r\n") try: while True: chunk = res.read(8192) if not chunk: break self.wfile.write(chunk) self.wfile.flush() except socket.error: # connection closed by client pass do_HEAD = do_GET do_POST = do_GET do_PUT = do_GET do_DELETE = do_GET do_OPTIONS = do_GET def filter_headers(self, headers): # http://tools.ietf.org/html/rfc2616#section-13.5.1 hop_by_hop = ( 'connection', 'keep-alive', 'proxy-authenticate', 'proxy-authorization', 'te', 'trailers', 'transfer-encoding', 'upgrade') for k in hop_by_hop: del headers[k] return headers def send_cacert(self): with open(self.cacert, 'rb') as f: data = f.read() self.wfile.write("%s %d %s\r\n" % (self.protocol_version, 200, 'OK')) self.send_header('Content-Type', 'application/x-x509-ca-cert') self.send_header('Content-Length', len(data)) self.send_header('Connection', 'close') self.end_headers() self.wfile.write(data) def run_proxy2(HandlerClass=ProxyRequestHandler, ServerClass=ThreadingHTTPServer, protocol="HTTP/1.1", hostname='127.0.0.1', port='8080'): server_address = (hostname, port) HandlerClass.protocol_version = protocol httpd = ServerClass(server_address, HandlerClass) sa = httpd.socket.getsockname() httpd.serve_forever() class Proxy(Module): """Run local proxy to pivot HTTP/HTTPS browsing through the target.""" def init(self): self.register_info( { 'author': [ 'Emilio Pinna' ], 'license': 'GPLv3' } ) self.register_arguments([ {'name': '-lhost', 'default': '127.0.0.1'}, {'name': '-lport', 'default': 8080, 'type': int}, {'name': '-no-background', 'action': 'store_true', 'default': False, 'help': 'Run foreground'} ]) def run(self): log.warning(messages.module_net_proxy.proxy_starting_s_i % (self.args['lhost'], self.args['lport'])) log.warning(messages.module_net_proxy.proxy_set_proxy) initialize_certificates() if self.args['no_background']: log.warning(messages.module_net_proxy.proxy_started_foreground) run_proxy2( hostname=self.args['lhost'], port=self.args['lport'] ) else: log.warning(messages.module_net_proxy.proxy_started_background) server_thread = threading.Thread(target=run_proxy2, kwargs={ 'hostname': self.args['lhost'], 'port': self.args['lport'] }) server_thread.daemon = True server_thread.start()

9,893

686

245

c1a9fb6d2776eae6bdea4a9ec9110150e80c3b44

65

py

Python

or_testbed/solvers/grasp/__init__.py

Fynardo/or-testbed

9cc58edd71f400da7a933f166cd325e43562cfb6

[ "MIT" ]

1

2020-07-23T14:59:03.000Z

or_testbed/solvers/grasp/__init__.py

Fynardo/or-testbed

9cc58edd71f400da7a933f166cd325e43562cfb6

[ "MIT" ]

null

or_testbed/solvers/grasp/__init__.py

Fynardo/or-testbed

9cc58edd71f400da7a933f166cd325e43562cfb6

[ "MIT" ]

null

from .construct import GraspConstruct, MultiStartGraspConstruct

32.5

64

0.876923

from .construct import GraspConstruct, MultiStartGraspConstruct

0

30c3599ff9f4efeffbeca8fb7c06634e903655c8

1,151

py

Python

tsengine/test/test_pool.py

ccccxjin/TsEngine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

1

2020-07-10T09:11:38.000Z

tsengine/test/test_pool.py

ccccxjin/tsengine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

null

tsengine/test/test_pool.py

ccccxjin/tsengine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

null

import pytest

27.404762

54

0.578627

import pytest class TestPool: def test_pool_get(self, pool): assert pool.checkedin == 1 assert pool.checkedout == 0 connection1 = pool.get() assert pool.checkedin == 0 assert pool.checkedout == 1 connection2 = pool.get() assert pool.checkedin == 0 assert pool.checkedout == 2 connection3 = pool.get() assert pool.checkedin == 0 assert pool.checkedout == 3 assert pool.busy pool.wait = False with pytest.raises(TimeoutError) as e: pool.get() exec_msg = e.value.args[0] assert exec_msg == 'get session timeout error' pool.wait = 3 with pytest.raises(TimeoutError) as e: pool.get() exec_msg = e.value.args[0] assert exec_msg == 'get session timeout error' pool.put(connection1) assert pool.checkedin == 1 assert pool.checkedout == 2 pool.put(connection2) assert pool.checkedin == 2 assert pool.checkedout == 1 pool.put(connection3) assert pool.checkedin == 3 assert pool.checkedout == 0

1,093

-6

49

e5d6df24af9bac17d018ee4f885d2b0a7d316e52

4,965

py

Python

course_grader/views/api/submitted_graderoster.py

uw-it-aca/gradepage

7059d715cc112ad0ecb0e5012f716e525ee7b3bc

[ "Apache-2.0" ]

1

2017-01-29T09:52:06.000Z

course_grader/views/api/submitted_graderoster.py

uw-it-aca/gradepage

7059d715cc112ad0ecb0e5012f716e525ee7b3bc

[ "Apache-2.0" ]

287

2017-03-09T00:17:20.000Z

2022-01-08T00:36:34.000Z

course_grader/views/api/submitted_graderoster.py

uw-it-aca/gradepage

7059d715cc112ad0ecb0e5012f716e525ee7b3bc

[ "Apache-2.0" ]

null

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 from django.conf import settings from django.http import HttpResponse from django.views.decorators.cache import never_cache from django.utils.decorators import method_decorator from uw_saml.decorators import group_required from course_grader.views.rest_dispatch import RESTDispatch from course_grader.models import ( SubmittedGradeRoster as SubmittedGradeRosterModel) from course_grader.dao.person import person_from_regid, person_display_name from course_grader.dao.section import section_from_label from course_grader.dao.term import term_from_param from uw_sws_graderoster.models import GradeRoster from lxml import etree from logging import getLogger import csv logger = getLogger(__name__) @method_decorator(group_required(settings.GRADEPAGE_SUPPORT_GROUP), name='dispatch') @method_decorator(never_cache, name='dispatch') @method_decorator(group_required(settings.GRADEPAGE_SUPPORT_GROUP), name='dispatch') @method_decorator(never_cache, name='dispatch')

35.978261

76

0.627593

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 from django.conf import settings from django.http import HttpResponse from django.views.decorators.cache import never_cache from django.utils.decorators import method_decorator from uw_saml.decorators import group_required from course_grader.views.rest_dispatch import RESTDispatch from course_grader.models import ( SubmittedGradeRoster as SubmittedGradeRosterModel) from course_grader.dao.person import person_from_regid, person_display_name from course_grader.dao.section import section_from_label from course_grader.dao.term import term_from_param from uw_sws_graderoster.models import GradeRoster from lxml import etree from logging import getLogger import csv logger = getLogger(__name__) @method_decorator(group_required(settings.GRADEPAGE_SUPPORT_GROUP), name='dispatch') @method_decorator(never_cache, name='dispatch') class SubmissionsByTerm(RESTDispatch): def get(self, request, *args, **kwargs): term_id = kwargs.get("term_id") try: selected_term = term_from_param(term_id) except Exception as ex: return self.error_response(400, "Invalid Term ID") graderosters = SubmittedGradeRosterModel.objects.get_status_by_term( selected_term) response = self.csv_response(filename=term_id) csv.register_dialect("unix_newline", lineterminator="\n") writer = csv.writer(response, dialect="unix_newline") writer.writerow([ "Section", "Secondary section", "Submitter", "Submission datetime" ]) for graderoster in graderosters: writer.writerow([ graderoster["section_id"], graderoster["secondary_section_id"], graderoster["submitted_by"], graderoster["submitted_date"], ]) return response @method_decorator(group_required(settings.GRADEPAGE_SUPPORT_GROUP), name='dispatch') @method_decorator(never_cache, name='dispatch') class SubmittedGradeRoster(RESTDispatch): def get(self, request, *args, **kwargs): graderoster_id = kwargs.get("graderoster_id") try: model = SubmittedGradeRosterModel.objects.get(pk=graderoster_id) section = section_from_label(model.section_id) instructor = person_from_regid(model.instructor_id) submitter = person_from_regid(model.submitted_by) graderoster = GradeRoster.from_xhtml( etree.fromstring(model.document.strip()), section=section, instructor=instructor) except SubmittedGradeRosterModel.DoesNotExist: return self.error_response(404, "Not Found") except Exception as ex: logger.error( "Download failed for graderoster model {}: {}".format( graderoster_id, ex)) return self.error_response(500, "{}".format(ex)) if model.secondary_section_id is not None: filename = model.secondary_section_id else: filename = model.section_id response = self.csv_response(filename=filename) csv.register_dialect("unix_newline", lineterminator="\n") writer = csv.writer(response, dialect="unix_newline") writer.writerow([ "Student number", "Student name", "Course", "Section", "Credits", "Incomplete", "Grade", "Writing credit", "Instructor name", "Instructor netid", "Submitter name", "Submitter netid" ]) secondary_section = getattr(graderoster, "secondary_section", None) for item in graderoster.items: if (secondary_section is not None and secondary_section.section_id != item.section_id): continue writer.writerow([ item.student_number, "{first_name} {last_name}".format( first_name=item.student_first_name, last_name=item.student_surname), "{curr_abbr} {course_num}".format( curr_abbr=section.curriculum_abbr, course_num=section.course_number), item.section_id, item.student_credits, "I" if item.has_incomplete else "", "X" if item.no_grade_now else str(item.grade), "W" if item.has_writing_credit else "", person_display_name(instructor), instructor.uwnetid, person_display_name(submitter), submitter.uwnetid ]) logger.info("Graderoster downloaded: {}-{}".format( model.section_id, model.instructor_id)) return response

3,738

37

96

72cac8fb30a2e307bde5d70d65c30b41c1787dec

1,235

py

Python

pipeline/boto_helpers.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

null

pipeline/boto_helpers.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

null

pipeline/boto_helpers.py

DMS-medical-informatics/beiwe-backend

55afe3a16e1c9b34501f3655288b5c19c663a083

[ "BSD-3-Clause" ]

null

import json import os.path import subprocess import boto3 # This is all cribbed from the django branch's cluster_management/deployment_helpers folder # TODO once the branches are merged, use that code and NOT this code

29.404762

99

0.728745

import json import os.path import subprocess import boto3 # This is all cribbed from the django branch's cluster_management/deployment_helpers folder # TODO once the branches are merged, use that code and NOT this code def get_aws_object_names(): configs_folder = get_configs_folder() with open(os.path.join(configs_folder, 'aws-object-names.json')) as fn: return json.load(fn) def get_boto_client(client_type): from config.settings import BEIWE_SERVER_AWS_ACCESS_KEY_ID, BEIWE_SERVER_AWS_SECRET_ACCESS_KEY aws_object_names = get_aws_object_names() return boto3.client( client_type, aws_access_key_id=BEIWE_SERVER_AWS_ACCESS_KEY_ID, aws_secret_access_key=BEIWE_SERVER_AWS_SECRET_ACCESS_KEY, region_name=aws_object_names['region_name'], ) def get_pipeline_folder(): return os.path.abspath(__file__).rsplit('/', 1)[0] def get_configs_folder(): return os.path.join(get_pipeline_folder(), 'configs') def set_default_region(): aws_object_names = get_aws_object_names() region_name = aws_object_names['region_name'] subprocess.check_call(['aws', 'configure', 'set', 'default.region', region_name])

871

0

125

e5e52448863aa3d2032ea0acf739006c4aeffca6

1,783

py

Python

WaltzControl/use_cases/tel_controller_boundarys.py

DaneSpaeth/WaltzControl_refactored

80aa3e28f1e0709bc7dd9472bc1d841e9b4da9e7

[ "MIT" ]

null

WaltzControl/use_cases/tel_controller_boundarys.py

DaneSpaeth/WaltzControl_refactored

80aa3e28f1e0709bc7dd9472bc1d841e9b4da9e7

[ "MIT" ]

null

WaltzControl/use_cases/tel_controller_boundarys.py

DaneSpaeth/WaltzControl_refactored

80aa3e28f1e0709bc7dd9472bc1d841e9b4da9e7

[ "MIT" ]

null

"""Boundarys for Responses from TelescopeController (TC) and Requests to TC. Data entry and exit point into use_cases layer. """ class TelescopeControllerResponseBoundary: """Contains Responses from TelescopeController Device. """ def __init__( self, ra_response = None, dec_response = None, validate_response = None): """Store Responses of Telescope Controller as floats. """ self.ra_response = ra_response self.dec_response = dec_response self.validate_response = validate_response def set_ra_response(self, ra): """Set ra response. Input: ra as float in hours """ self.ra_response = ra def set_dec_response(self, dec): """Set dec response. Input: dec as float in degrees """ self.dec_response = dec def set_validate_response(self, valid): """Set validate response. Input: valid as boolean (accounts for Returns of Telesope Controllere to set_target etc) """ self.validate_response = valid def reset_responses(self): """Reset all responses to None. """ self.ra_response = None self.dec_response = None self.validate_response = None def retrieve_position(self): """Returns ra and dec_responses. """ return (self.ra_response, self.dec_response) class TelescopeControllerRequestBoundary: """Interface for commands to TelescopeController Device. """

28.758065

80

0.574313

"""Boundarys for Responses from TelescopeController (TC) and Requests to TC. Data entry and exit point into use_cases layer. """ class TelescopeControllerResponseBoundary: """Contains Responses from TelescopeController Device. """ def __init__( self, ra_response = None, dec_response = None, validate_response = None): """Store Responses of Telescope Controller as floats. """ self.ra_response = ra_response self.dec_response = dec_response self.validate_response = validate_response def set_ra_response(self, ra): """Set ra response. Input: ra as float in hours """ self.ra_response = ra def set_dec_response(self, dec): """Set dec response. Input: dec as float in degrees """ self.dec_response = dec def set_validate_response(self, valid): """Set validate response. Input: valid as boolean (accounts for Returns of Telesope Controllere to set_target etc) """ self.validate_response = valid def reset_responses(self): """Reset all responses to None. """ self.ra_response = None self.dec_response = None self.validate_response = None def retrieve_position(self): """Returns ra and dec_responses. """ return (self.ra_response, self.dec_response) class TelescopeControllerRequestBoundary: """Interface for commands to TelescopeController Device. """ def __init__(self): pass def request_position(self): pass

30

0

61

2992c83e0ce52d8039899799790c8ae2a72523fc

3,505

py

Python

example_group_epochs.py

DraganaMana/mne_microstates

de3dc76e63e49fb4b61810bf737d4d5d11f5b2f0

[ "MIT" ]

1

2021-06-02T09:14:30.000Z

example_group_epochs.py

DraganaMana/mne_microstates

de3dc76e63e49fb4b61810bf737d4d5d11f5b2f0

[ "MIT" ]

null

example_group_epochs.py

DraganaMana/mne_microstates

de3dc76e63e49fb4b61810bf737d4d5d11f5b2f0

[ "MIT" ]

1

2020-06-15T13:59:07.000Z

# -*- coding: utf-8 -*- """ Created on Tue Feb 25 18:04:32 2020 @author: Dragana """ import mne import microstates as mst import numpy as np HC_RS_path = 'C:/Users/.../Documents/RS_EEG/' subj_folder = ['subj01', 'subj02', 'subj03', 'subj04', 'subj05'] # Parameteres setting up chan_to_drop = ['E67', 'E73', 'E247', 'E251', 'E256', 'E243', 'E246', 'E250', 'E255', 'E82', 'E91', 'E254', 'E249', 'E245', 'E242', 'E253', 'E252', 'E248', 'E244', 'E241', 'E92', 'E102', 'E103', 'E111', 'E112', 'E120', 'E121', 'E133', 'E134', 'E145', 'E146', 'E156', 'E165', 'E166', 'E174', 'E175', 'E187', 'E188', 'E199', 'E200', 'E208', 'E209', 'E216', 'E217', 'E228', 'E229', 'E232', 'E233', 'E236', 'E237', 'E240', 'E218', 'E227', 'E231', 'E235', 'E239', 'E219', 'E225', 'E226', 'E230', 'E234', 'E238'] pax = len(subj_folder) # number of participants n_states = 4 n_inits = 10 EGI256 = True if EGI256 == True: n_channels = 256 - len(chan_to_drop) grouped_maps = np.array([], dtype=np.int64).reshape(0, n_channels) for i, f in enumerate(subj_folder): fname = HC_RS_path + f + '/' + f +'_clean-epo.fif' epochs = mne.read_epochs(fname, preload=True) if EGI256 == True: epochs.drop_channels(chan_to_drop) data = epochs.get_data() # Segment the data in microstates maps, segmentation, gev, gfp_peaks = mst.segment(data, n_states, n_inits) grouped_maps = np.concatenate((grouped_maps, maps), axis=0) # Transpose the maps from maps(n_maps, n_channels) to maps(n_channels, n_maps) # and treat the n_maps as a sample in time. grouped_maps_T = grouped_maps.transpose() # Find the group maps using k-means clustering group_maps, group_gev = mst.segment(grouped_maps_T, n_states, n_inits, use_peaks=False) # Plot the maps mst.viz.plot_maps(group_maps, epochs.info) # Fitting the maps back to the original epoched data by subject grouped_segment, all_p = [], [] for i, f in enumerate(subj_folder): fname = HC_RS_path + f + '/' + f +'_clean-epo.fif' epochs = mne.read_epochs(fname, preload=True) if EGI256 == True: epochs.drop_channels(chan_to_drop) data = epochs.get_data() n_epochs, n_chans, n_samples = data.shape # Make the data 2D data = np.hstack(data) # Compute final microstate segmentations on the original data activation = group_maps.dot(data) segmentation = np.argmax(np.abs(activation), axis=0) # Add all the per subject segmentations in one array # (n_times, subjects) grouped_segment.append(segmentation) # Plot the segmentation per subject sfreq = epochs.info['sfreq'] times = np.arange(0, len(data[1])/sfreq, 1/sfreq) mst.viz.plot_segmentation(segmentation[:500], data[:, :500], times[:500]) # p_empirical epoched_data = True p_hat = mst.analysis.p_empirical(segmentation, n_epochs, n_samples, n_states, epoched_data) all_p.append(p_hat) # p_empirical printing print("\n\t Empirical symbol distribution (RTT) per subject:\n") for i in range(pax): print("\n Subject", i) for j in range(n_states): print("\n\t\t p", j, " = {0:.5f}".format(all_p[i][j])) all_p = np.vstack(all_p) all_p /= pax all_p_sum = np.sum(all_p, axis=0) print("\n\t Empirical symbol distribution (RTT) for all subjects:\n") for i in range(n_states): print("\n\t\t p", i, " = {0:.5f}".format(all_p_sum[i]))

36.510417

87

0.628531

# -*- coding: utf-8 -*- """ Created on Tue Feb 25 18:04:32 2020 @author: Dragana """ import mne import microstates as mst import numpy as np HC_RS_path = 'C:/Users/.../Documents/RS_EEG/' subj_folder = ['subj01', 'subj02', 'subj03', 'subj04', 'subj05'] # Parameteres setting up chan_to_drop = ['E67', 'E73', 'E247', 'E251', 'E256', 'E243', 'E246', 'E250', 'E255', 'E82', 'E91', 'E254', 'E249', 'E245', 'E242', 'E253', 'E252', 'E248', 'E244', 'E241', 'E92', 'E102', 'E103', 'E111', 'E112', 'E120', 'E121', 'E133', 'E134', 'E145', 'E146', 'E156', 'E165', 'E166', 'E174', 'E175', 'E187', 'E188', 'E199', 'E200', 'E208', 'E209', 'E216', 'E217', 'E228', 'E229', 'E232', 'E233', 'E236', 'E237', 'E240', 'E218', 'E227', 'E231', 'E235', 'E239', 'E219', 'E225', 'E226', 'E230', 'E234', 'E238'] pax = len(subj_folder) # number of participants n_states = 4 n_inits = 10 EGI256 = True if EGI256 == True: n_channels = 256 - len(chan_to_drop) grouped_maps = np.array([], dtype=np.int64).reshape(0, n_channels) for i, f in enumerate(subj_folder): fname = HC_RS_path + f + '/' + f +'_clean-epo.fif' epochs = mne.read_epochs(fname, preload=True) if EGI256 == True: epochs.drop_channels(chan_to_drop) data = epochs.get_data() # Segment the data in microstates maps, segmentation, gev, gfp_peaks = mst.segment(data, n_states, n_inits) grouped_maps = np.concatenate((grouped_maps, maps), axis=0) # Transpose the maps from maps(n_maps, n_channels) to maps(n_channels, n_maps) # and treat the n_maps as a sample in time. grouped_maps_T = grouped_maps.transpose() # Find the group maps using k-means clustering group_maps, group_gev = mst.segment(grouped_maps_T, n_states, n_inits, use_peaks=False) # Plot the maps mst.viz.plot_maps(group_maps, epochs.info) # Fitting the maps back to the original epoched data by subject grouped_segment, all_p = [], [] for i, f in enumerate(subj_folder): fname = HC_RS_path + f + '/' + f +'_clean-epo.fif' epochs = mne.read_epochs(fname, preload=True) if EGI256 == True: epochs.drop_channels(chan_to_drop) data = epochs.get_data() n_epochs, n_chans, n_samples = data.shape # Make the data 2D data = np.hstack(data) # Compute final microstate segmentations on the original data activation = group_maps.dot(data) segmentation = np.argmax(np.abs(activation), axis=0) # Add all the per subject segmentations in one array # (n_times, subjects) grouped_segment.append(segmentation) # Plot the segmentation per subject sfreq = epochs.info['sfreq'] times = np.arange(0, len(data[1])/sfreq, 1/sfreq) mst.viz.plot_segmentation(segmentation[:500], data[:, :500], times[:500]) # p_empirical epoched_data = True p_hat = mst.analysis.p_empirical(segmentation, n_epochs, n_samples, n_states, epoched_data) all_p.append(p_hat) # p_empirical printing print("\n\t Empirical symbol distribution (RTT) per subject:\n") for i in range(pax): print("\n Subject", i) for j in range(n_states): print("\n\t\t p", j, " = {0:.5f}".format(all_p[i][j])) all_p = np.vstack(all_p) all_p /= pax all_p_sum = np.sum(all_p, axis=0) print("\n\t Empirical symbol distribution (RTT) for all subjects:\n") for i in range(n_states): print("\n\t\t p", i, " = {0:.5f}".format(all_p_sum[i]))

0

d0c633f50b464b8e08988638cf34cd0815c70e55

1,142

py

Python

chunkymonkey/lib/base.py

shopzilla/chunky-monkey

2556055e87849e2a873a950a5e52429e516c8304

[ "Apache-2.0" ]

1

2016-10-24T15:16:26.000Z

chunkymonkey/lib/base.py

shopzilla/chunky-monkey

2556055e87849e2a873a950a5e52429e516c8304

[ "Apache-2.0" ]

null

chunkymonkey/lib/base.py

shopzilla/chunky-monkey

2556055e87849e2a873a950a5e52429e516c8304

[ "Apache-2.0" ]

null

# # Copyright 2011 Shopzilla.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this 1 except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """The base Controller API Provides the BaseController class for subclassing. """ from pylons.controllers import WSGIController from pylons.templating import render_mako as render

36.83871

74

0.75394

# # Copyright 2011 Shopzilla.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this 1 except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """The base Controller API Provides the BaseController class for subclassing. """ from pylons.controllers import WSGIController from pylons.templating import render_mako as render class BaseController(WSGIController): def __call__(self, environ, start_response): """Invoke the Controller""" # WSGIController.__call__ dispatches to the Controller method # the request is routed to. This routing information is # available in environ['pylons.routes_dict'] return WSGIController.__call__(self, environ, start_response)

0

359

23

7d5cf31371d57d1d5e01bffec3ad52101c96988a

134,221

py

Python

carculator/inventory.py

SimonVoelker/carculator

e40d664c9b5612250cf9ad2c6fa2a199b0bf88c5

[ "BSD-3-Clause" ]

null

carculator/inventory.py

SimonVoelker/carculator

e40d664c9b5612250cf9ad2c6fa2a199b0bf88c5

[ "BSD-3-Clause" ]

null

carculator/inventory.py

SimonVoelker/carculator

e40d664c9b5612250cf9ad2c6fa2a199b0bf88c5

[ "BSD-3-Clause" ]

null

from . import DATA_DIR import sys import glob from .background_systems import BackgroundSystemModel from .export import ExportInventory from inspect import currentframe, getframeinfo from pathlib import Path from scipy import sparse import csv import itertools import numexpr as ne import numpy as np import xarray as xr REMIND_FILES_DIR = DATA_DIR / "IAM" class InventoryCalculation: """ Build and solve the inventory for results characterization and inventory export Vehicles to be analyzed can be filtered by passing a `scope` dictionary. Some assumptions in the background system can also be adjusted by passing a `background_configuration` dictionary. .. code-block:: python scope = { 'powertrain':['BEV', 'FCEV', 'ICEV-p'], } bc = {'country':'CH', # considers electricity network losses for Switzerland 'custom electricity mix' : [[1,0,0,0,0,0,0,0,0,0], # in this case, 100% hydropower for the first year [0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0], ], # in this case, 100% nuclear for the second year 'fuel blend':{ 'cng':{ #specify fuel bland for compressed gas 'primary fuel':{ 'type':'biogas', 'share':[0.9, 0.8, 0.7, 0.6] # shares per year. Must total 1 for each year. }, 'secondary fuel':{ 'type':'syngas', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'diesel':{ 'primary fuel':{ 'type':'synthetic diesel', 'share':[0.9, 0.8, 0.7, 0.6] }, 'secondary fuel':{ 'type':'biodiesel - cooking oil', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'petrol':{ 'primary fuel':{ 'type':'petrol', 'share':[0.9, 0.8, 0.7, 0.6] }, 'secondary fuel':{ 'type':'bioethanol - wheat straw', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'hydrogen':{ 'primary fuel':{'type':'electrolysis', 'share':[1, 0, 0, 0]}, 'secondary fuel':{'type':'smr - natural gas', 'share':[0, 1, 1, 1]} } }, 'energy storage': { 'electric': { 'type':'NMC', 'origin': 'NO' }, 'hydrogen': { 'type':'carbon fiber' } } } InventoryCalculation(CarModel.array, background_configuration=background_configuration, scope=scope, scenario="RCP26") The `custom electricity mix` key in the background_configuration dictionary defines an electricity mix to apply, under the form of one or several array(s), depending on teh number of years to analyze, that should total 1, of which the indices correspond to: - [0]: hydro-power - [1]: nuclear - [2]: natural gas - [3]: solar power - [4]: wind power - [5]: biomass - [6]: coal - [7]: oil - [8]: geothermal - [9]: waste incineration If none is given, the electricity mix corresponding to the country specified in `country` will be selected. If no country is specified, Europe applies. The `primary` and `secondary` fuel keys contain an array with shares of alternative petrol fuel for each year, to create a custom blend. If none is provided, a blend provided by the Integrated Assessment model REMIND is used, which will depend on the REMIND energy scenario selected. Here is a list of available fuel pathways: Hydrogen technologies -------------------- electrolysis smr - natural gas smr - natural gas with CCS smr - biogas smr - biogas with CCS coal gasification wood gasification wood gasification with CCS Natural gas technologies ------------------------ cng biogas syngas Diesel technologies ------------------- diesel biodiesel - algae biodiesel - cooking oil synthetic diesel Petrol technologies ------------------- petrol bioethanol - wheat straw bioethanol - maize starch bioethanol - sugarbeet bioethanol - forest residues synthetic gasoline :ivar array: array from the CarModel class :vartype array: CarModel.array :ivar scope: dictionary that contains filters for narrowing the analysis :ivar background_configuration: dictionary that contains choices for background system :ivar scenario: REMIND energy scenario to use ("SSP2-Baseline": business-as-usual, "SSP2-PkBudg1100": limits cumulative GHG emissions to 1,100 gigatons by 2100, "static": no forward-looking modification of the background inventories). "SSP2-Baseline" selected by default. .. code-block:: python """ def __getitem__(self, key): """ Make class['foo'] automatically filter for the parameter 'foo' Makes the model code much cleaner :param key: Parameter name :type key: str :return: `array` filtered after the parameter selected """ return self.temp_array.sel(parameter=key) def get_results_table(self, split, sensitivity=False): """ Format an xarray.DataArray array to receive the results. :param split: "components" or "impact categories". Split by impact categories only applicable when "endpoint" level is applied. :return: xarrray.DataArray """ if split == "components": cat = [ "direct - exhaust", "direct - non-exhaust", "energy chain", "maintenance", "glider", "EoL", "powertrain", "energy storage", "road", ] dict_impact_cat = list(self.impact_categories.keys()) if sensitivity == False: response = xr.DataArray( np.zeros( ( self.B.shape[1], len(self.scope["size"]), len(self.scope["powertrain"]), len(self.scope["year"]), len(cat), self.iterations, ) ), coords=[ dict_impact_cat, self.scope["size"], self.scope["powertrain"], self.scope["year"], cat, np.arange(0, self.iterations), ], dims=[ "impact_category", "size", "powertrain", "year", "impact", "value", ], ) else: params = [a for a in self.array.value.values] response = xr.DataArray( np.zeros( ( self.B.shape[1], len(self.scope["size"]), len(self.scope["powertrain"]), len(self.scope["year"]), self.iterations, ) ), coords=[ dict_impact_cat, self.scope["size"], self.scope["powertrain"], self.scope["year"], params, ], dims=["impact_category", "size", "powertrain", "year", "parameter"], ) return response def get_split_indices(self): """ Return list of indices to split the results into categories. :return: list of indices :rtype: list """ filename = "dict_split.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError("The dictionary of splits could not be found.") with open(filepath) as f: csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()] (_, _, *header), *data = csv_list csv_dict = {} for row in data: key, sub_key, *values = row if key in csv_dict: if sub_key in csv_dict[key]: csv_dict[key][sub_key].append( {"search by": values[0], "search for": values[1]} ) else: csv_dict[key][sub_key] = [ {"search by": values[0], "search for": values[1]} ] else: csv_dict[key] = { sub_key: [{"search by": values[0], "search for": values[1]}] } flatten = itertools.chain.from_iterable d = {} l = [] d['direct - exhaust'] = [] d['direct - exhaust'].append( self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Cadmium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Copper", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Chromium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Nickel", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Selenium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Zinc", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Chromium VI", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].extend(self.index_emissions) d['direct - exhaust'].extend(self.index_noise) l.append(d['direct - exhaust']) for cat in csv_dict["components"]: d[cat] = list( flatten( [ self.get_index_of_flows([l["search for"]], l["search by"]) for l in csv_dict["components"][cat] ] ) ) l.append(d[cat]) list_ind = [d[x] for x in d] maxLen = max(map(len, list_ind)) for row in list_ind: while len(row) < maxLen: row.extend([len(self.inputs) - 1]) return list(d.keys()), list_ind def get_A_matrix(self): """ Load the A matrix. The A matrix contains exchanges of products (rows) between activities (columns). :return: A matrix with three dimensions of shape (number of values, number of products, number of activities). :rtype: numpy.ndarray """ filename = "A_matrix.csv" filepath = ( Path(getframeinfo(currentframe()).filename) .resolve() .parent.joinpath("data/" + filename) ) if not filepath.is_file(): raise FileNotFoundError("The technology matrix could not be found.") initial_A = np.genfromtxt(filepath, delimiter=";") new_A = np.identity(len(self.inputs)) new_A[0 : np.shape(initial_A)[0], 0 : np.shape(initial_A)[0]] = initial_A # Resize the matrix to fit the number of iterations in `array` new_A = np.resize(new_A, (self.array.shape[1], new_A.shape[0], new_A.shape[1])) return new_A def get_B_matrix(self): """ Load the B matrix. The B matrix contains impact assessment figures for a give impact assessment method, per unit of activity. Its length column-wise equals the length of the A matrix row-wise. Its length row-wise equals the number of impact assessment methods. :param method: only "recipe" and "ilcd" available at the moment. :param level: only "midpoint" available at the moment. :return: an array with impact values per unit of activity for each method. :rtype: numpy.ndarray """ if self.method == "recipe": if self.method_type == "midpoint": list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*recipe_midpoint*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 21, len(self.inputs))) else: list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*recipe_endpoint*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 3, len(self.inputs))) else: list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*ilcd*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 19, len(self.inputs))) for f in list_file_names: initial_B = np.genfromtxt(f, delimiter=";") new_B = np.zeros((np.shape(initial_B)[0], len(self.inputs),)) new_B[0 : np.shape(initial_B)[0], 0 : np.shape(initial_B)[1]] = initial_B B[list_file_names.index(f), :, :] = new_B list_impact_categories = list(self.impact_categories.keys()) if self.scenario != "static": response = xr.DataArray( B, coords=[ [2005, 2010, 2020, 2030, 2040, 2050], list_impact_categories, list(self.inputs.keys()), ], dims=["year", "category", "activity"], ) else: response = xr.DataArray( B, coords=[ [2020], list_impact_categories, list(self.inputs.keys()), ], dims=["year", "category", "activity"], ) return response def get_dict_input(self): """ Load a dictionary with tuple ("name of activity", "location", "unit", "reference product") as key, row/column indices as values. :return: dictionary with `label:index` pairs. :rtype: dict """ filename = "dict_inputs_A_matrix.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError( "The dictionary of activity labels could not be found." ) csv_dict = {} count = 0 with open(filepath) as f: input_dict = csv.reader(f, delimiter=";") for row in input_dict: if "(" in row[1]: new_str = row[1].replace("(", "") new_str = new_str.replace(")", "") new_str = [s.strip() for s in new_str.split(",") if s] t = () for s in new_str: if "low population" in s: s = "low population density, long-term" t += (s,) break else: t += (s.replace("'", ""),) csv_dict[(row[0], t, row[2])] = count else: csv_dict[(row[0], row[1], row[2], row[3])] = count count += 1 return csv_dict def get_dict_impact_categories(self): """ Load a dictionary with available impact assessment methods as keys, and assessment level and categories as values. ..code-block:: python {'recipe': {'midpoint': ['freshwater ecotoxicity', 'human toxicity', 'marine ecotoxicity', 'terrestrial ecotoxicity', 'metal depletion', 'agricultural land occupation', 'climate change', 'fossil depletion', 'freshwater eutrophication', 'ionising radiation', 'marine eutrophication', 'natural land transformation', 'ozone depletion', 'particulate matter formation', 'photochemical oxidant formation', 'terrestrial acidification', 'urban land occupation', 'water depletion', 'human noise', 'primary energy, non-renewable', 'primary energy, renewable'] } } :return: dictionary :rtype: dict """ filename = "dict_impact_categories.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError( "The dictionary of impact categories could not be found." ) csv_dict = {} with open(filepath) as f: input_dict = csv.reader(f, delimiter=";") for row in input_dict: if row[0] == self.method and row[3] == self.method_type: csv_dict[row[2]] = {'method':row[1], 'category':row[2], 'type':row[3], 'abbreviation':row[4], 'unit':row[5], 'source':row[6]} return csv_dict def get_rev_dict_input(self): """ Reverse the self.inputs dictionary. :return: reversed dictionary :rtype: dict """ return {v: k for k, v in self.inputs.items()} def get_index_vehicle_from_array( self, items_to_look_for, items_to_look_for_also=None, method="or" ): """ Return list of row/column indices of self.array of labels that contain the string defined in `items_to_look_for`. :param items_to_look_for: string to search for :return: list """ if not isinstance(items_to_look_for, list): items_to_look_for = [items_to_look_for] if not items_to_look_for_also is None: if not isinstance(items_to_look_for_also, list): items_to_look_for_also = [items_to_look_for_also] list_vehicles = self.array.desired.values.tolist() if method == "or": return [ list_vehicles.index(c) for c in list_vehicles if set(items_to_look_for).intersection(c) ] if method == "and": return [ list_vehicles.index(c) for c in list_vehicles if set(items_to_look_for).intersection(c) and set(items_to_look_for_also).intersection(c) ] def get_index_of_flows(self, items_to_look_for, search_by="name"): """ Return list of row/column indices of self.A of labels that contain the string defined in `items_to_look_for`. :param items_to_look_for: string :param search_by: "name" or "compartment" (for elementary flows) :return: list of row/column indices :rtype: list """ if search_by == "name": return [ int(self.inputs[c]) for c in self.inputs if all(ele in c[0].lower() for ele in items_to_look_for) ] if search_by == "compartment": return [ int(self.inputs[c]) for c in self.inputs if all(ele in c[1] for ele in items_to_look_for) ] def export_lci( self, presamples=True, ecoinvent_compatibility=True, ecoinvent_version="3.6", db_name="carculator db", ): """ Export the inventory as a dictionary. Also return a list of arrays that contain pre-sampled random values if :meth:`stochastic` of :class:`CarModel` class has been called. :param presamples: boolean. :param ecoinvent_compatibility: bool. If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent. :param ecoinvent_version: str. "3.5", "3.6" or "uvek" :return: inventory, and optionally, list of arrays containing pre-sampled values. :rtype: list """ # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) if presamples == True: lci, array = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci(presamples, ecoinvent_compatibility, ecoinvent_version) return (lci, array) else: lci = ExportInventory(self.A, self.rev_inputs, db_name=db_name).write_lci( presamples, ecoinvent_compatibility, ecoinvent_version ) return lci def export_lci_to_bw( self, presamples=True, ecoinvent_compatibility=True, ecoinvent_version="3.6", db_name="carculator db", ): """ Export the inventory as a `brightway2` bw2io.importers.base_lci.LCIImporter object with the inventory in the `data` attribute. .. code-block:: python # get the inventory i, _ = ic.export_lci_to_bw() # import it in a Brightway2 project i.match_database('ecoinvent 3.6 cutoff', fields=('name', 'unit', 'location', 'reference product')) i.match_database("biosphere3", fields=('name', 'unit', 'categories')) i.match_database(fields=('name', 'unit', 'location', 'reference product')) i.match_database(fields=('name', 'unit', 'categories')) # Create an additional biosphere database for the few flows that do not # exist in "biosphere3" i.create_new_biosphere("additional_biosphere", relink=True) # Check if all exchanges link i.statistics() # Register the database i.write_database() :return: LCIImport object that can be directly registered in a `brightway2` project. :rtype: bw2io.importers.base_lci.LCIImporter """ # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) if presamples == True: lci, array = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci_to_bw(presamples, ecoinvent_compatibility, ecoinvent_version) return (lci, array) else: lci = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci_to_bw(presamples, ecoinvent_compatibility, ecoinvent_version) return lci def export_lci_to_excel( self, directory=None, ecoinvent_compatibility=True, ecoinvent_version="3.6", software_compatibility="brightway2", filename=None, ): """ Export the inventory as an Excel file (if the destination software is Brightway2) or a CSV file (if the destination software is Simapro) file. Also return the file path where the file is stored. :param directory: directory where to save the file. :type directory: str :param ecoinvent_compatibility: If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent. :param ecoinvent_version: "3.6", "3.5" or "uvek" :param software_compatibility: "brightway2" or "simapro" :return: file path where the file is stored. :rtype: str """ if software_compatibility not in ("brightway2", "simapro"): raise NameError( "The destination software argument is not valid. Choose between 'brightway2' or 'simapro'." ) # Simapro inventory only for ecoinvent 3.5 or UVEK if software_compatibility == "simapro": if ecoinvent_version == "3.6": print( "Simapro-compatible inventory export is only available for ecoinvent 3.5 or UVEK." ) return ecoinvent_compatibility = True ecoinvent_version = "3.5" # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) fp = ExportInventory( self.A, self.rev_inputs, db_name=filename or "carculator db" ).write_lci_to_excel( directory, ecoinvent_compatibility, ecoinvent_version, software_compatibility, filename, ) return fp def define_electricity_mix_for_fuel_prep(self): """ This function defines a fuel mix based either on user-defined mix, or on default mixes for a given country. The mix is calculated as the average mix, weighted by the distribution of annually driven kilometers. :return: """ try: losses_to_low = float(self.bs.losses[self.country]["LV"]) except KeyError: # If losses for the country are not found, assume EU average losses_to_low = float(self.bs.losses["RER"]["LV"]) if "custom electricity mix" in self.background_configuration: # If a special electricity mix is specified, we use it mix = self.background_configuration["custom electricity mix"] else: use_year = [ int(i) for i in ( self.array.values[ self.array_inputs["lifetime kilometers"], :, self.get_index_vehicle_from_array( [ "BEV", "FCEV", "PHEV-p", "PHEV-d", "ICEV-p", "ICEV-d", "HEV-p", "HEV-d", "ICEV-g", ] ), ] / self.array.values[ self.array_inputs["kilometers per year"], :, self.get_index_vehicle_from_array( [ "BEV", "FCEV", "PHEV-p", "PHEV-d", "ICEV-p", "ICEV-d", "HEV-p", "HEV-d", "ICEV-g", ] ), ] ) .mean(axis=1) .reshape(-1, len(self.scope["year"])) .mean(axis=0) ] mix = [ self.bs.electricity_mix.sel( country=self.country, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp( year=np.arange(y, y + use_year[self.scope["year"].index(y)]), kwargs={"fill_value": "extrapolate"}, ) .mean(axis=0) .values if y + use_year[self.scope["year"].index(y)] <= 2050 else self.bs.electricity_mix.sel( country=self.country, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp(year=np.arange(y, 2051), kwargs={"fill_value": "extrapolate"}) .mean(axis=0) .values for y in self.scope["year"] ] return mix def create_electricity_market_for_fuel_prep(self): """ This function fills the electricity market that supplies battery charging operations and hydrogen production through electrolysis. """ try: losses_to_low = float(self.bs.losses[self.country]["LV"]) except KeyError: # If losses for the country are not found, assume EU average losses_to_low = float(self.bs.losses["RER"]["LV"]) # Fill the electricity markets for battery charging and hydrogen production for y in self.scope["year"]: m = np.array(self.mix[self.scope["year"].index(y)]).reshape(-1, 10, 1) # Add electricity technology shares self.A[ np.ix_( np.arange(self.iterations), [self.inputs[self.elec_map[t]] for t in self.elec_map], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ) ] = (m * -1 * losses_to_low) # Add transmission network for high and medium voltage self.A[ :, self.inputs[ ( "transmission network construction, electricity, high voltage", "CH", "kilometer", "transmission network, electricity, high voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (6.58e-9 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, electricity, medium voltage", "CH", "kilometer", "transmission network, electricity, medium voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (1.86e-8 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, long-distance", "UCTE", "kilometer", "transmission network, long-distance", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (3.17e-10 * -1 * losses_to_low) # Add distribution network, low voltage self.A[ :, self.inputs[ ( "distribution network construction, electricity, low voltage", "CH", "kilometer", "distribution network, electricity, low voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (8.74e-8 * -1 * losses_to_low) # Add supply of sulfur hexafluoride for transformers self.A[ :, self.inputs[ ( "market for sulfur hexafluoride, liquid", "RER", "kilogram", "sulfur hexafluoride, liquid", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) # Add SF_6 leakage self.A[ :, self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) def create_electricity_market_for_battery_production(self): """ This function fills in the column in `self.A` concerned with the electricity mix used for manufacturing battery cells :return: """ battery_tech = self.background_configuration["energy storage"]["electric"][ "type" ] battery_origin = self.background_configuration["energy storage"]["electric"][ "origin" ] try: losses_to_low = float(self.bs.losses[battery_origin]["LV"]) except KeyError: losses_to_low = float(self.bs.losses["CN"]["LV"]) mix_battery_manufacturing = ( self.bs.electricity_mix.sel( country=battery_origin, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"}) .values ) # Fill the electricity markets for battery production for y in self.scope["year"]: m = np.array( mix_battery_manufacturing[self.scope["year"].index(y)] ).reshape(-1, 10, 1) self.A[ np.ix_( np.arange(self.iterations), [self.inputs[self.elec_map[t]] for t in self.elec_map], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ) ] = (m * losses_to_low * -1) # Add transmission network for high and medium voltage self.A[ :, self.inputs[ ( "transmission network construction, electricity, high voltage", "CH", "kilometer", "transmission network, electricity, high voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (6.58e-9 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, electricity, medium voltage", "CH", "kilometer", "transmission network, electricity, medium voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (1.86e-8 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, long-distance", "UCTE", "kilometer", "transmission network, long-distance", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (3.17e-10 * -1 * losses_to_low) # Add distribution network, low voltage self.A[ :, self.inputs[ ( "distribution network construction, electricity, low voltage", "CH", "kilometer", "distribution network, electricity, low voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (8.74e-8 * -1 * losses_to_low) # Add supply of sulfur hexafluoride for transformers self.A[ :, self.inputs[ ( "market for sulfur hexafluoride, liquid", "RER", "kilogram", "sulfur hexafluoride, liquid", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) # Add SF_6 leakage self.A[ :, self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) def set_actual_range(self): """ Set the actual range considering the blend. Liquid bio-fuels and synthetic fuels typically have a lower calorific value. Hence, the need to recalculate the vehicle range. Modifies parameter `range` of `array` in place """ if {"ICEV-p", "HEV-p", "PHEV-p"}.intersection(set(self.scope["powertrain"])): for y in self.scope["year"]: share_primary = self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] lhv_primary = self.fuel_blends["petrol"]["primary"]["lhv"] share_secondary = self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] lhv_secondary = self.fuel_blends["petrol"]["secondary"]["lhv"] index = self.get_index_vehicle_from_array( ["ICEV-p", "HEV-p", "PHEV-p"], y, method="and" ) self.array.values[self.array_inputs["range"], :, index] = ( ( ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_primary * lhv_primary ) + ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_secondary * lhv_secondary ) ) * 1000 / self.array.values[self.array_inputs["TtW energy"], :, index] ) if {"ICEV-d", "HEV-d", "PHEV-d"}.intersection(set(self.scope["powertrain"])): for y in self.scope["year"]: share_primary = self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] lhv_primary = self.fuel_blends["diesel"]["primary"]["lhv"] share_secondary = self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] lhv_secondary = self.fuel_blends["diesel"]["secondary"]["lhv"] index = self.get_index_vehicle_from_array( ["ICEV-d", "PHEV-d", "HEV-d"], y, method="and" ) self.array.values[self.array_inputs["range"], :, index] = ( ( ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_primary * lhv_primary ) + ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_secondary * lhv_secondary ) ) * 1000 / self.array.values[self.array_inputs["TtW energy"], :, index] ) def define_fuel_blends(self): """ This function defines fuel blends from what is passed in `background_configuration`. It populates a dictionary `self.fuel_blends` that contains the respective shares, lower heating values and CO2 emission factors of the fuels used. :return: """ fuels_lhv = { "petrol": 42.4, "bioethanol - wheat straw": 26.8, "bioethanol - maize starch": 26.8, "bioethanol - sugarbeet": 26.8, "bioethanol - forest residues": 26.8, "synthetic gasoline": 42.4, "diesel": 42.8, "biodiesel - cooking oil": 31.7, "biodiesel - algae": 31.7, "synthetic diesel": 43.3, "cng": 55.5, "biogas": 55.5, "syngas": 55.5 } fuels_CO2 = { "petrol": 3.18, "bioethanol - wheat straw": 1.91, "bioethanol - maize starch": 1.91, "bioethanol - sugarbeet": 1.91, "bioethanol - forest residues": 1.91, "synthetic gasoline": 3.18, "diesel": 3.14, "biodiesel - cooking oil": 2.85, "biodiesel - algae": 2.85, "synthetic diesel": 3.16, "cng": 2.65, "biogas": 2.65, "syngas": 2.65 } if {"ICEV-p", "HEV-p", "PHEV-p"}.intersection(set(self.scope["powertrain"])): fuel_type = "petrol" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": { "type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary], }, "secondary": { "type": secondary, "share": secondary_share, "lhv": fuels_lhv[secondary], "CO2": fuels_CO2[secondary], }, } if {"ICEV-d", "HEV-d", "PHEV-d"}.intersection(set(self.scope["powertrain"])): fuel_type = "diesel" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": { "type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary], }, "secondary": { "type": secondary, "share": secondary_share, "lhv": fuels_lhv[secondary], "CO2": fuels_CO2[secondary], }, } if {"ICEV-g"}.intersection(set(self.scope["powertrain"])): fuel_type = "cng" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": {"type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary]}, "secondary": {"type": secondary, "share": secondary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary]}, } if {"FCEV"}.intersection(set(self.scope["powertrain"])): fuel_type = "hydrogen" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": {"type": primary, "share": primary_share}, "secondary": {"type": secondary, "share": secondary_share}, } if {"BEV", "PHEV-p", "PHEV-d"}.intersection(set(self.scope["powertrain"])): fuel_type = "electricity" self.create_fuel_markets(fuel_type) def create_fuel_markets( self, fuel_type, primary=None, secondary=None, primary_share=None, secondary_share=None, ): """ This function creates markets for fuel, considering a given blend, a given fuel type and a given year. It also adds separate electricity input in case hydrogen from electrolysis is needed somewhere in the fuel supply chain. :return: """ d_fuels = { "electrolysis": { "name": ( "Hydrogen, gaseous, 700 bar, from electrolysis, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from electrolysis, at H2 fuelling station", ), "additional electricity": 58, }, "smr - natural gas": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR NG w/o CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR NG w/o CCS, at H2 fuelling station", ), "additional electricity": 0, }, "smr - natural gas with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR NG w CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR NG w CCS, at H2 fuelling station", ), "additional electricity": 0, }, "smr - biogas": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR of biogas, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR of biogas, at H2 fuelling station", ), "additional electricity": 0, }, "smr - biogas with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR of biogas with CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR of biogas with CCS, at H2 fuelling station", ), "additional electricity": 0, }, "coal gasification": { "name": ( "Hydrogen, gaseous, 700 bar, from coal gasification, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from coal gasification, at H2 fuelling station", ), "additional electricity": 0, }, "wood gasification": { "name": ( "Hydrogen, gaseous, 700 bar, from dual fluidised bed gasification of woody biomass, at H2 fuelling station", "CH", "kilogram", "Hydrogen, gaseous, 700 bar", ), "additional electricity": 0, }, "wood gasification with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from dual fluidised bed gasification of woody biomass with CCS, at H2 fuelling station", "CH", "kilogram", "Hydrogen, gaseous, 700 bar", ), "additional electricity": 0, }, "cng": { "name": ( "market for natural gas, from high pressure network (1-5 bar), at service station", "GLO", "kilogram", "natural gas, from high pressure network (1-5 bar), at service station", ), "additional electricity": 0, }, "biogas": { "name": ( "biogas upgrading - sewage sludge - amine scrubbing - best", "CH", "kilogram", "biogas upgrading - sewage sludge - amine scrubbing - best", ), "additional electricity": 0, }, "syngas": { "name": ( "Methane production, synthetic, from electrochemical methanation", "RER", "kilogram", "Methane, synthetic", ), "additional electricity": 58 * 0.50779661, }, "diesel": { "name": ( "market for diesel", "Europe without Switzerland", "kilogram", "diesel", ), "additional electricity": 0, }, "biodiesel - algae": { "name": ( "Biodiesel from algae", "RER", "kilogram", "Biodiesel from algae", ), "additional electricity": 0, }, "biodiesel - cooking oil": { "name": ( "Biodiesel from cooking oil", "RER", "kilogram", "Biodiesel from cooking oil", ), "additional electricity": 0, }, "synthetic diesel": { "name": ( "Diesel production, synthetic, Fischer Tropsch process", "RER", "kilogram", "Diesel, synthetic", ), "additional electricity": 58 * 0.2875, }, "petrol": { "name": ( "market for petrol, low-sulfur", "Europe without Switzerland", "kilogram", "petrol, low-sulfur", ), "additional electricity": 0, }, "bioethanol - wheat straw": { "name": ( "Ethanol from wheat straw pellets", "RER", "kilogram", "Ethanol from wheat straw pellets", ), "additional electricity": 0, }, "bioethanol - forest residues": { "name": ( "Ethanol from forest residues", "RER", "kilogram", "Ethanol from forest residues", ), "additional electricity": 0, }, "bioethanol - sugarbeet": { "name": ( "Ethanol from sugarbeet", "RER", "kilogram", "Ethanol from sugarbeet", ), "additional electricity": 0, }, "bioethanol - maize starch": { "name": ( "Ethanol from maize starch", "RER", "kilogram", "Ethanol from maize starch", ), "additional electricity": 0, }, "synthetic gasoline": { "name": ( "Gasoline production, synthetic, from methanol", "RER", "kilogram", "Gasoline, synthetic", ), "additional electricity": 58 * 0.328, }, } d_dataset_name = { "petrol": "fuel supply for gasoline vehicles, ", "diesel": "fuel supply for diesel vehicles, ", "cng": "fuel supply for gas vehicles, ", "hydrogen": "fuel supply for hydrogen vehicles, ", "electricity": "electricity supply for electric vehicles, ", } if fuel_type != "electricity": for y in self.scope["year"]: dataset_name = d_dataset_name[fuel_type] + str(y) fuel_market_index = [ self.inputs[i] for i in self.inputs if i[0] == dataset_name ][0] primary_fuel_activity_index = self.inputs[d_fuels[primary]["name"]] secondary_fuel_activity_index = self.inputs[d_fuels[secondary]["name"]] self.A[:, primary_fuel_activity_index, fuel_market_index] = ( -1 * primary_share[self.scope["year"].index(y)] ) self.A[:, secondary_fuel_activity_index, fuel_market_index] = ( -1 * secondary_share[self.scope["year"].index(y)] ) additional_electricity = ( d_fuels[primary]["additional electricity"] * primary_share[self.scope["year"].index(y)] ) + ( d_fuels[secondary]["additional electricity"] * secondary_share[self.scope["year"].index(y)] ) if additional_electricity > 0: electricity_mix_index = [ self.inputs[i] for i in self.inputs if i[0] == "electricity market for fuel preparation, " + str(y) ][0] self.A[:, electricity_mix_index, fuel_market_index] = ( -1 * additional_electricity ) else: for y in self.scope["year"]: dataset_name = d_dataset_name[fuel_type] + str(y) electricity_market_index = [ self.inputs[i] for i in self.inputs if i[0] == dataset_name ][0] electricity_mix_index = [ self.inputs[i] for i in self.inputs if i[0] == "electricity market for fuel preparation, " + str(y) ][0] self.A[:, electricity_mix_index, electricity_market_index] = -1 def set_inputs_in_A_matrix(self, array): """ Fill-in the A matrix. Does not return anything. Modifies in place. Shape of the A matrix (values, products, activities). :param array: :attr:`array` from :class:`CarModel` class """ # Glider self.A[ :, self.inputs[ ( "market for glider, passenger car", "GLO", "kilogram", "glider, passenger car", ) ], -self.number_of_cars :, ] = ( (array[self.array_inputs["glider base mass"], :]) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ("Glider lightweighting", "GLO", "kilogram", "Glider lightweighting") ], -self.number_of_cars :, ] = ( ( array[self.array_inputs["lightweighting"], :] * array[self.array_inputs["glider base mass"], :] ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "maintenance, passenger car", "RER", "unit", "passenger car maintenance", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["curb mass"], :] / 1240 / 150000 * -1) # Glider EoL self.A[ :, self.inputs[ ( "market for manual dismantling of used electric passenger car", "GLO", "unit", "manual dismantling of used electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["curb mass"], :] * (1 - array[self.array_inputs["combustion power share"], :]) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for manual dismantling of used passenger car with internal combustion engine", "GLO", "unit", "manual dismantling of used passenger car with internal combustion engine", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["curb mass"], :] * array[self.array_inputs["combustion power share"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Powertrain components self.A[ :, self.inputs[ ( "market for charger, electric passenger car", "GLO", "kilogram", "charger, electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["charger mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for converter, for electric passenger car", "GLO", "kilogram", "converter, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["converter mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for electric motor, electric passenger car", "GLO", "kilogram", "electric motor, electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["electric engine mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for inverter, for electric passenger car", "GLO", "kilogram", "inverter, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["inverter mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for power distribution unit, for electric passenger car", "GLO", "kilogram", "power distribution unit, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["power distribution unit mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) l_elec_pt = [ "charger mass", "converter mass", "inverter mass", "power distribution unit mass", "electric engine mass", "fuel cell stack mass", "fuel cell ancillary BoP mass", "fuel cell essential BoP mass", "battery cell mass", "battery BoP mass", ] self.A[ :, self.inputs[ ( "market for used powertrain from electric passenger car, manual dismantling", "GLO", "kilogram", "used powertrain from electric passenger car, manual dismantling", ) ], -self.number_of_cars :, ] = ( array[[self.array_inputs[l] for l in l_elec_pt], :].sum(axis=0) / array[self.array_inputs["lifetime kilometers"], :] ) self.A[ :, self.inputs[ ( "market for internal combustion engine, passenger car", "GLO", "kilogram", "internal combustion engine, for passenger car", ) ], -self.number_of_cars :, ] = ( ( array[ [ self.array_inputs[l] for l in ["combustion engine mass", "powertrain mass"] ], :, ].sum(axis=0) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Ancillary BoP", "GLO", "kilogram", "Ancillary BoP")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell ancillary BoP mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Essential BoP", "GLO", "kilogram", "Essential BoP")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell essential BoP mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Stack", "GLO", "kilowatt", "Stack")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell stack mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Start of printout print( "****************** IMPORTANT BACKGROUND PARAMETERS ******************", end="\n * ", ) # Energy storage print( "The country of use is " + self.country, end="\n * ", ) battery_tech = self.background_configuration["energy storage"]["electric"][ "type" ] battery_origin = self.background_configuration["energy storage"]["electric"][ "origin" ] print( "Power and energy batteries produced in " + battery_origin + " using " + battery_tech + " chemistry.", end="\n * ", ) # Use the NMC inventory of Schmidt et al. 2019 self.A[ :, self.inputs[("Battery BoP", "GLO", "kilogram", "Battery BoP")], -self.number_of_cars :, ] = ( ( array[self.array_inputs["battery BoP mass"], :] * (1 + array[self.array_inputs["battery lifetime replacements"], :]) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) battery_cell_label = ( "Battery cell, " + battery_tech, "GLO", "kilogram", "Battery cell", ) self.A[:, self.inputs[battery_cell_label], -self.number_of_cars :,] = ( ( array[self.array_inputs["battery cell mass"], :] * (1 + array[self.array_inputs["fuel cell lifetime replacements"], :]) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Set an input of electricity, given the country of manufacture self.A[ :, self.inputs[ ( "market group for electricity, medium voltage", "World", "kilowatt hour", "electricity, medium voltage", ) ], self.inputs[battery_cell_label], ] = 0 for y in self.scope["year"]: index = self.get_index_vehicle_from_array(y) self.A[ np.ix_( np.arange(self.iterations), [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] ], ) ] = ( array[ self.array_inputs["battery cell production electricity"], :, index ].T * self.A[ :, self.inputs[battery_cell_label], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] ], ] ).reshape( self.iterations, 1, -1 ) index_A = [ self.inputs[c] for c in self.inputs if any( ele in c[0] for ele in ["ICEV-d", "ICEV-p", "HEV-p", "PHEV-p", "PHEV-d", "HEV-d"] ) ] index = self.get_index_vehicle_from_array( ["ICEV-d", "ICEV-p", "HEV-p", "PHEV-p", "PHEV-d", "HEV-d"] ) self.A[ :, self.inputs[ ( "polyethylene production, high density, granulate", "RER", "kilogram", "polyethylene, high density, granulate", ) ], index_A, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T index = self.get_index_vehicle_from_array("ICEV-g") self.A[ :, self.inputs[ ( "glass fibre reinforced plastic production, polyamide, injection moulded", "RER", "kilogram", "glass fibre reinforced plastic, polyamide, injection moulded", ) ], self.index_cng, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T if "hydrogen" in self.background_configuration["energy storage"]: # If a customization dict is passed hydro_tank_technology = self.background_configuration["energy storage"][ "hydrogen" ]["type"] else: hydro_tank_technology = "carbon fiber" dict_tank_map = { "carbon fiber": ( "Fuel tank, compressed hydrogen gas, 700bar", "GLO", "kilogram", "Fuel tank, compressed hydrogen gas, 700bar", ), "hdpe": ( "Fuel tank, compressed hydrogen gas, 700bar, with HDPE liner", "RER", "kilogram", "Hydrogen tank", ), "aluminium": ( "Fuel tank, compressed hydrogen gas, 700bar, with aluminium liner", "RER", "kilogram", "Hydrogen tank", ), } index = self.get_index_vehicle_from_array("FCEV") self.A[ :, self.inputs[dict_tank_map[hydro_tank_technology]], self.index_fuel_cell, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T for y in self.scope["year"]: sum_renew, co2_intensity_tech = self.define_renewable_rate_in_mix() if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + ", % of renewable: " + str(np.round(sum_renew * 100, 0)) + "%" + ", GHG intensity per kWh: " + str( int( np.sum( co2_intensity_tech * self.mix[self.scope["year"].index(y)] ) ) ) + " g. CO2-eq.", end=end_str, ) if any( True for x in ["BEV", "PHEV-p", "PHEV-d"] if x in self.scope["powertrain"] ): for y in self.scope["year"]: index = self.get_index_vehicle_from_array( ["BEV", "PHEV-p", "PHEV-d"], y, method="and" ) self.A[ np.ix_( np.arange(self.iterations), [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity supply for electric vehicles" in i[0] ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any( True for x in ["BEV", "PHEV-p", "PHEV-d"] if x in i[0] ) ], ) ] = ( array[self.array_inputs["electricity consumption"], :, index] * -1 ).T.reshape( self.iterations, 1, -1 ) if "FCEV" in self.scope["powertrain"]: index = self.get_index_vehicle_from_array("FCEV") print( "{} is completed by {}.".format( self.fuel_blends["hydrogen"]["primary"]["type"], self.fuel_blends["hydrogen"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["hydrogen"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) # Primary fuel share for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and "FCEV" in i[0] ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for hydrogen vehicles" in i[0] ], ind_A, ] = ( array[self.array_inputs["fuel mass"], :, ind_array] / array[self.array_inputs["range"], :, ind_array] * -1 ).T if "ICEV-g" in self.scope["powertrain"]: index = self.get_index_vehicle_from_array("ICEV-g") print( "{} is completed by {}.".format( self.fuel_blends["cng"]["primary"]["type"], self.fuel_blends["cng"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) # Primary fuel share for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and "ICEV-g" in i[0] ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for gas vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Fuel-based emissions from CNG, CO2 # The share and CO2 emissions factor of CNG is retrieved, if used share_fossil = 0 CO2_fossil = 0 if self.fuel_blends["cng"]["primary"]["type"] == "cng": share_fossil += self.fuel_blends["cng"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["cng"]["primary"]["CO2"] if self.fuel_blends["cng"]["secondary"]["type"] == "cng": share_fossil += self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["cng"]["primary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Fuel-based CO2 emission from alternative petrol # The share of non-fossil gas in the blend is retrieved # As well as the CO2 emission factor of the fuel share_non_fossil = 0 CO2_non_fossil = 0 if self.fuel_blends["cng"]["primary"]["type"] != "cng": share_non_fossil += self.fuel_blends["cng"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["cng"]["primary"]["CO2"] if self.fuel_blends["cng"]["secondary"]["type"] != "cng": share_non_fossil += self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["cng"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T if [i for i in self.scope["powertrain"] if i in ["ICEV-d", "PHEV-d", "HEV-d"]]: index = self.get_index_vehicle_from_array(["ICEV-d", "PHEV-d", "HEV-d"]) print( "{} is completed by {}.".format( self.fuel_blends["diesel"]["primary"]["type"], self.fuel_blends["diesel"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any(x in i[0] for x in ["ICEV-d", "PHEV-d", "HEV-d"]) ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] # Fuel supply self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for diesel vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_fossil = 0 CO2_fossil = 0 # Fuel-based CO2 emission from conventional petrol if self.fuel_blends["diesel"]["primary"]["type"] == "diesel": share_fossil += self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["diesel"]["primary"]["CO2"] if self.fuel_blends["diesel"]["secondary"]["type"] == "diesel": share_fossil += self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["diesel"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_non_fossil = 0 CO2_non_fossil = 0 # Fuel-based CO2 emission from alternative petrol # The share of non-fossil fuel in the blend is retrieved # As well as the CO2 emission factor of the fuel if self.fuel_blends["diesel"]["primary"]["type"] != "diesel": share_non_fossil += self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["diesel"]["primary"]["CO2"] if self.fuel_blends["diesel"]["secondary"]["type"] != "diesel": share_non_fossil += self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["diesel"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Heavy metals emissions from conventional diesel # Emission factors from Spielmann et al., Transport Services Data v.2 (2007) # Cadmium, 0.01 mg/kg diesel self.A[ :, self.inputs[ ("Cadmium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Copper, 1.7 mg/kg diesel self.A[ :, self.inputs[ ("Copper", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.7e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium, 0.05 mg/kg diesel self.A[ :, self.inputs[ ("Chromium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 5.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Nickel, 0.07 mg/kg diesel self.A[ :, self.inputs[ ("Nickel", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 7.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Selenium, 0.01 mg/kg diesel self.A[ :, self.inputs[ ("Selenium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Zinc, 1 mg/kg diesel self.A[ :, self.inputs[ ("Zinc", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium VI, 0.0001 mg/kg diesel self.A[ :, self.inputs[ ( "Chromium VI", ("air", "urban air close to ground"), "kilogram", ) ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-10 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T if [i for i in self.scope["powertrain"] if i in ["ICEV-p", "HEV-p", "PHEV-p"]]: index = self.get_index_vehicle_from_array(["ICEV-p", "HEV-p", "PHEV-p"]) print( "{} is completed by {}.".format( self.fuel_blends["petrol"]["primary"]["type"], self.fuel_blends["petrol"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any(x in i[0] for x in ["ICEV-p", "HEV-p", "PHEV-p"]) ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] # Fuel supply self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for gasoline vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_fossil = 0 CO2_fossil = 0 # Fuel-based CO2 emission from conventional petrol if self.fuel_blends["petrol"]["primary"]["type"] == "petrol": share_fossil += self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["petrol"]["primary"]["CO2"] if self.fuel_blends["petrol"]["secondary"]["type"] == "petrol": share_fossil += self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["petrol"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_non_fossil = 0 CO2_non_fossil = 0 # Fuel-based CO2 emission from alternative petrol # The share of non-fossil fuel in the blend is retrieved # As well as the CO2 emission factor of the fuel if self.fuel_blends["petrol"]["primary"]["type"] != "petrol": share_non_fossil += self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["petrol"]["primary"]["CO2"] if self.fuel_blends["petrol"]["secondary"]["type"] != "petrol": share_non_fossil += self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["petrol"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Heavy metals emissions from conventional petrol # Cadmium, 0.01 mg/kg gasoline self.A[ :, self.inputs[ ("Cadmium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Copper, 1.7 mg/kg gasoline self.A[ :, self.inputs[ ("Copper", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.7e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium, 0.05 mg/kg gasoline self.A[ :, self.inputs[ ("Chromium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 5.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Nickel, 0.07 mg/kg gasoline self.A[ :, self.inputs[ ("Nickel", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 7.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Selenium, 0.01 mg/kg gasoline self.A[ :, self.inputs[ ("Selenium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Zinc, 1 mg/kg gasoline self.A[ :, self.inputs[ ("Zinc", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium VI, 0.0001 mg/kg gasoline self.A[ :, self.inputs[ ( "Chromium VI", ("air", "urban air close to ground"), "kilogram", ) ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-10 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Non-exhaust emissions self.A[ :, self.inputs[ ( "market for road wear emissions, passenger car", "GLO", "kilogram", "road wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 1e-08) self.A[ :, self.inputs[ ( "market for tyre wear emissions, passenger car", "GLO", "kilogram", "tyre wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 6e-08) self.A[ :, self.inputs[ ( "market for brake wear emissions, passenger car", "GLO", "kilogram", "brake wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 5e-09) # Infrastructure self.A[ :, self.inputs[("market for road", "GLO", "meter-year", "road")], -self.number_of_cars :, ] = (5.37e-7 * array[self.array_inputs["driving mass"], :] * -1) # Infrastructure maintenance self.A[ :, self.inputs[ ("market for road maintenance", "RER", "meter-year", "road maintenance") ], -self.number_of_cars :, ] = (1.29e-3 * -1) # Exhaust emissions # Non-fuel based emissions self.A[:, self.index_emissions, -self.number_of_cars :] = ( array[ [ self.array_inputs[self.map_non_fuel_emissions[self.rev_inputs[x]]] for x in self.index_emissions ] ] * -1 ).transpose([1, 0, 2]) # Noise emissions self.A[:, self.index_noise, -self.number_of_cars :] = ( array[ [ self.array_inputs[self.map_noise_emissions[self.rev_inputs[x]]] for x in self.index_noise ] ] * -1 ).transpose([1, 0, 2]) print("*********************************************************************")

