xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

1d1440784b015c2af1bb3c792b09e92f2956dc6a

44

py

Python

app/handlers/homework/inline_mode/__init__.py

vitaliy-ukiru/math-bot

72c116b4f5a4aa6a5f8eaae67ecbbf3df821f9e9

[ "MIT" ]

1

2021-12-11T07:41:38.000Z

app/handlers/homework/inline_mode/__init__.py

vitaliy-ukiru/math-bot

72c116b4f5a4aa6a5f8eaae67ecbbf3df821f9e9

[ "MIT" ]

8

2021-05-08T21:48:34.000Z

2022-01-20T15:42:00.000Z

app/handlers/homework/inline_mode/__init__.py

vitaliy-ukiru/math-bot

72c116b4f5a4aa6a5f8eaae67ecbbf3df821f9e9

[ "MIT" ]

null

__all__ = ("dp",) from .handlers import dp

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1d2c00608b961b664ac9d3e64dbf1d33fd2aceb9

892

py

Python

src/tt_storage/tt_storage/exceptions.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

85

2017-11-21T12:22:02.000Z

2022-03-27T23:07:17.000Z

src/tt_storage/tt_storage/exceptions.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

545

2017-11-04T14:15:04.000Z

2022-03-27T14:19:27.000Z

src/tt_storage/tt_storage/exceptions.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

45

2017-11-11T12:36:30.000Z

2022-02-25T06:10:44.000Z

from tt_web import exceptions class StorageError(exceptions.BaseError): pass class OperationsError(StorageError): pass class ItemAlreadyCreated(OperationsError): MESSAGE = 'can not create item {item_id} for owner {owner_id}' class CanNotDeleteItem(OperationsError): MESSAGE = 'Can not delete item {item_id} from owner {owner_id}' class CanNotChangeItemOwner(OperationsError): MESSAGE = 'Can not change item {item_id} owner from {old_owner_id} to {new_owner_id}' class CanNotChangeItemOwnerSameOwner(OperationsError): MESSAGE = 'Can not change item {item_id} ownwer {owner_id} to same' class CanNotChangeItemStorage(OperationsError): MESSAGE = 'Can not move item {item_id} from storage {old_storage_id} to storage {new_storage_id}' class UnknownOperationTypeInProtobuf(OperationsError): MESSAGE = 'Unknown operation type in protobuf: "{type}"'

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1d48fdb0886237d37ece2a8f2b0d527a48b1bda4

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py

Python

spectrespecs/nightwatch/utils.py

Spacehug/loony-lovegood

fd860591d37bd18107243e4e4e86cb4fd3836c6f

[ "MIT" ]

1

2019-08-03T09:22:41.000Z

spectrespecs/nightwatch/utils.py

Spacehug/loony-lovegood

fd860591d37bd18107243e4e4e86cb4fd3836c6f

[ "MIT" ]

2

2021-04-30T20:58:49.000Z

2021-06-01T23:58:36.000Z

spectrespecs/nightwatch/utils.py

Spacehug/loony-lovegood

fd860591d37bd18107243e4e4e86cb4fd3836c6f

[ "MIT" ]

null

import re SPAM = re.compile(r"([-\w\d:%._+~#=]*\.[\w\d]{2,6})|(@[\w\d_]*)") CODE = re.compile(r"([0-9\s]{12})") def is_malicious(message): return re.search(SPAM, message) is not None def is_friend_code(message): return re.search(CODE, message) is not None

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1d5fe86bcb98105828cf2270bc8568f523213dba

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py

Python

lambdata_JonRivera/__init__.py

JonRivera/Package_Repo

c12dc07ce5ab04e6842403b6adc89e7f8e4024aa

[ "MIT" ]

null

lambdata_JonRivera/__init__.py

JonRivera/Package_Repo

c12dc07ce5ab04e6842403b6adc89e7f8e4024aa

[ "MIT" ]

null

lambdata_JonRivera/__init__.py

JonRivera/Package_Repo

c12dc07ce5ab04e6842403b6adc89e7f8e4024aa

[ "MIT" ]

2

2020-08-04T19:11:59.000Z

2020-08-07T01:29:21.000Z

""" lambdata-JonRivera - a collection of Data Science Helper Functions """ import pandas as pd import numpy as np ONES = pd.DataFrame(np.ones(10)) ZEROS = pd.DataFrame(np.zeros(50))

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1d610fc0f15125a457a1844b617c8a8eaf67d90b

234

py

Python

src/oscar/apps/address/apps.py

Jean1508/ya-madoa

1ffb1d11e15bf33e4c3a09698675a4357e887eaa

[ "BSD-3-Clause" ]

null

src/oscar/apps/address/apps.py

Jean1508/ya-madoa

1ffb1d11e15bf33e4c3a09698675a4357e887eaa

[ "BSD-3-Clause" ]

5

2021-05-28T19:38:28.000Z

2022-03-12T00:45:39.000Z

src/oscar/apps/address/apps.py

Jean1508/ya-madoa

1ffb1d11e15bf33e4c3a09698675a4357e887eaa

[ "BSD-3-Clause" ]

null

from django.utils.translation import gettext_lazy as _ from oscar.core.application import OscarConfig class AddressConfig(OscarConfig): label = 'address' name = 'oscar.apps.address' verbose_name = _('Address')

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1d69da8a62d50b6da7b32182bb1bb70047c27846

63

py

Python

test/hello_world/a.py

xuzizhou/fc-python-sdk

3964dc91de69263083ef62ab4b13b21c6fe4fc58

[ "MIT" ]

51

2017-08-02T01:35:03.000Z

2022-03-13T07:07:15.000Z

test/hello_world/a.py

xuzizhou/fc-python-sdk

3964dc91de69263083ef62ab4b13b21c6fe4fc58

[ "MIT" ]

61

2017-08-26T03:37:26.000Z

2022-01-23T21:20:56.000Z

test/hello_world/a.py

xuzizhou/fc-python-sdk

3964dc91de69263083ef62ab4b13b21c6fe4fc58

[ "MIT" ]

16

2017-09-27T07:58:19.000Z

2021-11-12T03:21:20.000Z

def my_handler(event, context): return 'new hello world'

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1d7e759f6a6332307c3c88ce555745f1b7552d51

163

py

Python

vivid/featureset/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

vivid/featureset/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

vivid/featureset/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

from .atoms import AbstractMergeAtom, AbstractAtom, StringContainsAtom from .molecules import create_molecule, find_molecule from .utils import create_data_loader

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1d820fd325920c61a4473dec5305b07108f20eb0

8,206

py

Python

SViTE/engine.py

VITA-Group/SViTE

b0c62fd153c8b0b99917ab935ee76925c9de1149

[ "MIT" ]

50

2021-05-29T00:52:45.000Z

2022-03-17T11:39:47.000Z

SViTE/engine.py

VITA-Group/SViTE

b0c62fd153c8b0b99917ab935ee76925c9de1149

[ "MIT" ]

2

2022-01-16T07:24:52.000Z

2022-03-29T01:56:24.000Z

SViTE/engine.py

VITA-Group/SViTE

b0c62fd153c8b0b99917ab935ee76925c9de1149

[ "MIT" ]

6

2021-06-27T22:24:16.000Z

2022-01-17T02:45:32.000Z

# Copyright (c) 2015-present, Facebook, Inc. # All rights reserved. """ Train and eval functions used in main.py """ import math import sys from typing import Iterable, Optional import time import torch from timm.data import Mixup from timm.utils import accuracy, ModelEma from losses import DistillationLoss import utils import pdb import warnings warnings.filterwarnings('ignore') def get_tau(start_tau, end_tau, ite, total): tau = start_tau + (end_tau - start_tau) * ite / total return tau ite_step = 0 def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss, data_loader: Iterable, optimizer: torch.optim.Optimizer, device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, set_training_mode=True, mask=None, args=None): model.train(set_training_mode) metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 10 # pdb.set_trace() total_iteration = len(data_loader) * (args.epochs) for samples, targets in metric_logger.log_every(data_loader, print_freq, header): samples = samples.to(device, non_blocking=True) targets = targets.to(device, non_blocking=True) global ite_step optimizer.zero_grad() if mixup_fn is not None: samples, targets = mixup_fn(samples, targets) if args.token_selection: tau = get_tau(10, 0.1, ite_step, total_iteration) else: tau = -1 with torch.cuda.amp.autocast(): if args.pruning_type == 'structure': outputs, atten_pruning_indicator = model(samples, tau=tau, number=args.token_number) else: outputs = model(samples, tau=tau, number=args.token_number) atten_pruning_indicator = None loss = criterion(samples, outputs, targets) loss_value = loss.item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) # this attribute is added by timm on one optimizer (adahessian) is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order loss_scaler(loss, optimizer, clip_grad=max_norm, parameters=model.parameters(), create_graph=is_second_order) if mask is not None: mask.step(pruning_type=args.pruning_type) torch.cuda.synchronize() if model_ema is not None: model_ema.update(model) metric_logger.update(loss=loss_value) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) # update sparse topology ite_step = mask.steps if ite_step % args.update_frequency == 0 and ite_step < args.t_end * total_iteration: mask.at_end_of_epoch(pruning_type=args.pruning_type, indicator_list=atten_pruning_indicator) # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def train_one_epoch_training_time(model: torch.nn.Module, criterion: DistillationLoss, data_loader: Iterable, optimizer: torch.optim.Optimizer, device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, set_training_mode=True, mask=None, args=None): model.train(set_training_mode) metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 10 # pdb.set_trace() total_time = 0 total_iteration = len(data_loader) * (args.epochs) for samples, targets in metric_logger.log_every(data_loader, print_freq, header): samples = samples.to(device, non_blocking=True) targets = targets.to(device, non_blocking=True) optimizer.zero_grad() if mixup_fn is not None: samples, targets = mixup_fn(samples, targets) with torch.cuda.amp.autocast(): start = time.time() if args.pruning_type == 'structure': outputs, atten_pruning_indicator = model(samples) elif args.token_selection: outputs = model(samples, tau=10, number=args.token_number) atten_pruning_indicator = None else: outputs = model(samples) atten_pruning_indicator = None loss = criterion(samples, outputs, targets) loss_value = loss.item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) # this attribute is added by timm on one optimizer (adahessian) is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order loss_scaler(loss, optimizer, clip_grad=max_norm, parameters=model.parameters(), create_graph=is_second_order) end = time.time() total_time += end-start global ite_step ite_step += 1 if ite_step % 100 == 0: print(total_time) total_time = 0 # if mask is not None: # mask.step(pruning_type=args.pruning_type) torch.cuda.synchronize() if model_ema is not None: model_ema.update(model) metric_logger.update(loss=loss_value) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) # update sparse topology # if ite_step % args.update_frequency == 0 and ite_step < args.t_end * total_iteration: # mask.at_end_of_epoch(pruning_type=args.pruning_type, # indicator_list=atten_pruning_indicator) # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def evaluate(data_loader, model, device, args=None): criterion = torch.nn.CrossEntropyLoss() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Test:' # switch to evaluation mode model.eval() if args.token_selection: tau = 1 else: tau = -1 for images, target in metric_logger.log_every(data_loader, 10, header): images = images.to(device, non_blocking=True) target = target.to(device, non_blocking=True) # compute output with torch.cuda.amp.autocast(): if args.pruning_type == 'structure': output, atten_pruning_indicator = model(images, tau=tau, number=args.token_number) else: output = model(images, tau=tau, number=args.token_number) atten_pruning_indicator = None # output = model(images) loss = criterion(output, target) acc1, acc5 = accuracy(output, target, topk=(1, 5)) batch_size = images.shape[0] metric_logger.update(loss=loss.item()) metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) # gather the stats from all processes metric_logger.synchronize_between_processes() print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

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1d8beb9a5b5a3f3b8e23f4d482b6642dbfa156f1

1,644

py

Python

sklearn_wrapper/modules/data_translaters/DataTranslater.py

hidetomo-watanabe/analyze_for_kaggle

d90dbad3d07c862271332c151bcc7229d7c353df

[ "Apache-2.0" ]

3

2018-01-04T06:53:03.000Z

2019-02-19T22:19:38.000Z

sklearn_wrapper/modules/data_translaters/DataTranslater.py

hidetomo-watanabe/analyze_for_kaggle

d90dbad3d07c862271332c151bcc7229d7c353df

[ "Apache-2.0" ]

null

sklearn_wrapper/modules/data_translaters/DataTranslater.py

hidetomo-watanabe/analyze_for_kaggle

d90dbad3d07c862271332c151bcc7229d7c353df

[ "Apache-2.0" ]

null

from logging import getLogger logger = getLogger('predict').getChild('DataTranslater') if 'ConfigReader' not in globals(): from ..ConfigReader import ConfigReader if 'TableDataTranslater' not in globals(): from .TableDataTranslater import TableDataTranslater if 'ImageDataTranslater' not in globals(): from .ImageDataTranslater import ImageDataTranslater class DataTranslater(ConfigReader): def __init__(self, kernel=False): self.kernel = kernel def get_translater(self): data_type = self.configs['data']['type'] if data_type == 'table': self.translater = TableDataTranslater(self.kernel) elif data_type == 'image': self.translater = ImageDataTranslater(self.kernel) else: logger.error('DATA MODE SHOULD BE table OR image') raise Exception('NOT IMPLEMENTED') # take over instance variable self.translater.__dict__.update(self.__dict__) return def get_df_data(self): return self.translater.get_df_data() def calc_train_data(self): return self.translater.calc_train_data() def write_train_data(self): return self.translater.write_train_data() def get_train_data(self): return self.translater.get_train_data() def get_pre_processers(self): return self.translater.get_pre_processers() def get_post_processers(self): return self.translater.get_post_processers() def _sample_with_under(self): return self.translater._sample_with_under() def _sample_with_over(self): return self.translater._sample_with_over()

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d52e31c396587f99ff03e58a1b845be97eb21a6c

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py

Python

src/djanban/apps/charts/views/public.py

diegojromerolopez/djanban

6451688d49cf235d03c604b19a6a8480b33eed87

[ "MIT" ]

33

2017-06-14T18:04:25.000Z

2021-06-15T07:07:56.000Z

src/djanban/apps/charts/views/public.py

diegojromerolopez/djanban

6451688d49cf235d03c604b19a6a8480b33eed87

[ "MIT" ]

1

2017-05-10T08:45:55.000Z

src/djanban/apps/charts/views/public.py

diegojromerolopez/djanban

6451688d49cf235d03c604b19a6a8480b33eed87

[ "MIT" ]

8

2017-08-27T11:14:25.000Z

2021-03-03T12:11:16.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from djanban.apps.base.auth import user_is_administrator from djanban.apps.boards.models import Board from djanban.apps.charts import cards, labels, members, boards, requirements # General burndown chart def burndown(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return boards.burndown(board=board) # Requirement burndown chart def requirement_burndown(request, board_public_access_code, requirement_code=None): requirement = None board = _get_user_board(request, board_public_access_code) if requirement_code is not None: requirement = board.requirements.get(code=requirement_code) return requirements.burndown(board, requirement) # Show the spent time by week by members def spent_time_by_week(request, week_of_year, board_public_access_code): board = _get_user_board(request, board_public_access_code) return members.spent_time_by_week(request.user, week_of_year=week_of_year, board=board) # Show a chart with the task forward movements by member def task_forward_movements_by_member(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return members.task_movements_by_member(request, "forward", board) # Show a chart with the task backward movements by member def task_backward_movements_by_member(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return members.task_movements_by_member(request, "backward", board) # Show average time each card lives in each list def avg_time_by_list(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return cards.avg_time_by_list(board) # Average card lead time def avg_lead_time(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return cards.avg_lead_time(request, board) # Average card cycle time def avg_cycle_time(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return cards.avg_cycle_time(request, board) # Average spent times def avg_spent_times(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return labels.avg_spent_times(request, board) # Average estimated times def avg_estimated_times(request, board_public_access_code): board = _get_user_board(request, board_public_access_code) return labels.avg_estimated_times(request, board) # Get user boards depending on if the user is a superuser # or a visitor def _get_user_board(request, board_public_access_code): if user_is_administrator(request.user): return Board.objects.get(public_access_code=board_public_access_code) return Board.objects.get(enable_public_access=True, public_access_code=board_public_access_code)

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null

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1

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4

d53b0fcc700a8ea4071c41b5598165cb774021a1

191

py

Python

weibo/test/testData.py

haiboz/weiboSpider

517cae2ef3e7bccd9e1d328a40965406707f5362

[ "Apache-2.0" ]

null

weibo/test/testData.py

haiboz/weiboSpider

517cae2ef3e7bccd9e1d328a40965406707f5362

[ "Apache-2.0" ]

null

weibo/test/testData.py

haiboz/weiboSpider

517cae2ef3e7bccd9e1d328a40965406707f5362

[ "Apache-2.0" ]

null

#coding:utf8 ''' Created on 2016年4月19日 @author: wb-zhaohaibo ''' datas = [] weibo_data = {} if weibo_data is None: print "sss" else: print "---" print len(weibo_data)

14.692308

25

0.596859

24

191

4.625

0.75

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191

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25

14.692308

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null

0

1

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1

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4

d53b3f387102b62bd20819bb8ed7a91b89c52b76

112

py

Python

great_international/apps.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2018-03-20T11:19:07.000Z

2021-10-05T07:53:11.000Z

great_international/apps.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

802

2018-02-05T14:16:13.000Z

2022-02-10T10:59:21.000Z

great_international/apps.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2019-01-22T13:19:37.000Z

2019-07-01T10:35:26.000Z

from django.apps import AppConfig class GreatInternationalConfig(AppConfig): name = 'great_international'

18.666667

42

0.803571

11

112

8.090909

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0.133929

112

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null

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d54b6954c5529cde63c332fe2c41b1b36d08863a

81

py

Python

django_ltree_field/test_utils/test_app/__init__.py

john-parton/django-ltree-field

a4378f0eb0d6a4abb2ed459c49b081d7e2a35c4b

[ "BSD-3-Clause" ]

1

2021-11-11T20:03:12.000Z

django_ltree_field/test_utils/test_app/__init__.py

john-parton/django-ltree-field

a4378f0eb0d6a4abb2ed459c49b081d7e2a35c4b

[ "BSD-3-Clause" ]

null

django_ltree_field/test_utils/test_app/__init__.py

john-parton/django-ltree-field

a4378f0eb0d6a4abb2ed459c49b081d7e2a35c4b

[ "BSD-3-Clause" ]

null

default_app_config = 'django_ltree_field.test_utils.test_app.apps.TestAppConfig'

40.5

80

0.876543

12

81

5.416667

0.833333

0

0.037037

81

1

81

0.833333

0

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1

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null

0

1

0

1

null

0

4

d54ef50b568c97cf4dd042bab1e7f918318a353a

2,852

py

Python

apps/users/migrations/0011_fishx.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

null

apps/users/migrations/0011_fishx.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

5

2021-06-09T17:54:51.000Z

2022-03-12T00:46:49.000Z

apps/users/migrations/0011_fishx.py

lucasjaroszewski/incremental-game

bae8823f986be0fd046bd50195d43fbc548fad90

[ "MIT" ]

1

2020-09-27T18:26:15.000Z

# Generated by Django 3.0.6 on 2020-08-03 20:39 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('users', '0010_profile_rod'), ] operations = [ migrations.CreateModel( name='FishX', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('number', models.PositiveIntegerField(default='0')), ('description', models.CharField(blank=True, default='M', max_length=1)), ('name', models.CharField(default='', max_length=30)), ('hook', models.CharField(default='S', max_length=1)), ('actual_catch', models.PositiveIntegerField(default='0')), ('stones', models.PositiveIntegerField(default='20')), ('rod', models.PositiveIntegerField(default='0')), ('bait', models.CharField(default='', max_length=30)), ('reel', models.CharField(default='', max_length=20)), ('xp', models.PositiveIntegerField(default='0')), ('icon', models.ImageField(default='default.jpg', upload_to='media/fish_pics')), ('lake_tiilen', models.BooleanField(default=False)), ('dragon_palace', models.BooleanField(default=False)), ('acteul', models.BooleanField(default=False)), ('vasu_mointains', models.BooleanField(default=False)), ('charol_plains', models.BooleanField(default=False)), ('man_eating_swamp', models.BooleanField(default=False)), ('baruoki', models.BooleanField(default=False)), ('nauru_uplands', models.BooleanField(default=False)), ('karek_swampland', models.BooleanField(default=False)), ('rinde_port', models.BooleanField(default=False)), ('serena_coast', models.BooleanField(default=False)), ('rucyana_sands', models.BooleanField(default=False)), ('elzion_airport', models.BooleanField(default=False)), ('nilva', models.BooleanField(default=False)), ('last_island', models.BooleanField(default=False)), ('dimension_rift', models.BooleanField(default=False)), ('zol_plains', models.BooleanField(default=False)), ('moonlight_forest', models.BooleanField(default=False)), ('snake_neck_igoma', models.BooleanField(default=False)), ('ancient_battlefield', models.BooleanField(default=False)), ('user', models.ManyToManyField(to=settings.AUTH_USER_MODEL)), ], ), ]

52.814815

114

0.596424

262

2,852

6.351145

0.431298

0.216346

0.300481

0.360577

0.101563

0.039663

0

0.015537

0.255259

2,852

53

115

53.811321

0.767891

0.015778

0

1

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0.134046

0

1

0

false

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0.042553

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0.106383

0

null

1

0

1

0

null

0

4

d5610e83a2492c6e7447f48e1d9220d30e4fb359

160

py

Python

Data Analytics/making data frames.py

bahuisman/NatGasModel

397423237b90a7638089f79492be0519e02fcc67

[ "MIT" ]

4

2019-09-09T08:05:46.000Z

2021-03-24T13:09:10.000Z

Data Analytics/making data frames.py

bahuisman/NatGasModel

397423237b90a7638089f79492be0519e02fcc67

[ "MIT" ]

null

Data Analytics/making data frames.py

bahuisman/NatGasModel

397423237b90a7638089f79492be0519e02fcc67

[ "MIT" ]

3

2019-09-09T08:05:48.000Z

2020-04-03T21:31:21.000Z

# -*- coding: utf-8 -*- """ Created on Fri Jul 14 01:43:16 2017 @author: Berend """ import pandas as pd import numpy as np np.linspace(2012,2020,7)

14.545455

36

0.61875

27

160

3.666667

0.888889

0

0.178862

0.23125

160

11

37

14.545455

0.626016

0.46875

0

1

0

true

0

0.666667

0

0.666667

0

1

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null

0

null

0

1

0

1

0

1

0

4

d576129df67b75d5a13213ef3de9ba608204071b

108

py

Python

mir/__init__.py

fenrir-z/ymir-cmd

6fbffd3c1ff5dd1c9a44b55de411523b50567661

[ "Apache-2.0" ]

64

2021-11-15T03:48:00.000Z

2022-03-25T07:08:46.000Z

mir/__init__.py

fenrir-z/ymir-cmd

6fbffd3c1ff5dd1c9a44b55de411523b50567661

[ "Apache-2.0" ]

35

2021-11-23T04:14:35.000Z

2022-03-26T09:03:43.000Z

mir/__init__.py

fenrir-z/ymir-cmd

6fbffd3c1ff5dd1c9a44b55de411523b50567661

[ "Apache-2.0" ]

57

2021-11-11T10:15:40.000Z

2022-03-29T07:27:54.000Z

import logging import sys logging.basicConfig(stream=sys.stdout, format='%(message)s', level=logging.INFO)

21.6

80

0.787037

15

108

5.666667

0.733333

0

0.074074

108

4

81

27

0.85

0

0.101852

0

1

0

true

0

0.666667

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null

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null

0

1

0

1

0

4

d594e650b232d55ab1ddc0481cc31694028496df

11,748

py

Python

tests/unit_tests/test_view_establishment.py

sspbft/BFTList

d73aee5bd0ab05995509f0fcfaf3c0a5944e617a

[ "MIT" ]

6

2019-11-12T01:45:55.000Z

2022-03-18T10:57:21.000Z

tests/unit_tests/test_view_establishment.py

practicalbft/BFTList

d73aee5bd0ab05995509f0fcfaf3c0a5944e617a

[ "MIT" ]

4

2019-02-14T10:57:09.000Z

2019-03-21T15:22:08.000Z

tests/unit_tests/test_view_establishment.py

sspbft/BFTList

d73aee5bd0ab05995509f0fcfaf3c0a5944e617a

[ "MIT" ]

1

2019-04-04T15:09:33.000Z

import unittest from unittest.mock import Mock, MagicMock, call from resolve.resolver import Resolver from modules.view_establishment.predicates import PredicatesAndAction from modules.view_establishment.module import ViewEstablishmentModule from modules.enums import ViewEstablishmentEnums from resolve.enums import Function, Module from modules.constants import VIEWS, PHASE, WITNESSES, VCHANGE class ViewEstablishmentModuleTest(unittest.TestCase): def setUp(self): self.resolver = Resolver(testing=True) def test_while_true_case_1_is_true_and_return_is_an_action(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) # (1)Predicates and action reset all should be called view_est_mod.pred_and_action.need_reset = MagicMock(return_value = True) view_est_mod.pred_and_action.reset_all = Mock() # (2) Processor i recent values are noticed and both processors have been witnessed view_est_mod.noticed_recent_value = MagicMock(return_value = True) view_est_mod.get_witnesses = MagicMock(return_value = {0,1}) # (3)Let predicate of case 0 be false and case 1 true view_est_mod.witnes_seen = MagicMock(return_value = True) view_est_mod.pred_and_action.automation = MagicMock(side_effect=(lambda t ,y, x: x)) # (4) Mocks the final calls view_est_mod.send_msg = Mock() # Run the method and check all statements above view_est_mod.run(testing=True) # (1) Predicates and action reset all should be called view_est_mod.pred_and_action.reset_all.assert_called_once() # (2) Processor i recent values are noticed and both processors have been witnessed self.assertTrue(view_est_mod.witnesses[view_est_mod.id]) self.assertEqual(view_est_mod.witnesses_set, {0,1}) # (3) Let predicate of case 0 be false and case 1 true, make sure function is called calls_automaton = [call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 0), call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 1), call( ViewEstablishmentEnums.ACTION,view_est_mod.phs[view_est_mod.id], 1) ] # any_order means that no other calls to the function should be made view_est_mod.pred_and_action.automation.assert_has_calls(calls_automaton, any_order = False) # (4) Check that the functions are called with correct input #view_est_mod.send_msg.assert_called_once() # Used for mocking predicate_and_action automaton for different values # When called with predicate : case 0 returns false, case 1 returns true. def side_effect_case_1_return_no_action(self, action, phase, case): if(action == ViewEstablishmentEnums.ACTION): return ViewEstablishmentEnums.NO_ACTION else: return case def test_while_true_case_1_is_true_and_return_is_no_action(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) # (1)Predicates and action reset all should be called view_est_mod.pred_and_action.need_reset = MagicMock(return_value = True) view_est_mod.pred_and_action.reset_all = Mock() # (2) Processor i recent values are noticed and both processors have been witnessed view_est_mod.noticed_recent_value = MagicMock(return_value = False) view_est_mod.get_witnesses = MagicMock(return_value = set()) # (3)Let predicate of case 0 be false and case 1 true view_est_mod.witnes_seen = MagicMock(return_value = True) view_est_mod.pred_and_action.automation = MagicMock(side_effect=self.side_effect_case_1_return_no_action) # (4) Mocks the final calls view_est_mod.next_phs = Mock() view_est_mod.send_msg = Mock() # Run the method and check all statements above view_est_mod.run(testing=True) # (3) Let predicate of case 0 be false and case 1 true, make sure function is called calls_automaton = [call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 0), call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 1), call( ViewEstablishmentEnums.ACTION,view_est_mod.phs[view_est_mod.id], 1) ] # any_order means that no other calls to the function should be made view_est_mod.pred_and_action.automation.assert_has_calls(calls_automaton, any_order = False) # (4) Check that the functions are called with correct input view_est_mod.next_phs.assert_not_called() #view_est_mod.send_msg.assert_called_once() def test_while_true_no_case_is_true(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) # (1)Predicates and action reset all should not be called view_est_mod.pred_and_action.need_reset = MagicMock(return_value = False) view_est_mod.pred_and_action.reset_all = Mock() # (2) Processor i recent values are noticed and both processors have been witnessed view_est_mod.noticed_recent_value = MagicMock(return_value = True) view_est_mod.get_witnesses = MagicMock(return_value = {0,1}) # (3) No predicate is true view_est_mod.witnes_seen = MagicMock(return_value = True) view_est_mod.pred_and_action.automation = MagicMock(return_value = False) # (4) Mocks the final calls view_est_mod.next_phs = Mock() view_est_mod.send_msg = Mock() # Run the method and check all statements above view_est_mod.run(testing=True) # (1) Predicates and action reset all should not be called view_est_mod.pred_and_action.reset_all.assert_not_called() # (3) calls_automaton = [call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 0), call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 1), call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 2), call( ViewEstablishmentEnums.PREDICATE,view_est_mod.phs[view_est_mod.id], 3) ] view_est_mod.pred_and_action.automation.assert_has_calls(calls_automaton, any_order = False) # (4) Check that next_phs is not called and send_msg are called with correct arguments view_est_mod.next_phs.assert_not_called() #view_est_mod.send_msg.assert_called_once() # Macros def test_echo_no_witn(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) # Both conditions are fulfilled view_est_mod.phs[view_est_mod.id] = 0 view_est_mod.pred_and_action.get_info = MagicMock(return_value = ({"current": 0, "next": 1}, False, False)) view_est_mod.echo[1] = {VIEWS: {"current": 0, "next": 1}, PHASE: 0, WITNESSES: None, VCHANGE: False} self.assertTrue(view_est_mod.echo_no_witn(1)) # The view in the echo is not correct view_est_mod.echo[1] = {VIEWS: {"current": 0, "next": 0}, PHASE: 0, WITNESSES: None} self.assertFalse(view_est_mod.echo_no_witn(1)) # The phase in the echo is not correct view_est_mod.echo[1] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} self.assertFalse(view_est_mod.echo_no_witn(1)) def test_witnes_seen(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 6, 1) # Both condition fulfilled with f = 0 view_est_mod.witnesses[view_est_mod.id] = True view_est_mod.witnesses_set = {1, 2, 3, 4, 5} view_est_mod.echo[0] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} view_est_mod.echo[2] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} view_est_mod.echo[3] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} view_est_mod.echo[4] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} view_est_mod.echo[5] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} view_est_mod.echo[1] = {VIEWS: {"current": 0, "next": 1}, PHASE: 1, WITNESSES: None} self.assertTrue(view_est_mod.witnes_seen()) # Processor i has not been witnessed view_est_mod.witnesses[view_est_mod.id] = False self.assertFalse(view_est_mod.witnes_seen()) # f = 1, meaning the set is not big enough view_est_mod.witnesses[view_est_mod.id] = True view_est_mod.witnesses_set = {1, 2, 3} self.assertFalse(view_est_mod.witnes_seen()) def test_next_phs(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) view_est_mod.phs[view_est_mod.id] = 0 # Move to phase 1 view_est_mod.next_phs() self.assertEqual(view_est_mod.phs[view_est_mod.id], 1) # Move to phase 2 view_est_mod.next_phs() self.assertEqual(view_est_mod.phs[view_est_mod.id], 0) # Interface functions def test_get_phs(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) view_est_mod.phs = [0, 1] self.assertEqual(view_est_mod.get_phs(0), 0) self.assertEqual(view_est_mod.get_phs(1), 1) def test_init(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) view_est_mod.phs = [0, 1] view_est_mod.witnesses_set = {0} view_est_mod.witnesses = [True, True] view_est_mod.init_module() self.assertEqual(view_est_mod.phs, [0, 0]) self.assertEqual(view_est_mod.witnesses_set, set()) self.assertEqual(view_est_mod.witnesses, [False, False]) # Function added for while true loop def test_noticed_recent_value(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 1) # All have noticed view_est_mod.echo_no_witn = MagicMock(return_value = True) self.assertTrue(view_est_mod.noticed_recent_value()) # Processor 1 has not noticed (0 False, 1 True) view_est_mod.echo_no_witn = MagicMock(side_effect=lambda x: not x) self.assertTrue(view_est_mod.noticed_recent_value()) # None have noticed view_est_mod.echo_no_witn = MagicMock(return_value = False) self.assertFalse(view_est_mod.noticed_recent_value()) def test_get_witnesses(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) # Both processors have been witnessed view_est_mod.witnesses=[True, True] self.assertEqual(view_est_mod.get_witnesses(), {0,1}) # Both processor 1 has been witnessed, not processor 0 view_est_mod.witnesses=[False, True] self.assertEqual(view_est_mod.get_witnesses(), {1}) # None of the processors have been witnessed view_est_mod.witnesses=[False, False] self.assertEqual(view_est_mod.get_witnesses(), set()) # Function added for re-routing inter-module communication def test_get_current_view_from_predicts_and_action(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) view_est_mod.pred_and_action.get_current_view = Mock() view_est_mod.get_current_view(0) view_est_mod.pred_and_action.get_current_view.assert_called_once_with(0) def test_allow_service_from_predicts_and_action(self): view_est_mod = ViewEstablishmentModule(0, self.resolver, 2, 0) view_est_mod.pred_and_action.allow_service = Mock() view_est_mod.allow_service() view_est_mod.pred_and_action.allow_service.assert_called_once()

44.839695

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null

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4

d597da931bb3b2d46eadaae0e53b5f56c67a78cc

1,943

py

Python

autograd/scipy/stats/norm.py

EnjoyLifeFund/py36pkgs

0ac677fbbfa7b6d8c527fe2c759ba05117b07fd2

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

4

2021-01-12T22:02:57.000Z

2021-04-02T15:24:18.000Z

autograd/scipy/stats/norm.py

EnjoyLifeFund/py36pkgs

0ac677fbbfa7b6d8c527fe2c759ba05117b07fd2

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

null

autograd/scipy/stats/norm.py

EnjoyLifeFund/py36pkgs

0ac677fbbfa7b6d8c527fe2c759ba05117b07fd2

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

1

2017-07-30T23:49:27.000Z

"""Gradients of the normal distribution.""" from __future__ import absolute_import import scipy.stats import autograd.numpy as anp from autograd.core import primitive from autograd.numpy.numpy_grads import unbroadcast pdf = primitive(scipy.stats.norm.pdf) cdf = primitive(scipy.stats.norm.cdf) logpdf = primitive(scipy.stats.norm.logpdf) logcdf = primitive(scipy.stats.norm.logcdf) pdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * ans * (x - loc) / scale**2)) pdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * ans * (x - loc) / scale**2), argnum=1) pdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * ans * (((x - loc)/scale)**2 - 1.0)/scale), argnum=2) cdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * pdf(x, loc, scale))) cdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * pdf(x, loc, scale)), argnum=1) cdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * pdf(x, loc, scale)*(x-loc)/scale), argnum=2) logpdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * (x - loc) / scale**2)) logpdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * (x - loc) / scale**2), argnum=1) logpdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * (-1.0/scale + (x - loc)**2/scale**3)), argnum=2) logcdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, g * anp.exp(logpdf(x, loc, scale) - logcdf(x, loc, scale)))) logcdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * anp.exp(logpdf(x, loc, scale) - logcdf(x, loc, scale))), argnum=1) logcdf.defvjp(lambda g, ans, vs, gvs, x, loc=0.0, scale=1.0: unbroadcast(vs, gvs, -g * anp.exp(logpdf(x, loc, scale) - logcdf(x, loc, scale))*(x-loc)/scale), argnum=2)

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1

0

null

0

4

d5a64f6c2783e8b94f9669bc21e2aeda3ec0783a

1,244

py

Python

django_analyses/filters/__init__.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

1

2020-12-30T12:43:34.000Z

django_analyses/filters/__init__.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

59

2019-12-25T13:14:56.000Z

2021-07-22T12:24:46.000Z

django_analyses/filters/__init__.py

TheLabbingProject/django_analyses

08cac40a32754a265b37524f08ec6160c69ebea8

[ "Apache-2.0" ]

2

2020-05-24T06:44:27.000Z

2020-07-09T15:47:31.000Z

""" Filters for the app's :ref:`models <modules/django_analyses.models:Models>`. References ---------- * `Django REST Framework`_ `filtering documentation`_. * django-filter_'s documentation for `Integration with DRF`_. .. _django-filter: https://django-filter.readthedocs.io/en/stable/index.html .. _Django REST Framework: https://www.django-rest-framework.org/ .. _filtering documentation: https://www.django-rest-framework.org/api-guide/filtering/ .. _Integration with DRF: https://django-filter.readthedocs.io/en/stable/guide/rest_framework.html """ from django_analyses.filters.analysis import AnalysisFilter from django_analyses.filters.analysis_version import AnalysisVersionFilter from django_analyses.filters.category import CategoryFilter from django_analyses.filters.input import (InputDefinitionFilter, InputFilter, InputSpecificationFilter) from django_analyses.filters.output import (OutputDefinitionFilter, OutputFilter, OutputSpecificationFilter) from django_analyses.filters.pipeline import (NodeFilter, PipeFilter, PipelineFilter)

44.428571

78

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120

1,244

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0.160757

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0.213023

1,244

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0

null

0

1

0

1

0

1

0

4

633922242bfb2084b69e826c8f5d4f9b1df633ef

253

py

Python

tips/*40.py

leolanese/python-playground

4cfa281243e48ea616387c2110444944aaba5b3d

[ "MIT" ]

1

2018-10-11T20:27:52.000Z

tips/*40.py

leolanese/python-playground

4cfa281243e48ea616387c2110444944aaba5b3d

[ "MIT" ]

null

tips/*40.py

leolanese/python-playground

4cfa281243e48ea616387c2110444944aaba5b3d

[ "MIT" ]

null

print("Hello!, Welcome to Python") print("*" * 40) print("Please Enter your name ") name = input() print("Your name is, " + name) # Hello!,Welcome to Python # **************************************** # Please Enter your name # Leo # Your name is, Leo

21.083333

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0.40625

0.230216

0.201439

0.28777

0

0.009434

0.162055

253

11

43

23

0.646226

0.438735

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0.463235

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false

0

0.8

0

null

1

0

null

0

1

0

4

6373a447352ea40984e9c81455efa67f18110321

64

py

Python

uniswap/__init__.py

mul53/uniswap-python

f24993bcea8cb4181be59dd7e4e9abcd40a375cb

[ "MIT" ]

3

2021-05-03T06:59:31.000Z

2021-11-02T05:18:54.000Z

uniswap/__init__.py

mul53/uniswap-python

f24993bcea8cb4181be59dd7e4e9abcd40a375cb

[ "MIT" ]

null

uniswap/__init__.py

mul53/uniswap-python

f24993bcea8cb4181be59dd7e4e9abcd40a375cb

[ "MIT" ]

4

2020-10-27T20:27:44.000Z

2022-03-23T22:07:55.000Z

from .uniswap import Uniswap, InvalidToken, InsufficientBalance

32

63

0.859375

6

64

9.166667

0.833333

0

0.09375

64

1

64

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null

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null

0

1

0

1

0

4

6397d998f4655e986e02ed4237c84c111c3b5cc1

93

py

Python

bloodcare/apps.py

tanmayag8958/bloodcare

b0d0a3920d9e39fc37d8471a2359603d589e1798

[ "MIT" ]

2

2019-11-19T07:38:06.000Z

2021-08-14T06:43:55.000Z

bloodcare/apps.py

tanmayag8958/bloodcare

b0d0a3920d9e39fc37d8471a2359603d589e1798

[ "MIT" ]

3

2021-06-04T23:04:34.000Z

2021-06-10T19:21:04.000Z

bloodcare/apps.py

tanmayag8958/bloodcare

b0d0a3920d9e39fc37d8471a2359603d589e1798

[ "MIT" ]

