xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

094d973f1a0b76ddedf08b03767accf5c0cfd497

7,467

py

Python

mlcomponents/encoderdecoder.py

microsoft/aaai21-copy-that

7dfb2ebabbbf1165a33c2430ef2f2571e487b4fd

[ "MIT" ]

7

2021-06-21T17:13:23.000Z

2022-02-25T06:28:24.000Z

mlcomponents/encoderdecoder.py

microsoft/aaai21-copy-that

7dfb2ebabbbf1165a33c2430ef2f2571e487b4fd

[ "MIT" ]

null

mlcomponents/encoderdecoder.py

microsoft/aaai21-copy-that

7dfb2ebabbbf1165a33c2430ef2f2571e487b4fd

[ "MIT" ]

2

2021-09-13T12:32:16.000Z

2022-02-19T13:28:35.000Z

import logging from typing import Optional, Dict, Any, List, Tuple, NamedTuple import torch from data.edits import Edit from dpu_utils.ptutils import BaseComponent from mlcomponents.seqdecoding import SeqDecoder from mlcomponents.seqencoder import SequenceEncoder class EncoderDecoder(BaseComponent): LOGGER = logging.getLogger('EncoderDecoder') def __init__(self, name: str, input_sequence_encoder: SequenceEncoder, output_sequence_decoder: SeqDecoder, hyperparameters: Optional[Dict[str, Any]] = None) -> None: super(EncoderDecoder, self).__init__(name, hyperparameters) self.__input_sequence_encoder = input_sequence_encoder self.__output_sequence_decoder = output_sequence_decoder @classmethod def default_hyperparameters(cls) -> Dict[str, Any]: return { } def _finalize_component_metadata_and_model(self) -> None: pass @property def input_sequence_encoder(self): return self.__input_sequence_encoder @property def output_sequence_decoder(self): return self.__output_sequence_decoder def _load_metadata_from_sample(self, data_to_load: Edit) -> None: self.__input_sequence_encoder.load_metadata_from_sample(data_to_load.input_sequence) self.__output_sequence_decoder.load_metadata_from_sample(SeqDecoder.InputOutputSequence( input_sequence=data_to_load.input_sequence, output_sequence=data_to_load.output_sequence )) TensorizedData = NamedTuple('EncoderDecoderTensorizedData', [ ('input_sequence', Any), ('output_sequence', Any), ]) def load_data_from_sample(self, data_to_load: Edit) -> Optional['EncoderDecoder.TensorizedData']: return self.TensorizedData( input_sequence=self.__input_sequence_encoder.load_data_from_sample([SeqDecoder.START] + data_to_load.input_sequence + [SeqDecoder.END]), output_sequence=self.__output_sequence_decoder.load_data_from_sample(SeqDecoder.InputOutputSequence( input_sequence=[SeqDecoder.START] + data_to_load.input_sequence + [SeqDecoder.END], output_sequence=data_to_load.output_sequence )) ) def initialize_minibatch(self) -> Dict[str, Any]: return { 'input_sequences': self.__input_sequence_encoder.initialize_minibatch(), 'output_sequences': self.__output_sequence_decoder.initialize_minibatch(), } def extend_minibatch_by_sample(self, datapoint: 'EncoderDecoder.TensorizedData', accumulated_minibatch_data: Dict[str, Any]) -> bool: continue_extending = self.__input_sequence_encoder.extend_minibatch_by_sample( datapoint=datapoint.input_sequence, accumulated_minibatch_data=accumulated_minibatch_data['input_sequences']) continue_extending &= self.__output_sequence_decoder.extend_minibatch_by_sample( datapoint=datapoint.output_sequence, accumulated_minibatch_data=accumulated_minibatch_data['output_sequences']) return continue_extending def finalize_minibatch(self, accumulated_minibatch_data: Dict[str, Any]) -> Dict[str, Any]: return { 'input_sequences': self.__input_sequence_encoder.finalize_minibatch(accumulated_minibatch_data['input_sequences']), 'output_sequences': self.__output_sequence_decoder.finalize_minibatch(accumulated_minibatch_data['output_sequences']) } def forward(self, *, input_sequences: Dict[str, Any], output_sequences: Dict[str, Any]): input_encoding = self.__input_sequence_encoder.forward( input_sequence_data=input_sequences, return_embedded_sequence=True ) memories, memories_lengths, output_state, input_sequence_token_embeddings = input_encoding decoder_loss = self.__output_sequence_decoder.forward(memories=memories, memories_lengths=memories_lengths, initial_state=output_state, input_sequence_token_embeddings=input_sequence_token_embeddings, **output_sequences) return decoder_loss def greedy_decode(self, input_sequences: Dict[str, Any], ground_input_sequences: List[List[str]], max_length: int=50) -> List[Tuple[List[List[str]], List[float]]]: with torch.no_grad(): ground_input_sequences, initial_state, memories, memory_lengths = self.__prepare_decoding(ground_input_sequences, input_sequences) return self.__output_sequence_decoder.greedy_decode(memories, memory_lengths, initial_state=initial_state, max_length=max_length, memories_str_representations=[[SeqDecoder.START] + g + [SeqDecoder.END] for g in ground_input_sequences]) def beam_decode(self, input_sequences: Dict[str, Any], ground_input_sequences: List[List[str]], max_length: int=150) -> List[Tuple[List[List[str]], List[float]]]: with torch.no_grad(): ground_input_sequences, initial_state, memories, memory_lengths = self.__prepare_decoding(ground_input_sequences, input_sequences) return self.__output_sequence_decoder.beam_decode(memories, memory_lengths, initial_state=initial_state, max_length=max_length, memories_str_representations=[[SeqDecoder.START] + g + [SeqDecoder.END] for g in ground_input_sequences], ) def __prepare_decoding(self, ground_input_sequences, input_sequences): memories, memory_lengths, output_state = self.__input_sequence_encoder.forward( input_sequence_data=input_sequences) return ground_input_sequences, output_state, memories, memory_lengths def compute_likelihood(self, *, input_sequences: Dict[str, Any], output_sequences: Dict[str, Any], return_debug_info: bool = False): with torch.no_grad(): memories, memories_lengths, output_state = self.__input_sequence_encoder.forward(input_sequence_data=input_sequences) return self.__output_sequence_decoder.compute_likelihood(memories=memories, memories_lengths=memories_lengths, initial_state=output_state, return_debug_info= return_debug_info, **output_sequences)