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from . import DATA_DIR import sys import glob from .background_systems import BackgroundSystemModel from .export import ExportInventory from inspect import currentframe, getframeinfo from pathlib import Path from scipy import sparse import csv import itertools import numexpr as ne import numpy as np import xarray as xr REMIND_FILES_DIR = DATA_DIR / "IAM" class InventoryCalculation: """ Build and solve the inventory for results characterization and inventory export Vehicles to be analyzed can be filtered by passing a `scope` dictionary. Some assumptions in the background system can also be adjusted by passing a `background_configuration` dictionary. .. code-block:: python scope = { 'powertrain':['BEV', 'FCEV', 'ICEV-p'], } bc = {'country':'CH', # considers electricity network losses for Switzerland 'custom electricity mix' : [[1,0,0,0,0,0,0,0,0,0], # in this case, 100% hydropower for the first year [0,1,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0], ], # in this case, 100% nuclear for the second year 'fuel blend':{ 'cng':{ #specify fuel bland for compressed gas 'primary fuel':{ 'type':'biogas', 'share':[0.9, 0.8, 0.7, 0.6] # shares per year. Must total 1 for each year. }, 'secondary fuel':{ 'type':'syngas', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'diesel':{ 'primary fuel':{ 'type':'synthetic diesel', 'share':[0.9, 0.8, 0.7, 0.6] }, 'secondary fuel':{ 'type':'biodiesel - cooking oil', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'petrol':{ 'primary fuel':{ 'type':'petrol', 'share':[0.9, 0.8, 0.7, 0.6] }, 'secondary fuel':{ 'type':'bioethanol - wheat straw', 'share': [0.1, 0.2, 0.3, 0.4] } }, 'hydrogen':{ 'primary fuel':{'type':'electrolysis', 'share':[1, 0, 0, 0]}, 'secondary fuel':{'type':'smr - natural gas', 'share':[0, 1, 1, 1]} } }, 'energy storage': { 'electric': { 'type':'NMC', 'origin': 'NO' }, 'hydrogen': { 'type':'carbon fiber' } } } InventoryCalculation(CarModel.array, background_configuration=background_configuration, scope=scope, scenario="RCP26") The `custom electricity mix` key in the background_configuration dictionary defines an electricity mix to apply, under the form of one or several array(s), depending on teh number of years to analyze, that should total 1, of which the indices correspond to: - [0]: hydro-power - [1]: nuclear - [2]: natural gas - [3]: solar power - [4]: wind power - [5]: biomass - [6]: coal - [7]: oil - [8]: geothermal - [9]: waste incineration If none is given, the electricity mix corresponding to the country specified in `country` will be selected. If no country is specified, Europe applies. The `primary` and `secondary` fuel keys contain an array with shares of alternative petrol fuel for each year, to create a custom blend. If none is provided, a blend provided by the Integrated Assessment model REMIND is used, which will depend on the REMIND energy scenario selected. Here is a list of available fuel pathways: Hydrogen technologies -------------------- electrolysis smr - natural gas smr - natural gas with CCS smr - biogas smr - biogas with CCS coal gasification wood gasification wood gasification with CCS Natural gas technologies ------------------------ cng biogas syngas Diesel technologies ------------------- diesel biodiesel - algae biodiesel - cooking oil synthetic diesel Petrol technologies ------------------- petrol bioethanol - wheat straw bioethanol - maize starch bioethanol - sugarbeet bioethanol - forest residues synthetic gasoline :ivar array: array from the CarModel class :vartype array: CarModel.array :ivar scope: dictionary that contains filters for narrowing the analysis :ivar background_configuration: dictionary that contains choices for background system :ivar scenario: REMIND energy scenario to use ("SSP2-Baseline": business-as-usual, "SSP2-PkBudg1100": limits cumulative GHG emissions to 1,100 gigatons by 2100, "static": no forward-looking modification of the background inventories). "SSP2-Baseline" selected by default. .. code-block:: python """ def __init__( self, array, scope=None, background_configuration=None, scenario="SSP2-Base", method="recipe", method_type="midpoint" ): if scope is None: scope = {} scope["size"] = array.coords["size"].values.tolist() scope["powertrain"] = array.coords["powertrain"].values.tolist() scope["year"] = array.coords["year"].values.tolist() else: scope["size"] = scope.get("size", array.coords["size"].values.tolist()) scope["powertrain"] = scope.get( "powertrain", array.coords["powertrain"].values.tolist() ) scope["year"] = scope.get("year", array.coords["year"].values.tolist()) self.scope = scope self.scenario = scenario array = array.sel( powertrain=self.scope["powertrain"], year=self.scope["year"], size=self.scope["size"], ) self.array = array.stack(desired=["size", "powertrain", "year"]) self.iterations = len(array.value.values) self.number_of_cars = ( len(self.scope["size"]) * len(self.scope["powertrain"]) * len(self.scope["year"]) ) self.array_inputs = { x: i for i, x in enumerate(list(self.array.parameter.values), 0) } self.array_powertrains = { x: i for i, x in enumerate(list(self.array.powertrain.values), 0) } if not background_configuration is None: self.background_configuration = background_configuration else: self.background_configuration = {} if "energy storage" not in self.background_configuration: self.background_configuration["energy storage"] = { "electric": {"type": "NMC", "origin": "CN"} } else: if "electric" not in self.background_configuration["energy storage"]: self.background_configuration["energy storage"]["electric"] = { "type": "NMC", "origin": "CN", } else: if ( "origin" not in self.background_configuration["energy storage"]["electric"] ): self.background_configuration["energy storage"]["electric"][ "origin" ] = "CN" if ( "type" not in self.background_configuration["energy storage"]["electric"] ): self.background_configuration["energy storage"]["electric"][ "type" ] = "NMC" self.inputs = self.get_dict_input() self.bs = BackgroundSystemModel() self.country = self.get_country_of_use() self.add_additional_activities() self.rev_inputs = self.get_rev_dict_input() self.A = self.get_A_matrix() self.mix = self.define_electricity_mix_for_fuel_prep() self.fuel_blends = {} self.define_fuel_blends() self.set_actual_range() self.index_cng = [self.inputs[i] for i in self.inputs if "ICEV-g" in i[0]] self.index_combustion_wo_cng = [ self.inputs[i] for i in self.inputs if any( ele in i[0] for ele in ["ICEV-p", "HEV-p", "PHEV-p", "ICEV-d", "PHEV-d", "HEV-d"] ) ] self.index_diesel = [self.inputs[i] for i in self.inputs if "ICEV-d" in i[0]] self.index_all_petrol = [ self.inputs[i] for i in self.inputs if any(ele in i[0] for ele in ["ICEV-p", "HEV-p", "PHEV-p"]) ] self.index_petrol = [self.inputs[i] for i in self.inputs if "ICEV-p" in i[0]] self.index_hybrid = [ self.inputs[i] for i in self.inputs if any(ele in i[0] for ele in ["HEV-p", "HEV-d"]) ] self.index_plugin_hybrid = [ self.inputs[i] for i in self.inputs if "PHEV" in i[0] ] self.index_fuel_cell = [self.inputs[i] for i in self.inputs if "FCEV" in i[0]] self.map_non_fuel_emissions = { ( "Methane, fossil", ("air", "non-urban air or from high stacks"), "kilogram", ): "Methane direct emissions, suburban", ( "Methane, fossil", ("air", "low population density, long-term"), "kilogram", ): "Methane direct emissions, rural", ( "Lead", ("air", "non-urban air or from high stacks"), "kilogram", ): "Lead direct emissions, suburban", ( "Ammonia", ("air", "non-urban air or from high stacks"), "kilogram", ): "Ammonia direct emissions, suburban", ( "NMVOC, non-methane volatile organic compounds, unspecified origin", ("air", "urban air close to ground"), "kilogram", ): "NMVOC direct emissions, urban", ( "PAH, polycyclic aromatic hydrocarbons", ("air", "urban air close to ground"), "kilogram", ): "Hydrocarbons direct emissions, urban", ( "Dinitrogen monoxide", ("air", "low population density, long-term"), "kilogram", ): "Dinitrogen oxide direct emissions, rural", ( "Nitrogen oxides", ("air", "urban air close to ground"), "kilogram", ): "Nitrogen oxides direct emissions, urban", ( "Ammonia", ("air", "urban air close to ground"), "kilogram", ): "Ammonia direct emissions, urban", ( "Particulates, < 2.5 um", ("air", "non-urban air or from high stacks"), "kilogram", ): "Particulate matters direct emissions, suburban", ( "Carbon monoxide, fossil", ("air", "urban air close to ground"), "kilogram", ): "Carbon monoxide direct emissions, urban", ( "Nitrogen oxides", ("air", "low population density, long-term"), "kilogram", ): "Nitrogen oxides direct emissions, rural", ( "NMVOC, non-methane volatile organic compounds, unspecified origin", ("air", "non-urban air or from high stacks"), "kilogram", ): "NMVOC direct emissions, suburban", ( "Benzene", ("air", "non-urban air or from high stacks"), "kilogram", ): "Benzene direct emissions, suburban", ( "Ammonia", ("air", "low population density, long-term"), "kilogram", ): "Ammonia direct emissions, rural", ( "Sulfur dioxide", ("air", "low population density, long-term"), "kilogram", ): "Sulfur dioxide direct emissions, rural", ( "NMVOC, non-methane volatile organic compounds, unspecified origin", ("air", "low population density, long-term"), "kilogram", ): "NMVOC direct emissions, rural", ( "Particulates, < 2.5 um", ("air", "urban air close to ground"), "kilogram", ): "Particulate matters direct emissions, urban", ( "Sulfur dioxide", ("air", "urban air close to ground"), "kilogram", ): "Sulfur dioxide direct emissions, urban", ( "Dinitrogen monoxide", ("air", "non-urban air or from high stacks"), "kilogram", ): "Dinitrogen oxide direct emissions, suburban", ( "Carbon monoxide, fossil", ("air", "low population density, long-term"), "kilogram", ): "Carbon monoxide direct emissions, rural", ( "Methane, fossil", ("air", "urban air close to ground"), "kilogram", ): "Methane direct emissions, urban", ( "Carbon monoxide, fossil", ("air", "non-urban air or from high stacks"), "kilogram", ): "Carbon monoxide direct emissions, suburban", ( "Lead", ("air", "urban air close to ground"), "kilogram", ): "Lead direct emissions, urban", ( "Particulates, < 2.5 um", ("air", "low population density, long-term"), "kilogram", ): "Particulate matters direct emissions, rural", ( "Sulfur dioxide", ("air", "non-urban air or from high stacks"), "kilogram", ): "Sulfur dioxide direct emissions, suburban", ( "Benzene", ("air", "low population density, long-term"), "kilogram", ): "Benzene direct emissions, rural", ( "Nitrogen oxides", ("air", "non-urban air or from high stacks"), "kilogram", ): "Nitrogen oxides direct emissions, suburban", ( "Lead", ("air", "low population density, long-term"), "kilogram", ): "Lead direct emissions, rural", ( "Benzene", ("air", "urban air close to ground"), "kilogram", ): "Benzene direct emissions, urban", ( "PAH, polycyclic aromatic hydrocarbons", ("air", "low population density, long-term"), "kilogram", ): "Hydrocarbons direct emissions, rural", ( "PAH, polycyclic aromatic hydrocarbons", ("air", "non-urban air or from high stacks"), "kilogram", ): "Hydrocarbons direct emissions, suburban", ( "Dinitrogen monoxide", ("air", "urban air close to ground"), "kilogram", ): "Dinitrogen oxide direct emissions, urban", } self.index_emissions = [ self.inputs[i] for i in self.map_non_fuel_emissions.keys() ] self.map_noise_emissions = { ( "noise, octave 1, day time, urban", ("octave 1", "day time", "urban"), "joule", ): "noise, octave 1, day time, urban", ( "noise, octave 2, day time, urban", ("octave 2", "day time", "urban"), "joule", ): "noise, octave 2, day time, urban", ( "noise, octave 3, day time, urban", ("octave 3", "day time", "urban"), "joule", ): "noise, octave 3, day time, urban", ( "noise, octave 4, day time, urban", ("octave 4", "day time", "urban"), "joule", ): "noise, octave 4, day time, urban", ( "noise, octave 5, day time, urban", ("octave 5", "day time", "urban"), "joule", ): "noise, octave 5, day time, urban", ( "noise, octave 6, day time, urban", ("octave 6", "day time", "urban"), "joule", ): "noise, octave 6, day time, urban", ( "noise, octave 7, day time, urban", ("octave 7", "day time", "urban"), "joule", ): "noise, octave 7, day time, urban", ( "noise, octave 8, day time, urban", ("octave 8", "day time", "urban"), "joule", ): "noise, octave 8, day time, urban", ( "noise, octave 1, day time, suburban", ("octave 1", "day time", "suburban"), "joule", ): "noise, octave 1, day time, suburban", ( "noise, octave 2, day time, suburban", ("octave 2", "day time", "suburban"), "joule", ): "noise, octave 2, day time, suburban", ( "noise, octave 3, day time, suburban", ("octave 3", "day time", "suburban"), "joule", ): "noise, octave 3, day time, suburban", ( "noise, octave 4, day time, suburban", ("octave 4", "day time", "suburban"), "joule", ): "noise, octave 4, day time, suburban", ( "noise, octave 5, day time, suburban", ("octave 5", "day time", "suburban"), "joule", ): "noise, octave 5, day time, suburban", ( "noise, octave 6, day time, suburban", ("octave 6", "day time", "suburban"), "joule", ): "noise, octave 6, day time, suburban", ( "noise, octave 7, day time, suburban", ("octave 7", "day time", "suburban"), "joule", ): "noise, octave 7, day time, suburban", ( "noise, octave 8, day time, suburban", ("octave 8", "day time", "suburban"), "joule", ): "noise, octave 8, day time, suburban", ( "noise, octave 1, day time, rural", ("octave 1", "day time", "rural"), "joule", ): "noise, octave 1, day time, rural", ( "noise, octave 2, day time, rural", ("octave 2", "day time", "rural"), "joule", ): "noise, octave 2, day time, rural", ( "noise, octave 3, day time, rural", ("octave 3", "day time", "rural"), "joule", ): "noise, octave 3, day time, rural", ( "noise, octave 4, day time, rural", ("octave 4", "day time", "rural"), "joule", ): "noise, octave 4, day time, rural", ( "noise, octave 5, day time, rural", ("octave 5", "day time", "rural"), "joule", ): "noise, octave 5, day time, rural", ( "noise, octave 6, day time, rural", ("octave 6", "day time", "rural"), "joule", ): "noise, octave 6, day time, rural", ( "noise, octave 7, day time, rural", ("octave 7", "day time", "rural"), "joule", ): "noise, octave 7, day time, rural", ( "noise, octave 8, day time, rural", ("octave 8", "day time", "rural"), "joule", ): "noise, octave 8, day time, rural", } self.elec_map = { "Hydro": ( "electricity production, hydro, run-of-river", "DE", "kilowatt hour", "electricity, high voltage", ), "Nuclear": ( "electricity production, nuclear, pressure water reactor", "DE", "kilowatt hour", "electricity, high voltage", ), "Gas": ( "electricity production, natural gas, conventional power plant", "DE", "kilowatt hour", "electricity, high voltage", ), "Solar": ( "electricity production, photovoltaic, 3kWp slanted-roof installation, multi-Si, panel, mounted", "DE", "kilowatt hour", "electricity, low voltage", ), "Wind": ( "electricity production, wind, 1-3MW turbine, onshore", "DE", "kilowatt hour", "electricity, high voltage", ), "Biomass": ( "heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014", "DE", "kilowatt hour", "electricity, high voltage", ), "Coal": ( "electricity production, hard coal", "DE", "kilowatt hour", "electricity, high voltage", ), "Oil": ( "electricity production, oil", "DE", "kilowatt hour", "electricity, high voltage", ), "Geo": ( "electricity production, deep geothermal", "DE", "kilowatt hour", "electricity, high voltage", ), "Waste": ( "treatment of municipal solid waste, incineration", "DE", "kilowatt hour", "electricity, for reuse in municipal waste incineration only", ), } self.index_noise = [self.inputs[i] for i in self.map_noise_emissions.keys()] self.list_cat, self.split_indices = self.get_split_indices() self.method = method if self.method == "recipe": self.method_type = method_type else: self.method_type = "midpoint" self.impact_categories = self.get_dict_impact_categories() # Load the B matrix self.B = self.get_B_matrix() def __getitem__(self, key): """ Make class['foo'] automatically filter for the parameter 'foo' Makes the model code much cleaner :param key: Parameter name :type key: str :return: `array` filtered after the parameter selected """ return self.temp_array.sel(parameter=key) def get_results_table(self, split, sensitivity=False): """ Format an xarray.DataArray array to receive the results. :param split: "components" or "impact categories". Split by impact categories only applicable when "endpoint" level is applied. :return: xarrray.DataArray """ if split == "components": cat = [ "direct - exhaust", "direct - non-exhaust", "energy chain", "maintenance", "glider", "EoL", "powertrain", "energy storage", "road", ] dict_impact_cat = list(self.impact_categories.keys()) if sensitivity == False: response = xr.DataArray( np.zeros( ( self.B.shape[1], len(self.scope["size"]), len(self.scope["powertrain"]), len(self.scope["year"]), len(cat), self.iterations, ) ), coords=[ dict_impact_cat, self.scope["size"], self.scope["powertrain"], self.scope["year"], cat, np.arange(0, self.iterations), ], dims=[ "impact_category", "size", "powertrain", "year", "impact", "value", ], ) else: params = [a for a in self.array.value.values] response = xr.DataArray( np.zeros( ( self.B.shape[1], len(self.scope["size"]), len(self.scope["powertrain"]), len(self.scope["year"]), self.iterations, ) ), coords=[ dict_impact_cat, self.scope["size"], self.scope["powertrain"], self.scope["year"], params, ], dims=["impact_category", "size", "powertrain", "year", "parameter"], ) return response def get_split_indices(self): """ Return list of indices to split the results into categories. :return: list of indices :rtype: list """ filename = "dict_split.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError("The dictionary of splits could not be found.") with open(filepath) as f: csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()] (_, _, *header), *data = csv_list csv_dict = {} for row in data: key, sub_key, *values = row if key in csv_dict: if sub_key in csv_dict[key]: csv_dict[key][sub_key].append( {"search by": values[0], "search for": values[1]} ) else: csv_dict[key][sub_key] = [ {"search by": values[0], "search for": values[1]} ] else: csv_dict[key] = { sub_key: [{"search by": values[0], "search for": values[1]}] } flatten = itertools.chain.from_iterable d = {} l = [] d['direct - exhaust'] = [] d['direct - exhaust'].append( self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Cadmium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Copper", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Chromium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Nickel", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Selenium", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Zinc", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].append( self.inputs[("Chromium VI", ("air", "urban air close to ground"), "kilogram")] ) d['direct - exhaust'].extend(self.index_emissions) d['direct - exhaust'].extend(self.index_noise) l.append(d['direct - exhaust']) for cat in csv_dict["components"]: d[cat] = list( flatten( [ self.get_index_of_flows([l["search for"]], l["search by"]) for l in csv_dict["components"][cat] ] ) ) l.append(d[cat]) list_ind = [d[x] for x in d] maxLen = max(map(len, list_ind)) for row in list_ind: while len(row) < maxLen: row.extend([len(self.inputs) - 1]) return list(d.keys()), list_ind def calculate_impacts( self, split="components", sensitivity=False ): # Prepare an array to store the results results = self.get_results_table(split, sensitivity=sensitivity) # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() # Fill in the A matrix with car parameters self.set_inputs_in_A_matrix(self.array.values) # Collect indices of activities contributing to the first level arr = self.A[0, : -self.number_of_cars, -self.number_of_cars :].sum(axis=1) ind = np.nonzero(arr)[0] new_arr = np.float32( np.zeros((self.A.shape[1], self.B.shape[1], len(self.scope["year"]))) ) f = np.float32(np.zeros((np.shape(self.A)[1]))) for y in self.scope["year"]: if self.scenario != "static": B = self.B.interp(year=y, kwargs={"fill_value": "extrapolate"}).values else: B = self.B[0].values for a in ind: f[:] = 0 f[a] = 1 X = np.float32(sparse.linalg.spsolve(self.A[0], f.T)) C = X * B new_arr[a, :, self.scope["year"].index(y)] = C.sum(axis=1) new_arr = new_arr.T.reshape( len(self.scope["year"]), B.shape[0], 1, 1, self.A.shape[-1] ) a = np.float32(self.A[:, :, -self.number_of_cars :].transpose(0, 2, 1)) arr = np.float32(ne.evaluate("a * new_arr * -1")) arr = arr.transpose(1, 3, 0, 4, 2) arr = arr[:, :, :, self.split_indices, :].sum(axis=4) if not sensitivity: for y in range(0, len(self.scope["year"])): results[:, :, :, y, :, :] = arr[ :, y :: len(self.scope["year"]), y, :, : ].reshape( ( B.shape[0], len(self.scope["size"]), len(self.scope["powertrain"]), len(results.impact.values), self.iterations, ) ) else: for y in range(0, len(self.scope["year"])): results[:, :, :, y, :] = ( arr[:, y :: len(self.scope["year"]), y, :] .sum(axis=2) .reshape( ( B.shape[0], len(self.scope["size"]), len(self.scope["powertrain"]), self.iterations, ) ) ) results /= results.sel(parameter="reference") return results.astype("float32") def add_additional_activities(self): # Add as many rows and columns as cars to consider # Also add additional columns and rows for electricity markets # for fuel preparation and energy battery production maximum = max(self.inputs.values()) for y in self.scope["year"]: if {"ICEV-p", "HEV-p", "PHEV-p"}.intersection( set(self.scope["powertrain"]) ): maximum += 1 self.inputs[ ( "fuel supply for gasoline vehicles, " + str(y), self.country, "kilogram", "fuel", ) ] = maximum if {"ICEV-d", "HEV-d", "PHEV-d"}.intersection( set(self.scope["powertrain"]) ): maximum += 1 self.inputs[ ( "fuel supply for diesel vehicles, " + str(y), self.country, "kilogram", "fuel", ) ] = maximum if {"ICEV-g"}.intersection(set(self.scope["powertrain"])): maximum += 1 self.inputs[ ( "fuel supply for gas vehicles, " + str(y), self.country, "kilogram", "fuel", ) ] = maximum if {"FCEV"}.intersection(set(self.scope["powertrain"])): maximum += 1 self.inputs[ ( "fuel supply for hydrogen vehicles, " + str(y), self.country, "kilogram", "fuel", ) ] = maximum if {"BEV", "PHEV-p", "PHEV-d"}.intersection(set(self.scope["powertrain"])): maximum += 1 self.inputs[ ( "electricity supply for electric vehicles, " + str(y), self.country, "kilowatt hour", "electricity, low voltage, for battery electric vehicles", ) ] = maximum maximum += 1 self.inputs[ ( "electricity market for fuel preparation, " + str(y), self.country, "kilowatt hour", "electricity, low voltage", ) ] = maximum maximum += 1 self.inputs[ ( "electricity market for energy storage production, " + str(y), self.background_configuration["energy storage"]["electric"][ "origin" ], "kilowatt hour", "electricity, low voltage, for energy storage production", ) ] = maximum for s in self.scope["size"]: for pt in self.scope["powertrain"]: for y in self.scope["year"]: maximum += 1 if y < 1993: euro_class = "EURO-0" if 1993 <= y < 1997: euro_class = "EURO-1" if 1997 <= y < 2001: euro_class = "EURO-2" if 2001 <= y < 2006: euro_class = "EURO-3" if 2006 <= y < 2011: euro_class = "EURO-4" if 2001 <= y < 2015: euro_class = "EURO-5" if y >= 2015: euro_class = "EURO-6" name = ( "Passenger car, " + pt + ", " + s + ", " + str(y) + ", " + euro_class ) self.inputs[ ( name, self.background_configuration["country"], "kilometer", "transport, passenger car, " + euro_class, ) ] = maximum def get_A_matrix(self): """ Load the A matrix. The A matrix contains exchanges of products (rows) between activities (columns). :return: A matrix with three dimensions of shape (number of values, number of products, number of activities). :rtype: numpy.ndarray """ filename = "A_matrix.csv" filepath = ( Path(getframeinfo(currentframe()).filename) .resolve() .parent.joinpath("data/" + filename) ) if not filepath.is_file(): raise FileNotFoundError("The technology matrix could not be found.") initial_A = np.genfromtxt(filepath, delimiter=";") new_A = np.identity(len(self.inputs)) new_A[0 : np.shape(initial_A)[0], 0 : np.shape(initial_A)[0]] = initial_A # Resize the matrix to fit the number of iterations in `array` new_A = np.resize(new_A, (self.array.shape[1], new_A.shape[0], new_A.shape[1])) return new_A def get_B_matrix(self): """ Load the B matrix. The B matrix contains impact assessment figures for a give impact assessment method, per unit of activity. Its length column-wise equals the length of the A matrix row-wise. Its length row-wise equals the number of impact assessment methods. :param method: only "recipe" and "ilcd" available at the moment. :param level: only "midpoint" available at the moment. :return: an array with impact values per unit of activity for each method. :rtype: numpy.ndarray """ if self.method == "recipe": if self.method_type == "midpoint": list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*recipe_midpoint*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 21, len(self.inputs))) else: list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*recipe_endpoint*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 3, len(self.inputs))) else: list_file_names = glob.glob( str(REMIND_FILES_DIR) + "/*ilcd*{}*.csv".format(self.scenario) ) B = np.zeros((len(list_file_names), 19, len(self.inputs))) for f in list_file_names: initial_B = np.genfromtxt(f, delimiter=";") new_B = np.zeros((np.shape(initial_B)[0], len(self.inputs),)) new_B[0 : np.shape(initial_B)[0], 0 : np.shape(initial_B)[1]] = initial_B B[list_file_names.index(f), :, :] = new_B list_impact_categories = list(self.impact_categories.keys()) if self.scenario != "static": response = xr.DataArray( B, coords=[ [2005, 2010, 2020, 2030, 2040, 2050], list_impact_categories, list(self.inputs.keys()), ], dims=["year", "category", "activity"], ) else: response = xr.DataArray( B, coords=[ [2020], list_impact_categories, list(self.inputs.keys()), ], dims=["year", "category", "activity"], ) return response def get_dict_input(self): """ Load a dictionary with tuple ("name of activity", "location", "unit", "reference product") as key, row/column indices as values. :return: dictionary with `label:index` pairs. :rtype: dict """ filename = "dict_inputs_A_matrix.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError( "The dictionary of activity labels could not be found." ) csv_dict = {} count = 0 with open(filepath) as f: input_dict = csv.reader(f, delimiter=";") for row in input_dict: if "(" in row[1]: new_str = row[1].replace("(", "") new_str = new_str.replace(")", "") new_str = [s.strip() for s in new_str.split(",") if s] t = () for s in new_str: if "low population" in s: s = "low population density, long-term" t += (s,) break else: t += (s.replace("'", ""),) csv_dict[(row[0], t, row[2])] = count else: csv_dict[(row[0], row[1], row[2], row[3])] = count count += 1 return csv_dict def get_dict_impact_categories(self): """ Load a dictionary with available impact assessment methods as keys, and assessment level and categories as values. ..code-block:: python {'recipe': {'midpoint': ['freshwater ecotoxicity', 'human toxicity', 'marine ecotoxicity', 'terrestrial ecotoxicity', 'metal depletion', 'agricultural land occupation', 'climate change', 'fossil depletion', 'freshwater eutrophication', 'ionising radiation', 'marine eutrophication', 'natural land transformation', 'ozone depletion', 'particulate matter formation', 'photochemical oxidant formation', 'terrestrial acidification', 'urban land occupation', 'water depletion', 'human noise', 'primary energy, non-renewable', 'primary energy, renewable'] } } :return: dictionary :rtype: dict """ filename = "dict_impact_categories.csv" filepath = DATA_DIR / filename if not filepath.is_file(): raise FileNotFoundError( "The dictionary of impact categories could not be found." ) csv_dict = {} with open(filepath) as f: input_dict = csv.reader(f, delimiter=";") for row in input_dict: if row[0] == self.method and row[3] == self.method_type: csv_dict[row[2]] = {'method':row[1], 'category':row[2], 'type':row[3], 'abbreviation':row[4], 'unit':row[5], 'source':row[6]} return csv_dict def get_rev_dict_input(self): """ Reverse the self.inputs dictionary. :return: reversed dictionary :rtype: dict """ return {v: k for k, v in self.inputs.items()} def get_index_vehicle_from_array( self, items_to_look_for, items_to_look_for_also=None, method="or" ): """ Return list of row/column indices of self.array of labels that contain the string defined in `items_to_look_for`. :param items_to_look_for: string to search for :return: list """ if not isinstance(items_to_look_for, list): items_to_look_for = [items_to_look_for] if not items_to_look_for_also is None: if not isinstance(items_to_look_for_also, list): items_to_look_for_also = [items_to_look_for_also] list_vehicles = self.array.desired.values.tolist() if method == "or": return [ list_vehicles.index(c) for c in list_vehicles if set(items_to_look_for).intersection(c) ] if method == "and": return [ list_vehicles.index(c) for c in list_vehicles if set(items_to_look_for).intersection(c) and set(items_to_look_for_also).intersection(c) ] def get_index_of_flows(self, items_to_look_for, search_by="name"): """ Return list of row/column indices of self.A of labels that contain the string defined in `items_to_look_for`. :param items_to_look_for: string :param search_by: "name" or "compartment" (for elementary flows) :return: list of row/column indices :rtype: list """ if search_by == "name": return [ int(self.inputs[c]) for c in self.inputs if all(ele in c[0].lower() for ele in items_to_look_for) ] if search_by == "compartment": return [ int(self.inputs[c]) for c in self.inputs if all(ele in c[1] for ele in items_to_look_for) ] def export_lci( self, presamples=True, ecoinvent_compatibility=True, ecoinvent_version="3.6", db_name="carculator db", ): """ Export the inventory as a dictionary. Also return a list of arrays that contain pre-sampled random values if :meth:`stochastic` of :class:`CarModel` class has been called. :param presamples: boolean. :param ecoinvent_compatibility: bool. If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent. :param ecoinvent_version: str. "3.5", "3.6" or "uvek" :return: inventory, and optionally, list of arrays containing pre-sampled values. :rtype: list """ # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) if presamples == True: lci, array = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci(presamples, ecoinvent_compatibility, ecoinvent_version) return (lci, array) else: lci = ExportInventory(self.A, self.rev_inputs, db_name=db_name).write_lci( presamples, ecoinvent_compatibility, ecoinvent_version ) return lci def export_lci_to_bw( self, presamples=True, ecoinvent_compatibility=True, ecoinvent_version="3.6", db_name="carculator db", ): """ Export the inventory as a `brightway2` bw2io.importers.base_lci.LCIImporter object with the inventory in the `data` attribute. .. code-block:: python # get the inventory i, _ = ic.export_lci_to_bw() # import it in a Brightway2 project i.match_database('ecoinvent 3.6 cutoff', fields=('name', 'unit', 'location', 'reference product')) i.match_database("biosphere3", fields=('name', 'unit', 'categories')) i.match_database(fields=('name', 'unit', 'location', 'reference product')) i.match_database(fields=('name', 'unit', 'categories')) # Create an additional biosphere database for the few flows that do not # exist in "biosphere3" i.create_new_biosphere("additional_biosphere", relink=True) # Check if all exchanges link i.statistics() # Register the database i.write_database() :return: LCIImport object that can be directly registered in a `brightway2` project. :rtype: bw2io.importers.base_lci.LCIImporter """ # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) if presamples == True: lci, array = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci_to_bw(presamples, ecoinvent_compatibility, ecoinvent_version) return (lci, array) else: lci = ExportInventory( self.A, self.rev_inputs, db_name=db_name ).write_lci_to_bw(presamples, ecoinvent_compatibility, ecoinvent_version) return lci def export_lci_to_excel( self, directory=None, ecoinvent_compatibility=True, ecoinvent_version="3.6", software_compatibility="brightway2", filename=None, ): """ Export the inventory as an Excel file (if the destination software is Brightway2) or a CSV file (if the destination software is Simapro) file. Also return the file path where the file is stored. :param directory: directory where to save the file. :type directory: str :param ecoinvent_compatibility: If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent. :param ecoinvent_version: "3.6", "3.5" or "uvek" :param software_compatibility: "brightway2" or "simapro" :return: file path where the file is stored. :rtype: str """ if software_compatibility not in ("brightway2", "simapro"): raise NameError( "The destination software argument is not valid. Choose between 'brightway2' or 'simapro'." ) # Simapro inventory only for ecoinvent 3.5 or UVEK if software_compatibility == "simapro": if ecoinvent_version == "3.6": print( "Simapro-compatible inventory export is only available for ecoinvent 3.5 or UVEK." ) return ecoinvent_compatibility = True ecoinvent_version = "3.5" # Create electricity and fuel market datasets self.create_electricity_market_for_fuel_prep() # Create electricity market dataset for battery production self.create_electricity_market_for_battery_production() self.set_inputs_in_A_matrix(self.array.values) fp = ExportInventory( self.A, self.rev_inputs, db_name=filename or "carculator db" ).write_lci_to_excel( directory, ecoinvent_compatibility, ecoinvent_version, software_compatibility, filename, ) return fp def get_country_of_use(self): if "country" not in self.background_configuration: self.background_configuration["country"] = "RER" return self.background_configuration["country"] def define_electricity_mix_for_fuel_prep(self): """ This function defines a fuel mix based either on user-defined mix, or on default mixes for a given country. The mix is calculated as the average mix, weighted by the distribution of annually driven kilometers. :return: """ try: losses_to_low = float(self.bs.losses[self.country]["LV"]) except KeyError: # If losses for the country are not found, assume EU average losses_to_low = float(self.bs.losses["RER"]["LV"]) if "custom electricity mix" in self.background_configuration: # If a special electricity mix is specified, we use it mix = self.background_configuration["custom electricity mix"] else: use_year = [ int(i) for i in ( self.array.values[ self.array_inputs["lifetime kilometers"], :, self.get_index_vehicle_from_array( [ "BEV", "FCEV", "PHEV-p", "PHEV-d", "ICEV-p", "ICEV-d", "HEV-p", "HEV-d", "ICEV-g", ] ), ] / self.array.values[ self.array_inputs["kilometers per year"], :, self.get_index_vehicle_from_array( [ "BEV", "FCEV", "PHEV-p", "PHEV-d", "ICEV-p", "ICEV-d", "HEV-p", "HEV-d", "ICEV-g", ] ), ] ) .mean(axis=1) .reshape(-1, len(self.scope["year"])) .mean(axis=0) ] mix = [ self.bs.electricity_mix.sel( country=self.country, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp( year=np.arange(y, y + use_year[self.scope["year"].index(y)]), kwargs={"fill_value": "extrapolate"}, ) .mean(axis=0) .values if y + use_year[self.scope["year"].index(y)] <= 2050 else self.bs.electricity_mix.sel( country=self.country, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp(year=np.arange(y, 2051), kwargs={"fill_value": "extrapolate"}) .mean(axis=0) .values for y in self.scope["year"] ] return mix def define_renewable_rate_in_mix(self): try: losses_to_low = float(self.bs.losses[self.country]["LV"]) except KeyError: # If losses for the country are not found, assume EU average losses_to_low = float(self.bs.losses["RER"]["LV"]) for y in self.scope["year"]: if self.scenario == "static": if self.method == "recipe": if self.method_type == "midpoint": co2_intensity_tech = ( self.B.sel( category="climate change", year=2020, activity=list(self.elec_map.values()), ).values * losses_to_low ) * 1000 else: co2_intensity_tech = 0 else: co2_intensity_tech = ( self.B.sel( category="climate change - climate change fossil", year=2020, activity=list(self.elec_map.values()), ).values * losses_to_low ) * 1000 else: if self.method == "recipe": if self.method_type == "midpoint": co2_intensity_tech = ( self.B.sel( category="climate change", activity=list(self.elec_map.values()) ) .interp(year=y, kwargs={"fill_value": "extrapolate"}) .values * losses_to_low ) * 1000 else: co2_intensity_tech = 0 else: co2_intensity_tech = ( self.B.sel( category="climate change - climate change fossil", activity=list(self.elec_map.values()) ) .interp(year=y, kwargs={"fill_value": "extrapolate"}) .values * losses_to_low ) * 1000 sum_renew = ( self.mix[self.scope["year"].index(y)][0] + self.mix[self.scope["year"].index(y)][3] + self.mix[self.scope["year"].index(y)][4] + self.mix[self.scope["year"].index(y)][5] + self.mix[self.scope["year"].index(y)][8] ) return sum_renew, co2_intensity_tech def create_electricity_market_for_fuel_prep(self): """ This function fills the electricity market that supplies battery charging operations and hydrogen production through electrolysis. """ try: losses_to_low = float(self.bs.losses[self.country]["LV"]) except KeyError: # If losses for the country are not found, assume EU average losses_to_low = float(self.bs.losses["RER"]["LV"]) # Fill the electricity markets for battery charging and hydrogen production for y in self.scope["year"]: m = np.array(self.mix[self.scope["year"].index(y)]).reshape(-1, 10, 1) # Add electricity technology shares self.A[ np.ix_( np.arange(self.iterations), [self.inputs[self.elec_map[t]] for t in self.elec_map], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ) ] = (m * -1 * losses_to_low) # Add transmission network for high and medium voltage self.A[ :, self.inputs[ ( "transmission network construction, electricity, high voltage", "CH", "kilometer", "transmission network, electricity, high voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (6.58e-9 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, electricity, medium voltage", "CH", "kilometer", "transmission network, electricity, medium voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (1.86e-8 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, long-distance", "UCTE", "kilometer", "transmission network, long-distance", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (3.17e-10 * -1 * losses_to_low) # Add distribution network, low voltage self.A[ :, self.inputs[ ( "distribution network construction, electricity, low voltage", "CH", "kilometer", "distribution network, electricity, low voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = (8.74e-8 * -1 * losses_to_low) # Add supply of sulfur hexafluoride for transformers self.A[ :, self.inputs[ ( "market for sulfur hexafluoride, liquid", "RER", "kilogram", "sulfur hexafluoride, liquid", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) # Add SF_6 leakage self.A[ :, self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for fuel preparation" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) def create_electricity_market_for_battery_production(self): """ This function fills in the column in `self.A` concerned with the electricity mix used for manufacturing battery cells :return: """ battery_tech = self.background_configuration["energy storage"]["electric"][ "type" ] battery_origin = self.background_configuration["energy storage"]["electric"][ "origin" ] try: losses_to_low = float(self.bs.losses[battery_origin]["LV"]) except KeyError: losses_to_low = float(self.bs.losses["CN"]["LV"]) mix_battery_manufacturing = ( self.bs.electricity_mix.sel( country=battery_origin, variable=[ "Hydro", "Nuclear", "Gas", "Solar", "Wind", "Biomass", "Coal", "Oil", "Geothermal", "Waste", ], ) .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"}) .values ) # Fill the electricity markets for battery production for y in self.scope["year"]: m = np.array( mix_battery_manufacturing[self.scope["year"].index(y)] ).reshape(-1, 10, 1) self.A[ np.ix_( np.arange(self.iterations), [self.inputs[self.elec_map[t]] for t in self.elec_map], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ) ] = (m * losses_to_low * -1) # Add transmission network for high and medium voltage self.A[ :, self.inputs[ ( "transmission network construction, electricity, high voltage", "CH", "kilometer", "transmission network, electricity, high voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (6.58e-9 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, electricity, medium voltage", "CH", "kilometer", "transmission network, electricity, medium voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (1.86e-8 * -1 * losses_to_low) self.A[ :, self.inputs[ ( "transmission network construction, long-distance", "UCTE", "kilometer", "transmission network, long-distance", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (3.17e-10 * -1 * losses_to_low) # Add distribution network, low voltage self.A[ :, self.inputs[ ( "distribution network construction, electricity, low voltage", "CH", "kilometer", "distribution network, electricity, low voltage", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = (8.74e-8 * -1 * losses_to_low) # Add supply of sulfur hexafluoride for transformers self.A[ :, self.inputs[ ( "market for sulfur hexafluoride, liquid", "RER", "kilogram", "sulfur hexafluoride, liquid", ) ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) # Add SF_6 leakage self.A[ :, self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], ] = ((5.4e-8 + 2.99e-9) * -1 * losses_to_low) def get_share_biofuel(self): region = self.bs.region_map[self.country]["RegionCode"] scenario = self.scenario if self.scenario != "static" else "SSP2-Base" share_biofuel = ( self.bs.biofuel.sel( region=region, value=0, fuel_type="Biomass fuel", scenario=scenario, ) .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"}) .values ) return share_biofuel def find_fuel_shares(self, fuel_type): default_fuels = { "petrol": {"primary": "petrol", "secondary": "bioethanol - wheat straw"}, "diesel": {"primary": "diesel", "secondary": "biodiesel - cooking oil"}, "cng": {"primary": "cng", "secondary": "biogas"}, "hydrogen": {"primary": "electrolysis", "secondary": "smr - natural gas"}, } if "fuel blend" in self.background_configuration: if fuel_type in self.background_configuration["fuel blend"]: primary = self.background_configuration["fuel blend"][fuel_type][ "primary fuel" ]["type"] try: secondary = self.background_configuration["fuel blend"][fuel_type][ "secondary fuel" ]["type"] except: secondary = default_fuels[fuel_type]["secondary"] primary_share = self.background_configuration["fuel blend"][fuel_type][ "primary fuel" ]["share"] secondary_share = 1 - np.array(primary_share) else: primary = default_fuels[fuel_type]["primary"] secondary = default_fuels[fuel_type]["secondary"] secondary_share = self.get_share_biofuel() primary_share = 1 - np.array(secondary_share) else: primary = default_fuels[fuel_type]["primary"] secondary = default_fuels[fuel_type]["secondary"] secondary_share = self.get_share_biofuel() primary_share = 1 - np.array(secondary_share) return (primary, secondary, primary_share, secondary_share) def set_actual_range(self): """ Set the actual range considering the blend. Liquid bio-fuels and synthetic fuels typically have a lower calorific value. Hence, the need to recalculate the vehicle range. Modifies parameter `range` of `array` in place """ if {"ICEV-p", "HEV-p", "PHEV-p"}.intersection(set(self.scope["powertrain"])): for y in self.scope["year"]: share_primary = self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] lhv_primary = self.fuel_blends["petrol"]["primary"]["lhv"] share_secondary = self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] lhv_secondary = self.fuel_blends["petrol"]["secondary"]["lhv"] index = self.get_index_vehicle_from_array( ["ICEV-p", "HEV-p", "PHEV-p"], y, method="and" ) self.array.values[self.array_inputs["range"], :, index] = ( ( ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_primary * lhv_primary ) + ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_secondary * lhv_secondary ) ) * 1000 / self.array.values[self.array_inputs["TtW energy"], :, index] ) if {"ICEV-d", "HEV-d", "PHEV-d"}.intersection(set(self.scope["powertrain"])): for y in self.scope["year"]: share_primary = self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] lhv_primary = self.fuel_blends["diesel"]["primary"]["lhv"] share_secondary = self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] lhv_secondary = self.fuel_blends["diesel"]["secondary"]["lhv"] index = self.get_index_vehicle_from_array( ["ICEV-d", "PHEV-d", "HEV-d"], y, method="and" ) self.array.values[self.array_inputs["range"], :, index] = ( ( ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_primary * lhv_primary ) + ( self.array.values[self.array_inputs["fuel mass"], :, index] * share_secondary * lhv_secondary ) ) * 1000 / self.array.values[self.array_inputs["TtW energy"], :, index] ) def define_fuel_blends(self): """ This function defines fuel blends from what is passed in `background_configuration`. It populates a dictionary `self.fuel_blends` that contains the respective shares, lower heating values and CO2 emission factors of the fuels used. :return: """ fuels_lhv = { "petrol": 42.4, "bioethanol - wheat straw": 26.8, "bioethanol - maize starch": 26.8, "bioethanol - sugarbeet": 26.8, "bioethanol - forest residues": 26.8, "synthetic gasoline": 42.4, "diesel": 42.8, "biodiesel - cooking oil": 31.7, "biodiesel - algae": 31.7, "synthetic diesel": 43.3, "cng": 55.5, "biogas": 55.5, "syngas": 55.5 } fuels_CO2 = { "petrol": 3.18, "bioethanol - wheat straw": 1.91, "bioethanol - maize starch": 1.91, "bioethanol - sugarbeet": 1.91, "bioethanol - forest residues": 1.91, "synthetic gasoline": 3.18, "diesel": 3.14, "biodiesel - cooking oil": 2.85, "biodiesel - algae": 2.85, "synthetic diesel": 3.16, "cng": 2.65, "biogas": 2.65, "syngas": 2.65 } if {"ICEV-p", "HEV-p", "PHEV-p"}.intersection(set(self.scope["powertrain"])): fuel_type = "petrol" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": { "type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary], }, "secondary": { "type": secondary, "share": secondary_share, "lhv": fuels_lhv[secondary], "CO2": fuels_CO2[secondary], }, } if {"ICEV-d", "HEV-d", "PHEV-d"}.intersection(set(self.scope["powertrain"])): fuel_type = "diesel" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": { "type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary], }, "secondary": { "type": secondary, "share": secondary_share, "lhv": fuels_lhv[secondary], "CO2": fuels_CO2[secondary], }, } if {"ICEV-g"}.intersection(set(self.scope["powertrain"])): fuel_type = "cng" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": {"type": primary, "share": primary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary]}, "secondary": {"type": secondary, "share": secondary_share, "lhv": fuels_lhv[primary], "CO2": fuels_CO2[primary]}, } if {"FCEV"}.intersection(set(self.scope["powertrain"])): fuel_type = "hydrogen" primary, secondary, primary_share, secondary_share = self.find_fuel_shares( fuel_type ) self.create_fuel_markets( fuel_type, primary, secondary, primary_share, secondary_share ) self.fuel_blends[fuel_type] = { "primary": {"type": primary, "share": primary_share}, "secondary": {"type": secondary, "share": secondary_share}, } if {"BEV", "PHEV-p", "PHEV-d"}.intersection(set(self.scope["powertrain"])): fuel_type = "electricity" self.create_fuel_markets(fuel_type) def create_fuel_markets( self, fuel_type, primary=None, secondary=None, primary_share=None, secondary_share=None, ): """ This function creates markets for fuel, considering a given blend, a given fuel type and a given year. It also adds separate electricity input in case hydrogen from electrolysis is needed somewhere in the fuel supply chain. :return: """ d_fuels = { "electrolysis": { "name": ( "Hydrogen, gaseous, 700 bar, from electrolysis, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from electrolysis, at H2 fuelling station", ), "additional electricity": 58, }, "smr - natural gas": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR NG w/o CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR NG w/o CCS, at H2 fuelling station", ), "additional electricity": 0, }, "smr - natural gas with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR NG w CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR NG w CCS, at H2 fuelling station", ), "additional electricity": 0, }, "smr - biogas": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR of biogas, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR of biogas, at H2 fuelling station", ), "additional electricity": 0, }, "smr - biogas with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from SMR of biogas with CCS, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from SMR of biogas with CCS, at H2 fuelling station", ), "additional electricity": 0, }, "coal gasification": { "name": ( "Hydrogen, gaseous, 700 bar, from coal gasification, at H2 fuelling station", "RER", "kilogram", "Hydrogen, gaseous, 700 bar, from coal gasification, at H2 fuelling station", ), "additional electricity": 0, }, "wood gasification": { "name": ( "Hydrogen, gaseous, 700 bar, from dual fluidised bed gasification of woody biomass, at H2 fuelling station", "CH", "kilogram", "Hydrogen, gaseous, 700 bar", ), "additional electricity": 0, }, "wood gasification with CCS": { "name": ( "Hydrogen, gaseous, 700 bar, from dual fluidised bed gasification of woody biomass with CCS, at H2 fuelling station", "CH", "kilogram", "Hydrogen, gaseous, 700 bar", ), "additional electricity": 0, }, "cng": { "name": ( "market for natural gas, from high pressure network (1-5 bar), at service station", "GLO", "kilogram", "natural gas, from high pressure network (1-5 bar), at service station", ), "additional electricity": 0, }, "biogas": { "name": ( "biogas upgrading - sewage sludge - amine scrubbing - best", "CH", "kilogram", "biogas upgrading - sewage sludge - amine scrubbing - best", ), "additional electricity": 0, }, "syngas": { "name": ( "Methane production, synthetic, from electrochemical methanation", "RER", "kilogram", "Methane, synthetic", ), "additional electricity": 58 * 0.50779661, }, "diesel": { "name": ( "market for diesel", "Europe without Switzerland", "kilogram", "diesel", ), "additional electricity": 0, }, "biodiesel - algae": { "name": ( "Biodiesel from algae", "RER", "kilogram", "Biodiesel from algae", ), "additional electricity": 0, }, "biodiesel - cooking oil": { "name": ( "Biodiesel from cooking oil", "RER", "kilogram", "Biodiesel from cooking oil", ), "additional electricity": 0, }, "synthetic diesel": { "name": ( "Diesel production, synthetic, Fischer Tropsch process", "RER", "kilogram", "Diesel, synthetic", ), "additional electricity": 58 * 0.2875, }, "petrol": { "name": ( "market for petrol, low-sulfur", "Europe without Switzerland", "kilogram", "petrol, low-sulfur", ), "additional electricity": 0, }, "bioethanol - wheat straw": { "name": ( "Ethanol from wheat straw pellets", "RER", "kilogram", "Ethanol from wheat straw pellets", ), "additional electricity": 0, }, "bioethanol - forest residues": { "name": ( "Ethanol from forest residues", "RER", "kilogram", "Ethanol from forest residues", ), "additional electricity": 0, }, "bioethanol - sugarbeet": { "name": ( "Ethanol from sugarbeet", "RER", "kilogram", "Ethanol from sugarbeet", ), "additional electricity": 0, }, "bioethanol - maize starch": { "name": ( "Ethanol from maize starch", "RER", "kilogram", "Ethanol from maize starch", ), "additional electricity": 0, }, "synthetic gasoline": { "name": ( "Gasoline production, synthetic, from methanol", "RER", "kilogram", "Gasoline, synthetic", ), "additional electricity": 58 * 0.328, }, } d_dataset_name = { "petrol": "fuel supply for gasoline vehicles, ", "diesel": "fuel supply for diesel vehicles, ", "cng": "fuel supply for gas vehicles, ", "hydrogen": "fuel supply for hydrogen vehicles, ", "electricity": "electricity supply for electric vehicles, ", } if fuel_type != "electricity": for y in self.scope["year"]: dataset_name = d_dataset_name[fuel_type] + str(y) fuel_market_index = [ self.inputs[i] for i in self.inputs if i[0] == dataset_name ][0] primary_fuel_activity_index = self.inputs[d_fuels[primary]["name"]] secondary_fuel_activity_index = self.inputs[d_fuels[secondary]["name"]] self.A[:, primary_fuel_activity_index, fuel_market_index] = ( -1 * primary_share[self.scope["year"].index(y)] ) self.A[:, secondary_fuel_activity_index, fuel_market_index] = ( -1 * secondary_share[self.scope["year"].index(y)] ) additional_electricity = ( d_fuels[primary]["additional electricity"] * primary_share[self.scope["year"].index(y)] ) + ( d_fuels[secondary]["additional electricity"] * secondary_share[self.scope["year"].index(y)] ) if additional_electricity > 0: electricity_mix_index = [ self.inputs[i] for i in self.inputs if i[0] == "electricity market for fuel preparation, " + str(y) ][0] self.A[:, electricity_mix_index, fuel_market_index] = ( -1 * additional_electricity ) else: for y in self.scope["year"]: dataset_name = d_dataset_name[fuel_type] + str(y) electricity_market_index = [ self.inputs[i] for i in self.inputs if i[0] == dataset_name ][0] electricity_mix_index = [ self.inputs[i] for i in self.inputs if i[0] == "electricity market for fuel preparation, " + str(y) ][0] self.A[:, electricity_mix_index, electricity_market_index] = -1 def set_inputs_in_A_matrix(self, array): """ Fill-in the A matrix. Does not return anything. Modifies in place. Shape of the A matrix (values, products, activities). :param array: :attr:`array` from :class:`CarModel` class """ # Glider self.A[ :, self.inputs[ ( "market for glider, passenger car", "GLO", "kilogram", "glider, passenger car", ) ], -self.number_of_cars :, ] = ( (array[self.array_inputs["glider base mass"], :]) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ("Glider lightweighting", "GLO", "kilogram", "Glider lightweighting") ], -self.number_of_cars :, ] = ( ( array[self.array_inputs["lightweighting"], :] * array[self.array_inputs["glider base mass"], :] ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "maintenance, passenger car", "RER", "unit", "passenger car maintenance", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["curb mass"], :] / 1240 / 150000 * -1) # Glider EoL self.A[ :, self.inputs[ ( "market for manual dismantling of used electric passenger car", "GLO", "unit", "manual dismantling of used electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["curb mass"], :] * (1 - array[self.array_inputs["combustion power share"], :]) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for manual dismantling of used passenger car with internal combustion engine", "GLO", "unit", "manual dismantling of used passenger car with internal combustion engine", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["curb mass"], :] * array[self.array_inputs["combustion power share"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Powertrain components self.A[ :, self.inputs[ ( "market for charger, electric passenger car", "GLO", "kilogram", "charger, electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["charger mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for converter, for electric passenger car", "GLO", "kilogram", "converter, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["converter mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for electric motor, electric passenger car", "GLO", "kilogram", "electric motor, electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["electric engine mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for inverter, for electric passenger car", "GLO", "kilogram", "inverter, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["inverter mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[ ( "market for power distribution unit, for electric passenger car", "GLO", "kilogram", "power distribution unit, for electric passenger car", ) ], -self.number_of_cars :, ] = ( array[self.array_inputs["power distribution unit mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) l_elec_pt = [ "charger mass", "converter mass", "inverter mass", "power distribution unit mass", "electric engine mass", "fuel cell stack mass", "fuel cell ancillary BoP mass", "fuel cell essential BoP mass", "battery cell mass", "battery BoP mass", ] self.A[ :, self.inputs[ ( "market for used powertrain from electric passenger car, manual dismantling", "GLO", "kilogram", "used powertrain from electric passenger car, manual dismantling", ) ], -self.number_of_cars :, ] = ( array[[self.array_inputs[l] for l in l_elec_pt], :].sum(axis=0) / array[self.array_inputs["lifetime kilometers"], :] ) self.A[ :, self.inputs[ ( "market for internal combustion engine, passenger car", "GLO", "kilogram", "internal combustion engine, for passenger car", ) ], -self.number_of_cars :, ] = ( ( array[ [ self.array_inputs[l] for l in ["combustion engine mass", "powertrain mass"] ], :, ].sum(axis=0) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Ancillary BoP", "GLO", "kilogram", "Ancillary BoP")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell ancillary BoP mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Essential BoP", "GLO", "kilogram", "Essential BoP")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell essential BoP mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) self.A[ :, self.inputs[("Stack", "GLO", "kilowatt", "Stack")], -self.number_of_cars :, ] = ( array[self.array_inputs["fuel cell stack mass"], :] / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Start of printout print( "****************** IMPORTANT BACKGROUND PARAMETERS ******************", end="\n * ", ) # Energy storage print( "The country of use is " + self.country, end="\n * ", ) battery_tech = self.background_configuration["energy storage"]["electric"][ "type" ] battery_origin = self.background_configuration["energy storage"]["electric"][ "origin" ] print( "Power and energy batteries produced in " + battery_origin + " using " + battery_tech + " chemistry.", end="\n * ", ) # Use the NMC inventory of Schmidt et al. 2019 self.A[ :, self.inputs[("Battery BoP", "GLO", "kilogram", "Battery BoP")], -self.number_of_cars :, ] = ( ( array[self.array_inputs["battery BoP mass"], :] * (1 + array[self.array_inputs["battery lifetime replacements"], :]) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) battery_cell_label = ( "Battery cell, " + battery_tech, "GLO", "kilogram", "Battery cell", ) self.A[:, self.inputs[battery_cell_label], -self.number_of_cars :,] = ( ( array[self.array_inputs["battery cell mass"], :] * (1 + array[self.array_inputs["fuel cell lifetime replacements"], :]) ) / array[self.array_inputs["lifetime kilometers"], :] * -1 ) # Set an input of electricity, given the country of manufacture self.A[ :, self.inputs[ ( "market group for electricity, medium voltage", "World", "kilowatt hour", "electricity, medium voltage", ) ], self.inputs[battery_cell_label], ] = 0 for y in self.scope["year"]: index = self.get_index_vehicle_from_array(y) self.A[ np.ix_( np.arange(self.iterations), [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity market for energy storage production" in i[0] ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] ], ) ] = ( array[ self.array_inputs["battery cell production electricity"], :, index ].T * self.A[ :, self.inputs[battery_cell_label], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] ], ] ).reshape( self.iterations, 1, -1 ) index_A = [ self.inputs[c] for c in self.inputs if any( ele in c[0] for ele in ["ICEV-d", "ICEV-p", "HEV-p", "PHEV-p", "PHEV-d", "HEV-d"] ) ] index = self.get_index_vehicle_from_array( ["ICEV-d", "ICEV-p", "HEV-p", "PHEV-p", "PHEV-d", "HEV-d"] ) self.A[ :, self.inputs[ ( "polyethylene production, high density, granulate", "RER", "kilogram", "polyethylene, high density, granulate", ) ], index_A, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T index = self.get_index_vehicle_from_array("ICEV-g") self.A[ :, self.inputs[ ( "glass fibre reinforced plastic production, polyamide, injection moulded", "RER", "kilogram", "glass fibre reinforced plastic, polyamide, injection moulded", ) ], self.index_cng, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T if "hydrogen" in self.background_configuration["energy storage"]: # If a customization dict is passed hydro_tank_technology = self.background_configuration["energy storage"][ "hydrogen" ]["type"] else: hydro_tank_technology = "carbon fiber" dict_tank_map = { "carbon fiber": ( "Fuel tank, compressed hydrogen gas, 700bar", "GLO", "kilogram", "Fuel tank, compressed hydrogen gas, 700bar", ), "hdpe": ( "Fuel tank, compressed hydrogen gas, 700bar, with HDPE liner", "RER", "kilogram", "Hydrogen tank", ), "aluminium": ( "Fuel tank, compressed hydrogen gas, 700bar, with aluminium liner", "RER", "kilogram", "Hydrogen tank", ), } index = self.get_index_vehicle_from_array("FCEV") self.A[ :, self.inputs[dict_tank_map[hydro_tank_technology]], self.index_fuel_cell, ] = ( array[self.array_inputs["fuel tank mass"], :, index] / array[self.array_inputs["lifetime kilometers"], :, index] * -1 ).T for y in self.scope["year"]: sum_renew, co2_intensity_tech = self.define_renewable_rate_in_mix() if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + ", % of renewable: " + str(np.round(sum_renew * 100, 0)) + "%" + ", GHG intensity per kWh: " + str( int( np.sum( co2_intensity_tech * self.mix[self.scope["year"].index(y)] ) ) ) + " g. CO2-eq.", end=end_str, ) if any( True for x in ["BEV", "PHEV-p", "PHEV-d"] if x in self.scope["powertrain"] ): for y in self.scope["year"]: index = self.get_index_vehicle_from_array( ["BEV", "PHEV-p", "PHEV-d"], y, method="and" ) self.A[ np.ix_( np.arange(self.iterations), [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "electricity supply for electric vehicles" in i[0] ], [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any( True for x in ["BEV", "PHEV-p", "PHEV-d"] if x in i[0] ) ], ) ] = ( array[self.array_inputs["electricity consumption"], :, index] * -1 ).T.reshape( self.iterations, 1, -1 ) if "FCEV" in self.scope["powertrain"]: index = self.get_index_vehicle_from_array("FCEV") print( "{} is completed by {}.".format( self.fuel_blends["hydrogen"]["primary"]["type"], self.fuel_blends["hydrogen"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["hydrogen"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) # Primary fuel share for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and "FCEV" in i[0] ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for hydrogen vehicles" in i[0] ], ind_A, ] = ( array[self.array_inputs["fuel mass"], :, ind_array] / array[self.array_inputs["range"], :, ind_array] * -1 ).T if "ICEV-g" in self.scope["powertrain"]: index = self.get_index_vehicle_from_array("ICEV-g") print( "{} is completed by {}.".format( self.fuel_blends["cng"]["primary"]["type"], self.fuel_blends["cng"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) # Primary fuel share for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and "ICEV-g" in i[0] ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for gas vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Fuel-based emissions from CNG, CO2 # The share and CO2 emissions factor of CNG is retrieved, if used share_fossil = 0 CO2_fossil = 0 if self.fuel_blends["cng"]["primary"]["type"] == "cng": share_fossil += self.fuel_blends["cng"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["cng"]["primary"]["CO2"] if self.fuel_blends["cng"]["secondary"]["type"] == "cng": share_fossil += self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["cng"]["primary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Fuel-based CO2 emission from alternative petrol # The share of non-fossil gas in the blend is retrieved # As well as the CO2 emission factor of the fuel share_non_fossil = 0 CO2_non_fossil = 0 if self.fuel_blends["cng"]["primary"]["type"] != "cng": share_non_fossil += self.fuel_blends["cng"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["cng"]["primary"]["CO2"] if self.fuel_blends["cng"]["secondary"]["type"] != "cng": share_non_fossil += self.fuel_blends["cng"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["cng"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T if [i for i in self.scope["powertrain"] if i in ["ICEV-d", "PHEV-d", "HEV-d"]]: index = self.get_index_vehicle_from_array(["ICEV-d", "PHEV-d", "HEV-d"]) print( "{} is completed by {}.".format( self.fuel_blends["diesel"]["primary"]["type"], self.fuel_blends["diesel"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any(x in i[0] for x in ["ICEV-d", "PHEV-d", "HEV-d"]) ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] # Fuel supply self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for diesel vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_fossil = 0 CO2_fossil = 0 # Fuel-based CO2 emission from conventional petrol if self.fuel_blends["diesel"]["primary"]["type"] == "diesel": share_fossil += self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["diesel"]["primary"]["CO2"] if self.fuel_blends["diesel"]["secondary"]["type"] == "diesel": share_fossil += self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["diesel"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_non_fossil = 0 CO2_non_fossil = 0 # Fuel-based CO2 emission from alternative petrol # The share of non-fossil fuel in the blend is retrieved # As well as the CO2 emission factor of the fuel if self.fuel_blends["diesel"]["primary"]["type"] != "diesel": share_non_fossil += self.fuel_blends["diesel"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["diesel"]["primary"]["CO2"] if self.fuel_blends["diesel"]["secondary"]["type"] != "diesel": share_non_fossil += self.fuel_blends["diesel"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["diesel"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Heavy metals emissions from conventional diesel # Emission factors from Spielmann et al., Transport Services Data v.2 (2007) # Cadmium, 0.01 mg/kg diesel self.A[ :, self.inputs[ ("Cadmium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Copper, 1.7 mg/kg diesel self.A[ :, self.inputs[ ("Copper", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.7e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium, 0.05 mg/kg diesel self.A[ :, self.inputs[ ("Chromium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 5.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Nickel, 0.07 mg/kg diesel self.A[ :, self.inputs[ ("Nickel", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 7.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Selenium, 0.01 mg/kg diesel self.A[ :, self.inputs[ ("Selenium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Zinc, 1 mg/kg diesel self.A[ :, self.inputs[ ("Zinc", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium VI, 0.0001 mg/kg diesel self.A[ :, self.inputs[ ( "Chromium VI", ("air", "urban air close to ground"), "kilogram", ) ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-10 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T if [i for i in self.scope["powertrain"] if i in ["ICEV-p", "HEV-p", "PHEV-p"]]: index = self.get_index_vehicle_from_array(["ICEV-p", "HEV-p", "PHEV-p"]) print( "{} is completed by {}.".format( self.fuel_blends["petrol"]["primary"]["type"], self.fuel_blends["petrol"]["secondary"]["type"], ), end="\n \t * ", ) for y in self.scope["year"]: if self.scope["year"].index(y) + 1 == len(self.scope["year"]): end_str = "\n * " else: end_str = "\n \t * " print( "in " + str(y) + " _________________________________________ " + str( np.round( self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] * 100, 0, ) ) + "%", end=end_str, ) for y in self.scope["year"]: ind_A = [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "Passenger" in i[0] and any(x in i[0] for x in ["ICEV-p", "HEV-p", "PHEV-p"]) ] ind_array = [ x for x in self.get_index_vehicle_from_array(y) if x in index ] # Fuel supply self.A[ :, [ self.inputs[i] for i in self.inputs if str(y) in i[0] and "fuel supply for gasoline vehicles" in i[0] ], ind_A, ] = ( (array[self.array_inputs["fuel mass"], :, ind_array]) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_fossil = 0 CO2_fossil = 0 # Fuel-based CO2 emission from conventional petrol if self.fuel_blends["petrol"]["primary"]["type"] == "petrol": share_fossil += self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["petrol"]["primary"]["CO2"] if self.fuel_blends["petrol"]["secondary"]["type"] == "petrol": share_fossil += self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_fossil = self.fuel_blends["petrol"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil * CO2_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T share_non_fossil = 0 CO2_non_fossil = 0 # Fuel-based CO2 emission from alternative petrol # The share of non-fossil fuel in the blend is retrieved # As well as the CO2 emission factor of the fuel if self.fuel_blends["petrol"]["primary"]["type"] != "petrol": share_non_fossil += self.fuel_blends["petrol"]["primary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["petrol"]["primary"]["CO2"] if self.fuel_blends["petrol"]["secondary"]["type"] != "petrol": share_non_fossil += self.fuel_blends["petrol"]["secondary"]["share"][ self.scope["year"].index(y) ] CO2_non_fossil = self.fuel_blends["petrol"]["secondary"]["CO2"] self.A[ :, self.inputs[("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_non_fossil * CO2_non_fossil) ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Heavy metals emissions from conventional petrol # Cadmium, 0.01 mg/kg gasoline self.A[ :, self.inputs[ ("Cadmium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Copper, 1.7 mg/kg gasoline self.A[ :, self.inputs[ ("Copper", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.7e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium, 0.05 mg/kg gasoline self.A[ :, self.inputs[ ("Chromium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 5.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Nickel, 0.07 mg/kg gasoline self.A[ :, self.inputs[ ("Nickel", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 7.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Selenium, 0.01 mg/kg gasoline self.A[ :, self.inputs[ ("Selenium", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-8 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Zinc, 1 mg/kg gasoline self.A[ :, self.inputs[ ("Zinc", ("air", "urban air close to ground"), "kilogram") ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-6 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Chromium VI, 0.0001 mg/kg gasoline self.A[ :, self.inputs[ ( "Chromium VI", ("air", "urban air close to ground"), "kilogram", ) ], ind_A, ] = ( ( (array[self.array_inputs["fuel mass"], :, ind_array] * share_fossil) * 1.0e-10 ) / array[self.array_inputs["range"], :, ind_array] * -1 ).T # Non-exhaust emissions self.A[ :, self.inputs[ ( "market for road wear emissions, passenger car", "GLO", "kilogram", "road wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 1e-08) self.A[ :, self.inputs[ ( "market for tyre wear emissions, passenger car", "GLO", "kilogram", "tyre wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 6e-08) self.A[ :, self.inputs[ ( "market for brake wear emissions, passenger car", "GLO", "kilogram", "brake wear emissions, passenger car", ) ], -self.number_of_cars :, ] = (array[self.array_inputs["driving mass"], :] * 5e-09) # Infrastructure self.A[ :, self.inputs[("market for road", "GLO", "meter-year", "road")], -self.number_of_cars :, ] = (5.37e-7 * array[self.array_inputs["driving mass"], :] * -1) # Infrastructure maintenance self.A[ :, self.inputs[ ("market for road maintenance", "RER", "meter-year", "road maintenance") ], -self.number_of_cars :, ] = (1.29e-3 * -1) # Exhaust emissions # Non-fuel based emissions self.A[:, self.index_emissions, -self.number_of_cars :] = ( array[ [ self.array_inputs[self.map_non_fuel_emissions[self.rev_inputs[x]]] for x in self.index_emissions ] ] * -1 ).transpose([1, 0, 2]) # Noise emissions self.A[:, self.index_noise, -self.number_of_cars :] = ( array[ [ self.array_inputs[self.map_noise_emissions[self.rev_inputs[x]]] for x in self.index_noise ] ] * -1 ).transpose([1, 0, 2]) print("*********************************************************************")