2

2020-04-08T16:17:09.000Z

2020-04-11T06:26:06.000Z

from django.apps import AppConfig class BloodcareConfig(AppConfig): name = 'bloodcare'

15.5

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null

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1

0

4

63abe3e259b96a5e0967b9ac9b090b0f5826710d

83

py

Python

scripts/jenkins/docs_release_check/check.py

pengpj/apm-agent-java

d10db0ca04d31e1cd9891eb694b0c49c5b535028

[ "Apache-2.0" ]

1

2021-08-04T05:10:14.000Z

scripts/jenkins/docs_release_check/check.py

pengpj/apm-agent-java

d10db0ca04d31e1cd9891eb694b0c49c5b535028

[ "Apache-2.0" ]

34

2021-01-18T07:04:28.000Z

2022-03-28T23:04:36.000Z

scripts/jenkins/docs_release_check/check.py

pengpj/apm-agent-java

d10db0ca04d31e1cd9891eb694b0c49c5b535028

[ "Apache-2.0" ]

3

2021-06-04T13:35:28.000Z

2021-07-16T08:42:42.000Z

#!/usr/bin/env python import lib if __name__ == "__main__": lib.entrypoint()

11.857143

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83

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null

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1

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4

893a7c27290dcf8017cc971df2d155df432fca7e

154

py

Python

python/row-of-the-odd-triangle/solution.py

hiljusti/codewars-solutions

1a423e8cb0fbcac94738f6e51dc333f057b0a731

[ "WTFPL" ]

2

2020-02-22T08:47:51.000Z

2021-05-21T22:21:55.000Z

python/row-of-the-odd-triangle/solution.py

hiljusti/codewars-solutions

1a423e8cb0fbcac94738f6e51dc333f057b0a731

[ "WTFPL" ]

null

python/row-of-the-odd-triangle/solution.py

hiljusti/codewars-solutions

1a423e8cb0fbcac94738f6e51dc333f057b0a731

[ "WTFPL" ]

1

2021-11-09T17:22:10.000Z

# https://www.codewars.com/kata/5d5a7525207a674b71aa25b5 def odd_row(row): base = row * (row - 1) return [base + n * 2 + 1 for n in range(row)]

22

56

0.636364

24

154

4.041667

0.708333

0.123711

0

0.165289

0.214286

154

6

57

25.666667

0.636364

0.350649

0

1

0.333333

false

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null

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1

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null

0

1

0

1

0

4

894f3d22c11e0a45e04bc9cf5924908b0f99befa

58

py

Python

mitmproxy/tools/console/grideditor/__init__.py

0x7c48/mitmproxy

f9d8f3bae3f4e681d5f4d406b7e06b099e60ecba

[ "MIT" ]

24,939

2015-01-01T17:13:21.000Z

2022-03-31T17:50:04.000Z

mitmproxy/tools/console/grideditor/__init__.py

0x7c48/mitmproxy

f9d8f3bae3f4e681d5f4d406b7e06b099e60ecba

[ "MIT" ]

3,655

2015-01-02T12:31:43.000Z

2022-03-31T20:24:57.000Z

mitmproxy/tools/console/grideditor/__init__.py

0x7c48/mitmproxy

f9d8f3bae3f4e681d5f4d406b7e06b099e60ecba

[ "MIT" ]

3,712

2015-01-06T06:47:06.000Z

2022-03-31T10:33:27.000Z

from .editors import * # noqa from . import base # noqa

19.333333

30

0.672414

8

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4.875

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58

2

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true

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null

0

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8980010713a9fe4e84501a6cc407c8bd192a9456

189

py

Python

polls/forms.py

kraupn3r/intranet

4cabf6f365ef0ea0f352f67f9322318e161ed265

[ "MIT" ]

null

polls/forms.py

kraupn3r/intranet

4cabf6f365ef0ea0f352f67f9322318e161ed265

[ "MIT" ]

null

polls/forms.py

kraupn3r/intranet

4cabf6f365ef0ea0f352f67f9322318e161ed265

[ "MIT" ]

null

from django import forms from .models import Poll class PollForm(forms.ModelForm): class Meta(): model = Poll fields = ['title','target_departament','target_location']

23.625

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false

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1

0

null

0

1

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1

0

4

8981dd4564ac09e0f71ab2a3985c14ff4fe90bb6

44,957

py

Python

Meters/IEC/Helpers/obis_codes.py

Runamook/PyCharmProjects

1b1a063345e052451f00e3fdea82e31bdd2a0cae

[ "MIT" ]

null

Meters/IEC/Helpers/obis_codes.py

Runamook/PyCharmProjects

1b1a063345e052451f00e3fdea82e31bdd2a0cae

[ "MIT" ]

null

Meters/IEC/Helpers/obis_codes.py

Runamook/PyCharmProjects

1b1a063345e052451f00e3fdea82e31bdd2a0cae

[ "MIT" ]

null

obis_codes = { "1.8.0": "Positive active energy (A+) total [kWh]", "1.8.1": "Positive active energy (A+) in tariff T1 [kWh]", "1.8.2": "Positive active energy (A+) in tariff T2 [kWh]", "1.8.3": "Positive active energy (A+) in tariff T3 [kWh]", "1.8.4": "Positive active energy (A+) in tariff T4 [kWh]", "2.8.0": "Negative active energy (A+) total [kWh]", "2.8.1": "Negative active energy (A+) in tariff T1 [kWh]", "2.8.2": "Negative active energy (A+) in tariff T2 [kWh]", "2.8.3": "Negative active energy (A+) in tariff T3 [kWh]", "2.8.4": "Negative active energy (A+) in tariff T4 [kWh]", "15.8.0": "Absolute active energy (A+) total [kWh]", "15.8.1": "Absolute active energy (A+) in tariff T1 [kWh]", "15.8.2": "Absolute active energy (A+) in tariff T2 [kWh]", "15.8.3": "Absolute active energy (A+) in tariff T3 [kWh]", "15.8.4": "Absolute active energy (A+) in tariff T4 [kWh]", "16.8.0": "Sum active energy without reverse blockade (A+ - A-) total [kWh]", "16.8.1": "Sum active energy without reverse blockade (A+ - A-) in tariff T1 [kWh]", "16.8.2": "Sum active energy without reverse blockade (A+ - A-) in tariff T2 [kWh]", "16.8.3": "Sum active energy without reverse blockade (A+ - A-) in tariff T3 [kWh]", "16.8.4": "Sum active energy without reverse blockade (A+ - A-) in tariff T4 [kWh]", "3.8.0": "Positive reactive energy (Q+) total [kvarh]", "3.8.1": "Positive reactive energy (Q+) in tariff T1 [kvarh]", "3.8.2": "Positive reactive energy (Q+) in tariff T2 [kvarh]", "3.8.3": "Positive reactive energy (Q+) in tariff T3 [kvarh]", "3.8.4": "Positive reactive energy (Q+) in tariff T4 [kvarh]", "4.8.0": "Negative reactive energy (Q-) total [kvarh]", "4.8.1": "Negative reactive energy (Q-) in tariff T1 [kvarh]", "4.8.2": "Negative reactive energy (Q-) in tariff T2 [kvarh]", "4.8.3": "Negative reactive energy (Q-) in tariff T3 [kvarh]", "4.8.4": "Negative reactive energy (Q-) in tariff T4 [kvarh]", "5.8.0": "Imported inductive reactive energy in 1-st quadrant (Q1) total [kvarh]", "5.8.1": "Imported inductive reactive energy in 1-st quadrant (Q1) in tariff T1 [kvarh]", "5.8.2": "Imported inductive reactive energy in 1-st quadrant (Q1) in tariff T2 [kvarh]", "5.8.3": "Imported inductive reactive energy in 1-st quadrant (Q1) in tariff T3 [kvarh]", "5.8.4": "Imported inductive reactive energy in 1-st quadrant (Q1) in tariff T4 [kvarh]", "6.8.0": "Imported capacitive reactive energy in 2-nd quadrant (Q2) total [kvarh]", "6.8.1": "Imported capacitive reactive energy in 2-nd quadr. (Q2) in tariff T1 [kvarh]", "6.8.2": "Imported capacitive reactive energy in 2-nd quadr. (Q2) in tariff T2 [kvarh]", "6.8.3": "Imported capacitive reactive energy in 2-nd quadr. (Q2) in tariff T3 [kvarh]", "6.8.4": "Imported capacitive reactive energy in 2-nd quadr. (Q2) in tariff T4 [kvarh]", "7.8.0": "Exported inductive reactive energy in 3-rd quadrant (Q3) total [kvarh]", "7.8.1": "Exported inductive reactive energy in 3-rd quadrant (Q3) in tariff T1 [kvarh]", "7.8.2": "Exported inductive reactive energy in 3-rd quadrant (Q3) in tariff T2 [kvarh]", "7.8.3": "Exported inductive reactive energy in 3-rd quadrant (Q3) in tariff T3 [kvarh]", "7.8.4": "Exported inductive reactive energy in 3-rd quadrant (Q3) in tariff T4 [kvarh]", "8.8.0": "Exported capacitive reactive energy in 4-th quadrant (Q4) total [kvarh]", "8.8.1": "Exported capacitive reactive energy in 4-th quadr. (Q4) in tariff T1 [kvarh]", "8.8.2": "Exported capacitive reactive energy in 4-th quadr. (Q4) in tariff T2 [kvarh]", "8.8.3": "Exported capacitive reactive energy in 4-th quadr. (Q4) in tariff T3 [kvarh]", "8.8.4": "Exported capacitive reactive energy in 4-th quadr. (Q4) in tariff T4 [kvarh]", "9.8.0": "Apparent energy (S+) total [kVAh]", "9.8.1": "Apparent energy (S+) in tariff T1 [kVAh]", "9.8.2": "Apparent energy (S+) in tariff T2 [kVAh]", "9.8.3": "Apparent energy (S+) in tariff T3 [kVAh]", "9.8.4": "Apparent energy (S+) in tariff T4 [kVAh]", "21.8.0": "Positive active energy (A+) in phase L1 total [kWh]", "41.8.0": "Positive active energy (A+) in phase L2 total [kWh]", "61.8.0": "Positive active energy (A+) in phase L3 total [kWh]", "22.8.0": "Negative active energy (A-) in phase L1 total [kWh]", "42.8.0": "Negative active energy (A-) in phase L2 total [kWh]", "62.8.0": "Negative active energy (A-) in phase L3 total [kWh]", "35.8.0": "Absolute active energy (|A|) in phase L1 total [kWh]", "55.8.0": "Absolute active energy (|A|) in phase L2 total [kWh]", "75.8.0": "Absolute active energy (|A|) in phase L3 total [kWh]", "1.6.0": "Positive active maximum demand (A+) total [kW]", "1.6.1": "Positive active maximum demand (A+) in tariff T1 [kW]", "1.6.2": "Positive active maximum demand (A+) in tariff T2 [kW]", "1.6.3": "Positive active maximum demand (A+) in tariff T3 [kW]", "1.6.4": "Positive active maximum demand (A+) in tariff T4 [kW]", "2.6.0": "Negative active maximum demand (A-) total [kW]", "2.6.1": "Negative active maximum demand (A-) in tariff T1 [kW]", "2.6.2": "Negative active maximum demand (A-) in tariff T2 [kW]", "2.6.3": "Negative active maximum demand (A-) in tariff T3 [kW]", "2.6.4": "Negative active maximum demand (A-) in tariff T4 [kW]", "15.6.0": "Absolute active maximum demand (|A|) total [kW]", "15.6.1": "Absolute active maximum demand (|A|) in tariff T1 [kW]", "15.6.2": "Absolute active maximum demand (|A|) in tariff T2 [kW]", "15.6.3": "Absolute active maximum demand (|A|) in tariff T3 [kW]", "15.6.4": "Absolute active maximum demand (|A|) in tariff T4 [kW]", "3.6.0": "Positive reactive maximum demand (Q+) total [kvar]", "4.6.0": "Negative reactive maximum demand (Q-) total [kvar]", "5.6.0": "Reactive maximum demand in Q1 (Q1) total [kvar]", "6.6.0": "Reactive maximum demand in Q2 (Q2) total [kvar]", "7.6.0": "Reactive maximum demand in Q3 (Q3) total [kvar]", "8.6.0": "Reactive maximum demand in Q4 (Q4) total [kvar]", "9.6.0": "Apparent maximum demand (S+) total [kVA]", "1.2.0": "Positive active cumulative maximum demand (A+) total [kW]", "1.2.1": "Positive active cumulative maximum demand (A+) in tariff T1 [kW]", "1.2.2": "Positive active cumulative maximum demand (A+) in tariff T2 [kW]", "1.2.3": "Positive active cumulative maximum demand (A+) in tariff T3 [kW]", "1.2.4": "Positive active cumulative maximum demand (A+) in tariff T4 [kW]", "2.2.0": "Negative active cumulative maximum demand (A-) total [kW]", "2.2.1": "Negative active cumulative maximum demand (A-) in tariff T1 [kW]", "2.2.2": "Negative active cumulative maximum demand (A-) in tariff T2 [kW]", "2.2.3": "Negative active cumulative maximum demand (A-) in tariff T3 [kW]", "2.2.4": "Negative active cumulative maximum demand (A-) in tariff T4 [kW]", "15.2.0": "Absolute active cumulative maximum demand (|A|) total [kW]", "15.2.1": "Absolute active cumulative maximum demand (|A|) in tariff T1 [kW]", "15.2.2": "Absolute active cumulative maximum demand (|A|) in tariff T2 [kW]", "15.2.3": "Absolute active cumulative maximum demand (|A|) in tariff T3 [kW]", "15.2.4": "Absolute active cumulative maximum demand (|A|) in tariff T4 [kW]", "3.2.0": "Positive reactive cumulative maximum demand (Q+) total [kvar]", "4.2.0": "Negative reactive cumulative maximum demand (Q-) total [kvar]", "5.2.0": "Reactive cumulative maximum demand in Q1 (Q1) total [kvar]", "6.2.0": "Reactive cumulative maximum demand in Q2 (Q2) total [kvar]", "7.2.0": "Reactive cumulative maximum demand in Q3 (Q3) total [kvar]", "8.2.0": "Reactive cumulative maximum demand in Q4 (Q4) total [kvar]", "9.2.0": "Apparent cumulative maximum demand (S+) total [kVA]", "1.4.0": "Positive active demand in a current demand period (A+) [kW]", "2.4.0": "Negative active demand in a current demand period (A-) [kW]", "15.4.0": "Absolute active demand in a current demand period (|A|) [kW]", "3.4.0": "Positive reactive demand in a current demand period (Q+) [kvar]", "4.4.0": "Negative reactive demand in a current demand period (Q-) [kvar]", "5.4.0": "Reactive demand in a current demand period in Q1 (Q1) [kvar]", "6.4.0": "Reactive demand in a current demand period in Q2 (Q2) [kvar]", "7.4.0": "Reactive demand in a current demand period in Q3 (Q3) [kvar]", "8.4.0": "Reactive demand in a current demand period in Q4 (Q4) [kvar]", "9.4.0": "Apparent demand in a current demand period (S+) [kVA]", "1.5.0": "Positive active demand in the last completed demand period (A+) [kW]", "2.5.0": "Negative active demand in the last completed demand period (A-) [kW]", "15.5.0": "Absolute active demand in the last completed demand period (|A|) [kW]", "3.5.0": "Positive reactive demand in the last completed demand period (Q+) [kvar]", "4.5.0": "Negative reactive demand in the last completed demand period (Q-) [kvar]", "5.5.0": "Reactive demand in the last completed demand period in Q1 (Q1) [kvar]", "6.5.0": "Reactive demand in the last completed demand period in Q2 (Q2) [kvar]", "7.5.0": "Reactive demand in the last completed demand period in Q3 (Q3) [kvar]", "8.5.0": "Reactive demand in the last completed demand period in Q4 (Q4) [kvar]", "9.5.0": "Apparent demand in the last completed demand period (S+) [kVA]", "1.7.0": "Positive active instantaneous power (A+) [kW]", "21.7.0": "Positive active instantaneous power (A+) in phase L1 [kW]", "41.7.0": "Positive active instantaneous power (A+) in phase L2 [kW]", "61.7.0": "Positive active instantaneous power (A+) in phase L3 [kW]", "2.7.0": "Negative active instantaneous power (A-) [kW]", "22.7.0": "Negative active instantaneous power (A-) in phase L1 [kW]", "42.7.0": "Negative active instantaneous power (A-) in phase L2 [kW]", "62.7.0": "Negative active instantaneous power (A-) in phase L3 [kW]", "15.7.0": "Absolute active instantaneous power (|A|) [kW]", "35.7.0": "Absolute active instantaneous power (|A|) in phase L1 [kW]", "55.7.0": "Absolute active instantaneous power (|A|) in phase L2 [kW]", "75.7.0": "Absolute active instantaneous power (|A|) in phase L3 [kW]", "16.7.0": "Sum active instantaneous power (A+ - A-) [kW]", "36.7.0": "Sum active instantaneous power (A+ - A-) in phase L1 [kW]", "56.7.0": "Sum active instantaneous power (A+ - A-) in phase L2 [kW]", "76.7.0": "Sum active instantaneous power (A+ - A-) in phase L3 [kW]", "3.7.0": "Positive reactive instantaneous power (Q+) [kvar]", "23.7.0": "Positive reactive instantaneous power (Q+) in phase L1 [kvar]", "43.7.0": "Positive reactive instantaneous power (Q+) in phase L2 [kvar]", "63.7.0": "Positive reactive instantaneous power (Q+) in phase L3 [kvar]", "4.7.0": "Negative reactive instantaneous power (Q-) [kvar]", "24.7.0": "Negative reactive instantaneous power (Q-) in phase L1 [kvar]", "44.7.0": "Negative reactive instantaneous power (Q-) in phase L2 [kvar]", "64.7.0": "Negative reactive instantaneous power (Q-) in phase L3 [kvar]", "9.7.0": "Apparent instantaneous power (S+) [kVA]", "29.7.0": "Apparent instantaneous power (S+) in phase L1 [kVA]", "49.7.0": "Apparent instantaneous power (S+) in phase L2 [kVA]", "69.7.0": "Apparent instantaneous power (S+) in phase L3 [kVA]", "11.7.0": "Instantaneous current (I) [A]", "31.7.0": "Instantaneous current (I) in phase L1 [A]", "51.7.0": "Instantaneous current (I) in phase L2 [A]", "71.7.0": "Instantaneous current (I) in phase L3 [A]", "91.7.0": "Instantaneous current (I) in neutral [A]", "11.6.0": "Maximum current (I max) [A]", "31.6.0": "Maximum current (I max) in phase L1 [A]", "51.6.0": "Maximum current (I max) in phase L2 [A]", "71.6.0": "Maximum current (I max) in phase L3 [A]", "91.6.0": "Maximum current (I max) in neutral [A]", "12.7.0": "Instantaneous voltage (U) [V]", "32.7.0": "Instantaneous voltage (U) in phase L1 [V]", "52.7.0": "Instantaneous voltage (U) in phase L2 [V]", "72.7.0": "Instantaneous voltage (U) in phase L3 [V]", "13.7.0": "Instantaneous power factor", "33.7.0": "Instantaneous power factor in phase L1", "53.7.0": "Instantaneous power factor in phase L2", "73.7.0": "Instantaneous power factor in phase L3", "14.7.0": "Frequency [Hz]", "C.53.1": "Tamper 1 energy register", "C.53.2": "Tamper 2 energy register", "C.53.3": "Tamper 3 energy register", "C.53.4": "Tamper 4 energy register", "C.53.11": "Tamper 5 energy register", "C.53.5": "Tamper 1 time counter register", "C.53.6": "Tamper 2 time counter register", "C.53.7": "Tamper 3 time counter register", "C.53.9": "Tamper 4 time counter register", "C.53.10": "Tamper 5 time counter register", "C.2.0": "Event parameters change - counter", "C.2.1": "Event parameters change - timestamp", "C.51.1": "Event terminal cover opened - counter", "C.51.2": "Event terminal cover opened - timestamp", "C.51.3": "Event main cover opened - counter", "C.51.5": "Event magnetic field detection start - counter", "C.51.6": "Event magnetic field detection start - timestamp", "C.51.7": "Event reverse power flow - counter", "C.51.8": "Event reverse power flow - timestamp", "C.7.10": "Event power down - timestamp", "C.51.13": "Event power up - counter", "C.51.14": "Event power up – timestamp", "C.51.15": "Event RTC (Real Time Clock) set - counter", "C.51.16": "Event RTC (Real Time Clock) set - timestamp", "C.51.21": "Event terminal cover closed - counter", "C.51.22": "Event terminal cover closed - timestamp", "C.51.23": "Event main cover closed - counter", "C.51.24": "Event main cover closed - timestamp", "C.51.25": "Event log-book 1 erased - counter", "C.51.26": "Event log-book 1 erased - timestamp", "C.51.27": "Event fraud start - counter", "C.51.28": "Event fraud start - timestamp", "C.51.29": "Event fraud stop - counter", "C.51.30": "Event fraud stop - timestamp", "0.9.1": "Current time (hh:mm:ss)", "0.9.2": "Date (YY.MM.DD or DD.MM.YY)", "0.9.4": "Date and Time (YYMMDDhhmmss)", "0.8.0": "Demand period [min]", "0.8.4": "Load profile period [min] (option)", "0.0.0": "Device address 1", "0.0.1": "Device address 2", "0.1.0": "MD reset counter", "0.1.2": "MD reset timestamp", "0.2.0": "Firmware version", "0.2.2": "Tariff program ID", "C.1.0": "Meter serial number", "C.1.2": "Parameters file code", "C.1.4": "Parameters check sum", "C.1.5": "Firmware built date", "C.1.6": "Firmware check sum", "C.6.0": "Power down time counter", "C.6.1": "Battery remaining capacity", "F.F.0": "Fatal error meter status", "C.87.0": "Active tariff", "0.2.1": "Parameters scheme ID", "C.60.9": "Fraud flag", "0.3.0": "Active energy meter constant", "0.4.2": "Current transformer ratio", "0.4.3": "Voltage transformer ratio", "0.0.9": "Identification number", "21.25": "Instantaneous value of active power phase L1", "41.25": "Instantaneous value of active power phase L2", "61.25": "Instantaneous value of active power phase L3", "1.25": "Instantaneous value of total power", "23.25": "Instantaneous value of reactive power phase L1", "43.25": "Instantaneous value of reactive power phase L2", "63.25": "Instantaneous value of reactive power phase L3", "3.25": "Instantaneous value of reactive power phase total", "29.25": "Instantaneous value of apparent power phase L1", "49.25": "Instantaneous value of apparent power phase L2", "69.25": "Instantaneous value of apparent power phase L3", "9.25": "Instantaneous value of total apparent power", "31.25": "Instantaneous value of current phase L1", "51.25": "Instantaneous value of current phase L2", "71.25": "Instantaneous value of current phase L3", "32.25": "Instantaneous value of voltage phase L1", "52.25": "Instantaneous value of voltage phase L2", "72.25": "Instantaneous value of voltage phase L3", "33.25": "Instantaneous value of power factor phase L1", "53.25": "Instantaneous value of power factor phase L2", "73.25": "Instantaneous value of power factor phase L3", "13.25": "Instantaneous value of average power factor", "14.25": "Instantaneous value of frequency", "C.3": "State of the in/out control signals", "C.4": "State of the internal control signals", "C.5": "Internal operating conditions", "C.7.0": "Total number of phase failures", "C.7.1": "Number of phase failures phase 1", "C.7.2": "Number of phase failures phase 2", "C.7.3": "Number of phase failures phase 3", "C.51.4": "DCF-77 last synchronization", "C.52.0": "Phase information", "C.86.0": "Installation check" } obis_codes_short = { "1.8.0": "Positive active energy total", "1.8.1": "Positive active energy in tariff T1", "1.8.2": "Positive active energy in tariff T2", "1.8.3": "Positive active energy in tariff T3", "1.8.4": "Positive active energy in tariff T4", "2.8.0": "Negative active energy total", "2.8.1": "Negative active energy in tariff T1", "2.8.2": "Negative active energy in tariff T2", "2.8.3": "Negative active energy in tariff T3", "2.8.4": "Negative active energy in tariff T4", "15.8.0": "Absolute active energy total", "15.8.1": "Absolute active energy in tariff T1", "15.8.2": "Absolute active energy in tariff T2", "15.8.3": "Absolute active energy in tariff T3", "15.8.4": "Absolute active energy in tariff T4", "16.8.0": "Sum active energy without reverse blockade total", "16.8.1": "Sum active energy without reverse blockade in tariff T1", "16.8.2": "Sum active energy without reverse blockade in tariff T2", "16.8.3": "Sum active energy without reverse blockade in tariff T3", "16.8.4": "Sum active energy without reverse blockade in tariff T4", "3.8.0": "Positive reactive energy total", "3.8.1": "Positive reactive energy in tariff T1", "3.8.2": "Positive reactive energy in tariff T2", "3.8.3": "Positive reactive energy in tariff T3", "3.8.4": "Positive reactive energy in tariff T4", "4.8.0": "Negative reactive energy total", "4.8.1": "Negative reactive energy in tariff T1", "4.8.2": "Negative reactive energy in tariff T2", "4.8.3": "Negative reactive energy in tariff T3", "4.8.4": "Negative reactive energy in tariff T4", "5.8.0": "Imported inductive reactive energy in 1-st quadrant total", "5.8.1": "Imported inductive reactive energy in 1-st quadrant in tariff T1", "5.8.2": "Imported inductive reactive energy in 1-st quadrant in tariff T2", "5.8.3": "Imported inductive reactive energy in 1-st quadrant in tariff T3", "5.8.4": "Imported inductive reactive energy in 1-st quadrant in tariff T4", "6.8.0": "Imported capacitive reactive energy in 2-nd quadrant total", "6.8.1": "Imported capacitive reactive energy in 2-nd quadr. in tariff T1", "6.8.2": "Imported capacitive reactive energy in 2-nd quadr. in tariff T2", "6.8.3": "Imported capacitive reactive energy in 2-nd quadr. in tariff T3", "6.8.4": "Imported capacitive reactive energy in 2-nd quadr. in tariff T4", "7.8.0": "Exported inductive reactive energy in 3-rd quadrant total", "7.8.1": "Exported inductive reactive energy in 3-rd quadrant in tariff T1", "7.8.2": "Exported inductive reactive energy in 3-rd quadrant in tariff T2", "7.8.3": "Exported inductive reactive energy in 3-rd quadrant in tariff T3", "7.8.4": "Exported inductive reactive energy in 3-rd quadrant in tariff T4", "8.8.0": "Exported capacitive reactive energy in 4-th quadrant total", "8.8.1": "Exported capacitive reactive energy in 4-th quadr. in tariff T1", "8.8.2": "Exported capacitive reactive energy in 4-th quadr. in tariff T2", "8.8.3": "Exported capacitive reactive energy in 4-th quadr. in tariff T3", "8.8.4": "Exported capacitive reactive energy in 4-th quadr. in tariff T4", "9.8.0": "Apparent energy total", "9.8.1": "Apparent energy in tariff T1", "9.8.2": "Apparent energy in tariff T2", "9.8.3": "Apparent energy in tariff T3", "9.8.4": "Apparent energy in tariff T4", "21.8.0": "Positive active energy in phase L1 total", "41.8.0": "Positive active energy in phase L2 total", "61.8.0": "Positive active energy in phase L3 total", "22.8.0": "Negative active energy in phase L1 total", "42.8.0": "Negative active energy in phase L2 total", "62.8.0": "Negative active energy in phase L3 total", "35.8.0": "Absolute active energy in phase L1 total", "55.8.0": "Absolute active energy in phase L2 total", "75.8.0": "Absolute active energy in phase L3 total", "1.6.0": "Positive active maximum demand total", "1.6.1": "Positive active maximum demand in tariff T1", "1.6.2": "Positive active maximum demand in tariff T2", "1.6.3": "Positive active maximum demand in tariff T3", "1.6.4": "Positive active maximum demand in tariff T4", "2.6.0": "Negative active maximum demand total", "2.6.1": "Negative active maximum demand in tariff T1", "2.6.2": "Negative active maximum demand in tariff T2", "2.6.3": "Negative active maximum demand in tariff T3", "2.6.4": "Negative active maximum demand in tariff T4", "15.6.0": "Absolute active maximum demand total", "15.6.1": "Absolute active maximum demand in tariff T1", "15.6.2": "Absolute active maximum demand in tariff T2", "15.6.3": "Absolute active maximum demand in tariff T3", "15.6.4": "Absolute active maximum demand in tariff T4", "3.6.0": "Positive reactive maximum demand total", "4.6.0": "Negative reactive maximum demand total", "5.6.0": "Reactive maximum demand in Q1 total", "6.6.0": "Reactive maximum demand in Q2 total", "7.6.0": "Reactive maximum demand in Q3 total", "8.6.0": "Reactive maximum demand in Q4 total", "9.6.0": "Apparent maximum demand total", "1.2.0": "Positive active cumulative maximum demand total", "1.2.1": "Positive active cumulative maximum demand in tariff T1", "1.2.2": "Positive active cumulative maximum demand in tariff T2", "1.2.3": "Positive active cumulative maximum demand in tariff T3", "1.2.4": "Positive active cumulative maximum demand in tariff T4", "2.2.0": "Negative active cumulative maximum demand total", "2.2.1": "Negative active cumulative maximum demand in tariff T1", "2.2.2": "Negative active cumulative maximum demand in tariff T2", "2.2.3": "Negative active cumulative maximum demand in tariff T3", "2.2.4": "Negative active cumulative maximum demand in tariff T4", "15.2.0": "Absolute active cumulative maximum demand total", "15.2.1": "Absolute active cumulative maximum demand in tariff T1", "15.2.2": "Absolute active cumulative maximum demand in tariff T2", "15.2.3": "Absolute active cumulative maximum demand in tariff T3", "15.2.4": "Absolute active cumulative maximum demand in tariff T4", "3.2.0": "Positive reactive cumulative maximum demand total", "4.2.0": "Negative reactive cumulative maximum demand total", "5.2.0": "Reactive cumulative maximum demand in Q1 total", "6.2.0": "Reactive cumulative maximum demand in Q2 total", "7.2.0": "Reactive cumulative maximum demand in Q3 total", "8.2.0": "Reactive cumulative maximum demand in Q4 total", "9.2.0": "Apparent cumulative maximum demand total", "1.4.0": "Positive active demand in current demand period", "2.4.0": "Negative active demand in current demand period", "15.4.0": "Absolute active demand in current demand period", "3.4.0": "Positive reactive demand in current demand period", "4.4.0": "Negative reactive demand in current demand period", "5.4.0": "Reactive demand in current demand period in Q1", "6.4.0": "Reactive demand in current demand period in Q2", "7.4.0": "Reactive demand in current demand period in Q3", "8.4.0": "Reactive demand in current demand period in Q4", "9.4.0": "Apparent demand in current demand period", "1.5.0": "Positive active demand in the last completed demand period", "2.5.0": "Negative active demand in the last completed demand period", "15.5.0": "Absolute active demand in the last completed demand period", "3.5.0": "Positive reactive demand in the last completed demand period", "4.5.0": "Negative reactive demand in the last completed demand period", "5.5.0": "Reactive demand in the last completed demand period in Q1", "6.5.0": "Reactive demand in the last completed demand period in Q2", "7.5.0": "Reactive demand in the last completed demand period in Q3", "8.5.0": "Reactive demand in the last completed demand period in Q4", "9.5.0": "Apparent demand in the last completed demand period", "1.7.0": "Positive active instantaneous power", "21.7.0": "Positive active instantaneous power in phase L1", "41.7.0": "Positive active instantaneous power in phase L2", "61.7.0": "Positive active instantaneous power in phase L3", "2.7.0": "Negative active instantaneous power", "22.7.0": "Negative active instantaneous power in phase L1", "42.7.0": "Negative active instantaneous power in phase L2", "62.7.0": "Negative active instantaneous power in phase L3", "15.7.0": "Absolute active instantaneous power", "35.7.0": "Absolute active instantaneous power in phase L1", "55.7.0": "Absolute active instantaneous power in phase L2", "75.7.0": "Absolute active instantaneous power in phase L3", "16.7.0": "Sum active instantaneous power", "36.7.0": "Sum active instantaneous power in phase L1", "56.7.0": "Sum active instantaneous power in phase L2", "76.7.0": "Sum active instantaneous power in phase L3", "3.7.0": "Positive reactive instantaneous power", "23.7.0": "Positive reactive instantaneous power in phase L1", "43.7.0": "Positive reactive instantaneous power in phase L2", "63.7.0": "Positive reactive instantaneous power in phase L3", "4.7.0": "Negative reactive instantaneous power", "24.7.0": "Negative reactive instantaneous power in phase L1", "44.7.0": "Negative reactive instantaneous power in phase L2", "64.7.0": "Negative reactive instantaneous power in phase L3", "9.7.0": "Apparent instantaneous power", "29.7.0": "Apparent instantaneous power in phase L1", "49.7.0": "Apparent instantaneous power in phase L2", "69.7.0": "Apparent instantaneous power in phase L3", "11.7.0": "Instantaneous current", "31.7.0": "Instantaneous current in phase L1", "51.7.0": "Instantaneous current in phase L2", "71.7.0": "Instantaneous current in phase L3", "91.7.0": "Instantaneous current in neutral", "11.6.0": "Maximum current", "31.6.0": "Maximum current in phase L1", "51.6.0": "Maximum current in phase L2", "71.6.0": "Maximum current in phase L3", "91.6.0": "Maximum current in neutral", "12.7.0": "Instantaneous voltage", "32.7.0": "Instantaneous voltage in phase L1", "52.7.0": "Instantaneous voltage in phase L2", "72.7.0": "Instantaneous voltage in phase L3", "13.7.0": "Instantaneous power factor", "33.7.0": "Instantaneous power factor in phase L1", "53.7.0": "Instantaneous power factor in phase L2", "73.7.0": "Instantaneous power factor in phase L3", "14.7.0": "Frequency", "C.53.1": "Tamper 1 energy register", "C.53.2": "Tamper 2 energy register", "C.53.3": "Tamper 3 energy register", "C.53.4": "Tamper 4 energy register", "C.53.11": "Tamper 5 energy register", "C.53.5": "Tamper 1 time counter register", "C.53.6": "Tamper 2 time counter register", "C.53.7": "Tamper 3 time counter register", "C.53.9": "Tamper 4 time counter register", "C.53.10": "Tamper 5 time counter register", "C.2.0": "Event parameters change - counter", "C.2.1": "Event parameters change - timestamp", "C.51.1": "Event terminal cover opened - counter", "C.51.2": "Event terminal cover opened - timestamp", "C.51.3": "Event main cover opened - counter", "C.51.5": "Event magnetic field detection start - counter", "C.51.6": "Event magnetic field detection start - timestamp", "C.51.7": "Event reverse power flow - counter", "C.51.8": "Event reverse power flow - timestamp", "C.7.10": "Event power down - timestamp", "C.51.13": "Event power up - counter", "C.51.14": "Event power up – timestamp", "C.51.15": "Event RTC (Real Time Clock) set - counter", "C.51.16": "Event RTC (Real Time Clock) set - timestamp", "C.51.21": "Event terminal cover closed - counter", "C.51.22": "Event terminal cover closed - timestamp", "C.51.23": "Event main cover closed - counter", "C.51.24": "Event main cover closed - timestamp", "C.51.25": "Event log-book 1 erased - counter", "C.51.26": "Event log-book 1 erased - timestamp", "C.51.27": "Event fraud start - counter", "C.51.28": "Event fraud start - timestamp", "C.51.29": "Event fraud stop - counter", "C.51.30": "Event fraud stop - timestamp", "0.9.1": "Current time", "0.9.2": "Date", "0.9.4": "Date and Time", "0.8.0": "Demand period", "0.8.4": "Load profile period", "0.0.0": "Device address 1", "0.0.1": "Device address 2", "0.1.0": "MD reset counter", "0.1.2": "MD reset timestamp", "0.2.0": "Firmware version", "0.2.2": "Tariff program ID", "C.1.0": "Meter serial number", "C.1.2": "Parameters file code", "C.1.4": "Parameters check sum", "C.1.5": "Firmware built date", "C.1.6": "Firmware check sum", "C.6.0": "Power down time counter", "C.6.1": "Battery remaining capacity", "F.F.0": "Fatal error meter status", "C.87.0": "Active tariff", "0.2.1": "Parameters scheme ID", "C.60.9": "Fraud flag", "0.3.0": "Active energy meter constant", "0.4.2": "Current transformer ratio", "0.4.3": "Voltage transformer ratio", "0.0.9": "Identification number", "21.25": "Instantaneous value of active power phase L1", "41.25": "Instantaneous value of active power phase L2", "61.25": "Instantaneous value of active power phase L3", "1.25": "Instantaneous value of total power", "23.25": "Instantaneous value of reactive power phase L1", "43.25": "Instantaneous value of reactive power phase L2", "63.25": "Instantaneous value of reactive power phase L3", "3.25": "Instantaneous value of reactive power phase total", "29.25": "Instantaneous value of apparent power phase L1", "49.25": "Instantaneous value of apparent power phase L2", "69.25": "Instantaneous value of apparent power phase L3", "9.25": "Instantaneous value of total apparent power", "31.25": "Instantaneous value of current phase L1", "51.25": "Instantaneous value of current phase L2", "71.25": "Instantaneous value of current phase L3", "32.25": "Instantaneous value of voltage phase L1", "52.25": "Instantaneous value of voltage phase L2", "72.25": "Instantaneous value of voltage phase L3", "33.25": "Instantaneous value of power factor phase L1", "53.25": "Instantaneous value of power factor phase L2", "73.25": "Instantaneous value of power factor phase L3", "13.25": "Instantaneous value of average power factor", "14.25": "Instantaneous value of frequency", "C.3": "State of the in/out control signals", "C.4": "State of the internal control signals", "C.5": "Internal operating conditions", "C.7.0": "Total number of phase failures", "C.7.1": "Number of phase failures phase 1", "C.7.2": "Number of phase failures phase 2", "C.7.3": "Number of phase failures phase 3", "C.51.4": "DCF-77 last synchronization", "C.52.0": "Phase information", "C.86.0": "Installation check" } table_obis_codes = { "1.5.0": "Positive active demand", "2.5.0": "Negative active demand", "5.5.0": "Reactive demand in Q1", "6.5.0": "Reactive demand in Q2", "7.5.0": "Reactive demand in Q3", "8.5.0": "Reactive demand in Q4", "0.9.1": "Current time", "0.9.2": "Date", "0.0.0": "Device address 1", "0.0.9": "Identification number", "21.25": "Active power phase L1", "41.25": "Active power phase L2", "61.25": "Active power phase L3", "1.25": "Total power", "23.25": "Reactive power phase L1", "43.25": "Reactive power phase L2", "63.25": "Reactive power phase L3", "3.25": "Reactive power phase total", "32.25": "Voltage phase L1", "52.25": "Voltage phase L2", "72.25": "Voltage phase L3", "33.25": "Power factor phase L1", "53.25": "Power factor phase L2", "73.25": "Power factor phase L3", "13.25": "Average power factor", "14.25": "Frequency", "C.3": "State of the in/out control signals", # Not implemented "C.4": "State of the internal control signals", # Not implemented "C.5": "Internal operating conditions", # Not implemented "C.7.0": "Total number of phase failures", "C.7.1": "Phase failures phase 1", "C.7.2": "Phase failures phase 2", "C.7.3": "Phase failures phase 3", "C.51.4": "DCF-77 last synchronization", # Not implemented "C.52.0": "Phase information", # Not implemented "C.86.0": "Installation check" # Not implemented } zabbix_obis_codes = { "1.5.0": "positiveActiveDemand", "2.5.0": "negativeActiveDemand", "5.5.0": "reactiveDemandQ1", "6.5.0": "reactiveDemandQ2", "7.5.0": "reactiveDemandQ3", "8.5.0": "reactiveDemandQ4", "bill-1.5.0": "Z-1-1.1.29.0", "bill-2.5.0": "Z-1-1.2.29.0", "bill-5.5.0": "Z-1-1.5.29.0", "bill-6.5.0": "Z-1-1.6.29.0", "bill-7.5.0": "Z-1-1.7.29.0", "bill-8.5.0": "Z-1-1.8.29.0", "bill-1.8.0": "Z-1-1.1.8.0-bill", "bill-2.8.0": "Z-1-1.2.8.0-bill", "bill-5.8.0": "Z-1-1.5.8.0-bill", "bill-6.8.0": "Z-1-1.6.8.0-bill", "bill-7.8.0": "Z-1-1.7.8.0-bill", "bill-8.8.0": "Z-1-1.8.8.0-bill", "bill-raw-1.5.0": "Z-1-1.1.29.0-raw", "bill-raw-2.5.0": "Z-1-1.2.29.0-raw", "bill-raw-5.5.0": "Z-1-1.5.29.0-raw", "bill-raw-6.5.0": "Z-1-1.6.29.0-raw", "bill-raw-7.5.0": "Z-1-1.7.29.0-raw", "bill-raw-8.5.0": "Z-1-1.8.29.0-raw", "bill-raw-1.8.0": "Z-1-1.1.8.0-bill-raw", "bill-raw-2.8.0": "Z-1-1.2.8.0-bill-raw", "bill-raw-5.8.0": "Z-1-1.5.8.0-bill-raw", "bill-raw-6.8.0": "Z-1-1.6.8.0-bill-raw", "bill-raw-7.8.0": "Z-1-1.7.8.0-bill-raw", "bill-raw-8.8.0": "Z-1-1.8.8.0-bill-raw", "bill-Log": "Z-1-1.Log", # "bill-1.5.0": "positiveActiveDemandBill", # "bill-2.5.0": "negativeActiveDemandBill", # "bill-5.5.0": "reactiveDemandQ1Bill", # "bill-6.5.0": "reactiveDemandQ2Bill", # "bill-7.5.0": "reactiveDemandQ3Bill", # "bill-8.5.0": "reactiveDemandQ4Bill", "0.9.1": "Current time", "0.9.2": "Date", "0.0.0": "Device address 1", "0.0.9": "Identification number", "21.25": "activePowerPhaseL1", "41.25": "activePowerPhaseL2", "61.25": "activePowerPhaseL3", "1.25": "totalPower", "23.25": "reactivePowerPhaseL1", "43.25": "reactivePowerPhaseL2", "63.25": "reactivePowerPhaseL3", "3.25": "reactivePowerTotal", "32.25": "voltagePhaseL1", "52.25": "voltagePhaseL2", "72.25": "voltagePhaseL3", "33.25": "powerFactorPhaseL1", "53.25": "powerFactorPhaseL2", "73.25": "powerFactorPhaseL3", "13.25": "powerFactorAvg", "14.25": "Frequency", "C.3": "State of the in/out control signals", # Not implemented "C.4": "State of the internal control signals", # Not implemented "C.5": "Internal operating conditions", # Not implemented "C.7.0": "phaseFailuresTotal", "C.7.1": "phaseFailuresPhase1", "C.7.2": "phaseFailuresPhase2", "C.7.3": "phaseFailuresPhase3", "C.51.4": "DCF-77 last synchronization", # Not implemented "C.52.0": "Phase information", # Not implemented "C.86.0": "Installation check", # Not implemented "29.25": "apparentPowerPhaseL1", "49.25": "apparentPowerPhaseL2", "69.25": "apparentPowerPhaseL3", "9.25": "apparentPowerTotal", "31.25": "currentPhaseL1", "51.25": "currentPhaseL2", "71.25": "currentPhaseL3", "cos_phi": "CosinusPhi", "tan_phi": "TangensPhi", "1.2.1": "Z-1-1.1.2.1", "1.2.2": "Z-1-1.1.2.2", "1.6.1": "Z-1-1.1.6.1", "1.6.2": "Z-1-1.1.6.2", "1.8.0": "Z-1-1.1.8.0", "1.8.1": "Z-1-1.1.8.1", "1.8.2": "Z-1-1.1.8.2", "2.2.1": "Z-1-1.2.2.1", "2.2.2": "Z-1-1.2.2.2", "2.6.1": "Z-1-1.2.6.1", "2.6.2": "Z-1-1.2.6.2", "2.8.0": "Z-1-1.2.8.0", "2.8.1": "Z-1-1.2.8.1", "2.8.2": "Z-1-1.2.8.2", "5.8.0": "Z-1-1.5.8.0", "5.8.1": "Z-1-1.5.8.1", "5.8.2": "Z-1-1.5.8.2", "6.8.0": "Z-1-1.6.8.0", "6.8.1": "Z-1-1.6.8.1", "6.8.2": "Z-1-1.6.8.2", "7.8.0": "Z-1-1.7.8.0", "7.8.1": "Z-1-1.7.8.1", "7.8.2": "Z-1-1.7.8.2", "8.8.0": "Z-1-1.8.8.0", "8.8.1": "Z-1-1.8.8.1", "8.8.2": "Z-1-1.8.8.2", "raw-1.2.1": "Z-1-1.1.2.1-raw", "raw-1.2.2": "Z-1-1.1.2.2-raw", "raw-1.6.1": "Z-1-1.1.6.1-raw", "raw-1.6.2": "Z-1-1.1.6.2-raw", "raw-1.8.0": "Z-1-1.1.8.0-raw", "raw-1.8.1": "Z-1-1.1.8.1-raw", "raw-1.8.2": "Z-1-1.1.8.2-raw", "raw-2.2.1": "Z-1-1.2.2.1-raw", "raw-2.2.2": "Z-1-1.2.2.2-raw", "raw-2.6.1": "Z-1-1.2.6.1-raw", "raw-2.6.2": "Z-1-1.2.6.2-raw", "raw-2.8.0": "Z-1-1.2.8.0-raw", "raw-2.8.1": "Z-1-1.2.8.1-raw", "raw-2.8.2": "Z-1-1.2.8.2-raw", "raw-5.8.0": "Z-1-1.5.8.0-raw", "raw-5.8.1": "Z-1-1.5.8.1-raw", "raw-5.8.2": "Z-1-1.5.8.2-raw", "raw-6.8.0": "Z-1-1.6.8.0-raw", "raw-6.8.1": "Z-1-1.6.8.1-raw", "raw-6.8.2": "Z-1-1.6.8.2-raw", "raw-7.8.0": "Z-1-1.7.8.0-raw", "raw-7.8.1": "Z-1-1.7.8.1-raw", "raw-7.8.2": "Z-1-1.7.8.2-raw", "raw-8.8.0": "Z-1-1.8.8.0-raw", "raw-8.8.1": "Z-1-1.8.8.1-raw", "raw-8.8.2": "Z-1-1.8.8.2-raw", "0.1.2": "Z-1-1.0.1.2", "1.6.1-time": "Z-1-1.1.6.1-time", "1.6.2-time": "Z-1-1.1.6.2-time", "2.6.1-time": "Z-1-1.2.6.1-time", "2.6.2-time": "Z-1-1.2.6.2-time", "P.200_Bit15": "Z-1-1.Bit15", "P.200_Bit14": "Z-1-1.Bit14", "P.200_Bit13": "Z-1-1.Bit13", "P.200_Bit12": "Z-1-1.Bit12", "P.200_Bit11": "Z-1-1.Bit11", "P.200_Bit10": "Z-1-1.Bit10", "P.200_Bit9": "Z-1-1.Bit9", "P.200_Bit8": "Z-1-1.Bit8", "P.200_Bit7": "Z-1-1.Bit7", "P.200_Bit6": "Z-1-1.Bit6", "P.200_Bit5": "Z-1-1.Bit5", "P.200_Bit4": "Z-1-1.Bit4", "P.200_Bit3": "Z-1-1.Bit3", "P.200_Bit2": "Z-1-1.Bit2", "P.200_Bit1": "Z-1-1.Bit1", "P.200_Bit0": "Z-1-1.Bit0", "2000": "Z-1-1.2000", "23A6": "Z-1-1.23A6", "234C": "Z-1-1.234C", "334C": "Z-1-1.334C", "234D": "Z-1-1.234D", "334D": "Z-1-1.334D", "234E": "Z-1-1.234E", "334E": "Z-1-1.334E", "0.9.1-value": "Z-1-1.0.9.1-value", "0.9.2-value": "Z-1-1.0.9.2-value", "0.9.1-trigger": "Z-1-1.0.9.1-trigger", "0.9.2-trigger": "Z-1-1.0.9.2-trigger" } transform_set = { "positiveActiveDemand": "totalFactor", "negativeActiveDemand": "totalFactor", "reactiveDemandQ1": "totalFactor", "reactiveDemandQ2": "totalFactor", "reactiveDemandQ3": "totalFactor", "reactiveDemandQ4": "totalFactor", # "positiveActiveDemandBill": "totalFactor", # "negativeActiveDemandBill": "totalFactor", # "reactiveDemandQ1Bill": "totalFactor", # "reactiveDemandQ2Bill": "totalFactor", # "reactiveDemandQ3Bill": "totalFactor", # "reactiveDemandQ4Bill": "totalFactor", "Z-1-1.1.29.0": "totalFactor", "Z-1-1.2.29.0": "totalFactor", "Z-1-1.5.29.0": "totalFactor", "Z-1-1.6.29.0": "totalFactor", "Z-1-1.7.29.0": "totalFactor", "Z-1-1.8.29.0": "totalFactor", "Z-1-1.1.29.0-raw": "None", "Z-1-1.2.29.0-raw": "None", "Z-1-1.5.29.0-raw": "None", "Z-1-1.6.29.0-raw": "None", "Z-1-1.7.29.0-raw": "None", "Z-1-1.8.29.0-raw": "None", "Z-1-1.Log": "None", "Current time": "None", "Date": "None", "Device address 1": "None", "Identification number": "", "activePowerPhaseL1": "totalFactor", "activePowerPhaseL2": "totalFactor", "activePowerPhaseL3": "totalFactor", "totalPower": "totalFactor", "reactivePowerPhaseL1": "totalFactor", "reactivePowerPhaseL2": "totalFactor", "reactivePowerPhaseL3": "totalFactor", "reactivePowerTotal": "totalFactor", "voltagePhaseL1": "voltageRatio", "voltagePhaseL2": "voltageRatio", "voltagePhaseL3": "voltageRatio", "powerFactorPhaseL1": "None", "powerFactorPhaseL2": "None", "powerFactorPhaseL3": "None", "powerFactorAvg": "None", "Frequency": "None", "phaseFailuresTotal": "None", "phaseFailuresPhase1": "None", "phaseFailuresPhase2": "None", "phaseFailuresPhase3": "None", "apparentPowerPhaseL1": "totalFactor", "apparentPowerPhaseL2": "totalFactor", "apparentPowerPhaseL3": "totalFactor", "apparentPowerTotal": "totalFactor", "currentPhaseL1": "currentRatio", "currentPhaseL2": "currentRatio", "currentPhaseL3": "currentRatio", "CosinusPhi": "None", "TangensPhi": "None", "Z-1-1.1.2.1": "totalFactor", "Z-1-1.1.2.2": "totalFactor", "Z-1-1.1.6.1": "totalFactor", "Z-1-1.1.6.2": "totalFactor", "Z-1-1.1.8.0": "totalFactor", "Z-1-1.1.8.1": "totalFactor", "Z-1-1.1.8.2": "totalFactor", "Z-1-1.2.2.1": "totalFactor", "Z-1-1.2.2.2": "totalFactor", "Z-1-1.2.6.1": "totalFactor", "Z-1-1.2.6.2": "totalFactor", "Z-1-1.2.8.0": "totalFactor", "Z-1-1.2.8.1": "totalFactor", "Z-1-1.2.8.2": "totalFactor", "Z-1-1.5.8.0": "totalFactor", "Z-1-1.5.8.1": "totalFactor", "Z-1-1.5.8.2": "totalFactor", "Z-1-1.6.8.0": "totalFactor", "Z-1-1.6.8.1": "totalFactor", "Z-1-1.6.8.2": "totalFactor", "Z-1-1.7.8.0": "totalFactor", "Z-1-1.7.8.1": "totalFactor", "Z-1-1.7.8.2": "totalFactor", "Z-1-1.8.8.0": "totalFactor", "Z-1-1.8.8.1": "totalFactor", "Z-1-1.8.8.2": "totalFactor", "Z-1-1.1.2.1-raw": "None", "Z-1-1.1.2.2-raw": "None", "Z-1-1.1.6.1-raw": "None", "Z-1-1.1.6.2-raw": "None", "Z-1-1.1.8.0-raw": "None", "Z-1-1.1.8.1-raw": "None", "Z-1-1.1.8.2-raw": "None", "Z-1-1.2.2.1-raw": "None", "Z-1-1.2.2.2-raw": "None", "Z-1-1.2.6.1-raw": "None", "Z-1-1.2.6.2-raw": "None", "Z-1-1.2.8.0-raw": "None", "Z-1-1.2.8.1-raw": "None", "Z-1-1.2.8.2-raw": "None", "Z-1-1.5.8.0-raw": "None", "Z-1-1.5.8.1-raw": "None", "Z-1-1.5.8.2-raw": "None", "Z-1-1.6.8.0-raw": "None", "Z-1-1.6.8.1-raw": "None", "Z-1-1.6.8.2-raw": "None", "Z-1-1.7.8.0-raw": "None", "Z-1-1.7.8.1-raw": "None", "Z-1-1.7.8.2-raw": "None", "Z-1-1.8.8.0-raw": "None", "Z-1-1.8.8.1-raw": "None", "Z-1-1.8.8.2-raw": "None", "Z-1-1.0.1.2": "None", "Z-1-1.1.6.1-time": "None", "Z-1-1.1.6.2-time": "None", "Z-1-1.2.6.1-time": "None", "Z-1-1.2.6.2-time": "None", "Z-1-1.Bit15": "None", "Z-1-1.Bit14": "None", "Z-1-1.Bit13": "None", "Z-1-1.Bit12": "None", "Z-1-1.Bit11": "None", "Z-1-1.Bit10": "None", "Z-1-1.Bit9": "None", "Z-1-1.Bit8": "None", "Z-1-1.Bit7": "None", "Z-1-1.Bit6": "None", "Z-1-1.Bit5": "None", "Z-1-1.Bit4": "None", "Z-1-1.Bit3": "None", "Z-1-1.Bit2": "None", "Z-1-1.Bit1": "None", "Z-1-1.Bit0": "None", "Z-1-1.2000": "None", "Z-1-1.23A6": "None", "Z-1-1.234C": "None", "Z-1-1.334C": "None", "Z-1-1.234D": "None", "Z-1-1.334D": "None", "Z-1-1.234E": "None", "Z-1-1.334E": "None", "Z-1-1.0.9.1-value": "None", "Z-1-1.0.9.2-value": "None", "Z-1-1.0.9.1-trigger": "None", "Z-1-1.0.9.2-trigger": "None", "Z-1-1.1.8.0-bill": "totalFactor", "Z-1-1.2.8.0-bill": "totalFactor", "Z-1-1.5.8.0-bill": "totalFactor", "Z-1-1.6.8.0-bill": "totalFactor", "Z-1-1.7.8.0-bill": "totalFactor", "Z-1-1.8.8.0-bill": "totalFactor", "Z-1-1.1.8.0-bill-raw": "None", "Z-1-1.2.8.0-bill-raw": "None", "Z-1-1.5.8.0-bill-raw": "None", "Z-1-1.6.8.0-bill-raw": "None", "Z-1-1.7.8.0-bill-raw": "None", "Z-1-1.8.8.0-bill-raw": "None" }