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7,467

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0.359223

0

null

0

null

0

1

0

094ecb7e5b2ee93093ff48eabf2ef2a4ed487787

8,195

py

Python

mplStyle/MplTickStyle.py

khanfarhan10/mplStyle

f657f54c6c101811b8bf0c44f4b16d4f4926685d

[ "BSD-3-Clause" ]

39

2015-03-08T23:05:01.000Z

2022-02-07T16:03:35.000Z

mplStyle/MplTickStyle.py

khanfarhan10/mplStyle

f657f54c6c101811b8bf0c44f4b16d4f4926685d

[ "BSD-3-Clause" ]

null

mplStyle/MplTickStyle.py

khanfarhan10/mplStyle

f657f54c6c101811b8bf0c44f4b16d4f4926685d

[ "BSD-3-Clause" ]

23

2015-03-08T19:56:59.000Z

2021-07-15T15:16:26.000Z

#=========================================================================== # # Copyright (c) 2014, California Institute of Technology. # U.S. Government Sponsorship under NASA Contract NAS7-03001 is # acknowledged. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #=========================================================================== """: A class containing tick mark style information.""" __version__ = "$Revision: #1 $" #=========================================================================== from . import types as S from .MplBasicLineStyle import MplBasicLineStyle from .MplTextStyle import MplTextStyle import matplotlib.axis as mplaxis #=========================================================================== __all__ = [ 'MplTickStyle' ] #=========================================================================== class MplTickStyle( S.SubStyle ): """: Style properties for managing matplotlib axis tick elements. """ labels = S.property.SubStyle( MplTextStyle, doc = """ The style properties for any text labels placed at tick marks along the primary axis edge. If this is on the X-Axis, then the primary edge is the bottom. If this is on the Y-Axis, then the primary edge is the left. = SEE ALSO - :ref:`MplTextStyle <mplStyle_MplTextStyle>` """ ) secondaryLabels = S.property.SubStyle( MplTextStyle, doc = """ The style properties for any text labels placed at tick marks along the secondary axis edge. If this is on the X-Axis, then the secondary edge is the top. If this is on the Y-Axis, then the secondary edge is the right. = SEE ALSO - :ref:`MplTextStyle <mplStyle_MplTextStyle>` """ ) marks = S.property.SubStyle( MplBasicLineStyle, doc = """ The style properties for the tick marks along the primary axis edge. If this is on the X-Axis, then the primary edge is the bottom. If this is on the Y-Axis, then the primary edge is the left. = SEE ALSO - :ref:`MplBasicLineStyle <mplStyle_MplBasicLineStyle>` """ ) secondaryMarks = S.property.SubStyle( MplBasicLineStyle, doc = """ The style properties for the tick marks along the secondary axis edge. If this is on the X-Axis, then the secondary edge is the top. If this is on the Y-Axis, then the secondary edge is the right. = SEE ALSO - :ref:`MplBasicLineStyle <mplStyle_MplBasicLineStyle>` """ ) grid = S.property.SubStyle( MplBasicLineStyle, doc = """ The style properties for the grid lines. Grid lines are present for each tick mark. This means that if there is no tick locator for an axis, then there are no ticks to use for grid lines. Setting the visibility of the tick marks to True will ensure that a tick locator is present to use for generating grid lines. = SEE ALSO - :ref:`MplBasicLineStyle <mplStyle_MplBasicLineStyle>` """ ) length = S.property.Float( min = 0.0, doc = """ The length of the ticks (in points). """ ) width = S.property.Float( min = 0.0, doc = """ The width of the ticks (in points). """ ) pad = S.property.Float( doc = """ The spacing between the ticks and their labels (in points). """ ) #----------------------------------------------------------------------- def apply( self, obj, defaults = {}, **kwargs ): """: Apply this style to the given object using the supplied defaults. = NOTE - This can apply to any matplotlib Tick. = INPUT VARIABLES - obj The object to apply the style to. - defaults Keyword-value dictionary with defaults values to use if a property value is not specified. - kwargs Keyword-value dictionary whose values will supercede any values set by the properties of this sub-style. """ if not isinstance( obj, mplaxis.Tick ): msg = "Unable to apply this sub-style to the given element." \ "Expected a matplotlib 'Tick' and instead received the " \ "following:\n%s" % (obj,) raise Exception( msg ) # Labels subKwargs = kwargs.get( 'labels', {} ) subDefaults = S.lib.resolveDefaults( defaults, ['text', 'labels'] ) self.labels.apply( obj.label1, subDefaults, **subKwargs ) value = self.labels.getValue( 'visible', subDefaults, **subKwargs ) if value is not None: obj.label1On = value # Secondary Labels subKwargs = kwargs.get( 'secondaryLabels', {} ) subDefaults = S.lib.resolveDefaults( defaults, ['text', 'labels', 'secondaryLabels'] ) self.secondaryLabels.apply( obj.label2, subDefaults, **subKwargs ) value = self.secondaryLabels.getValue( 'visible', subDefaults, **subKwargs ) if value is not None: obj.label2On = value # marks subKwargs = kwargs.get( 'marks', {} ) subDefaults = S.lib.resolveDefaults( defaults, ['marks'] ) self.marks.apply( obj.tick1line, subDefaults, **subKwargs ) value = self.marks.getValue( 'visible', subDefaults, **subKwargs ) if value is not None: obj.tick1On = value # Secondary Marks subKwargs = kwargs.get( 'secondaryMarks', {} ) subDefaults = S.lib.resolveDefaults( defaults, ['secondaryMarks'] ) self.secondaryMarks.apply( obj.tick2line, subDefaults, **subKwargs ) value = self.secondaryMarks.getValue( 'visible', subDefaults, **subKwargs ) if value is not None: obj.tick2On = value # Grid subKwargs = kwargs.get( 'grid', {} ) subDefaults = S.lib.resolveDefaults( defaults, ['grid'] ) self.grid.apply( obj.gridline, subDefaults, **subKwargs ) value = self.grid.getValue( 'visible', subDefaults, **subKwargs ) if value is not None: obj.gridOn = value # Activate the grid as appropriate #FUTURE: This should be here using Tick.major, but matplotlib #FUTURE: needs to be fixed first. #FUTURE obj.grid( self.grid.visible ) #FUTURE: Setup minor tick locators (as necessary) # Length value = self.getValue( 'length', defaults, **kwargs ) if value is not None: obj._size = value obj.tick1line.set_markersize( obj._size ) obj.tick2line.set_markersize( obj._size ) # Width value = self.getValue( 'width', defaults, **kwargs ) if value is not None: obj._width = value obj.tick1line.set_markeredgewidth( obj._width ) obj.tick2line.set_markeredgewidth( obj._width ) # Pad value = self.getValue( 'pad', defaults, **kwargs ) if value is not None: obj.set_pad( value ) #-----------------------------------------------------------------------