30,672

0

189

544162d5b108b9011d584715752e360c5e3a3bf6

4,955

py

Python

project/project/settings.py

gtrafimenkov/example-django-kubernetes

ddcf1d0b06152ca3615230be53cf9a5f837c09d9

[ "BSD-3-Clause" ]

null

project/project/settings.py

gtrafimenkov/example-django-kubernetes

ddcf1d0b06152ca3615230be53cf9a5f837c09d9

[ "BSD-3-Clause" ]

6

2021-02-02T22:59:52.000Z

2021-06-10T20:35:55.000Z

project/project/settings.py

gtrafimenkov/example-django-kubernetes

ddcf1d0b06152ca3615230be53cf9a5f837c09d9

[ "BSD-3-Clause" ]

null

# Django settings for gtd project. import os from django.contrib.messages import constants as message_constants DEBUG = get_debug_settings() BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # If running in a Windows environment this must be set to the same as your # system time zone. TIME_ZONE = "America/Los_Angeles" # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = "en-us" SITE_ID = 1 # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "project.urls" LOGIN_URL = "/login" LOGIN_REDIRECT_URL = "todo:lists" LOGOUT_REDIRECT_URL = "home" SESSION_EXPIRE_AT_BROWSER_CLOSE = True SESSION_SECURITY_WARN_AFTER = 5 SESSION_SECURITY_EXPIRE_AFTER = 12 # See: https://docs.djangoproject.com/en/dev/ref/settings/#wsgi-application WSGI_APPLICATION = "project.wsgi.application" INSTALLED_APPS = ( "django.contrib.admin", "django.contrib.admindocs", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.flatpages", "django.contrib.messages", "django.contrib.sessions", "django.contrib.sites", "django.contrib.staticfiles", "todo", "django_extensions", ) # Static files and uploads STATIC_URL = "/static/" STATICFILES_DIRS = [os.path.join(BASE_DIR, "project", "static")] STATIC_ROOT = os.path.join(BASE_DIR, "static") # Uploaded media MEDIA_ROOT = os.path.join(BASE_DIR, "media") MEDIA_URL = "/media/" # Without this, uploaded files > 4MB end up with perm 0600, unreadable by web server process FILE_UPLOAD_PERMISSIONS = 0o644 TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [os.path.join(BASE_DIR, "project", "templates")], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.template.context_processors.i18n", "django.template.context_processors.media", "django.template.context_processors.static", "django.template.context_processors.tz", "django.contrib.messages.context_processors.messages", # Your stuff: custom template context processors go here ] }, } ] # Override CSS class for the ERROR tag level to match Bootstrap class name MESSAGE_TAGS = {message_constants.ERROR: "danger"} #################################################################### # Environment specific settings #################################################################### SECRET_KEY = os.environ.get('SECRET_KEY', 'lksdf98wrhkjs88dsf8-324ksdm') # DEBUG = True ALLOWED_HOSTS = ["*"] DATABASES = get_db_settings() EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # TODO-specific settings TODO_STAFF_ONLY = False TODO_DEFAULT_LIST_SLUG = 'tickets' TODO_DEFAULT_ASSIGNEE = None TODO_PUBLIC_SUBMIT_REDIRECT = '/' #################################################################### # ####################################################################

32.81457

101

0.638143

# Django settings for gtd project. import os from django.contrib.messages import constants as message_constants def get_debug_settings(): return os.environ.get("DJANGO_DEBUG", "").lower() in ["true", "1", "yes", "y"] DEBUG = get_debug_settings() BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # If running in a Windows environment this must be set to the same as your # system time zone. TIME_ZONE = "America/Los_Angeles" # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = "en-us" SITE_ID = 1 # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "project.urls" LOGIN_URL = "/login" LOGIN_REDIRECT_URL = "todo:lists" LOGOUT_REDIRECT_URL = "home" SESSION_EXPIRE_AT_BROWSER_CLOSE = True SESSION_SECURITY_WARN_AFTER = 5 SESSION_SECURITY_EXPIRE_AFTER = 12 # See: https://docs.djangoproject.com/en/dev/ref/settings/#wsgi-application WSGI_APPLICATION = "project.wsgi.application" INSTALLED_APPS = ( "django.contrib.admin", "django.contrib.admindocs", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.flatpages", "django.contrib.messages", "django.contrib.sessions", "django.contrib.sites", "django.contrib.staticfiles", "todo", "django_extensions", ) # Static files and uploads STATIC_URL = "/static/" STATICFILES_DIRS = [os.path.join(BASE_DIR, "project", "static")] STATIC_ROOT = os.path.join(BASE_DIR, "static") # Uploaded media MEDIA_ROOT = os.path.join(BASE_DIR, "media") MEDIA_URL = "/media/" # Without this, uploaded files > 4MB end up with perm 0600, unreadable by web server process FILE_UPLOAD_PERMISSIONS = 0o644 TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [os.path.join(BASE_DIR, "project", "templates")], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.template.context_processors.i18n", "django.template.context_processors.media", "django.template.context_processors.static", "django.template.context_processors.tz", "django.contrib.messages.context_processors.messages", # Your stuff: custom template context processors go here ] }, } ] # Override CSS class for the ERROR tag level to match Bootstrap class name MESSAGE_TAGS = {message_constants.ERROR: "danger"} #################################################################### # Environment specific settings #################################################################### SECRET_KEY = os.environ.get('SECRET_KEY', 'lksdf98wrhkjs88dsf8-324ksdm') # DEBUG = True ALLOWED_HOSTS = ["*"] def get_db_settings(): CPHTEST_ENVIRONMENT = os.environ.get('CPHTEST_ENVIRONMENT', 'local') if CPHTEST_ENVIRONMENT == "local": return { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } if CPHTEST_ENVIRONMENT == "k8s": return { 'default': { 'ENGINE': os.environ.get('DB_ENGINE', 'django.db.backends.postgresql'), 'NAME': os.environ.get('DB_NAME', 'cphtest'), 'USER': os.environ.get('DB_USER', 'cphtestuser'), 'PASSWORD': os.environ.get('DB_PASSWORD', 'django'), 'HOST': os.environ.get('DB_HOST', 'p1-postgresql.default.svc.cluster.local'), 'PORT': os.environ.get('DB_PORT', ''), }, } return {} DATABASES = get_db_settings() EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # TODO-specific settings TODO_STAFF_ONLY = False TODO_DEFAULT_LIST_SLUG = 'tickets' TODO_DEFAULT_ASSIGNEE = None TODO_PUBLIC_SUBMIT_REDIRECT = '/' #################################################################### # ####################################################################

975

0

46

1f010d3368e8fe21a4c6b38d8a3a7ce2c8c7822f

964

py

Python

resources/search.py

DanielNery/api-list-mscs-genius

9febbbb4211ca86a210803981cb5968077d7de72

[ "MIT" ]

1

2021-11-20T22:09:23.000Z

resources/search.py

DanielNery/api-list-mscs-genius

9febbbb4211ca86a210803981cb5968077d7de72

[ "MIT" ]

null

resources/search.py

DanielNery/api-list-mscs-genius

9febbbb4211ca86a210803981cb5968077d7de72

[ "MIT" ]

null

from flask_restful import Resource import requests import json import os import redis HEADER = { 'User-Agent': 'CompuServe Classic/1.22', 'Accept': 'application/json', 'Host': os.getenv("HOST"), 'Authorization': f'Bearer {os.getenv("ACESS_TOKEN")}' } class Search(Resource): """Recurso responsável por retornar lista de artistas, para o usuário escolher""" def get(self, artist_name): """ Retorna lista de artistas """ querystring = {"q": artist_name} url = f"https://{os.getenv('HOST')}/search" try: response = requests.get(url=url, headers=HEADER, params=querystring) if response.status_code != '200': return json.loads(response.text), response.status_code except Exception as e: print(e) return {"message": "Internal server error."}, 500 data = json.loads(response.text) return data, 200

25.368421

85

0.607884

from flask_restful import Resource import requests import json import os import redis HEADER = { 'User-Agent': 'CompuServe Classic/1.22', 'Accept': 'application/json', 'Host': os.getenv("HOST"), 'Authorization': f'Bearer {os.getenv("ACESS_TOKEN")}' } class Search(Resource): """Recurso responsável por retornar lista de artistas, para o usuário escolher""" def get(self, artist_name): """ Retorna lista de artistas """ querystring = {"q": artist_name} url = f"https://{os.getenv('HOST')}/search" try: response = requests.get(url=url, headers=HEADER, params=querystring) if response.status_code != '200': return json.loads(response.text), response.status_code except Exception as e: print(e) return {"message": "Internal server error."}, 500 data = json.loads(response.text) return data, 200

0

7baca6067411cc1ecfa07468272839cd744972f8

441

py

Python

string/firstUniqueCharacterInAString.py

G-MontaG/leetcode

444e8ee3f395c191a86eae0e42d028060ecd1686

[ "MIT" ]

1

2021-02-10T18:14:55.000Z

string/firstUniqueCharacterInAString.py

G-MontaG/leetcode

444e8ee3f395c191a86eae0e42d028060ecd1686

[ "MIT" ]

null

string/firstUniqueCharacterInAString.py

G-MontaG/leetcode

444e8ee3f395c191a86eae0e42d028060ecd1686

[ "MIT" ]

null

# https://leetcode.com/problems/first-unique-character-in-a-string/

27.5625

67

0.519274

# https://leetcode.com/problems/first-unique-character-in-a-string/ class Solution: def firstUniqChar(self, s: str) -> int: mapping = {} for index, char in enumerate(s): if char in mapping.keys(): mapping[char] = -1 else: mapping[char] = index for char in mapping: if mapping[char] > -1: return mapping[char] return -1

331

-6

48

0cc5410e4e819af67fb7073f0bb5d856a89be207

453

py

Python

ktapp/migrations/0037_ktuser_fav_period.py

cu2/KT

8a0964b77dce150358637faa679d969a07e42f07

[ "CC-BY-3.0" ]

5

2015-04-13T09:44:31.000Z

2017-10-19T01:07:58.000Z

ktapp/migrations/0037_ktuser_fav_period.py

cu2/KT

8a0964b77dce150358637faa679d969a07e42f07

[ "CC-BY-3.0" ]

49

2015-02-15T07:12:05.000Z

2022-03-11T23:11:43.000Z

ktapp/migrations/0037_ktuser_fav_period.py

cu2/KT

8a0964b77dce150358637faa679d969a07e42f07

[ "CC-BY-3.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations

21.571429

74

0.602649

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('ktapp', '0036_ktuser_bio'), ] operations = [ migrations.AddField( model_name='ktuser', name='fav_period', field=models.CharField(max_length=250, null=True, blank=True), preserve_default=True, ), ]

0

323

23

45ca169aee71ee56ada82a211aa1e50134aad821

2,133

py

Python

mmtrack/datasets/youtube_vis_dataset.py

benxiao/mmtracking

4363a05659d5f26da97b9725075dcbb3b13f775f

[ "Apache-2.0" ]

1

2022-03-12T21:36:42.000Z

mmtrack/datasets/youtube_vis_dataset.py

Readpistol/mmtracking

131b8fb7c632324f88c3240229e411e801380f2a

[ "Apache-2.0" ]

null

mmtrack/datasets/youtube_vis_dataset.py

Readpistol/mmtracking

131b8fb7c632324f88c3240229e411e801380f2a

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. from mmdet.datasets import DATASETS from .coco_video_dataset import CocoVideoDataset @DATASETS.register_module() class YouTubeVISDataset(CocoVideoDataset): """YouTube VIS dataset for video instance segmentation.""" CLASSES_2019_version = ('person', 'giant_panda', 'lizard', 'parrot', 'skateboard', 'sedan', 'ape', 'dog', 'snake', 'monkey', 'hand', 'rabbit', 'duck', 'cat', 'cow', 'fish', 'train', 'horse', 'turtle', 'bear', 'motorbike', 'giraffe', 'leopard', 'fox', 'deer', 'owl', 'surfboard', 'airplane', 'truck', 'zebra', 'tiger', 'elephant', 'snowboard', 'boat', 'shark', 'mouse', 'frog', 'eagle', 'earless_seal', 'tennis_racket') CLASSES_2021_version = ('airplane', 'bear', 'bird', 'boat', 'car', 'cat', 'cow', 'deer', 'dog', 'duck', 'earless_seal', 'elephant', 'fish', 'flying_disc', 'fox', 'frog', 'giant_panda', 'giraffe', 'horse', 'leopard', 'lizard', 'monkey', 'motorbike', 'mouse', 'parrot', 'person', 'rabbit', 'shark', 'skateboard', 'snake', 'snowboard', 'squirrel', 'surfboard', 'tennis_racket', 'tiger', 'train', 'truck', 'turtle', 'whale', 'zebra') @classmethod

48.477273

79

0.506329

# Copyright (c) OpenMMLab. All rights reserved. from mmdet.datasets import DATASETS from .coco_video_dataset import CocoVideoDataset @DATASETS.register_module() class YouTubeVISDataset(CocoVideoDataset): """YouTube VIS dataset for video instance segmentation.""" CLASSES_2019_version = ('person', 'giant_panda', 'lizard', 'parrot', 'skateboard', 'sedan', 'ape', 'dog', 'snake', 'monkey', 'hand', 'rabbit', 'duck', 'cat', 'cow', 'fish', 'train', 'horse', 'turtle', 'bear', 'motorbike', 'giraffe', 'leopard', 'fox', 'deer', 'owl', 'surfboard', 'airplane', 'truck', 'zebra', 'tiger', 'elephant', 'snowboard', 'boat', 'shark', 'mouse', 'frog', 'eagle', 'earless_seal', 'tennis_racket') CLASSES_2021_version = ('airplane', 'bear', 'bird', 'boat', 'car', 'cat', 'cow', 'deer', 'dog', 'duck', 'earless_seal', 'elephant', 'fish', 'flying_disc', 'fox', 'frog', 'giant_panda', 'giraffe', 'horse', 'leopard', 'lizard', 'monkey', 'motorbike', 'mouse', 'parrot', 'person', 'rabbit', 'shark', 'skateboard', 'snake', 'snowboard', 'squirrel', 'surfboard', 'tennis_racket', 'tiger', 'train', 'truck', 'turtle', 'whale', 'zebra') def __init__(self, dataset_version, *args, **kwargs): self.set_dataset_classes(dataset_version) super().__init__(*args, **kwargs) @classmethod def set_dataset_classes(cls, dataset_version): if dataset_version == '2019': cls.CLASSES = cls.CLASSES_2019_version elif dataset_version == '2021': cls.CLASSES = cls.CLASSES_2021_version else: raise NotImplementedError('Not supported YouTubeVIS dataset' f'version: {dataset_version}')

485

0

53

b26e7b4e1b789021e57269548d99674e7d9e0fb6

2,198

py

Python

easyDiffractionApp/Logic/DisplayModels/StatusModel.py

rozyczko/easyDiffractionApp

6b088e3cb19f943e6eee0e86c3c23515b7c6a084

[ "BSD-3-Clause" ]

1

2021-05-25T15:26:44.000Z

easyDiffractionApp/Logic/DisplayModels/StatusModel.py

rozyczko/easyDiffractionApp

6b088e3cb19f943e6eee0e86c3c23515b7c6a084

[ "BSD-3-Clause" ]

138

2021-02-12T07:59:04.000Z

2022-03-26T12:07:19.000Z

easyDiffractionApp/Logic/DisplayModels/StatusModel.py

rozyczko/easyDiffractionApp

6b088e3cb19f943e6eee0e86c3c23515b7c6a084

[ "BSD-3-Clause" ]

3

2021-05-07T07:08:25.000Z

2021-11-02T09:53:26.000Z

# SPDX-FileCopyrightText: 2021 easyDiffraction contributors <support@easydiffraction.org> # SPDX-License-Identifier: BSD-3-Clause # © 2021 Contributors to the easyDiffraction project <https://github.com/easyScience/easyDiffractionApp> __author__ = 'github.com/andrewsazonov' __version__ = '0.0.1' from random import random from PySide2.QtCore import QPointF from PySide2.QtCharts import QtCharts

27.475

104

0.641492

# SPDX-FileCopyrightText: 2021 easyDiffraction contributors <support@easydiffraction.org> # SPDX-License-Identifier: BSD-3-Clause # © 2021 Contributors to the easyDiffraction project <https://github.com/easyScience/easyDiffractionApp> __author__ = 'github.com/andrewsazonov' __version__ = '0.0.1' from random import random from PySide2.QtCore import QPointF from PySide2.QtCharts import QtCharts class StatusModel: def __init__(self, paren=None): super().__init__(parent) def updateSeries(self): """ Generates new data and updates the GUI ChartView LineSeries. """ if not self._lowerSeriesRefs or not self._upperSeriesRefs: return lowerSeries = self._dataObj.get_lowerXY() upperSeries = self._dataObj.get_upperXY() for seriesRef in self._lowerSeriesRefs: seriesRef.replace(lowerSeries) for seriesRef in self._upperSeriesRefs: seriesRef.replace(upperSeries) def updateData(self, dataObj): """ Update ... """ self._dataObj = dataObj self.updateSeries() def addLowerSeriesRef(self, seriesRef): """ Sets series to be a reference to the GUI ChartView LineSeries. """ self._lowerSeriesRefs.append(seriesRef) def addUpperSeriesRef(self, seriesRef): """ Sets series to be a reference to the GUI ChartView LineSeries. """ self._upperSeriesRefs.append(seriesRef) class CalculatedDataModel: def __init__(self, dataObj=None): self._seriesRef = None self._dataObj = dataObj def updateSeries(self): """ Generates new data and updates the GUI ChartView LineSeries. """ if self._seriesRef is None: return series = self._dataObj.get_fit_XY() self._seriesRef.replace(series) def updateData(self, dataObj): """ Update ... """ self._dataObj = dataObj self.updateSeries() def setSeriesRef(self, seriesRef): """ Sets series to be a reference to the GUI ChartView LineSeries. """ self._seriesRef = seriesRef

118

1,633

46

41c794a5523ae6175185d6430eee0502fa65573d

1,349

py

Python

run.py

iustce/cesa-web

8b6b1fd8a66277b7319fdbf327e19948cc56917d

[ "MIT" ]

1

2018-10-13T19:48:05.000Z

run.py

iustce/cesa-web

8b6b1fd8a66277b7319fdbf327e19948cc56917d

[ "MIT" ]

null

run.py

iustce/cesa-web

8b6b1fd8a66277b7319fdbf327e19948cc56917d

[ "MIT" ]

null

# -*- coding: utf-8 -*- # python imports import os import subprocess import sys, traceback from flask.ext.migrate import MigrateCommand from flask.ext.script import Manager from database import manager as database_manager try: from project import app from project.application import configure_app from project.config import DefaultConfig, DevelopmentConfig, ProductionConfig except ImportError: print ' *** please install/update requirements or fix the problem ***' traceback.print_exc(file=sys.stdout) exit(0) manager = Manager(app) manager.add_command('database', database_manager) manager.add_command('migration', MigrateCommand) fwpath = os.path.abspath(os.path.dirname(__file__)) venv_dir = os.path.join(fwpath, 'venv') @manager.command @manager.command @manager.command @manager.command if __name__ == '__main__': manager.run()

24.981481

111

0.731653

# -*- coding: utf-8 -*- # python imports import os import subprocess import sys, traceback from flask.ext.migrate import MigrateCommand from flask.ext.script import Manager from database import manager as database_manager try: from project import app from project.application import configure_app from project.config import DefaultConfig, DevelopmentConfig, ProductionConfig except ImportError: print ' *** please install/update requirements or fix the problem ***' traceback.print_exc(file=sys.stdout) exit(0) manager = Manager(app) manager.add_command('database', database_manager) manager.add_command('migration', MigrateCommand) fwpath = os.path.abspath(os.path.dirname(__file__)) venv_dir = os.path.join(fwpath, 'venv') @manager.command def run(): configure_app(app, DevelopmentConfig()) app.run(host='0.0.0.0', port=5000) @manager.command def import_local_config_file(filename): if not os.path.isabs(filename): filename = os.path.join(os.getcwd(), filename) configure_app(app, filename, is_pyfile=True) app.run(host='0.0.0.0', port=5000) @manager.command def test(): pass @manager.command def update_requirements(): subprocess.call([os.path.join(venv_dir, 'bin/pip'), 'install', '-r', os.path.join(fwpath, 'requirements')]) if __name__ == '__main__': manager.run()

386

0

88

3622ebf53eb605a0ad50e3ba80cbe1fe001d8264

11,174

py

Python

docs/model.py

DLR-SC/gitlab2prov

0a548cf85121faa63ef9abbbf0d43aa4e0bc3d57

[ "MIT" ]

13

2019-10-14T19:28:04.000Z

2022-03-24T09:46:50.000Z

docs/model.py

DLR-SC/gitlab2prov

0a548cf85121faa63ef9abbbf0d43aa4e0bc3d57

[ "MIT" ]

50

2019-10-15T09:05:09.000Z

2022-03-28T10:51:22.000Z

docs/model.py

DLR-SC/gitlab2prov

0a548cf85121faa63ef9abbbf0d43aa4e0bc3d57

[ "MIT" ]

2

2020-05-16T15:40:04.000Z

2021-09-14T12:08:19.000Z

"""PROV model fpr GitLab2PROV.""" __author__ = "Claas de Boer, Andreas Schreiber, Lynn von Kurnatowski" __copyright__ = "Copyright 2020, German Aerospace Center (DLR) and individual contributors" __license__ = "MIT" __version__ = "0.5" __status__ = "Development" from prov.model import ProvDocument from prov.constants import PROV_LABEL from prov.dot import prov_to_dot add = ProvDocument() add.set_default_namespace("gitlab2prov:") add.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) add.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) add.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) add.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": ""}) add.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) add.entity("File Version", other_attributes={"prov:type": "file_version", "old_path": "", "new_path": ""}) add.wasInformedBy("Commit", "Parent Commit") add.wasAssociatedWith("Commit", "Committer") add.wasAssociatedWith("Commit", "Author") add.wasGeneratedBy("File", "Commit") add.wasGeneratedBy("File Version", "Commit") add.wasAttributedTo("File", "Author") add.wasAttributedTo("File Version", "Author") add.specializationOf("File Version", "File") mod = ProvDocument() mod.set_default_namespace("gitlab2prov:") mod.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""},) mod.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""},) mod.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) mod.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": "",}) mod.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) mod.entity("File Version N", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) mod.entity("File Version N-1", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) mod.wasInformedBy("Commit", "Parent Commit") mod.wasAssociatedWith("Commit", "Author") mod.wasAssociatedWith("Commit", "Committer") mod.used("Commit", "File Version N-1") mod.wasGeneratedBy("File Version N", "Commit") mod.wasRevisionOf("File Version N", "File Version N-1") mod.specializationOf("File Version N", "File") mod.specializationOf("File Version N-1", "File") mod.wasAttributedTo("File Version N", "Author") rem = ProvDocument() rem.set_default_namespace("gitlab2prov:") rem.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) rem.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) rem.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) rem.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": ""}) rem.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) rem.entity("File Version", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) rem.wasInformedBy("Commit", "Parent Commit") rem.wasAssociatedWith("Commit", "Committer") rem.wasAssociatedWith("Commit", "Author") rem.wasInvalidatedBy("File Version", "Commit") rem.specializationOf("File Version", "File") com = ProvDocument() com.set_default_namespace("gitlab2prov:") com.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""}) com.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) com.activity("Commit Creation", other_attributes={"prov:type": "creation", "prov:startedAt": "", "prov:endedAt": ""}) com.activity("Commit Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) com.activity("Git Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) com.wasInformedBy("Commit Creation", "Git Commit") com.entity("Commit", other_attributes={"prov:type": "commit_resource", "title": "", "message": "", "short_id": "", "id": ""}) com.entity("Commit Version", other_attributes={"prov:type": "commit_resource_version"}) com.entity("Annotated Commit Version", other_attributes={"prov:type": "commit_resource_version"},) com.wasAssociatedWith("Commit Creation", "Creator") com.wasAttributedTo("Commit", "Creator") com.wasAttributedTo("Commit Version", "Creator") com.wasGeneratedBy("Commit", "Commit Creation") com.wasGeneratedBy("Commit Version", "Commit Creation") com.wasAttributedTo("Annotated Commit Version", "Annotator") com.wasAssociatedWith("Commit Annotation", "Annotator") com.used("Commit Annotation", "Commit Version") com.wasInformedBy("Commit Annotation", "Commit Creation") com.wasGeneratedBy("Annotated Commit Version", "Commit Annotation") com.specializationOf("Commit Version", "Commit") com.specializationOf("Annotated Commit Version", "Commit") com.wasDerivedFrom("Annotated Commit Version", "Commit Version") mr = ProvDocument() mr.set_default_namespace("gitlab2prov:") mr.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""},) mr.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) mr.activity("Merge Request Creation", other_attributes={"prov:type": "merge_request_creation", "prov:startedAt": "", "prov:endedAt": ""}) mr.activity("Merge Request Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) mr.entity("Merge Request", other_attributes={"prov:type": "merge_request_resource", "id": "", "iid": "", "title": "", "description": "", "web_url": "", "project_id": "", "source_branch": "", "target_branch": "", "source_project_url": "", "target_project_url": ""}) mr.entity("Merge Request Version", other_attributes={"prov:type": "merge_request_resource_version"},) mr.entity("Annotated Merge Request Version", other_attributes={"prov:type": "merge_request_resource_version"},) mr.wasInformedBy("Merge Request Annotation", "Merge Request Creation") mr.wasGeneratedBy("Merge Request", "Merge Request Creation") mr.wasGeneratedBy("Merge Request Version", "Merge Request Creation") mr.wasGeneratedBy("Annotated Merge Request Version", "Merge Request Annotation") mr.used("Merge Request Annotation", "Merge Request Version") mr.specializationOf("Merge Request Version", "Merge Request") mr.specializationOf("Annotated Merge Request Version", "Merge Request") mr.wasDerivedFrom("Annotated Merge Request Version", "Merge Request Version") mr.wasAttributedTo("Annotated Merge Request Version", "Annotator") mr.wasAttributedTo("Merge Request Version", "Creator") mr.wasAttributedTo("Merge Request", "Creator") mr.wasAssociatedWith("Merge Request Creation", "Creator") mr.wasAssociatedWith("Merge Request Annotation", "Annotator") iss = ProvDocument() iss.set_default_namespace("gitlab2prov:") iss.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""}) iss.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) iss.activity("Issue Creation", other_attributes={"prov:type": "issue_creation", "prov:startedAt": "", "prov:endedAt": ""}) iss.activity("Issue Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) iss.entity("Issue", other_attributes={"prov:type": "issue_resource", "id": "", "iid": "", "title": "", "description": "", "project_id": "", "web_url": ""}) iss.entity("Issue Version", other_attributes={"prov:type": "issue_resource_version"}) iss.entity("Annotated Issue Version", other_attributes={"prov:type": "issue_resource_version"}) iss.wasInformedBy("Issue Annotation", "Issue Creation") iss.wasGeneratedBy("Issue", "Issue Creation") iss.wasGeneratedBy("Issue Version", "Issue Creation") iss.wasGeneratedBy("Annotated Issue Version", "Issue Annotation") iss.used("Issue Annotation", "Issue Version") iss.specializationOf("Issue Version", "Issue") iss.specializationOf("Annotated Issue Version", "Issue") iss.wasDerivedFrom("Annotated Issue Version", "Issue Version") iss.wasAttributedTo("Annotated Issue Version", "Annotator") iss.wasAttributedTo("Issue Version", "Creator") iss.wasAttributedTo("Issue", "Creator") iss.wasAssociatedWith("Issue Creation", "Creator") iss.wasAssociatedWith("Issue Annotation", "Annotator") release_tag_model = ProvDocument() release_tag_model.set_default_namespace("gitlab2prov:") release_tag_model.agent("User", {"name": "", "email": ""}) release_tag_model.activity("Release_Event") release_tag_model.activity("Tag_Event") release_tag_model.activity("Commit_Event") release_tag_model.entity("Tag", {"prov:type": "prov:Collection", "name": "", "message": "", "commit": "", "target_commit": ""}) release_tag_model.entity("Release", {"prov:type": "prov:Collection", "name": "", "tag_name": "", "description": "", "created_at": "", "released_at": "", "commit_path": "", "tag_path": ""}) release_tag_model.entity("Commit", {"id": "", "short_id": "", "title": "", "message": "", "web_url": "", "created_at": ""}) release_tag_model.entity("Release_Evidence", {"sha": "", "filepath": "", "collected_at": ""}) release_tag_model.entity("Release_Asset", {"uri": "", "format": "", "filepath": ""}) release_tag_model.hadMember("Release_Asset", "Release") release_tag_model.hadMember("Release_Evidence", "Release") release_tag_model.hadMember("Tag", "Release") release_tag_model.hadMember("Commit", "Tag") release_tag_model.wasAssociatedWith("Commit_Event", "User") release_tag_model.wasAssociatedWith("Release_Event", "User") release_tag_model.wasAssociatedWith("Tag_Event", "User") release_tag_model.wasAttributedTo("Release", "User") release_tag_model.wasAttributedTo("Tag", "User") release_tag_model.wasAttributedTo("Commit", "User") release_tag_model.wasGeneratedBy("Release", "Release_Event") release_tag_model.wasGeneratedBy("Tag", "Tag_Event") release_tag_model.wasGeneratedBy("Commit", "Commit_Event") for title, doc in [ ("git_commit_model_add", add), ("git_commit_model_mod", mod), ("git_commit_model_del", rem), ("gitlab_commit_model", com), ("gitlab_issue_model", iss), ("gitlab_merge_request_model", mr), ("gitlab_release_tag_model", release_tag_model) ]: prov_to_dot(doc, show_nary=False, use_labels=False, direction="BT").write_pdf( f"pdfs/{title}.pdf" ) prov_to_dot(doc, show_nary=False, use_labels=False, direction="BT").write_svg( f"svgs/{title}.svg" )