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null

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8994253b00d6e5d27e65563ed1f3b8a7b6074e7a

56

py

Python

hms_kivy/rfid/__init__.py

NottingHack/hms-kivy

38f0047517be15099f2f34b73f6aa43902de7c85

[ "MIT" ]

1

2021-12-17T04:24:22.000Z

hms_kivy/rfid/__init__.py

NottingHack/hms-kivy

38f0047517be15099f2f34b73f6aa43902de7c85

[ "MIT" ]

null

hms_kivy/rfid/__init__.py

NottingHack/hms-kivy

38f0047517be15099f2f34b73f6aa43902de7c85

[ "MIT" ]

null

""" RFID =============== """ # from .rfid import RFID

7

24

0.392857

5

56

4.4

0.6

0

0.196429

56

7

25

8

0.488889

0.785714

0

null

0

null

0

null

0

null

1

null

true

0

null

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0

null

0

1

0

1

0

null

0

1

0

4

899aac445ad00336b2aa58754d79698b04eca293

426

py

Python

diff/__init__.py

treebohotels/diff-and-patch

497c078ea5c1bc6caa361f14eb4c6206d84a5d24

[ "BSD-2-Clause-FreeBSD" ]

1

2020-01-01T15:34:30.000Z

diff/__init__.py

treebohotels/diff-and-patch

497c078ea5c1bc6caa361f14eb4c6206d84a5d24

[ "BSD-2-Clause-FreeBSD" ]

null

diff/__init__.py

treebohotels/diff-and-patch

497c078ea5c1bc6caa361f14eb4c6206d84a5d24

[ "BSD-2-Clause-FreeBSD" ]

null

# -*- coding: utf-8 -*- from diff.diff_strategy.base_diff_item import BaseDiffItem from diff.diff_strategy.integer_diff import IntegerDiff from diff.diff_strategy.string_diff import StringDiff from diff.diff_strategy.base_diff_strategy import BaseDiffStrategy from diff.differ import Differ from diff.patcher import Patcher from diff.patch_behaviours import BasePatchBehaviour from diff.patch_behaviours import BaseBehaviour

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426

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1

0

1

0

1

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4

89ab07a525358131a9c0e7f84a7b69337a2f2cca

104

py

Python

models/continent.py

morival/W04_project_TBL

a116bbda72bf61c55752fe1f4fdce2685ae0d024

[ "PostgreSQL", "Unlicense", "MIT" ]

null

models/continent.py

morival/W04_project_TBL

a116bbda72bf61c55752fe1f4fdce2685ae0d024

[ "PostgreSQL", "Unlicense", "MIT" ]

null

models/continent.py

morival/W04_project_TBL

a116bbda72bf61c55752fe1f4fdce2685ae0d024

[ "PostgreSQL", "Unlicense", "MIT" ]

null

class Continent: def __init__(self, name, id = None): self.name = name self.id = id

20.8

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14

104

3.928571

0.571429

0.290909

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0.326923

104

5

41

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1

0.25

false

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0

null

1

0

1

0

null

0

1

0

4

89b3b953ae664601d77ea8396659d68d4f2330e8

52

py

Python

probez/file_handling/file_handling_exceptions.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

null

probez/file_handling/file_handling_exceptions.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

3

2021-08-09T21:51:41.000Z

2021-08-09T21:51:45.000Z

probez/file_handling/file_handling_exceptions.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

3

2021-10-16T14:07:59.000Z

2021-10-16T17:09:03.000Z

class InconsistentNChanError(Exception): pass

17.333333

41

0.769231

4

52

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1

0

0.173077

52

2

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true

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1

0

null

0

1

0

null

0

1

0

4

984d7d175e90b6c00cda2c880cd9eb06bf678508

370

py

Python

awards/serializers.py

kilonzijnr/awards

d2ec991de8f161b88ae85f6520c1702ead21291e

[ "MIT" ]

null

awards/serializers.py

kilonzijnr/awards

d2ec991de8f161b88ae85f6520c1702ead21291e

[ "MIT" ]

null

awards/serializers.py

kilonzijnr/awards

d2ec991de8f161b88ae85f6520c1702ead21291e

[ "MIT" ]

null

from rest_framework import serializers from .models import Profile,Project class ProfileSerializer(serializers.ModelSerializer): class Meta: model = Profile fields = ('name','bio') class ProjectSerializer(serializers.ModelSerializer): class Meta: model = Project fields=('sitename','link','content', 'design')

24.666667

54

0.667568

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370

7.235294

0.617647

0.211382

0.252033

0.284553

0.325203

0

0.235135

370

15

54

24.666667

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0.6

0

null

1

0

null

0

1

0

4

984dd04ab5d04ac8ea4c9837a4fad414a731b448

98

py

Python

django_adminform/apps.py

humanscape-covy/django-jsonform

ddf2bf40022855d4969988e13a0a3db7abb8a365

[ "BSD-3-Clause" ]

null

django_adminform/apps.py

humanscape-covy/django-jsonform

ddf2bf40022855d4969988e13a0a3db7abb8a365

[ "BSD-3-Clause" ]

null

django_adminform/apps.py

humanscape-covy/django-jsonform

ddf2bf40022855d4969988e13a0a3db7abb8a365

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig class JsonappConfig(AppConfig): name = 'django_adminform'

16.333333

33

0.77551

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98

6.818182

0.818182

0

0.153061

98

5

34

19.6

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0

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false

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1

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null

0

1

0

1

0

4

986e9a1940b22c7bcff1ec87b0c917126cf577b9

135

py

Python

rules_default/castervoice/lib/ctrl/mgr/errors/tree_rule_config_error.py

MLH-Fellowship/LarynxCode

840fee18c689a357052825607c27fc8e3e56571c

[ "MIT" ]

1

2021-09-17T06:11:02.000Z

rules_default/castervoice/lib/ctrl/mgr/errors/tree_rule_config_error.py

soma2000-lang/LarynxCode

840fee18c689a357052825607c27fc8e3e56571c

[ "MIT" ]

5

2021-02-03T05:29:41.000Z

2021-02-08T01:14:11.000Z

rules_default/castervoice/lib/ctrl/mgr/errors/tree_rule_config_error.py

soma2000-lang/LarynxCode

840fee18c689a357052825607c27fc8e3e56571c

[ "MIT" ]

4

2021-02-03T05:05:00.000Z

2021-07-14T06:21:10.000Z

class TreeRuleConfigurationError(Exception): def __init__(self, msg): super(TreeRuleConfigurationError, self).__init__(msg)

45

61

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12

135

8

0.666667

0

0.133333

135

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61

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0

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0.666667

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null

0

1

0

null

0

1

0

1

0

4

98852ee7adf518e9d53b96045eb40fd60d4b87be

982

py

Python

pyengy/error/node_error.py

FreNeS1/PyEngy-2d

771112530ae039e8921369f700ba3b66c3df50c1

[ "MIT" ]

1

2020-07-09T12:42:30.000Z

pyengy/error/node_error.py

FreNeS1/PyEngy-2d

771112530ae039e8921369f700ba3b66c3df50c1

[ "MIT" ]

null

pyengy/error/node_error.py

FreNeS1/PyEngy-2d

771112530ae039e8921369f700ba3b66c3df50c1

[ "MIT" ]

null

"""Contains the NodeError class.""" from __future__ import annotations from typing import List, Optional from .pyengy_error import PyEngyError class NodeError(PyEngyError): """Error raised when a basic interaction with a node cannot be completed. For example, cyclic node dependencies.""" def __init__(self, node: str, message: str, caused_by: Optional[List[Exception]] = None) -> None: """ Instantiates a new NodeError. :param node: Valid identifier of the node that raised the error. :param message: Human readable description of the error. :param caused_by: List of exceptions that caused this one to be raised, if any. """ super().__init__(message, caused_by) self.node = node """Valid identifier of the node that raised the error. Usually the string representation of the node.""" def _error_string(self): return "NodeError for node ({}): {}.".format(self.node, self.message)

35.071429

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982

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982

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120

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null

0

null

0

1

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4

9891700284763a3f19380f342bc7448060c6da88

3,214

py

Python

sauron/parsers.py

luebbert42/sauron-engine

9d67ecb8254544ec7ac16fbb80b33edc7a9758e3

[ "MIT" ]

30

2019-12-17T09:59:15.000Z

2021-07-14T20:09:52.000Z

sauron/parsers.py

luebbert42/sauron-engine

9d67ecb8254544ec7ac16fbb80b33edc7a9758e3

[ "MIT" ]

367

2020-02-24T17:28:25.000Z

2022-03-15T15:47:46.000Z

sauron/parsers.py

luebbert42/sauron-engine

9d67ecb8254544ec7ac16fbb80b33edc7a9758e3

[ "MIT" ]

5

2020-04-15T10:14:55.000Z

2021-12-21T07:49:06.000Z

import json from json.decoder import JSONDecodeError from typing import List, Type, Union, Dict, Any from sauron.models import JobModel from ruamel.yaml import YAML class DefaultParser: single_model: Type[JobModel] = JobModel def __init__(self): self.yaml = YAML(typ="safe") def _parse_single_job(self, job_dict) -> JobModel: """ Method that know how to parse a single job dictionary """ return self.single_model(**job_dict) def _parse_jobs_from_list(self, jobs_input) -> List[JobModel]: """ Method that know how to parse a list for jobs """ parsed_jobs: List = [] for raw_job in jobs_input: current_job: JobModel = self._parse_single_job(raw_job) parsed_jobs.append(current_job) return parsed_jobs def _parse_jobs_from_string(self, jobs_input) -> List[JobModel]: """ Method that know how to parse a list for jobs described by a json-string with the list of jobs """ try: jobs: list = self.yaml.load(jobs_input) except JSONDecodeError: raise ValueError("jobs param is not a valid json string") else: return self._parse_jobs_from_list(jobs) def parse(self, jobs_input) -> List[JobModel]: """ Main method called to parse any jobs """ jobs_list_data: List[JobModel] = [] if isinstance(jobs_input, str): jobs_list_data = self._parse_jobs_from_string(jobs_input) elif isinstance(jobs_input, list): # jobs_input is a python list jobs_list_data = self._parse_jobs_from_list(jobs_input) else: raise ValueError("jobs param must be a list or json-string") return jobs_list_data class RuleEngineParser(DefaultParser): single_model: Type[JobModel] = JobModel def __init__(self): self.yaml = YAML(typ="safe") def _parse_jobs_from_string(self, jobs_input: str) -> List[JobModel]: """ Method that know how to parse a list for jobs described by a json-string with the list of jobs """ try: decoded_jobs: dict = self.yaml.load(jobs_input) jobs: list = decoded_jobs["conditions"] + decoded_jobs["actions"] except JSONDecodeError: raise ValueError("jobs param is not a valid json string") else: return self._parse_jobs_from_list(jobs) def parse(self, jobs_input: Union[List, str]) -> List[JobModel]: """ Main method called to parse any jobs """ jobs_list_data: List[JobModel] = [] if isinstance(jobs_input, str): jobs_list_data = self._parse_jobs_from_string(jobs_input) elif isinstance(jobs_input, list): # jobs_input is a python list jobs_list_data = self._parse_jobs_from_list(jobs_input) elif isinstance(jobs_input, dict): # jobs_input is a python list jobs_list_data = self._parse_jobs_from_string(str(jobs_input)) else: raise ValueError("jobs param must be a list or json-string") return jobs_list_data

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null

0

4

989a51d09054c4e781530d27242fa96e3092af04

1,099

py

Python

libotp/nametag/MarginPopup.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

1

2019-11-23T21:54:23.000Z

libotp/nametag/MarginPopup.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

1

2021-06-08T17:16:48.000Z

libotp/nametag/MarginPopup.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

3

2021-06-03T05:36:36.000Z

2021-06-22T15:07:31.000Z

from panda3d.core import * import NametagGlobals class MarginPopup(PandaNode): def __init__(self): PandaNode.__init__(self, 'MarginPopup') self.m_managed = False self.m_visible = False self.m_np = None self.m_cell_width = 1.0 self.m_seq = NametagGlobals._margin_prop_seq def getCellWidth(self): return self.m_cell_width def setManaged(self, value): self.m_managed = value if value: self.m_np = NodePath.anyPath(self) else: self.m_np = None def isManaged(self): return self.m_managed def setVisible(self, value): self.m_visible = value def isVisible(self): return self.m_visible def getScore(self): return 0.0 def getObjectCode(self): return 0 def considerVisible(self): if self.m_seq != NametagGlobals._margin_prop_seq: self.m_seq = NametagGlobals._margin_prop_seq self.updateContents() def updateContents(self): pass def frameCallback(self): pass

21.134615

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1,099

4.923664

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null

0

null

0

1

0

1

0

1

0

4

989f890f3cd119ce24bac176059068606afe256e

29,932

py

Python

source/plotting/create_plots.py

ml-jku/OfflineRL

d407457aba144587ce58fc47f4e8ae6099356f03

[ "MIT" ]

6

2021-11-30T09:41:54.000Z

2022-03-29T18:15:02.000Z

source/plotting/create_plots.py

kschweig/OfflineRL

d407457aba144587ce58fc47f4e8ae6099356f03

[ "MIT" ]

null

source/plotting/create_plots.py

kschweig/OfflineRL

d407457aba144587ce58fc47f4e8ae6099356f03

[ "MIT" ]