37.591743

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null

0

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0

095359d3e1736ca0199f424b5cea96012cb5fc24

3,286

py

Python

micolog/cache2.py

tclh123/micolog

9b6fcddb9f147fe20a0cbfe0e89eda07f69d0f68

[ "MIT" ]

null

micolog/cache2.py

tclh123/micolog

9b6fcddb9f147fe20a0cbfe0e89eda07f69d0f68

[ "MIT" ]

null

micolog/cache2.py

tclh123/micolog

9b6fcddb9f147fe20a0cbfe0e89eda07f69d0f68

[ "MIT" ]

null

#------------------------------------------------------------------------------- # Name: cache.py # Purpose: # # Author: xuming # # Created: 23-01-2011 # Copyright: (c) xuming 2011 # Licence: GPL #------------------------------------------------------------------------------- #!/usr/bin/env python """A simple cache warp for micolog The main purpose of this module is to design a common layer to deal with all methods which need been cached! """ from google.appengine.api import memcache from utils import format_date from datetime import datetime from settings import ENABLE_MEMCACHE def vcache(key="", time=0,args=()): """ Cache for normal method which return some object example:: @vcache("blog.hotposts",args=('count')) def hotposts(self,count=8): return Entry.all().filter('entrytype =', 'post').filter("published =", True).order('-readtimes').fetch(count) args: key: keyname fo memcache args: the list of cached args time: relative number of seconds from current time. """ def _decorate(method): def _wrapper(*cargs, **kwargs): if not ENABLE_MEMCACHE: return method(*cargs, **kwargs) skey=key if hasattr(cargs[0],"vkey"): skey=key+cargs[0].vkey for arg in args: if kwargs.has_key(arg): skey+="_"+str(arg)+"_"+str(kwargs[arg]) result=memcache.get(skey) if result==None: result = method(*cargs, **kwargs) memcache.set(skey, result, time) return result return _wrapper return _decorate def cache(key="",time=0): """ Cache for request handler method, such as: get or post. It will cache the web page. example:: @cache(time=600) def get(self,tags=None): args: key: optional key name. Request. path_qs as default. time: relative number of seconds from current time. """ def _decorate(method): def _wrapper(*args, **kwargs): if not ENABLE_MEMCACHE: method(*args, **kwargs) return request=args[0].request response=args[0].response skey=key+ request.path_qs #logging.info('skey:'+skey) html= memcache.get(skey) #arg[0] is BaseRequestHandler object if html: #logging.info('cache:'+skey) response.last_modified =html[1] ilen=len(html) if ilen>=3: response.set_status(html[2]) if ilen>=4: for skey,value in html[3].items(): response.headers[skey]=value response.out.write(html[0]) else: if 'last-modified' not in response.headers: response.last_modified = format_date(datetime.utcnow()) method(*args, **kwargs) result=response.body status_code = response.status_int memcache.set(skey,(result,response.last_modified,status_code,response.headers),time) return _wrapper return _decorate