61.060109

264

0.707625

"""PROV model fpr GitLab2PROV.""" __author__ = "Claas de Boer, Andreas Schreiber, Lynn von Kurnatowski" __copyright__ = "Copyright 2020, German Aerospace Center (DLR) and individual contributors" __license__ = "MIT" __version__ = "0.5" __status__ = "Development" from prov.model import ProvDocument from prov.constants import PROV_LABEL from prov.dot import prov_to_dot add = ProvDocument() add.set_default_namespace("gitlab2prov:") add.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) add.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) add.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) add.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": ""}) add.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) add.entity("File Version", other_attributes={"prov:type": "file_version", "old_path": "", "new_path": ""}) add.wasInformedBy("Commit", "Parent Commit") add.wasAssociatedWith("Commit", "Committer") add.wasAssociatedWith("Commit", "Author") add.wasGeneratedBy("File", "Commit") add.wasGeneratedBy("File Version", "Commit") add.wasAttributedTo("File", "Author") add.wasAttributedTo("File Version", "Author") add.specializationOf("File Version", "File") mod = ProvDocument() mod.set_default_namespace("gitlab2prov:") mod.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""},) mod.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""},) mod.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) mod.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": "",}) mod.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) mod.entity("File Version N", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) mod.entity("File Version N-1", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) mod.wasInformedBy("Commit", "Parent Commit") mod.wasAssociatedWith("Commit", "Author") mod.wasAssociatedWith("Commit", "Committer") mod.used("Commit", "File Version N-1") mod.wasGeneratedBy("File Version N", "Commit") mod.wasRevisionOf("File Version N", "File Version N-1") mod.specializationOf("File Version N", "File") mod.specializationOf("File Version N-1", "File") mod.wasAttributedTo("File Version N", "Author") rem = ProvDocument() rem.set_default_namespace("gitlab2prov:") rem.activity("Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) rem.activity("Parent Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) rem.agent("Committer", other_attributes={"prov:type": "user", "prov:role": "committer", "name": "", "email": ""}) rem.agent("Author", other_attributes={"prov:type": "user", "prov:role": "author", "name": "", "email": ""}) rem.entity("File", other_attributes={"prov:type": "file", "path_at_addition": ""}) rem.entity("File Version", other_attributes={"prov:type": "file_version", "new_path": "", "old_path": ""}) rem.wasInformedBy("Commit", "Parent Commit") rem.wasAssociatedWith("Commit", "Committer") rem.wasAssociatedWith("Commit", "Author") rem.wasInvalidatedBy("File Version", "Commit") rem.specializationOf("File Version", "File") com = ProvDocument() com.set_default_namespace("gitlab2prov:") com.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""}) com.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) com.activity("Commit Creation", other_attributes={"prov:type": "creation", "prov:startedAt": "", "prov:endedAt": ""}) com.activity("Commit Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) com.activity("Git Commit", other_attributes={"prov:type": "commit", "title": "", "message": "", "id": "", "short_id": "", "prov:startedAt": "", "prov:endedAt": ""}) com.wasInformedBy("Commit Creation", "Git Commit") com.entity("Commit", other_attributes={"prov:type": "commit_resource", "title": "", "message": "", "short_id": "", "id": ""}) com.entity("Commit Version", other_attributes={"prov:type": "commit_resource_version"}) com.entity("Annotated Commit Version", other_attributes={"prov:type": "commit_resource_version"},) com.wasAssociatedWith("Commit Creation", "Creator") com.wasAttributedTo("Commit", "Creator") com.wasAttributedTo("Commit Version", "Creator") com.wasGeneratedBy("Commit", "Commit Creation") com.wasGeneratedBy("Commit Version", "Commit Creation") com.wasAttributedTo("Annotated Commit Version", "Annotator") com.wasAssociatedWith("Commit Annotation", "Annotator") com.used("Commit Annotation", "Commit Version") com.wasInformedBy("Commit Annotation", "Commit Creation") com.wasGeneratedBy("Annotated Commit Version", "Commit Annotation") com.specializationOf("Commit Version", "Commit") com.specializationOf("Annotated Commit Version", "Commit") com.wasDerivedFrom("Annotated Commit Version", "Commit Version") mr = ProvDocument() mr.set_default_namespace("gitlab2prov:") mr.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""},) mr.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) mr.activity("Merge Request Creation", other_attributes={"prov:type": "merge_request_creation", "prov:startedAt": "", "prov:endedAt": ""}) mr.activity("Merge Request Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) mr.entity("Merge Request", other_attributes={"prov:type": "merge_request_resource", "id": "", "iid": "", "title": "", "description": "", "web_url": "", "project_id": "", "source_branch": "", "target_branch": "", "source_project_url": "", "target_project_url": ""}) mr.entity("Merge Request Version", other_attributes={"prov:type": "merge_request_resource_version"},) mr.entity("Annotated Merge Request Version", other_attributes={"prov:type": "merge_request_resource_version"},) mr.wasInformedBy("Merge Request Annotation", "Merge Request Creation") mr.wasGeneratedBy("Merge Request", "Merge Request Creation") mr.wasGeneratedBy("Merge Request Version", "Merge Request Creation") mr.wasGeneratedBy("Annotated Merge Request Version", "Merge Request Annotation") mr.used("Merge Request Annotation", "Merge Request Version") mr.specializationOf("Merge Request Version", "Merge Request") mr.specializationOf("Annotated Merge Request Version", "Merge Request") mr.wasDerivedFrom("Annotated Merge Request Version", "Merge Request Version") mr.wasAttributedTo("Annotated Merge Request Version", "Annotator") mr.wasAttributedTo("Merge Request Version", "Creator") mr.wasAttributedTo("Merge Request", "Creator") mr.wasAssociatedWith("Merge Request Creation", "Creator") mr.wasAssociatedWith("Merge Request Annotation", "Annotator") iss = ProvDocument() iss.set_default_namespace("gitlab2prov:") iss.agent("Creator", other_attributes={"prov:type": "user", "prov:role": "creator", "name": ""}) iss.agent("Annotator", other_attributes={"prov:type": "user", "prov:role": "initiator", "name": ""}) iss.activity("Issue Creation", other_attributes={"prov:type": "issue_creation", "prov:startedAt": "", "prov:endedAt": ""}) iss.activity("Issue Annotation", other_attributes={"prov:type": "event", "prov:startedAt": "", "prov:endedAt": "", "event": ""}) iss.entity("Issue", other_attributes={"prov:type": "issue_resource", "id": "", "iid": "", "title": "", "description": "", "project_id": "", "web_url": ""}) iss.entity("Issue Version", other_attributes={"prov:type": "issue_resource_version"}) iss.entity("Annotated Issue Version", other_attributes={"prov:type": "issue_resource_version"}) iss.wasInformedBy("Issue Annotation", "Issue Creation") iss.wasGeneratedBy("Issue", "Issue Creation") iss.wasGeneratedBy("Issue Version", "Issue Creation") iss.wasGeneratedBy("Annotated Issue Version", "Issue Annotation") iss.used("Issue Annotation", "Issue Version") iss.specializationOf("Issue Version", "Issue") iss.specializationOf("Annotated Issue Version", "Issue") iss.wasDerivedFrom("Annotated Issue Version", "Issue Version") iss.wasAttributedTo("Annotated Issue Version", "Annotator") iss.wasAttributedTo("Issue Version", "Creator") iss.wasAttributedTo("Issue", "Creator") iss.wasAssociatedWith("Issue Creation", "Creator") iss.wasAssociatedWith("Issue Annotation", "Annotator") release_tag_model = ProvDocument() release_tag_model.set_default_namespace("gitlab2prov:") release_tag_model.agent("User", {"name": "", "email": ""}) release_tag_model.activity("Release_Event") release_tag_model.activity("Tag_Event") release_tag_model.activity("Commit_Event") release_tag_model.entity("Tag", {"prov:type": "prov:Collection", "name": "", "message": "", "commit": "", "target_commit": ""}) release_tag_model.entity("Release", {"prov:type": "prov:Collection", "name": "", "tag_name": "", "description": "", "created_at": "", "released_at": "", "commit_path": "", "tag_path": ""}) release_tag_model.entity("Commit", {"id": "", "short_id": "", "title": "", "message": "", "web_url": "", "created_at": ""}) release_tag_model.entity("Release_Evidence", {"sha": "", "filepath": "", "collected_at": ""}) release_tag_model.entity("Release_Asset", {"uri": "", "format": "", "filepath": ""}) release_tag_model.hadMember("Release_Asset", "Release") release_tag_model.hadMember("Release_Evidence", "Release") release_tag_model.hadMember("Tag", "Release") release_tag_model.hadMember("Commit", "Tag") release_tag_model.wasAssociatedWith("Commit_Event", "User") release_tag_model.wasAssociatedWith("Release_Event", "User") release_tag_model.wasAssociatedWith("Tag_Event", "User") release_tag_model.wasAttributedTo("Release", "User") release_tag_model.wasAttributedTo("Tag", "User") release_tag_model.wasAttributedTo("Commit", "User") release_tag_model.wasGeneratedBy("Release", "Release_Event") release_tag_model.wasGeneratedBy("Tag", "Tag_Event") release_tag_model.wasGeneratedBy("Commit", "Commit_Event") for title, doc in [ ("git_commit_model_add", add), ("git_commit_model_mod", mod), ("git_commit_model_del", rem), ("gitlab_commit_model", com), ("gitlab_issue_model", iss), ("gitlab_merge_request_model", mr), ("gitlab_release_tag_model", release_tag_model) ]: prov_to_dot(doc, show_nary=False, use_labels=False, direction="BT").write_pdf( f"pdfs/{title}.pdf" ) prov_to_dot(doc, show_nary=False, use_labels=False, direction="BT").write_svg( f"svgs/{title}.svg" )

0

f495910cbad974850a149f592f1022624205f1c7

9,479

py

Python

scripts/go_stats_utils.py

kltm/go-site

fe6797ed1291bd0d12df83b7c9d670c91a0fb526

[ "BSD-3-Clause" ]

31

2016-11-01T13:11:43.000Z

2022-02-28T05:05:16.000Z

scripts/go_stats_utils.py

kltm/go-site

fe6797ed1291bd0d12df83b7c9d670c91a0fb526

[ "BSD-3-Clause" ]

1,172

2015-01-29T23:47:53.000Z

2022-03-30T05:22:01.000Z

scripts/go_stats_utils.py

kltm/go-site

fe6797ed1291bd0d12df83b7c9d670c91a0fb526

[ "BSD-3-Clause" ]

92

2015-02-11T03:10:55.000Z

2022-03-01T08:16:02.000Z

import json import requests from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry from enum import Enum # This is a hard coded list of evidence, better organized for readability ev_all = ['EXP', 'IDA', 'IMP', 'IGI', 'IPI', 'IEP', 'IGC', 'RCA', 'IBA', 'IKR', 'IC', 'NAS', 'ND', 'TAS', 'HDA', 'HEP', 'HGI', 'HMP', 'ISA', 'ISM', 'ISO', 'ISS', 'IEA'] # This is a hard coded list of reference genomes that should always be present in a GO release REFERENCE_GENOME_IDS = [ "NCBITaxon:9606", "NCBITaxon:10116", "NCBITaxon:10090", "NCBITaxon:3702", "NCBITaxon:7955", "NCBITaxon:6239", "NCBITaxon:559292", "NCBITaxon:7227", "NCBITaxon:44689", "NCBITaxon:4896", "NCBITaxon:83333" ] BP_TERM_ID = "GO:0008150" MF_TERM_ID = "GO:0003674" CC_TERM_ID = "GO:0005575" # useful grouping of evidences as discussed with Pascale EVIDENCE_GROUPS = { "EXP": ["EXP", "IDA", "IEP", "IGI", "IMP", "IPI"], "HTP": ["HDA", "HEP", "HGI", "HMP", "HTP"], "PHYLO": ["IBA", "IRD", "IKR", "IMR"], "IEA": ["IEA"], "ND": ["ND"], "OTHER": ["IC", "IGC", "ISA", "ISM", "ISO", "ISS", "NAS", "RCA", "TAS"] } EVIDENCE_MIN_GROUPS = { "EXPERIMENTAL" : EVIDENCE_GROUPS["EXP"] + EVIDENCE_GROUPS["HTP"], "COMPUTATIONAL" : EVIDENCE_GROUPS["PHYLO"] + EVIDENCE_GROUPS["IEA"] + EVIDENCE_GROUPS["OTHER"] } global_session = None def fetch(url): """ Error proof method to get data from HTTP request If an error occured, return None """ global global_session # Ensure we are using the same session - creating too many sessions could crash this script if global_session is None: global_session = requests_retry(global_session) try: r = global_session.get(url) return r except Exception as x: print("Query GET " , url , " failed: ", x) return None def golr_fetch(golr_base_url, select_query): """ Error proof method to get data from GOLr If an HTTP error occurs, return None, otherwise return the json object """ r = fetch(golr_base_url + select_query) if r is None: return None response = r.json() return response # utility function to build a list from a solr/golr facet array # utility function to transform a list [A, 1, B, 2] into a map {A: 1, B: 2} # utility function to build a reverse map: { "a": 1, "b": 1, "c": 2 } -> {1: ["a", "b"], 2: ["c"]} # utility function to cluster elements of an input map based on another map of synonyms # similar as above but the value of each key is also a map # reorder map (python 3.6 keeps order in which items are inserted in map: https://stackoverflow.com/questions/613183/how-do-i-sort-a-dictionary-by-value) def bioentity_type(str_type): """ In a nutshell, collapse all RNA related types into RNA """ if "RNA" in str_type or "ribozyme" in str_type or "transcript" in str_type: return "RNA_cluster" return str_type def sum_map_values(map): """ Utility function to sum up the values of a map. Assume the map values are all numbers """ total = 0 for key, val in map.items(): total += val return total

31.387417

169

0.606815

import json import requests from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry from enum import Enum # This is a hard coded list of evidence, better organized for readability ev_all = ['EXP', 'IDA', 'IMP', 'IGI', 'IPI', 'IEP', 'IGC', 'RCA', 'IBA', 'IKR', 'IC', 'NAS', 'ND', 'TAS', 'HDA', 'HEP', 'HGI', 'HMP', 'ISA', 'ISM', 'ISO', 'ISS', 'IEA'] class CLOSURE_LABELS(Enum): ISA = "isa_closure" ISA_PARTOF = "isa_partof_closure" REGULATES = "regulates_closure" # This is a hard coded list of reference genomes that should always be present in a GO release REFERENCE_GENOME_IDS = [ "NCBITaxon:9606", "NCBITaxon:10116", "NCBITaxon:10090", "NCBITaxon:3702", "NCBITaxon:7955", "NCBITaxon:6239", "NCBITaxon:559292", "NCBITaxon:7227", "NCBITaxon:44689", "NCBITaxon:4896", "NCBITaxon:83333" ] BP_TERM_ID = "GO:0008150" MF_TERM_ID = "GO:0003674" CC_TERM_ID = "GO:0005575" # useful grouping of evidences as discussed with Pascale EVIDENCE_GROUPS = { "EXP": ["EXP", "IDA", "IEP", "IGI", "IMP", "IPI"], "HTP": ["HDA", "HEP", "HGI", "HMP", "HTP"], "PHYLO": ["IBA", "IRD", "IKR", "IMR"], "IEA": ["IEA"], "ND": ["ND"], "OTHER": ["IC", "IGC", "ISA", "ISM", "ISO", "ISS", "NAS", "RCA", "TAS"] } EVIDENCE_MIN_GROUPS = { "EXPERIMENTAL" : EVIDENCE_GROUPS["EXP"] + EVIDENCE_GROUPS["HTP"], "COMPUTATIONAL" : EVIDENCE_GROUPS["PHYLO"] + EVIDENCE_GROUPS["IEA"] + EVIDENCE_GROUPS["OTHER"] } def is_experimental(evidence_type): return evidence_type in EVIDENCE_MIN_GROUPS["EXPERIMENTAL"] def is_computational(evidence_type): return evidence_type in EVIDENCE_MIN_GROUPS["COMPUTATIONAL"] def get_evidence_min_group(evidence_type): for group, codes in EVIDENCE_MIN_GROUPS.items(): if evidence_type in codes: return group return "ND" def aspect_from_source(source): if source == "molecular_function": return "MF" elif source == "biological_process": return "BP" elif source == "cellular_component": return "CC" return "UNK" global_session = None def requests_retry(retries = 3, backoff = 0.3, session = None): session = session or requests.Session() retry = Retry( total = retries, read = retries, connect = retries, backoff_factor = backoff, status_forcelist = (429, 500, 502, 503, 504) ) adapter = HTTPAdapter(max_retries=retry) session.mount("http://", adapter) session.mount("https://", adapter) return session def fetch(url): """ Error proof method to get data from HTTP request If an error occured, return None """ global global_session # Ensure we are using the same session - creating too many sessions could crash this script if global_session is None: global_session = requests_retry(global_session) try: r = global_session.get(url) return r except Exception as x: print("Query GET " , url , " failed: ", x) return None def post(url, params): global global_session global_session = requests_retry(global_session) try: r = global_session.post(url, data = params) return r except Exception as x: print("Query POST " , url , " failed: ", x) return None def golr_fetch(golr_base_url, select_query): """ Error proof method to get data from GOLr If an HTTP error occurs, return None, otherwise return the json object """ r = fetch(golr_base_url + select_query) if r is None: return None response = r.json() return response def golr_fetch_by_taxon(golr_base_url, select_query, taxon): return golr_fetch(golr_base_url, select_query + "&fq=taxon:\"" + taxon + "\"") def golr_fetch_by_taxa(golr_base_url, select_query, taxa): tmp = "" if isinstance(taxa, list): tmp = "&fq=taxon:(\"" + taxa.join("\" ") + "\")" else: tmp = "&fq=taxon:\"" + taxa + "\"" print("*** ", golr_base_url + select_query + tmp) return golr_fetch(golr_base_url, select_query + tmp) # utility function to build a list from a solr/golr facet array def build_list(items_list, min_size = None): ls = [] for i in range(0, len(items_list), 2): if min_size is None or items_list[i + 1] > min_size: ls.append(items_list[i]) return ls # utility function to transform a list [A, 1, B, 2] into a map {A: 1, B: 2} def build_map(items_list, min_size = None): map = {} for i in range(0, len(items_list), 2): if min_size is None or items_list[i + 1] > min_size: map[items_list[i]] = items_list[i + 1] return map # utility function to build a reverse map: { "a": 1, "b": 1, "c": 2 } -> {1: ["a", "b"], 2: ["c"]} def build_reverse_map(map): reverse_map = { } for key, val in map.items(): ls = [] if val in reverse_map: ls = reverse_map[val] else: reverse_map[val] = ls ls.append(key) return reverse_map # utility function to cluster elements of an input map based on another map of synonyms def cluster_map(input_map, synonyms): cluster = { } for key, val in input_map.items(): temp = synonyms[key] if temp in cluster: val_cluster = cluster[temp] cluster[temp] = val_cluster + val else: cluster[temp] = val return cluster # similar as above but the value of each key is also a map def cluster_complex_map(input_map, synonyms): cluster = { } for key, val in input_map.items(): temp = synonyms[key] # print("working on : " , key , val) if temp in cluster: temp_cluster = cluster[temp] # print("cluster already found : ", temp , temp_cluster) for key_cluster, val_cluster in temp_cluster.items(): temp_cluster[key_cluster] = val_cluster + val[key_cluster] else: cluster[temp] = val return cluster # reorder map (python 3.6 keeps order in which items are inserted in map: https://stackoverflow.com/questions/613183/how-do-i-sort-a-dictionary-by-value) def ordered_map(map): ordered_map = { } for w in sorted(map, key=map.get, reverse=True): ordered_map[w] = map[w] return ordered_map def extract_map(map, key_str): extracted = { } for key, val in map.items(): if key_str in key: extracted[key] = val return extracted def merge_dict(dict_total, dict_diff): new_dict = { } for key, val in dict_total.items(): if type(val) == str: new_dict[key] = val elif type(val) == int or type(val) == float: if val == 0: diff_val = dict_diff[key] if key in dict_diff else 0 new_dict[key] = str(diff_val) + " / " + str(val) + "\t0%" else: diff_val = dict_diff[key] if key in dict_diff else 0 new_dict[key] = str(diff_val) + " / " + str(val) + "\t" + str(round(100 * diff_val / val, 2)) + "%" elif type(val) == dict: diff_val = dict_diff[key] if key in dict_diff else { } new_dict[key] = merge_dict(val, diff_val) else: print("should not happened ! " , val , type(val)) return new_dict def minus_dict(dict1, dict2): new_dict = { } for key, val in dict1.items(): if type(val) == str: new_dict[key] = val elif type(val) == int or type(val) == float: diff_val = dict2[key] if key in dict2 else 0 new_dict[key] = val - diff_val elif type(val) == dict: diff_val = dict2[key] if key in dict2 else { } new_dict[key] = merge_dict(val, diff_val) else: print("should not happened ! " , val , type(val)) return new_dict def has_taxon(stats, taxon_id): for taxon in stats["annotations"]["by_taxon"]: if taxon_id in taxon: return True return False def added_removed_species(current_stats, previous_stats): results = { "added" : { }, "removed" : { } } for taxon in current_stats["annotations"]["by_taxon"]: taxon_id = taxon.split("|")[0] if not has_taxon(previous_stats, taxon_id): results["added"][taxon] = current_stats["annotations"]["by_taxon"][taxon] for taxon in previous_stats["annotations"]["by_taxon"]: taxon_id = taxon.split("|")[0] if not has_taxon(current_stats, taxon_id): results["removed"][taxon] = previous_stats["annotations"]["by_taxon"][taxon] return results def bioentity_type(str_type): """ In a nutshell, collapse all RNA related types into RNA """ if "RNA" in str_type or "ribozyme" in str_type or "transcript" in str_type: return "RNA_cluster" return str_type def sum_map_values(map): """ Utility function to sum up the values of a map. Assume the map values are all numbers """ total = 0 for key, val in map.items(): total += val return total def write_json(key, content): with open(key, 'w') as outfile: try: json.dump(content, outfile, indent=2) finally: outfile.close() def write_text(key, content): with open(key, 'w') as outfile: try: outfile.write(content) finally: outfile.close()

5,663

101

517

5fcb1e0039071dfe15f9923cb83138fdd37d3701

774

py

Python

tests/mixins/urls_mixin.py

Nyior/django-rest-paystack

fd74dd26703fe4ce63664736c2063ace7020f71a

[ "MIT" ]

9

2021-12-12T17:59:15.000Z

2022-02-05T17:13:46.000Z

tests/mixins/urls_mixin.py

Nyior/django-rest-paystack

fd74dd26703fe4ce63664736c2063ace7020f71a

[ "MIT" ]

null

tests/mixins/urls_mixin.py

Nyior/django-rest-paystack

fd74dd26703fe4ce63664736c2063ace7020f71a

[ "MIT" ]

1

2021-12-21T18:57:03.000Z

from django.urls import reverse

27.642857

78

0.706718

from django.urls import reverse class URLsMixin(object): def initiate_transaction_url(self): return reverse("transaction-initiate") def verify_transaction_url(self, trans_ref): return reverse("transaction-verify") + f"?transaction_ref={trans_ref}" def charge_customer_url(self): return reverse("transaction-charge-customer") def transaction_url(self, transaction_id): return reverse("transaction-detail") def all_transactions_url(self): return reverse("transaction-list") def webhook_handler_url(self): return reverse("webhook-handler") def get_customer_url(self, user_id): return reverse("customer-detail") def all_customers_url(self): return reverse("customer-list")

500

3

238

841c91691e5e3f9f8e364f9c80db23924bcbaafd

102

py

Python

notebooks/exercise_solutions/n00_python_intro_data-structures.py

pydy/pydy-tutorial-human-standing

72b1d8513e339e9b10e501bd3490caa3fa997bc4

[ "CC-BY-4.0" ]

134

2015-05-19T15:24:18.000Z

2022-03-12T09:39:03.000Z

notebooks/exercise_solutions/n00_python_intro_data-structures.py

pydy/pydy-tutorial-human-standing

72b1d8513e339e9b10e501bd3490caa3fa997bc4

[ "CC-BY-4.0" ]

46

2015-05-05T18:08:20.000Z

2022-01-28T11:12:42.000Z

notebooks/exercise_solutions/n00_python_intro_data-structures.py

pydy/pydy-tutorial-pycon-2014

72b1d8513e339e9b10e501bd3490caa3fa997bc4

[ "CC-BY-4.0" ]

62

2015-06-16T01:50:51.000Z

2022-02-26T07:39:41.000Z

num_list = [1,2,3,4] months = ['Jan', 'Feb', 'Mar', 'Apr'] months_dict = dict(zip(months, num_list))

20.4

41

0.607843

num_list = [1,2,3,4] months = ['Jan', 'Feb', 'Mar', 'Apr'] months_dict = dict(zip(months, num_list))

0

fe92b53f5b3777d23e5c45c05e94c9a44f57b7aa

345

py

Python

calingen/interfaces/__init__.py

Mischback/django-calingen

3354c751e29d301609ec44e64d69a8729ec36de4

[ "MIT" ]

null

calingen/interfaces/__init__.py

Mischback/django-calingen

3354c751e29d301609ec44e64d69a8729ec36de4

[ "MIT" ]

51

2021-11-15T20:44:19.000Z

2022-02-10T08:33:08.000Z

calingen/interfaces/__init__.py

Mischback/django-calingen

3354c751e29d301609ec44e64d69a8729ec36de4

[ "MIT" ]

null

# SPDX-License-Identifier: MIT """The application's interfaces that are used to connect the different components. Notes ----- This package's code is not really specific to the Django framework. It is an abstraction layer. Primary focus is the provision of a plugin API, that allows the app to be extendable with third-party applications. """

26.538462

82

0.773913

# SPDX-License-Identifier: MIT """The application's interfaces that are used to connect the different components. Notes ----- This package's code is not really specific to the Django framework. It is an abstraction layer. Primary focus is the provision of a plugin API, that allows the app to be extendable with third-party applications. """

0

8b3f5bf2170e6f1f55a1f584308e631727c5174c

1,387

py

Python

Final Project/src/main.py

tig3r66/CMPUT275

dd5b94dcf0436e281f4696959db07b56f5c0b9d8

[ "MIT" ]

1

2022-01-25T05:19:15.000Z

Final Project/src/main.py

tig3r66/CMPUT275

dd5b94dcf0436e281f4696959db07b56f5c0b9d8

[ "MIT" ]

null

Final Project/src/main.py

tig3r66/CMPUT275

dd5b94dcf0436e281f4696959db07b56f5c0b9d8

[ "MIT" ]

null

# =================================== # Name: Edward (Eddie) Guo # ID: 1576381 # Partner: Jason Kim # CMPUT 275, Fall 2020 # # Final Assignment: EEG Visualizer # =================================== """ Contains the QApplication which holds the PlotWindow QMainWindow object. The controller class is here for convenient additions of extra QMainWindows. """ import sys # for UI from PyQt5 import QtCore, QtWidgets from plot_window import PlotWindow class Controller: """Controller class for slave QMainWindows. Used for expandability in case the user wishes to create additional windows for the program (ex: home window). """ def show_plot_window(self): """Creates the main window (EEG and FFT plots) from plot_window.py. """ self.plot_window = QtWidgets.QMainWindow() self.ui = PlotWindow() self.ui.setup_ui(self.plot_window) self.plot_window.setWindowFlags(QtCore.Qt.Window) self.plot_window.show() app.aboutToQuit.connect(self.close_threads) def close_threads(self): """Helper function that closes all running threads when the application is about to quit. """ self.ui.close_threads() if __name__ == '__main__': app = QtWidgets.QApplication(sys.argv) controller = Controller() controller.show_plot_window() sys.exit(app.exec_())

26.673077

79

0.651045

# =================================== # Name: Edward (Eddie) Guo # ID: 1576381 # Partner: Jason Kim # CMPUT 275, Fall 2020 # # Final Assignment: EEG Visualizer # =================================== """ Contains the QApplication which holds the PlotWindow QMainWindow object. The controller class is here for convenient additions of extra QMainWindows. """ import sys # for UI from PyQt5 import QtCore, QtWidgets from plot_window import PlotWindow class Controller: """Controller class for slave QMainWindows. Used for expandability in case the user wishes to create additional windows for the program (ex: home window). """ def show_plot_window(self): """Creates the main window (EEG and FFT plots) from plot_window.py. """ self.plot_window = QtWidgets.QMainWindow() self.ui = PlotWindow() self.ui.setup_ui(self.plot_window) self.plot_window.setWindowFlags(QtCore.Qt.Window) self.plot_window.show() app.aboutToQuit.connect(self.close_threads) def close_threads(self): """Helper function that closes all running threads when the application is about to quit. """ self.ui.close_threads() if __name__ == '__main__': app = QtWidgets.QApplication(sys.argv) controller = Controller() controller.show_plot_window() sys.exit(app.exec_())

0

9bc6d6d5809746ae0dfad11e6d7e815c885010be

929

py

Python

utilipy/data_utils/tests/test_init.py

nstarman/utilipy

17984942145d31126724df23500bafba18fb7516

[ "BSD-3-Clause" ]

2

2020-11-15T01:48:45.000Z

2020-12-02T20:44:20.000Z

utilipy/data_utils/tests/test_init.py

nstarman/astroPHD

17984942145d31126724df23500bafba18fb7516

[ "BSD-3-Clause" ]

22

2020-09-13T17:58:24.000Z

2022-02-04T19:05:23.000Z

utilipy/data_utils/tests/test_init.py

nstarman/utilipy

17984942145d31126724df23500bafba18fb7516

[ "BSD-3-Clause" ]

1

2020-04-21T22:41:01.000Z

# -*- coding: utf-8 -*- """Test Code in __init__.""" __all__ = [ "test_get_path_to_file", ] ############################################################################## # IMPORTS # BUILT-IN import os.path # PROJECT-SPECIFIC from utilipy.data_utils.utils import get_path_to_file ############################################################################## # PARAMETERS ############################################################################## # CODE ############################################################################## # /def # ------------------------------------------------------------------- ############################################################################## # END

21.113636

78

0.339074

# -*- coding: utf-8 -*- """Test Code in __init__.""" __all__ = [ "test_get_path_to_file", ] ############################################################################## # IMPORTS # BUILT-IN import os.path # PROJECT-SPECIFIC from utilipy.data_utils.utils import get_path_to_file ############################################################################## # PARAMETERS ############################################################################## # CODE ############################################################################## def test_get_path_to_file(): path = get_path_to_file("__init__.py", package="utilipy.data_utils") assert isinstance(path, str) assert os.path.join("utilipy", "data_utils", "__init__.py") in path # /def # ------------------------------------------------------------------- ############################################################################## # END

187

0

23

22e65f52c1dd2e9a786884bce3811c3aa03273e2

2,579

py

Python

eStore/migrations/0005_auto_20210420_2220.py

masrufjaman/gas-n-go

435e574a1b1bbd875a8a7aeade4d4c2dc1636b07

[ "MIT" ]

null

eStore/migrations/0005_auto_20210420_2220.py

masrufjaman/gas-n-go

435e574a1b1bbd875a8a7aeade4d4c2dc1636b07

[ "MIT" ]

9

2021-03-22T18:36:25.000Z

2021-04-20T17:39:47.000Z

eStore/migrations/0005_auto_20210420_2220.py

masrufjaman/gas-n-go

435e574a1b1bbd875a8a7aeade4d4c2dc1636b07

[ "MIT" ]

2

2021-06-30T14:39:52.000Z

2021-08-12T19:41:11.000Z

# Generated by Django 3.1.7 on 2021-04-20 16:20 from django.db import migrations, models import django.db.models.deletion

40.936508

150

0.598682

# Generated by Django 3.1.7 on 2021-04-20 16:20 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('accounts', '0006_auto_20210406_1400'), ('eStore', '0004_item_discount_price'), ] operations = [ migrations.RemoveField( model_name='order', name='items', ), migrations.AddField( model_name='order', name='transaction_id', field=models.BooleanField(max_length=200, null=True), ), migrations.AddField( model_name='orderitem', name='order', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='eStore.order'), ), migrations.AddField( model_name='orderitem', name='quantity', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AddField( model_name='orderitem', name='username', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.customer'), ), migrations.AlterField( model_name='order', name='username', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.customer', to_field='username'), ), migrations.AlterField( model_name='orderitem', name='item', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='eStore.item'), ), migrations.CreateModel( name='ShippingAddress', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('address', models.CharField(max_length=200, null=True)), ('city', models.CharField(max_length=200, null=True)), ('area', models.CharField(max_length=200, null=True)), ('road_no', models.CharField(max_length=200, null=True)), ('date_added', models.DateTimeField(auto_now_add=True)), ('order', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='eStore.order')), ('username', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.customer')), ], ), ]

0

2,432

23

72dfafe3d10bd2db54f014bbf5184b6be818ecf0

9,310

py

Python

sdk/python/kfp/v2/dsl/experimental/for_loop.py

ryansteakley/pipelines

98677b2190fb327be68e4bb0d00c520593707f21

[ "Apache-2.0" ]

1

2021-10-23T00:39:47.000Z

sdk/python/kfp/v2/dsl/experimental/for_loop.py

ryansteakley/pipelines

98677b2190fb327be68e4bb0d00c520593707f21

[ "Apache-2.0" ]

null

sdk/python/kfp/v2/dsl/experimental/for_loop.py

ryansteakley/pipelines

98677b2190fb327be68e4bb0d00c520593707f21

[ "Apache-2.0" ]

null

# Copyright 2021 The Kubeflow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Classes and methods that supports argument for ParallelFor.""" import re from typing import Any, Dict, List, Optional, Tuple, Union, get_type_hints from kfp.v2.components.experimental import pipeline_channel ItemList = List[Union[int, float, str, Dict[str, Any]]] def _get_loop_item_type(type_name: str) -> Optional[str]: """Extracts the loop item type. This method is used for extract the item type from a collection type. For example: List[str] -> str typing.List[int] -> int typing.Sequence[str] -> str List -> None str -> None Args: type_name: The collection type name, like `List`, Sequence`, etc. Returns: The collection item type or None if no match found. """ match = re.match('(typing\.)?(?:\w+)(?:\[(?P<item_type>.+)\])', type_name) if match: return match.group('item_type').lstrip().rstrip() else: return None def _get_subvar_type(type_name: str) -> Optional[str]: """Extracts the subvar type. This method is used for extract the value type from a dictionary type. For example: Dict[str, int] -> int typing.Mapping[str, float] -> float Args: type_name: The dictionary type. Returns: The dictionary value type or None if no match found. """ match = re.match( '(typing\.)?(?:\w+)(?:\[\s*(?:\w+)\s*,\s*(?P<value_type>.+)\])', type_name) if match: return match.group('value_type').lstrip().rstrip() else: return None class LoopArgument(pipeline_channel.PipelineChannel): """Represents the argument that are looped over in a ParallelFor loop. The class shouldn't be instantiated by the end user, rather it is created automatically by a ParallelFor ops group. To create a LoopArgument instance, use one of its factory methods:: LoopArgument.from_pipeline_channel(...) LoopArgument.from_raw_items(...) Attributes: items_or_pipeline_channel: The raw items or the PipelineChannel object this LoopArgument is associated to. """ LOOP_ITEM_NAME_BASE = 'loop-item' LOOP_ITEM_PARAM_NAME_BASE = 'loop-item-param' def __init__( self, items: Union[ItemList, pipeline_channel.PipelineChannel], name_code: Optional[str] = None, name_override: Optional[str] = None, **kwargs, ): """Initializes a LoopArguments object. Args: items: List of items to loop over. If a list of dicts then, all dicts must have the same keys and every key must be a legal Python variable name. name_code: A unique code used to identify these loop arguments. Should match the code for the ParallelFor ops_group which created these LoopArguments. This prevents parameter name collisions. name_override: The override name for PipelineChannel. **kwargs: Any other keyword arguments passed down to PipelineChannel. """ if (name_code is None) == (name_override is None): raise ValueError( 'Expect one and only one of `name_code` and `name_override` to ' 'be specified.') if name_override is None: super().__init__(name=self._make_name(name_code), **kwargs) else: super().__init__(name=name_override, **kwargs) if not isinstance(items, (list, tuple, pipeline_channel.PipelineChannel)): raise TypeError( f'Expected list, tuple, or PipelineChannel, got {items}.') if isinstance(items, tuple): items = list(items) self.items_or_pipeline_channel = items self._referenced_subvars: Dict[str, LoopArgumentVariable] = {} if isinstance(items, list) and isinstance(items[0], dict): subvar_names = set(items[0].keys()) # then this block creates loop_arg.variable_a and loop_arg.variable_b for subvar_name in subvar_names: loop_arg_var = LoopArgumentVariable( loop_argument=self, subvar_name=subvar_name, ) self._referenced_subvars[subvar_name] = loop_arg_var setattr(self, subvar_name, loop_arg_var) def _make_name(self, code: str): """Makes a name for this loop argument from a unique code.""" return '{}-{}'.format(self.LOOP_ITEM_PARAM_NAME_BASE, code) @classmethod def from_pipeline_channel( cls, channel: pipeline_channel.PipelineChannel, ) -> 'LoopArgument': """Creates a LoopArgument object from a PipelineChannel object.""" return LoopArgument( items=channel, name_override=channel.name + '-' + cls.LOOP_ITEM_NAME_BASE, task_name=channel.task_name, channel_type=_get_loop_item_type(channel.channel_type), ) @classmethod def from_raw_items( cls, raw_items: ItemList, name_code: str, ) -> 'LoopArgument': """Creates a LoopArgument object from raw item list.""" if len(raw_items) == 0: raise ValueError('Got an empty item list for loop argument.') return LoopArgument( items=raw_items, name_code=name_code, channel_type=type(raw_items[0]).__name__, ) @classmethod def name_is_loop_argument(cls, name: str) -> bool: """Returns True if the given channel name looks like a loop argument. Either it came from a withItems loop item or withParams loop item. """ return ('-' + cls.LOOP_ITEM_NAME_BASE) in name \ or (cls.LOOP_ITEM_PARAM_NAME_BASE + '-') in name class LoopArgumentVariable(pipeline_channel.PipelineChannel): """Represents a subvariable for a loop argument. This is used for cases where we're looping over maps, each of which contains several variables. If the user ran: with dsl.ParallelFor([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]) as item: ... Then there's one LoopArgumentVariable for 'a' and another for 'b'. Attributes: loop_argument: The original LoopArgument object this subvariable is attached to. subvar_name: The subvariable name. """ SUBVAR_NAME_DELIMITER = '-subvar-' LEGAL_SUBVAR_NAME_REGEX = re.compile(r'^[a-zA-Z_][0-9a-zA-Z_]*$') def __init__( self, loop_argument: LoopArgument, subvar_name: str, ): """Initializes a LoopArgumentVariable instance. Args: loop_argument: The LoopArgument object this subvariable is based on a subvariable to. subvar_name: The name of this subvariable, which is the name of the dict key that spawned this subvariable. Raises: ValueError is subvar name is illegal. """ if not self._subvar_name_is_legal(subvar_name): raise ValueError( f'Tried to create subvariable named {subvar_name}, but that is ' 'not a legal Python variable name.') self.subvar_name = subvar_name self.loop_argument = loop_argument super().__init__( name=self._get_name_override( loop_arg_name=loop_argument.name, subvar_name=subvar_name, ), task_name=loop_argument.task_name, channel_type=_get_subvar_type(loop_argument.channel_type), ) def _subvar_name_is_legal(self, proposed_variable_name: str) -> bool: """Returns True if the subvar name is legal.""" return re.match(self.LEGAL_SUBVAR_NAME_REGEX, proposed_variable_name) is not None def _get_name_override(self, loop_arg_name: str, subvar_name: str) -> str: """Gets the name. Args: loop_arg_name: the name of the loop argument parameter that this LoopArgumentVariable is attached to. subvar_name: The name of this subvariable. Returns: The name of this loop arg variable. """ return f'{loop_arg_name}{self.SUBVAR_NAME_DELIMITER}{subvar_name}'

34.868914

81

0.628249

# Copyright 2021 The Kubeflow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Classes and methods that supports argument for ParallelFor.""" import re from typing import Any, Dict, List, Optional, Tuple, Union, get_type_hints from kfp.v2.components.experimental import pipeline_channel ItemList = List[Union[int, float, str, Dict[str, Any]]] def _get_loop_item_type(type_name: str) -> Optional[str]: """Extracts the loop item type. This method is used for extract the item type from a collection type. For example: List[str] -> str typing.List[int] -> int typing.Sequence[str] -> str List -> None str -> None Args: type_name: The collection type name, like `List`, Sequence`, etc. Returns: The collection item type or None if no match found. """ match = re.match('(typing\.)?(?:\w+)(?:\[(?P<item_type>.+)\])', type_name) if match: return match.group('item_type').lstrip().rstrip() else: return None def _get_subvar_type(type_name: str) -> Optional[str]: """Extracts the subvar type. This method is used for extract the value type from a dictionary type. For example: Dict[str, int] -> int typing.Mapping[str, float] -> float Args: type_name: The dictionary type. Returns: The dictionary value type or None if no match found. """ match = re.match( '(typing\.)?(?:\w+)(?:\[\s*(?:\w+)\s*,\s*(?P<value_type>.+)\])', type_name) if match: return match.group('value_type').lstrip().rstrip() else: return None class LoopArgument(pipeline_channel.PipelineChannel): """Represents the argument that are looped over in a ParallelFor loop. The class shouldn't be instantiated by the end user, rather it is created automatically by a ParallelFor ops group. To create a LoopArgument instance, use one of its factory methods:: LoopArgument.from_pipeline_channel(...) LoopArgument.from_raw_items(...) Attributes: items_or_pipeline_channel: The raw items or the PipelineChannel object this LoopArgument is associated to. """ LOOP_ITEM_NAME_BASE = 'loop-item' LOOP_ITEM_PARAM_NAME_BASE = 'loop-item-param' def __init__( self, items: Union[ItemList, pipeline_channel.PipelineChannel], name_code: Optional[str] = None, name_override: Optional[str] = None, **kwargs, ): """Initializes a LoopArguments object. Args: items: List of items to loop over. If a list of dicts then, all dicts must have the same keys and every key must be a legal Python variable name. name_code: A unique code used to identify these loop arguments. Should match the code for the ParallelFor ops_group which created these LoopArguments. This prevents parameter name collisions. name_override: The override name for PipelineChannel. **kwargs: Any other keyword arguments passed down to PipelineChannel. """ if (name_code is None) == (name_override is None): raise ValueError( 'Expect one and only one of `name_code` and `name_override` to ' 'be specified.') if name_override is None: super().__init__(name=self._make_name(name_code), **kwargs) else: super().__init__(name=name_override, **kwargs) if not isinstance(items, (list, tuple, pipeline_channel.PipelineChannel)): raise TypeError( f'Expected list, tuple, or PipelineChannel, got {items}.') if isinstance(items, tuple): items = list(items) self.items_or_pipeline_channel = items self._referenced_subvars: Dict[str, LoopArgumentVariable] = {} if isinstance(items, list) and isinstance(items[0], dict): subvar_names = set(items[0].keys()) # then this block creates loop_arg.variable_a and loop_arg.variable_b for subvar_name in subvar_names: loop_arg_var = LoopArgumentVariable( loop_argument=self, subvar_name=subvar_name, ) self._referenced_subvars[subvar_name] = loop_arg_var setattr(self, subvar_name, loop_arg_var) def __getattr__(self, name: str): # this is being overridden so that we can access subvariables of the # LoopArgument (i.e.: item.a) without knowing the subvariable names ahead # of time. return self._referenced_subvars.setdefault( name, LoopArgumentVariable( loop_argument=self, subvar_name=name, )) def _make_name(self, code: str): """Makes a name for this loop argument from a unique code.""" return '{}-{}'.format(self.LOOP_ITEM_PARAM_NAME_BASE, code) @classmethod def from_pipeline_channel( cls, channel: pipeline_channel.PipelineChannel, ) -> 'LoopArgument': """Creates a LoopArgument object from a PipelineChannel object.""" return LoopArgument( items=channel, name_override=channel.name + '-' + cls.LOOP_ITEM_NAME_BASE, task_name=channel.task_name, channel_type=_get_loop_item_type(channel.channel_type), ) @classmethod def from_raw_items( cls, raw_items: ItemList, name_code: str, ) -> 'LoopArgument': """Creates a LoopArgument object from raw item list.""" if len(raw_items) == 0: raise ValueError('Got an empty item list for loop argument.') return LoopArgument( items=raw_items, name_code=name_code, channel_type=type(raw_items[0]).__name__, ) @classmethod def name_is_loop_argument(cls, name: str) -> bool: """Returns True if the given channel name looks like a loop argument. Either it came from a withItems loop item or withParams loop item. """ return ('-' + cls.LOOP_ITEM_NAME_BASE) in name \ or (cls.LOOP_ITEM_PARAM_NAME_BASE + '-') in name class LoopArgumentVariable(pipeline_channel.PipelineChannel): """Represents a subvariable for a loop argument. This is used for cases where we're looping over maps, each of which contains several variables. If the user ran: with dsl.ParallelFor([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]) as item: ... Then there's one LoopArgumentVariable for 'a' and another for 'b'. Attributes: loop_argument: The original LoopArgument object this subvariable is attached to. subvar_name: The subvariable name. """ SUBVAR_NAME_DELIMITER = '-subvar-' LEGAL_SUBVAR_NAME_REGEX = re.compile(r'^[a-zA-Z_][0-9a-zA-Z_]*$') def __init__( self, loop_argument: LoopArgument, subvar_name: str, ): """Initializes a LoopArgumentVariable instance. Args: loop_argument: The LoopArgument object this subvariable is based on a subvariable to. subvar_name: The name of this subvariable, which is the name of the dict key that spawned this subvariable. Raises: ValueError is subvar name is illegal. """ if not self._subvar_name_is_legal(subvar_name): raise ValueError( f'Tried to create subvariable named {subvar_name}, but that is ' 'not a legal Python variable name.') self.subvar_name = subvar_name self.loop_argument = loop_argument super().__init__( name=self._get_name_override( loop_arg_name=loop_argument.name, subvar_name=subvar_name, ), task_name=loop_argument.task_name, channel_type=_get_subvar_type(loop_argument.channel_type), ) def _subvar_name_is_legal(self, proposed_variable_name: str) -> bool: """Returns True if the subvar name is legal.""" return re.match(self.LEGAL_SUBVAR_NAME_REGEX, proposed_variable_name) is not None def _get_name_override(self, loop_arg_name: str, subvar_name: str) -> str: """Gets the name. Args: loop_arg_name: the name of the loop argument parameter that this LoopArgumentVariable is attached to. subvar_name: The name of this subvariable. Returns: The name of this loop arg variable. """ return f'{loop_arg_name}{self.SUBVAR_NAME_DELIMITER}{subvar_name}'

368

0

27

e0cb3175c59da0065800bb2675b16b000572cbc4

9,948

py

Python

.github/workflows/templates/generate.py

s0undt3ch/salt-bootstrap

11e5a237a922425c0e11608eec37bb4fde8d4577

[ "Apache-2.0" ]

null

.github/workflows/templates/generate.py

s0undt3ch/salt-bootstrap

11e5a237a922425c0e11608eec37bb4fde8d4577

[ "Apache-2.0" ]

null

.github/workflows/templates/generate.py

s0undt3ch/salt-bootstrap

11e5a237a922425c0e11608eec37bb4fde8d4577

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import datetime os.chdir(os.path.abspath(os.path.dirname(__file__))) LINUX_DISTROS = [ "almalinux-8", "amazon-2", "arch", "centos-7", "centos-8", "debian-10", "debian-11", "debian-9", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "opensuse-15", "opensuse-tumbleweed", "oraclelinux-7", "oraclelinux-8", "rockylinux-8", "ubuntu-1804", "ubuntu-2004", "ubuntu-2104", ] OSX = WINDOWS = [] STABLE_DISTROS = [ "amazon-2", "centos-7", "centos-8", "debian-10", "debian-11", "debian-9", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "oraclelinux-7", "oraclelinux-8", "ubuntu-1804", "ubuntu-2004", "ubuntu-2104", ] PY2_BLACKLIST = [ "almalinux-8", "centos-8", "debian-10", "debian-11", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "opensuse-15", "opensuse-tumbleweed", "oraclelinux-8", "rockylinux-8", "ubuntu-2004", "ubuntu-2104", ] BLACKLIST_3000 = [ "almalinux-8", "debian-11", "fedora-33", "fedora-34", "fedora-35", "opensuse-tumbleweed", "rockylinux-8", "ubuntu-2004", "ubuntu-2104", ] BLACKLIST_3001 = [ "almalinux-8", "debian-11", "rockylinux-8", "ubuntu-2104", ] BLACKLIST_3001_0 = [ "almalinux-8", "debian-11", "gentoo", "gentoo-systemd", "rockylinux-8", "ubuntu-2104", ] BLACKLIST_3002_0 = [ "almalinux-8", "debian-11", "gentoo", "gentoo-systemd", "rockylinux-8", "ubuntu-2104", ] SALT_BRANCHES = [ "3000", "3001", "3001-0", "3002", "3002-0", "master", "latest", ] BRANCH_DISPLAY_NAMES = { "3000": "v3000", "3001": "v3001", "3001-0": "v3001.0", "3002": "v3002", "3002-0": "v3002.0", "master": "Master", "latest": "Latest", } STABLE_BRANCH_BLACKLIST = [] LATEST_PKG_BLACKLIST = [] DISTRO_DISPLAY_NAMES = { "almalinux-8": "AlmaLinux 8", "amazon-2": "Amazon 2", "arch": "Arch", "centos-7": "CentOS 7", "centos-8": "CentOS 8", "debian-10": "Debian 10", "debian-11": "Debian 11", "debian-9": "Debian 9", "fedora-33": "Fedora 33", "fedora-34": "Fedora 34", "fedora-35": "Fedora 35", "gentoo": "Gentoo", "gentoo-systemd": "Gentoo (systemd)", "opensuse-15": "Opensuse 15", "opensuse-tumbleweed": "Opensuse Tumbleweed", "oraclelinux-7": "Oracle Linux 7", "oraclelinux-8": "Oracle Linux 8", "rockylinux-8": "Rocky Linux 8", "ubuntu-1804": "Ubuntu 18.04", "ubuntu-2004": "Ubuntu 20.04", "ubuntu-2104": "Ubuntu 21.04", } TIMEOUT_DEFAULT = 20 TIMEOUT_OVERRIDES = { "gentoo": 90, "gentoo-systemd": 90, } BRANCH_ONLY_OVERRIDES = [ "gentoo", "gentoo-systemd", ] if __name__ == "__main__": generate_test_jobs()