2

2021-11-04T16:47:59.000Z

2022-02-15T14:30:21.000Z

import os import glob import scipy import pickle import numpy as np from source.offline_ds_evaluation.metrics_manager import MetricsManager import matplotlib import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.colors import Normalize # Turn interactive plotting off plt.ioff() import seaborn as sns sns.set() matplotlib.rc('xtick', labelsize=10) matplotlib.rc('ytick', labelsize=10) folder = "main_figures_paper" image_type = "pdf" """ folder = "main_figures" image_type = "png" """ figsize = (12, 6) figsize_legend = (12, 1) figsize_half = (12, 3.5) figsize_half_half = (9.25, 3.5) figsize_small_avg = (9, 3.2) figsize_small = (16, 3) figsize_comp = (12, 6) figsize_envs = (12, 7.2) figsize_theplot = (13, 12) figsize_thesmallplot = (9, 8) # metric manager experiments = ["ex1", "ex2", "ex3", "ex4", "ex5", "ex6"] mm = MetricsManager(0) useruns = 5 for ex in experiments: for userun in range(1, 6): paths = glob.glob(os.path.join("..", "..", "data", ex, f"metrics_*_run{userun}.pkl")) for path in paths: with open(path, "rb") as f: m = pickle.load(f) m.recode(userun) mm.data.update(m.data) # static stuff envs = {'CartPole-v1': 0, 'MountainCar-v0': 1, "MiniGrid-LavaGapS7-v0": 2, "MiniGrid-Dynamic-Obstacles-8x8-v0": 3, 'Breakout-MinAtar-v0': 4, "SpaceInvaders-MinAtar-v0": 5} algos = ["BC", "BVE", "MCE", "DQN", "QRDQN", "REM", "BCQ", "CQL", "CRR"] buffer = {"random": "Random", "mixed": "Mixed", "er": "Replay", "noisy": "Noisy", "fully": "Expert"} mc_actions = ["Acc. to the Left", "Don't accelerate", "Acc. to the Right"] def plt_csv(ax, csv, algo, mode, ylims=None, set_title=True, color=None, set_label=True): est = np.mean(csv, axis=1) sd = np.std(csv, axis=1) cis = (est - sd, est + sd) ax.fill_between(np.arange(0, len(est) * 100, 100), cis[0], cis[1], alpha=0.2, color=color) ax.plot(np.arange(0, len(est) * 100, 100), est, label=(algo if set_label else None), color=color) ax.ticklabel_format(axis="x", style="sci", scilimits=(0, 0)) if set_title: ax.set_title(buffer[mode]) if ylims != None: ax.set_ylim(bottom=ylims[0], top=ylims[1]) ## get data indir = os.path.join("..", "..", "results", "csv_per_userun", "return") files = [] for file in glob.iglob(os.path.join(indir, "**", "*.csv"), recursive=True): files.append(file) data = dict() for file in files: name = file.split("/")[-1] userun = int(file.split("/")[-2][-1]) env = file.split("/")[-3] algo = name.split("_")[-2] mode = name.split("_")[-1].split(".")[0] try: csv = np.loadtxt(file, delimiter=";") except: print("Error in ", env, mode, algo) if len(csv.shape) == 1: csv = csv.reshape(-1, 1) if not data.keys() or env not in data.keys(): data[env] = dict() if not data[env].keys() or userun not in data[env].keys(): data[env][userun] = dict() if not data[env][userun].keys() or mode not in data[env][userun].keys(): data[env][userun][mode] = dict() data[env][userun][mode][algo] = csv ############### # plot metrics for policies ############### modes = list(buffer.keys()) outdir = os.path.join("..", "..", "results", folder, "metrics") os.makedirs(outdir, exist_ok=True) # titles x_label = "Dataset" # compact representation averaged over envs f, axs = plt.subplots(1, 3, figsize=figsize_half, sharex=True) for m, metric in enumerate([(0, 0), 2, (3, 0)]): x_all = [] for mode in modes: x = [] for e, env in enumerate(envs): for userun in range(1, useruns + 1): random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] if m == 1: result = mm.get_data(env, mode, userun)[metric] else: result = mm.get_data(env, mode, userun)[metric[0]][metric[1]] if m == 0: csv = data[env][userun]["online"]["DQN"] x.append((result - random_return) / (np.max(csv) - random_return)) elif m == 1: x.append(result / online_usap) else: x.append(result) x_all.append(x) axs[m].boxplot(x_all, positions=range(len(modes)), zorder=20, medianprops={"c": f"darkcyan", "linewidth": 1.5}, boxprops={"c": f"darkcyan", "linewidth": 1.5}, whiskerprops={"c": f"darkcyan", "linewidth": 1.5}, capprops={"c": f"darkcyan", "linewidth": 1.5}, flierprops={"markeredgecolor": f"darkcyan"})#, "markeredgewidth": 1.5}) if m == 0: axs[m].set_ylabel("Relative Trajectory Quality") axs[m].axhline(y=1, color="silver") elif m == 1: axs[m].set_ylabel("Relative State-Action Coverage") axs[m].axhline(y=1, color="silver") elif m == 2: axs[m].set_ylabel("Entropy") axs[m].set_ylim(bottom=-0.05, top=1.45) axs[m].set_xticks([i for i in range(len(modes))]) axs[m].set_xticklabels([buffer[m] for m in modes],fontsize="small")#, rotation=15, rotation_mode="anchor") axs[-1].set_ylim(bottom=-0.05, top=1.05) f.tight_layout(rect=(0, 0.022, 1, 1)) f.text(0.52, 0.01, x_label, ha='center') plt.savefig(os.path.join(outdir, f"overview_3_avg." + image_type)) plt.close() # compact representation averaged over envs f, axs = plt.subplots(1, 2, figsize=figsize_small_avg, sharex=True) for m, metric in enumerate([(0, 0), 2]): x_all = [] for mode in modes: x = [] for e, env in enumerate(envs): for userun in range(1, useruns + 1): random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] if m == 1: result = mm.get_data(env, mode, userun)[metric] else: result = mm.get_data(env, mode, userun)[metric[0]][metric[1]] if m == 0: csv = data[env][userun]["online"]["DQN"] x.append((result - random_return) / (np.max(csv) - random_return)) elif m == 1: x.append(result / online_usap) x_all.append(x) axs[m].boxplot(x_all, positions=range(len(modes)), zorder=20, medianprops={"c": "darkcyan", "linewidth": 1.5}, boxprops={"c": "darkcyan", "linewidth": 1.5}, whiskerprops={"c": "darkcyan", "linewidth": 1.5}, capprops={"c": "darkcyan", "linewidth": 1.5}, flierprops={"markeredgecolor": "darkcyan"})#, "markeredgewidth": 1.5}) if m == 0: axs[m].set_ylabel("Relative Trajectory Quality") axs[m].axhline(y=1, color="silver") elif m == 1: axs[m].set_ylabel("Relative State-Action Coverage") axs[m].axhline(y=1, color="silver") axs[m].set_ylim(bottom=-0.05, top=1.45) axs[m].set_xticks([i for i in range(len(modes))]) axs[m].set_xticklabels([buffer[m] for m in modes],fontsize="small")#, rotation=15, rotation_mode="anchor") f.tight_layout(rect=(0, 0.022, 1, 1)) f.text(0.52, 0.01, x_label, ha='center') plt.savefig(os.path.join(outdir, f"overview_2_avg." + image_type)) plt.close() # compact representation f, axs = plt.subplots(1, 3, figsize=figsize_half, sharex=True) for m, metric in enumerate([(0, 0), 2, (3, 0)]): for e, env in enumerate(envs): x_all = [] for mode in modes: x = [] for userun in range(1, useruns + 1): random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] if m == 1: result = mm.get_data(env, mode, userun)[metric] else: result = mm.get_data(env, mode, userun)[metric[0]][metric[1]] if m == 0: csv = data[env][userun]["online"]["DQN"] x.append((result - random_return) / (np.max(csv) - random_return)) elif m == 1: x.append(result / online_usap) else: x.append(result) x_all.append(x) pos = [0.2 + 0.12 * e + m_ for m_ in range(len(modes))] axs[m].boxplot(x_all, positions=pos, widths=0.1, sym="", zorder=20, medianprops={"c": f"C{e}", "linewidth": 1.5}, boxprops={"color": f"C{e}", "linewidth": 1.5}, whiskerprops={"color": f"C{e}", "linewidth": 1.5}, capprops={"color": f"C{e}", "linewidth": 1.5}, flierprops={"color": f"C{e}", "linewidth": 1.5}) if m == 0: axs[m].set_ylabel("Relative Trajectory Quality") axs[m].axhline(y=1, color="silver") elif m == 1: axs[m].set_ylabel("Relative State-Action Coverage") axs[m].axhline(y=1, color="silver") elif m == 2: axs[m].set_ylabel("Entropy") axs[m].set_ylim(bottom=-0.05, top=1.45) axs[m].set_xticks([i for i in range(len(modes) + 1)]) names = [buffer[m] for m in modes] names.append("") axs[m].set_xticklabels(names,fontsize="small")#, rotation=15, rotation_mode="anchor") offset = matplotlib.transforms.ScaledTranslation(0.29, 0, f.dpi_scale_trans) for label in axs[m].xaxis.get_majorticklabels(): label.set_transform(label.get_transform() + offset) axs[-1].set_ylim(bottom=-0.05, top=1.05) labels = [mpatches.Patch(color=f"C{e_}", fill=False, linewidth=1.5, label="-".join(env_.split("-")[:-1])) for e_, env_ in enumerate(envs)] f.legend(handles=labels, handlelength=1, loc="upper center", ncol=len(envs), fontsize="small") f.tight_layout(rect=(0, 0.022, 1, 0.92)) f.text(0.52, 0.01, x_label, ha='center') plt.savefig(os.path.join(outdir, f"overview_3." + image_type)) plt.close() # compact representation f, axs = plt.subplots(1, 2, figsize=figsize_half_half, sharex=True) for m, metric in enumerate([(0, 0), 2]): for e, env in enumerate(envs): x_all = [] for mode in modes: x = [] for userun in range(1, useruns + 1): random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] if m == 1: result = mm.get_data(env, mode, userun)[metric] else: result = mm.get_data(env, mode, userun)[metric[0]][metric[1]] if m == 0: csv = data[env][userun]["online"]["DQN"] x.append((result - random_return) / (np.max(csv) - random_return)) elif m == 1: x.append(result / online_usap) else: x.append(result) x_all.append(x) pos = [0.2 + 0.12 * e + m_ for m_ in range(len(modes))] axs[m].boxplot(x_all, positions=pos, widths=0.1, sym="", zorder=20, medianprops={"c": f"C{e}", "linewidth": 1.5}, boxprops={"color": f"C{e}", "linewidth": 1.5}, whiskerprops={"color": f"C{e}", "linewidth": 1.5}, capprops={"color": f"C{e}", "linewidth": 1.5}, flierprops={"color": f"C{e}", "linewidth": 1.5}) #axs[m].plot(range(len(x)), x, "-o", label = "-".join(env.split("-")[:-1]) if m == 0 else None, zorder=20) if m == 0: axs[m].set_ylabel("Relative Trajectory Quality") axs[m].axhline(y=1, color="silver") elif m == 1: axs[m].set_ylabel("Relative State-Action Coverage") axs[m].axhline(y=1, color="silver") axs[m].set_ylim(bottom=-0.05, top=1.45) axs[m].set_xticks([i for i in range(len(modes) + 1)]) names = [buffer[m] for m in modes] names.append("") axs[m].set_xticklabels(names, fontsize="small")#, rotation=15, rotation_mode="anchor") offset = matplotlib.transforms.ScaledTranslation(0.33, 0, f.dpi_scale_trans) for label in axs[m].xaxis.get_majorticklabels(): label.set_transform(label.get_transform() + offset) labels = [mpatches.Patch(color=f"C{e_}", fill=False, linewidth=1.5, label="-".join(env_.split("-")[:-1])) for e_, env_ in enumerate(envs)] f.legend(handles=labels, handlelength=1, loc="upper center", ncol=len(envs), fontsize="x-small") f.tight_layout(rect=(0, 0.022, 1, 0.92)) f.text(0.52, 0.01, x_label, ha='center') plt.savefig(os.path.join(outdir, f"overview_2." + image_type)) plt.close() ############### # Main Results ############### outdir = os.path.join("..", "..", "results", folder, "tq_vs_sac") os.makedirs(outdir, exist_ok=True) from matplotlib.colors import LinearSegmentedColormap #c = ["seagreen", "darkcyan", ""]#["red", "tomato", "lightsalmon", "wheat", "palegreen", "limegreen", "green"] #v = [i / (len(c) - 1) for i in range(len(c))] #print(v) #l = list(zip(v, c)) cmap = "viridis" #LinearSegmentedColormap.from_list('grnylw',l, N=256) normalize = Normalize(vmin=0, vmax=120, clip=True) offset_ann = 0.025 # titles x_label = r"Relative $\bf{{State}{-}{Action} \; Coverage}$ of Dataset" y_label = r"Relative $\bf{Trajectory \; Quality}$ of Dataset" # plot for discussion ### algos not averaged types = ["all", "noMinAtar", "MinAtar"] for t, environments in enumerate([list(envs), list(envs)[:4], list(envs)[4:]]): if t == 2: f, axs = plt.subplots(2, 2, figsize=(figsize_thesmallplot[0], figsize_thesmallplot[1]), sharex=True, sharey=True) axs = [item for sublist in zip(axs[:, 0], axs[:, 1]) for item in sublist] algos_ = ["BC", "DQN", "BCQ", "CQL"] else: f, axs = plt.subplots(3, 3, figsize=(figsize_theplot[0], figsize_theplot[1]), sharex=True, sharey=True) axs = [item for sublist in zip(axs[:, 0], axs[:, 1], axs[:, 2]) for item in sublist] algos_ = algos for a, algo in enumerate(algos_): ax = axs[a] ax.axhline(y=1, color="silver") ax.axvline(x=1, color="silver") ax.set_title(algo, fontsize="large") x, y, performance = [], [], [] for e, env in enumerate(list(environments)): for userun in range(1, useruns + 1): online_return = np.max(data[env][userun]["online"]["DQN"]) random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] for m, mode in enumerate(modes): try: performance.append((np.max(np.mean(data[env][userun][mode][algo], axis=1)) - random_return) / ( online_return - random_return) * 100) x.append(mm.get_data(env, mode, userun)[2] / online_usap) y.append((mm.get_data(env, mode, userun)[0][0] - random_return) / (online_return - random_return)) except: continue ax.scatter(x, y, s = 70, c=performance, cmap=cmap, norm=normalize, zorder=10) """ for i in range(len(performance)): ax.annotate(f"{int(performance[i])}%", (x[i] + offset_ann, y[i] + offset_ann), fontsize="x-small", zorder=20) """ if a == 0: print("-" * 30) print(types[t]) print("(TQ - SAC):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(x, y)])) print("-" * 30) print(algo, " (TQ - P):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(y, performance)])) print(algo, " (SAC - P):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(x, performance)])) print("-" * 30) f.colorbar(matplotlib.cm.ScalarMappable(norm=normalize, cmap=cmap), ax=axs, anchor=(1.35, 0.55), shrink=0.5 if t < 2 else 0.5).set_label(label="Performance in % of Online Policy", size=14) f.tight_layout(rect=(0.022, 0.022, 0.92, 1)) f.text(0.5, 0.01, x_label, ha='center', fontsize="large") f.text(0.005, 0.5, y_label, va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"algos_{types[t]}." + image_type)) plt.close() ### algos averaged for t, environments in enumerate([list(envs), list(envs)[:4], list(envs)[4:]]): if t == 2: f, axs = plt.subplots(2, 2, figsize=(figsize_thesmallplot[0], figsize_thesmallplot[1]), sharex=True, sharey=True) axs = [item for sublist in zip(axs[:, 0], axs[:, 1]) for item in sublist] algos_ = ["BC", "DQN", "BCQ", "CQL"] else: f, axs = plt.subplots(3, 3, figsize=(figsize_theplot[0], figsize_theplot[1]), sharex=True, sharey=True) axs = [item for sublist in zip(axs[:, 0], axs[:, 1], axs[:, 2]) for item in sublist] algos_ = algos for a, algo in enumerate(algos_): ax = axs[a] ax.axhline(y=1, color="silver") ax.axvline(x=1, color="silver") ax.set_title(algo, fontsize="large") x_, y_, performance_ = [], [], [] for e, env in enumerate(list(environments)): x, y, performance = [], [], [] for userun in range(1, useruns + 1): online_return = np.max(data[env][userun]["online"]["DQN"]) random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] for m, mode in enumerate(modes): try: performance.append((np.max(np.mean(data[env][userun][mode][algo], axis=1)) - random_return) / ( online_return - random_return) * 100) x.append(mm.get_data(env, mode, userun)[2] / online_usap) y.append((mm.get_data(env, mode, userun)[0][0] - random_return) / (online_return - random_return)) except: continue x_.extend(np.mean(np.asarray(x).reshape(useruns, -1), axis=0).tolist()) y_.extend(np.mean(np.asarray(y).reshape(useruns, -1), axis=0).tolist()) performance_.extend(np.mean(np.asarray(performance).reshape(useruns, -1), axis=0).tolist()) ax.scatter(x_, y_, s = 140, c=performance_, cmap=cmap, norm=normalize, zorder=10) """ for i in range(len(performance_)): ax.annotate(f"{int(performance_[i])}%", (x_[i] + offset_ann, y_[i] + offset_ann), fontsize="x-small", zorder=20) """ if a == 0: print("-" * 30) print(types[t]) print("(TQ - SAC):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(x_, y_)])) print("-" * 30) print(algo, " (TQ - P):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(y_, performance_)])) print(algo, " (SAC - P):", " ".join([f"{round(i, 3)}" for i in scipy.stats.pearsonr(x_, performance_)])) print("-" * 30) f.colorbar(matplotlib.cm.ScalarMappable(norm=normalize, cmap=cmap), ax=axs, anchor=(1.35, 0.55), shrink=0.5 if t < 2 else 0.5).set_label(label="Performance in % of Online Policy", size=14) f.tight_layout(rect=(0.022, 0.022, 0.92, 1)) f.text(0.5, 0.01, x_label, ha='center', fontsize="large") f.text(0.005, 0.5, y_label, va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"algos_avg_{types[t]}." + image_type)) plt.close() ### for environments for method in ["Mean", "Maximum", "Minimum", "Median", "Mean + STD", "Mean - STD"]: f, axs = plt.subplots(2, 3, figsize=figsize_envs, sharex=True, sharey=True) axs = [item for sublist in zip(axs[0], axs[1]) for item in sublist] for e, env in enumerate(envs): ax = axs[e] ax.axhline(y=1, color="silver") ax.axvline(x=1, color="silver") for userun in range(1, useruns + 1): online_return = np.max(data[env][userun]["online"]["DQN"]) random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] x, y, performance = [], [], [] for m, mode in enumerate(modes): x.append(mm.get_data(env, mode, userun)[2] / online_usap) y.append((mm.get_data(env, mode, userun)[0][0] - random_return) / (online_return - random_return)) p = [] for algo in algos: ax.set_title("-".join(env.split("-")[:-1]), fontsize="large") try: p.append((np.max(np.mean(data[env][userun][mode][algo], axis=1)) - random_return) / (online_return - random_return) * 100) except: pass performance.append(p) if method == "Mean": performance = np.mean(np.asarray(performance), axis=1) elif method == "Maximum": performance = np.max(np.asarray(performance), axis=1) elif method == "Minimum": performance = np.min(np.asarray(performance), axis=1) elif method == "Mean + STD": performance = np.mean(np.asarray(performance), axis=1) + np.std(np.asarray(performance), axis=1) elif method == "Mean - STD": performance = np.mean(np.asarray(performance), axis=1) - np.std(np.asarray(performance), axis=1) elif method == "Median": performance = np.median(np.asarray(performance), axis=1) ax.scatter(x, y, s=100, c=performance, cmap=cmap, norm=normalize, zorder=10) """ for i in range(len(performance)): ax.annotate(f"{int(performance[i])}%", (x[i] + offset_ann, y[i] + offset_ann), fontsize="x-small", va="bottom", ha="left",zorder=20) ax.annotate(annotations[i], (x[i] - offset_ann, y[i] + offset_ann), fontsize="x-small", va="bottom", ha="right", zorder=30) """ f.colorbar(matplotlib.cm.ScalarMappable(norm=normalize, cmap=cmap), ax=axs, anchor=(1.35, 0.55), shrink=0.5 if t < 2 else 0.5).set_label(label="Performance in % of Online Policy", size=14) f.tight_layout(rect=(0.022, 0.022, 0.92, 0.96)) f.text(0.5, 0.96, f"{method} performance across algorithms", ha='center', fontsize="x-large") f.text(0.5, 0.01, x_label, ha='center', fontsize="large") f.text(0.005, 0.5, y_label, va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"envs_{method}." + image_type)) plt.close() #### for Environments average for method in ["Mean", "Maximum", "Minimum", "Median", "Mean + STD", "Mean - STD"]: f, axs = plt.subplots(2, 3, figsize=figsize_envs, sharex=True, sharey=True) axs = [item for sublist in zip(axs[0], axs[1]) for item in sublist] for e, env in enumerate(envs): ax = axs[e] ax.axhline(y=1, color="silver") ax.axvline(x=1, color="silver") for userun in range(1, useruns + 1): online_return = np.max(data[env][userun]["online"]["DQN"]) random_return = mm.get_data(env, "random", userun)[0][0] online_usap = mm.get_data(env, "er", userun)[2] x, y, performance = [], [], [] for m, mode in enumerate(modes): x.append(mm.get_data(env, mode, userun)[2] / online_usap) y.append((mm.get_data(env, mode, userun)[0][0] - random_return) / (online_return - random_return)) p = [] for algo in algos: ax.set_title("-".join(env.split("-")[:-1]), fontsize="large") try: p.append((np.max(np.mean(data[env][userun][mode][algo], axis=1)) - random_return) / (online_return - random_return) * 100) except: pass performance.append(p) if method == "Mean": performance = np.mean(np.asarray(performance), axis=1) elif method == "Maximum": performance = np.max(np.asarray(performance), axis=1) elif method == "Minimum": performance = np.min(np.asarray(performance), axis=1) elif method == "Mean + STD": performance = np.mean(np.asarray(performance), axis=1) + np.std(np.asarray(performance), axis=1) elif method == "Mean - STD": performance = np.mean(np.asarray(performance), axis=1) - np.std(np.asarray(performance), axis=1) elif method == "Median": performance = np.median(np.asarray(performance), axis=1) ax.scatter(x, y, s=100, c=performance, cmap=cmap, norm=normalize, zorder=10) """ for i in range(len(performance)): ax.annotate(f"{int(performance[i])}%", (x[i] + offset_ann, y[i] + offset_ann), fontsize="x-small", va="bottom", ha="left",zorder=20) ax.annotate(annotations[i], (x[i] - offset_ann, y[i] + offset_ann), fontsize="x-small", va="bottom", ha="right", zorder=30) """ f.colorbar(matplotlib.cm.ScalarMappable(norm=normalize, cmap=cmap), ax=axs, anchor=(1.35, 0.55), shrink=0.5 if t < 2 else 0.5).set_label(label="Performance in % of Online Policy", size=14) f.tight_layout(rect=(0.022, 0.022, 0.92, 0.96)) f.text(0.5, 0.96, f"{method} performance across algorithms", ha='center', fontsize="x-large") f.text(0.5, 0.01, x_label, ha='center', fontsize="large") f.text(0.005, 0.5, y_label, va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"envs_avg_{method}." + image_type)) plt.close() ############################# # Comparisons # ############################# ################## # load reward data ################## outdir = os.path.join("..", "..", "results", folder, "comp_return") os.makedirs(outdir, exist_ok=True) ############### # plot metrics + policy for reward ############### # titles y_label = "Maximum Average Return" x_label = "Dataset" ### buffertypes per userun for userun in range(1, useruns + 1): # plot for modes f, axs = plt.subplots(2, 3, figsize=figsize_comp, sharex=True) axs = [item for sublist in zip(axs[0], axs[1]) for item in sublist] for e, env in enumerate(envs): ax = axs[e] ax.set_title(env[:-3]) x, y = list(range(len(buffer))), [] for mode in modes: y.append(mm.get_data(env, mode, userun)[0][0]) x, y = [list(tuple) for tuple in zip(*sorted(zip(x, y)))] ax.plot(x, y, "o:", label=("Behav." if e == 0 else None), color="black") # Online Policy csv = data[env][userun]["online"]["DQN"] ax.axhline(y=np.max(csv), color="black", label=("Online" if e == 0 else None)) for a, algo in enumerate(algos): x, y, sd = [], [], [] for m, mode in enumerate(modes): try: y.append(np.mean(data[env][userun][mode][algo])) sd.append(np.std(data[env][userun][mode][algo])) x.append(m) except: # print(env, userun, mode, algo) pass if len(x) == 0 or len(y) == 0 or len(sd) == 0: continue x, y, sd = [list(tuple) for tuple in zip(*sorted(zip(x, y, sd)))] cis = (np.asarray(y) - np.asarray(sd), np.asarray(y) + np.asarray(sd)) ax.fill_between(x, cis[0], cis[1], alpha=0.2, color=f"C{a}") ax.plot(x, y, "o-", label=(algo if e == 0 else None), color=f"C{a}") x = [] for m, mode in enumerate(modes): x.append(m) ax.set_xticks(range(len(modes))) ax.set_xticklabels([buffer[m] for m in modes], fontsize="small")#, rotation=15, rotation_mode="anchor") f.legend(loc="upper center", ncol=len(algos) + 2, fontsize="small") f.tight_layout(rect=(0.008, 0.022, 1, 0.95)) f.text(0.52, 0.01, x_label, ha='center', fontsize="large") f.text(0.005, 0.5, y_label, va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"buffertypes_userun{userun}." + image_type)) plt.close() ### MountainCar outdir = os.path.join("..", "..", "results", folder, "mountainCar") os.makedirs(outdir, exist_ok=True) colors=["#39568CFF", "#ffcf20FF", "#29AF7FFF"] samples = 10000 np.random.seed(42) ind = np.random.choice(10**5, (samples, ), replace=False) f, axs = plt.subplots(5, 5, figsize=figsize_theplot, sharex=True, sharey=True) axs = [item for sublist in zip(axs[0], axs[1], axs[2], axs[3], axs[4]) for item in sublist] for m, bt in enumerate(buffer): for userun in range(1, useruns + 1): ax = axs[m*5 + userun - 1] # load saved buffer with open(os.path.join("..", "..", "data", f"ex2", f"MountainCar-v0_run{userun}_{bt}.pkl"), "rb") as file: data = pickle.load(file) if userun == 1: ax.set_title(buffer[bt]) ax.scatter(data.state[ind, 0], data.state[ind, 1], c=[colors[a] for a in data.action[ind, 0]], s=0.5) ax.text(0.02, 0.92, f"Run {userun}", fontsize="small", transform=ax.transAxes) f.tight_layout(rect=(0.022, 0.022, 1, 0.97)) labels = [mpatches.Patch(color=colors[a], fill=True, linewidth=1, label=mc_actions[a]) for a in range(3)] f.legend(handles=labels, handlelength=1, loc="upper right", ncol=3, fontsize="small") f.text(0.53, 0.98, "Dataset", ha='center', fontsize="large") f.text(0.53, 0.01, "Position in m", ha='center', fontsize="large") f.text(0.005, 0.5, "Velocity in m/s", va='center', rotation='vertical', fontsize="large") plt.savefig(os.path.join(outdir, f"mountaincar.png")) plt.close()

40.394062

144

0.5587

4,221

29,932

3.885572

0.094764

0.023901

0.018109

0.024145

0.788976

0.769831

0.720078

0.706298

0.694287

0

0.037516

0.256415

29,932

740

145

40.448649

0.699375

0.037452

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0.007605

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1

0.001916

false

0.005747

0.022989

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0.024904

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0

null

0

1

0

1

0

null

0

4

98b98fad99a549679ab71ed1e6fe0f75606accc6

249

py

Python

fastapi_admin/apps/wxPay/tools.py

Chise1/fastapi-admin

74693cf8dd854d61ae5bd931ebe85f5b94f48121

[ "Apache-2.0" ]

null

fastapi_admin/apps/wxPay/tools.py

Chise1/fastapi-admin

74693cf8dd854d61ae5bd931ebe85f5b94f48121

[ "Apache-2.0" ]

null

fastapi_admin/apps/wxPay/tools.py

Chise1/fastapi-admin

74693cf8dd854d61ae5bd931ebe85f5b94f48121

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- """ @File : tools.py @Time : 2020/4/11 10:43 @Author : chise @Email : chise123@live.com @Software: PyCharm @info :一些处理数据的工具函数 """ from typing import Dict def dict2xml(d:Dict[str,str]): """字典转xml""" pass

17.785714

30

0.606426

34

249

4.441176

0.941176

0

0.081218

0.208835

249

14

31

17.785714

0.685279

0.662651

0

1

0.333333

false

0.333333

0

0.666667

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null

0

null

0

1

0

1

0

1

0

4

7f28f13048650c2961d83d7539ae3569c83af87b

1,295

py

Python

test.py

SriMethan/python-tictactoe

40dcd64c7a42c1e4a94eb40d54e985f7165f46c2

[ "MIT" ]

null

test.py

SriMethan/python-tictactoe

40dcd64c7a42c1e4a94eb40d54e985f7165f46c2

[ "MIT" ]

null

test.py

SriMethan/python-tictactoe

40dcd64c7a42c1e4a94eb40d54e985f7165f46c2

[ "MIT" ]

null

from tictactoe import Board def draw_board(): board = Board() board.push((0, 0)) board.push((0, 1)) board.push((0, 2)) board.push((1, 1)) board.push((1, 0)) board.push((2, 0)) board.push((1, 2)) board.push((2, 2)) board.push((2, 1)) return board def x_win_board(): board = Board() board.push((0, 0)) board.push((0, 1)) board.push((0, 2)) board.push((1, 1)) board.push((1, 0)) board.push((1, 2)) board.push((2, 0)) return board def o_win_board(): board = Board() board.push((0, 0)) board.push((0, 1)) board.push((0, 2)) board.push((1, 1)) board.push((1, 0)) board.push((2, 0)) board.push((1, 2)) board.push((2, 1)) return board def unfinished_board(): board = Board() board.push((0, 0)) board.push((0, 1)) board.push((0, 2)) board.push((1, 1)) board.push((1, 0)) board.push((2, 0)) board.push((1, 2)) board.push((2, 2)) return board def test_result(): assert x_win_board().result() == 1 assert o_win_board().result() == 2 assert draw_board().result() == 0 assert unfinished_board().result() is None if __name__ == "__main__": test_result()

19.923077

47

0.51583

193

1,295

3.34715

0.119171

0.44582

0.185759

0.085139

0.688854

0.608359

0

0.072826

0.289575

1,295

64

48

20.234375

0.629348

0

0.769231

0

0.006499

0

0.076923

1

0.096154

false

0

0.019231

0

0.192308

0

null

1

0

1

0

1

0

null

0

4

7f29a37ba7ca9d8b45eadd4c85b6f59cdd98a5b1

671

py

Python

theorems/admin.py

austindjones/mathreview

34cd22103d5880bb283e695d2114bb6ddf4c2219

[ "MIT" ]

1

2021-02-25T20:57:50.000Z

theorems/admin.py

austindjones/mathreview

34cd22103d5880bb283e695d2114bb6ddf4c2219

[ "MIT" ]

null

theorems/admin.py

austindjones/mathreview

34cd22103d5880bb283e695d2114bb6ddf4c2219

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import Subject from .models import Theorem from .models import Theorem_Statement from .models import Theorem_Proof from .models import Definition from .models import Proof_Definition_Link from .models import Question from .models import Keyword from .models import Theorem_Keyword_Link admin.site.register(Subject) admin.site.register(Theorem) admin.site.register(Theorem_Statement) admin.site.register(Theorem_Proof) admin.site.register(Definition) admin.site.register(Proof_Definition_Link) admin.site.register(Question) admin.site.register(Keyword) admin.site.register(Theorem_Keyword_Link)

29.173913

42

0.845007

93

671

5.967742

0.193548

0.162162

0.259459

0.165766

0

0.083458

671

22

43

30.5

0.902439

0.038748

0

1

0

true

0

0.526316

0

0.526316

0

null

0

1

0

null

0

1

0

1

0

4

7f3ae844e2e9d48df0c926798ecbbafdf0478afc

803

py

Python

ivreg/services.py

sirex/internet-voting-registration

b60915507966ec150db36ef63782971d0d5e1a2b

[ "MIT" ]

null

ivreg/services.py

sirex/internet-voting-registration

b60915507966ec150db36ef63782971d0d5e1a2b

[ "MIT" ]

null

ivreg/services.py

sirex/internet-voting-registration

b60915507966ec150db36ef63782971d0d5e1a2b

[ "MIT" ]

null

import os import uuid import json import hashlib import base64 import requests def generate_request_id(): return base64.b32encode(uuid.uuid4().bytes).decode('ascii').rstrip('=') def generate_candidate_codes(candidates): return {x: base64.b32encode(os.urandom(5)).decode('ascii')[:5] for x in candidates} def generate_ballot_id(): return base64.b32encode(uuid.uuid4().bytes).decode('ascii')[:10] def verify_vote(data): vote_hash = hashlib.sha256((data['ballot_id'] + data['candidate_id'] + data['vcode']).encode('utf-8')).hexdigest() for line in requests.get('http://log.rk.sub.lt/').text.splitlines(): ballot = json.loads(line.strip()) if ballot['ballot_id'] == data['ballot_id'] and ballot['vote_hash'] == vote_hash: return True return False

28.678571

118

0.689913

111

803

4.864865

0.477477

0.059259

0.051852

0.085185

0.177778

0

0.034985

0.145704

803

27

119

29.740741

0.752187

0

1

0

0.118306

0

1

0.210526

false

0

0.315789

0.157895

0.789474

0

null

0

1

0

null

0

1

0

1

0

4

7f6db1e9953197c6e430f0417d4e9234193cc958

136

py

Python

slackpicam.py

ricklon/slackpicam

dc4b3f74cc4f22a9f9d642878e5649bc1ee86ef9

[ "Apache-2.0" ]

1

2019-07-12T15:36:06.000Z

slackpicam.py

ricklon/slackpicam

dc4b3f74cc4f22a9f9d642878e5649bc1ee86ef9

[ "Apache-2.0" ]

null

slackpicam.py

ricklon/slackpicam

dc4b3f74cc4f22a9f9d642878e5649bc1ee86ef9

[ "Apache-2.0" ]

null

from time import sleep from picamera import PiCamera camera = PiCamera() camera.resolution = (1024, 768) camera.capture('foobar.jpg')

17

31

0.764706

18

136

5.777778

0.666667

0.269231

0

0.059322

0.132353

136

7

32

19.428571

0.822034

0

0.073529

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

4

7f735174c1fbd8a50460c5cb2d32e73a2ceb1276

74

py

Python

smrf/envphys/__init__.py

scotthavens/smrf

a492d01a5eef994e00728c1cbed9f693879bbade

[ "CC0-1.0" ]

null

smrf/envphys/__init__.py

scotthavens/smrf

a492d01a5eef994e00728c1cbed9f693879bbade

[ "CC0-1.0" ]

null

smrf/envphys/__init__.py

scotthavens/smrf

a492d01a5eef994e00728c1cbed9f693879bbade

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- from . import core, phys, radiation, snow, storms

24.666667

49

0.635135

10

74

4.7

1

0

0.016393

0.175676

74

2

50

37

0.754098

0.283784

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f6887a1bc28ae32c0dd52cdd49a32ee38e668ed8

218

py

Python

helpcenter/tests/dummy_classes.py

smalls12/django_helpcenter

e0118447871abad701056bc137d63e6fcd8abde6

[ "MIT" ]

4

2017-07-30T17:43:36.000Z

2021-09-14T04:26:37.000Z

helpcenter/tests/dummy_classes.py

cdriehuys/django_helpcenter

e0118447871abad701056bc137d63e6fcd8abde6

[ "MIT" ]

20

2016-06-30T04:52:26.000Z

2016-09-30T04:52:15.000Z

helpcenter/tests/dummy_classes.py

cdriehuys/django_helpcenter

e0118447871abad701056bc137d63e6fcd8abde6

[ "MIT" ]

null

from django import forms class BlankForm(forms.Form): """A blank form for testing purposes.""" def __init__(self, *args, **kwargs): """Consume arguments.""" super(BlankForm, self).__init__()

21.8

44

0.637615

25

218

5.24

0.8

0

0.215596

218

9

45

24.222222

0.766082

0.243119

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f68a9fd9a2a3b0664886738789562dcab483d747

166

py

Python

materi/pertemuan_5/python/login.py

ai-qadrlabs/dasar-pemrograman

73dbc87ef33159542be89f12f89b2873a06a4e3a

[ "MIT" ]

null

materi/pertemuan_5/python/login.py

ai-qadrlabs/dasar-pemrograman

73dbc87ef33159542be89f12f89b2873a06a4e3a

[ "MIT" ]

null

materi/pertemuan_5/python/login.py

ai-qadrlabs/dasar-pemrograman

73dbc87ef33159542be89f12f89b2873a06a4e3a

[ "MIT" ]

null

username = 'admin' password = 'garahasia' if (username == 'admin') and (password == 'rahasia'): print('user berhasil login') else: print('user gagal login')

20.75

53

0.650602

19

166

5.684211

0.684211

0.240741

0

0.180723

166

7

54

23.714286

0.794118

0

0.36747

0

1

0

false

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

4

f6914ec37499c1b6f5fdf1173c659e29406e4aee

60

py

Python

pc_processor/models/__init__.py

MasterHow/PanoLiSeg

56bd09fe3c85251c46532dba5fcec5fb03951c36

[ "MIT" ]

65

2021-08-03T02:37:14.000Z

2022-03-28T17:11:23.000Z

pc_processor/models/__init__.py

MasterHow/PanoLiSeg

56bd09fe3c85251c46532dba5fcec5fb03951c36

[ "MIT" ]

12

2021-10-30T03:11:00.000Z

2022-03-27T11:36:11.000Z

pc_processor/models/__init__.py

MasterHow/PanoLiSeg

56bd09fe3c85251c46532dba5fcec5fb03951c36

[ "MIT" ]

23

2021-10-14T02:44:34.000Z

2022-03-18T11:45:23.000Z

from .salsanext import SalsaNext from .pmf_net import PMFNet

30

32

0.85

9

60

5.555556

0.666667

0

0.116667

60

2

33

30

0.943396

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f6d69e0e4ee62c1f2a3f87de58751f58ea4ec399

87

py

Python

knowledge_sim/ontology/__init__.py

t3pleni9/KnowledgeSimulator

2c3df1e49372176daa20bbda2bf53910358373a0

[ "Apache-2.0" ]

1

2021-06-18T02:22:54.000Z

knowledge_sim/ontology/__init__.py

t3pleni9/KnowledgeSimulator

2c3df1e49372176daa20bbda2bf53910358373a0

[ "Apache-2.0" ]

null

knowledge_sim/ontology/__init__.py

t3pleni9/KnowledgeSimulator

2c3df1e49372176daa20bbda2bf53910358373a0

[ "Apache-2.0" ]

null

from .reasoner import Reasoner from .behavior import Behavior from .state import State

21.75

30

0.827586

12

87

6

0.416667

0

0.137931

87

3

31

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f6f0419aeaa7066172b9955e6da6dc1b5331e9ec

2,246

py

Python

tests/structure/test_singles.py

nakaken88/NKSSG

8f635bef3c466afd0842178e9a9a3501c3f39119

[ "MIT" ]

2

2021-04-28T11:52:08.000Z

2021-11-16T11:32:47.000Z

tests/structure/test_singles.py

nakaken88/NKSSG

8f635bef3c466afd0842178e9a9a3501c3f39119

[ "MIT" ]

null

tests/structure/test_singles.py

nakaken88/NKSSG

8f635bef3c466afd0842178e9a9a3501c3f39119

[ "MIT" ]

null

import datetime from pathlib import Path from nkssg.structure.singles import Singles, Single def test_get_url_from_permalink_no_change(): single = Single('', '') single.date = datetime.datetime.now() ret = single.get_url_from_permalink('/sample/', None) assert ret == '/sample/' def test_get_url_from_permalink_YMD(): single = Single('', '') now = datetime.datetime.now() single.date = datetime.datetime.now() ret = single.get_url_from_permalink('/%Y/%m/%d/', None) assert ret == now.strftime('/%Y/%m/%d/') def test_get_url_from_permalink_YMD_HMS(): single = Single('', '') now = datetime.datetime.now() single.date = datetime.datetime.now() ret = single.get_url_from_permalink('/%Y/%m/%d/%H%M%S/', None) assert ret == now.strftime('/%Y/%m/%d/%H%M%S/') def test_get_url_from_permalink_slug(): single = Single('', '') single.date = datetime.datetime.now() single.slug = 'sample' ret = single.get_url_from_permalink('/{slug}/', None) assert ret == '/sample/' def test_get_url_from_permalink_filename(): single = Single('', '') single.date = datetime.datetime.now() single.filename = 'sample' ret = single.get_url_from_permalink('/{filename}/', None) assert ret == '/sample/' def test_get_url_from_permalink_filename_dirty_name(): single = Single('', '') single.date = datetime.datetime.now() single.filename = 'A of C' ret = single.get_url_from_permalink('/{filename}/', None) assert ret == '/a-of-c/' def test_get_url_from_permalink_filename_index(): single = Single('', '') single.date = datetime.datetime.now() single.filename = 'index' single.src_dir = Path('post_type', 'dir1', 'dir2') ret = single.get_url_from_permalink('/{filename}/', None) assert ret == '/dir2/' def test_get_url_from_permalink_filename_top_index(): single = Single('', '') single.date = datetime.datetime.now() single.filename = 'index' single.post_type = 'sample_post_type' single.src_dir = Path(single.post_type) config = {'post_type': [{'sample_post_type': {'slug': 'new_post_type'}}]} ret = single.get_url_from_permalink('/{filename}/', config) assert ret == '/new_post_type/'

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4.724832

0.157718

0.068182

0.113636

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31.194444

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0.203704

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null

0

1

0

null

0

4

100a4046af88fc4895dc10d3d1832d4b693b3e9c

253

py

Python

tests/mockups/firstDiscoveryRequest.py

securesonic/safekiddo-mdm

05ebd6ba0d01c7b1aa85b473c764b870c0b8e182

[ "BSD-2-Clause" ]

null

tests/mockups/firstDiscoveryRequest.py

securesonic/safekiddo-mdm

05ebd6ba0d01c7b1aa85b473c764b870c0b8e182

[ "BSD-2-Clause" ]

null

tests/mockups/firstDiscoveryRequest.py

securesonic/safekiddo-mdm

05ebd6ba0d01c7b1aa85b473c764b870c0b8e182

[ "BSD-2-Clause" ]

null

import urllib2 import mockupsLib opener = urllib2.build_opener() request = urllib2.Request(mockupsLib.getDiscoveryRequestUrl()+"/EnrollmentServer/Discovery.svc", headers={'Content-Type': 'unknown'}) response = opener.open(request) print response.info()

36.142857

133

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253

7.444444

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0.012712

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7

134

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null

0

1

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null

0

1

0

1

0

4

100dec975c9ad9898f05a884e1f3edcd0a4bc7cd

28

py

Python

sentry/__init__.py

justquick/django-sentry

07988759144524ba49bc63b308663244d1a69d04

[ "BSD-3-Clause" ]

1

2016-03-21T18:56:31.000Z

sentry/__init__.py

justquick/django-sentry

07988759144524ba49bc63b308663244d1a69d04

[ "BSD-3-Clause" ]

null

sentry/__init__.py

justquick/django-sentry

07988759144524ba49bc63b308663244d1a69d04

[ "BSD-3-Clause" ]

null

__version__ = (1, 0, '-dev')

28

0.571429

4

28

3

1

0

0.083333

0.142857

28

1

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0.416667

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false

0

1

0

null

0

1

0

1

0

null

0

4

101274f29925300afaefe843031ffe5b06b383a8

221

py

Python

yawhois/parser/durban_whois_registry_net_za.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

yawhois/parser/durban_whois_registry_net_za.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

yawhois/parser/durban_whois_registry_net_za.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

from .za_central_registry import ZaCentralRegistryParser class DurbanWhoisRegistryNetZaParser(ZaCentralRegistryParser): def __init__(self, *args): super(DurbanWhoisRegistryNetZaParser, self).__init__(*args)

31.571429

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0.81448

18

221

9.444444

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221

6

68

36.833333

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false

0

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null

0

1

0

null

0

1

0

1

0

4

122742f1e3fd8eddf7df483de6428e0f81b55333

92

py

Python

test/single_layer.py

gregvw/pyQAOA

59b5abda36d90b45913878e7ffb588a1c146bc38

[ "BSD-3-Clause" ]

null

test/single_layer.py

gregvw/pyQAOA

59b5abda36d90b45913878e7ffb588a1c146bc38

[ "BSD-3-Clause" ]

null

test/single_layer.py

gregvw/pyQAOA

59b5abda36d90b45913878e7ffb588a1c146bc38

[ "BSD-3-Clause" ]

null

import numpy as np import qaoa import matplotlib.pyplot as plt if __name__ == '__main__':

13.142857

31

0.76087

14

92

4.428571

0.785714

0

0.173913

92

6

32

15.333333

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0

null

0

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null

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null

0

1

0

null

0

1

0

1

0

4

123ee803ea3bc55ebbc04b94b215d90ef6f7a611

70

py

Python

danceschool/guestlist/__init__.py

benjwrdill/django-danceschool

9ecb2754502e62d0f49aa23d08ca6de6cae3c99a

[ "BSD-3-Clause" ]

1

2019-02-04T02:11:32.000Z

danceschool/guestlist/__init__.py

benjwrdill/django-danceschool

9ecb2754502e62d0f49aa23d08ca6de6cae3c99a

[ "BSD-3-Clause" ]

2

2019-03-26T22:37:49.000Z

2019-12-02T15:39:35.000Z

danceschool/guestlist/__init__.py

benjwrdill/django-danceschool

9ecb2754502e62d0f49aa23d08ca6de6cae3c99a

[ "BSD-3-Clause" ]

1

2019-03-19T22:49:01.000Z

default_app_config = 'danceschool.guestlist.apps.GuestListAppConfig'

35

69

0.857143

7

70

8.285714

1

0

0.057143

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1

70

0.878788

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0.652174

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false

0

1

0

null

0

1

0

1

null

0

4

1251cd114b18bdc67612a8b040ba9243a77f076f

67

py

Python

setup.py

shamanoor/devops-for-data

87e4773b45844d12ff96d1be58ade665b5d36d85

[ "Apache-2.0" ]

2

2020-10-29T15:27:52.000Z

2021-04-10T14:08:20.000Z

setup.py

shamanoor/devops-for-data

87e4773b45844d12ff96d1be58ade665b5d36d85

[ "Apache-2.0" ]

null

setup.py

shamanoor/devops-for-data

87e4773b45844d12ff96d1be58ade665b5d36d85

[ "Apache-2.0" ]

16

2020-10-28T12:21:51.000Z

2022-02-04T12:25:25.000Z

from setuptools import setup # All config is in setup.cfg setup()

13.4

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0.761194

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67

4.636364

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4

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1

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89efddebbe37d517ff525c9672af20117f46300d

1,286

py

Python

astropy/tests/__init__.py

REMeyer/astropy

28c49fb618538a01812e586cd07bccdf0591a6c6

[ "BSD-3-Clause" ]

3

2018-03-20T15:09:16.000Z

2021-05-27T11:17:33.000Z

astropy/tests/__init__.py

REMeyer/astropy

28c49fb618538a01812e586cd07bccdf0591a6c6

[ "BSD-3-Clause" ]

null

astropy/tests/__init__.py

REMeyer/astropy

28c49fb618538a01812e586cd07bccdf0591a6c6

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This package contains utilities to run the astropy test suite, tools for writing tests, and general tests that are not associated with a particular package. """ # NOTE: This is retained only for backwards compatibility. Affiliated packages # should no longer import `disable_internet` from `astropy.tests`. It is now # available from `pytest_remotedata`. However, this is not the recommended # mechanism for controlling access to remote data in tests. Instead, packages # should make use of decorators provided by the pytest_remotedata plugin: # - `@pytest.mark.remote_data` for tests that require remote data access # - `@pytest.mark.internet_off` for tests that should only run when remote data # access is disabled. # Remote data access for the test suite is controlled by the `--remote-data` # command line flag. This is either passed to `pytest` directly or to the # `setup.py test` command. # # TODO: This import should eventually be removed once backwards compatibility # is no longer supported. from pkgutil import find_loader if find_loader('pytest_remotedata') is not None: from pytest_remotedata import disable_internet else: from ..extern.plugins.pytest_remotedata import disable_internet

44.344828

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0

1

0

1

0

1

0

4

d63571f14d5098676b716fc361353085c3dfd8ca

368

py

Python

setup.py

COHRINT/etddf_minau

b2770aaaeff37bf580cc23a566f83432300b715c

[ "Apache-2.0" ]

null

setup.py

COHRINT/etddf_minau

b2770aaaeff37bf580cc23a566f83432300b715c

[ "Apache-2.0" ]

null

setup.py

COHRINT/etddf_minau

b2770aaaeff37bf580cc23a566f83432300b715c

[ "Apache-2.0" ]

null

#!/usr/bin/env python from distutils.core import setup from catkin_pkg.python_setup import generate_distutils_setup d = generate_distutils_setup() d['packages'] = ['etddf', 'cuprint', 'cuquantization', 'deltatier'] d['package_dir'] = {'etddf': 'src/etddf/etddf', "cuprint":'src/cuprint', 'cuquantization':"src/cuquantization", "deltatier":"src/deltatier"} setup(**d)

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null

0

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4

d6430fc3118621cb938a86fd9fd8009e19b2be00

1,624

py

Python

examples/10_Example_DSSInterface.py

davilamds/py_dss_interface

a447c97787aeac962381db88dd622ccb235eef4b

[ "MIT" ]

8

2020-08-15T12:56:03.000Z

2022-01-04T15:51:14.000Z

examples/10_Example_DSSInterface.py

rodolfoplondero/py_dss_interface

cb6771b34ed322a5df7ef1cc194611e794f26441

[ "MIT" ]

24

2021-04-24T18:33:19.000Z

2021-11-13T14:59:54.000Z

examples/10_Example_DSSInterface.py

rodolfoplondero/py_dss_interface

cb6771b34ed322a5df7ef1cc194611e794f26441

[ "MIT" ]