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0

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095b1d80f095695d78ca8e32322128326b38659a

5,112

py

Python

LeetCode-All-Solution/Python3/LC-0189-Rotate-Array.py

YuweiYin/Algorithm_YuweiYin

28648fac59c5a4e3c907978cbd1b3e662ba18fd5

[ "MIT" ]

null

LeetCode-All-Solution/Python3/LC-0189-Rotate-Array.py

YuweiYin/Algorithm_YuweiYin

28648fac59c5a4e3c907978cbd1b3e662ba18fd5

[ "MIT" ]

null

LeetCode-All-Solution/Python3/LC-0189-Rotate-Array.py

YuweiYin/Algorithm_YuweiYin

28648fac59c5a4e3c907978cbd1b3e662ba18fd5

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- """================================================================= @Project : Algorithm_YuweiYin/LeetCode-All-Solution/Python3 @File : LC-0189-Rotate-Array.py @Author : [YuweiYin](https://github.com/YuweiYin) @Date : 2022-01-02 ==================================================================""" import sys import time from typing import List """ LeetCode - 0189 - (Medium) - Rotate Array https://leetcode.com/problems/rotate-array/ Description: Given an array, rotate the array to the right by k steps, where k is non-negative. Example 1: Input: nums = [1,2,3,4,5,6,7], k = 3 Output: [5,6,7,1,2,3,4] Explanation: rotate 1 steps to the right: [7,1,2,3,4,5,6] rotate 2 steps to the right: [6,7,1,2,3,4,5] rotate 3 steps to the right: [5,6,7,1,2,3,4] Example 2: Input: nums = [-1,-100,3,99], k = 2 Output: [3,99,-1,-100] Explanation: rotate 1 steps to the right: [99,-1,-100,3] rotate 2 steps to the right: [3,99,-1,-100] Constraints: 1 <= nums.length <= 105 -231 <= nums[i] <= 231 - 1 0 <= k <= 105 Follow up: Try to come up with as many solutions as you can. There are at least three different ways to solve this problem. Could you do it in-place with O(1) extra space? """ class Solution: def rotate(self, nums: List[int], k: int) -> None: """ Do not return anything, modify nums in-place instead. """ # exception case if not isinstance(nums, list) or len(nums) <= 1 or k <= 0: return # main method self._rotate_double_reverse(nums, k) # Warning: the following method is correct only if gcd(len_num, k) == 1 # def _rotate_gcd1(self, nums: List[int], k: int) -> None: # len_num = len(nums) # if k > len_num: # k %= len_num # avoid unnecessary rotate # # old_start_index, old_end_index = 0, len_num - 1 # # new_start_index = len_num - k # # new_end_index = new_start_index - 1 # # nums[old_start_index]...nums[new_end_index] go right (gap = +k) # # nums[new_start_index]...nums[old_end_index] go left (gap = -(len_num - k)) # forward_move_gap = k # backward_move_gap = len_num - k # watershed = len_num - k # # move one by one in order to get O(1) space efficiency # # if O(n) space, then just init a new list and append numbers, quite easy # cur_move_index = 0 # temp_from_num = nums[cur_move_index] # store the number that is going to replace another number # move_counter = 0 # move len_num times in total # while move_counter < len_num: # if cur_move_index < watershed: # go right (gap = +k) # temp_to_num = nums[cur_move_index + forward_move_gap] # store the number that is going to be replaced # nums[cur_move_index + forward_move_gap] = temp_from_num # temp_from_num = temp_to_num # cur_move_index = cur_move_index + forward_move_gap # else: # go left (gap = -(len_num - k)) # temp_to_num = nums[cur_move_index - backward_move_gap] # store the number that is going to be replaced # nums[cur_move_index - backward_move_gap] = temp_from_num # temp_from_num = temp_to_num # cur_move_index = cur_move_index - backward_move_gap # # move_counter += 1 # 1. reverse the whole list; 2. split; 3. reserve two small lists respectively; 4. combine. def _rotate_double_reverse(self, nums: List[int], k: int) -> None: len_num = len(nums) if k > len_num: k %= len_num # avoid unnecessary rotate # split nums[0]...nums[k] and nums[k]...nums[len_num] watershed = len_num - k nums.reverse() # Way 1: # nums[0: k] = reversed(nums[0: k]) # nums[k: len_num] = reversed(nums[k: len_num]) # Way 2 self._reverse_list_in_place(nums, 0, k - 1) self._reverse_list_in_place(nums, k, len_num - 1) @staticmethod def _reverse_list_in_place(nums: List[int], start_index: int, end_index: int) -> None: while start_index < end_index: temp_num = nums[start_index] nums[start_index] = nums[end_index] nums[end_index] = temp_num start_index += 1 end_index -= 1 def main(): # Example 1: Output: [5,6,7,1,2,3,4] nums = [1, 2, 3, 4, 5, 6, 7] k = 3 # Example 2: Output: [3,99,-1,-100] # nums = [-1, -100, 3, 99] # k = 2 # Example 3: Output: [4, 5, 6, 1, 2, 3] # nums = [1, 2, 3, 4, 5, 6] # k = 3 # init instance solution = Solution() # run & time start = time.process_time() solution.rotate(nums, k) ans = nums end = time.process_time() # show answer print('\nAnswer:') print(ans) # show time consumption print('Running Time: %.5f ms' % ((end - start) * 1000)) if __name__ == "__main__": sys.exit(main())