28.918605

157

0.441998

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import datetime os.chdir(os.path.abspath(os.path.dirname(__file__))) LINUX_DISTROS = [ "almalinux-8", "amazon-2", "arch", "centos-7", "centos-8", "debian-10", "debian-11", "debian-9", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "opensuse-15", "opensuse-tumbleweed", "oraclelinux-7", "oraclelinux-8", "rockylinux-8", "ubuntu-1804", "ubuntu-2004", "ubuntu-2104", ] OSX = WINDOWS = [] STABLE_DISTROS = [ "amazon-2", "centos-7", "centos-8", "debian-10", "debian-11", "debian-9", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "oraclelinux-7", "oraclelinux-8", "ubuntu-1804", "ubuntu-2004", "ubuntu-2104", ] PY2_BLACKLIST = [ "almalinux-8", "centos-8", "debian-10", "debian-11", "fedora-33", "fedora-34", "fedora-35", "gentoo", "gentoo-systemd", "opensuse-15", "opensuse-tumbleweed", "oraclelinux-8", "rockylinux-8", "ubuntu-2004", "ubuntu-2104", ] BLACKLIST_3000 = [ "almalinux-8", "debian-11", "fedora-33", "fedora-34", "fedora-35", "opensuse-tumbleweed", "rockylinux-8", "ubuntu-2004", "ubuntu-2104", ] BLACKLIST_3001 = [ "almalinux-8", "debian-11", "rockylinux-8", "ubuntu-2104", ] BLACKLIST_3001_0 = [ "almalinux-8", "debian-11", "gentoo", "gentoo-systemd", "rockylinux-8", "ubuntu-2104", ] BLACKLIST_3002_0 = [ "almalinux-8", "debian-11", "gentoo", "gentoo-systemd", "rockylinux-8", "ubuntu-2104", ] SALT_BRANCHES = [ "3000", "3001", "3001-0", "3002", "3002-0", "master", "latest", ] BRANCH_DISPLAY_NAMES = { "3000": "v3000", "3001": "v3001", "3001-0": "v3001.0", "3002": "v3002", "3002-0": "v3002.0", "master": "Master", "latest": "Latest", } STABLE_BRANCH_BLACKLIST = [] LATEST_PKG_BLACKLIST = [] DISTRO_DISPLAY_NAMES = { "almalinux-8": "AlmaLinux 8", "amazon-2": "Amazon 2", "arch": "Arch", "centos-7": "CentOS 7", "centos-8": "CentOS 8", "debian-10": "Debian 10", "debian-11": "Debian 11", "debian-9": "Debian 9", "fedora-33": "Fedora 33", "fedora-34": "Fedora 34", "fedora-35": "Fedora 35", "gentoo": "Gentoo", "gentoo-systemd": "Gentoo (systemd)", "opensuse-15": "Opensuse 15", "opensuse-tumbleweed": "Opensuse Tumbleweed", "oraclelinux-7": "Oracle Linux 7", "oraclelinux-8": "Oracle Linux 8", "rockylinux-8": "Rocky Linux 8", "ubuntu-1804": "Ubuntu 18.04", "ubuntu-2004": "Ubuntu 20.04", "ubuntu-2104": "Ubuntu 21.04", } TIMEOUT_DEFAULT = 20 TIMEOUT_OVERRIDES = { "gentoo": 90, "gentoo-systemd": 90, } BRANCH_ONLY_OVERRIDES = [ "gentoo", "gentoo-systemd", ] def generate_test_jobs(): test_jobs = "" branch_only_test_jobs = "" for distro in LINUX_DISTROS + OSX + WINDOWS: timeout_minutes = ( TIMEOUT_OVERRIDES[distro] if distro in TIMEOUT_OVERRIDES else TIMEOUT_DEFAULT ) needs = " needs: lint" if distro in BRANCH_ONLY_OVERRIDES: needs = "" current_test_jobs = "" for branch in SALT_BRANCHES: if branch == "latest": if distro in LATEST_PKG_BLACKLIST: continue if distro in LINUX_DISTROS: template = "linux.yml" elif distro in OSX: template = "osx.yml" elif distro in WINDOWS: template = "windows.yml" else: print("Don't know how to handle {}".format(distro)) with open(template) as rfh: current_test_jobs += "\n{}\n".format( rfh.read() .replace( "{python_version}-{bootstrap_type}-{branch}-{distro}", "{branch}-{distro}", ) .format( distro=distro, branch=branch, display_name="{} Latest packaged release".format( DISTRO_DISPLAY_NAMES[distro], ), timeout_minutes=timeout_minutes, needs=needs, ) ) continue for python_version in ("py2", "py3"): if branch == "master" and python_version == "py2": # Salt's master branch no longer supports Python 2 continue try: if int(branch.split("-")[0]) >= 3000 and python_version == "py2": # Salt's 300X versions no longer supports Python 2 continue except ValueError: pass for bootstrap_type in ("stable", "git"): if bootstrap_type == "stable": if branch == "master": # For the master branch there's no stable build continue if distro not in STABLE_DISTROS: continue if branch in STABLE_BRANCH_BLACKLIST: continue if distro.startswith("fedora") and branch != "latest": # Fedora does not keep old builds around continue if bootstrap_type == "git": # .0 versions are a virtual version for pinning to the first point release of a major release, such as 3001, there is no git version. if branch.endswith("-0"): continue if python_version == "py3": if distro in ("arch"): allowed_branches = ["master"] try: int_branch = int(branch) if int_branch > 3000: allowed_branches.append(branch) except ValueError: pass if branch not in allowed_branches: # Arch and Fedora default to py3.8 continue if branch == "3000" and distro in BLACKLIST_3000: continue if branch == "3001" and distro in BLACKLIST_3001: continue if branch == "3001-0" and distro in BLACKLIST_3001_0: continue if branch == "3002-0" and distro in BLACKLIST_3002_0: continue if python_version == "py2" and distro in PY2_BLACKLIST: continue if distro in LINUX_DISTROS: template = "linux.yml" elif distro in OSX: template = "osx.yml" elif distro in WINDOWS: template = "windows.yml" else: print("Don't know how to handle {}".format(distro)) with open(template) as rfh: current_test_jobs += "\n{}\n".format( rfh.read().format( distro=distro, branch=branch, python_version=python_version, bootstrap_type=bootstrap_type, display_name="{} {} {} {}".format( DISTRO_DISPLAY_NAMES[distro], BRANCH_DISPLAY_NAMES[branch], python_version.capitalize(), bootstrap_type.capitalize(), ), timeout_minutes=timeout_minutes, needs=needs, ) ) if distro in BRANCH_ONLY_OVERRIDES: branch_only_test_jobs += current_test_jobs else: test_jobs += current_test_jobs with open("lint.yml") as rfh: lint_job = "\n{}\n".format(rfh.read()) with open("pre-commit.yml") as rfh: pre_commit_job = "\n{}\n".format(rfh.read()) with open("../main.yml", "w") as wfh: with open("main.yml") as rfh: wfh.write( "{}\n".format( rfh.read() .format( jobs="{pre_commit}{lint}{test}".format( lint=lint_job, test=test_jobs, pre_commit=pre_commit_job, ), on="push, pull_request", name="Testing", ) .strip() ) ) with open("../main-branch-only.yml", "w") as wfh: with open("main.yml") as rfh: wfh.write( "{}\n".format( rfh.read() .format( jobs="{test}".format(test=branch_only_test_jobs,), on="push", name="Branch Testing", ) .strip() ) ) if __name__ == "__main__": generate_test_jobs()

6,927

0

23

4c2b178af364b6b782db82646942cb0a6c95a702

17,831

py

Python

qiskit/aqua/algorithms/single_sample/shor/shor.py

Nick-Singstock/qiskit-aqua

8c2bc57b78dec447faec3adbc966471a3206c2ef

[ "Apache-2.0" ]

1

2020-11-06T01:09:28.000Z

qiskit/aqua/algorithms/single_sample/shor/shor.py

Nick-Singstock/qiskit-aqua

8c2bc57b78dec447faec3adbc966471a3206c2ef

[ "Apache-2.0" ]

null

qiskit/aqua/algorithms/single_sample/shor/shor.py

Nick-Singstock/qiskit-aqua

8c2bc57b78dec447faec3adbc966471a3206c2ef

[ "Apache-2.0" ]

1

2020-11-06T01:09:43.000Z

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM Corp. 2017 and later. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """ The Shor's Factoring algorithm. """ import math import array import fractions import logging import numpy as np from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister from qiskit.aqua.utils.arithmetic import is_power from qiskit.aqua import AquaError, Pluggable from qiskit.aqua.utils import get_subsystem_density_matrix from qiskit.aqua.algorithms import QuantumAlgorithm from qiskit.aqua.circuits import FourierTransformCircuits as ftc from qiskit.aqua.circuits.gates import mcu1 from qiskit.aqua.utils import summarize_circuits logger = logging.getLogger(__name__) class Shor(QuantumAlgorithm): """ The Shor's Factoring algorithm. Adapted from https://github.com/ttlion/ShorAlgQiskit """ PROP_N = 'N' PROP_A = 'a' CONFIGURATION = { 'name': 'Shor', 'description': "The Shor's Factoring Algorithm", 'input_schema': { '$schema': 'http://json-schema.org/schema#', 'id': 'shor_schema', 'type': 'object', 'properties': { PROP_N: { 'type': 'integer', 'default': 15, 'minimum': 3 }, PROP_A: { 'type': 'integer', 'default': 2, 'minimum': 2 }, }, 'additionalProperties': False }, 'problems': ['factoring'], } def __init__(self, N=15, a=2): """ Constructor. Args: N (int): The integer to be factored. a (int): A random integer a that satisfies a < N and gcd(a, N) = 1 """ self.validate(locals()) super().__init__() # check the input integer if N < 1 or N % 2 == 0: raise AquaError('The input needs to be an odd integer greater than 1.') self._N = N if a >= N or math.gcd(a, self._N) != 1: raise AquaError('The integer a needs to satisfy a < N and gcd(a, N) = 1.') self._a = a self._ret = {'factors': []} # check if the input integer is a power tf, b, p = is_power(N, return_decomposition=True) if tf: logger.info('The input integer is a power: {}={}^{}.'.format(N, b, p)) self._ret['factors'].append(b) @classmethod def init_params(cls, params, algo_input): """ Initialize via parameters dictionary and algorithm input instance. Args: params: parameters dictionary algo_input: input instance """ if algo_input is not None: raise AquaError("Input instance not supported.") shor_params = params.get(Pluggable.SECTION_KEY_ALGORITHM) N = shor_params.get(Shor.PROP_N) return cls(N) def _get_angles(self, a): """ Calculate the array of angles to be used in the addition in Fourier Space """ s = bin(int(a))[2:].zfill(self._n + 1) angles = np.zeros([self._n + 1]) for i in range(0, self._n + 1): for j in range(i, self._n + 1): if s[j] == '1': angles[self._n - i] += math.pow(2, -(j - i)) angles[self._n - i] *= np.pi return angles def _phi_add(self, circuit, q, inverse=False): """ Creation of the circuit that performs addition by a in Fourier Space Can also be used for subtraction by setting the parameter inverse=True """ angle = self._get_angles(self._N) for i in range(0, self._n + 1): circuit.u1(-angle[i] if inverse else angle[i], q[i]) def _controlled_phi_add(self, circuit, q, ctl, inverse=False): """ Single controlled version of the _phi_add circuit """ angles = self._get_angles(self._N) for i in range(0, self._n + 1): angle = (-angles[i] if inverse else angles[i]) / 2 circuit.u1(angle, ctl) circuit.cx(ctl, q[i]) circuit.u1(-angle, q[i]) circuit.cx(ctl, q[i]) circuit.u1(angle, q[i]) def _controlled_controlled_phi_add(self, circuit, q, ctl1, ctl2, a, inverse=False): """ Doubly controlled version of the _phi_add circuit """ angle = self._get_angles(a) for i in range(self._n + 1): # ccphase(circuit, -angle[i] if inverse else angle[i], ctl1, ctl2, q[i]) circuit.mcu1(-angle[i] if inverse else angle[i], [ctl1, ctl2], q[i]) def _controlled_controlled_phi_add_mod_N(self, circuit, q, ctl1, ctl2, aux, a): """ Circuit that implements doubly controlled modular addition by a """ self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) self._phi_add(circuit, q, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.cx(q[self._n], aux) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_phi_add(circuit, q, aux) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.u3(np.pi, 0, np.pi, q[self._n]) circuit.cx(q[self._n], aux) circuit.u3(np.pi, 0, np.pi, q[self._n]) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) def _controlled_controlled_phi_add_mod_N_inv(self, circuit, q, ctl1, ctl2, aux, a): """ Circuit that implements the inverse of doubly controlled modular addition by a """ self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.u3(np.pi, 0, np.pi, q[self._n]) circuit.cx(q[self._n], aux) circuit.u3(np.pi, 0, np.pi, q[self._n]) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) self._controlled_phi_add(circuit, q, aux, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.cx(q[self._n], aux) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._phi_add(circuit, q) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) def _controlled_multiple_mod_N(self, circuit, ctl, q, aux, a): """ Circuit that implements single controlled modular multiplication by a """ ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False ) for i in range(0, self._n): self._controlled_controlled_phi_add_mod_N( circuit, aux, q[i], ctl, aux[self._n + 1], (2 ** i) * a % self._N ) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) for i in range(0, self._n): circuit.cswap(ctl, q[i], aux[i]) a_inv = modinv(a, self._N) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False ) for i in reversed(range(self._n)): self._controlled_controlled_phi_add_mod_N_inv( circuit, aux, q[i], ctl, aux[self._n + 1], math.pow(2, i) * a_inv % self._N ) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) def construct_circuit(self): """Construct circuit. Returns: QuantumCircuit: quantum circuit. """ # Get n value used in Shor's algorithm, to know how many qubits are used self._n = math.ceil(math.log(self._N, 2)) # quantum register where the sequential QFT is performed self._up_qreg = QuantumRegister(2 * self._n, name='up') # quantum register where the multiplications are made self._down_qreg = QuantumRegister(self._n, name='down') # auxilliary quantum register used in addition and multiplication self._aux_qreg = QuantumRegister(self._n + 2, name='aux') # Create Quantum Circuit circuit = QuantumCircuit(self._up_qreg, self._down_qreg, self._aux_qreg) # Initialize down register to 1 and create maximal superposition in top register circuit.u2(0, np.pi, self._up_qreg) circuit.u3(np.pi, 0, np.pi, self._down_qreg[0]) # Apply the multiplication gates as showed in the report in order to create the exponentiation for i in range(0, 2 * self._n): self._controlled_multiple_mod_N( circuit, self._up_qreg[i], self._down_qreg, self._aux_qreg, int(pow(self._a, pow(2, i))) ) # Apply inverse QFT ftc.construct_circuit(circuit=circuit, qubits=self._up_qreg, do_swaps=True, inverse=True) logger.info(summarize_circuits(circuit)) return circuit def _get_factors(self, output_desired, t_upper): """ Apply the continued fractions to find r and the gcd to find the desired factors. """ x_value = int(output_desired, 2) logger.info('In decimal, x_final value for this result is: {0}.'.format(x_value)) if x_value <= 0: self._ret['results'][output_desired] = 'x_value is <= 0, there are no continued fractions.' return False logger.debug('Running continued fractions for this case.') # Calculate T and x/T T = pow(2, t_upper) x_over_T = x_value / T # Cycle in which each iteration corresponds to putting one more term in the # calculation of the Continued Fraction (CF) of x/T # Initialize the first values according to CF rule i = 0 b = array.array('i') t = array.array('f') b.append(math.floor(x_over_T)) t.append(x_over_T - b[i]) while i >= 0: # From the 2nd iteration onwards, calculate the new terms of the CF based # on the previous terms as the rule suggests if i > 0: b.append(math.floor(1 / t[i - 1])) t.append((1 / t[i - 1]) - b[i]) # Calculate the CF using the known terms aux = 0 j = i while j > 0: aux = 1 / (b[j] + aux) j = j - 1 aux = aux + b[0] # Get the denominator from the value obtained frac = fractions.Fraction(aux).limit_denominator() denominator = frac.denominator logger.debug('Approximation number {0} of continued fractions:'.format(i + 1)) logger.debug("Numerator:{0} \t\t Denominator: {1}.".format(frac.numerator, frac.denominator)) # Increment i for next iteration i = i + 1 if denominator % 2 == 1: if i >= self._N: self._ret['results'][output_desired] = 'unable to find factors after too many attempts.' return False logger.debug('Odd denominator, will try next iteration of continued fractions.') continue # If denominator even, try to get factors of N # Get the exponential a^(r/2) exponential = 0 if denominator < 1000: exponential = pow(self._a, denominator / 2) # Check if the value is too big or not if math.isinf(exponential) or exponential > 1000000000: self._ret['results'][output_desired] = 'denominator of continued fraction is too big.' return False # If the value is not to big (infinity), then get the right values and do the proper gcd() putting_plus = int(exponential + 1) putting_minus = int(exponential - 1) one_factor = math.gcd(putting_plus, self._N) other_factor = math.gcd(putting_minus, self._N) # Check if the factors found are trivial factors or are the desired factors if one_factor == 1 or one_factor == self._N or other_factor == 1 or other_factor == self._N: logger.debug('Found just trivial factors, not good enough.') # Check if the number has already been found, use i-1 because i was already incremented if t[i - 1] == 0: self._ret['results'][output_desired] = 'the continued fractions found exactly x_final/(2^(2n)).' return False if i >= self._N: self._ret['results'][output_desired] = 'unable to find factors after too many attempts.' return False else: logger.debug('The factors of {0} are {1} and {2}.'.format(self._N, one_factor, other_factor)) logger.debug('Found the desired factors.') self._ret['results'][output_desired] = (one_factor, other_factor) factors = sorted((one_factor, other_factor)) if factors not in self._ret['factors']: self._ret['factors'].append(factors) return True

36.464213

119

0.555213

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM Corp. 2017 and later. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """ The Shor's Factoring algorithm. """ import math import array import fractions import logging import numpy as np from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister from qiskit.aqua.utils.arithmetic import is_power from qiskit.aqua import AquaError, Pluggable from qiskit.aqua.utils import get_subsystem_density_matrix from qiskit.aqua.algorithms import QuantumAlgorithm from qiskit.aqua.circuits import FourierTransformCircuits as ftc from qiskit.aqua.circuits.gates import mcu1 from qiskit.aqua.utils import summarize_circuits logger = logging.getLogger(__name__) class Shor(QuantumAlgorithm): """ The Shor's Factoring algorithm. Adapted from https://github.com/ttlion/ShorAlgQiskit """ PROP_N = 'N' PROP_A = 'a' CONFIGURATION = { 'name': 'Shor', 'description': "The Shor's Factoring Algorithm", 'input_schema': { '$schema': 'http://json-schema.org/schema#', 'id': 'shor_schema', 'type': 'object', 'properties': { PROP_N: { 'type': 'integer', 'default': 15, 'minimum': 3 }, PROP_A: { 'type': 'integer', 'default': 2, 'minimum': 2 }, }, 'additionalProperties': False }, 'problems': ['factoring'], } def __init__(self, N=15, a=2): """ Constructor. Args: N (int): The integer to be factored. a (int): A random integer a that satisfies a < N and gcd(a, N) = 1 """ self.validate(locals()) super().__init__() # check the input integer if N < 1 or N % 2 == 0: raise AquaError('The input needs to be an odd integer greater than 1.') self._N = N if a >= N or math.gcd(a, self._N) != 1: raise AquaError('The integer a needs to satisfy a < N and gcd(a, N) = 1.') self._a = a self._ret = {'factors': []} # check if the input integer is a power tf, b, p = is_power(N, return_decomposition=True) if tf: logger.info('The input integer is a power: {}={}^{}.'.format(N, b, p)) self._ret['factors'].append(b) @classmethod def init_params(cls, params, algo_input): """ Initialize via parameters dictionary and algorithm input instance. Args: params: parameters dictionary algo_input: input instance """ if algo_input is not None: raise AquaError("Input instance not supported.") shor_params = params.get(Pluggable.SECTION_KEY_ALGORITHM) N = shor_params.get(Shor.PROP_N) return cls(N) def _get_angles(self, a): """ Calculate the array of angles to be used in the addition in Fourier Space """ s = bin(int(a))[2:].zfill(self._n + 1) angles = np.zeros([self._n + 1]) for i in range(0, self._n + 1): for j in range(i, self._n + 1): if s[j] == '1': angles[self._n - i] += math.pow(2, -(j - i)) angles[self._n - i] *= np.pi return angles def _phi_add(self, circuit, q, inverse=False): """ Creation of the circuit that performs addition by a in Fourier Space Can also be used for subtraction by setting the parameter inverse=True """ angle = self._get_angles(self._N) for i in range(0, self._n + 1): circuit.u1(-angle[i] if inverse else angle[i], q[i]) def _controlled_phi_add(self, circuit, q, ctl, inverse=False): """ Single controlled version of the _phi_add circuit """ angles = self._get_angles(self._N) for i in range(0, self._n + 1): angle = (-angles[i] if inverse else angles[i]) / 2 circuit.u1(angle, ctl) circuit.cx(ctl, q[i]) circuit.u1(-angle, q[i]) circuit.cx(ctl, q[i]) circuit.u1(angle, q[i]) def _controlled_controlled_phi_add(self, circuit, q, ctl1, ctl2, a, inverse=False): """ Doubly controlled version of the _phi_add circuit """ angle = self._get_angles(a) for i in range(self._n + 1): # ccphase(circuit, -angle[i] if inverse else angle[i], ctl1, ctl2, q[i]) circuit.mcu1(-angle[i] if inverse else angle[i], [ctl1, ctl2], q[i]) def _controlled_controlled_phi_add_mod_N(self, circuit, q, ctl1, ctl2, aux, a): """ Circuit that implements doubly controlled modular addition by a """ self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) self._phi_add(circuit, q, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.cx(q[self._n], aux) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_phi_add(circuit, q, aux) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.u3(np.pi, 0, np.pi, q[self._n]) circuit.cx(q[self._n], aux) circuit.u3(np.pi, 0, np.pi, q[self._n]) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) def _controlled_controlled_phi_add_mod_N_inv(self, circuit, q, ctl1, ctl2, aux, a): """ Circuit that implements the inverse of doubly controlled modular addition by a """ self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.u3(np.pi, 0, np.pi, q[self._n]) circuit.cx(q[self._n], aux) circuit.u3(np.pi, 0, np.pi, q[self._n]) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a) self._controlled_phi_add(circuit, q, aux, inverse=True) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) circuit.cx(q[self._n], aux) ftc.construct_circuit( circuit=circuit, qubits=[q[i] for i in reversed(range(self._n + 1))], do_swaps=False ) self._phi_add(circuit, q) self._controlled_controlled_phi_add(circuit, q, ctl1, ctl2, a, inverse=True) def _controlled_multiple_mod_N(self, circuit, ctl, q, aux, a): """ Circuit that implements single controlled modular multiplication by a """ ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False ) for i in range(0, self._n): self._controlled_controlled_phi_add_mod_N( circuit, aux, q[i], ctl, aux[self._n + 1], (2 ** i) * a % self._N ) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) for i in range(0, self._n): circuit.cswap(ctl, q[i], aux[i]) def modinv(a, m): def egcd(a, b): if a == 0: return (b, 0, 1) else: g, y, x = egcd(b % a, a) return (g, x - (b // a) * y, y) g, x, y = egcd(a, m) if g != 1: raise Exception('modular inverse does not exist') else: return x % m a_inv = modinv(a, self._N) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False ) for i in reversed(range(self._n)): self._controlled_controlled_phi_add_mod_N_inv( circuit, aux, q[i], ctl, aux[self._n + 1], math.pow(2, i) * a_inv % self._N ) ftc.construct_circuit( circuit=circuit, qubits=[aux[i] for i in reversed(range(self._n + 1))], do_swaps=False, inverse=True ) def construct_circuit(self): """Construct circuit. Returns: QuantumCircuit: quantum circuit. """ # Get n value used in Shor's algorithm, to know how many qubits are used self._n = math.ceil(math.log(self._N, 2)) # quantum register where the sequential QFT is performed self._up_qreg = QuantumRegister(2 * self._n, name='up') # quantum register where the multiplications are made self._down_qreg = QuantumRegister(self._n, name='down') # auxilliary quantum register used in addition and multiplication self._aux_qreg = QuantumRegister(self._n + 2, name='aux') # Create Quantum Circuit circuit = QuantumCircuit(self._up_qreg, self._down_qreg, self._aux_qreg) # Initialize down register to 1 and create maximal superposition in top register circuit.u2(0, np.pi, self._up_qreg) circuit.u3(np.pi, 0, np.pi, self._down_qreg[0]) # Apply the multiplication gates as showed in the report in order to create the exponentiation for i in range(0, 2 * self._n): self._controlled_multiple_mod_N( circuit, self._up_qreg[i], self._down_qreg, self._aux_qreg, int(pow(self._a, pow(2, i))) ) # Apply inverse QFT ftc.construct_circuit(circuit=circuit, qubits=self._up_qreg, do_swaps=True, inverse=True) logger.info(summarize_circuits(circuit)) return circuit def _get_factors(self, output_desired, t_upper): """ Apply the continued fractions to find r and the gcd to find the desired factors. """ x_value = int(output_desired, 2) logger.info('In decimal, x_final value for this result is: {0}.'.format(x_value)) if x_value <= 0: self._ret['results'][output_desired] = 'x_value is <= 0, there are no continued fractions.' return False logger.debug('Running continued fractions for this case.') # Calculate T and x/T T = pow(2, t_upper) x_over_T = x_value / T # Cycle in which each iteration corresponds to putting one more term in the # calculation of the Continued Fraction (CF) of x/T # Initialize the first values according to CF rule i = 0 b = array.array('i') t = array.array('f') b.append(math.floor(x_over_T)) t.append(x_over_T - b[i]) while i >= 0: # From the 2nd iteration onwards, calculate the new terms of the CF based # on the previous terms as the rule suggests if i > 0: b.append(math.floor(1 / t[i - 1])) t.append((1 / t[i - 1]) - b[i]) # Calculate the CF using the known terms aux = 0 j = i while j > 0: aux = 1 / (b[j] + aux) j = j - 1 aux = aux + b[0] # Get the denominator from the value obtained frac = fractions.Fraction(aux).limit_denominator() denominator = frac.denominator logger.debug('Approximation number {0} of continued fractions:'.format(i + 1)) logger.debug("Numerator:{0} \t\t Denominator: {1}.".format(frac.numerator, frac.denominator)) # Increment i for next iteration i = i + 1 if denominator % 2 == 1: if i >= self._N: self._ret['results'][output_desired] = 'unable to find factors after too many attempts.' return False logger.debug('Odd denominator, will try next iteration of continued fractions.') continue # If denominator even, try to get factors of N # Get the exponential a^(r/2) exponential = 0 if denominator < 1000: exponential = pow(self._a, denominator / 2) # Check if the value is too big or not if math.isinf(exponential) or exponential > 1000000000: self._ret['results'][output_desired] = 'denominator of continued fraction is too big.' return False # If the value is not to big (infinity), then get the right values and do the proper gcd() putting_plus = int(exponential + 1) putting_minus = int(exponential - 1) one_factor = math.gcd(putting_plus, self._N) other_factor = math.gcd(putting_minus, self._N) # Check if the factors found are trivial factors or are the desired factors if one_factor == 1 or one_factor == self._N or other_factor == 1 or other_factor == self._N: logger.debug('Found just trivial factors, not good enough.') # Check if the number has already been found, use i-1 because i was already incremented if t[i - 1] == 0: self._ret['results'][output_desired] = 'the continued fractions found exactly x_final/(2^(2n)).' return False if i >= self._N: self._ret['results'][output_desired] = 'unable to find factors after too many attempts.' return False else: logger.debug('The factors of {0} are {1} and {2}.'.format(self._N, one_factor, other_factor)) logger.debug('Found the desired factors.') self._ret['results'][output_desired] = (one_factor, other_factor) factors = sorted((one_factor, other_factor)) if factors not in self._ret['factors']: self._ret['factors'].append(factors) return True def _run(self): if not self._ret['factors']: logger.debug('Running with N={} and a={}.'.format(self._N, self._a)) circuit = self.construct_circuit() if self._quantum_instance.is_statevector: logger.warning('The statevector_simulator might lead to subsequent computation using too much memory.') result = self._quantum_instance.execute(circuit) complete_state_vec = result.get_statevector(circuit) # TODO: this uses too much memory up_qreg_density_mat = get_subsystem_density_matrix( complete_state_vec, range(2 * self._n, 4 * self._n + 2) ) up_qreg_density_mat_diag = np.diag(up_qreg_density_mat) counts = dict() for i, v in enumerate(up_qreg_density_mat_diag): if not v == 0: counts[bin(int(i))[2:].zfill(2 * self._n)] = v ** 2 else: up_cqreg = ClassicalRegister(2 * self._n, name='m') circuit.add_register(up_cqreg) circuit.measure(self._up_qreg, up_cqreg) counts = self._quantum_instance.execute(circuit).get_counts(circuit) self._ret['results'] = dict() # For each simulation result, print proper info to user and try to calculate the factors of N for output_desired in list(counts.keys()): # Get the x_value from the final state qubits logger.info("------> Analyzing result {0}.".format(output_desired)) self._ret['results'][output_desired] = None success = self._get_factors(output_desired, int(2 * self._n)) if success: logger.info('Found factors {} from measurement {}.'.format( self._ret['results'][output_desired], output_desired )) else: logger.info('Cannot find factors from measurement {} because {}'.format( output_desired, self._ret['results'][output_desired] )) return self._ret

2,572

0

58

14d1776a23dbeff91b7b113a7ec6193886a74ae5

3,802

py

Python

src/game.py

Ale-XYX/Contrast

6daf08e14826fbe382a6a8bbaa53f6c5a0494383

[ "Apache-2.0" ]

null

src/game.py

Ale-XYX/Contrast

6daf08e14826fbe382a6a8bbaa53f6c5a0494383

[ "Apache-2.0" ]

null

src/game.py

Ale-XYX/Contrast

6daf08e14826fbe382a6a8bbaa53f6c5a0494383

[ "Apache-2.0" ]

2

2020-02-03T14:04:11.000Z

2020-05-15T16:44:33.000Z

import re import bz2 import pygame import public import sprites import functions import dictionaries import random # :^)

25.689189

80

0.583377

import re import bz2 import pygame import public import sprites import functions import dictionaries import random def title(debug): pygame.display.set_caption('Contrast') pygame.display.set_icon(pygame.image.fromstring(bz2.decompress( dictionaries.MEDIA['icon']), (32, 32), 'RGBA')) info_text = public.FONT_LG.render( 'ENTER TO BEGIN', False, [public.WHITE] * 3) play_button = sprites.Button((343, 290), 'Play') music_button = sprites.Button((407, 290), 'Music') button_cover = pygame.Surface((public.SWIDTH, 10)) button_cover.fill([public.BLACK] * 3) if len(debug) != 1: public.music = False m = re.search('map_(.+?).tmx', debug[1]) if m: public.level = int(m.group(1)) while True: for event in pygame.event.get(): if event.type == pygame.QUIT: return 0 public.all_sprites.update() if public.end_title: game() return 0 public.screen.fill([public.BLACK] * 3) public.screen.blit(dictionaries.IMAGES['Logo'], functions.center( dictionaries.IMAGES['Logo'])) for sprite in public.all_sprites: sprite.draw() public.screen.blit(button_cover, (0, 345)) pygame.display.flip() public.clock.tick(public.FPS) def game(): if public.music: dictionaries.MEDIA['greetings'].play(-1) functions.generate_clouds() functions.generate_level(True) dt = public.clock.tick(public.FPS) / 1000 cover_alpha = 0 cover_surf = pygame.Surface((public.SWIDTH, public.SHEIGHT)) cover_surf.set_alpha(cover_alpha) while True: for event in pygame.event.get(): if event.type == pygame.QUIT: return 0 elif event.type == pygame.KEYDOWN: if event.key == pygame.K_w: public.player.jump() elif event.key == pygame.K_SPACE: public.player.flip() keys = pygame.key.get_pressed() if keys[pygame.K_d] and not (public.player.died or public.player.won): public.player.move('right') elif keys[pygame.K_a] and not (public.player.died or public.player.won): public.player.move('left') else: public.player.accelerating = False if public.player.won and cover_alpha != 255: cover_alpha += 1 cover_surf.set_alpha(cover_alpha) if cover_alpha == 255: end('A GAME BY TEAM-ABSTRACTANDROID') return 0 if public.level == public.level_max: end('More levels to come soon!') return 0 public.all_sprites.update() sorted_sprites = sorted( public.all_sprites.sprites(), key=lambda x: x.layer) public.screen.fill([public.bg_type] * 3) for sprite in sorted_sprites: sprite.draw() public.screen.blit(cover_surf, (0, 0)) pygame.display.flip() public.clock.tick(public.FPS) def end(msg): text_alpha = 0 credits_text = public.FONT_LG.render( msg, False, [public.WHITE] * 3) credits_text.set_alpha(text_alpha) while True: for event in pygame.event.get(): if event.type == pygame.QUIT: return 0 elif event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: return 0 if text_alpha != 255: text_alpha += 5 credits_text.set_alpha(text_alpha) public.screen.fill([public.BLACK] * 3) public.screen.blit(credits_text, functions.center(credits_text)) pygame.display.flip() public.clock.tick(public.FPS) # :^)

3,608

0

69

2e0aa5f7b3230ca90001a4c7c190460a296a87de

6,243

py

Python

tabledataextractor/input/from_html.py

ELchem/tabledataextractor

9eb38faf57611c26cdcaa8df13fd4e1cf36a4c21

[ "MIT" ]

4

2021-09-01T18:28:10.000Z

2022-03-29T09:43:34.000Z

tabledataextractor/input/from_html.py

ELchem/tabledataextractor

9eb38faf57611c26cdcaa8df13fd4e1cf36a4c21

[ "MIT" ]

3

2021-11-13T21:17:27.000Z

2021-11-15T18:29:14.000Z

tabledataextractor/input/from_html.py

ELchem/tabledataextractor

9eb38faf57611c26cdcaa8df13fd4e1cf36a4c21

[ "MIT" ]

2

2021-10-07T01:20:39.000Z

2021-11-02T17:56:06.000Z

# -*- coding: utf-8 -*- """ Reads an `html` formatted table. """ import numpy as np from bs4 import BeautifulSoup import requests from selenium import webdriver from selenium.webdriver.firefox.options import Options as FirefoxOptions from selenium.webdriver.chrome.options import Options as ChromeOptions from selenium.webdriver.edge.options import Options as EdgeOptions from selenium.webdriver.ie.options import Options as IeOptions import copy import logging from tabledataextractor.exceptions import InputError log = logging.getLogger(__name__) def makearray(html_table): """ Creates a numpy array from an `.html` file, taking `rowspan` and `colspan` into account. Modified from: John Ricco, https://johnricco.github.io/2017/04/04/python-html/, *Using Python to scrape HTML tables with merged cells* Added functionality for duplicating cell content for cells with `rowspan`/`colspan`. The table has to be :math:`n*m`, rectangular, with the same number of columns in every row. """ n_cols = 0 n_rows = 0 for row in html_table.findAll("tr"): col_tags = row.find_all(["td", "th"]) if len(col_tags) > 0: n_rows += 1 if len(col_tags) > n_cols: n_cols = len(col_tags) # according to numpy documentation fill_value should be of type Union[int, float, complex] # however, 'str' works just fine array = np.full((n_rows, n_cols), fill_value="", dtype='<U60') # list to store rowspan values skip_index = [0 for i in range(0, n_cols)] # iterating over each row in the table row_counter = 0 for row in html_table.findAll("tr"): # skip row if it's empty if len(row.find_all(["td", "th"])) == 0: continue else: # get all the cells containing data in this row columns = row.find_all(["td", "th"]) col_dim = [] row_dim = [] col_dim_counter = -1 row_dim_counter = -1 col_counter = -1 this_skip_index = copy.deepcopy(skip_index) for col in columns: # determine all cell dimensions colspan = col.get("colspan") if not colspan: col_dim.append(1) else: col_dim.append(int(colspan)) col_dim_counter += 1 rowspan = col.get("rowspan") if not rowspan: row_dim.append(1) else: row_dim.append(int(rowspan)) row_dim_counter += 1 # adjust column counter if col_counter == -1: col_counter = 0 else: col_counter = col_counter + col_dim[col_dim_counter - 1] while skip_index[col_counter] > 0: col_counter += 1 # get cell contents cell_data = col.get_text() # insert data into cell array[row_counter, col_counter] = cell_data # Insert data into neighbouring rowspan/colspan cells if colspan: for spanned_col in range(col_counter+1, col_counter + int(colspan)): array[row_counter, spanned_col] = cell_data if rowspan: for spanned_row in range(row_counter+1, row_counter + int(rowspan)): array[spanned_row, col_counter] = cell_data #record column skipping index if row_dim[row_dim_counter] > 1: this_skip_index[col_counter] = row_dim[row_dim_counter] # adjust row counter row_counter += 1 # adjust column skipping index skip_index = [i - 1 if i > 0 else i for i in this_skip_index] return array def read_file(file_path, table_number=1): """Reads an .html file and returns a numpy array.""" file = open(file_path, encoding='UTF-8') html_soup = BeautifulSoup(file, features='lxml') file.close() html_table = html_soup.find_all("table")[table_number-1] array = makearray(html_table) return array def configure_selenium(browser='Firefox'): """ Configuration for `Selenium <https://selenium-python.readthedocs.io/>`_. Sets the path to ``geckodriver.exe`` :param browser: Which browser to use :type browser: str :return: Selenium driver """ if browser == 'Firefox': options = FirefoxOptions() options.headless = True driver = webdriver.Firefox(options=options, executable_path=r'C:\Users\juras\System\geckodriver\geckodriver.exe') return driver else: return None def read_url(url, table_number=1): """ Reads in a table from an URL and returns a numpy array. Will try `Requests <http://docs.python-requests.org/en/master/>`_ first. If it doesn't succeed, `Selenium <https://selenium-python.readthedocs.io/>`_ will be used. :param url: Url of the page where the table is located :type url: str :param table_number: Number of Table on the web page. :type table_number: int """ if not isinstance(table_number, int): msg = 'Table number is not valid.' log.critical(msg) raise TypeError(msg) # first try the requests package, if it fails do the selenium, which is much slower try: html_file = requests.get(url) html_soup = BeautifulSoup(html_file.text, features='lxml') html_table = html_soup.find_all("table")[table_number - 1] array = makearray(html_table) log.info("Package 'requests' was used.") return array except Exception: driver = configure_selenium() driver.get(url) html_file = driver.page_source html_soup = BeautifulSoup(html_file, features='lxml') try: html_table = html_soup.find_all("table")[table_number-1] except IndexError: raise InputError("table_number={} is out of range".format(table_number)) else: array = makearray(html_table) log.info("Package 'selenium' was used.") return array

33.745946

223

0.606279

# -*- coding: utf-8 -*- """ Reads an `html` formatted table. """ import numpy as np from bs4 import BeautifulSoup import requests from selenium import webdriver from selenium.webdriver.firefox.options import Options as FirefoxOptions from selenium.webdriver.chrome.options import Options as ChromeOptions from selenium.webdriver.edge.options import Options as EdgeOptions from selenium.webdriver.ie.options import Options as IeOptions import copy import logging from tabledataextractor.exceptions import InputError log = logging.getLogger(__name__) def makearray(html_table): """ Creates a numpy array from an `.html` file, taking `rowspan` and `colspan` into account. Modified from: John Ricco, https://johnricco.github.io/2017/04/04/python-html/, *Using Python to scrape HTML tables with merged cells* Added functionality for duplicating cell content for cells with `rowspan`/`colspan`. The table has to be :math:`n*m`, rectangular, with the same number of columns in every row. """ n_cols = 0 n_rows = 0 for row in html_table.findAll("tr"): col_tags = row.find_all(["td", "th"]) if len(col_tags) > 0: n_rows += 1 if len(col_tags) > n_cols: n_cols = len(col_tags) # according to numpy documentation fill_value should be of type Union[int, float, complex] # however, 'str' works just fine array = np.full((n_rows, n_cols), fill_value="", dtype='<U60') # list to store rowspan values skip_index = [0 for i in range(0, n_cols)] # iterating over each row in the table row_counter = 0 for row in html_table.findAll("tr"): # skip row if it's empty if len(row.find_all(["td", "th"])) == 0: continue else: # get all the cells containing data in this row columns = row.find_all(["td", "th"]) col_dim = [] row_dim = [] col_dim_counter = -1 row_dim_counter = -1 col_counter = -1 this_skip_index = copy.deepcopy(skip_index) for col in columns: # determine all cell dimensions colspan = col.get("colspan") if not colspan: col_dim.append(1) else: col_dim.append(int(colspan)) col_dim_counter += 1 rowspan = col.get("rowspan") if not rowspan: row_dim.append(1) else: row_dim.append(int(rowspan)) row_dim_counter += 1 # adjust column counter if col_counter == -1: col_counter = 0 else: col_counter = col_counter + col_dim[col_dim_counter - 1] while skip_index[col_counter] > 0: col_counter += 1 # get cell contents cell_data = col.get_text() # insert data into cell array[row_counter, col_counter] = cell_data # Insert data into neighbouring rowspan/colspan cells if colspan: for spanned_col in range(col_counter+1, col_counter + int(colspan)): array[row_counter, spanned_col] = cell_data if rowspan: for spanned_row in range(row_counter+1, row_counter + int(rowspan)): array[spanned_row, col_counter] = cell_data #record column skipping index if row_dim[row_dim_counter] > 1: this_skip_index[col_counter] = row_dim[row_dim_counter] # adjust row counter row_counter += 1 # adjust column skipping index skip_index = [i - 1 if i > 0 else i for i in this_skip_index] return array def read_file(file_path, table_number=1): """Reads an .html file and returns a numpy array.""" file = open(file_path, encoding='UTF-8') html_soup = BeautifulSoup(file, features='lxml') file.close() html_table = html_soup.find_all("table")[table_number-1] array = makearray(html_table) return array def configure_selenium(browser='Firefox'): """ Configuration for `Selenium <https://selenium-python.readthedocs.io/>`_. Sets the path to ``geckodriver.exe`` :param browser: Which browser to use :type browser: str :return: Selenium driver """ if browser == 'Firefox': options = FirefoxOptions() options.headless = True driver = webdriver.Firefox(options=options, executable_path=r'C:\Users\juras\System\geckodriver\geckodriver.exe') return driver else: return None def read_url(url, table_number=1): """ Reads in a table from an URL and returns a numpy array. Will try `Requests <http://docs.python-requests.org/en/master/>`_ first. If it doesn't succeed, `Selenium <https://selenium-python.readthedocs.io/>`_ will be used. :param url: Url of the page where the table is located :type url: str :param table_number: Number of Table on the web page. :type table_number: int """ if not isinstance(table_number, int): msg = 'Table number is not valid.' log.critical(msg) raise TypeError(msg) # first try the requests package, if it fails do the selenium, which is much slower try: html_file = requests.get(url) html_soup = BeautifulSoup(html_file.text, features='lxml') html_table = html_soup.find_all("table")[table_number - 1] array = makearray(html_table) log.info("Package 'requests' was used.") return array except Exception: driver = configure_selenium() driver.get(url) html_file = driver.page_source html_soup = BeautifulSoup(html_file, features='lxml') try: html_table = html_soup.find_all("table")[table_number-1] except IndexError: raise InputError("table_number={} is out of range".format(table_number)) else: array = makearray(html_table) log.info("Package 'selenium' was used.") return array

0

8a2527c8ebf711cd89d50a2c1b007f80d07a457b

924

py

Python

07/script.py

has-ctrl/advent-of-code-2021

09d309feb5082f108ab690f9e37abf6150b7283d

[ "MIT" ]

null

07/script.py

has-ctrl/advent-of-code-2021

09d309feb5082f108ab690f9e37abf6150b7283d

[ "MIT" ]

null

07/script.py

has-ctrl/advent-of-code-2021

09d309feb5082f108ab690f9e37abf6150b7283d

[ "MIT" ]

null

import numpy as np test_data = np.array([16, 1, 2, 0, 4, 2, 7, 1, 2, 14]) np_data = np.loadtxt("data.txt", delimiter=",", dtype=int) def one(data: np.ndarray) -> int: """ Determine the horizontal position that the crabs can align to using the least fuel possible. How much fuel must they spend to align to that position? """ median = np.median(data).astype(int) return np.absolute(data - median).sum() def two(data: np.ndarray) -> int: """ Determine the horizontal position that the crabs can align to using the least fuel possible so they can make you an escape route! How much fuel must they spend to align to that position? """ mean = np.mean(data).astype(int) diff = np.absolute(data - mean) # 'Factorial for addition' is the same as (X^2 + X) / 2 return ((diff * diff + diff) / 2).astype(int).sum() print(f"1. {one(np_data)}") print(f"2. {two(np_data)}")

30.8

120

0.650433

import numpy as np test_data = np.array([16, 1, 2, 0, 4, 2, 7, 1, 2, 14]) np_data = np.loadtxt("data.txt", delimiter=",", dtype=int) def one(data: np.ndarray) -> int: """ Determine the horizontal position that the crabs can align to using the least fuel possible. How much fuel must they spend to align to that position? """ median = np.median(data).astype(int) return np.absolute(data - median).sum() def two(data: np.ndarray) -> int: """ Determine the horizontal position that the crabs can align to using the least fuel possible so they can make you an escape route! How much fuel must they spend to align to that position? """ mean = np.mean(data).astype(int) diff = np.absolute(data - mean) # 'Factorial for addition' is the same as (X^2 + X) / 2 return ((diff * diff + diff) / 2).astype(int).sum() print(f"1. {one(np_data)}") print(f"2. {two(np_data)}")

0

46445e6276cdd339ed1cb28a14605af7c00ee8a9

787

py

Python

docs/src/callbackgen.py

aristanetworks/ctypegen

379f8e5c712c8deb0ed27cbf005d7706fa11e6e8

[ "Apache-2.0" ]

17

2018-06-12T10:07:42.000Z

2022-03-23T14:03:33.000Z

docs/src/callbackgen.py

aristanetworks/ctypegen

379f8e5c712c8deb0ed27cbf005d7706fa11e6e8

[ "Apache-2.0" ]

4

2018-10-29T17:55:34.000Z

2021-10-08T07:19:12.000Z

docs/src/callbackgen.py

aristanetworks/ctypegen

379f8e5c712c8deb0ed27cbf005d7706fa11e6e8

[ "Apache-2.0" ]

7

2018-12-20T19:35:45.000Z

2021-05-18T03:42:17.000Z

# Copyright (c) 2018 Arista Networks, Inc. All rights reserved. # Arista Networks, Inc. Confidential and Proprietary. # # DON'T EDIT THIS FILE. It was generated by # /usr/local/lib/python2.7/dist-packages/CTypeGen.py # Please see AID/3558 for details on the contents of this file # from ctypes import * # pylint: disable=wildcard-import from CTypeGenRun import * # pylint: disable=wildcard-import # pylint: disable=unnecessary-pass,protected-access Callback = CFUNCTYPE( c_int, c_int , c_int ) functionTypes = { 'callme': CFUNCTYPE( c_int, c_int , c_int , Callback ), } if __name__ == "__main__": test_classes()

21.861111

64

0.684879

# Copyright (c) 2018 Arista Networks, Inc. All rights reserved. # Arista Networks, Inc. Confidential and Proprietary. # # DON'T EDIT THIS FILE. It was generated by # /usr/local/lib/python2.7/dist-packages/CTypeGen.py # Please see AID/3558 for details on the contents of this file # from ctypes import * # pylint: disable=wildcard-import from CTypeGenRun import * # pylint: disable=wildcard-import # pylint: disable=unnecessary-pass,protected-access Callback = CFUNCTYPE( c_int, c_int , c_int ) def decorateFunctions( lib ): lib.callme.restype = c_int lib.callme.argtypes = [ c_int, c_int, Callback ] functionTypes = { 'callme': CFUNCTYPE( c_int, c_int , c_int , Callback ), } if __name__ == "__main__": test_classes()

108

0

23

f5ccf91f07f564599f0a2cf7b1cc3268aa005d97

1,296

py

Python

generated-libraries/python/ports.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

2

2017-03-28T15:31:26.000Z

2018-08-16T22:15:18.000Z

generated-libraries/python/ports.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

null

generated-libraries/python/ports.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

null

from netapp.connection import NaErrorResponse, NaPagedResponse from netapp.net import NetConnection from netapp.net.net_port_info import NetPortInfo conn = NetConnection("192.168.135.100", "admin", "mehmeh123") print "LISTING ALL PORTS:" print "-----------------------------------------------" query = NetPortInfo(node="radontap-02") response = conn.net_port_get_iter( desired_attributes="node,port".split(","), query=query ) if isinstance(response, NaPagedResponse): for npi in response.output: print "{}: {}".format( npi.port, npi ) while response.has_more(): next = response.next() if isinstance(next.result, NaErrorResponse): print "There was an error: {} : {}".format( next.result.error_code, next.result.reason ) else: for npi in next.output: print "{}: {}".format( npi.port, npi ) elif isinstance(response, NaErrorResponse): print "There was an error: {} : {}".format( response.error_code, response.reason ) else: for npi in response: print "{}: {}".format( npi.port, npi ) print "GET A SINGLE PORT:" print "-----------------------------------------------" port_info = conn.net_port_get( node="radontap-02", port="e0c", desired_attributes="node,port".split(",") ) print port_info

35.027027

106

0.622685

from netapp.connection import NaErrorResponse, NaPagedResponse from netapp.net import NetConnection from netapp.net.net_port_info import NetPortInfo conn = NetConnection("192.168.135.100", "admin", "mehmeh123") print "LISTING ALL PORTS:" print "-----------------------------------------------" query = NetPortInfo(node="radontap-02") response = conn.net_port_get_iter( desired_attributes="node,port".split(","), query=query ) if isinstance(response, NaPagedResponse): for npi in response.output: print "{}: {}".format( npi.port, npi ) while response.has_more(): next = response.next() if isinstance(next.result, NaErrorResponse): print "There was an error: {} : {}".format( next.result.error_code, next.result.reason ) else: for npi in next.output: print "{}: {}".format( npi.port, npi ) elif isinstance(response, NaErrorResponse): print "There was an error: {} : {}".format( response.error_code, response.reason ) else: for npi in response: print "{}: {}".format( npi.port, npi ) print "GET A SINGLE PORT:" print "-----------------------------------------------" port_info = conn.net_port_get( node="radontap-02", port="e0c", desired_attributes="node,port".split(",") ) print port_info

0

4041a20fc51def3b3801556656d9b21062ae0f2d

185

py

Python

torch/fx/experimental/unification/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

60,067

2017-01-18T17:21:31.000Z

2022-03-31T21:37:45.000Z

torch/fx/experimental/unification/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

66,955

2017-01-18T17:21:38.000Z

2022-03-31T23:56:11.000Z

torch/fx/experimental/unification/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

19,210

2017-01-18T17:45:04.000Z

2022-03-31T23:51:56.000Z

# type: ignore[attr-defined] from .core import unify, reify # noqa: F403 from .more import unifiable # noqa: F403 from .variable import var, isvar, vars, variables, Var # noqa: F403

37

68

0.724324

# type: ignore[attr-defined] from .core import unify, reify # noqa: F403 from .more import unifiable # noqa: F403 from .variable import var, isvar, vars, variables, Var # noqa: F403

0

3be09ddb058024d53f0d37a425c547e2ad46cc57

2,147

py

Python

psinsights/rules.py

paulcronk/psinsights

cd465f20254fbdb30032ce40b6fe30d32de0d524

[ "Apache-2.0" ]

null

psinsights/rules.py

paulcronk/psinsights

cd465f20254fbdb30032ce40b6fe30d32de0d524

[ "Apache-2.0" ]

null

psinsights/rules.py

paulcronk/psinsights

cd465f20254fbdb30032ce40b6fe30d32de0d524

[ "Apache-2.0" ]

null

############################################################################### # Copyright 2012 FastSoft Inc. # Copyright 2012 Devin Anderson <danderson (at) fastsoft (dot) com> # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy # of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. ############################################################################### from psinsights.rule import Rule as _Rule

28.626667

79

0.583605

############################################################################### # Copyright 2012 FastSoft Inc. # Copyright 2012 Devin Anderson <danderson (at) fastsoft (dot) com> # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy # of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. ############################################################################### from psinsights.rule import Rule as _Rule class Rules(object): def __contains__(self, name): return name in self.__data def __del__(self): self.__data = None self.__rule_map = None def __getitem__(self, name): rule = self.get(name) if rule is None: raise KeyError(name) return rule def __init__(self, data): self.__data = data self.__rule_map = {} def __iter__(self): return self.__data.iterkeys() def __len__(self): return len(self.__data) def get(self, name, default=None): rule_map = self.__rule_map rule = rule_map.get(name) if rule is None: data = self.__data rule_data = data.get(name) if rule_data is None: return default rule = _Rule(rule_data) rule_map[name] = rule return rule def items(self): return list(self.iteritems()) def iteritems(self): get = self.get return ((k, get(k)) for k in self.__data.iterkeys()) iterkeys = __iter__ def itervalues(self): get = self.get return (get(k) for k in self.__data.iterkeys()) def keys(self): return list(iter(self)) def values(self): return list(self.itervalues())

926

348

23

662efd1261fb763f2ca5bdab861633e763419ddb

552

py

Python

exercicio-condicional/questao-1.py

maumneto/exercicio-python

bd57cd9f3b48c76ea3f8195544d347bc1b0c943e

[ "MIT" ]

null

exercicio-condicional/questao-1.py

maumneto/exercicio-python

bd57cd9f3b48c76ea3f8195544d347bc1b0c943e

[ "MIT" ]

null

exercicio-condicional/questao-1.py

maumneto/exercicio-python

bd57cd9f3b48c76ea3f8195544d347bc1b0c943e

[ "MIT" ]

1

2020-04-27T15:01:10.000Z

''' Faça um programa que leia o salário de um trabalhador e o valor da prestação de um empréstimo. Se a prestação for maior que 20% do salário imprima: “Empréstimo não concedido”; caso contrário imprima: “Empréstimo concedido”. ''' # entrada de dados salarao = float(input('Digite o valor do salario: ')) prestacao = float(input('Digite o valor da prestacao: ')) # condicional if (prestacao > 0.2*salarao): print('Emprestimo nao concedido!') else: print('Emprestimo concedido!') # mensagem de término de algoritmo print('Fim do algoritmo!')

30.666667

95

0.733696

''' Faça um programa que leia o salário de um trabalhador e o valor da prestação de um empréstimo. Se a prestação for maior que 20% do salário imprima: “Empréstimo não concedido”; caso contrário imprima: “Empréstimo concedido”. ''' # entrada de dados salarao = float(input('Digite o valor do salario: ')) prestacao = float(input('Digite o valor da prestacao: ')) # condicional if (prestacao > 0.2*salarao): print('Emprestimo nao concedido!') else: print('Emprestimo concedido!') # mensagem de término de algoritmo print('Fim do algoritmo!')