7

2020-08-15T12:56:04.000Z

2021-10-04T16:14:30.000Z

# -*- encoding: utf-8 -*- """ Created by Ênio Viana at 15/05/2021 """ from py_dss_interface.models.Example.ExampleBase import ExampleBase dss = ExampleBase("13").dss # Integer methods print(45 * '=' + ' Integer Methods' + 45 * '=') print(f'dss.dss_num_circuits(): {dss.dss_num_circuits()}') print(f'dss.dss_clear_all(): {dss.dss_clear_all()}') print(f'dss.dss_show_panel(): {dss.dss_show_panel()}') print(f'dss.dss_start(): {dss.dss_start()}') print(f'dss.dss_num_classes(): {dss.dss_num_classes()}') print(f'dss.dss_num_user_classes(): {dss.dss_num_user_classes()}') print(f'dss.dss_reset(): {dss.dss_reset()}') print(f'dss.dss_read_allow_forms(): {dss.dss_read_allow_forms()}') print(f'dss.dss_write_allow_forms(): {dss.dss_write_allow_forms(0)}') print(f'dss.dss_read_allow_forms(): {dss.dss_read_allow_forms()}') # String methods print(45 * '=' + ' String Methods ' + 45 * '=') print(f'dss.dss_new_circuit(): {dss.dss_new_circuit("new_rest_circuit")}') print(f'dss.dss_version(): {dss.dss_version()}') print(f'dss.dss_read_datapath(): {dss.dss_read_datapath()}') # PAY ATTENTION: According with the OpenDSS original source there is no error here, dss.dss_write_datapath(r"C:\Users\eniocc\Desktop\epri_projects\fork\py_dss_interface\src\py_dss_interface\models" r"\Capacitors\CapacitorsS.py") print(f'dss.dss_read_datapath(): {dss.dss_read_datapath()}') print(f'dss.dss_default_editor(): {dss.dss_default_editor()}') # Variant methods print(45 * '=' + ' Variant Methods ' + 45 * '=') print(f'dss.dss_classes(): {dss.dss_classes()}') print(f'dss.dss_user_classes(): {dss.dss_user_classes()}')

42.736842

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256

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1

null

0

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c38f8a137a8ab674b0f9ad3ffa00628b4cf57150

98

py

Python

matizla/helpers/types.py

neotje/matizla

23afbc9ad3972c04e5882e0fed2de1ce0b7f397b

[ "MIT" ]

null

matizla/helpers/types.py

neotje/matizla

23afbc9ad3972c04e5882e0fed2de1ce0b7f397b

[ "MIT" ]

null

matizla/helpers/types.py

neotje/matizla

23afbc9ad3972c04e5882e0fed2de1ce0b7f397b

[ "MIT" ]

null

from webview.window import Window class JSwindow: title: str uuid: str hidden: bool

12.25

33

0.683673

13

98

5.153846

0.846154

0

0.265306

98

7

34

14

0.930556

0

1

0

true

0

0.2

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

4

c39522539ddd16df4a65be551b45fae7ef2e1827

93

py

Python

WebApp/ImgSketch/apps.py

devanshsolani/sketchyourlife

ff9cec4eeb9fe3ebcb07432f9d12e6bcc10c4322

[ "BSD-3-Clause" ]

1

2021-06-03T19:18:41.000Z

WebApp/ImgSketch/apps.py

devanshsolani/sketchyourlife

ff9cec4eeb9fe3ebcb07432f9d12e6bcc10c4322

[ "BSD-3-Clause" ]

1

2021-05-12T10:19:12.000Z

WebApp/ImgSketch/apps.py

devanshsolani/sketchyourlife

ff9cec4eeb9fe3ebcb07432f9d12e6bcc10c4322

[ "BSD-3-Clause" ]

4

2021-05-12T10:19:44.000Z

2021-07-03T07:57:31.000Z

from django.apps import AppConfig class ImgSketchConfig(AppConfig): name = 'ImgSketch'

15.5

33

0.763441

10

93

7.1

0.9

0

0.16129

93

5

34

18.6

0.910256

0

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0

1

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false

0

0.333333

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null

0

1

0

null

0

1

0

1

0

4

c3957d577ec1fa3898313456a867fe9f6ec2a40f

124

py

Python

plugins/docker/__init__.py

ajenti/ajen

177c1a67278a7763ed06eb2f773d7b409a85ec77

[ "MIT" ]

3,777

2015-02-21T00:10:12.000Z

2022-03-30T15:33:22.000Z

plugins/docker/__init__.py

ajenti/ajen

177c1a67278a7763ed06eb2f773d7b409a85ec77

[ "MIT" ]

749

2015-03-12T14:17:03.000Z

2022-03-25T13:22:28.000Z

plugins/docker/__init__.py

ajenti/ajen

177c1a67278a7763ed06eb2f773d7b409a85ec77

[ "MIT" ]

687

2015-03-21T10:42:33.000Z

2022-03-21T23:18:12.000Z

import logging from .main import ItemProvider from .views import Handler logging.info('docker.__init__.py: docker loaded')

20.666667

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0.806452

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124

5.647059

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0

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124

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1

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true

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0

1

0

null

0

1

0

1

0

4

c3b74f7c7a6c11b158f2affbdf969a225aae6118

344

py

Python

qt.py

DaelonSuzuka/qtenv

ee27d8989664aacd93ef1d8bb58ac6fa14418387

[ "MIT" ]

null

qt.py

DaelonSuzuka/qtenv

ee27d8989664aacd93ef1d8bb58ac6fa14418387

[ "MIT" ]

null

qt.py

DaelonSuzuka/qtenv

ee27d8989664aacd93ef1d8bb58ac6fa14418387

[ "MIT" ]

null

import PySide2 from PySide2 import QtCore, QtGui, QtWidgets from PySide2.QtCore import * from PySide2.QtGui import * from PySide2.QtWidgets import * from PySide2.QtNetwork import * from PySide2.QtWebSockets import * from PySide2.QtCharts import * from PySide2.QtMultimedia import * from PySide2.QtSerialPort import * from PySide2.QtSql import *

31.272727

44

0.81686

44

344

6.386364

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0.391459

0.483986

0

0.036545

0.125

344

11

45

31.272727

0.89701

0

1

0

true

0

1

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1

0

null

1

0

null

0

1

0

1

0

4

c3bc7b903f7d70f70f266720379cbbc77be71313

35

py

Python

ssh2net/core/juniper_junos/__init__.py

carlmontanari/ssh2net

55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b

[ "MIT" ]

10

2020-01-13T03:28:33.000Z

2022-02-08T17:05:59.000Z

ssh2net/core/juniper_junos/__init__.py

carlmontanari/ssh2net

55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b

[ "MIT" ]

null

ssh2net/core/juniper_junos/__init__.py

carlmontanari/ssh2net

55e969b6d44ec3f2bd2ebbd8dedd68b99bee4c5b

[ "MIT" ]

1

2020-05-26T13:35:46.000Z

"""ssh2net juniper junos driver"""

17.5

34

0.714286

4

35

6.25

1

0

0.032258

0.114286

35

1

35

0.774194

0.8

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

c3c096c6bf5140a9cca00f20e76eddbc5cb9115f

99,519

py

Python

spinsim/__init__.py

alexander-tritt-monash/spinsim

30a635464730e95c6e236931e3103ff6dcf119fe

[ "BSD-3-Clause" ]

2

2021-11-09T08:45:42.000Z

2022-02-09T22:36:54.000Z

spinsim/__init__.py

alexander-tritt-monash/spinsim

30a635464730e95c6e236931e3103ff6dcf119fe

[ "BSD-3-Clause" ]

null

spinsim/__init__.py

alexander-tritt-monash/spinsim

30a635464730e95c6e236931e3103ff6dcf119fe

[ "BSD-3-Clause" ]