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null

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null

0

1

0

095c5cd039268240cc1f5e992dfc55ceb574b9a1

921

py

Python

d1/repeatedcharacter.py

jwbat/python-datastructures

82ff91f1ee8c76a382bd5d43cdefe4ffcde00528

[ "MIT" ]

null

d1/repeatedcharacter.py

jwbat/python-datastructures

82ff91f1ee8c76a382bd5d43cdefe4ffcde00528

[ "MIT" ]

null

d1/repeatedcharacter.py

jwbat/python-datastructures

82ff91f1ee8c76a382bd5d43cdefe4ffcde00528

[ "MIT" ]

null

''' One fcn returns the first nonrepeated character from string, s. The other returns the first repeated character from the string. ''' def print_dict(d): for k, v in d.items(): print('\t', k, '=> ', v) def char_count_dict(s): d = dict() for char in s: count = d.get(char, 0) d[char] = count + 1 return d def first_nonrepeated_char(s, d): for char in s: if d[char] == 1: return char return None def first_repeated_char(s): st = set() for char in s: if char in st: return char st.add(char) return None #s = 'Where have I heard that?' #s = 'Wherefore art thou Romeo?' s = 'Grilled cheeses are great with mustard.' s = s.lower() d = char_count_dict(s) print('\n string: ', s) print('\n\t 1st nonrepeated char: ', first_nonrepeated_char(s, d)) print('\t 1st repeated char: ', first_repeated_char(s), '\n')

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0.185185

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null

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null

0

1

0

095ca8346512b61fba41d3997f22f02f7c9433ae

2,652

py

Python

alipay/aop/api/domain/KoubeiCateringPosDishcateTransferModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/domain/KoubeiCateringPosDishcateTransferModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/domain/KoubeiCateringPosDishcateTransferModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class KoubeiCateringPosDishcateTransferModel(object): def __init__(self): self._cate_id = None self._cook_id = None self._dish_ids = None self._shop_id = None @property def cate_id(self): return self._cate_id @cate_id.setter def cate_id(self, value): self._cate_id = value @property def cook_id(self): return self._cook_id @cook_id.setter def cook_id(self, value): self._cook_id = value @property def dish_ids(self): return self._dish_ids @dish_ids.setter def dish_ids(self, value): if isinstance(value, list): self._dish_ids = list() for i in value: self._dish_ids.append(i) @property def shop_id(self): return self._shop_id @shop_id.setter def shop_id(self, value): self._shop_id = value def to_alipay_dict(self): params = dict() if self.cate_id: if hasattr(self.cate_id, 'to_alipay_dict'): params['cate_id'] = self.cate_id.to_alipay_dict() else: params['cate_id'] = self.cate_id if self.cook_id: if hasattr(self.cook_id, 'to_alipay_dict'): params['cook_id'] = self.cook_id.to_alipay_dict() else: params['cook_id'] = self.cook_id if self.dish_ids: if isinstance(self.dish_ids, list): for i in range(0, len(self.dish_ids)): element = self.dish_ids[i] if hasattr(element, 'to_alipay_dict'): self.dish_ids[i] = element.to_alipay_dict() if hasattr(self.dish_ids, 'to_alipay_dict'): params['dish_ids'] = self.dish_ids.to_alipay_dict() else: params['dish_ids'] = self.dish_ids if self.shop_id: if hasattr(self.shop_id, 'to_alipay_dict'): params['shop_id'] = self.shop_id.to_alipay_dict() else: params['shop_id'] = self.shop_id return params @staticmethod def from_alipay_dict(d): if not d: return None o = KoubeiCateringPosDishcateTransferModel() if 'cate_id' in d: o.cate_id = d['cate_id'] if 'cook_id' in d: o.cook_id = d['cook_id'] if 'dish_ids' in d: o.dish_ids = d['dish_ids'] if 'shop_id' in d: o.shop_id = d['shop_id'] return o

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0.061629

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0

null

0

null

0

1

0

0961afc6929e36d21b0d3df4440d37f44a042e71

3,557

py

Python

main.py

darshanvjani/Wrapper_EVAI_Pytorch

cd3f11ad8b36f5d512288b8e5f7e15174bd2bbd1

[ "MIT" ]

null

main.py

darshanvjani/Wrapper_EVAI_Pytorch

cd3f11ad8b36f5d512288b8e5f7e15174bd2bbd1

[ "MIT" ]

null

main.py

darshanvjani/Wrapper_EVAI_Pytorch

cd3f11ad8b36f5d512288b8e5f7e15174bd2bbd1

[ "MIT" ]