0

4b998a8b759bc5a4cf2d3b91ee6979cd04cfc889

12,997

py

Python

Firefly/services/firefly_security_and_monitoring/firefly_monitoring.py

Firefly-Automation/Firefly

fccf40b8f6e015ef34c292264184090eb8d860b7

[ "Apache-2.0" ]

20

2017-03-24T08:25:50.000Z

2020-07-07T16:09:34.000Z

Firefly/services/firefly_security_and_monitoring/firefly_monitoring.py

Firefly-Automation/Firefly

fccf40b8f6e015ef34c292264184090eb8d860b7

[ "Apache-2.0" ]

1

2017-11-02T17:46:48.000Z

Firefly/services/firefly_security_and_monitoring/firefly_monitoring.py

Firefly-Automation/Firefly

fccf40b8f6e015ef34c292264184090eb8d860b7

[ "Apache-2.0" ]

5

2017-04-11T02:27:38.000Z

2020-12-11T07:44:00.000Z

""" Firefly Security and Monitoring This is the core Firefly Security and Monitoring Service. There should be almost zero config to the user and firefly will monitor the entire house. - Alarm System (Away) - Alarm System (Night) - Vacation Lighting - Battery Monitor - Smoke Alerts - Flooding Alerts """ from Firefly import logging, scheduler, aliases from Firefly.const import COMMAND_NOTIFY, EVENT_TYPE_BROADCAST, FIREFLY_SECURITY_MONITORING, SERVICE_NOTIFICATION, SOURCE_LOCATION, TYPE_DEVICE, WATER, SENSOR_DRY, SENSOR_WET from Firefly.helpers.device import BATTERY, CONTACT, CONTACT_CLOSE, CONTACT_OPEN, MOTION, MOTION_ACTIVE, MOTION_INACTIVE from Firefly.helpers.events import Command, Event from Firefly.services.firefly_security_and_monitoring.battery_monitor import check_battery_from_event, generate_battery_notification_message from Firefly.services.firefly_security_and_monitoring.secueity_settings import FireflySecuritySettings from Firefly.services.firefly_security_and_monitoring.security_monitor import (check_all_security_contact_sensors, check_all_security_motion_sensors, generate_contact_warning_message, process_contact_change, process_motion_change) from Firefly.util.firefly_util import command_from_dict from .const import ALARM_ARMED_MESSAGE_MOTION, ALARM_ARMED_MESSAGE_NO_MOTION, BATTERY_LOW, BATTERY_NO_NOTIFY_STATES, STATUS_TEMPLATE ALARM_DISARMED = 'disarmed' ALARM_ARMED = 'armed' ALARM_ARMED_MOTION = 'armed_motion' ALARM_ARMED_NO_MOTION = 'armed_no_motion' ALARM_TRIGGERED = 'triggered' SYSTEM_DISABLED = 'system_diabled'

38.11437

183

0.701239

""" Firefly Security and Monitoring This is the core Firefly Security and Monitoring Service. There should be almost zero config to the user and firefly will monitor the entire house. - Alarm System (Away) - Alarm System (Night) - Vacation Lighting - Battery Monitor - Smoke Alerts - Flooding Alerts """ from Firefly import logging, scheduler, aliases from Firefly.const import COMMAND_NOTIFY, EVENT_TYPE_BROADCAST, FIREFLY_SECURITY_MONITORING, SERVICE_NOTIFICATION, SOURCE_LOCATION, TYPE_DEVICE, WATER, SENSOR_DRY, SENSOR_WET from Firefly.helpers.device import BATTERY, CONTACT, CONTACT_CLOSE, CONTACT_OPEN, MOTION, MOTION_ACTIVE, MOTION_INACTIVE from Firefly.helpers.events import Command, Event from Firefly.services.firefly_security_and_monitoring.battery_monitor import check_battery_from_event, generate_battery_notification_message from Firefly.services.firefly_security_and_monitoring.secueity_settings import FireflySecuritySettings from Firefly.services.firefly_security_and_monitoring.security_monitor import (check_all_security_contact_sensors, check_all_security_motion_sensors, generate_contact_warning_message, process_contact_change, process_motion_change) from Firefly.util.firefly_util import command_from_dict from .const import ALARM_ARMED_MESSAGE_MOTION, ALARM_ARMED_MESSAGE_NO_MOTION, BATTERY_LOW, BATTERY_NO_NOTIFY_STATES, STATUS_TEMPLATE ALARM_DISARMED = 'disarmed' ALARM_ARMED = 'armed' ALARM_ARMED_MOTION = 'armed_motion' ALARM_ARMED_NO_MOTION = 'armed_no_motion' ALARM_TRIGGERED = 'triggered' SYSTEM_DISABLED = 'system_diabled' class FireflySecurityAndMonitoring(object): def __init__(self, firefly, enabled=True): self.firefly = firefly self.enabled = enabled self.status = STATUS_TEMPLATE self.alarm_status = ALARM_DISARMED self.settings = FireflySecuritySettings() def shutdown(self, **kwargs): self.settings.save_config() def get_alarm_status(self, **kwargs): if not self.enabled: return SYSTEM_DISABLED return self.alarm_status def event(self, event: Event, **kwargs): logging.info('[FIREFLY SECURITY] event received: %s' % str(event)) if not self.enabled: logging.info('[FIREFLY SECURITY] security and monitoring not enabled') return # Process Battery Notifications if BATTERY in event.event_action: self.process_battery_event(event) # Process water event only if monitoring is enabled for the device. if WATER in event.event_action: if self.check_security_enabled(event.source): self.process_water_event(event) # Enter Secure Mode if event.source == SOURCE_LOCATION and 'mode' in event.event_action: mode = event.event_action['mode'] if self.check_secure_mode(mode): self.enter_secure_mode() # Exit secure mode last_mode = self.firefly.location.lastMode if not self.check_secure_mode(mode) and self.check_secure_mode(last_mode): self.alarm_status = ALARM_DISARMED self.status['status']['alarm'] = self.alarm_status self.firefly.update_security_firebase(self.status) self.send_notification('Security alarm disabled.') self.broadcast_status() return if event.source not in self.firefly.components: logging.info('[FIREFLY SECURITY] event source not in components: %s' % event.source) return # Process Events while in secure mode if self.check_secure_mode(): if not self.check_security_enabled(event.source): logging.info('[FIREFLY SECURITY] event source is not device') return self.process_event_secure_mode(event) self.update_status(event) def startup(self, **kwargs): if self.check_secure_mode(): self.enter_secure_mode() def check_secure_mode(self, mode=None, no_motion=True, motion=True): """ Args: mode: The mode to check. no_motion: Check for modes with no motion active. motion: Check for modes with motion active. Returns: (bool) is in secure mode """ if mode is None: mode = self.firefly.location.mode mode_secure_no_motion = mode in self.settings.secure_modes_no_motion mode_secure_motion = mode in self.settings.secure_modes_motion if no_motion and motion: return mode_secure_motion or mode_secure_no_motion elif no_motion: return mode_secure_no_motion elif motion: return mode_secure_motion return False # TODO: Move this into security monitor def generate_status(self, **kwargs): if not self.enabled: return contact_states = check_all_security_contact_sensors(self.firefly.components, self.firefly.current_state) motion_states = check_all_security_motion_sensors(self.firefly.components, self.firefly.current_state) status_data = { 'status': { 'message': 'Message Placeholder', 'alarm': self.alarm_status }, CONTACT: { 'message': '', CONTACT_OPEN: { 'count': len(contact_states[CONTACT_OPEN]), 'devices': contact_states[CONTACT_OPEN] }, CONTACT_CLOSE: { 'count': len(contact_states[CONTACT_CLOSE]), 'devices': contact_states[CONTACT_CLOSE] } }, MOTION: { 'message': '', MOTION_ACTIVE: { 'count': len(motion_states[MOTION_ACTIVE]), 'devices': motion_states[MOTION_ACTIVE] }, MOTION_INACTIVE: { 'count': len(motion_states[MOTION_INACTIVE]), 'devices': motion_states[MOTION_INACTIVE] } } } self.status = status_data self.firefly.update_security_firebase(self.status) def check_security_enabled(self, ff_id: str, filter_type=TYPE_DEVICE) -> bool: if ff_id not in self.firefly.components: logging.info('[FIREFLY SECURITY] component not found: %s' % ff_id) return False try: component = self.firefly.components[ff_id] return component.security and component.type == filter_type except: return False # TODO: Move this into security monitor def update_status(self, event: Event): ff_id = event.source if not self.check_security_enabled(ff_id): return # Update Contact Status if CONTACT in event.event_action: if event.event_action[CONTACT] == CONTACT_OPEN: self.status[CONTACT][CONTACT_OPEN]['devices'].append(ff_id) self.status[CONTACT][CONTACT_OPEN]['count'] = len(self.status[CONTACT][CONTACT_OPEN]['devices']) try: self.status[CONTACT][CONTACT_CLOSE]['devices'].remove(ff_id) self.status[CONTACT][CONTACT_CLOSE]['count'] = len(self.status[CONTACT][CONTACT_CLOSE]['devices']) except Exception as e: logging.error('[FIREFLY SECURITY] error updating status: %s' % e) if event.event_action[CONTACT] == CONTACT_CLOSE: self.status[CONTACT][CONTACT_CLOSE]['devices'].append(ff_id) self.status[CONTACT][CONTACT_CLOSE]['count'] = len(self.status[CONTACT][CONTACT_CLOSE]['devices']) try: self.status[CONTACT][CONTACT_OPEN]['devices'].remove(ff_id) self.status[CONTACT][CONTACT_OPEN]['count'] = len(self.status[CONTACT][CONTACT_OPEN]['devices']) except Exception as e: logging.error('[FIREFLY SECURITY] error updating status: %s' % e) # Update Motion Status if MOTION in event.event_action: if event.event_action[MOTION] == MOTION_ACTIVE: self.status[MOTION][MOTION_ACTIVE]['devices'].append(ff_id) self.status[MOTION][MOTION_ACTIVE]['count'] = len(self.status[MOTION][MOTION_ACTIVE]['devices']) try: self.status[MOTION][MOTION_INACTIVE]['devices'].remove(ff_id) self.status[MOTION][MOTION_INACTIVE]['count'] = len(self.status[MOTION][MOTION_INACTIVE]['devices']) except Exception as e: logging.error('[FIREFLY SECURITY] error updating status: %s' % e) if event.event_action[MOTION] == MOTION_INACTIVE: self.status[MOTION][MOTION_INACTIVE]['devices'].append(ff_id) self.status[MOTION][MOTION_INACTIVE]['count'] = len(self.status[MOTION][MOTION_INACTIVE]['devices']) try: self.status[MOTION][MOTION_ACTIVE]['devices'].remove(ff_id) self.status[MOTION][MOTION_ACTIVE]['count'] = len(self.status[MOTION][MOTION_ACTIVE]['devices']) except Exception as e: logging.error('[FIREFLY SECURITY] error updating status: %s' % e) self.firefly.update_security_firebase(self.status) def process_event_secure_mode(self, event: Event): alarm_triggered = False contact_data = process_contact_change(event) if contact_data['contact_event']: self.send_notification(contact_data['message']) if contact_data['alarm']: alarm_triggered = True logging.info('[FIREFLY SECURITY] ALARM TRIGGERED') # TODO: Turn on listed lights, if no lights listed then turn on all lights if self.check_secure_mode(no_motion=False): motion_data = process_motion_change(event) if motion_data['alarm']: alarm_triggered = True self.send_notification(motion_data['message']) logging.info('[FIREFLY SECURITY] ALARM TRIGGERED') if alarm_triggered: self.trigger_alarm() def trigger_alarm(self, **kwargs): logging.info('TRIGGERING ALARM') self.alarm_status = ALARM_TRIGGERED lights = self.settings.lights if not lights: lights = self.get_devices_by_tag() for ff_id in lights: command = command_from_dict(ff_id, self.id, self.settings.light_command) logging.info('FIREFLY SECURITY] sending command %s' % str(command)) self.firefly.send_command(command) alarms = self.settings.alarms if not alarms: alarms = self.get_devices_by_tag(tags=['alarm']) for ff_id in alarms: command = Command(ff_id, self.id, self.settings.alarm_command) self.firefly.send_command(command) self.broadcast_status() self.status['status']['alarm'] = self.alarm_status.replace('_', ' ') self.firefly.update_security_firebase(self.status) def enter_secure_mode(self, **kwargs): logging.info('[FIREFLY SECURITY] Entering Secure Mode.') # Grab snapshot of current state current_state = self.firefly.current_state.copy() components = self.firefly.components contact_states = check_all_security_contact_sensors(components, current_state) if contact_states[CONTACT_OPEN]: message = generate_contact_warning_message(contact_states) self.send_notification(message) # If no contacts open then send notification that alarm is now armed. if self.check_secure_mode(no_motion=False): self.send_notification(ALARM_ARMED_MESSAGE_MOTION) self.alarm_status = ALARM_ARMED_MOTION else: self.send_notification(ALARM_ARMED_MESSAGE_NO_MOTION) self.alarm_status = ALARM_ARMED_NO_MOTION self.status['status']['alarm'] = self.alarm_status.replace('_', ' ') self.firefly.update_security_firebase(self.status) self.broadcast_status() def broadcast_status(self, **kwargs): event = Event(self.id, EVENT_TYPE_BROADCAST, { 'status': self.alarm_status, }) self.firefly.send_event(event) def get_devices_by_tag(self, tags=['light'], **kwargs): devices = [] for ff_id, component in self.firefly.components.items(): if component.type != TYPE_DEVICE: continue try: for tag in component.tags: if tag in tags: devices.append(ff_id) continue except: pass return devices def process_water_event(self, event: Event, **kwargs): alias = aliases.get_alias(event.source) if event.event_action.get(WATER) == SENSOR_WET: self.send_notification('ALERT!!! Water detected by: %s' % alias) self.trigger_alarm() return if event.event_action.get(WATER) == SENSOR_DRY: self.send_notification('ALERT!!! Water no longer detected by: %s' % alias) return def process_battery_event(self, event: Event, **kwargs): (battery_state, battery_level) = check_battery_from_event(event) if battery_state in BATTERY_NO_NOTIFY_STATES: if scheduler.cancel('%s_battery_notify' % event.source): self.send_notification('Battery in %s has been replaced.') return message = generate_battery_notification_message(event.source, battery_state, battery_level) self.send_notification(message) if battery_state == BATTERY_LOW: return scheduler.runEveryH(4, self.send_notification, job_id='%s_battery_notify' % event.source, message=message) return def send_notification(self, message): notify = Command(SERVICE_NOTIFICATION, self.id, COMMAND_NOTIFY, message=message) self.firefly.send_command(notify) @property def id(self): return FIREFLY_SECURITY_MONITORING

10,104

1,232

23

0c76885b70fe7b575d9278df97a40daf190c7e04

324

py

Python

optirocket/library/constants.py

Keith-Maxwell/OptiRocket

d99ac8d2b868b60a2bbf32f5a8a31ecdcaeea5b0

[ "MIT" ]

null

optirocket/library/constants.py

Keith-Maxwell/OptiRocket

d99ac8d2b868b60a2bbf32f5a8a31ecdcaeea5b0

[ "MIT" ]

3

2021-01-14T15:09:51.000Z

2021-02-12T17:05:18.000Z

optirocket/library/constants.py

Keith-Maxwell/OptiRocket

d99ac8d2b868b60a2bbf32f5a8a31ecdcaeea5b0

[ "MIT" ]

1

2021-01-11T02:34:29.000Z

# standard gravitational parameter for Earth = G*M EARTH_GRAV_CONST = 3.986005e5 # (km^3/s^2) # Earth Radius EARTH_RADIUS = 6378.137 # (km) # Earth rotation speed (calculated from sideral period) EARTH_ROT_RATE = 6.300387486749 / 86164 # (rad/s) # Earth gravitation at sea leve EARTH_GRAV_SEA_LVL = 9.80665 # (m^2/s)

27

55

0.725309

# standard gravitational parameter for Earth = G*M EARTH_GRAV_CONST = 3.986005e5 # (km^3/s^2) # Earth Radius EARTH_RADIUS = 6378.137 # (km) # Earth rotation speed (calculated from sideral period) EARTH_ROT_RATE = 6.300387486749 / 86164 # (rad/s) # Earth gravitation at sea leve EARTH_GRAV_SEA_LVL = 9.80665 # (m^2/s)

0

42488845e1b00797f2c42f02abc38006597e292a

4,539

py

Python

my_utils/misc.py

Jennifercheukyin/High-Speed-Pedestrian-Crossing-Prediction

09cceb0efaf4d074ee16d11d8f91292ce9dec854

[ "MIT" ]

4

2021-10-22T01:33:16.000Z

2022-03-09T06:39:54.000Z

my_utils/misc.py

Jennifercheukyin/High-Speed-Pedestrian-Crossing-Prediction

09cceb0efaf4d074ee16d11d8f91292ce9dec854

[ "MIT" ]

null

my_utils/misc.py

Jennifercheukyin/High-Speed-Pedestrian-Crossing-Prediction

09cceb0efaf4d074ee16d11d8f91292ce9dec854

[ "MIT" ]

null

'''Some helper functions for PyTorch, including: - get_mean_and_std: calculate the mean and std value of dataset. - msr_init: net parameter initialization. - progress_bar: progress bar mimic xlua.progress. ''' import errno import os import sys import time import math import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable import numpy as np import pdb import torch __all__ = ['get_mean_and_std', 'init_params', 'mkdir_p', 'AverageMeter', 'MovingAverage', 'AverageMeter_Mat', 'Timer'] def get_mean_and_std(dataset): '''Compute the mean and std value of dataset.''' dataloader = trainloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2) mean = torch.zeros(3) std = torch.zeros(3) print('==> Computing mean and std..') for inputs, targets in dataloader: for i in range(3): mean[i] += inputs[:,i,:,:].mean() std[i] += inputs[:,i,:,:].std() mean.div_(len(dataset)) std.div_(len(dataset)) return mean, std def init_params(net): '''Init layer parameters.''' for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal(m.weight, mode='fan_out') if m.bias: init.constant(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant(m.weight, 1) init.constant(m.bias, 0) elif isinstance(m, nn.Linear): init.normal(m.weight, std=1e-3) if m.bias: init.constant(m.bias, 0) def mkdir_p(path): '''make dir if not exist''' try: os.makedirs(path) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise class AverageMeter(object): """Computes and stores the average and current value Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262 """

30.463087

118

0.592642

'''Some helper functions for PyTorch, including: - get_mean_and_std: calculate the mean and std value of dataset. - msr_init: net parameter initialization. - progress_bar: progress bar mimic xlua.progress. ''' import errno import os import sys import time import math import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable import numpy as np import pdb import torch __all__ = ['get_mean_and_std', 'init_params', 'mkdir_p', 'AverageMeter', 'MovingAverage', 'AverageMeter_Mat', 'Timer'] def get_mean_and_std(dataset): '''Compute the mean and std value of dataset.''' dataloader = trainloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2) mean = torch.zeros(3) std = torch.zeros(3) print('==> Computing mean and std..') for inputs, targets in dataloader: for i in range(3): mean[i] += inputs[:,i,:,:].mean() std[i] += inputs[:,i,:,:].std() mean.div_(len(dataset)) std.div_(len(dataset)) return mean, std def init_params(net): '''Init layer parameters.''' for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal(m.weight, mode='fan_out') if m.bias: init.constant(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant(m.weight, 1) init.constant(m.bias, 0) elif isinstance(m, nn.Linear): init.normal(m.weight, std=1e-3) if m.bias: init.constant(m.bias, 0) def mkdir_p(path): '''make dir if not exist''' try: os.makedirs(path) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise class MovingAverage(object): def __init__(self, length): self.length = length self.count = 0 self.pointer = 0 self.values = np.zeros(length) # self.avg = 0 def update(self, val): self.values[self.pointer] = val self.pointer += 1 if self.pointer == self.length: self.pointer = 0 self.count += 1 self.count = np.minimum(self.count, self.length) def avg(self): return self.values.sum() / float(self.count) def reset(self): self.count = 0 self.pointer = 0 # self.avg = 0 self.values.fill(0) class AverageMeter(object): """Computes and stores the average and current value Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262 """ def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n # pdb.set_trace() self.count += n self.avg = self.sum / self.count class AverageMeter_Mat(object): def __init__(self,number_ID): self.number_ID = number_ID self.reset() def reset(self): # self.sum = Variable(torch.Tensor(self.number_ID,64).fill_(0).cuda()) # self.num = Variable(torch.Tensor(self.number_ID,64).fill_(0).cuda()) self.center = Variable(torch.Tensor(self.number_ID,64).fill_(0).cuda(), requires_grad=False) # self.dif = Variable(torch.Tensor(self.number_ID,64).fill_(0).cuda()) self.sum = torch.Tensor(self.number_ID,64).fill_(0).cuda() self.num = torch.Tensor(self.number_ID,64).fill_(0).cuda() # self.center = torch.Tensor(self.number_ID,64).fill_(0).cuda() # self.dif = torch.Tensor(self.number_ID,64).fill_(0).cuda() # self.sum = torch.Tensor(self.number_ID,64).fill_(0) # self.num = torch.Tensor(self.number_ID,64).fill_(0) # self.center = torch.Tensor(self.number_ID,64).fill_(0) # self.dif = torch.Tensor(self.number_ID,64).fill_(0) def update(self, SIR, ID, n): # pdb.set_trace() self.sum[ID,:] += SIR.data # pdb.set_trace() self.num[ID,:] += 1*n self.center[ID,:] = self.sum[ID] / self.num[ID] # self.dif[ID,:] = SIR - Variable(self.center[ID]) # self.avg = 0.5*torch.mean(self.dif**2) class Timer(object): def __init__(self): pass def reset(self): self.T = time.time() def time(self, reset=False): ti = time.time() - self.T if reset: self.reset() return ti

2,101

16

428

ca91f55ea74fe8da53eabdf2dc43a829dbcf7253

1,697

py

Python

gradient_decent_simple_linear_regression.py

eshanmherath/linear-regression

5b473586679a4b4594706faeb2bb7e4922c7ab38

[ "MIT" ]

1

2020-12-09T04:19:46.000Z

gradient_decent_simple_linear_regression.py

eshanmherath/linear-regression

5b473586679a4b4594706faeb2bb7e4922c7ab38

[ "MIT" ]

null

gradient_decent_simple_linear_regression.py

eshanmherath/linear-regression

5b473586679a4b4594706faeb2bb7e4922c7ab38

[ "MIT" ]

null

import numpy as np np.random.seed(111) ''' The data is generated adding noise to the values from y = 0.8x + 2 equation Therefore the expectation of the auto encoder is to get the values w and b closer to 0.8 and 2 respectively ''' '''generate random x values''' X_train = np.random.random((1, 50))[0] '''get the reference y value''' y_reference = 0.8*X_train + 2 '''add noise to the reference y value''' y_train = y_reference + np.sqrt(0.01)*np.random.random((1, 50))[0] W = np.random.random() b = np.random.random() '''number of training examples''' m = len(X_train) '''parameters''' learning_rate = 0.01 epochs = 5000 '''send data to the gradient optimizer to optimize values for W and b''' gradient_descent(X_train, y_train) print('\nGradient optimization completed') print('W Expected : 0.8' + ' Learned : ' + str(W)) print('b Expected : 2.0' + ' Learned : ' + str(b))

28.762712

107

0.625221

import numpy as np np.random.seed(111) ''' The data is generated adding noise to the values from y = 0.8x + 2 equation Therefore the expectation of the auto encoder is to get the values w and b closer to 0.8 and 2 respectively ''' '''generate random x values''' X_train = np.random.random((1, 50))[0] '''get the reference y value''' y_reference = 0.8*X_train + 2 '''add noise to the reference y value''' y_train = y_reference + np.sqrt(0.01)*np.random.random((1, 50))[0] W = np.random.random() b = np.random.random() '''number of training examples''' m = len(X_train) '''parameters''' learning_rate = 0.01 epochs = 5000 def gradient_descent(X, y): global W, b, learning_rate, epochs for _epoch in range(epochs): hypothesis = W*X + b '''cost function''' cost = np.divide(1, 2*m) * np.sum((hypothesis-y) ** 2) print(hypothesis) exit() '''partial derivatives of the cost function with respect to W and b''' gradient_w = np.divide(1, m) * np.sum((hypothesis-y)*X) gradient_b = np.divide(1, m) * np.sum(hypothesis-y) '''calculating new W and b values simultaneously''' temp_w = W - learning_rate*gradient_w temp_b = b - learning_rate*gradient_b '''updating W and b simultaneously''' W = temp_w b = temp_b print('\nepoch ' + str(_epoch) + ' W : ' + str(W) + ' b : ' + str(b) + ' Cost : ' + str(cost)) '''send data to the gradient optimizer to optimize values for W and b''' gradient_descent(X_train, y_train) print('\nGradient optimization completed') print('W Expected : 0.8' + ' Learned : ' + str(W)) print('b Expected : 2.0' + ' Learned : ' + str(b))

788

0

23

b2107b59ecdecdb0d53f298a0ed4ee2762c4cc8c

458

py

Python

1_mundo_exercicios/ex018.py

GuilhermeLima182/CursoDePython

7e72b117142794c38cbb14284d0fa6e1dbee5bf6

[ "MIT" ]

null

1_mundo_exercicios/ex018.py

GuilhermeLima182/CursoDePython

7e72b117142794c38cbb14284d0fa6e1dbee5bf6

[ "MIT" ]

null

1_mundo_exercicios/ex018.py

GuilhermeLima182/CursoDePython

7e72b117142794c38cbb14284d0fa6e1dbee5bf6

[ "MIT" ]

null

#Faça um programa que leia um ângulo qualquer e mostre na tela #o valor do seno,cosseno e tangente desse ângulo. from math import radians, sin, cos, tan angulo = int(input('Digite um ângulo: ')) sen = sin(radians(angulo)) cos = cos(radians(angulo)) tan = tan(radians(angulo)) print('O seno do ângulo {} é {:.2f}'.format(angulo, sen)) print('O Cosseno do ângulo {} é {:.2f}'.format(angulo, cos)) print('A tangente do ângulo {} é {:.2f}'.format(angulo, tan))

38.166667

62

0.696507

#Faça um programa que leia um ângulo qualquer e mostre na tela #o valor do seno,cosseno e tangente desse ângulo. from math import radians, sin, cos, tan angulo = int(input('Digite um ângulo: ')) sen = sin(radians(angulo)) cos = cos(radians(angulo)) tan = tan(radians(angulo)) print('O seno do ângulo {} é {:.2f}'.format(angulo, sen)) print('O Cosseno do ângulo {} é {:.2f}'.format(angulo, cos)) print('A tangente do ângulo {} é {:.2f}'.format(angulo, tan))

0

624916c3d5ec04f32ee59e6547283d5f7ef4f28e

1,313

py

Python

source/_sample/sympy/stereograph.py

showa-yojyo/notebook

82c15074c24d64a1dfcb70a526bc1deb2ecffe68

[ "MIT" ]

14

2016-04-13T08:10:02.000Z

2021-04-19T09:42:51.000Z

source/_sample/sympy/stereograph.py

showa-yojyo/note

5f262ecda3df132cb66206c465d16e174061d6b9

[ "MIT" ]

88

2017-09-27T15:07:05.000Z

2019-10-02T04:05:03.000Z

source/_sample/sympy/stereograph.py

showa-yojyo/note

5f262ecda3df132cb66206c465d16e174061d6b9

[ "MIT" ]

null

#!/usr/bin/env python """stereograph.py: Compute length of a geodesic in the unit sphere. """ from sympy import (symbols, Function, Matrix, factor, simplify, latex, sqrt) from sympy.abc import (t, xi, eta) from sympy.printing import print_latex if __name__ == '__main__': main()

32.825

76

0.581112

#!/usr/bin/env python """stereograph.py: Compute length of a geodesic in the unit sphere. """ from sympy import (symbols, Function, Matrix, factor, simplify, latex, sqrt) from sympy.abc import (t, xi, eta) from sympy.printing import print_latex def main(): u, v, R = symbols('u v R', real=True) xi, eta = symbols(r'\xi \eta', cls=Function) numer = 4*R**2 denom = u**2 + v**2 + numer # inverse of a stereographic projection from the south pole # onto the XY plane: pinv = Matrix([numer * u / denom, numer * v / denom, -(2 * R * (u**2 + v**2)) / denom]) # OK if False: # textbook style Dpinv = simplify(pinv.jacobian([u, v])) print_latex(Dpinv, mat_str='pmatrix', mat_delim=None) # OK? tDpinvDpinv = factor(Dpinv.transpose() @ Dpinv) print_latex(tDpinvDpinv, mat_str='pmatrix', mat_delim=None) # OK tDpinvDpinv = tDpinvDpinv.subs([(u, xi(t)), (v, eta(t))]) dcdt = Matrix([xi(t).diff(), eta(t).diff()]) print_latex(simplify( sqrt((dcdt.transpose() @ tDpinvDpinv).dot(dcdt)))) else: # directly dpinvc = pinv.subs([(u, xi(t)), (v, eta(t))]).diff(t, 1) print_latex(sqrt(factor(dpinvc.dot(dpinvc)))) if __name__ == '__main__': main()

1,005

0

23

9ee6af17b80095ba1ce3ce97e7b719c8cc0ba35d

357

py

Python

visdialch/decoders/__init__.py

mohitsudhakar/visual-dialog-experiments

77cc65938b0ce99fc52b839b7821f29c7a6b32a0

[ "BSD-3-Clause" ]

1

2020-11-15T07:40:18.000Z

visdialch/decoders/__init__.py

mohitsudhakar/visual-dialog-experiments

77cc65938b0ce99fc52b839b7821f29c7a6b32a0

[ "BSD-3-Clause" ]

3

2020-11-13T19:53:06.000Z

2020-11-16T01:23:10.000Z

visdialch/decoders/__init__.py

mohitsudhakar/visual-dialog-experiments

77cc65938b0ce99fc52b839b7821f29c7a6b32a0

[ "BSD-3-Clause" ]

null

# from visdialch.decoders.gen import GenerativeDecoder #from visdialch.decoders.disc import DiscriminativeDecoder from visdialch.decoders.decoder import DiscriminativeDecoder

44.625

76

0.812325

# from visdialch.decoders.gen import GenerativeDecoder #from visdialch.decoders.disc import DiscriminativeDecoder from visdialch.decoders.decoder import DiscriminativeDecoder def Decoder(model_config, *args): name_dec_map = {"disc": DiscriminativeDecoder, "gen": GenerativeDecoder} return name_dec_map[model_config["decoder"]](model_config, *args)

159

0

23

8389323ee21ddfba844127da575ffe9542fde2b5

1,761

py

Python

test/unit/graph/test_node.py

uSpike/ansible-discover

74ed24d01bf305f45d0bb3485846291d8b3ca473

[ "MIT" ]

4

2018-08-22T19:56:47.000Z

2021-11-15T16:11:21.000Z

test/unit/graph/test_node.py

uSpike/ansible-discover

74ed24d01bf305f45d0bb3485846291d8b3ca473

[ "MIT" ]

11

2018-03-09T08:35:47.000Z

2018-08-17T20:05:58.000Z

test/unit/graph/test_node.py

uSpike/ansible-discover

74ed24d01bf305f45d0bb3485846291d8b3ca473

[ "MIT" ]

3

2018-08-14T15:35:31.000Z

2021-11-15T16:11:24.000Z

import pytest from ansiblediscover.graph.node import Node @pytest.mark.parametrize('this, other, equal', [ (('myname', 'mytype', 'mypath'), ('myname', 'mytype', 'mypath'), True), (('myname', 'mytype', 'mypath'), ('othername', 'mytype', 'mypath'), False), (('myname', 'mytype', 'mypath'), ('myname', 'othertype', 'mypath'), False), (('myname', 'mytype', 'mypath'), ('myname', 'othertype', 'otherpath'), False), ]) @pytest.mark.parametrize('other', [ None, [], ('myname', 'mytype', 'mypath'), ])

27.092308

93

0.651902

import pytest from ansiblediscover.graph.node import Node def test_build_identifier(): assert 'role:server_base' == Node.build_identifier('server_base', 'role') def test_identifier(): node = Node('server_base', 'role', 'irrelevant') assert 'role:server_base' == node.identifier() def test_add_successor(): parent = Node('appserver', 'playbook', 'appserver.yml') child = Node('server_base', 'role', 'roles/server_base') parent.add_successor(child) assert child in parent.successors assert parent in child.predecessors def test_add_predecessor(): parent = Node('appserver', 'playbook', 'appserver.yml') child = Node('server_base', 'role', 'roles/server_base') child.add_predecessor(parent) assert child in parent.successors assert parent in child.predecessors def test_str(): name = 'myname' typestring = 'mytype' path = 'mypath' node = Node(name, typestring, path) assert str((typestring, name, path)) == str(node) @pytest.mark.parametrize('this, other, equal', [ (('myname', 'mytype', 'mypath'), ('myname', 'mytype', 'mypath'), True), (('myname', 'mytype', 'mypath'), ('othername', 'mytype', 'mypath'), False), (('myname', 'mytype', 'mypath'), ('myname', 'othertype', 'mypath'), False), (('myname', 'mytype', 'mypath'), ('myname', 'othertype', 'otherpath'), False), ]) def test_eq(this, other, equal): this_node = Node(*this) other_node = Node(*other) assert (equal and (this_node == other_node)) or (not equal and (this_node != other_node)) @pytest.mark.parametrize('other', [ None, [], ('myname', 'mytype', 'mypath'), ]) def test_eq_unequal_types(other): this = Node('myname', 'mytype', 'mypath') assert this != other

1,070

0

159

92a5c4abb10045ba60521150fcb257f838c2d9c5

3,288

py

Python

Core/Grader.py

brnomendes/grader-edx

d5a168bf82100f6b1196d927d1dff81ca9ad7070

[ "MIT" ]

null

Core/Grader.py

brnomendes/grader-edx

d5a168bf82100f6b1196d927d1dff81ca9ad7070

[ "MIT" ]

1

2017-04-11T23:58:00.000Z

Core/Grader.py

brnomendes/grader-edx

d5a168bf82100f6b1196d927d1dff81ca9ad7070

[ "MIT" ]

null

import datetime from Models.Submission import Submission from Core.Database import Database from Core.Scorer import Scorer from Core.Executer import Executer from Core.Parser import Parser

43.263158

129

0.667579

import datetime from Models.Submission import Submission from Core.Database import Database from Core.Scorer import Scorer from Core.Executer import Executer from Core.Parser import Parser class Grader(): def __init__(self): self._session = Database.session() def run(self, anonymous_student_id, student_response, problem_id): submission = self._save_submission(anonymous_student_id, student_response, problem_id) if submission.error: return Grader._response(False) fail_messages = {} submissions = Submission.get_last_submissions_each_user(submission.problem_id) for s in submissions: messages = self._grader_execute(submission, s) if messages: fail_messages[s.student_id] = messages if not s.id == submission.id: self._grader_execute(s, submission) return Grader._response(fail_messages=fail_messages) def _grader_execute(self, submission_program, submission_test): test_result, fail_messages = Executer.run_test(submission_program, submission_test) self._session.add(test_result) self._session.commit() Scorer(submission_program.student_id, submission_test.student_id, test_result).start() return fail_messages def _save_submission(self, anonymous_student_id, student_response, problem_id): program, test = Parser.parse(student_response) new_submission = Submission(datetime.datetime.now(), anonymous_student_id, problem_id, program, test, False) test_result, fail_messages = Executer.run_test(new_submission, new_submission) new_submission.error = True if test_result.errors > 0 else False submission_exists = Submission.get_submission_user(new_submission.student_id, problem_id) if submission_exists: Scorer.resubmission_score(new_submission.student_id, -100) Scorer(None, None, None).get_score(new_submission.student_id) self._session.add(new_submission) self._session.commit() self._session.expunge(new_submission) self._session.close() return new_submission @staticmethod def _response(correct=True, fail_messages=None): if not correct: title = "<h3 style='color:red'>Erro encontrado no Código.</h3>" msg = "Execute localmente em sua máquina os testes do seu programa antes de submetê-lo." else: title = "<h3>Submissão aceita e pontuada.</h3>" if fail_messages: if len(fail_messages) > 1: msg = "Os casos de testes de {} alunos encontraram falhas no seu programa.".format(len(fail_messages)) else: msg = "Os casos de testes de 1 aluno encontrou falhas no seu programa." fail_msg = "<pre style='color:red;'>{}</pre>".format(list(fail_messages.values())[0][0]) msg = "{}Mensagem de falha:{}".format(msg, fail_msg) else: msg = "Não foram encontradas falhas no seu programa por outros alunos." return {"correct": correct, "score": 1, "msg": "{}\n{}".format(title, msg)}

2,933

147

23

3687c80748ad58f744cedde41cab9e69281efc9e

44,472

py

Python

nitorch/io/volumes/mapping.py

balbasty/nitorch

d30c3125a8a66ea1434f2b39ed03338afd9724b4

[ "MIT" ]

46

2020-07-31T10:14:05.000Z

2022-03-24T12:51:46.000Z

nitorch/io/volumes/mapping.py

balbasty/nitorch

d30c3125a8a66ea1434f2b39ed03338afd9724b4

[ "MIT" ]

36

2020-10-06T19:01:38.000Z

2022-02-03T18:07:35.000Z

nitorch/io/volumes/mapping.py

balbasty/nitorch

d30c3125a8a66ea1434f2b39ed03338afd9724b4

[ "MIT" ]

6

2021-01-05T14:59:05.000Z

2021-11-18T18:26:45.000Z

from copy import copy import torch from nitorch.core.py import make_list from nitorch.core import dtypes from nitorch.spatial import affine_sub, affine_permute, voxel_size as affvx from nitorch.io.utils.indexing import (expand_index, guess_shape, compose_index, neg2pos, is_droppedaxis, is_newaxis, is_sliceaxis, invert_permutation, invert_slice, slice_navigator) from ..utils import volutils from ..mapping import MappedFile class MappedArray(MappedFile): """Base class for mapped arrays. Mapped arrays are usually stored on-disk, along with (diverse) metadata. They can be symbolically sliced, allowing for partial reading and (sometimes) writing of data from/to disk. Chaining of symbolic slicing operations is implemented in this base class. The actual io must be implemented by the child class. Abstract Methods ---------------- Child classes MUST implement: * self.data(...) Child classes SHOULD implement: * self.metadata(...) default -> returns empty dict Child classes MAY implement: * self.set_data(...) default -> raises cls.FailedWriteError * self.set_metadata(...) default -> raises cls.FailedWriteError * cls.save_new(...) default -> raises cls.FailedWriteError * cls.savef_new(...) default -> raises cls.FailedWriteError Child classes SHOULD register themselves in `readers.reader_classes`. If they implement `save_new`, child classes SHOULD register themselves in `writers.writer_classes`. Properties ---------- dtype : np.dtype On-disk data type slope : float Intensity slope from on-disk to unit inter : float Intensity shift from on-disk to unit affine : tensor Orientation matrix: maps spatial axes to 'world' spatial : tuple[bool] Mask of 'spatial' axes (x, y, z, ...) slicer : tuple[index_like] Indexing into the full on-disk array permutation : tuple[int] Permutation of the original in-disk axes. dim : int Number of axes voxel_size : tuple[float] World size of the spatial dimensions readable : AccessType See `AccessType` writable : AccessType See `AccessType` Types ----- FailedReadError Error raised when failing to load FailedWriteError Error raised when failing to save Methods ------- slice(tuple[index_like]) Subslice the array permute(tuple[int]) Permute axes transpose(int, int) Permute two axes unsqueeze(int) Insert singleton dimension squeeze(int) Remove singleton dimension unbind -> tuple Unstack arrays along a dimension chunk -> tuple Unstack arrays along a dimension by chunks split -> tuple Unstack arrays along a dimension by chunks data(...) -> tensor Load raw data to memory fdata(...) -> tensor Load scaled floating-point data to memory metadata(...) -> dict Load metadata to memory set_data(dat, ...) Write raw data to disk set_fdata(dat, ...) Write scaled floating-point data to disk set_metadata(**meta) Write metadata to disk Class methods ------------- save_new(dat, file_like) Write new file populated with `dat` savef_new(dat, file_like) Write new file populated with (scaled) `dat` External functions ------------------ map(file_like) -> MappedArray Build a MappedArray load(file_like) -> tensor Load raw data to memory from a file loadf(file_like) -> tensor Load scaled data to memory from a file save(dat, file_like) -> Save raw data into a new file savef(dat, file_like) -> Save scaled data into a new file cat(tuple[MappedArray]) Concatenate arrays along a dimension Syntaxic sugar -------------- __call__ -> fdata Load scaled floating-point data to memory __array__ -> fdata Load scaled floating-point data to memory __getitem__ -> slice Subslice the array __setitem__ -> set_fdata Write scaled floating-point data to disk __len__ Size of the first dimension (or 0 if scalar) """ fname: str = None # filename (can be None if in-memory proxy) fileobj = None # file-like object (`write`, `seek`, etc) is_compressed: bool = None # is compressed dtype: torch.dtype = None # on-disk data type slope: float = 1 # intensity slope inter: float = 0 # intensity shift affine = None # sliced voxel-to-world _affine = None # original voxel-to-world spatial: tuple = None # sliced spatial mask (len -> dim) _spatial: tuple = None # original spatial mask (len -> _dim) shape: tuple = None # sliced shape (len -> dim) _shape: tuple = None # original shape (len -> _dim) slicer: tuple = None # indexing into the parent permutation: tuple = None # permutation of original dim (len -> _dim) dim = property(lambda self: len(self.shape)) # Nb of sliced dimensions _dim = property(lambda self: len(self._shape)) # Nb of original dimensions voxel_size = property(lambda self: affvx(self.affine)) __repr__ = __str__ @classmethod def possible_extensions(cls): """List all possible extensions""" return tuple() def __getitem__(self, index): """Extract a sub-part of the array. Indices can only be slices, ellipses, integers or None. Parameters ---------- index : tuple[slice or ellipsis or int or None] Returns ------- subarray : type(self) MappedArray object, with the indexing operations and affine matrix relating to the new sub-array. """ return self.slice(index) def slice(self, index, new_shape=None, _pre_expanded=False): """Extract a sub-part of the array. Indices can only be slices, ellipses, integers or None. Parameters ---------- index : tuple[slice or ellipsis or int or None] Other Parameters ---------------- new_shape : sequence[int], optional Output shape of the sliced object _pre_expanded : bool, default=False Set to True of `expand_index` has already been called on `index` Returns ------- subarray : type(self) MappedArray object, with the indexing operations and affine matrix relating to the new sub-array. """ index = expand_index(index, self.shape) new_shape = guess_shape(index, self.shape) if any(isinstance(idx, list) for idx in index) > 1: raise ValueError('List indices not currently supported ' '(otherwise we enter advanced indexing ' 'territory and it becomes too complicated).') new = copy(self) new.shape = new_shape # compute new affine if self.affine is not None: spatial_shape = [sz for sz, msk in zip(self.shape, self.spatial) if msk] spatial_index = [idx for idx in index if not is_newaxis(idx)] spatial_index = [idx for idx, msk in zip(spatial_index, self.spatial) if msk] affine, _ = affine_sub(self.affine, spatial_shape, tuple(spatial_index)) else: affine = None new.affine = affine # compute new slicer perm_shape = [self._shape[d] for d in self.permutation] new.slicer = compose_index(self.slicer, index, perm_shape) # compute new spatial mask spatial = [] i = 0 for idx in new.slicer: if is_newaxis(idx): spatial.append(False) else: # original axis if not is_droppedaxis(idx): spatial.append(self._spatial[self.permutation[i]]) i += 1 new.spatial = tuple(spatial) return new def __setitem__(self, index, value): """Write scaled data to disk. Parameters ---------- index : tuple Tuple of indices (see `__getitem__`) value : array or tensor Array-like with shape `self[index].shape` Returns ------- self : type(self) """ if isinstance(value, MappedArray): raise NotImplementedError else: self.__getitem__(index).set_fdata(value) return self def __call__(self, *args, **kwargs): """Get floating point data. See `fdata()`""" return self.fdata(*args, **kwargs) def __array__(self, dtype=None): """Convert to numpy array""" return self.fdata(dtype=dtype, numpy=True) def permute(self, dims): """Permute dimensions Parameters ---------- dims : sequence[int] A permutation of `range(self.dim)` Returns ------- permarray : type(self) MappedArray object, with the indexing operations and affine matrix reflecting the permutation. """ dims = list(dims) if len(dims) != self.dim or len(dims) != len(set(dims)): raise ValueError('there should be as many (unique) dimensions ' 'as the array\'s dimension. Got {} and {}.' .format(len(set(dims)), self.dim)) # permute tuples that relate to the current spatial dimensions # (that part is easy) shape = tuple(self.shape[d] for d in dims) spatial = tuple(self.spatial[d] for d in dims) # permute slicer # 1) permute non-dropped dimensions slicer_nodrop = list(filter(lambda x: not is_droppedaxis(x), self.slicer)) slicer_nodrop = [slicer_nodrop[d] for d in dims] # 2) insert dropped dimensions slicer = [] for idx in self.slicer: if is_droppedaxis(idx): slicer.append(idx) else: new_idx, *slicer_nodrop = slicer_nodrop slicer.append(new_idx) # permute permutation # 1) insert None where new axes and remove dropped axes old_perm = self.permutation new_perm = [] drop_perm = [] for idx in self.slicer: if is_newaxis(idx): new_perm.append(None) continue p, *old_perm = old_perm if not is_droppedaxis(idx): new_perm.append(p) else: drop_perm.append(p) # 2) permute new_perm = [new_perm[d] for d in dims] # 3) insert back dropped axes and remove new axes perm = [] for idx in self.slicer: if is_droppedaxis(idx): p, *drop_perm = drop_perm perm.append(p) continue p, *new_perm = new_perm if not is_newaxis(p): perm.append(p) # permute affine # (it's a bit more complicated: we need to find the # permutation of the *current* *spatial* dimensions) perm_spatial = [p for p in dims if self.spatial[p]] perm_spatial = sorted(range(len(perm_spatial)), key=lambda k: perm_spatial[k]) affine, _ = affine_permute(self.affine, perm_spatial, self.shape) # create new object new = copy(self) new.shape = shape new.spatial = spatial new.permutation = tuple(perm) new.slicer = tuple(slicer) new.affine = affine return new def transpose(self, dim0, dim1): """Transpose two dimensions Parameters ---------- dim0 : int First dimension dim1 : int Second dimension Returns ------- permarray : type(self) MappedArray object, with the indexing operations and affine matrix reflecting the transposition. """ permutation = list(range(self.dim)) permutation[dim0] = dim1 permutation[dim1] = dim0 return self.permute(permutation) def data(self, dtype=None, device=None, casting='unsafe', rand=True, cutoff=None, dim=None, numpy=False): """Load the array in memory Parameters ---------- dtype : type or torch.dtype or np.dtype, optional Output data type. By default, keep the on-disk data type. device : torch.device, default='cpu' Output device. rand : bool, default=False If the on-disk dtype is not floating point, sample noise in the uncertainty interval. cutoff : float or (float, float), default=(0, 1) Percentile cutoff. If only one value is provided, it is assumed to relate to the upper percentile. dim : int or list[int], optional Dimensions along which to compute percentiles. By default, they are computed on the flattened array. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur: * 'no': the data types should not be cast at all. * 'equiv': only byte-order changes are allowed. * 'safe': only casts which can preserve values are allowed. * 'same_kind': only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe': any data conversions may be done. * 'rescale': the input data is rescaled to match the dynamic range of the output type. The minimum value in the data is mapped to the minimum value of the data type and the maximum value in the data is mapped to the maximum value of the data type. * 'rescale_zero': the input data is rescaled to match the dynamic range of the output type, but ensuring that zero maps to zero. > If the data is signed and cast to a signed datatype, zero maps to zero, and the scaling is chosen so that both the maximum and minimum value in the data fit in the output dynamic range. > If the data is signed and cast to an unsigned datatype, negative values "wrap around" (as with an unsafe cast). > If the data is unsigned and cast to a signed datatype, values are kept positive (the negative range is unused). numpy : bool, default=False Return a numpy array rather than a torch tensor. Returns ------- dat : tensor[dtype] """ pass def fdata(self, dtype=None, device=None, rand=False, cutoff=None, dim=None, numpy=False): """Load the scaled array in memory This function differs from `data` in several ways: * The output data type should be a floating point type. * If an affine scaling (slope, intercept) is defined in the file, it is applied to the data. * the default output data type is `torch.get_default_dtype()`. Parameters ---------- dtype : dtype_like, optional Output data type. By default, use `torch.get_default_dtype()`. Should be a floating point type. device : torch.device, default='cpu' Output device. rand : bool, default=False If the on-disk dtype is not floating point, sample noise in the uncertainty interval. cutoff : float or (float, float), default=(0, 1) Percentile cutoff. If only one value is provided, it is assumed to relate to the upper percentile. dim : int or list[int], optional Dimensions along which to compute percentiles. By default, they are computed on the flattened array. numpy : bool, default=False Return a numpy array rather than a torch tensor. Returns ------- dat : tensor[dtype] """ # --- sanity check --- dtype = torch.get_default_dtype() if dtype is None else dtype info = dtypes.dtype(dtype) if not info.is_floating_point: raise TypeError('Output data type should be a floating point ' 'type but got {}.'.format(dtype)) # --- get unscaled data --- dat = self.data(dtype=dtype, device=device, rand=rand, cutoff=cutoff, dim=dim, numpy=numpy) # --- scale --- if self.slope != 1: dat *= float(self.slope) if self.inter != 0: dat += float(self.inter) return dat def set_data(self, dat, casting='unsafe'): """Write (partial) data to disk. Parameters ---------- dat : tensor Tensor to write on disk. It should have shape `self.shape`. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur: * 'no': the data types should not be cast at all. * 'equiv': only byte-order changes are allowed. * 'safe': only casts which can preserve values are allowed. * 'same_kind': only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe': any data conversions may be done. * 'rescale': the input data is rescaled to match the dynamic range of the output type. The minimum value in the data is mapped to the minimum value of the data type and the maximum value in the data is mapped to the maximum value of the data type. * 'rescale_zero': the input data is rescaled to match the dynamic range of the output type, but ensuring that zero maps to zero. > If the data is signed and cast to a signed datatype, zero maps to zero, and the scaling is chosen so that both the maximum and minimum value in the data fit in the output dynamic range. > If the data is signed and cast to an unsigned datatype, negative values "wrap around" (as with an unsafe cast). > If the data is unsigned and cast to a signed datatype, values are kept positive (the negative range is unused). Returns ------- self : type(self) """ raise self.FailedWriteError("Method not implemented in class {}." .format(type(self).__name__)) def set_fdata(self, dat): """Write (partial) scaled data to disk. Parameters ---------- dat : tensor Tensor to write on disk. It should have shape `self.shape` and a floating point data type. Returns ------- self : type(self) """ # --- sanity check --- info = dtypes.dtype(dat.dtype) if not info.is_floating_point: raise TypeError('Input data type should be a floating point ' 'type but got {}.'.format(dat.dtype)) if dat.shape != self.shape: raise TypeError('Expected input shape {} but got {}.' .format(self.shape, dat.shape)) # --- detach --- if torch.is_tensor(dat): dat = dat.detach() # --- unscale --- if self.inter != 0 or self.slope != 1: dat = dat.clone() if torch.is_tensor(dat) else dat.copy() if self.inter != 0: dat -= float(self.inter) if self.slope != 1: dat /= float(self.slope) # --- set unscaled data --- self.set_data(dat) return self def metadata(self, keys=None): """Read metadata .. note:: The values returned by this function always relate to the full volume, even if we're inside a view. That is, we always return the affine of the original volume. To get an affine matrix that relates to the view, use `self.affine`. Parameters ---------- keys : sequence[str], optional List of metadata to load. They can either be one of the generic metadata keys define in `io.metadata`, or a format-specific metadata key. By default, all generic keys that are found in the file are returned. Returns ------- metadata : dict A dictionary of metadata """ return dict() def set_metadata(self, **meta): """Write metadata Parameters ---------- meta : dict, optional Dictionary of metadata. Fields that are absent from the dictionary or that have value `None` are kept untouched. Returns ------- self : type(self) """ raise NotImplementedError("Method not implemented in class {}." .format(type(self).__name__)) @classmethod def save_new(cls, dat, file_like, like=None, casting='unsafe', **metadata): """Write an array to disk. This function makes educated choices for the file format and its metadata based on the file extension, the data type and the other options provided. Parameters ---------- dat : tensor or array or MappedArray Data to write file_like : str or file object Path to file or file object (with methods `seek`, `read`). If the extension is known, it gets priority over `like` when choosing the output format. like : file or MappedArray An array on-disk that should be used as a template for the new file. Its metadata/layout/etc will be mimicked as much as possible. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur. See `MappedArray.set_data` metadata : dict Metadata to store on disk. Values provided there will have priority over `like`. Returns ------- dat : array or tensor The array loaded in memory attributes : dict, if attributes is not None Dictionary of attributes loaded as well """ raise cls.FailedWriteError("Method not implemented in class {}." .format(cls.__name__)) @classmethod def savef_new(cls, dat, file_like, like=None, **metadata): """Write a scaled array to disk. This function makes educated choices for the file format and its metadata based on the file extension, the data type and the other options provided. The input data type must be a floating point type. Parameters ---------- dat : tensor or array or MappedArray Data to write file_like : str or file object Path to file or file object (with methods `seek`, `read`). If the extension is known, it gets priority over `like` when choosing the output format. like : file or MappedArray An array on-disk that should be used as a template for the new file. Its metadata/layout/etc will be mimicked as much as possible. metadata : dict Metadata to store on disk. Values provided there will have priority over `like`. Returns ------- dat : array or tensor The array loaded in memory attributes : dict, if attributes is not None Dictionary of attributes loaded as well """ raise cls.FailedWriteError("Method not implemented in class {}." .format(cls.__name__)) def unsqueeze(self, dim, ndim=1): """Add a dimension of size 1 in position `dim`. Parameters ---------- dim : int The dimension is added to the right of `dim` if `dim < 0` else it is added to the left of `dim`. Returns ------- MappedArray """ index = [slice(None)] * self.dim if dim < 0: dim = self.dim + dim + 1 index = index[:dim] + ([None] * ndim) + index[dim:] return self[tuple(index)] def squeeze(self, dim): """Remove all dimensions of size 1. Parameters ---------- dim : int or sequence[int], optional If provided, only this dimension is squeezed. It *must* be a dimension of size 1. Returns ------- MappedArray """ if dim is None: dim = [d for d in range(self.dim) if self.shape[d] == 1] dim = make_list(dim) ndim = len(self.shape) dim = [ndim + d if d < 0 else d for d in dim] if any(self.shape[d] != 1 for d in dim): raise ValueError('Impossible to squeeze non-singleton dimensions.') index = [slice(None) if d not in dim else 0 for d in range(self.dim)] return self[tuple(index)] def unbind(self, dim=0, keepdim=False): """Extract all arrays along dimension `dim` and drop that dimension. Parameters ---------- dim : int, default=0 Dimension along which to unstack. keepdim : bool, default=False Do not drop the unstacked dimension. Returns ------- list[MappedArray] """ index = [slice(None)] * self.dim if keepdim: index = index[:dim+1] + [None] + index[dim+1:] out = [] for i in range(self.shape[dim]): index[dim] = i out.append(self[tuple(index)]) return out def chunk(self, chunks, dim=0): """Split the array into smaller arrays of size `chunk` along `dim`. Parameters ---------- chunks : int Number of chunks. dim : int, default=0 Dimensions along which to split. Returns ------- list[MappedArray] """ index = [slice(None)] * self.dim out = [] for i in range(self.shape[dim]): index[dim] = slice(i*chunks, (i+1)*chunks) out.append(self[tuple(index)]) return out def split(self, chunks, dim=0): """Split the array into smaller arrays along `dim`. Parameters ---------- chunks : int or list[int] If `int`: Number of chunks (see `self.chunk`) Else: Size of each chunk. Must sum to `self.shape[dim]`. dim : int, default=0 Dimensions along which to split. Returns ------- list[MappedArray] """ if isinstance(chunks, int): return self.chunk(chunks, dim) chunks = make_list(chunks) if sum(chunks) != self.shape[dim]: raise ValueError('Chunks must cover the full dimension. ' 'Got {} and {}.' .format(sum(chunks), self.shape[dim])) index = [slice(None)] * self.dim previous_chunks = 0 out = [] for chunk in chunks: index[dim] = slice(previous_chunks, previous_chunks+chunk) out.append(self[tuple(index)]) previous_chunks += chunk return out def channel_first(self, atleast=0): """Permute the dimensions such that all spatial axes are on the right. Parameters ---------- atleast : int, default=0 Make sure that at least this number of non-spatial dimensions exist (new axes are inserted accordingly). Returns ------- MappedArray """ # 1) move spatial dimensions to the right perm = [] spatial = [] for d, is_spatial in enumerate(self.spatial): if is_spatial: spatial.append(d) else: perm.append(d) nb_channels = len(perm) perm = perm + spatial new = self.permute(perm) # 2) add channel axes add_channels = max(0, atleast - nb_channels) if add_channels: index = [slice(None)] * nb_channels \ + [None] * add_channels \ + [Ellipsis] new = new.slice(tuple(index)) return new def channel_last(self, atleast=0): """Permute the dimensions such that all spatial axes are on the left. Parameters ---------- atleast : int, default=0 Make sure that at least this number of non-spatial dimensions exist (new axes are inserted accordingly). Returns ------- MappedArray """ # 1) move spatial dimensions to the right perm = [] spatial = [] for d, is_spatial in enumerate(self.spatial): if is_spatial: spatial.append(d) else: perm.append(d) nb_channels = len(perm) perm = spatial + perm new = self.permute(perm) # 2) add channel axes add_channels = max(0, atleast - nb_channels) if add_channels: index = [Ellipsis] + [None] * add_channels new = new.slice(tuple(index)) return new class CatArray(MappedArray): """A concatenation of mapped arrays. This is largely inspired by virtual concatenation of file_array in SPM: https://github.com/spm/spm12/blob/master/@file_array/cat.m """ _arrays: tuple = [] _dim_cat: int = None # defer attributes fname = property(lambda self: tuple(a.fname for a in self._arrays)) fileobj = property(lambda self: tuple(a.fileobj for a in self._arrays)) is_compressed = property(lambda self: tuple(a.is_compressed for a in self._arrays)) dtype = property(lambda self: tuple(a.dtype for a in self._arrays)) slope = property(lambda self: tuple(a.slope for a in self._arrays)) inter = property(lambda self: tuple(a.inter for a in self._arrays)) _shape = property(lambda self: tuple(a._shape for a in self._arrays)) _dim = property(lambda self: tuple(a._dim for a in self._arrays)) affine = property(lambda self: tuple(a.affine for a in self._arrays)) _affine = property(lambda self: tuple(a._affine for a in self._arrays)) spatial = property(lambda self: tuple(a.spatial for a in self._arrays)) _spatial = property(lambda self: tuple(a._spatial for a in self._arrays)) slicer = property(lambda self: tuple(a.slicer for a in self._arrays)) permutation = property(lambda self: tuple(a.permutation for a in self._arrays)) voxel_size = property(lambda self: tuple(a.voxel_size for a in self._arrays)) def __init__(self, arrays, dim=0): """ Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays """ super().__init__() arrays = list(arrays) dim = dim or 0 self._dim_cat = dim # sanity checks shapes = [] for i, array in enumerate(arrays): if not isinstance(array, MappedArray): raise TypeError('Input arrays should be `MappedArray` ' 'instances. Got {}.',format(type(array))) shape = list(array.shape) del shape[dim] shapes.append(shape) shape0, *shapes = shapes if not all(shape == shape0 for shape in shapes): raise ValueError('Shapes of all concatenated arrays should ' 'be equal except in the concatenation dimension.') # compute output shape shape = list(arrays[0].shape) dims = [array.shape[dim] for array in arrays] shape[dim] = sum(dims) self.shape = tuple(shape) # concatenate self._arrays = tuple(arrays) __repr__ = __str__ def cat(arrays, dim=0): """Concatenate mapped arrays along a dimension. Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays Returns ------- CatArray A symbolic concatenation of all input arrays. Its shape along dimension `dim` is the sum of all input shapes along dimension `dim`. """ return CatArray(arrays, dim) def stack(arrays, dim=0): """Stack mapped arrays along a dimension. Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays Returns ------- CatArray A symbolic stack of all input arrays. """ arrays = [array.unsqueeze(dim=dim) for array in arrays] return cat(arrays, dim=dim)