1

2021-06-02T10:28:50.000Z

""" """ from enum import Enum import numpy as np import numba as nb from numba import cuda from numba import roc import math import cmath sqrt2 = math.sqrt(2) sqrt3 = math.sqrt(3) class SpinQuantumNumber(Enum): """ Options for the spin quantum number of a system. Parameters ---------- value : :obj:`float` The numerical value of the spin quantum number. dimension : :obj:`int` Dimension of the hilbert space the states with this spin belong to. label : :obj:`str` A text label that can be used for archiving. plus_x, plus_y, plus_z, zero_x, zero_y, zero_z, minus_x, minus_y, minus_z : :obj:`numpy.ndarray` of :obj:`numpy.complex128` Eigenstates of the spin operators for quick reference. """ def __init__(self, value:np.float64, dimension:int, label:str): super().__init__() self._value_ = value self.dimension = dimension self.label = label if self.label == "half": self.plus_x = np.array([1, 1], np.complex128)/math.sqrt(2) self.minus_x = np.array([-1, 1], np.complex128)/math.sqrt(2) self.plus_y = np.array([1, 1j], np.complex128)/math.sqrt(2) self.minus_y = np.array([1, -1j], np.complex128)/math.sqrt(2) self.plus_z = np.array([1, 0], np.complex128) self.minus_z = np.array([0, 1], np.complex128) else: self.plus_x = np.array([1, math.sqrt(2), 1], np.complex128)/2 self.zero_x = np.array([-1, 0, 1], np.complex128)/math.sqrt(2) self.minus_x = np.array([1, -math.sqrt(2), 1], np.complex128)/2 self.plus_y = np.array([-1, -1j*math.sqrt(2), 1], np.complex128)/2 self.zero_y = np.array([1, 0, 1], np.complex128)/math.sqrt(2) self.minus_y = np.array([1, -1j*math.sqrt(2), 1], np.complex128)/2 self.plus_z = np.array([1, 0, 0], np.complex128) self.zero_z = np.array([0, 1, 0], np.complex128) self.minus_z = np.array([0, 0, 1], np.complex128) HALF = (1/2, 2, "half") """ For two level systems. """ ONE = (1, 3, "one") """ For three level systems. """ class IntegrationMethod(Enum): """ Options for describing which method is used during the integration. Parameters ---------- value : :obj:`str` A text label that can be used for archiving. """ MAGNUS_CF4 = "magnus_cf4" """ Commutator free, fourth order Magnus based integrator. """ EULER = "euler" """ Euler integration method. """ HEUN = "heun" """ Integration method from AtomicPy. Makes two Euler integration steps, one sampling the field from the start of the time step, one sampling the field from the end of the time step. The equivalent of the trapezoidal method. """ class ExponentiationMethod(Enum): """ The implementation to use for matrix exponentiation within the integrator. Parameters ---------- value : :obj:`str` A text label that can be used for archiving. index : :obj:`int` A reference number, used when compiling the integrator, where higher level objects like enums cannot be interpreted. """ def __init__(self, value:str, index:int): super().__init__() self._value_ = value self.index = index ANALYTIC = ("analytic", 0) """ Analytic expression of the matrix exponential. For spin-half :obj:`SpinQuantumNumber.HALF` systems only. See :obj:`Utilities.matrix_exponential_analytic()` for more information. """ LIE_TROTTER = ("lie_trotter", 1) """ Approximation using the Lie Trotter theorem, using the Pauli matrices and a single quadratic operator. See :obj:`Utilities.matrix_exponential_lie_trotter()` for more information. """ LIE_TROTTER_8 = ("lie_trotter_8", 2) """ Approximation using the Lie Trotter theorem, using all basis elements of su(3). For spin-one :obj:`SpinQuantumNumber.HALF` systems only. See :obj:`Utilities.matrix_exponential_lie_trotter_8()` for more information. """ class Device(Enum): """ The target device that the integrator is being compiled for. .. _Supported Python features: http://numba.pydata.org/numba-doc/latest/reference/pysupported.html .. _Supported Numpy features: http://numba.pydata.org/numba-doc/latest/reference/numpysupported.html .. _Supported CUDA Python features: http://numba.pydata.org/numba-doc/latest/cuda/cudapysupported.html """ def __init__(self, value:str, index:int): super().__init__() self._value_ = value self.index = index if value == "python": def jit_host(template, max_registers): def jit_host(func): return func return jit_host self.jit_host = jit_host def jit_device(func): return func self.jit_device = jit_device def jit_device_template(template): def jit_device_template(func): return func return jit_device_template self.jit_device_template = jit_device_template elif value == "cpu_single": def jit_host(template, max_registers): def jit_host(func): return nb.njit(template)(func) return jit_host self.jit_host = jit_host def jit_device(func): return nb.njit()(func) self.jit_device = jit_device def jit_device_template(template): def jit_device_template(func): return nb.njit(template)(func) return jit_device_template self.jit_device_template = jit_device_template elif value == "cpu": def jit_host(template, max_registers): def jit_host(func): return nb.njit(template, parallel = True)(func) return jit_host self.jit_host = jit_host def jit_device(func): return nb.njit()(func) self.jit_device = jit_device def jit_device_template(template): def jit_device_template(func): return nb.njit(template)(func) return jit_device_template self.jit_device_template = jit_device_template elif value == "cuda": def jit_host(template, max_registers): def jit_host(func): return cuda.jit(template, debug = False, max_registers = max_registers)(func) return jit_host self.jit_host = jit_host def jit_device(func): return cuda.jit(device = True, inline = True)(func) self.jit_device = jit_device def jit_device_template(template): def jit_device_template(func): return cuda.jit(template, device = True, inline = True)(func) return jit_device_template self.jit_device_template = jit_device_template elif value == "roc": def jit_host(template, max_registers): def jit_host(func): return roc.jit(template)(func) return jit_host self.jit_host = jit_host def jit_device(func): return roc.jit(device = True)(func) self.jit_device = jit_device def jit_device_template(template): def jit_device_template(func): return roc.jit(template, device = True)(func) return jit_device_template self.jit_device_template = jit_device_template PYTHON = ("python", 0) """ Use pure python interpreted code for the integrator, ie, don't compile the integrator. """ CPU_SINGLE = ("cpu_single", 0) """ Use the :func:`numba.jit()` LLVM compiler to compile the integrator to run on a single CPU core. .. note :: To use this device option, the user defined field function must be :func:`numba.jit()` compilable. See `Supported Python features`_ for compilable python features, and `Supported Numpy features`_ for compilable numpy features. """ CPU = ("cpu", 0) """ Use the :func:`numba.jit()` LLVM compiler to compile the integrator to run on all CPU cores, in parallel. .. note :: To use this device option, the user defined field function must be :func:`numba.jit()` compilable. See `Supported Python features`_ for compilable python features, and `Supported Numpy features`_ for compilable numpy features. """ CUDA = ("cuda", 1) """ Use the :func:`numba.cuda.jit()` LLVM compiler to compile the integrator to run on an Nvidia cuda compatible GPU, in parallel. .. note :: To use this device option, the user defined field function must be :func:`numba.cuda.jit()` compilable. See `Supported CUDA Python features`_ for compilable python features. """ ROC = ("roc", 2) """ Use the :func:`numba.roc.jit()` LLVM compiler to compile the integrator to run on an AMD ROCm compatible GPU, in parallel. .. warning :: Work in progress, not currently functional! """ class Results: """ The results of a an evaluation of the integrator. Attributes ---------- time : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index) The times that `state` was evaluated at. time_evolution : :obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index) The evaluated time evolution operator between each time step. See :ref:`architecture` for some information. state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number) The evaluated quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. spin : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction) The expected spin projection (Bloch vector) over time. This is calculated just in time using the JITed :obj:`callable` `spin_calculator`. spin_calculator : :obj:`callable` Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. Used to calculate `spin` the first time it is referenced by the user. Parameters: * **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. Returns: * **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time. """ def __init__(self, time:np.ndarray, time_evolution:np.ndarray, state:np.ndarray, spin_calculator:callable): """ Parameters ---------- time : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index) The times that `state` was evaluated at. time_evolution : :obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index) The evaluated time evolution operator between each time step. See :ref:`architecture` for some information. state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number) The evaluated quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. spin_calculator : :obj:`callable` Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. Used to calculate `spin` the first time it is referenced by the user. Parameters: * **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. Returns: * **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time. """ self.time = time self.time_evolution = time_evolution self.state = state self.spin_calculator = spin_calculator def __getattr__(self, attr_name:str) -> np.ndarray: if attr_name == "spin": spin = self.spin_calculator(self.state) setattr(self, attr_name, spin) return self.spin raise AttributeError("{} has no attribute called {}.".format(self, attr_name)) class Simulator: """ Attributes ---------- spin_quantum_number : :obj:`SpinQuantumNumber` The option to select whether the simulator will integrate a spin-half :obj:`SpinQuantumNumber.HALF`, or spin-one :obj:`SpinQuantumNumber.ONE` quantum system. threads_per_block : :obj:`int` The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models. device : :obj:`Device` The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details. number_of_threads : :obj:`int` The number of CPU threads to use when running on a CPU device. get_time_evolution : :obj:`callable` The internal function for evaluating the time evolution operator in parallel. Compiled for chosen device on object constrution. Parameters: * **sweep_parameters** (:obj:`numpy.ndarray` of :obj:`numpy.float64`) - The input to the :obj:`get_field()` function supplied by the user. Modifies the field function so the integrator can be used for many experiments, without the need for slow recompilation. For example, if the `sweep_parameters` is used to define the bias field strength in :obj:`get_field()`, then one can run many simulations, sweeping through bias values, by calling this method multiple times, each time varying `sweep_parameters`. * **time_coarse** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index)) - The times that `state` was evaluated at. * **time_end_points** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (start/end)) - The time offset that the experiment is to start at, and the time that the experiment is to finish at. Measured in s. * **time_step_integration** (:obj:`float`) - The integration time step. Measured in s. * **time_step_output** (:obj:`float`) - The sample resolution of the output timeseries for the state. Must be a whole number multiple of `time_step_integration`. Measured in s. * **time_evolution_output** (:obj:`numpy.ndarray` of :obj:`numpy.float128` (time_index, y_index, x_index)) - The evaluated time evolution operator between each time step. See :ref:`architecture` for some information. spin_calculator : :obj:`callable` Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. This :obj:`callable` is passed to the :obj:`Results` object returned from :func:`Simulator.evaluate()`, and is executed there just in time if the `spin` property is needed. Compiled for chosen device on object constrution. Parameters: * **state** (:obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number)) - The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. Returns: * **spin** (:obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction)) - The expected spin projection (Bloch vector) over time. """ def __init__(self, get_field:callable, spin_quantum_number:SpinQuantumNumber, device:Device = None, exponentiation_method:ExponentiationMethod = None, use_rotating_frame:bool = True, integration_method:IntegrationMethod = IntegrationMethod.MAGNUS_CF4, number_of_squares:int = 24, threads_per_block:int = 64, max_registers:int = None, number_of_threads:int = None): """ .. _Achieved Occupancy: https://docs.nvidia.com/gameworks/content/developertools/desktop/analysis/report/cudaexperiments/kernellevel/achievedoccupancy.htm Parameters ---------- get_field : :obj:`callable` A python function that describes the field that the spin system is being put under. It must have three arguments: * **time_sample** (:obj:`float`) - the time to sample the field at, in units of s. * **simulation_index** (:obj:`int`) - a parameter that can be swept over when multiple simulations need to be run. For example, it is used to sweep over dressing frequencies during the simulations that `spinsim` was designed for. * **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64` (spatial_index)) the returned value of the field. This is a four dimensional vector, with the first three entries being x, y, z spatial directions (to model a magnetic field, for example), and the fourth entry being the amplitude of the quadratic shift (only appearing, and required, in spin-one systems). .. note:: This function must be compilable for the device that the integrator is being compiled for. See :class:`Device` for more information and links. spin_quantum_number : :obj:`SpinQuantumNumber` The option to select whether the simulator will integrate a spin-half :obj:`SpinQuantumNumber.HALF`, or spin-one :obj:`SpinQuantumNumber.ONE` quantum system. device : :obj:`Device` The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details. exponentiation_method : :obj:`ExponentiationMethod` Which method to use for matrix exponentiation in the integration algorithm. Defaults to :obj:`ExponentiationMethod.LIE_TROTTER` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.ONE`, and defaults to :obj:`ExponentiationMethod.ANALYTIC` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.HALF`. See :obj:`ExponentiationMethod` for more details. use_rotating_frame : :obj:`bool` Whether or not to use the rotating frame optimisation. Defaults to :obj:`True`. If set to :obj:`True`, the integrator moves into a frame rotating in the z axis by an amount defined by the field in the z direction. This removes the (possibly large) z component of the field, which increases the accuracy of the output since the integrator will on average take smaller steps. .. note :: The use of a rotating frame is commonly associated with the use of a rotating wave approximation, a technique used to get approximate analytic solutions of spin system dynamics. This is not done when this option is set to :obj:`True` - no such approximations are made, and the output state in given out of the rotating frame. One can, of course, use :mod:`spinsim` to integrate states in the rotating frame, using the rating wave approximation: just define :obj:`get_field()` with field functions that use the rotating wave approximation in the rotating frame. integration_method : :obj:`IntegrationMethod` Which integration method to use in the integration. Defaults to :obj:`IntegrationMethod.MAGNUS_CF4`. See :obj:`IntegrationMethod` for more details. number_of_squares : :obj:`int` The number of squares made by the matrix exponentiator, if :obj:`ExponentiationMethod.LIE_TROTTER` is chosen. threads_per_block : :obj:`int` The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models. max_registers : :obj:`int` The maximum number of registers allocated per thread when using :obj:`Device.CUDA` as the target device, and can be modified to increase the execution speed for a specific GPU model. Raising this value allocates more registers (fast memory) to each thread, out of a maximum number for the whole GPU, for each specific GPU model. This means that if more registers are allocated than are available for the GPU model, the GPU must run fewer threads concurrently than it has Cuda cores, meaning some cores are inactive, and the GPU is said to have less occupancy. Lowering the value increases GPU occupancy, meaning more threads run concurrently, at the expense of fewer resgiters being avaliable to each thread, meaning slower memory must be used. Thus, there will be an optimal value of `max_registers` for each model of GPU running :mod:`spinsim`, balancing more threads vs faster running threads, and changing this value could increase performance for your GPU. See `Achieved Occupancy`_ for Nvidia's official explanation. number_of_threads : :obj:`int` The number of CPU threads to use when running on a CPU device. """ if not device: if cuda.is_available(): device = Device.CUDA else: device = Device.CPU self.threads_per_block = threads_per_block self.spin_quantum_number = spin_quantum_number self.device = device self.number_of_threads = number_of_threads self.get_time_evolution = None try: self.compile_time_evolver(get_field, spin_quantum_number, device, use_rotating_frame, integration_method, exponentiation_method, number_of_squares, threads_per_block, max_registers) except: print("\033[31mspinsim error!!!\nnumba could not jit get_field() function into a device function.\033[0m\n") raise def compile_time_evolver(self, get_field:callable, spin_quantum_number:SpinQuantumNumber, device:Device, use_rotating_frame:bool = True, integration_method:IntegrationMethod = IntegrationMethod.MAGNUS_CF4, exponentiation_method:ExponentiationMethod = None, number_of_squares:int = 24, threads_per_block:int = 64, max_registers:int = None): """ Compiles the integrator and spin calculation functions of the simulator. Parameters ---------- get_field : :obj:`callable` A python function that describes the field that the spin system is being put under. It must have three arguments: * **time_sample** (:obj:`float`) - the time to sample the field at, in units of s. * **sweep_parameters** (:obj:`numpy.ndarray` of :obj:`numpy.float64`) - an array of parameters that can be swept over when multiple simulations need to be run. For example, it is used to sweep over dressing frequencies during the magnetometry experiments that `spinsim` was designed for. * **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64` (spatial_index)) the returned value of the field. This is a four dimensional vector, with the first three entries being x, y, z spatial directions (to model a magnetic field, for example), and the fourth entry being the amplitude of the quadratic shift (only appearing, and required, in spin-one systems). .. note:: This function must be compilable for the device that the integrator is being compiled for. See :class:`Device` for more information and links. spin_quantum_number : :obj:`SpinQuantumNumber` The option to select whether the simulator will integrate a spin-half :obj:`SpinQuantumNumber.HALF`, or spin-one :obj:`SpinQuantumNumber.ONE` quantum system. device : :obj:`Device` The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details. exponentiation_method : :obj:`ExponentiationMethod` Which method to use for matrix exponentiation in the integration algorithm. Defaults to :obj:`ExponentiationMethod.LIE_TROTTER` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.ONE`, and defaults to :obj:`ExponentiationMethod.ANALYTIC` when `spin_quantum_number` is set to :obj:`SpinQuantumNumber.HALF`. See :obj:`ExponentiationMethod` for more details. use_rotating_frame : :obj:`bool` Whether or not to use the rotating frame optimisation. Defaults to :obj:`True`. If set to :obj:`True`, the integrator moves into a frame rotating in the z axis by an amount defined by the field in the z direction. This removes the (possibly large) z component of the field, which increases the accuracy of the output since the integrator will on average take smaller steps. .. note :: The use of a rotating frame is commonly associated with the use of a rotating wave approximation, a technique used to get approximate analytic solutions of spin system dynamics. This is not done when this option is set to :obj:`True` - no such approximations are made, and the output state in given out of the rotating frame. One can, of course, use :mod:`spinsim` to integrate states in the rotating frame, using the rating wave approximation: just define :obj:`get_field()` with field functions that use the rotating wave approximation in the rotating frame. integration_method : :obj:`IntegrationMethod` Which integration method to use in the integration. Defaults to :obj:`IntegrationMethod.MAGNUS_CF4`. See :obj:`IntegrationMethod` for more details. number_of_squares : :obj:`int` The number of squares made by the matrix exponentiator, if :obj:`ExponentiationMethod.LIE_TROTTER` is chosen. threads_per_block : :obj:`int` The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models. max_registers : :obj:`int` The maximum number of registers allocated per thread when using :obj:`Device.CUDA` as the target device, and can be modified to increase the execution speed for a specific GPU model. Defaults to 63 (optimal for GTX1070, the device used for testing. Note that one extra register per thread is always added to the number specified for control, so really this number is 64). Raising this value allocates more registers (fast memory) to each thread, out of a maximum number for the whole GPU, for each specific GPU model. This means that if more registers are allocated than are available for the GPU model, the GPU must run fewer threads concurrently than it has Cuda cores, meaning some cores are inactive, and the GPU is said to have less occupancy. Lowering the value increases GPU occupancy, meaning more threads run concurrently, at the expense of fewer registers being avaliable to each thread, meaning slower memory must be used. Thus, there will be an optimal value of `max_registers` for each model of GPU running :mod:`spinsim`, balancing more threads vs faster running threads, and changing this value could increase performance for your GPU. See `Achieved Occupancy`_ for Nvidia's official explanation. """ utilities = Utilities(spin_quantum_number, device, threads_per_block, number_of_squares) conj = utilities.conj set_to = utilities.set_to set_to_one = utilities.set_to_one matrix_multiply = utilities.matrix_multiply matrix_exponential_analytic = utilities.matrix_exponential_analytic matrix_exponential_lie_trotter = utilities.matrix_exponential_lie_trotter matrix_exponential_lie_trotter_8 = utilities.matrix_exponential_lie_trotter_8 jit_host = device.jit_host jit_device = device.jit_device jit_device_template = device.jit_device_template device_index = device.index if not exponentiation_method: if spin_quantum_number == SpinQuantumNumber.ONE: exponentiation_method = ExponentiationMethod.LIE_TROTTER elif spin_quantum_number == SpinQuantumNumber.HALF: exponentiation_method = ExponentiationMethod.ANALYTIC if integration_method == IntegrationMethod.MAGNUS_CF4: sample_index_max = 3 sample_index_end = 4 elif integration_method == IntegrationMethod.HEUN: sample_index_max = 3 sample_index_end = 4 elif integration_method == IntegrationMethod.EULER: sample_index_max = 1 sample_index_end = 1 exponentiation_method_index = exponentiation_method.index dimension = spin_quantum_number.dimension if spin_quantum_number == SpinQuantumNumber.HALF: lie_dimension = 3 elif spin_quantum_number == SpinQuantumNumber.ONE: if exponentiation_method == ExponentiationMethod.LIE_TROTTER: lie_dimension = 4 elif exponentiation_method == ExponentiationMethod.LIE_TROTTER_8: lie_dimension = 8 if (exponentiation_method == ExponentiationMethod.ANALYTIC) and (spin_quantum_number != SpinQuantumNumber.HALF): print("\033[31mspinsim warning!!!\n_attempting to use an analytic exponentiation method outside of spin-half. Switching to a Lie Trotter method.\033[0m") exponentiation_method = ExponentiationMethod.LIE_TROTTER exponentiation_method_index = 1 @jit_device_template("(float64[:], complex128[:, :], complex128[:, :])") def append_exponentiation(field_sample, time_evolution_fine, time_evolution_output): if device_index == 0: time_evolution_old = np.empty((dimension, dimension), dtype = np.complex128) elif device_index == 1: time_evolution_old = cuda.local.array((dimension, dimension), dtype = np.complex128) elif device_index == 2: time_evolution_old_group = roc.shared.array((threads_per_block, dimension, dimension), dtype = np.complex128) time_evolution_old = time_evolution_old_group[roc.get_local_id(1), :, :] # Calculate the exponential if exponentiation_method_index == 0: matrix_exponential_analytic(field_sample, time_evolution_fine) elif exponentiation_method_index == 1: matrix_exponential_lie_trotter(field_sample, time_evolution_fine) elif exponentiation_method_index == 2: matrix_exponential_lie_trotter_8(field_sample, time_evolution_fine) # Premultiply to the exitsing time evolution operator set_to(time_evolution_output, time_evolution_old) matrix_multiply(time_evolution_fine, time_evolution_old, time_evolution_output) if use_rotating_frame: if dimension == 3: if exponentiation_method_index == 2: @jit_device_template("(float64[:], float64, complex128)") def transform_frame_spin_one_rotating_8(field_sample, rotating_wave, rotating_wave_winding): X = (field_sample[0] + 1j*field_sample[1])*conj(rotating_wave_winding) field_sample[0] = X.real field_sample[1] = X.imag field_sample[2] = field_sample[2] - rotating_wave X = (field_sample[4] + 1j*field_sample[5])*conj(rotating_wave_winding)*conj(rotating_wave_winding) field_sample[4] = X.real field_sample[5] = X.imag X = (field_sample[6] + 1j*field_sample[7])*conj(rotating_wave_winding) field_sample[6] = X.real field_sample[7] = X.imag transform_frame = transform_frame_spin_one_rotating_8 else: @jit_device_template("(float64[:], float64, complex128)") def transform_frame_spin_one_rotating(field_sample, rotating_wave, rotating_wave_winding): X = (field_sample[0] + 1j*field_sample[1])/rotating_wave_winding field_sample[0] = X.real field_sample[1] = X.imag field_sample[2] = field_sample[2] - rotating_wave transform_frame = transform_frame_spin_one_rotating else: @jit_device_template("(float64[:], float64, complex128)") def transform_frame_spin_half_rotating(field_sample, rotating_wave, rotating_wave_winding): X = (field_sample[0] + 1j*field_sample[1])/(rotating_wave_winding**2) field_sample[0] = X.real field_sample[1] = X.imag field_sample[2] = field_sample[2] - 2*rotating_wave transform_frame = transform_frame_spin_half_rotating else: @jit_device_template("(float64[:], float64, complex128)") def transform_frame_lab(field_sample, rotating_wave, rotating_wave_winding): return transform_frame = transform_frame_lab get_field_jit = jit_device_template("(float64, float64[:], float64[:])")(get_field) if integration_method == IntegrationMethod.MAGNUS_CF4: @jit_device_template("(float64[:], float64, float64, float64, float64[:, :], float64, complex128[:])") def get_field_integration_magnus_cf4(sweep_parameters, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding): time_sample = ((time_fine + 0.5*time_step_integration*(1 - 1/sqrt3)) - time_coarse) rotating_wave_winding[0] = cmath.exp(1j*rotating_wave*time_sample) time_sample += time_coarse get_field_jit(time_sample, sweep_parameters, field_sample[0, :]) time_sample = ((time_fine + 0.5*time_step_integration*(1 + 1/sqrt3)) - time_coarse) rotating_wave_winding[1] = cmath.exp(1j*rotating_wave*time_sample) time_sample += time_coarse get_field_jit(time_sample, sweep_parameters, field_sample[1, :]) @jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])") def append_exponentiation_integration_magnus_cf4(time_evolution_fine, time_evolution_output, field_sample, time_step_integration, rotating_wave, rotating_wave_winding): transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0]) transform_frame(field_sample[1, :], rotating_wave, rotating_wave_winding[1]) w0 = (1.5 + sqrt3)/6 w1 = (1.5 - sqrt3)/6 field_sample[2, 0] = time_step_integration*(w0*field_sample[0, 0] + w1*field_sample[1, 0]) field_sample[2, 1] = time_step_integration*(w0*field_sample[0, 1] + w1*field_sample[1, 1]) field_sample[2, 2] = time_step_integration*(w0*field_sample[0, 2] + w1*field_sample[1, 2]) if dimension > 2: field_sample[2, 3] = time_step_integration*(w0*field_sample[0, 3] + w1*field_sample[1, 3]) append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_output) field_sample[2, 0] = time_step_integration*(w1*field_sample[0, 0] + w0*field_sample[1, 0]) field_sample[2, 1] = time_step_integration*(w1*field_sample[0, 1] + w0*field_sample[1, 1]) field_sample[2, 2] = time_step_integration*(w1*field_sample[0, 2] + w0*field_sample[1, 2]) if dimension > 2: field_sample[2, 3] = time_step_integration*(w1*field_sample[0, 3] + w0*field_sample[1, 3]) append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_output) get_field_integration = get_field_integration_magnus_cf4 append_exponentiation_integration = append_exponentiation_integration_magnus_cf4 elif integration_method == IntegrationMethod.HEUN: @jit_device_template("(float64[:], float64, float64, float64, float64[:, :], float64, complex128[:])") def get_field_integration_heun(sweep_parameters, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding): time_sample = time_fine - time_coarse rotating_wave_winding[0] = cmath.exp(1j*rotating_wave*time_sample) time_sample += time_coarse get_field_jit(time_sample, sweep_parameters, field_sample[0, :]) time_sample = time_fine + time_step_integration - time_coarse rotating_wave_winding[1] = cmath.exp(1j*rotating_wave*time_sample) time_sample += time_coarse get_field_jit(time_sample, sweep_parameters, field_sample[1, :]) @jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])") def append_exponentiation_integration_heun(time_evolution_fine, time_evolution_output, field_sample, time_step_integration, rotating_wave, rotating_wave_winding): transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0]) transform_frame(field_sample[1, :], rotating_wave, rotating_wave_winding[1]) field_sample[2, 0] = time_step_integration*field_sample[0, 0]/2 field_sample[2, 1] = time_step_integration*field_sample[0, 1]/2 field_sample[2, 2] = time_step_integration*field_sample[0, 2]/2 if dimension > 2: field_sample[2, 3] = time_step_integration*field_sample[0, 3]/2 append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_output) field_sample[2, 0] = time_step_integration*field_sample[1, 0]/2 field_sample[2, 1] = time_step_integration*field_sample[1, 1]/2 field_sample[2, 2] = time_step_integration*field_sample[1, 2]/2 if dimension > 2: field_sample[2, 3] = time_step_integration*field_sample[1, 3]/2 append_exponentiation(field_sample[2, :], time_evolution_fine, time_evolution_output) get_field_integration = get_field_integration_heun append_exponentiation_integration = append_exponentiation_integration_heun elif integration_method == IntegrationMethod.EULER: @jit_device_template("(float64[:], float64, float64, float64, float64[:, :], float64, complex128[:])") def get_field_integration_euler(sweep_parameters, time_fine, time_coarse, time_step_integration, field_sample, rotating_wave, rotating_wave_winding): time_sample = time_fine + 0.5*time_step_integration - time_coarse rotating_wave_winding[0] = cmath.exp(1j*rotating_wave*time_sample) time_sample += time_coarse get_field_jit(time_sample, sweep_parameters, field_sample[0, :]) @jit_device_template("(complex128[:, :], complex128[:, :], float64[:, :], float64, float64, complex128[:])") def append_exponentiation_integration_euler(time_evolution_fine, time_evolution_output, field_sample, time_step_integration, rotating_wave, rotating_wave_winding): transform_frame(field_sample[0, :], rotating_wave, rotating_wave_winding[0]) field_sample[0, 0] = time_step_integration*field_sample[0, 0] field_sample[0, 1] = time_step_integration*field_sample[0, 1] field_sample[0, 2] = time_step_integration*field_sample[0, 2] if dimension > 2: field_sample[0, 3] = time_step_integration*field_sample[0, 3] append_exponentiation(field_sample[0, :], time_evolution_fine, time_evolution_output) get_field_integration = get_field_integration_euler append_exponentiation_integration = append_exponentiation_integration_euler @jit_device_template("(int64, float64[:], float64, float64, float64[:], complex128[:, :, :], float64[:])") def get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_output, sweep_parameters): # Declare variables if device_index == 0: time_evolution_fine = np.empty((dimension, dimension), dtype = np.complex128) field_sample = np.empty((sample_index_max, lie_dimension), dtype = np.float64) rotating_wave_winding = np.empty(sample_index_end, dtype = np.complex128) elif device_index == 1: time_evolution_fine = cuda.local.array((dimension, dimension), dtype = np.complex128) field_sample = cuda.local.array((sample_index_max, lie_dimension), dtype = np.float64) rotating_wave_winding = cuda.local.array(sample_index_end, dtype = np.complex128) elif device_index == 2: time_evolution_fine_group = roc.shared.array((threads_per_block, dimension, dimension), dtype = np.complex128) time_evolution_fine = time_evolution_fine_group[roc.get_local_id(1), :, :] field_sample_group = roc.shared.array((threads_per_block, sample_index_max, lie_dimension), dtype = np.float64) field_sample = field_sample_group[roc.get_local_id(1), :, :] rotating_wave_winding_group = roc.shared.array((threads_per_block, sample_index_end), dtype = np.complex128) rotating_wave_winding = rotating_wave_winding_group[roc.get_local_id(1), :] time_coarse[time_index] = time_end_points[0] + time_step_output*time_index time_fine = time_coarse[time_index] # Initialise time evolution operator to 1 set_to_one(time_evolution_output[time_index, :]) field_sample[0, 2] = 0 if use_rotating_frame: time_sample = time_coarse[time_index] + time_step_output/2 get_field_jit(time_sample, sweep_parameters, field_sample[0, :]) rotating_wave = field_sample[0, 2] if dimension == 2: rotating_wave /= 2 # For every fine step for time_fine_index in range(math.floor(time_step_output/time_step_integration + 0.5)): get_field_integration(sweep_parameters, time_fine, time_coarse[time_index], time_step_integration, field_sample, rotating_wave, rotating_wave_winding) append_exponentiation_integration(time_evolution_fine, time_evolution_output[time_index, :], field_sample, time_step_integration, rotating_wave, rotating_wave_winding) time_fine += time_step_integration # Take out of rotating frame if use_rotating_frame: rotating_wave_winding[0] = cmath.exp(1j*rotating_wave*time_step_output) time_evolution_output[time_index, 0, 0] /= rotating_wave_winding[0] time_evolution_output[time_index, 0, 1] /= rotating_wave_winding[0] if dimension > 2: time_evolution_output[time_index, 0, 2] /= rotating_wave_winding[0] time_evolution_output[time_index, 2, 0] *= rotating_wave_winding[0] time_evolution_output[time_index, 2, 1] *= rotating_wave_winding[0] time_evolution_output[time_index, 2, 2] *= rotating_wave_winding[0] else: time_evolution_output[time_index, 1, 0] *= rotating_wave_winding[0] time_evolution_output[time_index, 1, 1] *= rotating_wave_winding[0] @jit_host("(float64[:], float64[:], float64[:], float64, float64, complex128[:, :, :])", max_registers) def get_time_evolution(sweep_parameters, time_coarse, time_end_points, time_step_integration, time_step_output, time_evolution_output): """ Find the stepwise time evolution opperator. Parameters ---------- sweep_parameters : :obj:`numpy.ndarray` of :obj:`numpy.float64` time_coarse : :class:`numpy.ndarray` of :class:`numpy.float64` (time_index) A coarse grained list of time samples that the time evolution operator is found for. In units of s. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`. time_end_points : :class:`numpy.ndarray` of :class:`numpy.float64` (start time (0) or end time (1)) The time values for when the experiment is to start and finishes. In units of s. time_step_integration : :obj:`float` The time step used within the integration algorithm. In units of s. time_step_output : :obj:`float` The time difference between each element of `time_coarse`. In units of s. Determines the sample rate of the outputs `time_coarse` and `time_evolution_output`. time_evolution_output : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, bra_state_index, ket_state_index) Time evolution operator (matrix) between the current and next timesteps, for each time sampled. See :math:`U(t)` in :ref:`overview_of_simulation_method`. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`. """ if device_index == 0: for time_index in nb.prange(time_coarse.size): get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_output, sweep_parameters) elif device_index == 1: # Run calculation for each coarse timestep in parallel time_index = cuda.grid(1) if time_index < time_coarse.size: get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_output, sweep_parameters) elif device_index == 2: # Run calculation for each coarse timestep in parallel time_index = roc.get_global_id(1) if time_index < time_coarse.size: get_time_evolution_loop(time_index, time_coarse, time_step_output, time_step_integration, time_end_points, time_evolution_output, sweep_parameters) return @jit_host("(complex128[:, :], float64[:, :])", max_registers = max_registers) def get_spin(state, spin): """ Calculate each expected spin value in parallel. For spin-half: .. math:: \\begin{align*} \\langle F\\rangle(t) = \\begin{pmatrix} \\Re(\\psi_{+\\frac{1}{2}}(t)\\psi_{-\\frac{1}{2}}(t)^*)\\\\ -\\Im(\\psi_{+\\frac{1}{2}}(t)\\psi_{-\\frac{1}{2}}(t)^*)\\\\ \\frac{1}{2}(|\\psi_{+\\frac{1}{2}}(t)|^2 - |\\psi_{-\\frac{1}{2}}(t)|^2) \\end{pmatrix} \\end{align*} For spin-one: .. math:: \\begin{align*} \\langle F\\rangle(t) = \\begin{pmatrix} \\Re(\\sqrt{2}\\psi_{0}(t)^*(\\psi_{+1}(t) + \\psi_{-1}(t))\\\\ -\\Im(\\sqrt{2}\\psi_{0}(t)^*(\\psi_{+1}(t) - \\psi_{-1}(t))\\\\ |\\psi_{+1}(t)|^2 - |\\psi_{-1}(t)|^2 \\end{pmatrix} \\end{align*} Parameters ---------- state : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, state_index) The state (wavefunction) of the spin system in the lab frame, for each time sampled. See :math:`\\psi(t)` in :ref:`overview_of_simulation_method`. spin : :class:`numpy.ndarray` of :class:`numpy.float64` (time_index, spatial_index) The expected value for hyperfine spin of the spin system in the lab frame, for each time sampled. Units of :math:`\\hbar`. This is an output, so use an empty :class:`numpy.ndarray` with :func:`numpy.empty()`, or declare a :class:`numpy.ndarray` using :func:`numba.cuda.device_array_like()`. """ if device_index == 0: for time_index in nb.prange(spin.shape[0]): if dimension == 2: spin[time_index, 0] = (state[time_index, 0]*conj(state[time_index, 1])).real spin[time_index, 1] = (1j*state[time_index, 0]*conj(state[time_index, 1])).real spin[time_index, 2] = 0.5*(state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 1].real**2 - state[time_index, 1].imag**2) else: spin[time_index, 0] = (2*conj(state[time_index, 1])*(state[time_index, 0] + state[time_index, 2])/sqrt2).real spin[time_index, 1] = (2j*conj(state[time_index, 1])*(state[time_index, 0] - state[time_index, 2])/sqrt2).real spin[time_index, 2] = state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 2].real**2 - state[time_index, 2].imag**2 elif device_index > 0: if device_index == 1: time_index = cuda.grid(1) elif device_index == 1: time_index = roc.get_global_id(1) if time_index < spin.shape[0]: if dimension == 2: spin[time_index, 0] = (state[time_index, 0]*conj(state[time_index, 1])).real spin[time_index, 1] = (1j*state[time_index, 0]*conj(state[time_index, 1])).real spin[time_index, 2] = 0.5*(state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 1].real**2 - state[time_index, 1].imag**2) else: spin[time_index, 0] = (2*conj(state[time_index, 1])*(state[time_index, 0] + state[time_index, 2])/sqrt2).real spin[time_index, 1] = (2j*conj(state[time_index, 1])*(state[time_index, 0] - state[time_index, 2])/sqrt2).real spin[time_index, 2] = state[time_index, 0].real**2 + state[time_index, 0].imag**2 - state[time_index, 2].real**2 - state[time_index, 2].imag**2 return def spin_calculator(state): """ Calculates the expected spin projection (Bloch vector) over time for a given time series of a quantum state. Parameters ---------- state : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (time_index, magnetic_quantum_number) The quantum state of the spin system over time, written in terms of the eigenstates of the spin projection operator in the z direction. Returns ------- spin : :obj:`numpy.ndarray` of :obj:`numpy.float64` (time_index, spatial_direction) The expected spin projection (Bloch vector) over time. """ if device.index == 0: spin = np.empty((state.shape[0], 3), np.float64) get_spin(state, spin) elif device == Device.CUDA: spin = cuda.device_array((state.shape[0], 3), np.float64) blocks_per_grid = (state.shape[0] + (threads_per_block - 1)) // threads_per_block get_spin[blocks_per_grid, threads_per_block](cuda.to_device(state), spin) spin = spin.copy_to_host() elif device == Device.ROC: spin = roc.device_array((state.shape[0], 3), np.float64) blocks_per_grid = (state.shape[0] + (threads_per_block - 1)) // threads_per_block get_spin[blocks_per_grid, threads_per_block](roc.to_device(state), spin) spin = spin.copy_to_host() return spin self.get_time_evolution = get_time_evolution self.spin_calculator = spin_calculator def evaluate(self, time_start:np.float64, time_end:np.float64, time_step_integration:np.float64, time_step_output:np.float64, state_init:np.ndarray, sweep_parameters:np.ndarray = [0]) -> Results: """ Integrates the time dependent Schroedinger equation and returns the quantum state of the spin system over time. Parameters ---------- sweep_parameters : :obj:`numpy.ndarray` of :obj:`numpy.float64` The input to the :obj:`get_field()` function supplied by the user. Modifies the field function so the integrator can be used for many experiments, without the need for slow recompilation. For example, if the `sweep_parameters` is used to define the bias field strength in :obj:`get_field()`, then one can run many simulations, sweeping through bias values, by calling this method multiple times, each time varying `sweep_parameters`. time_start : :obj:`float` The time offset that the experiment is to start at. Measured in s. time_end : :obj:`float` The time that the experiment is to finish at. Measured in s. The duration of the experiment is `time_end - time_start`. time_step_integration : :obj:`float` The integration time step. Measured in s. time_step_output : :obj:`float` The sample resolution of the output timeseries for the state. Must be a whole number multiple of `time_step_integration`. Measured in s. state_init : :obj:`numpy.ndarray` of :obj:`numpy.complex128` (magnetic_quantum_number) The initial quantum state of the spin system, written in terms of the eigenstates of the spin projection operator in the z direction. Returns ------- results : :obj:`Results` An object containing the results of the simulation. """ time_step_integration_old = time_step_integration time_step_integration = time_step_output/round(max(time_step_output/time_step_integration, 1)) if math.fabs(time_step_output/time_step_integration_old - round(time_step_output/time_step_integration_old)) > 1e-6: print(f"\033[33mspinsim warning!!!\ntime_step_output ({time_step_output:8.4e}) not an integer multiple of time_step_integration ({time_step_integration_old:8.4e}). Resetting time_step_integration to {time_step_integration:8.4e}.\033[0m\n") time_end_points = np.asarray([time_start, time_end], np.float64) state_init = np.asarray(state_init, np.complex128) sweep_parameters = np.asarray(sweep_parameters, np.float64) time_index_max = int((time_end_points[1] - time_end_points[0])/time_step_output) if self.device.index == 0: if self.device == Device.CPU: if self.number_of_threads: old_threads = nb.get_num_threads() nb.set_num_threads(self.number_of_threads) time = np.empty(time_index_max, np.float64) time_evolution_output = np.empty((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128) self.get_time_evolution(sweep_parameters, time, time_end_points, time_step_integration, time_step_output, time_evolution_output) if self.device == Device.CPU: if self.number_of_threads: nb.set_num_threads(old_threads) elif self.device == Device.CUDA: try: time = cuda.device_array(time_index_max, np.float64) time_evolution_output = cuda.device_array((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128) sweep_parameters_device = cuda.to_device(sweep_parameters) blocks_per_grid = (time.size + (self.threads_per_block - 1)) // self.threads_per_block self.get_time_evolution[blocks_per_grid, self.threads_per_block](sweep_parameters_device, time, time_end_points, time_step_integration, time_step_output, time_evolution_output) except: print("\033[31mspinsim error!!!\nnumba.cuda could not jit get_field() function into a cuda device function.\033[0m\n") raise time_evolution_output = time_evolution_output.copy_to_host() time = time.copy_to_host() elif self.device == Device.ROC: try: time = roc.device_array(time_index_max, np.float64) time_evolution_output = roc.device_array((time_index_max, self.spin_quantum_number.dimension, self.spin_quantum_number.dimension), np.complex128) sweep_parameters_device = roc.to_device(sweep_parameters) blocks_per_grid = (time.size + (self.threads_per_block - 1)) // self.threads_per_block self.get_time_evolution[blocks_per_grid, self.threads_per_block](sweep_parameters_device, time, time_end_points, time_step_integration, time_step_output, time_evolution_output) except: print("\033[31mspinsim error!!!\nnumba.roc could not jit get_field() function into a roc device function.\033[0m\n") raise time_evolution_output = time_evolution_output.copy_to_host() time = time.copy_to_host() state = np.empty((time_index_max, self.spin_quantum_number.dimension), np.complex128) self.get_state(state_init, state, time_evolution_output) results = Results(time, time_evolution_output, state, self.spin_calculator) return results @staticmethod @nb.njit def get_state(state_init:np.ndarray, state:np.ndarray, time_evolution:np.ndarray): """ Use the stepwise time evolution operators in succession to find the quantum state timeseries of the 3 level atom. Parameters ---------- state_init : :class:`numpy.ndarray` of :class:`numpy.complex128` The state (spin wavefunction) of the system at the start of the simulation. state : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, state_index) The state (wavefunction) of the spin system in the lab frame, for each time sampled. time_evolution : :class:`numpy.ndarray` of :class:`numpy.complex128` (time_index, bra_state_index, ket_state_index) The evaluated time evolution operator between each time step. See :ref:`architecture` for some information. """ for time_index in range(state.shape[0]): # State = time evolution * previous state for x_index in nb.prange(state.shape[1]): state[time_index, x_index] = 0 if time_index > 0: for z_index in range(state.shape[1]): state[time_index, x_index] += time_evolution[time_index - 1, x_index, z_index]*state[time_index - 1, z_index] else: state[time_index, x_index] += state_init[x_index] class Utilities: """ A on object that contains definitions of all of the device functions (functions compiled for use on the target device) used in the integrator. These device functions are compiled for the chosen target device on construction of the object. Attributes ---------- conj(z) : :obj:`callable` Conjugate of a complex number. .. math:: \\begin{align*} (a + ib)^* &= a - ib\\\\ a, b &\\in \\mathbb{R} \\end{align*} Parameters: * **z** (:class:`numpy.complex128`) - The complex number to take the conjugate of. Returns * **cz** (:class:`numpy.complex128`) - The conjugate of z. expm1i(b) : :obj:`callable` For real input :math:`b`, returns :math:`\\exp(ib) - 1`, while avoiding floating point cancellation errors. Parameters: * **b** (:class:`numpy.float64`) - The imaginary component to exponentiate. Returns * **em1i** (:class:`numpy.complex128`) - The evalauted output. cos_exp_m1(a, b) : :obj:`callable` For real input :math:`a`, :math:`b`, returns :math:`\\cos(a)\\exp(ib) - 1`, while avoiding floating point cancellation errors. Parameters: * **a** (:class:`numpy.float64`) - The real component to take the cosine of. * **b** (:class:`numpy.float64`) - The imaginary component to exponentiate. Returns * **cem1** (:class:`numpy.complex128`) - The evalauted output. cos_m1(a, b) : :obj:`callable` For real input :math:`a`, returns :math:`\\cos(a) - 1`, while avoiding floating point cancellation errors. Parameters: * **a** (:class:`numpy.float64`) - The real component to take the cosine of. Returns * **cm1** (:class:`numpy.complex128`) - The evalauted output. set_to(operator, result) : :obj:`callable` Copy the contents of one matrix into another. .. math:: (A)_{i, j} = (B)_{i, j} Parameters: * **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to copy from. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to copy to. set_to_one(operator) : :obj:`callable` Make a matrix the multiplicative identity, ie, :math:`1`. .. math:: \\begin{align*} (A)_{i, j} &= \\delta_{i, j}\\\\ &= \\begin{cases} 1,&i = j\\\\ 0,&i\\neq j \\end{cases} \\end{align*} Parameters: * **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to set to :math:`1`. set_to_zero(operator) : :obj:`callable` Make a matrix the zero matrix. .. math:: \\begin{align*} (A)_{i, j} &= \\0 \\end{align*} Parameters: * **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to set to :math:`0`. matrix_multiply(left, right, result) : :obj:`callable` Multiply matrices left and right together, to be returned in result. .. math:: \\begin{align*} (LR)_{i,k} = \\sum_j (L)_{i,j} (R)_{j,k} \\end{align*} Parameters: * **left** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to left multiply by. * **right** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix to right multiply by. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - A matrix to be filled with the result of the product. matrix_square_m1(operator, result) : :obj:`callable` For matrix :math:`A = 1 + a` :math:`S = A^2 = 1 + s`. Here the input is the residuals :math:`a`, and the output is :math:`s`. This is a way to evaluate :math:`s` without floating point cancellation error. Specifically, .. math:: \\begin{align*} s &= S - 1\\\\ &= A^2 - 1\\\\ &= (2\\cdot 1 + a)a \\end{align*} Parameters: * **operator** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The residual of the matrix to square. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - A matrix to be filled with the residual of the result of the product. matrix_multiply_m1(left, right, result) : :obj:`callable` For matrices :math:`L = 1 + l` and :math:`R = 1 + r`, evaluates :math:`O = LR = 1 + o`. Here the inputs are the residuals :math:`l` and :math:`r`, and the output is :math:`o`. This is a way to evaluate :math:`o` without floating point cancellation error. Specifically, .. math:: \\begin{align*} o &= O - 1\\\\ &= LR - 1\\\\ &= l + r + lr \\end{align*} Parameters: * **left** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The residual of the matrix to left multiply by. * **right** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The residual of the matrix to right multiply by. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - A matrix to be filled with the residual of the result of the product. matrix_exponential_analytic(field_sample, result) : :obj:`callable` Calculates a :math:`\\mathfrak{su}(2)` matrix exponential based on its analytic form. .. warning:: Only available for use with spin-half systems. Will not work with spin-one systems. Assumes the exponent is an imaginary linear combination of :math:`\\mathfrak{su}(2)`, being, .. math:: \\begin{align*} A &= -i(\\omega_x J_x + \\omega_y J_y + \\omega_z J_z), \\end{align*} with .. math:: \\begin{align*} J_x &= \\frac{1}{2}\\begin{pmatrix} 0 & 1 \\\\ 1 & 0 \\end{pmatrix},& J_y &= \\frac{1}{2}\\begin{pmatrix} 0 & -i \\\\ i & 0 \\end{pmatrix},& J_z &= \\frac{1}{2}\\begin{pmatrix} 1 & 0 \\\\ 0 & -1 \\end{pmatrix} \\end{align*} Then the exponential can be calculated as .. math:: \\begin{align*} \\exp(A) &= \\exp(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z)\\\\ &= \\begin{pmatrix} \\cos(\\frac{\\omega_r}{2}) - i\\frac{\\omega_z}{\\omega_r}\\sin(\\frac{\\omega_r}{2}) & -\\frac{\\omega_y + i\\omega_x}{\\omega_r}\\sin(\\frac{\\omega_r}{2})\\\\ \\frac{\\omega_y - i\\omega_x}{\\omega_r}\\sin(\\frac{\\omega_r}{2}) & \\cos(\\frac{\\omega_r}{2}) + i\\frac{\\omega_z}{\\omega_r}\\sin(\\frac{\\omega_r}{2}) \\end{pmatrix} \\end{align*} with :math:`\\omega_r = \\sqrt{\\omega_x^2 + \\omega_y^2 + \\omega_z^2}`. Parameters: * **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64`, (y_index, x_index)) - The values of :math:`\\omega_x`, :math:`\\omega_y` and :math:`\\omega_z` respectively, as described above. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix which the result of the exponentiation is to be written to. matrix_exponential_lie_trotter(field_sample, result) : :obj:`callable` Calculates a matrix exponential based on the Lie Product Formula, .. math:: \\exp(A + B) = \\lim_{c \\to \\infty} \\left(\\exp\\left(\\frac{1}{c}A\\right) \\exp\\left(\\frac{1}{c}B\\right)\\right)^c. **For spin-half systems:** Assumes the exponent is an imaginary linear combination of a subspace of :math:`\\mathfrak{su}(2)`, being, .. math:: \\begin{align*} A &= -i(\\omega_x J_x + \\omega_y J_y + \\omega_z J_z), \\end{align*} with .. math:: \\begin{align*} J_x &= \\frac{1}{2}\\begin{pmatrix} 0 & 1 \\\\ 1 & 0 \\end{pmatrix},& J_y &= \\frac{1}{2}\\begin{pmatrix} 0 & -i \\\\ i & 0 \\end{pmatrix},& J_z &= \\frac{1}{2}\\begin{pmatrix} 1 & 0 \\\\ 0 & -1 \\end{pmatrix} \\end{align*} Then the exponential can be approximated as, for large :math:`\\tau`, .. math:: \\begin{align*} \\exp(A) =& \\exp\\left(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z\\right)\\\\ =& \\exp\\left(2^{-\\tau}\\left(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z\\right)\\right)^{2^\\tau}\\\\ \\approx& \\biggl(\\exp\\left(-i\\frac12 2^{-\\tau} \\omega_z J_z\\right)\\exp\\left(-i\\left(2^{-\\tau} \\omega_\\phi J_\\phi\\right)\\right)\\exp\\left(-i\\frac12 2^{-\\tau} \\omega_z J_z\\right)\\biggr)^{2^\\tau}\\\\ =& \\begin{pmatrix} \\cos\\left(\\frac{\\Phi}{2}\\right)e^{-iz} & -i\\sin\\left(\\frac{\\Phi}{2}\\right) e^{i\\phi}\\\\ -i\\sin\\left(\\frac{\\Phi}{2}\\right) e^{-i\\phi} & \\cos\\left(\\frac{\\Phi}{2}\\right)e^{iz} \\end{pmatrix}^{2^\\tau}\\\\ =& T^{2^\\tau}. \\end{align*} Here :math:`z = 2^{-\\tau}\\frac{\\omega_z}{2}`, :math:`\\Phi = 2^{-\\tau}\\sqrt{\\omega_x^2 + \\omega_y^2}`, and :math:`\\phi = \\mathrm{atan}2(\\omega_y, \\omega_x)`. **For spin-one systems** Assumes the exponent is an imaginary linear combination of a subspace of :math:`\\mathfrak{su}(3)`, being, .. math:: \\begin{align*} A &= -i(\\omega_x J_x + \\omega_y J_y + \\omega_z J_z + \\omega_q Q), \\end{align*} with .. math:: \\begin{align*} J_x &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & 1 & 0 \\\\ 1 & 0 & 1 \\\\ 0 & 1 & 0 \\end{pmatrix},& J_y &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & -i & 0 \\\\ i & 0 & -i \\\\ 0 & i & 0 \\end{pmatrix},\\\\ J_z &= \\begin{pmatrix} 1 & 0 & 0 \\\\ 0 & 0 & 0 \\\\ 0 & 0 & -1 \\end{pmatrix},& Q &= \\frac{1}{3}\\begin{pmatrix} 1 & 0 & 0 \\\\ 0 & -2 & 0 \\\\ 0 & 0 & 1 \\end{pmatrix} \\end{align*} Then the exponential can be approximated as, for large :math:`\\tau`, .. math:: \\begin{align*} \\exp(A) =& \\exp\\left(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z - i\\omega_q Q\\right)\\\\ =& \\exp\\left(2^{-\\tau}\\left(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z - i\\omega_q Q\\right)\\right)^{2^\\tau}\\\\ \\approx& \\biggl(\\exp\\left(-i\\frac12\\left(2^{-\\tau} \\omega_z J_z + 2^{-\\tau}\\omega_q Q\\right)\\right)\\nonumber\\\\ &\\cdot\\exp\\left(-i\\left(2^{-\\tau} \\omega_\\phi J_\\phi\\right)\\right)\\nonumber\\\\ &\\cdot\\exp\\left(-i\\frac12\\left(2^{-\\tau} \\omega_z J_z + 2^{-\\tau} \\omega_q Q\\right)\\right)\\biggr)^{2^\\tau}\\\\ =& \\begin{pmatrix} \\left(\\cos\\left(\\frac{\\Phi}{2}\\right) e^{-iz}e^{-iq}\\right)^2 & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{iq}e^{-iz}e^{-i\\phi} & -\\left(\\sin\\left(\\frac{\\Phi}{2}\\right)e^{iq}e^{-i\\phi}\\right)^2\\\\ \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{iq}e^{-iz}e^{i\\phi} & \\cos(\\Phi)e^{i4q} & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{iq}e^{iz}e^{-i\\phi}\\\\ -\\left(\\sin\\left(\\frac{\\Phi}{2}\\right)e^{-iq}e^{i\\phi}\\right)^2 & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{iq}e^{iz}e^{i\\phi} & \\left(\\cos\\left(\\frac{\\Phi}{2}\\right) e^{iz}e^{-iq}\\right)^2 \\end{pmatrix}^{2^\\tau}.\\\\ \\end{align*} Here :math:`z = 2^{-\\tau}\\frac{\\omega_z}{2}`, :math:`q = 2^{-\\tau}\\frac{\\omega_q}{6}`, :math:`\\Phi = 2^{-\\tau}\\sqrt{\\omega_x^2 + \\omega_y^2}`, and :math:`\\phi = \\mathrm{atan}2(\\omega_y, \\omega_x)`. Once :math:`T` is calculated, it is then recursively squared :math:`\\tau` times to obtain :math:`\\exp(A)`. Parameters: * **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64`, (y_index, x_index)) - The values of x, y and z (and q for spin-one) respectively, as described above. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix which the result of the exponentiation is to be written to. * **number_of_squares** (:obj:`int`) - The number of squares to make to the approximate matrix (:math:`\\tau` above). matrix_exponential_lie_trotter_8(field_sample, result) : :obj:`callable` Calculates a matrix exponential based on the Lie Product Formula, .. math:: \\exp(A + B) = \\lim_{c \\to \\infty} \\left(\\exp\\left(\\frac{1}{c}A\\right) \\exp\\left(\\frac{1}{c}B\\right)\\right)^c. .. warning:: Only available for use with spin-one systems. Will not work with spin-half systems. Assumes the exponent is an imaginary linear combination elements of :math:`\\mathfrak{su}(3)`, being, .. math:: \\begin{align*} A &= -i(\\omega_x J_x + \\omega_y J_y + \\omega_z J_z + \\omega_q Q + \\omega_{u1} U_1 + \\omega_{u2} U_2 + \\omega_{v1} V_1 + \\omega_{v2} V_2), \\end{align*} with .. math:: \\begin{align*} J_x &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & 1 & 0 \\\\ 1 & 0 & 1 \\\\ 0 & 1 & 0 \\end{pmatrix},& J_y &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & -i & 0 \\\\ i & 0 & -i \\\\ 0 & i & 0 \\end{pmatrix},\\\\ J_z &= \\begin{pmatrix} 1 & 0 & 0 \\\\ 0 & 0 & 0 \\\\ 0 & 0 & -1 \\end{pmatrix},& Q &= \\frac{1}{3}\\begin{pmatrix} 1 & 0 & 0 \\\\ 0 & -2 & 0 \\\\ 0 & 0 & 1 \\end{pmatrix},\\\\ U_1 &= \\begin{pmatrix} 0 & 0 & 1 \\\\ 0 & 0 & 0 \\\\ 1 & 0 & 0 \\end{pmatrix},& U_2 &= \\begin{pmatrix} 0 & 0 & -i \\\\ 0 & 0 & 0 \\\\ i & 0 & 0 \\end{pmatrix},\\\\ V_1 &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & 1 & 0 \\\\ 1 & 0 & -1 \\\\ 0 & -1 & 0 \\end{pmatrix},& V_2 &= \\frac{1}{\\sqrt{2}}\\begin{pmatrix} 0 & -i & 0 \\\\ i & 0 & i \\\\ 0 & -i & 0 \\end{pmatrix}.\\\\ \\end{align*} Then the exponential can be approximated as, for large :math:`\\tau`, .. math:: \\begin{align*} \\exp(A) =& \\exp\\biggl(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z - i\\omega_q Q\\\\ &- i\\omega_{u1} U_1 - i\\omega_{u2} U_2 - i\\omega_{v1} V_1 - i\\omega_{v2} V_2\\biggr)\\\\ & \\exp\\biggl(2^{-\\tau}\\biggl(-i\\omega_x J_x - i\\omega_y J_y - i\\omega_z J_z - i\\omega_q Q\\\\ &- i\\omega_{u1} U_1 - i\\omega_{u2} U_2 - i\\omega_{v1} V_1 - i\\omega_{v2} V_2\\biggr)\\biggr)^{2^\\tau}\\\\ \\approx& \\biggl(\\exp\\left(-i2^{-\\tau} \\omega_\\phi J_\\phi\\right)\\exp\\left(-i2^{-\\tau} \\omega_{u\\phi} U_{u\\phi}\\right)\\\\ &\\cdot\\exp\\left(-i2^{-\\tau} \\omega_{v\\phi} V_{v\\phi}\\right)\\exp\\left(-i2^{-\\tau} \\omega_z J_z -i2^{-\\tau} \\omega_q Q \\right)\\biggr)^{2^\\tau}\\\\ =& \\biggl(\\begin{pmatrix} \\cos^2\\left(\\frac{\\Phi}{2}\\right) & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{-i\\phi} & -\\left(\\sin\\left(\\frac{\\Phi}{2}\\right)e^{-i\\phi}\\right)^2\\\\ \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{i\\phi} & \\cos\\left(\\Phi\\right) & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{-i\\phi}\\\\ -\\left(\\sin\\left(\\frac{\\Phi}{2}\\right)e^{i\\phi}\\right)^2 & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi)e^{i\\phi} & \\cos^2\\left(\\frac{\\Phi}{2}\\right) \\end{pmatrix}\\\\ &\\cdot \\begin{pmatrix} \\cos\\left(\\Phi_u\\right) & 0 & -i \\sin\\left(\\Phi_u\\right)e^{-i\\phi_u}\\\\ 0 & 1 & 0\\\\ -i \\sin\\left(\\Phi_u\\right)e^{i\\phi_u} & 0 & \\cos\\left(\\Phi_u\\right) \\end{pmatrix}\\\\ &\\cdot \\begin{pmatrix} \\cos^2\\left(\\frac{\\Phi_v}{2}\\right) & \\frac{-i}{\\sqrt{2}} \\sin(\\Phi_v)e^{-i\\phi_v} & \\left(\\sin\\left(\\frac{\\Phi_v}{2}\\right)e^{-i\\phi_v}\\right)^2\\\\ \\frac{-i}{\\sqrt{2}} \\sin(\\Phi_v)e^{i\\phi_v} & \\cos\\left(\\Phi_v\\right) & \\frac{i}{\\sqrt{2}} \\sin(\\Phi_v)e^{-i\\phi_v}\\\\ \\left(\\sin\\left(\\frac{\\Phi_v}{2}\\right)e^{i\\phi_v}\\right)^2 & \\frac{i}{\\sqrt{2}} \\sin(\\Phi_v)e^{i\\phi_v} & \\cos^2\\left(\\frac{\\Phi_v}{2}\\right) \\end{pmatrix}\\\\ &\\cdot \\begin{pmatrix} e^{-iz - iq} & 0 & 0\\\\ 0 & e^{i2q} & 0\\\\ 0 & 0 & e^{iz - iq} \\end{pmatrix}\\biggr)^{2^\\tau}\\\\ =& T^{2^\\tau}. \\end{align*} Here :math:`z = 2^{-\\tau}\\frac{\\omega_z}{2}`, :math:`q = 2^{-\\tau}\\frac{\\omega_q}{6}`, :math:`\\Phi = 2^{-\\tau}\\sqrt{\\omega_x^2 + \\omega_y^2}`, :math:`\\phi = \\mathrm{atan}2(\\omega_y, \\omega_x)`, :math:`\\Phi_u = 2^{-\\tau}\\sqrt{\\omega_{u1}^2 + \\omega_{u2}^2}`, :math:`\\phi_u = \\mathrm{atan}2(\\omega_{u1}, \\omega_{u2})`, :math:`\\Phi_v = 2^{-\\tau}\\sqrt{\\omega_{v1}^2 + \\omega_{v2}^2}`, and :math:`\\phi_v = \\mathrm{atan}2(\\omega_{v1}, \\omega_{v2})`. Once :math:`T` is calculated, it is then recursively squared :math:`\\tau` times to obtain :math:`\\exp(A)`. Parameters: * **field_sample** (:class:`numpy.ndarray` of :class:`numpy.float64`, (y_index, x_index)) - The values of x, y, z, q, u1, u2, v1 and v2 respectively, as described above. * **result** (:class:`numpy.ndarray` of :class:`numpy.complex128`, (y_index, x_index)) - The matrix which the result of the exponentiation is to be written to. * **number_of_squares** (:obj:`int`) - The number of squares to make to the approximate matrix (:math:`\\tau` above). """ def __init__(self, spin_quantum_number:SpinQuantumNumber, device:Device, threads_per_block:int, number_of_squares:int): """ Parameters ---------- spin_quantum_number : :obj:`SpinQuantumNumber` The option to select whether the simulator will integrate a spin-half :obj:`SpinQuantumNumber.HALF`, or spin-one :obj:`SpinQuantumNumber.ONE` quantum system. device : :obj:`Device` The option to select which device will be targeted for integration. That is, whether the integrator is compiled for a CPU or GPU. Defaults to :obj:`Device.CUDA` if the system it is being run on is Nvidia Cuda compatible, and defaults to :obj:`Device.CPU` otherwise. See :obj:`Device` for all options and more details. threads_per_block : :obj:`int` The size of each thread block (workgroup), in terms of the number of threads (workitems) they each contain, when running on the GPU target devices :obj:`Device.CUDA` (:obj:`Device.ROC`). Defaults to 64. Modifying might be able to increase execution time for different GPU models. """ jit_device = device.jit_device device_index = device.index number_of_hypercubes = math.ceil(number_of_squares/2) if number_of_hypercubes < 0: number_of_hypercubes = 0 trotter_precision = 4**number_of_hypercubes @jit_device def conj(z): return (z.real - 1j*z.imag) @jit_device def expm1i(i): return -2*(math.sin(i/2)**2) + 1j*math.sin(i) @jit_device def cos_exp_m1(c, e): return (expm1i(c + e) + expm1i(-c + e))/2 @jit_device def cos_m1(t): return -2*(math.sin(t/2)**2) if spin_quantum_number == SpinQuantumNumber.HALF: @jit_device def set_to(operator, result): result[0, 0] = operator[0, 0] result[1, 0] = operator[1, 0] result[0, 1] = operator[0, 1] result[1, 1] = operator[1, 1] @jit_device def set_to_one(operator): operator[0, 0] = 1 operator[1, 0] = 0 operator[0, 1] = 0 operator[1, 1] = 1 @jit_device def set_to_zero(operator): operator[0, 0] = 0 operator[1, 0] = 0 operator[0, 1] = 0 operator[1, 1] = 0 @jit_device def matrix_multiply(left, right, result): result[0, 0] = left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0] result[1, 0] = left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0] result[0, 1] = left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1] result[1, 1] = left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1] @jit_device def matrix_square_m1(operator, result): result[0, 0] = (2 + operator[0, 0])*operator[0, 0] + operator[0, 1]*operator[1, 0] result[1, 0] = operator[1, 0]*operator[0, 0] + (2 + operator[1, 1])*operator[1, 0] result[0, 1] = (2 + operator[0, 0])*operator[0, 1] + operator[0, 1]*operator[1, 1] result[1, 1] = operator[1, 0]*operator[0, 1] + (2 + operator[1, 1])*operator[1, 1] @jit_device def matrix_multiply_m1(left, right, result): result[0, 0] = (left[0, 0] + right[0, 0]) + (left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0]) result[1, 0] = (left[1, 0] + right[1, 0]) + (left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0]) result[0, 1] = (left[0, 1] + right[0, 1]) + (left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1]) result[1, 1] = (left[1, 1] + right[1, 1]) + (left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1]) @jit_device def matrix_exponential_analytic(field_sample, result): x = field_sample[0] y = field_sample[1] z = field_sample[2] r = math.sqrt(x**2 + y**2 + z**2) if r > 0: x /= r y /= r z /= r c = cos_exp_m1(r/2, 0) s = math.sin(r/2) result[0, 0] = c - 1j*z*s + 1 result[1, 0] = (y - 1j*x)*s result[0, 1] = -(y + 1j*x)*s result[1, 1] = c + 1j*z*s + 1 else: result[0, 0] = 1 result[1, 0] = 0 result[0, 1] = 0 result[1, 1] = 1 @jit_device def matrix_exponential_lie_trotter(field_sample, result): a = math.sqrt(field_sample[0]*field_sample[0] + field_sample[1]*field_sample[1]) if a > 0: ep = (field_sample[0] + 1j*field_sample[1])/a else: ep = 1 a = a/trotter_precision Sa = -1j*math.sin(a/2) z = field_sample[2]/(2*trotter_precision) result[0, 0] = cos_exp_m1(a/2, -z) result[1, 0] = Sa*ep result[0, 1] = Sa/ep result[1, 1] = cos_exp_m1(a/2, z) if device_index == 0: temporary = np.empty((2, 2), dtype = np.complex128) elif device_index == 1: temporary = cuda.local.array((2, 2), dtype = np.complex128) elif device_index == 2: temporary_group = roc.shared.array((threads_per_block, 2, 2), dtype = np.complex128) temporary = temporary_group[roc.get_local_id(1), :, :] for power_index in range(number_of_hypercubes): matrix_square_m1(result, temporary) matrix_square_m1(temporary, result) result[0, 0] += 1 result[1, 1] += 1 def matrix_exponential_lie_trotter_8(field_sample, result): pass else: @jit_device def set_to(operator, result): result[0, 0] = operator[0, 0] result[1, 0] = operator[1, 0] result[2, 0] = operator[2, 0] result[0, 1] = operator[0, 1] result[1, 1] = operator[1, 1] result[2, 1] = operator[2, 1] result[0, 2] = operator[0, 2] result[1, 2] = operator[1, 2] result[2, 2] = operator[2, 2] @jit_device def set_to_one(operator): operator[0, 0] = 1 operator[1, 0] = 0 operator[2, 0] = 0 operator[0, 1] = 0 operator[1, 1] = 1 operator[2, 1] = 0 operator[0, 2] = 0 operator[1, 2] = 0 operator[2, 2] = 1 @jit_device def set_to_zero(operator): operator[0, 0] = 0 operator[1, 0] = 0 operator[2, 0] = 0 operator[0, 1] = 0 operator[1, 1] = 0 operator[2, 1] = 0 operator[0, 2] = 0 operator[1, 2] = 0 operator[2, 2] = 0 @jit_device def matrix_multiply(left, right, result): result[0, 0] = left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0] + left[0, 2]*right[2, 0] result[1, 0] = left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0] + left[1, 2]*right[2, 0] result[2, 0] = left[2, 0]*right[0, 0] + left[2, 1]*right[1, 0] + left[2, 2]*right[2, 0] result[0, 1] = left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1] + left[0, 2]*right[2, 1] result[1, 1] = left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1] + left[1, 2]*right[2, 1] result[2, 1] = left[2, 0]*right[0, 1] + left[2, 1]*right[1, 1] + left[2, 2]*right[2, 1] result[0, 2] = left[0, 0]*right[0, 2] + left[0, 1]*right[1, 2] + left[0, 2]*right[2, 2] result[1, 2] = left[1, 0]*right[0, 2] + left[1, 1]*right[1, 2] + left[1, 2]*right[2, 2] result[2, 2] = left[2, 0]*right[0, 2] + left[2, 1]*right[1, 2] + left[2, 2]*right[2, 2] @jit_device def matrix_square_m1(operator, result): result[0, 0] = (2 + operator[0, 0])*operator[0, 0] + operator[0, 1]*operator[1, 0] + operator[0, 2]*operator[2, 0] result[1, 0] = operator[1, 0]*operator[0, 0] + (2 + operator[1, 1])*operator[1, 0] + operator[1, 2]*operator[2, 0] result[2, 0] = operator[2, 0]*operator[0, 0] + operator[2, 1]*operator[1, 0] + (2 + operator[2, 2])*operator[2, 0] result[0, 1] = (2 + operator[0, 0])*operator[0, 1] + operator[0, 1]*operator[1, 1] + operator[0, 2]*operator[2, 1] result[1, 1] = operator[1, 0]*operator[0, 1] + (2 + operator[1, 1])*operator[1, 1] + operator[1, 2]*operator[2, 1] result[2, 1] = operator[2, 0]*operator[0, 1] + operator[2, 1]*operator[1, 1] + (2 + operator[2, 2])*operator[2, 1] result[0, 2] = (2 + operator[0, 0])*operator[0, 2] + operator[0, 1]*operator[1, 2] + operator[0, 2]*operator[2, 2] result[1, 2] = operator[1, 0]*operator[0, 2] + (2 + operator[1, 1])*operator[1, 2] + operator[1, 2]*operator[2, 2] result[2, 2] = operator[2, 0]*operator[0, 2] + operator[2, 1]*operator[1, 2] + (2 + operator[2, 2])*operator[2, 2] @jit_device def matrix_multiply_m1(left, right, result): result[0, 0] = (left[0, 0] + right[0, 0]) + (left[0, 0]*right[0, 0] + left[0, 1]*right[1, 0] + left[0, 2]*right[2, 0]) result[1, 0] = (left[1, 0] + right[1, 0]) + (left[1, 0]*right[0, 0] + left[1, 1]*right[1, 0] + left[1, 2]*right[2, 0]) result[2, 0] = (left[2, 0] + right[2, 0]) + (left[2, 0]*right[0, 0] + left[2, 1]*right[1, 0] + left[2, 2]*right[2, 0]) result[0, 1] = (left[0, 1] + right[0, 1]) + (left[0, 0]*right[0, 1] + left[0, 1]*right[1, 1] + left[0, 2]*right[2, 1]) result[1, 1] = (left[1, 1] + right[1, 1]) + (left[1, 0]*right[0, 1] + left[1, 1]*right[1, 1] + left[1, 2]*right[2, 1]) result[2, 1] = (left[2, 1] + right[2, 1]) + (left[2, 0]*right[0, 1] + left[2, 1]*right[1, 1] + left[2, 2]*right[2, 1]) result[0, 2] = (left[0, 2] + right[0, 2]) + (left[0, 0]*right[0, 2] + left[0, 1]*right[1, 2] + left[0, 2]*right[2, 2]) result[1, 2] = (left[1, 2] + right[1, 2]) + (left[1, 0]*right[0, 2] + left[1, 1]*right[1, 2] + left[1, 2]*right[2, 2]) result[2, 2] = (left[2, 2] + right[2, 2]) + (left[2, 0]*right[0, 2] + left[2, 1]*right[1, 2] + left[2, 2]*right[2, 2]) @jit_device def matrix_exponential_analytic(field_sample, result, number_of_squares): pass @jit_device def matrix_exponential_lie_trotter(field_sample, result): a = math.sqrt(field_sample[0]*field_sample[0] + field_sample[1]*field_sample[1]) if a > 0: p = math.atan2(field_sample[1], field_sample[0]) else: p = 0 a = a/trotter_precision Sa = math.sin(a/2) sa = -1j*math.sin(a)/sqrt2 z = field_sample[2]/(2*trotter_precision) q = field_sample[3]/(6*trotter_precision) save_cos_exp_m1 = cos_exp_m1(a/2, -z - q) result[0, 0] = save_cos_exp_m1*(save_cos_exp_m1 + 2) result[1, 0] = sa*cmath.exp(1j*(q + p - z)) result[2, 0] = -(Sa**2)*cmath.exp(2*1j*(p - q)) result[0, 1] = sa*cmath.exp(1j*(q - p - z)) result[1, 1] = cos_exp_m1(a, 4*q) result[2, 1] = sa*cmath.exp(1j*(q + p + z)) result[0, 2] = -(Sa**2)*cmath.exp(2*1j*(q - p)) result[1, 2] = sa*cmath.exp(1j*(q - p + z)) save_cos_exp_m1 = cos_exp_m1(a/2, z - q) result[2, 2] = save_cos_exp_m1*(save_cos_exp_m1 + 2) if device_index == 0: temporary = np.empty((3, 3), dtype = np.complex128) elif device_index == 1: temporary = cuda.local.array((3, 3), dtype = np.complex128) elif device_index == 2: temporary_group = roc.shared.array((threads_per_block, 3, 3), dtype = np.complex128) temporary = temporary_group[roc.get_local_id(1), :, :] for power_index in range(number_of_hypercubes): matrix_square_m1(result, temporary) matrix_square_m1(temporary, result) result[0, 0] += 1 result[1, 1] += 1 result[2, 2] += 1 @jit_device def matrix_exponential_lie_trotter_8(field_sample, result): if device_index == 0: temporary_1 = np.empty((3, 3), dtype = np.complex128) temporary_2 = np.empty((3, 3), dtype = np.complex128) elif device_index == 1: temporary_1 = cuda.local.array((3, 3), dtype = np.complex128) temporary_2 = cuda.local.array((3, 3), dtype = np.complex128) elif device_index == 2: temporary_group_1 = roc.shared.array((threads_per_block, 3, 3), dtype = np.complex128) temporary_group_2 = roc.shared.array((threads_per_block, 3, 3), dtype = np.complex128) temporary_1 = temporary_group_1[roc.get_local_id(1), :, :] temporary_2 = temporary_group_2[roc.get_local_id(1), :, :] a = math.sqrt(field_sample[0]*field_sample[0] + field_sample[1]*field_sample[1]) if a > 0: ep = (field_sample[0] + 1j*field_sample[1])/a a = a/trotter_precision Sa = math.sin(a/2) sa = -1j*math.sin(a)/sqrt2 Cam1 = cos_m1(a/2) else: ep = 1 Sa = 0 sa = 0 Cam1 = 0 result[0, 0] = Cam1*(Cam1 + 2) result[1, 0] = sa*ep result[2, 0] = -(Sa*ep)**2 result[0, 1] = sa*conj(ep) result[1, 1] = cos_m1(a) result[2, 1] = sa*ep result[0, 2] = -(Sa*conj(ep))**2 result[1, 2] = sa*conj(ep) result[2, 2] = Cam1*(Cam1 + 2) a = math.sqrt(field_sample[6]*field_sample[6] + field_sample[7]*field_sample[7]) if a > 0: ep = (field_sample[6] + 1j*field_sample[7])/a a = a/trotter_precision Sa = math.sin(a/2) sa = -1j*math.sin(a)/sqrt2 Cam1 = cos_m1(a/2) else: ep = 1 Sa = 0 sa = 0 Cam1 = 0 temporary_1[0, 0] = Cam1*(Cam1 + 2) temporary_1[1, 0] = sa*ep temporary_1[2, 0] = -(Sa*ep)**2 temporary_1[0, 1] = sa*conj(ep) temporary_1[1, 1] = cos_m1(a) temporary_1[2, 1] = -sa*ep temporary_1[0, 2] = -(Sa*conj(ep))**2 temporary_1[1, 2] = -sa*conj(ep) temporary_1[2, 2] = Cam1*(Cam1 + 2) matrix_multiply_m1(result, temporary_1, temporary_2) a = math.sqrt(field_sample[4]*field_sample[4] + field_sample[5]*field_sample[5]) if a > 0: ep = (field_sample[4] + 1j*field_sample[5])/a a = a/trotter_precision Sa = math.sin(a) Cam1 = cos_m1(a) else: ep = 1 ep = 1 Sa = 0 sa = 0 Cam1 = 0 result[0, 0] = Cam1 result[1, 0] = 0.0 result[2, 0] = -(Sa*ep)**2 result[0, 1] = sa*conj(ep) result[1, 1] = 0.0 result[2, 1] = -sa*ep result[0, 2] = -(Sa*conj(ep))**2 result[1, 2] = 0.0 result[2, 2] = Cam1 matrix_multiply_m1(result, temporary_2, temporary_1) a = field_sample[2]/trotter_precision ep = field_sample[3]/(3*trotter_precision) temporary_2[0, 0] = expm1i(-a - ep) temporary_2[1, 0] = 0.0 temporary_2[2, 0] = 0.0 temporary_2[0, 1] = 0.0 temporary_2[1, 1] = expm1i(2*ep) temporary_2[2, 1] = 0.0 temporary_2[0, 2] = 0.0 temporary_2[1, 2] = 0.0 temporary_2[2, 2] = expm1i(a - ep) matrix_multiply_m1(temporary_1, temporary_2, result) for power_index in range(number_of_hypercubes): matrix_square_m1(result, temporary_1) matrix_square_m1(temporary_1, result) result[0, 0] += 1 result[1, 1] += 1 result[2, 2] += 1 self.conj = conj self.expm1i = expm1i self.cos_exp_m1 = cos_exp_m1 self.cos_m1 = cos_m1 self.set_to = set_to self.set_to_one = set_to_one self.set_to_zero = set_to_zero self.matrix_multiply = matrix_multiply self.matrix_multiply_m1 = matrix_multiply_m1 self.matrix_exponential_analytic = matrix_exponential_analytic self.matrix_exponential_lie_trotter = matrix_exponential_lie_trotter self.matrix_exponential_lie_trotter_8 = matrix_exponential_lie_trotter_8 self.matrix_square_m1 = matrix_square_m1