null

import torch import torchvision import torchvision.transforms as transforms import albumentations import numpy as np # from __future__ import print_function import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets , transforms import torchvision from Wrapper_EVAI_Pytorch.dataloader import albumentation as A from Wrapper_EVAI_Pytorch.utils.helper import * from Wrapper_EVAI_Pytorch.utils.gradcam import * from Wrapper_EVAI_Pytorch.utils.plot_metrics import * from Wrapper_EVAI_Pytorch.utils.test import * from Wrapper_EVAI_Pytorch.utils.train import * from Wrapper_EVAI_Pytorch.models import resnet class main(): def __init__(self,device): self.classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] self.device = device self.train_losses = [] self.test_losses = [] self.train_accuracy = [] self.test_accuracy = [] self.plot_train_acc=[] self.lrs=[] pass def dataloading_aubumentation(self,mean,std,batch_size): albu_obj = A.CIFAR10Albumentation() train_transform = albu_obj.train_transform(mean,std) test_transform = albu_obj.test_transform(mean,std) trainset = torchvision.datasets.CIFAR10(root='/content',train=True,download=True,transform=train_transform) testset = torchvision.datasets.CIFAR10(root='/content',train=False,download=True,transform=test_transform) train_dataloader = torch.utils.data.DataLoader(trainset,num_workers=2,shuffle=True,batch_size=batch_size) test_dataloader = torch.utils.data.DataLoader(testset,num_workers=2,shuffle=True,batch_size=batch_size) self.train_dataloader = train_dataloader self.test_dataloader = test_dataloader self.trainset = trainset self.testset = testset def show_augmented_img(self,no_of_images): helper.plot_images(self.trainset,no_of_images,self.classes) def model(self,model_name,set_seed_no,show_summery): if model_name == 'resnet34': net = resnet.ResNet34() self.net = net if set_seed_no != None: set_seed(set_seed_no,True) if show_summery == True: model_summary(self.net,(3,32,32)) return net def train_model(self,optimizer,epochs,lam_reg,schedular,criterian,show_plots=True): for epoch in range(epochs): train(self.net,self.device,self.train_dataloader,optimizer,epoch,self.train_accuracy,self.train_losses,lam_reg,schedular,criterian,self.lrs) test(self.net,self.device,self.test_dataloader,self.test_accuracy,self.test_losses,criterian) if show_plots==True: plot_metrics([self.train_accuracy,self.train_losses,self.test_accuracy,self.test_losses]) conf_matrix = compute_confusion_matrix(self.net,self.test_dataloader,self.device) plot_confusion_matrix(conf_matrix) def examination(self,no_of_images): wrong_pred = wrong_predictions(self.net,self.test_dataloader,no_of_images,self.device,self.classes) target_layers = ["layer1","layer2","layer3","layer4"] gradcam_output, probs, predicted_classes = generate_gradcam(wrong_pred[:10],self.net,target_layers,self.device) plot_gradcam(gradcam_output, target_layers, self.classes, (3, 32, 32),predicted_classes, wrong_pred[:10])

39.966292

153

0.699185

449

3,557

5.287305

0.280624

0.033698

0.044229

0.064869

0.277169

0.176917

0.033698

0

0.010608

0.204948

3,557

88

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40.420455

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0

1

0.09375

false

0.015625

0.265625

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0.390625

0

null

0

null

0

1

0

117383a0b92d5b2084b9f6d9093f315d7ed4bad1

1,501

py

Python

util/visualizer.py

google/tim-gan

0139ad452b3c74e3c12791ebb719ea5979eb0d1f

[ "Apache-2.0" ]

7

2020-11-17T21:38:46.000Z

2022-02-15T02:33:10.000Z

util/visualizer.py

google/tim-gan

0139ad452b3c74e3c12791ebb719ea5979eb0d1f

[ "Apache-2.0" ]

null

util/visualizer.py

google/tim-gan

0139ad452b3c74e3c12791ebb719ea5979eb0d1f

[ "Apache-2.0" ]

2

2021-01-23T12:13:29.000Z

2021-03-27T21:20:49.000Z

# Copyright 2020 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import numpy as np import os import ntpath import time from . import util from . import html import scipy.misc from io import BytesIO def save_images(webpage, visuals, image_path, win_size=512): image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims = [] txts = [] links = [] for label, image_numpy in visuals.items(): if label.startswith('output'): fulllabel = label label = 'output' else: fulllabel = label image_name = '%s_%s.jpg' % (name, label) save_path = os.path.join(image_dir, image_name) util.save_image(image_numpy, save_path) ims.append(image_name) txts.append(fulllabel) links.append(image_name) webpage.add_images(ims, txts, links, width=win_size)

30.632653

80

0.688874

214

1,501

4.724299

0.523364

0.059347

0.025717

0.031652

0

0.01045

0.171219

1,501

48

81

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0.802251

0.429047

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false

0

0.275862

0

0.310345

0

null

0

null

0

1

0

117624463673d5965908bc5b693e638ca894611b

407

py

Python

authorize/apis/base_api.py

aryeh/py-authorize

e0a2e8d9828efa2146b22cb855fa28d723913e41

[ "MIT" ]

30

2015-03-13T01:31:52.000Z

2021-06-11T08:49:43.000Z

authorize/apis/base_api.py

aryeh/py-authorize

e0a2e8d9828efa2146b22cb855fa28d723913e41

[ "MIT" ]

41

2015-01-30T20:01:05.000Z

2022-03-31T23:11:56.000Z

authorize/apis/base_api.py

aryeh/py-authorize

e0a2e8d9828efa2146b22cb855fa28d723913e41

[ "MIT" ]

34

2015-01-11T20:22:03.000Z

2022-03-28T20:34:22.000Z

import colander from authorize.exceptions import AuthorizeInvalidError class BaseAPI(object): def __init__(self, api): self.api = api self.config = api.config def _deserialize(self, schema, params={}): try: deserialized = schema.deserialize(params) except colander.Invalid as e: raise AuthorizeInvalidError(e) return deserialized