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from copy import copy import torch from nitorch.core.py import make_list from nitorch.core import dtypes from nitorch.spatial import affine_sub, affine_permute, voxel_size as affvx from nitorch.io.utils.indexing import (expand_index, guess_shape, compose_index, neg2pos, is_droppedaxis, is_newaxis, is_sliceaxis, invert_permutation, invert_slice, slice_navigator) from ..utils import volutils from ..mapping import MappedFile class MappedArray(MappedFile): """Base class for mapped arrays. Mapped arrays are usually stored on-disk, along with (diverse) metadata. They can be symbolically sliced, allowing for partial reading and (sometimes) writing of data from/to disk. Chaining of symbolic slicing operations is implemented in this base class. The actual io must be implemented by the child class. Abstract Methods ---------------- Child classes MUST implement: * self.data(...) Child classes SHOULD implement: * self.metadata(...) default -> returns empty dict Child classes MAY implement: * self.set_data(...) default -> raises cls.FailedWriteError * self.set_metadata(...) default -> raises cls.FailedWriteError * cls.save_new(...) default -> raises cls.FailedWriteError * cls.savef_new(...) default -> raises cls.FailedWriteError Child classes SHOULD register themselves in `readers.reader_classes`. If they implement `save_new`, child classes SHOULD register themselves in `writers.writer_classes`. Properties ---------- dtype : np.dtype On-disk data type slope : float Intensity slope from on-disk to unit inter : float Intensity shift from on-disk to unit affine : tensor Orientation matrix: maps spatial axes to 'world' spatial : tuple[bool] Mask of 'spatial' axes (x, y, z, ...) slicer : tuple[index_like] Indexing into the full on-disk array permutation : tuple[int] Permutation of the original in-disk axes. dim : int Number of axes voxel_size : tuple[float] World size of the spatial dimensions readable : AccessType See `AccessType` writable : AccessType See `AccessType` Types ----- FailedReadError Error raised when failing to load FailedWriteError Error raised when failing to save Methods ------- slice(tuple[index_like]) Subslice the array permute(tuple[int]) Permute axes transpose(int, int) Permute two axes unsqueeze(int) Insert singleton dimension squeeze(int) Remove singleton dimension unbind -> tuple Unstack arrays along a dimension chunk -> tuple Unstack arrays along a dimension by chunks split -> tuple Unstack arrays along a dimension by chunks data(...) -> tensor Load raw data to memory fdata(...) -> tensor Load scaled floating-point data to memory metadata(...) -> dict Load metadata to memory set_data(dat, ...) Write raw data to disk set_fdata(dat, ...) Write scaled floating-point data to disk set_metadata(**meta) Write metadata to disk Class methods ------------- save_new(dat, file_like) Write new file populated with `dat` savef_new(dat, file_like) Write new file populated with (scaled) `dat` External functions ------------------ map(file_like) -> MappedArray Build a MappedArray load(file_like) -> tensor Load raw data to memory from a file loadf(file_like) -> tensor Load scaled data to memory from a file save(dat, file_like) -> Save raw data into a new file savef(dat, file_like) -> Save scaled data into a new file cat(tuple[MappedArray]) Concatenate arrays along a dimension Syntaxic sugar -------------- __call__ -> fdata Load scaled floating-point data to memory __array__ -> fdata Load scaled floating-point data to memory __getitem__ -> slice Subslice the array __setitem__ -> set_fdata Write scaled floating-point data to disk __len__ Size of the first dimension (or 0 if scalar) """ fname: str = None # filename (can be None if in-memory proxy) fileobj = None # file-like object (`write`, `seek`, etc) is_compressed: bool = None # is compressed dtype: torch.dtype = None # on-disk data type slope: float = 1 # intensity slope inter: float = 0 # intensity shift affine = None # sliced voxel-to-world _affine = None # original voxel-to-world spatial: tuple = None # sliced spatial mask (len -> dim) _spatial: tuple = None # original spatial mask (len -> _dim) shape: tuple = None # sliced shape (len -> dim) _shape: tuple = None # original shape (len -> _dim) slicer: tuple = None # indexing into the parent permutation: tuple = None # permutation of original dim (len -> _dim) dim = property(lambda self: len(self.shape)) # Nb of sliced dimensions _dim = property(lambda self: len(self._shape)) # Nb of original dimensions voxel_size = property(lambda self: affvx(self.affine)) def __init__(self, **kwargs): self._init(**kwargs) def _init(self, **kwargs): for key, val in kwargs: setattr(self, key, val) if self.permutation is None: self.permutation = tuple(range(self._dim)) if self.slicer is None: # same layout as on-disk self.spatial = self._spatial self.affine = self._affine self.shape = self._shape self.slicer = expand_index([Ellipsis], self._shape) return self def __str__(self): return '{}(shape={}, dtype={})'.format( type(self).__name__, self.shape, self.dtype) __repr__ = __str__ def __len__(self): if len(self.shape) > 0: return self.shape[0] else: return 0 @classmethod def possible_extensions(cls): """List all possible extensions""" return tuple() def __getitem__(self, index): """Extract a sub-part of the array. Indices can only be slices, ellipses, integers or None. Parameters ---------- index : tuple[slice or ellipsis or int or None] Returns ------- subarray : type(self) MappedArray object, with the indexing operations and affine matrix relating to the new sub-array. """ return self.slice(index) def slice(self, index, new_shape=None, _pre_expanded=False): """Extract a sub-part of the array. Indices can only be slices, ellipses, integers or None. Parameters ---------- index : tuple[slice or ellipsis or int or None] Other Parameters ---------------- new_shape : sequence[int], optional Output shape of the sliced object _pre_expanded : bool, default=False Set to True of `expand_index` has already been called on `index` Returns ------- subarray : type(self) MappedArray object, with the indexing operations and affine matrix relating to the new sub-array. """ index = expand_index(index, self.shape) new_shape = guess_shape(index, self.shape) if any(isinstance(idx, list) for idx in index) > 1: raise ValueError('List indices not currently supported ' '(otherwise we enter advanced indexing ' 'territory and it becomes too complicated).') new = copy(self) new.shape = new_shape # compute new affine if self.affine is not None: spatial_shape = [sz for sz, msk in zip(self.shape, self.spatial) if msk] spatial_index = [idx for idx in index if not is_newaxis(idx)] spatial_index = [idx for idx, msk in zip(spatial_index, self.spatial) if msk] affine, _ = affine_sub(self.affine, spatial_shape, tuple(spatial_index)) else: affine = None new.affine = affine # compute new slicer perm_shape = [self._shape[d] for d in self.permutation] new.slicer = compose_index(self.slicer, index, perm_shape) # compute new spatial mask spatial = [] i = 0 for idx in new.slicer: if is_newaxis(idx): spatial.append(False) else: # original axis if not is_droppedaxis(idx): spatial.append(self._spatial[self.permutation[i]]) i += 1 new.spatial = tuple(spatial) return new def __setitem__(self, index, value): """Write scaled data to disk. Parameters ---------- index : tuple Tuple of indices (see `__getitem__`) value : array or tensor Array-like with shape `self[index].shape` Returns ------- self : type(self) """ if isinstance(value, MappedArray): raise NotImplementedError else: self.__getitem__(index).set_fdata(value) return self def __call__(self, *args, **kwargs): """Get floating point data. See `fdata()`""" return self.fdata(*args, **kwargs) def __array__(self, dtype=None): """Convert to numpy array""" return self.fdata(dtype=dtype, numpy=True) def permute(self, dims): """Permute dimensions Parameters ---------- dims : sequence[int] A permutation of `range(self.dim)` Returns ------- permarray : type(self) MappedArray object, with the indexing operations and affine matrix reflecting the permutation. """ dims = list(dims) if len(dims) != self.dim or len(dims) != len(set(dims)): raise ValueError('there should be as many (unique) dimensions ' 'as the array\'s dimension. Got {} and {}.' .format(len(set(dims)), self.dim)) # permute tuples that relate to the current spatial dimensions # (that part is easy) shape = tuple(self.shape[d] for d in dims) spatial = tuple(self.spatial[d] for d in dims) # permute slicer # 1) permute non-dropped dimensions slicer_nodrop = list(filter(lambda x: not is_droppedaxis(x), self.slicer)) slicer_nodrop = [slicer_nodrop[d] for d in dims] # 2) insert dropped dimensions slicer = [] for idx in self.slicer: if is_droppedaxis(idx): slicer.append(idx) else: new_idx, *slicer_nodrop = slicer_nodrop slicer.append(new_idx) # permute permutation # 1) insert None where new axes and remove dropped axes old_perm = self.permutation new_perm = [] drop_perm = [] for idx in self.slicer: if is_newaxis(idx): new_perm.append(None) continue p, *old_perm = old_perm if not is_droppedaxis(idx): new_perm.append(p) else: drop_perm.append(p) # 2) permute new_perm = [new_perm[d] for d in dims] # 3) insert back dropped axes and remove new axes perm = [] for idx in self.slicer: if is_droppedaxis(idx): p, *drop_perm = drop_perm perm.append(p) continue p, *new_perm = new_perm if not is_newaxis(p): perm.append(p) # permute affine # (it's a bit more complicated: we need to find the # permutation of the *current* *spatial* dimensions) perm_spatial = [p for p in dims if self.spatial[p]] perm_spatial = sorted(range(len(perm_spatial)), key=lambda k: perm_spatial[k]) affine, _ = affine_permute(self.affine, perm_spatial, self.shape) # create new object new = copy(self) new.shape = shape new.spatial = spatial new.permutation = tuple(perm) new.slicer = tuple(slicer) new.affine = affine return new def movedim(self, source, destination): dim = self.dim source = make_list(source) destination = make_list(destination) if len(destination) == 1: # we assume that the user wishes to keep moved dimensions # in the order they were provided destination = destination[0] if destination >= 0: destination = list(range(destination, destination + len(source))) else: destination = list(range(destination + 1 - len(source), destination + 1)) if len(source) != len(destination): raise ValueError('Expected as many source as destination positions.') source = [dim + src if src < 0 else src for src in source] destination = [dim + dst if dst < 0 else dst for dst in destination] if len(set(source)) != len(source): raise ValueError(f'Expected source positions to be unique but got ' f'{source}') if len(set(destination)) != len(destination): raise ValueError(f'Expected destination positions to be unique but got ' f'{destination}') # compute permutation positions_in = list(range(dim)) positions_out = [None] * dim for src, dst in zip(source, destination): positions_out[dst] = src positions_in[src] = None positions_in = filter(lambda x: x is not None, positions_in) for i, pos in enumerate(positions_out): if pos is None: positions_out[i], *positions_in = positions_in return self.permute(positions_out) def transpose(self, dim0, dim1): """Transpose two dimensions Parameters ---------- dim0 : int First dimension dim1 : int Second dimension Returns ------- permarray : type(self) MappedArray object, with the indexing operations and affine matrix reflecting the transposition. """ permutation = list(range(self.dim)) permutation[dim0] = dim1 permutation[dim1] = dim0 return self.permute(permutation) def data(self, dtype=None, device=None, casting='unsafe', rand=True, cutoff=None, dim=None, numpy=False): """Load the array in memory Parameters ---------- dtype : type or torch.dtype or np.dtype, optional Output data type. By default, keep the on-disk data type. device : torch.device, default='cpu' Output device. rand : bool, default=False If the on-disk dtype is not floating point, sample noise in the uncertainty interval. cutoff : float or (float, float), default=(0, 1) Percentile cutoff. If only one value is provided, it is assumed to relate to the upper percentile. dim : int or list[int], optional Dimensions along which to compute percentiles. By default, they are computed on the flattened array. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur: * 'no': the data types should not be cast at all. * 'equiv': only byte-order changes are allowed. * 'safe': only casts which can preserve values are allowed. * 'same_kind': only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe': any data conversions may be done. * 'rescale': the input data is rescaled to match the dynamic range of the output type. The minimum value in the data is mapped to the minimum value of the data type and the maximum value in the data is mapped to the maximum value of the data type. * 'rescale_zero': the input data is rescaled to match the dynamic range of the output type, but ensuring that zero maps to zero. > If the data is signed and cast to a signed datatype, zero maps to zero, and the scaling is chosen so that both the maximum and minimum value in the data fit in the output dynamic range. > If the data is signed and cast to an unsigned datatype, negative values "wrap around" (as with an unsafe cast). > If the data is unsigned and cast to a signed datatype, values are kept positive (the negative range is unused). numpy : bool, default=False Return a numpy array rather than a torch tensor. Returns ------- dat : tensor[dtype] """ pass def fdata(self, dtype=None, device=None, rand=False, cutoff=None, dim=None, numpy=False): """Load the scaled array in memory This function differs from `data` in several ways: * The output data type should be a floating point type. * If an affine scaling (slope, intercept) is defined in the file, it is applied to the data. * the default output data type is `torch.get_default_dtype()`. Parameters ---------- dtype : dtype_like, optional Output data type. By default, use `torch.get_default_dtype()`. Should be a floating point type. device : torch.device, default='cpu' Output device. rand : bool, default=False If the on-disk dtype is not floating point, sample noise in the uncertainty interval. cutoff : float or (float, float), default=(0, 1) Percentile cutoff. If only one value is provided, it is assumed to relate to the upper percentile. dim : int or list[int], optional Dimensions along which to compute percentiles. By default, they are computed on the flattened array. numpy : bool, default=False Return a numpy array rather than a torch tensor. Returns ------- dat : tensor[dtype] """ # --- sanity check --- dtype = torch.get_default_dtype() if dtype is None else dtype info = dtypes.dtype(dtype) if not info.is_floating_point: raise TypeError('Output data type should be a floating point ' 'type but got {}.'.format(dtype)) # --- get unscaled data --- dat = self.data(dtype=dtype, device=device, rand=rand, cutoff=cutoff, dim=dim, numpy=numpy) # --- scale --- if self.slope != 1: dat *= float(self.slope) if self.inter != 0: dat += float(self.inter) return dat def set_data(self, dat, casting='unsafe'): """Write (partial) data to disk. Parameters ---------- dat : tensor Tensor to write on disk. It should have shape `self.shape`. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur: * 'no': the data types should not be cast at all. * 'equiv': only byte-order changes are allowed. * 'safe': only casts which can preserve values are allowed. * 'same_kind': only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe': any data conversions may be done. * 'rescale': the input data is rescaled to match the dynamic range of the output type. The minimum value in the data is mapped to the minimum value of the data type and the maximum value in the data is mapped to the maximum value of the data type. * 'rescale_zero': the input data is rescaled to match the dynamic range of the output type, but ensuring that zero maps to zero. > If the data is signed and cast to a signed datatype, zero maps to zero, and the scaling is chosen so that both the maximum and minimum value in the data fit in the output dynamic range. > If the data is signed and cast to an unsigned datatype, negative values "wrap around" (as with an unsafe cast). > If the data is unsigned and cast to a signed datatype, values are kept positive (the negative range is unused). Returns ------- self : type(self) """ raise self.FailedWriteError("Method not implemented in class {}." .format(type(self).__name__)) def set_fdata(self, dat): """Write (partial) scaled data to disk. Parameters ---------- dat : tensor Tensor to write on disk. It should have shape `self.shape` and a floating point data type. Returns ------- self : type(self) """ # --- sanity check --- info = dtypes.dtype(dat.dtype) if not info.is_floating_point: raise TypeError('Input data type should be a floating point ' 'type but got {}.'.format(dat.dtype)) if dat.shape != self.shape: raise TypeError('Expected input shape {} but got {}.' .format(self.shape, dat.shape)) # --- detach --- if torch.is_tensor(dat): dat = dat.detach() # --- unscale --- if self.inter != 0 or self.slope != 1: dat = dat.clone() if torch.is_tensor(dat) else dat.copy() if self.inter != 0: dat -= float(self.inter) if self.slope != 1: dat /= float(self.slope) # --- set unscaled data --- self.set_data(dat) return self def metadata(self, keys=None): """Read metadata .. note:: The values returned by this function always relate to the full volume, even if we're inside a view. That is, we always return the affine of the original volume. To get an affine matrix that relates to the view, use `self.affine`. Parameters ---------- keys : sequence[str], optional List of metadata to load. They can either be one of the generic metadata keys define in `io.metadata`, or a format-specific metadata key. By default, all generic keys that are found in the file are returned. Returns ------- metadata : dict A dictionary of metadata """ return dict() def set_metadata(self, **meta): """Write metadata Parameters ---------- meta : dict, optional Dictionary of metadata. Fields that are absent from the dictionary or that have value `None` are kept untouched. Returns ------- self : type(self) """ raise NotImplementedError("Method not implemented in class {}." .format(type(self).__name__)) @classmethod def save_new(cls, dat, file_like, like=None, casting='unsafe', **metadata): """Write an array to disk. This function makes educated choices for the file format and its metadata based on the file extension, the data type and the other options provided. Parameters ---------- dat : tensor or array or MappedArray Data to write file_like : str or file object Path to file or file object (with methods `seek`, `read`). If the extension is known, it gets priority over `like` when choosing the output format. like : file or MappedArray An array on-disk that should be used as a template for the new file. Its metadata/layout/etc will be mimicked as much as possible. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe', 'rescale'}, default='unsafe' Controls what kind of data casting may occur. See `MappedArray.set_data` metadata : dict Metadata to store on disk. Values provided there will have priority over `like`. Returns ------- dat : array or tensor The array loaded in memory attributes : dict, if attributes is not None Dictionary of attributes loaded as well """ raise cls.FailedWriteError("Method not implemented in class {}." .format(cls.__name__)) @classmethod def savef_new(cls, dat, file_like, like=None, **metadata): """Write a scaled array to disk. This function makes educated choices for the file format and its metadata based on the file extension, the data type and the other options provided. The input data type must be a floating point type. Parameters ---------- dat : tensor or array or MappedArray Data to write file_like : str or file object Path to file or file object (with methods `seek`, `read`). If the extension is known, it gets priority over `like` when choosing the output format. like : file or MappedArray An array on-disk that should be used as a template for the new file. Its metadata/layout/etc will be mimicked as much as possible. metadata : dict Metadata to store on disk. Values provided there will have priority over `like`. Returns ------- dat : array or tensor The array loaded in memory attributes : dict, if attributes is not None Dictionary of attributes loaded as well """ raise cls.FailedWriteError("Method not implemented in class {}." .format(cls.__name__)) def unsqueeze(self, dim, ndim=1): """Add a dimension of size 1 in position `dim`. Parameters ---------- dim : int The dimension is added to the right of `dim` if `dim < 0` else it is added to the left of `dim`. Returns ------- MappedArray """ index = [slice(None)] * self.dim if dim < 0: dim = self.dim + dim + 1 index = index[:dim] + ([None] * ndim) + index[dim:] return self[tuple(index)] def squeeze(self, dim): """Remove all dimensions of size 1. Parameters ---------- dim : int or sequence[int], optional If provided, only this dimension is squeezed. It *must* be a dimension of size 1. Returns ------- MappedArray """ if dim is None: dim = [d for d in range(self.dim) if self.shape[d] == 1] dim = make_list(dim) ndim = len(self.shape) dim = [ndim + d if d < 0 else d for d in dim] if any(self.shape[d] != 1 for d in dim): raise ValueError('Impossible to squeeze non-singleton dimensions.') index = [slice(None) if d not in dim else 0 for d in range(self.dim)] return self[tuple(index)] def unbind(self, dim=0, keepdim=False): """Extract all arrays along dimension `dim` and drop that dimension. Parameters ---------- dim : int, default=0 Dimension along which to unstack. keepdim : bool, default=False Do not drop the unstacked dimension. Returns ------- list[MappedArray] """ index = [slice(None)] * self.dim if keepdim: index = index[:dim+1] + [None] + index[dim+1:] out = [] for i in range(self.shape[dim]): index[dim] = i out.append(self[tuple(index)]) return out def chunk(self, chunks, dim=0): """Split the array into smaller arrays of size `chunk` along `dim`. Parameters ---------- chunks : int Number of chunks. dim : int, default=0 Dimensions along which to split. Returns ------- list[MappedArray] """ index = [slice(None)] * self.dim out = [] for i in range(self.shape[dim]): index[dim] = slice(i*chunks, (i+1)*chunks) out.append(self[tuple(index)]) return out def split(self, chunks, dim=0): """Split the array into smaller arrays along `dim`. Parameters ---------- chunks : int or list[int] If `int`: Number of chunks (see `self.chunk`) Else: Size of each chunk. Must sum to `self.shape[dim]`. dim : int, default=0 Dimensions along which to split. Returns ------- list[MappedArray] """ if isinstance(chunks, int): return self.chunk(chunks, dim) chunks = make_list(chunks) if sum(chunks) != self.shape[dim]: raise ValueError('Chunks must cover the full dimension. ' 'Got {} and {}.' .format(sum(chunks), self.shape[dim])) index = [slice(None)] * self.dim previous_chunks = 0 out = [] for chunk in chunks: index[dim] = slice(previous_chunks, previous_chunks+chunk) out.append(self[tuple(index)]) previous_chunks += chunk return out def channel_first(self, atleast=0): """Permute the dimensions such that all spatial axes are on the right. Parameters ---------- atleast : int, default=0 Make sure that at least this number of non-spatial dimensions exist (new axes are inserted accordingly). Returns ------- MappedArray """ # 1) move spatial dimensions to the right perm = [] spatial = [] for d, is_spatial in enumerate(self.spatial): if is_spatial: spatial.append(d) else: perm.append(d) nb_channels = len(perm) perm = perm + spatial new = self.permute(perm) # 2) add channel axes add_channels = max(0, atleast - nb_channels) if add_channels: index = [slice(None)] * nb_channels \ + [None] * add_channels \ + [Ellipsis] new = new.slice(tuple(index)) return new def channel_last(self, atleast=0): """Permute the dimensions such that all spatial axes are on the left. Parameters ---------- atleast : int, default=0 Make sure that at least this number of non-spatial dimensions exist (new axes are inserted accordingly). Returns ------- MappedArray """ # 1) move spatial dimensions to the right perm = [] spatial = [] for d, is_spatial in enumerate(self.spatial): if is_spatial: spatial.append(d) else: perm.append(d) nb_channels = len(perm) perm = spatial + perm new = self.permute(perm) # 2) add channel axes add_channels = max(0, atleast - nb_channels) if add_channels: index = [Ellipsis] + [None] * add_channels new = new.slice(tuple(index)) return new class CatArray(MappedArray): """A concatenation of mapped arrays. This is largely inspired by virtual concatenation of file_array in SPM: https://github.com/spm/spm12/blob/master/@file_array/cat.m """ _arrays: tuple = [] _dim_cat: int = None # defer attributes fname = property(lambda self: tuple(a.fname for a in self._arrays)) fileobj = property(lambda self: tuple(a.fileobj for a in self._arrays)) is_compressed = property(lambda self: tuple(a.is_compressed for a in self._arrays)) dtype = property(lambda self: tuple(a.dtype for a in self._arrays)) slope = property(lambda self: tuple(a.slope for a in self._arrays)) inter = property(lambda self: tuple(a.inter for a in self._arrays)) _shape = property(lambda self: tuple(a._shape for a in self._arrays)) _dim = property(lambda self: tuple(a._dim for a in self._arrays)) affine = property(lambda self: tuple(a.affine for a in self._arrays)) _affine = property(lambda self: tuple(a._affine for a in self._arrays)) spatial = property(lambda self: tuple(a.spatial for a in self._arrays)) _spatial = property(lambda self: tuple(a._spatial for a in self._arrays)) slicer = property(lambda self: tuple(a.slicer for a in self._arrays)) permutation = property(lambda self: tuple(a.permutation for a in self._arrays)) voxel_size = property(lambda self: tuple(a.voxel_size for a in self._arrays)) def __init__(self, arrays, dim=0): """ Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays """ super().__init__() arrays = list(arrays) dim = dim or 0 self._dim_cat = dim # sanity checks shapes = [] for i, array in enumerate(arrays): if not isinstance(array, MappedArray): raise TypeError('Input arrays should be `MappedArray` ' 'instances. Got {}.',format(type(array))) shape = list(array.shape) del shape[dim] shapes.append(shape) shape0, *shapes = shapes if not all(shape == shape0 for shape in shapes): raise ValueError('Shapes of all concatenated arrays should ' 'be equal except in the concatenation dimension.') # compute output shape shape = list(arrays[0].shape) dims = [array.shape[dim] for array in arrays] shape[dim] = sum(dims) self.shape = tuple(shape) # concatenate self._arrays = tuple(arrays) def __str__(self): dtype_str = tuple(str(dt) for dt in self.dtype) dtype_str = '(' + ', '.join(dtype_str) + ')' return '{}(shape={}, dtype={})'.format( type(self).__name__, self.shape, dtype_str) __repr__ = __str__ def slice(self, index, new_shape=None): # overload slicer -> slice individual arrays index = expand_index(index, self.shape) new_shape = guess_shape(index, self.shape) assert len(index) > 0, "index should never be empty here" if any(isinstance(idx, list) for idx in index) > 1: raise ValueError('List indices not currently supported ' '(otherwise we enter advanced indexing ' 'territory and it becomes too complicated).') index = list(index) shape_cat = self.shape[self._dim_cat] # find out which index corresponds to the concatenated dimension # + compute the concatenated dimension in the output array new_dim_cat = self._dim_cat nb_old_dim = -1 for map_dim_cat, idx in enumerate(index): if is_newaxis(idx): # an axis was added: dim_cat moves to the right new_dim_cat = new_dim_cat + 1 elif is_droppedaxis(idx): # an axis was dropped: dim_cat moves to the left new_dim_cat = new_dim_cat - 1 nb_old_dim += 1 else: nb_old_dim += 1 if nb_old_dim >= self._dim_cat: # found the concatenated dimension break index_cat = index[map_dim_cat] index_cat = neg2pos(index_cat, shape_cat) # /!\ do not call it again if is_droppedaxis(index_cat): # if the concatenated dimension is dropped, return the # corresponding array (sliced) if index_cat < 0 or index_cat >= shape_cat: raise IndexError('Index {} out of bounds [0, {}]' .format(index_cat, shape_cat)) nb_pre = 0 for i in range(len(self._arrays)): if nb_pre < index_cat: # we haven't found the volume yet nb_pre += self._arrays[i].shape[self._dim_cat] continue if i > index_cat: # we've passed the volume i = i - 1 nb_pre -= self._arrays[i].shape[self._dim_cat] index_cat = index_cat - nb_pre index[map_dim_cat] = index_cat return self._arrays[i].slice(tuple(index), new_shape) # else, we may have to drop some volumes and slice the others assert is_sliceaxis(index_cat), "This should not happen" arrays = self._arrays step = index_cat.step or 1 if step < 0: # if negative step: # 1) invert everything invert_index = [slice(None)] * self.dim invert_index[self._dim_cat] = slice(None, None, -1) arrays = [array[tuple(invert_index)] for array in arrays] # 2) update index_cat index_cat = invert_slice(index_cat, shape_cat, neg2pos=False) # compute navigator # (step is positive) start, step, nb_elem_total = slice_navigator(index_cat, shape_cat, do_neg2pos=False) nb_pre = 0 # nb of slices on the left of the cursor kept_arrays = [] # arrays at least partly in bounds starts = [] # start in each kept array stops = [] # stop in each kept array size_since_start = 0 # nb of in-bounds slices left of the cursor while len(arrays) > 0: # pop array array, *arrays = arrays size_cat = array.shape[self._dim_cat] if nb_pre + size_cat < start: # discarded volumes at the beginning nb_pre += size_cat continue if nb_pre < start: # first volume kept_arrays.append(array) starts.append(start - nb_pre) elif index_cat.stop is None or nb_pre < index_cat.stop: # other kept volume kept_arrays.append(array) skip = size_since_start - (size_since_start // step) * step starts.append(skip) # compute stopping point nb_elem_prev = size_since_start // step nb_elem_remaining = nb_elem_total - nb_elem_prev nb_elem_this_volume = (size_cat - starts[-1]) // step if nb_elem_remaining <= nb_elem_this_volume: # last volume stops.append(nb_elem_remaining) break # read as much as possible size_since_start += size_cat nb_pre += size_cat stops.append(None) continue # slice kept arrays arrays = [] for array, start, stop in zip(kept_arrays, starts, stops): index[map_dim_cat] = slice(start, stop, step) arrays.append(array[tuple(index)]) # create new CatArray new = copy(self) new._arrays = arrays new._dim_cat = new_dim_cat new.shape = new_shape return new def permute(self, dims): # overload permutation -> permute individual arrays new = copy(self) new._arrays = [array.permute(dims) for array in new._arrays] iperm = invert_permutation(dims) new._dim_cat = iperm[new._dim_cat] new.shape = tuple(self.shape[d] for d in dims) return new def data(self, *args, **kwargs): # read individual arrays and concatenate them # TODO: it would be more efficient to preallocate the whole # array and pass the appropriate buffer to each reader but # (1) we don't have the option to provide a buffer yet # (2) everything's already quite inefficient dats = [array.data(*args, **kwargs) for array in self._arrays] print([dat.shape for dat in dats]) return volutils.cat(dats, dim=self._dim_cat) def fdata(self, *args, **kwargs): # read individual arrays and concatenate them # TODO: it would be more efficient to preallocate the whole # array and pass the appropriate buffer to each reader but # (1) we don't have the option to provide a buffer yet # (2) everything's already quite inefficient dats = [array.fdata(*args, **kwargs) for array in self._arrays] return volutils.cat(dats, dim=self._dim_cat) def set_data(self, dat, *args, **kwargs): # slice the input data and write it into each array size_prev = 0 index = [None] * self.dim for array in self._arrays: size_cat = array.shape[self._dim_cat] index[self._dim_cat] = slice(size_prev, size_prev + size_cat) array._set_data(dat[tuple(index)], *args, **kwargs) def set_fdata(self, dat, *args, **kwargs): # slice the input data and write it into each array size_prev = 0 index = [None] * self.dim for array in self._arrays: size_cat = array.shape[self._dim_cat] index[self._dim_cat] = slice(size_prev, size_prev + size_cat) array._set_fdata(dat[tuple(index)], *args, **kwargs) def metadata(self, *args, **kwargs): return tuple(array.metadata(*args, **kwargs) for array in self._arrays) def set_metadata(self, **meta): raise NotImplementedError('Cannot write metadata into concatenated ' 'array') def cat(arrays, dim=0): """Concatenate mapped arrays along a dimension. Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays Returns ------- CatArray A symbolic concatenation of all input arrays. Its shape along dimension `dim` is the sum of all input shapes along dimension `dim`. """ return CatArray(arrays, dim) def stack(arrays, dim=0): """Stack mapped arrays along a dimension. Parameters ---------- arrays : sequence[MappedArray] Arrays to concatenate. Their shapes should be identical except along dimension `dim`. dim : int, default=0 Dimension along white to concatenate the arrays Returns ------- CatArray A symbolic stack of all input arrays. """ arrays = [array.unsqueeze(dim=dim) for array in arrays] return cat(arrays, dim=dim)

9,800

0

378

1a2deaef0215145916e743664ab5b8b9ed9d9543

302

py

Python

blit.py

rwberendsen/blit

f025a286b04774ec6dc6a47823254484d3942b78

[ "MIT" ]

null

blit.py

rwberendsen/blit

f025a286b04774ec6dc6a47823254484d3942b78

[ "MIT" ]

null

blit.py

rwberendsen/blit

f025a286b04774ec6dc6a47823254484d3942b78

[ "MIT" ]

null

""" blit.py Call if you want to run everything """ import json import os import sys import integrate if __name__ == '__main__': sys.exit(main(sys.argv))

10.785714

39

0.63245

""" blit.py Call if you want to run everything """ import json import os import sys import integrate def main(argv): with open('config.json', 'r') as f: config = json.load(f) integrate.integrate(**config) return 0 if __name__ == '__main__': sys.exit(main(sys.argv))

112

0

23

33a51d04c0e22dbd80245e03d033a309d7a8fdfd

367

py

Python

pacote dowlond/curso python/exercicio100.py

Kaue-Marin/Curso-Python

45f7920e288a49724a4284f14c7212bb1662ab5b

[ "MIT" ]

null

pacote dowlond/curso python/exercicio100.py

Kaue-Marin/Curso-Python

45f7920e288a49724a4284f14c7212bb1662ab5b

[ "MIT" ]

null

pacote dowlond/curso python/exercicio100.py

Kaue-Marin/Curso-Python

45f7920e288a49724a4284f14c7212bb1662ab5b

[ "MIT" ]

null

from random import randint numeros = [] # programa principal sorteia() somapar()

22.9375

47

0.577657

from random import randint numeros = [] def sorteia(): for c in range(1, 5): c = randint(1, 9) numeros.append(c) print(f'os valores da lista são {numeros}') def somapar(): spar = 0 for c2 in numeros: if c2 % 2 == 0: spar += c2 print(f'a soma dos numeros pares é {spar}') # programa principal sorteia() somapar()

245

0

44

7f79dcf3d85037aa0b27e51ab5ee77202b2f17ac

3,802

py

Python

ch03/pro1.py

Lucid-ak/deeplearnig_practice

e196d733ee9b910a9c7648e61e6934aea9d255b3

[ "MIT" ]

null

ch03/pro1.py

Lucid-ak/deeplearnig_practice

e196d733ee9b910a9c7648e61e6934aea9d255b3

[ "MIT" ]

null

ch03/pro1.py

Lucid-ak/deeplearnig_practice

e196d733ee9b910a9c7648e61e6934aea9d255b3

[ "MIT" ]

null

import pickle import numpy as np #비선형 퍼셉트론 import matplotlib.pylab as plt import sys, os sys.path.append(os.pardir) from dataset.mnist import load_mnist from PIL import Image def step_function(x): ''' y = x > 0 return y.astype(np.int) #np.int와 dtype=int의 역할은 같다. ''' return np.array(x>0, dtype=int) #dtype의 역할은 출력 결과를 dtype=int등으로 통해 원하는 자료형으로 변형하는 것 ''' network=init_network() x=np.array([100,40]) y=forward(network, x) print(y) #print(y) #plt.plot(x,y) #plt.ylim(-0.1,1.1) #plt.show() ''' x_test,t_test = get_data() network=init_networrk_mnist() batch_size=100 accuracy_ct=0 for i in range(0, len(x_test), batch_size):#x_train의 실제 개수 몰라도 len함수 쓰면 된다 x_batch = x_test[i:i+batch_size] y_batch = predict(network, x_batch) p = np.argmax(y_batch, axis=1) #가장 확률이 높은 원소 가져오기? print(np.sum(p == t_test[i:i+batch_size])) accuracy_ct += np.sum(p == t_test[i:i+batch_size]) ''' y=predict(network, x_test[i]) p=np.argmax(y) if p==t_test[i] : accuracy_ct+=1 ''' print("Accuracy:",str(float(accuracy_ct)/len(x_test))) print(accuracy_ct) ''' img = x_train[0] label = t_train[0] print(label) print(img.shape) img = img.reshape(28,28) #이거 패턴화 28,28로 안하면 원하는 이미지 안나온다.-> 이거 이용해서 암호화나 용량 줄이기도 가능? print(img.shape) img_show(img) '''

22.104651

99

0.584955

import pickle import numpy as np #비선형 퍼셉트론 import matplotlib.pylab as plt import sys, os sys.path.append(os.pardir) from dataset.mnist import load_mnist from PIL import Image def AND(x1, x2): x=np.array([x1,x2]) w=np.array([0.5, 0.5]) b= -0.7 theta = 0 tmp = np.sum(w*x)+b if tmp<=theta: return 0 elif tmp>theta: return 1 def OR(x1, x2): x = np.array([x1,x2]) w = np.array([0.5,0.5]) b = -0.3 theta = 0 sig=np.sum(w*x)+b if sig>=theta: return 1 else : return 0 def NAND(x1, x2): x=np.array([x1,x2]) w=np.array([0.5, 0.5]) b=-0.7 theta=0 sig=np.sum(w*x)+b if sig<theta : return 1 else : return 0 def XOR(x1, x2): y1=OR(x1,x2) y2=NAND(x1,x2) y=AND(y1,y2) return y def step_function(x): ''' y = x > 0 return y.astype(np.int) #np.int와 dtype=int의 역할은 같다. ''' return np.array(x>0, dtype=int) #dtype의 역할은 출력 결과를 dtype=int등으로 통해 원하는 자료형으로 변형하는 것 def sigmoid(x): return 1/(1+np.exp(-x)) #브로드 캐스트 적용, 각 배열의 원소값에 대해 계산 후 결과값들을 배열로 변환 def ReLU(x): return np.array(np.maximum(0, x)) def softmax(x): #c는 입력 값 중 최대 c=np.max(x) exp_x=np.exp(x-c) sum_exp_x=sum(exp_x) y = exp_x/sum_exp_x #return exp_x/sum_exp_x로 바로 나타낼 수도 있지만 "가시성"을 위해 y로 따로 배정하여 계산 return y def identity_function(x): return x def init_network(): #network 배열에 라벨링을 통해 각 가중치 및 편향 저장 network={} network['W1']=np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]]) network['b1']=np.array([0.1, 0.2, 0.3]) network['W2']=np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]]) network['b2']=np.array([0.1,0.2]) network['W3']=np.array([[0.1,0.3],[0.2,0.4]]) network['b3']=np.array([0.1,0.2]) return network def init_networrk_mnist(): #라이브러리에서 weight, bias 가져오기 with open("sample_weight.pkl",'rb') as f: network = pickle.load(f) return network def predict(network, x): W1,W2, W3= network['W1'], network['W2'], network['W3'] b1,b2,b3 = network['b1'],network['b2'],network['b3'] a1 = np.dot(x,W1)+b1 z1 = sigmoid(a1) a2 = np.dot(z1, W2) + b2 z2 = sigmoid(a2) a3 = np.dot(z2, W3) + b3 y=softmax(a3) return y def forward(network, x): #순방향(입력->출력) 구현 항상 비슷한 값을 도출한다. W1,W2, W3= network['W1'], network['W2'], network['W3'] b1,b2,b3=network['b1'],network['b2'],network['b3'] a1 = np.dot(x,W1)+b1 z1 = softmax(a1) a2 = np.dot(z1, W2) + b2 z2 = softmax(a2) a3 = np.dot(z2, W3) + b3 y=identity_function(a3) return y def img_show(img): pil_img=Image.fromarray(np.uint8(img)) pil_img.show() def get_data(): (x_train, t_train), (x_test, t_test) = \ load_mnist(flatten=True, normalize=True, one_hot_label=False) return x_test, t_test ''' network=init_network() x=np.array([100,40]) y=forward(network, x) print(y) #print(y) #plt.plot(x,y) #plt.ylim(-0.1,1.1) #plt.show() ''' x_test,t_test = get_data() network=init_networrk_mnist() batch_size=100 accuracy_ct=0 for i in range(0, len(x_test), batch_size):#x_train의 실제 개수 몰라도 len함수 쓰면 된다 x_batch = x_test[i:i+batch_size] y_batch = predict(network, x_batch) p = np.argmax(y_batch, axis=1) #가장 확률이 높은 원소 가져오기? print(np.sum(p == t_test[i:i+batch_size])) accuracy_ct += np.sum(p == t_test[i:i+batch_size]) ''' y=predict(network, x_test[i]) p=np.argmax(y) if p==t_test[i] : accuracy_ct+=1 ''' print("Accuracy:",str(float(accuracy_ct)/len(x_test))) print(accuracy_ct) ''' img = x_train[0] label = t_train[0] print(label) print(img.shape) img = img.reshape(28,28) #이거 패턴화 28,28로 안하면 원하는 이미지 안나온다.-> 이거 이용해서 암호화나 용량 줄이기도 가능? print(img.shape) img_show(img) '''

2,368

0

319

206b2d2a2c251900c661943dfaa5e9366d3668b1

9,055

py

Python

slideatlas/security/blueprint.py

SlideAtlas/SlideAtlas-Server

3b9cbd56eaa29ae08ae521e75616ea230fe26397

[ "Apache-2.0" ]

3

2015-10-10T10:17:26.000Z

2020-12-14T09:42:19.000Z

slideatlas/security/blueprint.py

SlideAtlas/SlideAtlas-Server

3b9cbd56eaa29ae08ae521e75616ea230fe26397

[ "Apache-2.0" ]

41

2015-02-03T19:47:28.000Z

2017-02-06T23:24:26.000Z

slideatlas/security/blueprint.py

SlideAtlas/SlideAtlas-Server

3b9cbd56eaa29ae08ae521e75616ea230fe26397

[ "Apache-2.0" ]

2

2016-04-04T18:23:27.000Z

2017-11-14T22:34:58.000Z

# coding=utf-8 import copy from flask import Markup, url_for from flask.ext.security import Security, MongoEngineUserDatastore, user_registered from flask.ext.security.core import _SecurityState from flask.ext.security.core import _context_processor as security_default_context_processor from flask.ext.security.views import create_blueprint as security_create_blueprint from flask.ext.security.views import send_confirmation as security_send_confirmation from flask.ext.security.utils import send_mail from slideatlas import models from . import forms, views, login_provider from .principal import register_principal ################################################################################ __all__ = ('blueprint', 'register_with_app') ################################################################################ ################################################################################ # TODO: find a way of automatically registering Shibboleth users with the # appropriate group, similar to facebook_id ################################################################################ def add_config(app): """ Set Flask application configuration options. These are options that should never change. """ # Flask-Security configuration app.config.update( ### Frontend ### SECURITY_FLASH_MESSAGES=True, SECURITY_LOGIN_URL='/login', SECURITY_LOGIN_USER_TEMPLATE='security/login.html', SECURITY_MSG_DISABLED_ACCOUNT=('Password login is disabled for this account.', 'error'), SECURITY_LOGOUT_URL='/logout', # TODO: change '/sessions' to an endpoint name SECURITY_POST_LOGIN_VIEW='/sessions', SECURITY_POST_LOGOUT_VIEW='home', ### Password login options ### SECURITY_DEFAULT_REMEMBER_ME=False, ## New account registration SECURITY_REGISTERABLE=True, SECURITY_REGISTER_URL='/login/password/register', SECURITY_REGISTER_USER_TEMPLATE='security/register.html', SECURITY_SEND_REGISTER_EMAIL=True, SECURITY_EMAIL_SUBJECT_REGISTER='SlideAtlas: Account Created', # uses 'welcome' email body template # TODO: change the email body template, as the default contains a password confirmation link, and we want non-password users to receive a welcome email too ## Confirmation of user's email address SECURITY_CONFIRMABLE=True, SECURITY_CONFIRM_URL='/login/password/confirm', SECURITY_SEND_CONFIRMATION_TEMPLATE='security/resend_confirmation.html', SECURITY_EMAIL_SUBJECT_CONFIRM='SlideAtlas: Account Confirmation', # uses 'confirmation_instructions' email body template SECURITY_CONFIRM_EMAIL_WITHIN='5 days', SECURITY_LOGIN_WITHOUT_CONFIRMATION=False, SECURITY_MSG_EMAIL_CONFIRMED=( Markup( 'Welcome to SlideAtlas! Your account has been confirmed. ' ' ' 'Site administrators may now grant you access to additional content. ' 'You can also contact <a href="mailto:%(email)s">%(email)s</a> with any requests.' % {'email': app.config['SLIDEATLAS_ADMIN_EMAIL']} ), 'success'), ## Recover / reset a lost password SECURITY_RECOVERABLE=True, SECURITY_RESET_URL='/login/password/reset', SECURITY_FORGOT_PASSWORD_TEMPLATE='security/password_reset_1.html', # step 1 SECURITY_RESET_PASSWORD_TEMPLATE='security/password_reset_2.html', # step 2 SECURITY_EMAIL_SUBJECT_PASSWORD_RESET='SlideAtlas: Password Reset Instructions', # uses 'reset_instructions' email body template SECURITY_RESET_PASSWORD_WITHIN='5 days', SECURITY_SEND_PASSWORD_RESET_NOTICE_EMAIL=False, # TODO: do we want to send a confirmation email? SECURITY_EMAIL_SUBJECT_PASSWORD_NOTICE='SlideAtlas: Password Reset Successful', # uses 'reset_notice' email body template ## Change a password SECURITY_CHANGEABLE=True, SECURITY_CHANGE_URL='/login/password/change', SECURITY_CHANGE_PASSWORD_TEMPLATE='security/password_change.html', SECURITY_SEND_PASSWORD_CHANGE_EMAIL=False, # TODO: do we want to send a confirmation email? SECURITY_EMAIL_SUBJECT_PASSWORD_CHANGE_NOTICE='SlideAtlas: Password Change Successful', # uses 'change notice' email body template ### Other options ### SECURITY_TRACKABLE=True, # record login statistics in User model SECURITY_PASSWORDLESS=False, # an experimental feature # custom salts can also be set for several other tokens, but this shouldn't be necessary # TODO: there are a few other undocumented config settings in Flask-Security, explore them ) # Flask-Login configuration app.config.update( SESSION_PROTECTION='basic', # some extra security for cookies, see documentation for details REMEMBER_COOKIE_DOMAIN=app.session_interface.get_cookie_domain(app), REMEMBER_COOKIE_HTTPONLY=True, REMEMBER_COOKIE_SECURE=app.config['SLIDEATLAS_HTTPS'], ) ################################################################################ ################################################################################

45.049751

163

0.661182

# coding=utf-8 import copy from flask import Markup, url_for from flask.ext.security import Security, MongoEngineUserDatastore, user_registered from flask.ext.security.core import _SecurityState from flask.ext.security.core import _context_processor as security_default_context_processor from flask.ext.security.views import create_blueprint as security_create_blueprint from flask.ext.security.views import send_confirmation as security_send_confirmation from flask.ext.security.utils import send_mail from slideatlas import models from . import forms, views, login_provider from .principal import register_principal ################################################################################ __all__ = ('blueprint', 'register_with_app') ################################################################################ def register_with_app(app): add_config(app) security, blueprint = create_security(app) register_principal(app, security) login_provider.add_views(app, blueprint) # TODO: move the 'site_url' value to config file security.mail_context_processor(lambda: dict(site_url='https://slide-atlas.org/')) # TODO: make logins timeout # may use the 'flask.ext.login.user_loaded_from_*' signals for this, to update the timeout # furthermore, see the documentation 'flask.ext.login.needs_refresh', and implement re-login # redirection directly to the user's corresponding login provider if a user's session becomes stale user_registered.connect(on_user_registered, app) ################################################################################ # TODO: find a way of automatically registering Shibboleth users with the # appropriate group, similar to facebook_id def on_user_registered(app, user, confirm_token): if isinstance(user, models.ShibbolethUser) or user.email.endswith('brown.edu'): brown_group = models.Group.objects.with_id('529d244959a3aee20f8a00ae') user.groups.append(brown_group) user.save() send_mail( 'SlideAtlas: New User Registered', app.config['SLIDEATLAS_ADMIN_EMAIL'], 'new_user_notify', user=user, admin_user_url=url_for('%sview.edit_view' % user.__class__.__name__.lower(), id=str(user.id), _external=True) ) ################################################################################ def add_config(app): """ Set Flask application configuration options. These are options that should never change. """ # Flask-Security configuration app.config.update( ### Frontend ### SECURITY_FLASH_MESSAGES=True, SECURITY_LOGIN_URL='/login', SECURITY_LOGIN_USER_TEMPLATE='security/login.html', SECURITY_MSG_DISABLED_ACCOUNT=('Password login is disabled for this account.', 'error'), SECURITY_LOGOUT_URL='/logout', # TODO: change '/sessions' to an endpoint name SECURITY_POST_LOGIN_VIEW='/sessions', SECURITY_POST_LOGOUT_VIEW='home', ### Password login options ### SECURITY_DEFAULT_REMEMBER_ME=False, ## New account registration SECURITY_REGISTERABLE=True, SECURITY_REGISTER_URL='/login/password/register', SECURITY_REGISTER_USER_TEMPLATE='security/register.html', SECURITY_SEND_REGISTER_EMAIL=True, SECURITY_EMAIL_SUBJECT_REGISTER='SlideAtlas: Account Created', # uses 'welcome' email body template # TODO: change the email body template, as the default contains a password confirmation link, and we want non-password users to receive a welcome email too ## Confirmation of user's email address SECURITY_CONFIRMABLE=True, SECURITY_CONFIRM_URL='/login/password/confirm', SECURITY_SEND_CONFIRMATION_TEMPLATE='security/resend_confirmation.html', SECURITY_EMAIL_SUBJECT_CONFIRM='SlideAtlas: Account Confirmation', # uses 'confirmation_instructions' email body template SECURITY_CONFIRM_EMAIL_WITHIN='5 days', SECURITY_LOGIN_WITHOUT_CONFIRMATION=False, SECURITY_MSG_EMAIL_CONFIRMED=( Markup( 'Welcome to SlideAtlas! Your account has been confirmed. ' ' ' 'Site administrators may now grant you access to additional content. ' 'You can also contact <a href="mailto:%(email)s">%(email)s</a> with any requests.' % {'email': app.config['SLIDEATLAS_ADMIN_EMAIL']} ), 'success'), ## Recover / reset a lost password SECURITY_RECOVERABLE=True, SECURITY_RESET_URL='/login/password/reset', SECURITY_FORGOT_PASSWORD_TEMPLATE='security/password_reset_1.html', # step 1 SECURITY_RESET_PASSWORD_TEMPLATE='security/password_reset_2.html', # step 2 SECURITY_EMAIL_SUBJECT_PASSWORD_RESET='SlideAtlas: Password Reset Instructions', # uses 'reset_instructions' email body template SECURITY_RESET_PASSWORD_WITHIN='5 days', SECURITY_SEND_PASSWORD_RESET_NOTICE_EMAIL=False, # TODO: do we want to send a confirmation email? SECURITY_EMAIL_SUBJECT_PASSWORD_NOTICE='SlideAtlas: Password Reset Successful', # uses 'reset_notice' email body template ## Change a password SECURITY_CHANGEABLE=True, SECURITY_CHANGE_URL='/login/password/change', SECURITY_CHANGE_PASSWORD_TEMPLATE='security/password_change.html', SECURITY_SEND_PASSWORD_CHANGE_EMAIL=False, # TODO: do we want to send a confirmation email? SECURITY_EMAIL_SUBJECT_PASSWORD_CHANGE_NOTICE='SlideAtlas: Password Change Successful', # uses 'change notice' email body template ### Other options ### SECURITY_TRACKABLE=True, # record login statistics in User model SECURITY_PASSWORDLESS=False, # an experimental feature # custom salts can also be set for several other tokens, but this shouldn't be necessary # TODO: there are a few other undocumented config settings in Flask-Security, explore them ) # Flask-Login configuration app.config.update( SESSION_PROTECTION='basic', # some extra security for cookies, see documentation for details REMEMBER_COOKIE_DOMAIN=app.session_interface.get_cookie_domain(app), REMEMBER_COOKIE_HTTPONLY=True, REMEMBER_COOKIE_SECURE=app.config['SLIDEATLAS_HTTPS'], ) ################################################################################ def create_security(app): # register Flask-Security with app and get blueprint security = Security(app, SlideatlasMongoEngineUserDatastore(), register_blueprint=False, confirm_register_form=forms.RegisterForm, login_form=forms.LoginForm) # prevent Flask-Security from automatically creating register and confirm views # by calling 'security_create_blueprint' with a different state security_blueprint_state = copy.copy(security._state) security_blueprint_state.registerable = False security_blueprint_state.confirmable = False blueprint = security_create_blueprint(security_blueprint_state, 'flask_security.core') # add SlideAtlas's own register view, which doesn't immediately require a password blueprint.add_url_rule(security.register_url, endpoint='register', view_func=views.register, methods=['GET', 'POST']) # use the Flask-Security's built-in view for re-sending a confirmation, which # needs to be manually added, since 'confirmable' was set to False blueprint.add_url_rule(security.confirm_url, endpoint='send_confirmation', view_func=security_send_confirmation, methods=['GET', 'POST']) # add SlideAtlas's own confirm view, which requires the user to set a password blueprint.add_url_rule(security.confirm_url + '/<token>', endpoint='confirm_email', view_func=views.confirm_email, methods=['GET', 'POST']) # do work that Flask-Security would have done if 'register_blueprint' were True app.register_blueprint(blueprint) app.context_processor(security_default_context_processor) return security, blueprint ################################################################################ class SlideatlasMongoEngineUserDatastore(MongoEngineUserDatastore): def __init__(self): # 'db' parameter is not necessary for this subclass super(SlideatlasMongoEngineUserDatastore, self).__init__(None, models.User, None) self.user_creation_model = models.PasswordUser def create_user(self, **kwargs): """Creates and returns a new user from the given parameters.""" user = self.user_creation_model(**kwargs) return self.put(user)