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c3e7044b9a8d6bc7e21256b2b463446aa536d23e

356

py

Python

allegation/tests/utils/filter_tags_test_mixin.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

16

2016-05-20T09:03:32.000Z

2020-09-13T14:23:06.000Z

allegation/tests/utils/filter_tags_test_mixin.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

2

2016-05-24T01:44:14.000Z

2016-06-17T22:19:45.000Z

allegation/tests/utils/filter_tags_test_mixin.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

2

2016-10-10T16:14:19.000Z

2020-10-26T00:17:02.000Z

class FilterTagsTestMixin(object): def assert_have_filter_tags(self, category, value): filter_tags = self.find('#filter-tags').text.lower() filter_tags.should.contain(category.lower()) filter_tags.should.contain(str(value).lower()) def assert_no_filter_tags(self): self.find_all('.filter').should.have.length_of(0)

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null

0

1

0

4

613658a10de0abe34b68df0e796ca735776a6a36

198

py

Python

util/plt_setting.py

mnm-analytics/nklearn

333b1cadd49b63bdbdbd121b814ade03d19ada49

[ "MIT" ]

null

util/plt_setting.py

mnm-analytics/nklearn

333b1cadd49b63bdbdbd121b814ade03d19ada49

[ "MIT" ]

null

util/plt_setting.py

mnm-analytics/nklearn

333b1cadd49b63bdbdbd121b814ade03d19ada49

[ "MIT" ]

null

import pandas as pd import numpy as np import matplotlib.pyplot as plt import japanize_matplotlib plt.style.use("ggplot") plt.rcParams["figure.figsize"] = (16,10) plt.rcParams["font.fontsize"] = 12

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4

615876c3c8ddef74095cbcb8ab41e04c858d2edd

161

py

Python

pygdpr/specifications/dpa_node_type_specification/__init__.py

GDPRxiv/crawler

178ef9ff6c3641ba8b761a49e42c2579e453c1ca

[ "MIT" ]

null

pygdpr/specifications/dpa_node_type_specification/__init__.py

GDPRxiv/crawler

178ef9ff6c3641ba8b761a49e42c2579e453c1ca

[ "MIT" ]

2

2022-02-19T06:56:03.000Z

2022-02-19T07:00:00.000Z

pygdpr/specifications/dpa_node_type_specification/__init__.py

GDPRxiv/crawler

178ef9ff6c3641ba8b761a49e42c2579e453c1ca

[ "MIT" ]

null

class DPANodeTypeSpecification: def is_satisfied_by(self, dpa_node): classname = dpa_node.__class__.__name__ return (classname == 'DPANode')

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6159f8ff5e82e8b94750316d4fdfa0da3b6c400d

80

py

Python

apps/node/src/app/main/model_centric/processes/__init__.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

7

2020-04-20T22:22:08.000Z

2020-07-25T17:32:08.000Z

apps/node/src/app/main/model_centric/processes/__init__.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

3

2020-04-24T21:20:57.000Z

2020-05-28T09:17:02.000Z

apps/node/src/app/main/model_centric/processes/__init__.py

hivecell-io/federated_learning

e251bfa65c32abd83359c2b6847b9d0b62c4f5c3

[ "Apache-2.0" ]

4

2020-04-24T22:32:37.000Z

2020-05-25T19:29:20.000Z

from .process_manager import ProcessManager process_manager = ProcessManager()

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618357e099de41e86313f27afebc444ef9b76033

1,141

py

Python

model/utils.py

PetrovSergSerg/python_lection7

3214e89f8413442c6ffe07bdc20b842209ff6253

[ "Apache-2.0" ]

null

model/utils.py

PetrovSergSerg/python_lection7

3214e89f8413442c6ffe07bdc20b842209ff6253

[ "Apache-2.0" ]

null

model/utils.py

PetrovSergSerg/python_lection7

3214e89f8413442c6ffe07bdc20b842209ff6253

[ "Apache-2.0" ]

null

import datetime from random import randint, choice, randrange import re import data.constants as c def get_random_date(start, end): """Generate a random datetime between `start` and `end`""" return start + datetime.timedelta( # Get a random amount of seconds between `start` and `end` seconds=randint(0, int((end - start).total_seconds())), ) def get_random_word(alphabet: str, length: int): """Generate a random word on alphabet with given length""" return ''.join([choice(alphabet) for i in range(length)]) def get_random_email(alphabet: str): """Generate random email using function get_random_word(alphabet, length)""" return get_random_word(alphabet, randint(3, 10)) + '@' + get_random_word(alphabet, randint(2, 10)) + '.ru' def random_string(prefix: str, max_length: int): return prefix+"".join([choice(c.SYMBOLS) for x in range(randrange(max_length))]) def clear(string): return re.sub("[() -/]", "", string) def xstr(string): if string is None: return "" return str(string) def remove_spaces(string): return ' '.join(xstr(string).strip().split())

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0

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null

0

1

0

1

0

4

61bbc3af714e2181d128dd2b7228751ee1012c63

1,915

py

Python

app/generator/view.py

LifeLaboratory/Laboratory_EnergyHack

716f5a16b5fd0338fdf895882260b9b284e601fb

[ "MIT" ]

null

app/generator/view.py

LifeLaboratory/Laboratory_EnergyHack

716f5a16b5fd0338fdf895882260b9b284e601fb

[ "MIT" ]

null

app/generator/view.py

LifeLaboratory/Laboratory_EnergyHack

716f5a16b5fd0338fdf895882260b9b284e601fb

[ "MIT" ]

null

from json import loads from app.generator.creator import GenerateCode def get_code(struct): """ Публичный метод для построения кода по правилам :param struct: :return: """ return GenerateCode().create_code(struct) def get_file(struct): """ Публичный метод для построения кода по правилам и формирования exe :param struct: :return: """ return GenerateCode().create_file(struct) data = loads(''' [ {"name": "open", "action": "open_file", "file_path": "C:\\tmp"}, {"name": "cycle", "action": [ {"name": "write", "action": "write", "filter": "flt", "code": "code"} ] }, {"name": "close", "action": "close_file"} ] ''') data = loads(''' [ {"name": "open", "action": "open_file", "file_path": "B:/Program Files (x86)/AbilityCash/AbilityCash.exe"}, {"name": "open", "action": "open_file", "file_path": "B:/Program Files (x86)/AbilityCash/AbilityCash.xls"}, {"name": "Condition", "action": "click", "object": "AC-E"}, {"name": "cycle", "action": "cycle", "index": 10, "for": [ {"name": "Condition", "action": "click", "object": "AC-E"}, {"name": "Condition", "action": "save_value", "object": "AC-E", "value": "123"}, {"name": "Condition", "action": "save_value", "object": "AC-E", "source": "A"}, {"name": "cycle", "action": "cycle", "index": 10, "for": [ {"name": "Condition", "action": "click", "object": "AC-E"}, {"name": "Condition", "action": "save_value", "object": "AC-E", "value": "123"}, {"name": "Condition", "action": "save_value", "object": "AC-E", "source": "B"} ] } ] }, {"name": "Condition", "action": "save_value", "object": "AC-E", "value": "123"}, {"name": "Condition", "action": "save_value", "object": "AC-E", "source": "A1"} ] ''') # print(GenerateCode().create_file(data))

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0

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null

0

4

61be3cf346d2e41699e209d40684c448ba1daab6

562

py

Python

misc/gen_yupik_words.py

cwtliu/yupik-mt

b0c53dc5577a8b66f3bb60da64679bba6ce8bbd9

[ "MIT" ]

7

2018-05-14T06:07:36.000Z

2021-04-29T02:56:54.000Z

misc/gen_yupik_words.py

cwtliu/yupik-mt

b0c53dc5577a8b66f3bb60da64679bba6ce8bbd9

[ "MIT" ]

null

misc/gen_yupik_words.py

cwtliu/yupik-mt

b0c53dc5577a8b66f3bb60da64679bba6ce8bbd9

[ "MIT" ]

null

from __future__ import print_function import pickle ''' Appends generated english to english word mappings to two files. This will provide an additional corpus to train an NN which takes an unaranged set of english words and translates them to a coherent sentence. The first set of english words are taken from raw translations of yupik roots, postbases, and endings from dictionary lookups. The second set of english words are taken from the provided english translations. author: kechavez ''' # Retrieve all root, postbases, endings from pickled format.

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null

0

1

0

1

0

1

0

4

f60723dfbed875059ef8d85bdd6279e791d24df9

285

py

Python

Soma.py

MatheusSouza70/Exerc-cios-Python

f8878a0c9d62e49db61dcbce0ee10a161e12a894

[ "MIT" ]

1

2022-03-14T01:35:09.000Z

Soma.py

MatheusSouza70/Exerc-cios-Python

f8878a0c9d62e49db61dcbce0ee10a161e12a894

[ "MIT" ]

null

Soma.py

MatheusSouza70/Exerc-cios-Python

f8878a0c9d62e49db61dcbce0ee10a161e12a894

[ "MIT" ]

null

nota1 = float (input("Informe a primeira nota: ")) nota2 = float (input("Informe a segunda nota: ")) nota3 = float (input("Informe a terceira nota: ")) nota4 = float (input("Informe a quarta nota: ")) media = (nota1+nota2+nota3+nota4) / 4 print("A média é: {}" .format(media))

35.625

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0.652632

40

285

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0.365591

0.387097

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0.038298

0.175439

285

8

52

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0

null

1

0

1

0

null

0

4

f60e31e4e5fd5cf2e8629566b00cdd6b967d7337

36

py

Python

AlgorithmsPractice/python/test.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

58

2019-03-03T04:42:23.000Z

2022-01-13T04:36:31.000Z

AlgorithmsPractice/python/test.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

null

AlgorithmsPractice/python/test.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

28

2019-08-11T01:25:00.000Z

2021-08-22T06:46:06.000Z

# coding:utf-8 s = 3 print(s*(1/s))

9

14

0.555556

9

36

2.222222

0.777778

0

0.1

0.166667

36

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0.333333

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

f61a5046f32da237e548fc60980985b0b135b418

799

py

Python

toontown/coghq/FactorySpecs.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

8

2017-10-10T11:41:01.000Z

2021-02-23T12:55:47.000Z

toontown/coghq/FactorySpecs.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

1

2021-06-08T17:16:48.000Z

toontown/coghq/FactorySpecs.py

AnonymousDeveloper65535/open-toontown

3d05c22a7d960ad843dde231140447c46973dba5

[ "BSD-3-Clause" ]

3

2021-06-03T05:36:36.000Z

2021-06-22T15:07:31.000Z

from toontown.toonbase import ToontownGlobals import SellbotLegFactorySpec import SellbotLegFactoryCogs import LawbotLegFactorySpec import LawbotLegFactoryCogs def getFactorySpecModule(factoryId): return FactorySpecModules[factoryId] def getCogSpecModule(factoryId): return CogSpecModules[factoryId] FactorySpecModules = {ToontownGlobals.SellbotFactoryInt: SellbotLegFactorySpec, ToontownGlobals.LawbotOfficeInt: LawbotLegFactorySpec} CogSpecModules = {ToontownGlobals.SellbotFactoryInt: SellbotLegFactoryCogs, ToontownGlobals.LawbotOfficeInt: LawbotLegFactoryCogs} if __dev__: import FactoryMockupSpec FactorySpecModules[ToontownGlobals.MockupFactoryId] = FactoryMockupSpec import FactoryMockupCogs CogSpecModules[ToontownGlobals.MockupFactoryId] = FactoryMockupCogs

33.291667

79

0.861076

53

799

12.90566

0.433962

0.04386

0

0.093867

799

23

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34.73913

0.944751

0

1

0.111111

false

0

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0.111111

0.611111

0

1

null

0

null

0

1

0

4

f63bff37ac5336cecf554327a5b0d31204bd5905

122

py

Python

olivertwist/ruleengine/__init__.py

octoenergy/oliver-twist

7496208d9de4c21cd9e0d553f24bf07612ddc720

[ "Apache-2.0" ]

37

2020-12-17T13:32:12.000Z

2022-03-16T07:19:56.000Z

olivertwist/ruleengine/__init__.py

Norina-Sun/oliver-twist

5bb9b2cddc097d89d4a3eff78c63036682dd19f8

[ "Apache-2.0" ]

28

2020-12-17T16:20:14.000Z

2022-01-21T09:00:15.000Z

olivertwist/ruleengine/__init__.py

octoenergy/oliver-twist

7496208d9de4c21cd9e0d553f24bf07612ddc720

[ "Apache-2.0" ]

2

2021-08-09T17:07:23.000Z

2021-11-05T14:37:18.000Z

15.25

34

0.639344

19

122

3.894737

0.947368

0

0.15

0.180328

122

7

35

17.428571

0.59

0.918033

0

null

0

null

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null

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null

true

0

null

1

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null

0

1

0

1

0

null

0

1

0

4

f648045e68d81a9f300839dd2e3bdd8844d024cb

318

py

Python

utils/local_config.py

afcarl/fg-gating

9896447e2f91122ecc5d4153db127cbb30d7b5c9

[ "MIT" ]

1

2019-04-22T16:43:23.000Z

utils/local_config.py

afcarl/fg-gating

9896447e2f91122ecc5d4153db127cbb30d7b5c9

[ "MIT" ]

null

utils/local_config.py

afcarl/fg-gating

9896447e2f91122ecc5d4153db127cbb30d7b5c9

[ "MIT" ]

null

import os CUR_DIRECTORY = os.path.dirname(os.path.abspath(__file__)) NER_MODEL_PATH = CUR_DIRECTORY + 'english.all.3class.distsim.crf.ser.gz' NER_JAR_PATH = CUR_DIRECTORY + 'stanford-ner.jar' POS_MODEL_PATH = CUR_DIRECTORY + 'english-left3words-distsim.tagger' POS_JAR_PATH = CUR_DIRECTORY + 'stanford-postagger.jar'

39.75

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0.798742

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0.253165

0.270042

0.177215

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0

0.006849

0.081761

318

8

73

39.75

0.804795

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0.339623

0.289308

0

1

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false

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0.166667

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0.166667

0

null

1

0

1

0

null

0

4

f6536fbf3338d5121f74e348e2a369e8473489a6

132

py

Python

allegation/views/landing_view.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

16

2016-05-20T09:03:32.000Z

2020-09-13T14:23:06.000Z

allegation/views/landing_view.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

2

2016-05-24T01:44:14.000Z

2016-06-17T22:19:45.000Z

allegation/views/landing_view.py

invinst/CPDB

c2d8ae8888b13d956cc1068742f18d45736d4121

[ "Apache-2.0" ]

2

2016-10-10T16:14:19.000Z

2020-10-26T00:17:02.000Z

from django.views.generic.base import TemplateView class LandingView(TemplateView): template_name = "allegation/landing.html"

22

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0.80303

15

132

7

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132

5

51

26.4

0.897436

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false

0

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1

0

null

0

1

0

1

0

4

9ca8e3b276479c7eabc9dab17d2fe8f9fa1e7133

24,551

py

Python

pureples/experiments/pole_balancing/run_all_pole_balancing.py

kevinrpb/pureples

c591fefd5b20085f1d0537553631e29733374b16

[ "MIT" ]

51

2019-02-01T19:43:37.000Z

2022-03-16T09:07:03.000Z

pureples/experiments/pole_balancing/run_all_pole_balancing.py

kevinrpb/pureples

c591fefd5b20085f1d0537553631e29733374b16

[ "MIT" ]

2

2019-02-23T18:54:22.000Z

2019-11-09T01:30:32.000Z

pureples/experiments/pole_balancing/run_all_pole_balancing.py

kevinrpb/pureples

c591fefd5b20085f1d0537553631e29733374b16

[ "MIT" ]

35

2019-02-08T02:00:31.000Z

2022-03-01T23:17:00.000Z

import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import neat_pole_balancing import hyperneat_pole_balancing import es_hyperneat_pole_balancing_small import es_hyperneat_pole_balancing_medium import es_hyperneat_pole_balancing_large import gym import multiprocessing as multi from multiprocessing import Manager # Initialize lists to keep track during run. manager = Manager() neat_stats, hyperneat_stats, es_hyperneat_small_stats = manager.list([]), manager.list([]), manager.list([]) es_hyperneat_medium_stats, es_hyperneat_large_stats = manager.list([]), manager.list([]) neat_run_one_fitnesses, hyperneat_run_one_fitnesses, es_hyperneat_small_run_one_fitnesses = [], [], [] es_hyperneat_medium_run_one_fitnesses, es_hyperneat_large_run_one_fitnesses = [], [] neat_run_ten_fitnesses, hyperneat_run_ten_fitnesses, es_hyperneat_small_run_ten_fitnesses = [], [], [] es_hyperneat_medium_run_ten_fitnesses, es_hyperneat_large_run_ten_fitnesses = [], [] neat_run_hundred_fitnesses, hyperneat_run_hundred_fitnesses, es_hyperneat_small_run_hundred_fitnesses = [], [], [] es_hyperneat_medium_run_hundred_fitnesses, es_hyperneat_large_run_hundred_fitnesses = [], [] neat_one_solved, hyperneat_one_solved, es_hyperneat_small_one_solved = 0, 0, 0 es_hyperneat_medium_one_solved, es_hyperneat_large_one_solved = 0, 0 neat_ten_solved, hyperneat_ten_solved, es_hyperneat_small_ten_solved = 0, 0, 0 es_hyperneat_medium_ten_solved, es_hyperneat_large_ten_solved = 0, 0 neat_hundred_solved, hyperneat_hundred_solved, es_hyperneat_small_hundred_solved = 0, 0, 0 es_hyperneat_medium_hundred_solved, es_hyperneat_large_hundred_solved = 0, 0 runs = 16 inputs = range(runs) gens = 50 fit_threshold = 475 max_fit = 475 env = gym.make("CartPole-v1") # Run the experiments. def run(i): print("This is run #" + str(i)) neat_stats.append(neat_pole_balancing.run(gens, env)[1]) hyperneat_stats.append(hyperneat_pole_balancing.run(gens, env)[1]) es_hyperneat_small_stats.append(es_hyperneat_pole_balancing_small.run(gens, env)[1]) es_hyperneat_medium_stats.append(es_hyperneat_pole_balancing_medium.run(gens, env)[1]) es_hyperneat_large_stats.append(es_hyperneat_pole_balancing_large.run(gens, env)[1]) p = multi.Pool(multi.cpu_count()) p.map(run,range(runs)) # Average the NEAT runs. temp_fit_one = [0.0] * gens temp_fit_ten = [0.0] * gens temp_fit_hundred = [0.0] * gens for (stat_one, stat_ten, stat_hundred) in neat_stats: if stat_one.best_genome().fitness > max_fit: neat_run_one_fitnesses.append(max_fit) else: neat_run_one_fitnesses.append(stat_one.best_genome().fitness) if stat_ten.best_genome().fitness > max_fit: neat_run_ten_fitnesses.append(max_fit) else: neat_run_ten_fitnesses.append(stat_one.best_genome().fitness) if stat_hundred.best_genome().fitness > max_fit: neat_run_hundred_fitnesses.append(max_fit) else: neat_run_hundred_fitnesses.append(stat_one.best_genome().fitness) if stat_one.best_genome().fitness >= fit_threshold: neat_one_solved += 1 if stat_ten.best_genome().fitness >= fit_threshold: neat_ten_solved += 1 if stat_hundred.best_genome().fitness >= fit_threshold: neat_hundred_solved += 1 for i in range(gens): if i < len(stat_one.most_fit_genomes): if stat_one.most_fit_genomes[i].fitness > max_fit: temp_fit_one[i] += max_fit else: temp_fit_one[i] += stat_one.most_fit_genomes[i].fitness else: temp_fit_one[i] += max_fit if i < len(stat_ten.most_fit_genomes): if stat_ten.most_fit_genomes[i].fitness > max_fit: temp_fit_ten[i] += max_fit else: temp_fit_ten[i] += stat_ten.most_fit_genomes[i].fitness else: temp_fit_ten[i] += max_fit if i < len(stat_hundred.most_fit_genomes): if stat_hundred.most_fit_genomes[i].fitness > max_fit: temp_fit_hundred[i] += max_fit else: temp_fit_hundred[i] += stat_hundred.most_fit_genomes[i].fitness else: temp_fit_hundred[i] += max_fit neat_one_average_fit = [x / runs for x in temp_fit_one] neat_ten_average_fit = [x / runs for x in temp_fit_ten] neat_hundred_average_fit = [x / runs for x in temp_fit_hundred] # Average the HyperNEAT runs. temp_fit_one = [0.0] * gens temp_fit_ten = [0.0] * gens temp_fit_hundred = [0.0] * gens for (stat_one, stat_ten, stat_hundred) in hyperneat_stats: if stat_one.best_genome().fitness > max_fit: hyperneat_run_one_fitnesses.append(max_fit) else: hyperneat_run_one_fitnesses.append(stat_one.best_genome().fitness) if stat_ten.best_genome().fitness > max_fit: hyperneat_run_ten_fitnesses.append(max_fit) else: hyperneat_run_ten_fitnesses.append(stat_one.best_genome().fitness) if stat_hundred.best_genome().fitness > max_fit: hyperneat_run_hundred_fitnesses.append(max_fit) else: hyperneat_run_hundred_fitnesses.append(stat_one.best_genome().fitness) if stat_one.best_genome().fitness >= fit_threshold: hyperneat_one_solved += 1 if stat_ten.best_genome().fitness >= fit_threshold: hyperneat_ten_solved += 1 if stat_hundred.best_genome().fitness >= fit_threshold: hyperneat_hundred_solved += 1 for i in range(gens): if i < len(stat_one.most_fit_genomes): if stat_one.most_fit_genomes[i].fitness > max_fit: temp_fit_one[i] += max_fit else: temp_fit_one[i] += stat_one.most_fit_genomes[i].fitness else: temp_fit_one[i] += max_fit if i < len(stat_ten.most_fit_genomes): if stat_ten.most_fit_genomes[i].fitness > max_fit: temp_fit_ten[i] += max_fit else: temp_fit_ten[i] += stat_ten.most_fit_genomes[i].fitness else: temp_fit_ten[i] += max_fit if i < len(stat_hundred.most_fit_genomes): if stat_hundred.most_fit_genomes[i].fitness > max_fit: temp_fit_hundred[i] += max_fit else: temp_fit_hundred[i] += stat_hundred.most_fit_genomes[i].fitness else: temp_fit_hundred[i] += max_fit hyperneat_one_average_fit = [x / runs for x in temp_fit_one] hyperneat_ten_average_fit = [x / runs for x in temp_fit_ten] hyperneat_hundred_average_fit = [x / runs for x in temp_fit_hundred] # Average the small ES-HyperNEAT runs. temp_fit_one = [0.0] * gens temp_fit_ten = [0.0] * gens temp_fit_hundred = [0.0] * gens for (stat_one, stat_ten, stat_hundred) in es_hyperneat_small_stats: if stat_one.best_genome().fitness > max_fit: es_hyperneat_small_run_one_fitnesses.append(max_fit) else: es_hyperneat_small_run_one_fitnesses.append(stat_one.best_genome().fitness) if stat_ten.best_genome().fitness > max_fit: es_hyperneat_small_run_ten_fitnesses.append(max_fit) else: es_hyperneat_small_run_ten_fitnesses.append(stat_one.best_genome().fitness) if stat_hundred.best_genome().fitness > max_fit: es_hyperneat_small_run_hundred_fitnesses.append(max_fit) else: es_hyperneat_small_run_hundred_fitnesses.append(stat_one.best_genome().fitness) if stat_one.best_genome().fitness >= fit_threshold: es_hyperneat_small_one_solved += 1 if stat_ten.best_genome().fitness >= fit_threshold: es_hyperneat_small_ten_solved += 1 if stat_hundred.best_genome().fitness >= fit_threshold: es_hyperneat_small_hundred_solved += 1 for i in range(gens): if i < len(stat_one.most_fit_genomes): if stat_one.most_fit_genomes[i].fitness > max_fit: temp_fit_one[i] += max_fit else: temp_fit_one[i] += stat_one.most_fit_genomes[i].fitness else: temp_fit_one[i] += max_fit if i < len(stat_ten.most_fit_genomes): if stat_ten.most_fit_genomes[i].fitness > max_fit: temp_fit_ten[i] += max_fit else: temp_fit_ten[i] += stat_ten.most_fit_genomes[i].fitness else: temp_fit_ten[i] += max_fit if i < len(stat_hundred.most_fit_genomes): if stat_hundred.most_fit_genomes[i].fitness > max_fit: temp_fit_hundred[i] += max_fit else: temp_fit_hundred[i] += stat_hundred.most_fit_genomes[i].fitness else: temp_fit_hundred[i] += max_fit es_hyperneat_small_one_average_fit = [x / runs for x in temp_fit_one] es_hyperneat_small_ten_average_fit = [x / runs for x in temp_fit_ten] es_hyperneat_small_hundred_average_fit = [x / runs for x in temp_fit_hundred] # Average the medium ES-HyperNEAT runs. temp_fit_one = [0.0] * gens temp_fit_ten = [0.0] * gens temp_fit_hundred = [0.0] * gens for (stat_one, stat_ten, stat_hundred) in es_hyperneat_medium_stats: if stat_one.best_genome().fitness > max_fit: es_hyperneat_medium_run_one_fitnesses.append(max_fit) else: es_hyperneat_medium_run_one_fitnesses.append(stat_one.best_genome().fitness) if stat_ten.best_genome().fitness > max_fit: es_hyperneat_medium_run_ten_fitnesses.append(max_fit) else: es_hyperneat_medium_run_ten_fitnesses.append(stat_one.best_genome().fitness) if stat_hundred.best_genome().fitness > max_fit: es_hyperneat_medium_run_hundred_fitnesses.append(max_fit) else: es_hyperneat_medium_run_hundred_fitnesses.append(stat_one.best_genome().fitness) if stat_one.best_genome().fitness >= fit_threshold: es_hyperneat_medium_one_solved += 1 if stat_ten.best_genome().fitness >= fit_threshold: es_hyperneat_medium_ten_solved += 1 if stat_hundred.best_genome().fitness >= fit_threshold: es_hyperneat_medium_hundred_solved += 1 for i in range(gens): if i < len(stat_one.most_fit_genomes): if stat_one.most_fit_genomes[i].fitness > max_fit: temp_fit_one[i] += max_fit else: temp_fit_one[i] += stat_one.most_fit_genomes[i].fitness else: temp_fit_one[i] += max_fit if i < len(stat_ten.most_fit_genomes): if stat_ten.most_fit_genomes[i].fitness > max_fit: temp_fit_ten[i] += max_fit else: temp_fit_ten[i] += stat_ten.most_fit_genomes[i].fitness else: temp_fit_ten[i] += max_fit if i < len(stat_hundred.most_fit_genomes): if stat_hundred.most_fit_genomes[i].fitness > max_fit: temp_fit_hundred[i] += max_fit else: temp_fit_hundred[i] += stat_hundred.most_fit_genomes[i].fitness else: temp_fit_hundred[i] += max_fit es_hyperneat_medium_one_average_fit = [x / runs for x in temp_fit_one] es_hyperneat_medium_ten_average_fit = [x / runs for x in temp_fit_ten] es_hyperneat_medium_hundred_average_fit = [x / runs for x in temp_fit_hundred] # Average the large ES-HyperNEAT runs. temp_fit_one = [0.0] * gens temp_fit_ten = [0.0] * gens temp_fit_hundred = [0.0] * gens for (stat_one, stat_ten, stat_hundred) in es_hyperneat_large_stats: if stat_one.best_genome().fitness > max_fit: es_hyperneat_large_run_one_fitnesses.append(max_fit) else: es_hyperneat_large_run_one_fitnesses.append(stat_one.best_genome().fitness) if stat_ten.best_genome().fitness > max_fit: es_hyperneat_large_run_ten_fitnesses.append(max_fit) else: es_hyperneat_large_run_ten_fitnesses.append(stat_one.best_genome().fitness) if stat_hundred.best_genome().fitness > max_fit: es_hyperneat_large_run_hundred_fitnesses.append(max_fit) else: es_hyperneat_large_run_hundred_fitnesses.append(stat_one.best_genome().fitness) if stat_one.best_genome().fitness >= fit_threshold: es_hyperneat_large_one_solved += 1 if stat_ten.best_genome().fitness >= fit_threshold: es_hyperneat_large_ten_solved += 1 if stat_hundred.best_genome().fitness >= fit_threshold: es_hyperneat_large_hundred_solved += 1 for i in range(gens): if i < len(stat_one.most_fit_genomes): if stat_one.most_fit_genomes[i].fitness > max_fit: temp_fit_one[i] += max_fit else: temp_fit_one[i] += stat_one.most_fit_genomes[i].fitness else: temp_fit_one[i] += max_fit if i < len(stat_ten.most_fit_genomes): if stat_ten.most_fit_genomes[i].fitness > max_fit: temp_fit_ten[i] += max_fit else: temp_fit_ten[i] += stat_ten.most_fit_genomes[i].fitness else: temp_fit_ten[i] += max_fit if i < len(stat_hundred.most_fit_genomes): if stat_hundred.most_fit_genomes[i].fitness > max_fit: temp_fit_hundred[i] += max_fit else: temp_fit_hundred[i] += stat_hundred.most_fit_genomes[i].fitness else: temp_fit_hundred[i] += max_fit es_hyperneat_large_one_average_fit = [x / runs for x in temp_fit_one] es_hyperneat_large_ten_average_fit = [x / runs for x in temp_fit_ten] es_hyperneat_large_hundred_average_fit = [x / runs for x in temp_fit_hundred] # Write fitnesses to files. # NEAT. thefile = open('neat_pole_balancing_run_fitnesses.txt', 'w+') thefile.write("NEAT one\n") for item in neat_run_one_fitnesses: thefile.write("%s\n" % item) if max_fit in neat_one_average_fit: thefile.write("NEAT one solves pole_balancing at generation: " + str(neat_one_average_fit.index(max_fit))) else: thefile.write("NEAT one does not solve pole_balancing with best fitness: " + str(neat_one_average_fit[gens-1])) thefile.write("\nNEAT one solves pole_balancing in " + str(neat_one_solved) + " out of " + str(runs) + " runs.\n") thefile.write("NEAT ten\n") for item in neat_run_ten_fitnesses: thefile.write("%s\n" % item) if max_fit in neat_ten_average_fit: thefile.write("NEAT ten solves pole_balancing at generation: " + str(neat_ten_average_fit.index(max_fit))) else: thefile.write("NEAT ten does not solve pole_balancing with best fitness: " + str(neat_ten_average_fit[gens-1])) thefile.write("\nNEAT ten solves pole_balancing in " + str(neat_ten_solved) + " out of " + str(runs) + " runs.\n") thefile.write("NEAT hundred\n") for item in neat_run_hundred_fitnesses: thefile.write("%s\n" % item) if max_fit in neat_hundred_average_fit: thefile.write("NEAT hundred solves pole_balancing at generation: " + str(neat_hundred_average_fit.index(max_fit))) else: thefile.write("NEAT hundred does not solve pole_balancing with best fitness: " + str(neat_hundred_average_fit[gens-1])) thefile.write("\nNEAT hundred solves pole_balancing in " + str(neat_hundred_solved) + " out of " + str(runs) + " runs.\n") # HyperNEAT. thefile = open('hyperneat_pole_balancing_run_fitnesses.txt', 'w+') thefile.write("HyperNEAT one\n") for item in hyperneat_run_one_fitnesses: thefile.write("%s\n" % item) if max_fit in hyperneat_one_average_fit: thefile.write("HyperNEAT one solves pole_balancing at generation: " + str(hyperneat_one_average_fit.index(max_fit))) else: thefile.write("HyperNEAT one does not solve pole_balancing with best fitness: " + str(hyperneat_one_average_fit[gens-1])) thefile.write("\nHyperNEAT one solves pole_balancing in " + str(hyperneat_one_solved) + " out of " + str(runs) + " runs.\n") thefile.write("HyperNEAT ten\n") for item in hyperneat_run_ten_fitnesses: thefile.write("%s\n" % item) if max_fit in hyperneat_ten_average_fit: thefile.write("HyperNEAT ten solves pole_balancing at generation: " + str(hyperneat_ten_average_fit.index(max_fit))) else: thefile.write("HyperNEAT ten does not solve pole_balancing with best fitness: " + str(hyperneat_ten_average_fit[gens-1])) thefile.write("\nHyperNEAT ten solves pole_balancing in " + str(hyperneat_ten_solved) + " out of " + str(runs) + " runs.\n") thefile.write("HyperNEAT hundred\n") for item in hyperneat_run_hundred_fitnesses: thefile.write("%s\n" % item) if max_fit in hyperneat_hundred_average_fit: thefile.write("HyperNEAT hundred solves pole_balancing at generation: " + str(hyperneat_hundred_average_fit.index(max_fit))) else: thefile.write("HyperNEAT hundred does not solve pole_balancing with best fitness: " + str(hyperneat_hundred_average_fit[gens-1])) thefile.write("\nHyperNEAT hundred solves pole_balancing in " + str(hyperneat_hundred_solved) + " out of " + str(runs) + " runs.\n") # ES-HyperNEAT small. thefile = open('es_hyperneat_pole_balancing_small_run_fitnesses.txt', 'w+') thefile.write("ES-HyperNEAT small one\n") for item in es_hyperneat_small_run_one_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_small_one_average_fit: thefile.write("ES-HyperNEAT small one solves pole_balancing at generation: " + str(es_hyperneat_small_one_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT small one does not solve pole_balancing with best fitness: " + str(es_hyperneat_small_one_average_fit[gens-1])) thefile.write("\nES-HyperNEAT small one solves pole_balancing in " + str(es_hyperneat_small_one_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT small ten\n") for item in es_hyperneat_small_run_ten_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_small_ten_average_fit: thefile.write("ES-HyperNEAT small ten solves pole_balancing at generation: " + str(es_hyperneat_small_ten_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT small ten does not solve pole_balancing with best fitness: " + str(es_hyperneat_small_ten_average_fit[gens-1])) thefile.write("\nES-HyperNEAT small ten solves pole_balancing in " + str(es_hyperneat_small_ten_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT small hundred\n") for item in es_hyperneat_small_run_hundred_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_small_hundred_average_fit: thefile.write("ES-HyperNEAT small hundred solves pole_balancing at generation: " + str(es_hyperneat_small_hundred_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT small hundred does not solve pole_balancing with best fitness: " + str(es_hyperneat_small_hundred_average_fit[gens-1])) thefile.write("\nES-HyperNEAT small hundred solves pole_balancing in " + str(es_hyperneat_small_hundred_solved) + " out of " + str(runs) + " runs.\n") # ES-HyperNEAT medium. thefile = open('es_hyperneat_pole_balancing_medium_run_fitnesses.txt', 'w+') thefile.write("ES-HyperNEAT medium one\n") for item in es_hyperneat_medium_run_one_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_medium_one_average_fit: thefile.write("ES-HyperNEAT medium one solves pole_balancing at generation: " + str(es_hyperneat_medium_one_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT medium one does not solve pole_balancing with best fitness: " + str(es_hyperneat_medium_one_average_fit[gens-1])) thefile.write("\nES-HyperNEAT medium one solves pole_balancing in " + str(es_hyperneat_medium_one_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT medium ten\n") for item in es_hyperneat_medium_run_ten_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_medium_ten_average_fit: thefile.write("ES-HyperNEAT medium ten solves pole_balancing at generation: " + str(es_hyperneat_medium_ten_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT medium ten does not solve pole_balancing with best fitness: " + str(es_hyperneat_medium_ten_average_fit[gens-1])) thefile.write("\nES-HyperNEAT medium ten solves pole_balancing in " + str(es_hyperneat_medium_ten_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT medium hundred\n") for item in es_hyperneat_medium_run_hundred_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_medium_hundred_average_fit: thefile.write("ES-HyperNEAT medium hundred solves pole_balancing at generation: " + str(es_hyperneat_medium_hundred_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT medium hundred does not solve pole_balancing with best fitness: " + str(es_hyperneat_medium_hundred_average_fit[gens-1])) thefile.write("\nES-HyperNEAT medium hundred solves pole_balancing in " + str(es_hyperneat_medium_hundred_solved) + " out of " + str(runs) + " runs.\n") # ES-HyperNEAT large. thefile = open('es_hyperneat_pole_balancing_large_run_fitnesses.txt', 'w+') thefile.write("ES-HyperNEAT large one\n") for item in es_hyperneat_large_run_one_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_large_one_average_fit: thefile.write("ES-HyperNEAT large one solves pole_balancing at generation: " + str(es_hyperneat_large_one_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT large one does not solve pole_balancing with best fitness: " + str(es_hyperneat_large_one_average_fit[gens-1])) thefile.write("\nES-HyperNEAT large one solves pole_balancing in " + str(es_hyperneat_large_one_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT large ten\n") for item in es_hyperneat_large_run_ten_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_large_ten_average_fit: thefile.write("ES-HyperNEAT large ten solves pole_balancing at generation: " + str(es_hyperneat_large_ten_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT large ten does not solve pole_balancing with best fitness: " + str(es_hyperneat_large_ten_average_fit[gens-1])) thefile.write("\nES-HyperNEAT large ten solves pole_balancing in " + str(es_hyperneat_large_ten_solved) + " out of " + str(runs) + " runs.\n") thefile.write("ES-HyperNEAT large hundred\n") for item in es_hyperneat_large_run_hundred_fitnesses: thefile.write("%s\n" % item) if max_fit in es_hyperneat_large_hundred_average_fit: thefile.write("ES-HyperNEAT large hundred solves pole_balancing at generation: " + str(es_hyperneat_large_hundred_average_fit.index(max_fit))) else: thefile.write("ES-HyperNEAT large hundred does not solve pole_balancing with best fitness: " + str(es_hyperneat_large_hundred_average_fit[gens-1])) thefile.write("\nES-HyperNEAT large hundred solves pole_balancing in " + str(es_hyperneat_large_hundred_solved) + " out of " + str(runs) + " runs.\n") # Plot one fitnesses. plt.plot(range(gens), neat_one_average_fit, 'r-', label="NEAT") plt.plot(range(gens), hyperneat_one_average_fit, 'g--', label="HyperNEAT") plt.plot(range(gens), es_hyperneat_small_one_average_fit, 'b-.', label="ES-HyperNEAT small") plt.plot(range(gens), es_hyperneat_medium_one_average_fit, 'c-.', label="ES-HyperNEAT medium") plt.plot(range(gens), es_hyperneat_large_one_average_fit, 'm-.', label="ES-HyperNEAT large") plt.title("Average pole_balancing fitnesses one episode") plt.xlabel("Generations") plt.ylabel("Fitness") plt.grid() plt.legend(loc="best") plt.savefig('pole_balancing_one_fitnesses.svg') plt.close() # Plot ten fitnesses. plt.plot(range(gens), neat_ten_average_fit, 'r-', label="NEAT") plt.plot(range(gens), hyperneat_ten_average_fit, 'g--', label="HyperNEAT") plt.plot(range(gens), es_hyperneat_small_ten_average_fit, 'b-.', label="ES-HyperNEAT small") plt.plot(range(gens), es_hyperneat_medium_ten_average_fit, 'c-.', label="ES-HyperNEAT medium") plt.plot(range(gens), es_hyperneat_large_ten_average_fit, 'm-.', label="ES-HyperNEAT large") plt.title("Average pole_balancing fitnesses ten episodes") plt.xlabel("Generations") plt.ylabel("Fitness") plt.grid() plt.legend(loc="best") plt.savefig('pole_balancing_ten_fitnesses.svg') plt.close() # Plot hundred fitnesses. plt.plot(range(gens), neat_hundred_average_fit, 'r-', label="NEAT") plt.plot(range(gens), hyperneat_hundred_average_fit, 'g--', label="HyperNEAT") plt.plot(range(gens), es_hyperneat_small_hundred_average_fit, 'b-.', label="ES-HyperNEAT small") plt.plot(range(gens), es_hyperneat_medium_hundred_average_fit, 'c-.', label="ES-HyperNEAT medium") plt.plot(range(gens), es_hyperneat_large_hundred_average_fit, 'm-.', label="ES-HyperNEAT large") plt.title("Average pole_balancing fitnesses hundred episodes") plt.xlabel("Generations") plt.ylabel("Fitness") plt.grid() plt.legend(loc="best") plt.savefig('pole_balancing_hundred_fitnesses.svg') plt.close()