22.611111

54

0.65602

41

407

6.390244

0.609756

0.053435

0

0.27027

407

17

55

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0.882155

0

1

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false

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0

0.5

0

null

0

null

0

1

0

11766920d1f9e38bf6634e7585ed79d49348e3c1

1,237

py

Python

stable_baselines/trpo_mpi/run_mujoco.py

emadboctorx/stable-baselines

9bce185538e8bf69836371286e23919fd85eec64

[ "MIT" ]

null

stable_baselines/trpo_mpi/run_mujoco.py

emadboctorx/stable-baselines

9bce185538e8bf69836371286e23919fd85eec64

[ "MIT" ]

null

stable_baselines/trpo_mpi/run_mujoco.py

emadboctorx/stable-baselines

9bce185538e8bf69836371286e23919fd85eec64

[ "MIT" ]

null

import stable_baselines.common.tf_util as tf_util from mpi4py import MPI from stable_baselines import logger from stable_baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser from stable_baselines.common.policies import MlpPolicy from stable_baselines.trpo_mpi import TRPO def train(env_id, num_timesteps, seed): with tf_util.single_threaded_session(): rank = MPI.COMM_WORLD.Get_rank() if rank == 0: logger.configure() else: logger.configure(format_strs=[]) logger.set_level(logger.DISABLED) workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() env = make_mujoco_env(env_id, workerseed) model = TRPO( MlpPolicy, env, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1, entcoeff=0.0, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3, ) model.learn(total_timesteps=num_timesteps) env.close() def main(): args = mujoco_arg_parser().parse_args() train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) if __name__ == '__main__': main()

28.767442

79

0.631366

160

1,237

4.575

0.4625

0.102459

0.103825

0.068306

0.051913

0

0.032511

0.278901

1,237

42

80

29.452381

0.788117

0

0.006467

0

1

0.055556

false

0

0.166667

0

0.222222

0

null

0

null

0

1

0

11767e77d6369d63f87f78e8e4f1ac800b3e2e86

929

py

Python

Day-16/part1.py

archanpatkar/advent2020

86065eb744c885ce0e29ea8228b8e8ebbd38c939

[ "MIT" ]

null

Day-16/part1.py

archanpatkar/advent2020

86065eb744c885ce0e29ea8228b8e8ebbd38c939

[ "MIT" ]

null

Day-16/part1.py

archanpatkar/advent2020

86065eb744c885ce0e29ea8228b8e8ebbd38c939

[ "MIT" ]

null

import sys sys.path.append("..") from common import * def parse(data): data = list(map(lambda s: s.strip(),filter(lambda s: len(s) > 1,data.split("\n")))) conds = {} for i in range(20): kv = data[i].split(":") cs = kv[1].split("or") conds[kv[0]] = ([int(v) for v in cs[0].split("-")], [int(v) for v in cs[1].split("-")]) myticket = [int(n) for n in data[21].split(",")] nearbytickets = [[int(n) for n in data[l].split(",")] for l in range(23,len(data))] return (conds,myticket,nearbytickets) data = aoci(parse); p(data[0]); count = 0 error_rate = 0 for tic in data[2]: for val in tic: flag = False for field in data[0]: c = data[0][field] if bi(val,c[0][0],c[0][1]) or bi(val,c[1][0],c[1][1]): flag = True if not flag: count += 1 error_rate += val print(error_rate) print(count)

28.151515

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154

929

3.123377

0.350649

0.049896

0.029106

0.033264

0.108108

0

0.038806

0.278794

929

33

88

28.151515

0.679104

0

0.011828

0

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0.033333

false

0

0.066667

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0.133333

0.066667

0

null

0

null

0

1

0

11791aa64e3598994f4ffc54bf2c561214125d3a

1,931

py

Python

NewQshop/AppStore/Buyer/views.py

bestwishfang/FlaskFrameWork

e5f2af0b82be6d5b32febadc244a72aadceaa58b

[ "MIT" ]

null

NewQshop/AppStore/Buyer/views.py

bestwishfang/FlaskFrameWork

e5f2af0b82be6d5b32febadc244a72aadceaa58b

[ "MIT" ]

3

2020-04-30T15:16:56.000Z

2022-02-13T08:02:39.000Z

NewQshop/AppStore/Buyer/views.py

bestwishfang/FlaskFrameWork

e5f2af0b82be6d5b32febadc244a72aadceaa58b

[ "MIT" ]

null

import time import json import datetime from flask import request from flask import render_template from flask_restful import Resource from . import buyer, apibuyer from AppStore.models import Class_Info @buyer.route('/buyer/index/') def index(): return 'Hello World! This is buyer index.' @buyer.route('/api/buyer/page/') def page(): return render_template('buyer/page.html') @buyer.route('/buyer/data/') def demo_data(): return render_template('buyer/datapage.html') @apibuyer.resource('/api/buyer/') class BuyerApi(Resource): def __init__(self, *args, **kwargs): super(BuyerApi, self).__init__(*args, **kwargs) self.ret = { 'code': 200, 'version': 1.0, 'frame': 'flask 1.1.1', 'data': [] } def get(self): class_all = Class_Info.query.all() print(type(class_all)) # <class 'list'> for obj in class_all: obj_data = { 'class_num': obj.class_num, 'class_name': obj.class_name, 'entrance_time': obj.entrance_time.strftime('%Y-%m-%d'), 'college': obj.college, } # print(type(obj.entrance_time)) # new_date = obj.entrance_time.strftime('%Y-%m-%d') # print(new_date) # print(type(new_date)) self.ret['data'].append(obj_data) # print(self.ret) return self.ret def post(self): data = request.form class_obj = Class_Info() class_obj.class_num = data.get('class_num') class_obj.class_name = data.get('class_name') class_obj.entrance_time = data.get('entrance_time') class_obj.college = data.get('college') class_obj.save() self.ret['data'] = '保存成功' return self.ret def put(self): return self.ret def delete(self): return self.ret