3,373

262

88

6e1958f96728d11d2e7418e4925be857a7286b3c

1,616

py

Python

flight/views.py

NedyalkoKr/airline

d704e8cd98901dc4bb0bf672cc2363432ada3f84

[ "MIT" ]

null

flight/views.py

NedyalkoKr/airline

d704e8cd98901dc4bb0bf672cc2363432ada3f84

[ "MIT" ]

null

flight/views.py

NedyalkoKr/airline

d704e8cd98901dc4bb0bf672cc2363432ada3f84

[ "MIT" ]

null

from django.shortcuts import render from django.urls import reverse from django.http import Http404, HttpResponseRedirect from flight.models import Flight, Passenger def index(request): ''' display all flights ''' context = { 'main_header': 'Flights', 'title': 'Flights', 'flights': Flight.objects.all() } return render(request, 'flight/index.html', context) def flight(request, flight_id): ''' return individual flight details and passengers on this flight''' try: flight = Flight.objects.get(pk=flight_id) except Flight.DoesNotExist: raise Http404(f'Flight {flight} does not exist.') context = { 'flight': flight, 'passengers': flight.passengers.all(), 'non_passengers': Passenger.objects.exclude(flight=flight).all(), 'number_of_passengers': flight.passengers.count() } return render(request, 'flight/flight.html', context)

36.727273

99

0.678218

from django.shortcuts import render from django.urls import reverse from django.http import Http404, HttpResponseRedirect from flight.models import Flight, Passenger def index(request): ''' display all flights ''' context = { 'main_header': 'Flights', 'title': 'Flights', 'flights': Flight.objects.all() } return render(request, 'flight/index.html', context) def flight(request, flight_id): ''' return individual flight details and passengers on this flight''' try: flight = Flight.objects.get(pk=flight_id) except Flight.DoesNotExist: raise Http404(f'Flight {flight} does not exist.') context = { 'flight': flight, 'passengers': flight.passengers.all(), 'non_passengers': Passenger.objects.exclude(flight=flight).all(), 'number_of_passengers': flight.passengers.count() } return render(request, 'flight/flight.html', context) def book(request, flight_id): try: passenger_id = int(request.POST['passenger']) passenger = Passenger.objects.get(pk=passenger_id) flight = Flight.objects.get(pk=flight_id) except KeyError: return render(request, 'flight/error.html', {'message': 'No passenger selected'}) except Flight.DoesNotExist: return render(request, 'flight/error.html', {'message': 'No such flight exist'}) except Passenger.DoesNotExist: return render(request, 'flight/error.html', {'message': 'No passenger with that id exist'}) passenger.flight.add(flight) return HttpResponseRedirect(reverse('flight', args=(flight_id,)))

651

0

23

c2f3cfc4cf7bad08a1bd21dc39bb6765de3670b2

419

py

Python

setup1.py

Alexander437/Learning_repo

4e40ad419f8117d014f789119f4b3583067020bb

[ "CC0-1.0" ]

null

setup1.py

Alexander437/Learning_repo

4e40ad419f8117d014f789119f4b3583067020bb

[ "CC0-1.0" ]

null

setup1.py

Alexander437/Learning_repo

4e40ad419f8117d014f789119f4b3583067020bb

[ "CC0-1.0" ]

null

from setuptools import setup, find_packages, Extension from torch.utils import cpp_extension setup( name='my_lib', version='0.0', description='Learning setup', packages=find_packages(), ext_package='trt_pose', ext_modules=[cpp_extension.CppExtension('plugins', [ 'Learn_cpp/learn.cpp', ])], cmdclass={'build_ext': cpp_extension.BuildExtension}, install_requires=[ ], )

23.277778

57

0.687351

from setuptools import setup, find_packages, Extension from torch.utils import cpp_extension setup( name='my_lib', version='0.0', description='Learning setup', packages=find_packages(), ext_package='trt_pose', ext_modules=[cpp_extension.CppExtension('plugins', [ 'Learn_cpp/learn.cpp', ])], cmdclass={'build_ext': cpp_extension.BuildExtension}, install_requires=[ ], )

0

8678ddca56a8e9b76f05e9a0a06fe329c6224b43

8,342

py

Python

bookops_callno/normalizer.py

BookOps-CAT/bookops-callno

a8f1d2744b3b53844dc97a5400ae87a2db92cd4c

[ "MIT" ]

null

bookops_callno/normalizer.py

BookOps-CAT/bookops-callno

a8f1d2744b3b53844dc97a5400ae87a2db92cd4c

[ "MIT" ]

null

bookops_callno/normalizer.py

BookOps-CAT/bookops-callno

a8f1d2744b3b53844dc97a5400ae87a2db92cd4c

[ "MIT" ]

null

# -*- coding: utf-8 -*- from typing import Optional from pymarc import Field from unidecode import unidecode, UnidecodeError from bookops_callno.errors import CallNoConstructorError def remove_trailing_punctuation(value: str) -> str: """ Removes any trailing periods, commas, etc. Args: value: string to be processed Returns: value """ if not isinstance(value, str): raise CallNoConstructorError( "Invalid 'value' type used in argument. Must be a string." ) while value[-1] in ".,:;-() ": value = value[:-1] return value def normalize_value(value: str) -> str: """ Removes diacritics from string and changes to uppercase """ if not value: return "" elif not isinstance(value, str): raise CallNoConstructorError( "Invalid 'value' type used in argument. Must be a string." ) try: value = value.replace("\u02b9", "") # Russian: modifier letter prime value = value.replace("\u02bb", "") # Arabic modifier letter turned comma value = value.replace("'", "") value = unidecode(value, errors="strict") value = remove_trailing_punctuation(value).upper() return value except UnidecodeError as exc: raise CallNoConstructorError( f"Unsupported character encountered. Error: '{exc}'." ) def corporate_name_first_word(field: Field = None) -> Optional[str]: """ Returns the uppdercase first word of the corporate entity from the 110 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "110": return None words = field["a"].strip().split(" ") name = normalize_value(words[0]) return name def corporate_name_full(field: Field = None) -> Optional[str]: """ Returns an uppercase full name of corporate entity. Uses the 110 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag not in ("110", "610"): return None phrases = field["a"].strip().split("(") name = normalize_value(phrases[0]) return name def corporate_name_initial(field: Field = None) -> Optional[str]: """ Returns the uppercase first letter of the corporate entity based on the 110 MARC tag Args: field: pymarc.Field instance Returns: initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "110": return None name = field["a"] name = normalize_value(name) initial = name[0] return initial def personal_name_initial(field: Field = None) -> Optional[str]: """ Returns the first letter of the last name of a personal author Args: field: pymarc.Field instance Returns initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "100": return None name = field["a"].strip() name = normalize_value(name) initial = name[0] return initial def personal_name_surname(field: Field = None) -> Optional[str]: """ Returns an uppercase surname of personal author. Includes any numeration from the subield $b of 100 or 600 MARC tag. Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag not in ("100", "600"): return None elif field.indicator1 not in ("0", "1"): return None sub_a = field["a"].strip() # include subfield $b if present try: sub_b = field["b"].strip() name = f"{sub_a} {sub_b}" except AttributeError: name = sub_a name = normalize_value(name) # stop at comma to select surname try: stop = name.index(",") name = name[:stop] except ValueError: pass return name def subject_corporate_name(field: Field = None) -> Optional[str]: """ Returns an uppercase corporate name to be used in subject segment of the call number based on MARC tag 610 Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "610": return None name = corporate_name_full(field) return name def subject_family_name(field: Field = None) -> Optional[str]: """ Returns an uppercase family name based on the 600 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "600": return None elif field.indicator1 != "3": return None try: stop = field["a"].index("family") name = field["a"][:stop] except ValueError: return None name = normalize_value(name) return name def subject_personal_name(field: Field = None) -> Optional[str]: """ Returns personal name to be used in subject segment of the call number. Use for biography or Dewey + Name patters, examples: biography: B LOUIS XIV C criticizm of works of an author: 813 ADAMS C Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "600": return None name = personal_name_surname(field) return name def subject_topic(field: Field = None) -> Optional[str]: """ Returns an uppercase topic to be used in the subject segment of the call number based on MARC tag 650. Valid only for BPL call numbers. Examples: programming language, name of operating system, etc. Args: field: pymarc.Field instance Returns: topic """ pass def title_first_word(field: Field = None) -> Optional[str]: """ Returns an uppercase first word (skipping any articles) of the title field (245 MARC tag subfield $a). Args: field: pymarc.Field instance Returns: word """ pass def title_initial(field: Field = None) -> Optional[str]: """ Returns an uppercase initial (skipping any articles) of the title field (245 MARC tag subfield $a). Args: field: pymarc.Field instance Returns: initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "245": return None try: ind2 = int(field.indicator2) except ValueError: return None title = field["a"][ind2:] title = normalize_value(title) initial = title[0] return initial

23.902579

82

0.593743

# -*- coding: utf-8 -*- from typing import Optional from pymarc import Field from unidecode import unidecode, UnidecodeError from bookops_callno.errors import CallNoConstructorError def remove_trailing_punctuation(value: str) -> str: """ Removes any trailing periods, commas, etc. Args: value: string to be processed Returns: value """ if not isinstance(value, str): raise CallNoConstructorError( "Invalid 'value' type used in argument. Must be a string." ) while value[-1] in ".,:;-() ": value = value[:-1] return value def normalize_value(value: str) -> str: """ Removes diacritics from string and changes to uppercase """ if not value: return "" elif not isinstance(value, str): raise CallNoConstructorError( "Invalid 'value' type used in argument. Must be a string." ) try: value = value.replace("\u02b9", "") # Russian: modifier letter prime value = value.replace("\u02bb", "") # Arabic modifier letter turned comma value = value.replace("'", "") value = unidecode(value, errors="strict") value = remove_trailing_punctuation(value).upper() return value except UnidecodeError as exc: raise CallNoConstructorError( f"Unsupported character encountered. Error: '{exc}'." ) def corporate_name_first_word(field: Field = None) -> Optional[str]: """ Returns the uppdercase first word of the corporate entity from the 110 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "110": return None words = field["a"].strip().split(" ") name = normalize_value(words[0]) return name def corporate_name_full(field: Field = None) -> Optional[str]: """ Returns an uppercase full name of corporate entity. Uses the 110 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag not in ("110", "610"): return None phrases = field["a"].strip().split("(") name = normalize_value(phrases[0]) return name def corporate_name_initial(field: Field = None) -> Optional[str]: """ Returns the uppercase first letter of the corporate entity based on the 110 MARC tag Args: field: pymarc.Field instance Returns: initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "110": return None name = field["a"] name = normalize_value(name) initial = name[0] return initial def personal_name_initial(field: Field = None) -> Optional[str]: """ Returns the first letter of the last name of a personal author Args: field: pymarc.Field instance Returns initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "100": return None name = field["a"].strip() name = normalize_value(name) initial = name[0] return initial def personal_name_surname(field: Field = None) -> Optional[str]: """ Returns an uppercase surname of personal author. Includes any numeration from the subield $b of 100 or 600 MARC tag. Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag not in ("100", "600"): return None elif field.indicator1 not in ("0", "1"): return None sub_a = field["a"].strip() # include subfield $b if present try: sub_b = field["b"].strip() name = f"{sub_a} {sub_b}" except AttributeError: name = sub_a name = normalize_value(name) # stop at comma to select surname try: stop = name.index(",") name = name[:stop] except ValueError: pass return name def subject_corporate_name(field: Field = None) -> Optional[str]: """ Returns an uppercase corporate name to be used in subject segment of the call number based on MARC tag 610 Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "610": return None name = corporate_name_full(field) return name def subject_family_name(field: Field = None) -> Optional[str]: """ Returns an uppercase family name based on the 600 MARC tag Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "600": return None elif field.indicator1 != "3": return None try: stop = field["a"].index("family") name = field["a"][:stop] except ValueError: return None name = normalize_value(name) return name def subject_personal_name(field: Field = None) -> Optional[str]: """ Returns personal name to be used in subject segment of the call number. Use for biography or Dewey + Name patters, examples: biography: B LOUIS XIV C criticizm of works of an author: 813 ADAMS C Args: field: pymarc.Field instance Returns: name """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "600": return None name = personal_name_surname(field) return name def subject_topic(field: Field = None) -> Optional[str]: """ Returns an uppercase topic to be used in the subject segment of the call number based on MARC tag 650. Valid only for BPL call numbers. Examples: programming language, name of operating system, etc. Args: field: pymarc.Field instance Returns: topic """ pass def title_first_word(field: Field = None) -> Optional[str]: """ Returns an uppercase first word (skipping any articles) of the title field (245 MARC tag subfield $a). Args: field: pymarc.Field instance Returns: word """ pass def title_initial(field: Field = None) -> Optional[str]: """ Returns an uppercase initial (skipping any articles) of the title field (245 MARC tag subfield $a). Args: field: pymarc.Field instance Returns: initial """ if field is None: return None elif not isinstance(field, Field): raise CallNoConstructorError( "Invalid 'field' argument type. Must be pymarc.Field instance." ) if field.tag != "245": return None try: ind2 = int(field.indicator2) except ValueError: return None title = field["a"][ind2:] title = normalize_value(title) initial = title[0] return initial

0

b721c28c4d1d01229eaf38efadeba74addb10f97

1,310

py

Python

ex31.py

Lorranysousc/ExerciciosDeRepeticao

4b8ac1c4eb3ac5d2739456a4f967e094fad70256

[ "MIT" ]

null

ex31.py

Lorranysousc/ExerciciosDeRepeticao

4b8ac1c4eb3ac5d2739456a4f967e094fad70256

[ "MIT" ]

null

ex31.py

Lorranysousc/ExerciciosDeRepeticao

4b8ac1c4eb3ac5d2739456a4f967e094fad70256

[ "MIT" ]

null

'''O Sr. Manoel Joaquim expandiu seus negócios para além dos negócios de 1,99 e agora possui uma loja de conveniências. Faça um programa que implemente uma caixa registradora rudimentar. O programa deverá receber um número desconhecido de valores referentes aos preços das mercadorias. Um valor zero deve ser informado pelo operador para indicar o final da compra. O programa deve então mostrar o total da compra e perguntar o valor em dinheiro que o cliente forneceu, para então calcular e mostrar o valor do troco. Após esta operação, o programa deverá voltar ao ponto inicial, para registrar a próxima compra. A saída deve ser conforme o exemplo abaixo: ''' from time import sleep start = 1 while start == 1: #Reinicia o programa quando chega ao final. print('LOJAS TABAJARA') cont = 1 valor_produto = '' total_compra = 0 while valor_produto != 0: #Recebe valor dos produtos comprados. valor_produto = float(input(f'Produto {cont}: R$ ')) total_compra += valor_produto cont += 1 if valor_produto == 0: #Finaliza o programa. print(f'Total: R$ {total_compra:.2f}') dinheiro_cliente = float(input('Dinheiro: R$ ')) troco = dinheiro_cliente - total_compra print(f'Troco: R$ {troco:.2f}') sleep(3)

65.5

660

0.703817

'''O Sr. Manoel Joaquim expandiu seus negócios para além dos negócios de 1,99 e agora possui uma loja de conveniências. Faça um programa que implemente uma caixa registradora rudimentar. O programa deverá receber um número desconhecido de valores referentes aos preços das mercadorias. Um valor zero deve ser informado pelo operador para indicar o final da compra. O programa deve então mostrar o total da compra e perguntar o valor em dinheiro que o cliente forneceu, para então calcular e mostrar o valor do troco. Após esta operação, o programa deverá voltar ao ponto inicial, para registrar a próxima compra. A saída deve ser conforme o exemplo abaixo: ''' from time import sleep start = 1 while start == 1: #Reinicia o programa quando chega ao final. print('LOJAS TABAJARA') cont = 1 valor_produto = '' total_compra = 0 while valor_produto != 0: #Recebe valor dos produtos comprados. valor_produto = float(input(f'Produto {cont}: R$ ')) total_compra += valor_produto cont += 1 if valor_produto == 0: #Finaliza o programa. print(f'Total: R$ {total_compra:.2f}') dinheiro_cliente = float(input('Dinheiro: R$ ')) troco = dinheiro_cliente - total_compra print(f'Troco: R$ {troco:.2f}') sleep(3)

0

c9bdceebaee8f789e4c6a4a1d04b4ef5a1c5d7f9

399

py

Python

tests/unit/dummy/__init__.py

fabiannagel/schnetkit

bf0b9055bdc393d01ac6c3d5f17bb9db13297e32

[ "MIT" ]

1

2021-11-03T15:13:48.000Z

tests/unit/dummy/__init__.py

fabiannagel/schnetkit

bf0b9055bdc393d01ac6c3d5f17bb9db13297e32

[ "MIT" ]

null

tests/unit/dummy/__init__.py

fabiannagel/schnetkit

bf0b9055bdc393d01ac6c3d5f17bb9db13297e32

[ "MIT" ]

1

2022-02-02T17:34:05.000Z

from schnetkit.engine import Stateful models = [Dummy]

15.96

37

0.573935

from schnetkit.engine import Stateful class Dummy(Stateful): def __init__(self, a=2): self.a = a self.state = "great" def get_dict(self): return {"a": self.a} def get_state(self): return {"state": self.state} def restore(self, payload): self.state = payload["state"] def work(self): self.state = "tired" models = [Dummy]

182

1

158

4324c7df6b13227127944ca0a19c1650df6f0e53

7,020

py

Python

zigbear/custom_protocol/SecurityLayer.py

philippnormann/zigbear

3cfdb4c9b13adf1e785f27109194b575edf241af

[ "BSD-3-Clause" ]

14

2020-04-15T09:43:20.000Z

2022-01-29T19:36:27.000Z

zigbear/custom_protocol/SecurityLayer.py

philippnormann1337/zigbear

3cfdb4c9b13adf1e785f27109194b575edf241af

[ "BSD-3-Clause" ]

null

zigbear/custom_protocol/SecurityLayer.py

philippnormann1337/zigbear

3cfdb4c9b13adf1e785f27109194b575edf241af

[ "BSD-3-Clause" ]

1

2020-06-06T21:41:10.000Z

import secrets from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives import serialization from cryptography.hazmat.primitives.asymmetric import ec from cryptography.hazmat.primitives.ciphers.aead import AESGCM from cryptography.hazmat.primitives.kdf.hkdf import HKDF from zigbear.custom_protocol.scapy_layers import ZigbearSecurityLayer

43.602484

110

0.670228

import secrets from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives import serialization from cryptography.hazmat.primitives.asymmetric import ec from cryptography.hazmat.primitives.ciphers.aead import AESGCM from cryptography.hazmat.primitives.kdf.hkdf import HKDF from zigbear.custom_protocol.scapy_layers import ZigbearSecurityLayer class SecurityLayer: def __init__(self, networkLayer, network_key=None): self.networkLayer = networkLayer self.framecount = secrets.randbelow(2 ** 32) self.key_cache = {} self.framecount_cache = {} # Can and should be none for non-coordinators (has to be 128, 192 or 256 bit) self.network_key = network_key self.receive_callback = lambda source, port, data: source self.networkLayer.set_receive_callback(self.receive) def new_framecount(self): s = self.framecount self.framecount = (self.framecount + 1) % 2 ** 32 return s def check_framecount(self, source, framecount): if source in self.framecount_cache: result = self.framecount_cache[source] < framecount else: self.framecount_cache[source] = framecount result = True return result def set_source_framecount(self, source, framecount): self.framecount_cache[source] = framecount def set_receive_callback(self, callback): self.receive_callback = callback def enable_pairing_mode(self): self.network_key = None def make_security_packet(self, data): try: sec = ZigbearSecurityLayer(data) except: sec = None return sec def receive(self, source, port, data): sec = self.make_security_packet(data) if sec: applayer_data = None if self.check_framecount(source, sec.fc): if sec.message_type == 0: applayer_data = sec.data elif sec.message_type == 1: self.handle_pairing_request(source, port, sec.data, sec.flags & 1) elif sec.message_type == 2 and not self.network_key: self.handle_network_key(source, sec.fc, sec.data, sec.mac) else: applayer_data = self.handle_encrypted_data(source, sec.fc, sec.data, sec.mac) if applayer_data: self.receive_callback(source, port, applayer_data) def handle_pairing_request(self, source, port, secdata, reply): self.generate_public_key(source) peer_public_key = self.deserialize_public_key(secdata) self.key_cache[source]["peer_public_key"] = peer_public_key self.generate_derived_keys(source, peer_public_key, b"test") if reply: self.send(source, port, self.serialize_public_key(self.key_cache[source]["public_key"]), 1, 0) def handle_network_key(self, source, framecount, secdata, mac): error, network_key = self.decryption(framecount, secdata, mac, source, True) if not error: self.network_key = network_key self.key_cache.pop(source, None) self.set_source_framecount(source, framecount) def handle_encrypted_data(self, source, framecount, secdata, mac): error, applayer_data = self.decryption(framecount, secdata, mac, source) if not error: self.set_source_framecount(source, framecount) return applayer_data def send(self, destination, port, data, message_type=3, flags=0): packet = ZigbearSecurityLayer(flags=flags, message_type=message_type, fc=self.new_framecount()) packet_data = mac = None if message_type == 0: packet.data = data elif message_type == 1: packet.data = self.handle_prepare_pk(destination) elif message_type == 2: packet.data, packet.mac = self.handle_prepare_nwk(destination, packet.fc) else: packet.data, packet.mac = self.handle_prepare_encdata(destination, packet.fc, data) self.networkLayer.send(destination, port, packet) def handle_prepare_pk(self, destination): self.generate_public_key(destination) return self.serialize_public_key(self.key_cache[destination]["public_key"]) def handle_prepare_nwk(self, destination, framecount): return self.encryption(framecount, self.network_key, destination, True) def handle_prepare_encdata(self, destination, framecount, data): return self.encryption(framecount, data.build(), destination) def get_connection_attempts(self): return list(self.key_cache.keys()) def generate_public_key(self, source): if source not in self.key_cache or "public_key" not in self.key_cache[source]: self.key_cache[source] = {} new_private_key = ec.generate_private_key(ec.SECP224R1(), default_backend()) self.key_cache[source]["public_key"] = new_private_key.public_key() self.key_cache[source]["private_key"] = new_private_key def serialize_public_key(self, public_key): return public_key.public_bytes(encoding=serialization.Encoding.DER, format=serialization.PublicFormat.SubjectPublicKeyInfo) def deserialize_public_key(self, serialized_key): return serialization.load_der_public_key(serialized_key, backend=default_backend()) def generate_derived_keys(self, source, peer_public_key, salt): shared_key = self.key_cache[source]["private_key"].exchange(ec.ECDH(), peer_public_key) self.key_cache[source]["shared_encryption_key"] = self.derive_key(b"encryption key", salt, shared_key) def derive_key(self, info, salt, shared_key): return HKDF(algorithm=hashes.SHA256(), length=32, salt=salt, info=info, backend=default_backend() ).derive(shared_key) def get_nonce(self, framecount, destination): return framecount.to_bytes(4, byteorder='big') def encryption(self, framecount, data, destination, shared=False): key = self.key_cache[destination]["shared_encryption_key"] if shared else self.network_key nonce = self.get_nonce(framecount, destination) aesgcm = AESGCM(key) sk_encrypted = aesgcm.encrypt(nonce, data, None) return (sk_encrypted[:-16], int.from_bytes(sk_encrypted[-16:], 'big')) def decryption(self, framecount, data, mac, source, shared=False): key = self.key_cache[source]["shared_encryption_key"] if shared else self.network_key if shared: self.key_cache.pop(source, None) nonce = self.get_nonce(framecount, source) aesgcm = AESGCM(key) error = result = None try: result = aesgcm.decrypt(nonce, data + mac.to_bytes(16, 'big'), None) except: error = 1 return (error, result)

5,922

-1

670

9af2d928d6cc2a53fd788a67b6c0a78899bbda9e

1,106

py

Python

tracks/BamFeatures.py

goeckslab/jbrowse-archive-creator

438557136c9dd4eb0db89835e5d253e44b50a7a3

[ "AFL-3.0" ]

null

tracks/BamFeatures.py

goeckslab/jbrowse-archive-creator

438557136c9dd4eb0db89835e5d253e44b50a7a3

[ "AFL-3.0" ]

null

tracks/BamFeatures.py

goeckslab/jbrowse-archive-creator

438557136c9dd4eb0db89835e5d253e44b50a7a3

[ "AFL-3.0" ]

null

#!/usr/bin/env python2 import os import json import logging from TrackDb import TrackDb from util import subtools from util import santitizer

38.137931

114

0.699819

#!/usr/bin/env python2 import os import json import logging from TrackDb import TrackDb from util import subtools from util import santitizer class BamFeatures(TrackDb): def __init__(self, trackName, trackLabel, trackDataURL, trackType, dataType, extraSettings=None): super(BamFeatures, self).__init__(trackName, trackLabel, trackDataURL, trackType, dataType, extraSettings) def prepareExtraSetting(self): if 'category' not in self.extraSettings or not self.extraSettings['category']: self.extraSettings['category'] = "Default group" bam_track = dict() bam_track['type'] = 'JBrowse/View/Track/Alignments2' bam_track['storeClass'] = 'JBrowse/Store/SeqFeature/BAM' bam_track['urlTemplate'] = os.path.join('bbi', self.trackName) bam_track['baiUrlTemplate'] = os.path.join('bbi', self.extraSettings['index']) bam_track['label'] = self.trackLabel bam_track['category'] = self.extraSettings['category'] #extraConfigs = json.dumps(bam_track) extraConfigs = bam_track return extraConfigs

874

6

77

defb13f18fc11dc096d17386bf5d7d31a9e0c762

5,573

py

Python

coolamqp/uplink/handshake.py

smok-serwis/coolamqp

d57ada0d478bd1ca94743ae341f6819ba85ea253

[ "MIT" ]

4

2018-06-20T13:59:35.000Z

2021-08-31T12:03:59.000Z

coolamqp/uplink/handshake.py

piotrmaslanka/coolamqp

d57ada0d478bd1ca94743ae341f6819ba85ea253

[ "MIT" ]

33

2016-06-03T11:41:09.000Z

2020-07-09T17:48:28.000Z

coolamqp/uplink/handshake.py

smok-serwis/coolamqp

d57ada0d478bd1ca94743ae341f6819ba85ea253

[ "MIT" ]

null

# coding=UTF-8 from __future__ import absolute_import, division, print_function """ Provides reactors that can authenticate an AQMP session """ import six import typing as tp import copy import logging from coolamqp.framing.definitions import ConnectionStart, ConnectionStartOk, \ ConnectionTune, ConnectionTuneOk, ConnectionOpen, ConnectionOpenOk from coolamqp.framing.frames import AMQPMethodFrame from coolamqp.uplink.connection.states import ST_ONLINE from coolamqp.uplink.heartbeat import Heartbeater from coolamqp import __version__ PUBLISHER_CONFIRMS = b'publisher_confirms' CONSUMER_CANCEL_NOTIFY = b'consumer_cancel_notify' CONNECTION_BLOCKED = b'connection.blocked' SUPPORTED_EXTENSIONS = [ PUBLISHER_CONFIRMS, CONSUMER_CANCEL_NOTIFY, # half assed support - we just .cancel the consumer, see #12 CONNECTION_BLOCKED ] CLIENT_DATA = [ # because RabbitMQ is some kind of a fascist and does not allow # these fields to be of type short-string (b'product', (b'CoolAMQP', 'S')), (b'version', (__version__.encode('utf8'), 'S')), (b'copyright', (b'Copyright (C) 2016-2021 SMOK sp. z o.o.', 'S')), ( b'information', ( b'Licensed under the MIT License.\nSee https://github.com/smok-serwis/coolamqp for details', 'S')), (b'capabilities', ([(capa, (True, 't')) for capa in SUPPORTED_EXTENSIONS], 'F')), ] WATCHDOG_TIMEOUT = 10 logger = logging.getLogger(__name__) class Handshaker(object): """ Object that given a connection rolls the handshake. """ def __init__(self, connection, # type: coolamqp.uplink.connection.Connection node_definition, # type: coolamqp.objects.NodeDefinition on_success, # type: tp.Callable[[], None] extra_properties=None # type: tp.Dict[bytes, tp.Tuple[tp.Any, str]] ): """ :param connection: Connection instance to use :type node_definition: NodeDefinition :param on_success: callable/0, on success """ self.connection = connection self.login = node_definition.user.encode('utf8') self.password = node_definition.password.encode('utf8') self.virtual_host = node_definition.virtual_host.encode('utf8') self.heartbeat = node_definition.heartbeat or 0 self.connection.watch_for_method(0, ConnectionStart, self.on_connection_start) # Callbacks self.on_success = on_success self.EXTRA_PROPERTIES = extra_properties or [] # Called by internal setup def on_watchdog(self): """ Called WATCHDOG_TIMEOUT seconds after setup begins If we are not ST_ONLINE after that much, something is wrong and pwn this connection. """ # Not connected in 20 seconds - abort if self.connection.state != ST_ONLINE: # closing the connection this way will get to Connection by channels of ListenerThread self.connection.send(None)

39.524823

104

0.624978

# coding=UTF-8 from __future__ import absolute_import, division, print_function """ Provides reactors that can authenticate an AQMP session """ import six import typing as tp import copy import logging from coolamqp.framing.definitions import ConnectionStart, ConnectionStartOk, \ ConnectionTune, ConnectionTuneOk, ConnectionOpen, ConnectionOpenOk from coolamqp.framing.frames import AMQPMethodFrame from coolamqp.uplink.connection.states import ST_ONLINE from coolamqp.uplink.heartbeat import Heartbeater from coolamqp import __version__ PUBLISHER_CONFIRMS = b'publisher_confirms' CONSUMER_CANCEL_NOTIFY = b'consumer_cancel_notify' CONNECTION_BLOCKED = b'connection.blocked' SUPPORTED_EXTENSIONS = [ PUBLISHER_CONFIRMS, CONSUMER_CANCEL_NOTIFY, # half assed support - we just .cancel the consumer, see #12 CONNECTION_BLOCKED ] CLIENT_DATA = [ # because RabbitMQ is some kind of a fascist and does not allow # these fields to be of type short-string (b'product', (b'CoolAMQP', 'S')), (b'version', (__version__.encode('utf8'), 'S')), (b'copyright', (b'Copyright (C) 2016-2021 SMOK sp. z o.o.', 'S')), ( b'information', ( b'Licensed under the MIT License.\nSee https://github.com/smok-serwis/coolamqp for details', 'S')), (b'capabilities', ([(capa, (True, 't')) for capa in SUPPORTED_EXTENSIONS], 'F')), ] WATCHDOG_TIMEOUT = 10 logger = logging.getLogger(__name__) class Handshaker(object): """ Object that given a connection rolls the handshake. """ def __init__(self, connection, # type: coolamqp.uplink.connection.Connection node_definition, # type: coolamqp.objects.NodeDefinition on_success, # type: tp.Callable[[], None] extra_properties=None # type: tp.Dict[bytes, tp.Tuple[tp.Any, str]] ): """ :param connection: Connection instance to use :type node_definition: NodeDefinition :param on_success: callable/0, on success """ self.connection = connection self.login = node_definition.user.encode('utf8') self.password = node_definition.password.encode('utf8') self.virtual_host = node_definition.virtual_host.encode('utf8') self.heartbeat = node_definition.heartbeat or 0 self.connection.watch_for_method(0, ConnectionStart, self.on_connection_start) # Callbacks self.on_success = on_success self.EXTRA_PROPERTIES = extra_properties or [] # Called by internal setup def on_watchdog(self): """ Called WATCHDOG_TIMEOUT seconds after setup begins If we are not ST_ONLINE after that much, something is wrong and pwn this connection. """ # Not connected in 20 seconds - abort if self.connection.state != ST_ONLINE: # closing the connection this way will get to Connection by channels of ListenerThread self.connection.send(None) def on_connection_start(self, payload # type: coolamqp.framing.base.AMQPPayload ): sasl_mechanisms = payload.mechanisms.tobytes().split(b' ') locale_supported = payload.locales.tobytes().split(b' ') # Select a mechanism if b'PLAIN' not in sasl_mechanisms: raise ValueError('Server does not support PLAIN') # Select capabilities server_props = dict(payload.server_properties) if b'capabilities' in server_props: for label, fv in server_props[b'capabilities'][0]: if label in SUPPORTED_EXTENSIONS: if fv[0]: self.connection.extensions.append(label) self.connection.watchdog(WATCHDOG_TIMEOUT, self.on_watchdog) self.connection.watch_for_method(0, ConnectionTune, self.on_connection_tune) CLIENT_DATA_c = copy.copy(CLIENT_DATA) CLIENT_DATA_c.extend(self.EXTRA_PROPERTIES) self.connection.send([ AMQPMethodFrame(0, ConnectionStartOk(CLIENT_DATA_c, b'PLAIN', b'\x00' + self.login + b'\x00' + self.password, locale_supported[0] )) ]) def on_connection_tune(self, payload # type: coolamqp.framing.base.AMQPPayload ): self.connection.frame_max = payload.frame_max self.connection.heartbeat = min(payload.heartbeat, self.heartbeat) self.connection.free_channels.extend(six.moves.xrange(1, ( 65535 if payload.channel_max == 0 else payload.channel_max) + 1)) self.connection.watch_for_method(0, ConnectionOpenOk, self.on_connection_open_ok) self.connection.send([ AMQPMethodFrame(0, ConnectionTuneOk(payload.channel_max, payload.frame_max, self.connection.heartbeat)), AMQPMethodFrame(0, ConnectionOpen(self.virtual_host)) ]) # Install heartbeat handlers NOW, if necessary if self.connection.heartbeat > 0: Heartbeater(self.connection, self.connection.heartbeat) def on_connection_open_ok(self, payload # type: coolamqp.framing.base.AMQPPayload ): self.on_success()

2,447

0

81

08045555ebdef5af831c50bb02363844d684733e

10,852

py

Python

nodejs-mobile/test/testpy/__init__.py

xuelongqy/cnode

ac256264d329e68b6c5ae3281b0e7bb5a95ae164

[ "MIT" ]

null

nodejs-mobile/test/testpy/__init__.py

xuelongqy/cnode

ac256264d329e68b6c5ae3281b0e7bb5a95ae164

[ "MIT" ]

4

2020-03-13T14:45:49.000Z

2020-03-15T16:31:22.000Z

nodejs-mobile/test/testpy/__init__.py

xuelongqy/cnode

ac256264d329e68b6c5ae3281b0e7bb5a95ae164

[ "MIT" ]

1

2020-03-15T16:02:18.000Z

# Copyright 2008 the V8 project authors. All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import test import os from os.path import join, dirname, exists, splitext, isdir, basename import re import ast FLAGS_PATTERN = re.compile(r"//\s+Flags:(.*)") FILES_PATTERN = re.compile(r"//\s+Files:(.*)") chakraBannedFlags = ["--expose_externalize_string"]

39.176895

89

0.646056

# Copyright 2008 the V8 project authors. All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import test import os from os.path import join, dirname, exists, splitext, isdir, basename import re import ast FLAGS_PATTERN = re.compile(r"//\s+Flags:(.*)") FILES_PATTERN = re.compile(r"//\s+Files:(.*)") chakraBannedFlags = ["--expose_externalize_string"] class SimpleTestCase(test.TestCase): def __init__(self, path, file, arch, mode, context, config, jsEngine, additional=None): super(SimpleTestCase, self).__init__(context, path, arch, mode) self.file = file self.config = config self.arch = arch self.mode = mode self.jsEngine = jsEngine if additional is not None: self.additional_flags = additional else: self.additional_flags = [] def GetLabel(self): return "%s %s" % (self.mode, self.GetName()) def GetName(self): return self.path[-1] def GetCommand(self): result = [self.config.context.GetVm(self.arch, self.mode)] source = open(self.file).read() flags_match = FLAGS_PATTERN.search(source) if flags_match: flag = flags_match.group(1).strip().split() if self.jsEngine == "chakracore": flag = filter(lambda x: x not in chakraBannedFlags, flag) # The following block reads config.gypi to extract the v8_enable_inspector # value. This is done to check if the inspector is disabled in which case # the '--inspect' flag cannot be passed to the node process as it will # cause node to exit and report the test as failed. The use case # is currently when Node is configured --without-ssl and the tests should # still be runnable but skip any tests that require ssl (which includes # the inspector related tests). Also, if there is no ssl support the # options '--use-bundled-ca' and '--use-openssl-ca' will also cause a # similar failure so such tests are also skipped. if len(flag) == 0: pass elif ('--inspect' in flag[0] or \ '--use-bundled-ca' in flag[0] or \ '--use-openssl-ca' in flag[0]) and \ self.context.v8_enable_inspector == 0: print('Skipping as node was configured --without-ssl') else: result += flag files_match = FILES_PATTERN.search(source); additional_files = [] if files_match: additional_files += files_match.group(1).strip().split() for a_file in additional_files: result.append(join(dirname(self.config.root), '..', a_file)) if self.additional_flags: result += self.additional_flags result += [self.file] return result def GetSource(self): return open(self.file).read() class MessageTestCase(SimpleTestCase): def __init__(self, path, file, arch, mode, context, config, expected, jsEngine, additional=None): super(MessageTestCase, self).__init__(path, file, arch, mode, context, config, jsEngine, additional) self.expected = expected def IgnoreLine(self, str): """Ignore empty lines and valgrind output.""" if not str.strip(): return True else: return str.startswith('==') or str.startswith('**') def IsFailureOutput(self, output): f = file(self.expected) # Skip initial '#' comment and spaces #for line in f: # if (not line.startswith('#')) and (not line.strip()): # break # Convert output lines to regexps that we can match env = { 'basename': basename(self.file) } patterns = [ ] for line in f: if not line.strip(): continue pattern = re.escape(line.rstrip() % env) pattern = pattern.replace('\\*', '.*') pattern = '^%s$' % pattern patterns.append(pattern) # Compare actual output with the expected raw_lines = (output.stdout + output.stderr).split('\n') outlines = [ s for s in raw_lines if not self.IgnoreLine(s) ] if len(outlines) != len(patterns): print "length differs." print "expect=%d" % len(patterns) print "actual=%d" % len(outlines) print "patterns:" for i in xrange(len(patterns)): print "pattern = %s" % patterns[i] print "outlines:" for i in xrange(len(outlines)): print "outline = %s" % outlines[i] return True for i in xrange(len(patterns)): if not re.match(patterns[i], outlines[i]): print "match failed" print "line=%d" % i print "expect=%s" % patterns[i] print "actual=%s" % outlines[i] return True return False def GetSource(self): return (open(self.file).read() + "\n--- expected output ---\n" + open(self.expected).read()) class SimpleTestConfiguration(test.TestConfiguration): def __init__(self, context, root, section, additional=None): super(SimpleTestConfiguration, self).__init__(context, root) self.section = section if additional is not None: self.additional_flags = additional else: self.additional_flags = [] def Ls(self, path): return [f for f in os.listdir(path) if re.match('^test-.*\.m?js$', f)] def ListTests(self, current_path, path, arch, mode, jsEngine): all_tests = [current_path + [t] for t in self.Ls(join(self.root))] result = [] for test in all_tests: if self.Contains(path, test): file_path = join(self.root, reduce(join, test[1:], "")) test_name = test[:-1] + [splitext(test[-1])[0]] result.append(SimpleTestCase(test_name, file_path, arch, mode, self.context, self, jsEngine, self.additional_flags)) return result def GetBuildRequirements(self): return ['sample', 'sample=shell'] def GetTestStatus(self, sections, defs): status_file = join(self.root, '%s.status' % (self.section)) if exists(status_file): test.ReadConfigurationInto(status_file, sections, defs) class ParallelTestConfiguration(SimpleTestConfiguration): def __init__(self, context, root, section, additional=None): super(ParallelTestConfiguration, self).__init__(context, root, section, additional) def ListTests(self, current_path, path, arch, mode, jsEngine): result = super(ParallelTestConfiguration, self).ListTests( current_path, path, arch, mode, jsEngine) for test in result: test.parallel = True return result class AddonTestConfiguration(SimpleTestConfiguration): def __init__(self, context, root, section, additional=None): super(AddonTestConfiguration, self).__init__(context, root, section, additional) def Ls(self, path): def SelectTest(name): return name.endswith('.js') result = [] for subpath in os.listdir(path): if os.path.isdir(join(path, subpath)): for f in os.listdir(join(path, subpath)): if SelectTest(f): result.append([subpath, f[:-3]]) return result def ListTests(self, current_path, path, arch, mode, jsEngine): all_tests = [current_path + t for t in self.Ls(join(self.root))] result = [] for test in all_tests: if self.Contains(path, test): file_path = join(self.root, reduce(join, test[1:], "") + ".js") result.append( SimpleTestCase(test, file_path, arch, mode, self.context, self, jsEngine, self.additional_flags)) return result class AbortTestConfiguration(SimpleTestConfiguration): def __init__(self, context, root, section, additional=None): super(AbortTestConfiguration, self).__init__(context, root, section, additional) def ListTests(self, current_path, path, arch, mode, jsEngine): result = super(AbortTestConfiguration, self).ListTests( current_path, path, arch, mode, jsEngine) for test in result: test.disable_core_files = True return result class MessageTestConfiguration(SimpleTestConfiguration): def __init__(self, context, root, section, additional=None): super(MessageTestConfiguration, self).__init__(context, root, section, additional) def Ls(self, path): if isdir(path): return [f for f in os.listdir(path) if f.endswith('.js') or f.endswith('.mjs')] else: return [] def ListTests(self, current_path, path, arch, mode, jsEngine): all_tests = [current_path + [t] for t in self.Ls(join(self.root))] result = [] for test in all_tests: if self.Contains(path, test): test_name = test[:-1] + [splitext(test[-1])[0]] file_path = join(self.root, reduce(join, test[1:], '')) file_prefix = file_path[:file_path.rfind('.')] engine_output_path = file_prefix + (".%s.out" % jsEngine) output_path = file_prefix + '.out' if exists(engine_output_path): output_path = engine_output_path else: if not exists(output_path): raise Exception("Could not find %s" % output_path) result.append(MessageTestCase(test_name, file_path, arch, mode, self.context, self, output_path, jsEngine, self.additional_flags)) return result

7,907

455

657

3e13d9f04c5b9e380942a3048140fa5f7f9bee3d

919

py

Python

patterns/creational/factory_method.py

zhaijingrong/patterns_in_python

8cb53a58cbb78dc7ed578887a8e7c481cfa72c80

[ "MIT" ]

null

patterns/creational/factory_method.py

zhaijingrong/patterns_in_python

8cb53a58cbb78dc7ed578887a8e7c481cfa72c80

[ "MIT" ]

null

patterns/creational/factory_method.py

zhaijingrong/patterns_in_python

8cb53a58cbb78dc7ed578887a8e7c481cfa72c80

[ "MIT" ]

null

""" 抽象工厂方法--对象创建型模式 1. 目标定义一个用于创建对象的接口，让子类决定实例化哪一个类，使一个类的实例化延迟到子类。 """ if __name__ == '__main__': cream_cake_factory = CreamCakeFactory() cream_cake = cream_cake_factory.make_cake() print(cream_cake) fruit_cake_factory = FruitCakeFactory() fruit_cake = fruit_cake_factory.make_cake() print(fruit_cake)

19.145833

47

0.671382

""" 抽象工厂方法--对象创建型模式 1. 目标定义一个用于创建对象的接口，让子类决定实例化哪一个类，使一个类的实例化延迟到子类。 """ class CakeFactory(object): def make_cake(self): print('make a cake') class CreamCakeFactory(CakeFactory): def make_cake(self): print('make a cream cake') return CreamCake() class FruitCakeFactory(CakeFactory): def make_cake(self): print('make a fruit cake') return FruitCake() class Cake(object): def __repr__(self): return 'This is a cake' class CreamCake(Cake): def __repr__(self): return 'This is a cream cake' class FruitCake(Cake): def __repr__(self): return 'This is a fruit cake' if __name__ == '__main__': cream_cake_factory = CreamCakeFactory() cream_cake = cream_cake_factory.make_cake() print(cream_cake) fruit_cake_factory = FruitCakeFactory() fruit_cake = fruit_cake_factory.make_cake() print(fruit_cake)

252

35

294

8a8e864f9097a33ac84f3576473fa8671c78d0e2

1,583

py

Python

website/account/models.py

divmoe/DASHBOARD

42927dfca3797e0bde3e59288a156e33aec6790d

[ "MIT" ]

null

website/account/models.py

divmoe/DASHBOARD

42927dfca3797e0bde3e59288a156e33aec6790d

[ "MIT" ]

null

website/account/models.py

divmoe/DASHBOARD

42927dfca3797e0bde3e59288a156e33aec6790d

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User # Create your models here.

34.413043

77

0.722678

from django.db import models from django.contrib.auth.models import User # Create your models here. class Customer (models.Model): user=models.OneToOneField(User,null=True,on_delete=models.CASCADE) name = models.CharField(max_length=100,null=True) email= models.CharField(max_length=100,null=True) phone= models.CharField(max_length=100,null=True) photo=models.ImageField(null=True,blank=True) def __str__ (self): return self. name class Tag(models.Model): name = models.CharField(max_length=100,null=True) def __str__ (self): return self. name class Product(models.Model): CATOGORY={ ('indoor','indoor'), ('OUT DOOR','OUT DOOR') } name=models.CharField(max_length=100,null=True) price=models.FloatField(null=True) catogory=models.CharField(max_length=100,null=True,choices=CATOGORY) description=models.CharField(max_length=100,null=True,blank=True) date_created=models.DateTimeField(auto_now_add=True,null=True) tag = models.ManyToManyField(Tag) def __str__ (self): return self. name class Order(models.Model): STATUS={('pending','pending'), ('out for delivery','out for delivery'), ('Delivered','Delivered') } customer =models.ForeignKey(Customer,null=True,on_delete=models.SET_NULL) product =models.ForeignKey(Product,null=True,on_delete=models.SET_NULL) status= models.CharField(max_length=100,null=True,choices=STATUS) date_created = models.DateTimeField(auto_now_add=True,null=True) def __str__(self): return self.product.name

102

1,290

90

0ce133badac8ace62355d38651dd265c044af4eb

1,169

py

Python

chromeos/tools/concat_dbus_conf_files.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

14,668

2015-01-01T01:57:10.000Z

2022-03-31T23:33:32.000Z

chromeos/tools/concat_dbus_conf_files.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

86

2015-10-21T13:02:42.000Z

2022-03-14T07:50:50.000Z

chromeos/tools/concat_dbus_conf_files.py

zealoussnow/chromium

fd8a8914ca0183f0add65ae55f04e287543c7d4a

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

5,941

2015-01-02T11:32:21.000Z

2022-03-31T16:35:46.000Z

#!/usr/bin/env python # Copyright 2019 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Concatenates D-Bus busconfig files.""" import sys import xml.etree.ElementTree _BUSCONFIG_FILE_HEADER = b"""<!DOCTYPE busconfig PUBLIC "-//freedesktop//DTD D-Bus Bus Configuration 1.0//EN" "http://www.freedesktop.org/standards/dbus/1.0/busconfig.dtd"> """ if __name__ == '__main__': main()

26.568182

72

0.6929

#!/usr/bin/env python # Copyright 2019 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Concatenates D-Bus busconfig files.""" import sys import xml.etree.ElementTree _BUSCONFIG_FILE_HEADER = b"""<!DOCTYPE busconfig PUBLIC "-//freedesktop//DTD D-Bus Bus Configuration 1.0//EN" "http://www.freedesktop.org/standards/dbus/1.0/busconfig.dtd"> """ def main(): if len(sys.argv) < 3: sys.stderr.write('Usage: %s OUTFILE INFILES\n' % (sys.argv[0])) sys.exit(1) out_path = sys.argv[1] in_paths = sys.argv[2:] # Parse the first input file. tree = xml.etree.ElementTree.parse(in_paths[0]) assert(tree.getroot().tag == 'busconfig') # Append the remaining input files to the first file. for path in in_paths[1:]: current_tree = xml.etree.ElementTree.parse(path) assert(current_tree.getroot().tag == 'busconfig') for child in current_tree.getroot(): tree.getroot().append(child) # Output the result. with open(out_path, "wb") as f: f.write(_BUSCONFIG_FILE_HEADER) tree.write(f) if __name__ == '__main__': main()

657

0

23

02525ed7d476b11f1d77ac07f48e44ec57a3ff58

282

py

Python

rand.py

sriharikapu/RandomSequenceGenerator

7491e43b117be3e24eb5b7d66762699ef4d7593a

[ "CC0-1.0" ]

1

2022-02-08T01:47:03.000Z

rand.py

sriharikapu/RandomSequenceGenerator

7491e43b117be3e24eb5b7d66762699ef4d7593a

[ "CC0-1.0" ]

null

rand.py

sriharikapu/RandomSequenceGenerator

7491e43b117be3e24eb5b7d66762699ef4d7593a

[ "CC0-1.0" ]

null

import sys; import numpy as np; import pandas as pd; np.set_printoptions(threshold=sys.maxsize) # replace the range, sample size with your custom numbers arr = np.array(np.random.choice(range(10000), 10000, replace=False)) print(arr) DF = pd.DataFrame(arr) DF.to_csv("temp.csv")

25.636364

69

0.755319

import sys; import numpy as np; import pandas as pd; np.set_printoptions(threshold=sys.maxsize) # replace the range, sample size with your custom numbers arr = np.array(np.random.choice(range(10000), 10000, replace=False)) print(arr) DF = pd.DataFrame(arr) DF.to_csv("temp.csv")

0

f78a75c01086c2ca55a46920abf7034c2037b15f

2,062

py

Python

src/dataset.py

kantharajucn/job_seniority_prediction

cad9147ffddab1c5ead878c2f9d9e48199dc0da9

[ "Unlicense" ]

null

src/dataset.py

kantharajucn/job_seniority_prediction

cad9147ffddab1c5ead878c2f9d9e48199dc0da9

[ "Unlicense" ]

null

src/dataset.py

kantharajucn/job_seniority_prediction

cad9147ffddab1c5ead878c2f9d9e48199dc0da9

[ "Unlicense" ]

null

import torch from sklearn.preprocessing import LabelEncoder from torch.utils.data import Dataset, DataLoader

33.258065

97

0.612027

import torch from sklearn.preprocessing import LabelEncoder from torch.utils.data import Dataset, DataLoader class JobsDataset(Dataset): def __init__(self, X, y, tokenizer, max_len=512): self.len = len(X) self.data = X self.y = y self.tokenizer = tokenizer self.max_len = max_len self._label_encode() def _label_encode(self): self.label_encoder = LabelEncoder() self.y = self.label_encoder.fit_transform(self.y) def __getitem__(self, index): title = str(self.data.title[index]) title = " ".join(title.split()) description = str(self.data.description[index]) description = " ".join(description.split()) inputs = self.tokenizer.encode_plus( text=title, text_pair=description, add_special_tokens=True, max_length=self.max_len, padding='max_length', return_token_type_ids=True, truncation=True ) ids = inputs['input_ids'] mask = inputs['attention_mask'] return { 'ids': torch.tensor(ids, dtype=torch.long), 'mask': torch.tensor(mask, dtype=torch.long), 'targets': torch.tensor(self.y[index], dtype=torch.long) } def __len__(self): return self.len def get_data_loader(X_train, X_valid, y_train, y_valid, tokenizer, batch_size=16, num_workers=1): training_set = JobsDataset(X_train, y_train, tokenizer, max_len=512) validation_set = JobsDataset(X_valid, y_valid, tokenizer, max_len=512) train_params = {'batch_size': batch_size, 'shuffle': True, 'num_workers': num_workers } test_params = {'batch_size': batch_size, 'shuffle': True, 'num_workers': num_workers } training_loader = DataLoader(training_set, **train_params) validation_loader = DataLoader(validation_set, **test_params) return training_loader, validation_loader

1,792

6

153