49.39839

153

0.731864

3,729

24,551

4.450523

0.031912

0.111352

0.050133

0.027115

0.938118

0.898891

0.82785

0.751024

0.694023

0.586949

0

0.00443

0.163374

24,551

496

154

49.497984

0.803554

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0.002288

0

null

0

1

0

null

0

4

9cc32e6307652528b4d24d4098dd08d354afda23

454

py

Python

ontraportlib/models/sort_dir_enum.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

ontraportlib/models/sort_dir_enum.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

ontraportlib/models/sort_dir_enum.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ ontraportlib.models.sort_dir_enum This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ) on 11/14/2017 """ class SortDirEnum(object): """Implementation of the 'SortDir' enum. TODO: type enum description here. Attributes: ASC: TODO: type description here. DESC: TODO: type description here. """ ASC = 'asc' DESC = 'desc'

18.16

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0.142857

0.172932

0

0.034268

0.292952

454

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0.794393

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0

1

0

null

0

1

0

1

0

4

9cc49692629e3b37e0fc9542527c315936c6a87c

276

py

Python

AxePy3Lib/01/textwrap/textwrap_fill.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

1

2019-01-04T05:47:50.000Z

AxePy3Lib/01/textwrap/textwrap_fill.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

null

AxePy3Lib/01/textwrap/textwrap_fill.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # encoding: utf-8 # # Copyright (c) 2008 Doug Hellmann All rights reserved. # """ """ # end_pymotw_header import textwrap from textwrap_example import sample_text print(textwrap.fill(sample_text, width=50)) print(textwrap.fill(sample_text, width=40))

18.4

55

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0.167488

0.226601

0.315271

0

0.040984

0.115942

276

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56

19.714286

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1

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true

0

0.5

0

0.5

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0

1

0

null

0

1

0

1

0

1

0

4

9ce066e555ecb3359f16e7f599fa26998b432783

3,403

py

Python

spdb/spatialdb/test/int_test_AWS_object_store.py

jhuapl-boss/spdb

44d41e2b7a7b961e55746e1a5527d5419a74c2ce

[ "Apache-2.0" ]

5

2016-05-12T19:48:45.000Z

2018-11-17T00:15:23.000Z

spdb/spatialdb/test/int_test_AWS_object_store.py

jhuapl-boss/spdb

44d41e2b7a7b961e55746e1a5527d5419a74c2ce

[ "Apache-2.0" ]

5

2018-01-15T18:14:42.000Z

2020-07-30T21:59:16.000Z

spdb/spatialdb/test/int_test_AWS_object_store.py

jhuapl-boss/spdb

44d41e2b7a7b961e55746e1a5527d5419a74c2ce

[ "Apache-2.0" ]

3

2017-09-21T11:40:06.000Z

2018-05-14T20:15:40.000Z

# Copyright 2016 The Johns Hopkins University Applied Physics Laboratory # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from spdb.spatialdb import AWSObjectStore from spdb.spatialdb.test.test_AWS_object_store import AWSObjectStoreTestMixin from spdb.spatialdb.test.setup import AWSSetupLayer from spdb.project import BossResourceBasic class AWSObjectStoreTestIntegrationMixin(object): # TODO: implement tests here or remove def test_put_get_objects_async(self): """Method to test putting and getting objects to and from S3""" #os = AWSObjectStore(self.object_store_config) #cached_cuboid_keys = ["CACHED-CUBOID&1&1&1&0&0&12", "CACHED-CUBOID&1&1&1&0&0&13"] #fake_data = [b"aaaadddffffaadddfffaadddfff", b"fffddaaffddffdfffaaa"] #object_keys = os.cached_cuboid_to_object_keys(cached_cuboid_keys) #os.put_objects(object_keys, fake_data) #returned_data = os.get_objects_async(object_keys) #for rdata, sdata in zip(returned_data, fake_data): # assert rdata == sdata pass def test_page_in_objects(self): """Test method for paging in objects from S3 via lambda""" # os = AWSObjectStore(self.object_store_config) # # cached_cuboid_keys = ["CACHED-CUBOID&1&1&1&0&0&12", "CACHED-CUBOID&1&1&1&0&0&13"] # page_in_channel = "dummy_channel" # kv_config = {"param1": 1, "param2": 2} # state_config = {"param1": 1, "param2": 2} # # object_keys = os.page_in_objects(cached_cuboid_keys, # page_in_channel, # kv_config, # state_config) pass def test_trigger_page_out(self): """Test method for paging out objects to S3 via lambda""" # os = AWSObjectStore(self.object_store_config) # # cached_cuboid_keys = ["CACHED-CUBOID&1&1&1&0&0&12", "CACHED-CUBOID&1&1&1&0&0&13"] # page_in_channel = "dummy_channel" # kv_config = {"param1": 1, "param2": 2} # state_config = {"param1": 1, "param2": 2} # # object_keys = os.page_in_objects(cached_cuboid_keys, # page_in_channel, # kv_config, # state_config) pass class TestAWSObjectStoreInt(AWSObjectStoreTestIntegrationMixin, AWSObjectStoreTestMixin, unittest.TestCase): layer = AWSSetupLayer def setUp(self): """ Copy params from the Layer setUpClass """ # Setup Data self.data = self.layer.setup_helper.get_image8_dict() self.resource = BossResourceBasic(self.data) self.setup_helper = self.layer.setup_helper # Setup config self.object_store_config = self.layer.object_store_config

37.395604

108

0.644138

422

3,403

5.007109

0.329384

0.073829

0.045433

0.039754

0.302414

0.280644

0

0.027978

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3,403

90

109

37.811111

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0.631795

0

0.157895

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0.011111

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0.210526

false

0.157895

0.263158

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0.631579

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null

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1

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1

0

1

0

1

0

4

9ce7c44edfb802039a3e75fbd02681c6bc9c65e6

783

py

Python

tests/code_sample/todo/string_formats.py

brendanator/laziest

63098402cf7c3b320e0dfd46ca14d2700ed87056

[ "Apache-2.0" ]

1

2020-03-31T11:21:33.000Z

tests/code_sample/todo/string_formats.py

brendanator/laziest

63098402cf7c3b320e0dfd46ca14d2700ed87056

[ "Apache-2.0" ]

null

tests/code_sample/todo/string_formats.py

brendanator/laziest

63098402cf7c3b320e0dfd46ca14d2700ed87056

[ "Apache-2.0" ]

null

def string_format_s(arg1): return 'this is %s' % arg1 def string_format(arg1): return 'this is {}'.format(arg1) def string_format_f(arg1): return f'this is {arg1}' def string_format_f_multiple(arg1, arg2, arg3): return f'{arg2} this is {arg1}! {arg3}' def string_format_multiple(arg1, arg2, arg3): return ' {} this is {}! {}'.format(arg1, arg2, arg3) def string_format_named(arg1): return 'this is {name}'.format(name=arg1) def string_format_named_three_args(arg1, arg2, arg3): return '{first} this is {name} ! {last}'.format(name=arg1, first=arg2, last=arg3) def string_format_with_un_op(arg1, arg2, arg3): var = '{first} this is {name} ! {last}'.format(name=arg1, first=arg2, last=arg3) var += '. End.' var *= 3 return var

23.029412

85

0.662835

120

783

4.158333

0.2

0.144289

0.240481

0.152305

0.452906

0.200401

0

0.051563

0.182631

783

33

86

23.727273

0.728125

0

0.208174

0

1

0.421053

false

0

0.368421

0.842105

0

null

0

1

0

null

0

1

0

1

0

4

9cfd166f6930ac35c8c87e63aa513d3e78fb241a

790

py

Python

boto3_type_annotations/boto3_type_annotations/cloudformation/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

119

2018-12-01T18:20:57.000Z

2022-02-02T10:31:29.000Z

boto3_type_annotations/boto3_type_annotations/cloudformation/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

15

2018-11-16T00:16:44.000Z

2021-11-13T03:44:18.000Z

boto3_type_annotations/boto3_type_annotations/cloudformation/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

11

2019-05-06T05:26:51.000Z

2021-09-28T15:27:59.000Z

from typing import Dict from botocore.waiter import Waiter class ChangeSetCreateComplete(Waiter): def wait(self, ChangeSetName: str, StackName: str = None, NextToken: str = None, WaiterConfig: Dict = None): pass class StackCreateComplete(Waiter): def wait(self, StackName: str = None, NextToken: str = None, WaiterConfig: Dict = None): pass class StackDeleteComplete(Waiter): def wait(self, StackName: str = None, NextToken: str = None, WaiterConfig: Dict = None): pass class StackExists(Waiter): def wait(self, StackName: str = None, NextToken: str = None, WaiterConfig: Dict = None): pass class StackUpdateComplete(Waiter): def wait(self, StackName: str = None, NextToken: str = None, WaiterConfig: Dict = None): pass

28.214286

112

0.692405

91

790

6.010989

0.241758

0.127971

0.11883

0.155393

0.672761

0

0.208861

790

27

113

29.259259

0.8752

0

0.529412

0

1

0.294118

false

0.294118

0.117647

0

0.705882

0

null

0

1

0

null

0

1

0

1

0

1

0

4

140e67d033c79e366acb9a3b2745faa866fab465

1,346

py

Python

src/utils/hashing.py

DiceNameIsMy/fin-a-log

0eb47f9ed9f3fbc205b50b7217fabe67e50cea0e

[ "MIT" ]

null

src/utils/hashing.py

DiceNameIsMy/fin-a-log

0eb47f9ed9f3fbc205b50b7217fabe67e50cea0e

[ "MIT" ]

null

src/utils/hashing.py

DiceNameIsMy/fin-a-log

0eb47f9ed9f3fbc205b50b7217fabe67e50cea0e

[ "MIT" ]

null

from secrets import randbelow from hashids import Hashids from passlib.context import CryptContext pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") class InvalidHash(Exception): pass class InvalidObjectWithID(Exception): pass class IDHasher: def __init__(self, salt: str, min_length: int = 0): self.hashids = Hashids(salt, min_length=min_length) def encode(self, num: int) -> str: return self.hashids.encode(num) def decode(self, hash_id: str) -> int: try: return self.hashids.decode(hash_id)[0] except IndexError: raise InvalidHash() def encode_obj(self, obj): try: setattr(obj, "id", self.encode(getattr(obj, "id"))) return obj except AttributeError: raise InvalidObjectWithID(f"Object {obj} does not have an `id` attribute") def get_hashid(salt: str, min_length: int = 0) -> Hashids: return IDHasher(salt, min_length=min_length) def verify_password(plain_password: str, hashed_password: str) -> bool: return pwd_context.verify(plain_password, hashed_password) def get_password_hash(password: str) -> str: return pwd_context.hash(password) def generate_verification_code() -> int: """Generate a random 6-digit integer""" return randbelow(900000) + 100000

24.925926

86

0.679792

168

1,346

5.291667

0.392857

0.060742

0.040495

0.035996

0.101237

0

0.01518

0.216939

1,346

53

87

25.396226

0.828273

0.024517

0

0.125

0

0.044376

0

1

0.25

false

0.21875

0.09375

0.125

0.65625

0

null

0

null

0

1

0

1

0

1

0

4

141bbc8596c73b9859075f3010e29ba6684fa308

2,688

py

Python

TrackerDash/schemas/api.py

wedgieedward/TrackerDash

53c3ecc7b9124740f05847dbd235b068601c621e

[ "Beerware" ]

null

TrackerDash/schemas/api.py

wedgieedward/TrackerDash

53c3ecc7b9124740f05847dbd235b068601c621e

[ "Beerware" ]

2

2015-04-08T23:20:35.000Z

2015-04-08T23:21:57.000Z

TrackerDash/schemas/api.py

wedgieedward/TrackerDash

53c3ecc7b9124740f05847dbd235b068601c621e

[ "Beerware" ]

null

""" Schemas needed to validate incoming api requests """ import colander SUPPORTED_GRAPHS = ( 'line', 'bar', 'area', 'column', 'scatter', 'bar', "pie", "gauge") class ShowreelItem(colander.MappingSchema): title = colander.SchemaNode(colander.String()) item_type = colander.SchemaNode( colander.String(), validator=colander.OneOf(["graph", "dashboard"])) class ShowreelItems(colander.SequenceSchema): item = ShowreelItem() class Showreel(colander.MappingSchema): """ schema for a showreel document """ title = colander.SchemaNode(colander.String()) refresh_interval = colander.SchemaNode(colander.Int()) reels = ShowreelItems() class GraphDimension(colander.MappingSchema): width = colander.SchemaNode(colander.Int(), validator=colander.OneOf([4, 6, 8, 12])) height = colander.SchemaNode(colander.Int(), validator=colander.Range(1, 5)) class DashGraph(colander.MappingSchema): title = colander.SchemaNode(colander.String()) dimensions = GraphDimension() class GraphRow(colander.SequenceSchema): """ a list of graph names """ row_data = DashGraph() class GraphRows(colander.SequenceSchema): """ A list of graph rows """ rows = GraphRow() class Dashboard(colander.MappingSchema): """ Schema for a dashboard document """ title = colander.SchemaNode(colander.String()) row_data = GraphRows() class DataRange(colander.MappingSchema): """ Schema for a data range dictionary """ minutes = colander.SchemaNode(colander.Int(), missing=0) hours = colander.SchemaNode(colander.Int(), missing=0) days = colander.SchemaNode(colander.Int(), missing=0) weeks = colander.SchemaNode(colander.Int(), missing=0) seconds = colander.SchemaNode(colander.Int(), missing=0) class Graph(colander.MappingSchema): """ Schema for a graph document """ title = colander.SchemaNode(colander.String()) data_source = colander.SchemaNode(colander.String()) # Optional args description = colander.SchemaNode(colander.String(), missing="") data_range = DataRange(missing={"minutes": 0, "hours": 0, "days": 0, "weeks": 1, "seconds": 0}) graph_type = colander.SchemaNode( colander.String(), validator=colander.OneOf(SUPPORTED_GRAPHS), missing="line") stacked = colander.SchemaNode(colander.Bool(), missing=False) url = colander.SchemaNode(colander.String(), missing='')

27.71134

72

0.630208

257

2,688

6.55642

0.303502

0.202967

0.293175

0.189911

0.542433

0.422552

0.137685

0.068843

0

0.00835

0.24256

2,688

96

73

28

0.819253

0.08631

0

0.14

0

0.034351

0

1

0

false

0

0.02

0

0.74

0

null

1

0

null

0

1

0

4

142617c1149fb198c94820251dde11dd708ad448

229

py

Python

djangoapi/courses/serializer.py

ptyadana/django-REST-API-course-info

9247d5085e28418053975b0800fd42786b6742be

[ "MIT" ]

null

djangoapi/courses/serializer.py

ptyadana/django-REST-API-course-info

9247d5085e28418053975b0800fd42786b6742be

[ "MIT" ]

null

djangoapi/courses/serializer.py

ptyadana/django-REST-API-course-info

9247d5085e28418053975b0800fd42786b6742be

[ "MIT" ]

null

from rest_framework import serializers from .models import Course class CourseSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Course fields = ('id', 'url', 'name', 'langauge', 'price')

32.714286

63

0.71179

23

229

7.043478

0.782609

0

0.183406

229

7

64

32.714286

0.86631

0

0.095652

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

14355fd96dad21ad9be55c6f3e4a53c0f0123d16

28

py

Python

venv-lib/lib/python3.7/copy.py

migmaciasdiaz/venvs

bcdbb75931cb27fc4b5b30f12fc44be85952157e

[ "MIT" ]

2

2020-03-30T14:17:10.000Z

2020-10-04T12:33:00.000Z

venv-lib/lib/python3.7/copy.py

migmaciasdiaz/venvs

bcdbb75931cb27fc4b5b30f12fc44be85952157e

[ "MIT" ]

1

2020-11-24T03:31:13.000Z

venv/lib/python3.7/copy.py

wensu425/aws-eb-webapp

4b149c75c11fe5b33c9a080313ec336fabb45824

[ "MIT" ]

1

2021-05-04T09:18:22.000Z

/usr/lib64/python3.7/copy.py

28

0.785714

6

28

3.666667

1

0

0.142857

0

28

1

28

0.642857

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

4

1444a0098bfc98d4d2642192bfe348fc5d1b4df5

8,683

py

Python

avlDecoder.py

karticr/Teltonika_FMBXXX_TCP_Server

130a91c033ca0335e48b9aa275186103fc221a75

[ "Apache-2.0" ]

5

2021-01-05T03:14:00.000Z

2021-09-03T21:50:44.000Z

avlDecoder.py

karticr/Teltonika_FMBXXX_TCP_Server

130a91c033ca0335e48b9aa275186103fc221a75

[ "Apache-2.0" ]

3

2021-05-01T14:56:05.000Z

2022-03-03T09:53:27.000Z

avlDecoder.py

karticr/Teltonika_FMBXXX_TCP_Server

130a91c033ca0335e48b9aa275186103fc221a75

[ "Apache-2.0" ]

1

2022-02-10T04:38:18.000Z

import binascii import datetime import math from IO_decoder import IODecoder io = IODecoder() class avlDecoder(): def __init__(self): self.raw_data = "" self.initVars() def initVars(self): # initilizing variables self.codecid = 0 self.no_records_i = 0 self.no_records_e = 0 self.crc_16 = 0 self.avl_entries = [] self.avl_latest = "" self.d_time_unix = 0 self.d_time_local = "" self.avl_io_raw = "" self.priority = 0 self.lon = 0 self.lat = 0 self.alt = 0 self.angle = 0 self.satellites = 0 self.speed = 0 self.decoded_io = {} def decodeAVL(self, data): self.raw_data = data self.data_field_l = int(data[8:16],16)*2 # Data Field Length – size is calculated starting from Codec ID to Number of Data 2. self.total_io_size = self.data_field_l-4-2 #-4=> subtract codecid and no of data, -2=> no of data at the end. self.io_end = 20+self.total_io_size # 20=> start from timestamp self.codecid = int(data[16:18], 16) # codecid self.no_record_i = int(data[18:20], 16) # first no of total records self.no_record_e = int(data[-10:-8], 16) # no of total records before crc-16 check self.crc_16 = int(data[-8:],16) # crc-16 check self.first_io_start= 20 # first io starting pos self.first_io_end = math.ceil(self.total_io_size/ self.no_record_e) # end pos for first io entry if(self.codecid == 8 and (self.no_record_i == self.no_record_e)): # record_entries = data[20:-10] # entry data record_entries = data[self.first_io_start: self.io_end ] # entry data entries_size = len(record_entries) # total no of entries division_size = int(len(record_entries)/ self.no_record_i) # division size self.avl_entries = [] print("old size:", entries_size, "division:", division_size) print("new size:", self.total_io_size, "division:", self.total_io_size/ self.no_record_e) for i in range(0, entries_size, division_size): self.avl_entries.append(record_entries[i:i+division_size]) # splitting into chunks self.avl_latest = record_entries[0:self.first_io_end] # latest avl data packets self.avl_latest_1 = self.avl_entries[0] print("________________________________________") print("old:", self.avl_entries[0]) print("new:", self.avl_latest) print("‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾") self.d_time_unix = int(self.avl_latest[0:16],16) # device time unix self.d_time_local = self.unixtoLocal(self.d_time_unix) # device time local self.priority = int(record_entries[16:18], 16) # device data priority self.lon = int(record_entries[18:26], 16) # longitude self.lat = int(record_entries[26:34], 16) # latitude self.alt = int(record_entries[34:38], 16) # altitude self.angle = int(record_entries[38:42], 16) # angle self.satellites = int(record_entries[42:44], 16) # no of satellites self.speed = int(record_entries[44:48], 16) # speed self.avl_io_raw = self.avl_latest[48:] # avl io data raw print("raw io",self.avl_io_raw) self.decoded_io = io.dataDecoder(self.avl_io_raw) # decoded avl data return self.getAvlData() else: return -1 def getDateTime(self): # system time return datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S") def unixtoLocal(self, unix_time): # unix to local time time = datetime.datetime.fromtimestamp(unix_time/1000) return f"{time:%Y-%m-%d %H:%M:%S}" def getAvlData(self): data = { "sys_time" : self.getDateTime(), "codecid" : self.codecid, "no_record_i": self.no_record_i, "no_record_e": self.no_record_e, "crc-16" : self.crc_16, # "avl_entries": self.avl_entries, # "avl_latest" : self.avl_latest, "d_time_unix" : self.d_time_unix, "d_time_local": self.d_time_local, "priority" :self.priority, "lon" :self.lon, "lat" :self.lat, "alt" :self.alt, "angle" :self.angle, "satellites" :self.satellites, "speed" :self.speed, "io_data" :self.decoded_io } return data def getRawData(self): return self.raw_data if __name__ == "__main__": data = b'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' # data = b'000000000000003608010000016B40D8EA30010000000000000000000000000000000105021503010101425E0F01F10000601A014E0000000000000000010000C7CF' # data = b'000000000000004308020000016B40D57B480100000000000000000000000000000001010101000000000000016B40D5C198010000000000000000000000000000000101010101000000020000252C' avl = avlDecoder() res = avl.decodeAVL(data) print(res) # avldata = avl.getAvlData() # print(avldata)

66.282443

2,506

0.64609

699

8,683

7.829757

0.184549

0.014252

0.02083

0.027042

0.064681

0.025215

0.017541

0.007309

0

0.43913

0.284809

8,683

130

2,507

66.792308

0.435588

0.126569

0

0.020202

0

0.368588

0.340888

0

1

0

1

0.070707

false

0

0.040404

0.020202

0.181818

0.080808

0

null

0

1

0

1

0

1

0

null

1

0

4

1461c579bd796a534c823ae58d48b47fae36abdb

95

py

Python

src/mozloc/__init__.py

scivision/mozilla-location-wifi

fb41aa88bd89f0734c34fdd14fb3db0697d9b88a

[ "MIT" ]

4

2020-11-23T06:25:55.000Z

2021-11-04T02:11:53.000Z

src/mozloc/__init__.py

scivision/mozilla-location-wifi-python

fb41aa88bd89f0734c34fdd14fb3db0697d9b88a

[ "MIT" ]

null

src/mozloc/__init__.py

scivision/mozilla-location-wifi-python

fb41aa88bd89f0734c34fdd14fb3db0697d9b88a

[ "MIT" ]

null

from .base import log_wifi_loc from .modules import get_signal, parse_signal, cli_config_check

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0.852632

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1463af8b1fc3a452cf428a092fc4c5e6f04b24b2

238

py

Python

Section02_Builder/BuilderInheritance/PersonBirthDateBuilder.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

1

2020-10-20T07:41:51.000Z

Section02_Builder/BuilderInheritance/PersonBirthDateBuilder.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

Section02_Builder/BuilderInheritance/PersonBirthDateBuilder.py

enriqueescobar-askida/Kinito.Python

e4c5521e771c4de0ceaf81776a4a61f7de01edb4

[ "MIT" ]

null

from Section02_Builder.BuilderInheritance.PersonJobBuilder import PersonJobBuilder class PersonBirthDateBuilder(PersonJobBuilder): def born(self, date_of_birth): self.person.date_of_birth = date_of_birth return self

29.75

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147997bd302efc04f1376c6194416d2d473ad26b

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py

Python

ctrl-hyper/ctrl-r.py

MTfirst/cmd-ctrl_onLinux

38a6db67796bdc8d438ca63171d9fea03e84f5f7

[ "MIT" ]

1

2020-05-02T03:46:10.000Z

ctrl-hyper/ctrl-r.py

MTfirst/cmd-ctrl_onLinux

38a6db67796bdc8d438ca63171d9fea03e84f5f7

[ "MIT" ]

null

ctrl-hyper/ctrl-r.py

MTfirst/cmd-ctrl_onLinux

38a6db67796bdc8d438ca63171d9fea03e84f5f7

[ "MIT" ]

null

keyboard.send_keys("<ctrl>+<shift>+r")

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null

0

1

0

null

0

1

0

4

14986af2d1b45efa6e844fc93b02dbd9e29b1629

242

py

Python

python/analysis/beam_ana/__init__.py

ACTCollaboration/moby2

b0f6bd6add7170999eb964d18f16d795520426e9

[ "BSD-2-Clause" ]

3

2020-06-23T15:59:37.000Z

2022-03-29T16:04:35.000Z

python/analysis/beam_ana/__init__.py

ACTCollaboration/moby2

b0f6bd6add7170999eb964d18f16d795520426e9

[ "BSD-2-Clause" ]

1

2020-04-08T15:10:46.000Z

python/analysis/beam_ana/__init__.py

ACTCollaboration/moby2

b0f6bd6add7170999eb964d18f16d795520426e9

[ "BSD-2-Clause" ]

null

from __future__ import print_function from __future__ import absolute_import from past.builtins import basestring from .beam_obs import BeamObs, BeamObsList from .beam_plot import plot_beam_image from . import solid_angle from . import util

26.888889

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1

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4

14ae89744d23180ef351421ffe231d8cbfbc615a

22

py

Python

notebooks/dminteract/version.py

chapmanbe/isys90069_w2020_explore

305a129ee32035b62741fed2eb722aa4086a1167

[ "MIT" ]

8

2020-12-20T02:59:59.000Z

2021-09-23T06:04:01.000Z

notebooks/dminteract/version.py

chapmanbe/isys90069_w2020_explore

305a129ee32035b62741fed2eb722aa4086a1167

[ "MIT" ]

5

2021-06-08T21:54:49.000Z

2022-03-12T00:38:51.000Z

notebooks/dminteract/version.py

chapmanbe/isys90069_w2020_explore

305a129ee32035b62741fed2eb722aa4086a1167

[ "MIT" ]

9

2020-06-26T06:00:15.000Z

2022-01-06T04:07:38.000Z

__version__="0.0.1.5"

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null

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1

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null

0

4

14bf30645e89a98e38db0b7d2622f85c989d6c58

111

py

Python

tests/pipe_proc_tests/dev.py

genematx/nmrglue

8a24cf6cbd18451e552fc0673b84c42d1dcb69a2

[ "BSD-3-Clause" ]

150

2015-01-16T12:24:13.000Z

2022-03-03T18:01:18.000Z

tests/pipe_proc_tests/dev.py

genematx/nmrglue

8a24cf6cbd18451e552fc0673b84c42d1dcb69a2

[ "BSD-3-Clause" ]

129

2015-01-13T04:58:56.000Z

2022-03-02T13:39:16.000Z

tests/pipe_proc_tests/dev.py

genematx/nmrglue

8a24cf6cbd18451e552fc0673b84c42d1dcb69a2

[ "BSD-3-Clause" ]

88

2015-02-16T20:04:12.000Z

2022-03-10T06:50:30.000Z

#! /usr/bin/env python """ Create files for dev unit test """ # do nothing as NMRPipe goes into infinite loop.

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111

4.333333

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null

0

1

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1ae79a0e222d499f9abf55654da6499867c03d60

277

py

Python

warpfield/__init__.py

xr0038/jasmine_warpfield

d3dc8306c30c955eea997e7cb69c1910df6a9515

[ "MIT" ]

null

warpfield/__init__.py

xr0038/jasmine_warpfield

d3dc8306c30c955eea997e7cb69c1910df6a9515

[ "MIT" ]

7

2021-07-04T07:07:34.000Z

2021-09-09T05:22:09.000Z

warpfield/__init__.py

xr0038/jasmine_warpfield

d3dc8306c30c955eea997e7cb69c1910df6a9515

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from .util import get_projection from .source import retrieve_gaia_sources from .source import display_sources, display_gaia_sources from .telescope import Optics, Detector, Telescope # from .distortion import distortion_generator

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7

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null

0

1

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1

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1

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4

1aea61bcc9df6cd17982b810157a304388bea2e1

48

py

Python

hello.py

AbhijeetSrivastava96/Python-Programs

d5a7ff9698150a5bc809129214a0b3edc0fa9d91

[ "MIT" ]

null

hello.py

AbhijeetSrivastava96/Python-Programs

d5a7ff9698150a5bc809129214a0b3edc0fa9d91

[ "MIT" ]

null

hello.py

AbhijeetSrivastava96/Python-Programs

d5a7ff9698150a5bc809129214a0b3edc0fa9d91

[ "MIT" ]

null

a=4 b=5 c=a+b print("the value of a+b is : ",c)

9.6

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1.8

0.666667

0.148148

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0.052632

0.208333

48

4

34

12

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false

0

0.25

1

null

0

1

0

null

0

4

210643efde344f1910eb7a98b108382fc7041c16

330

py

Python

anidbcli/__init__.py

infinityb/anidbcli

fb5ea89b3690190f8f92f23111b44c3cfade92c1

[ "MIT" ]

null

anidbcli/__init__.py

infinityb/anidbcli

fb5ea89b3690190f8f92f23111b44c3cfade92c1

[ "MIT" ]

null

anidbcli/__init__.py

infinityb/anidbcli

fb5ea89b3690190f8f92f23111b44c3cfade92c1

[ "MIT" ]

null

from .anidbconnector import AnidbConnector from .libed2k import get_ed2k_link,hash_file from .cli import main from .protocol import FileRequest, AnimeAmaskField, FileFmaskField, FileAmaskField __all__ = ['AnidbConnector', "FileRequest", "AnimeAmaskField", "FileFmaskField", "FileAmaskField", "main", "hash_file", "get_ed2k_link"]

55

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0.809091

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330

7.342857

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0.054475

0.085603

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330

6

136

55

0.843854

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1

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1

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null

0

1

0

1

0

4

212f11894e29ef6f76020614604e0276162d9dd3

119

py

Python

awspice/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

1

2020-08-04T18:22:41.000Z

awspice/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

null

awspice/__init__.py

Telefonica/awspice

da6f6ee0a8d7a7206c1ea5e7ca8bbc83716b29fb

[ "Apache-2.0" ]

2

2019-04-03T16:56:19.000Z

2019-05-06T19:41:26.000Z

from .manager import AwsManager as connect from .servicemanager import ServiceManager from .helpers import ClsEncoder

23.8

42

0.848739

14

119

7.214286

0.642857

0

0.12605

119

4

43

29.75

0.971154

0

1

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true

0

1

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1

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1

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null

0

1

0

null

0

1

0

1

0

4

2135f82bed51051638952f0f902530653e48bfe9

554

py

Python

src/purgatory/domain/messages/events.py

mardiros/purgatory

5905619c0f153eae090c46ed5cd7f165c86eafd5

[ "BSD-3-Clause" ]

null

src/purgatory/domain/messages/events.py

mardiros/purgatory

5905619c0f153eae090c46ed5cd7f165c86eafd5

[ "BSD-3-Clause" ]

11

2021-12-29T21:28:50.000Z

2022-01-17T08:09:38.000Z

src/purgatory/domain/messages/events.py

mardiros/purgatory

5905619c0f153eae090c46ed5cd7f165c86eafd5

[ "BSD-3-Clause" ]

null

from dataclasses import dataclass from typing import Optional from purgatory.typing import TTL, StateName, Threshold from .base import Event @dataclass(frozen=True) class CircuitBreakerCreated(Event): name: str threshold: Threshold ttl: TTL @dataclass(frozen=True) class ContextChanged(Event): name: str state: StateName opened_at: Optional[float] @dataclass(frozen=True) class CircuitBreakerFailed(Event): name: str failure_count: int @dataclass(frozen=True) class CircuitBreakerRecovered(Event): name: str

17.3125

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0.752708

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554

6.484375

0.4375

0.144578

0.183133

0.231325

0

0.169675

554

31

55

17.870968

0.902174

0

0.380952

0

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true

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0.190476

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0.809524

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null

0

1

0

null

0

1

0

1

0

4

2148c24bf5b42cbd93b875b82aa1b5edb5e85002

193

py

Python

leads/apps.py

coderj001/Django-CRM

7cca0df5d39b92082781047c1f0a11129179f257

[ "MIT" ]

null

leads/apps.py

coderj001/Django-CRM

7cca0df5d39b92082781047c1f0a11129179f257

[ "MIT" ]

null

leads/apps.py

coderj001/Django-CRM

7cca0df5d39b92082781047c1f0a11129179f257

[ "MIT" ]

null

from django.apps import AppConfig class LeadsConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'leads' def ready(self): import leads.signals

19.3

56

0.709845

23

193

5.869565

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0.202073

193

9

57

21.444444

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0

0.176166

0.150259

0

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0.166667

false

0

0.333333

0

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null

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