25.407895

72

0.578975

239

1,931

4.493724

0.292887

0.052142

0.055866

0.044693

0.048417

0

0.00581

0.286898

1,931

75

73

25.746667

0.774147

0.077162

0

0.075472

0

0.139718

0

1

0.150943

false

0

0.150943

0.09434

0.45283

0.018868

0

null

0

null

0

1

0

1180dde3665390d3a54ac55203c8a6a39a04cbc1

1,993

py

Python

addpapers.py

tarrow/librarybase-pwb

6c86aba7cbcb6200bfade0170aea8be3593cafec

[ "MIT" ]

1

2017-05-23T14:14:16.000Z

addpapers.py

tarrow/librarybase-pwb

6c86aba7cbcb6200bfade0170aea8be3593cafec

[ "MIT" ]

7

2015-12-09T10:14:37.000Z

2016-01-19T12:55:33.000Z

addpapers.py

tarrow/librarybase-pwb

6c86aba7cbcb6200bfade0170aea8be3593cafec

[ "MIT" ]

null

import queryCiteFile import librarybase import pywikibot from epmclib.getPMCID import getPMCID from epmclib.exceptions import IDNotResolvedException import queue import threading import time def rununthreaded(): citefile = queryCiteFile.CiteFile() citations = citefile.findRowsWithIDType('pmc') for idx, citation in enumerate(citations[10513:]): addpaper(idx, citation) def runthreaded(): threads = [] for i in range(10): t = threading.Thread(target=worker()) t.start() threads.append(t) citefile = queryCiteFile.CiteFile() citations = citefile.findRowsWithIDType('pmc') for citation in enumerate(citations[10513:]): q.put(citation) q.join() for i in range(10): q.put(None) for t in threads: t.join() def worker(): while True: idx, citation = q.get() addpaper( idx, citation ) q.task_done() def addpaper( idx, citation ): start=time.time() print(citation) if citation is None: return print('trying to add {} number {}'.format(citation[5], idx)) site = pywikibot.Site("librarybase", "librarybase") item = librarybase.JournalArticlePage(site) pmcidobj = getPMCID(citation[5]) try: pmcidobj.getBBasicMetadata() except IDNotResolvedException: print('Couldn\'t find in EPMC:' + citation[5]) return metadata = pmcidobj.metadata print("Got metadata in:" + str(time.time()-start)) if not item.articleAlreadyExists(metadata['pmcid']): print('Item doesn\'t seem to exist. Setting metadata for: ' + metadata['pmcid']) item.setMetaData(metadata) print("set metadata in" + str(time.time()-start)) else: print("{} already exists. Doing nothing".format(metadata['pmcid'])) q=queue.Queue() rununthreaded()

29.746269

93

0.606121

208

1,993

5.802885

0.394231

0.045568

0.047225

0.062966

0.235294

0.159072

0.11599

0

0.011888

0.282489

1,993

67

94

29.746269

0.832168

0

0.175439

0

0.081432

0

1

0.070175

false

0

0.140351

0

0.245614

0.122807

0

null

0

null

0

1

0

118141ccfae972e74c17e2b33370161c604fe8c4

2,491

py

Python

day18/script2.py

Moremar/advent_of_code_2019

572200ad8c24efd38fb3fac428d086bcd7090ca9

[ "Apache-2.0" ]

null

day18/script2.py

Moremar/advent_of_code_2019

572200ad8c24efd38fb3fac428d086bcd7090ca9

[ "Apache-2.0" ]

null

day18/script2.py

Moremar/advent_of_code_2019

572200ad8c24efd38fb3fac428d086bcd7090ca9

[ "Apache-2.0" ]

null

from day18.script1 import parse_matrix, read_char_matrix, alpha_lower, solve_world # We could use the same logic as part 1 with a state made of a distance and 4 (x, y) pairs (one for each bot) # This works with examples, but there are too many combinations for the real input so it runs for very long # # To get it quicker, we will resolve the 4 sub-mazes independently, assuming we have all keys from other 3 mazes. # Then we sum the 4 results. # # There could be cases where this logic does not work (basically if the keys from other mazes that we assume we have # cannot be obtained in the order we assumed, because they require the current bot to get keys in a different order) # # I did not have such problematic cases with my input file and this logic gave the correct result. def solve(world): (world1, keys1, world2, keys2, world3, keys3, world4, keys4) = world return solve_world(world1, keys1) \ + solve_world(world2, keys2) \ + solve_world(world3, keys3) \ + solve_world(world4, keys4) def parse(file_name): matrix = read_char_matrix(file_name) middle_i = (len(matrix) + 1) // 2 middle_j = (len(matrix[0]) + 1) // 2 # split into 4 sub-matrices (mx1, mx2, mx3, mx4) = [], [], [], [] (keys1, keys2, keys3, keys4) = [], [], [], [] for i in range(middle_i): row = [] for j in range(middle_j): row.append(matrix[i][j]) if matrix[i][j] in alpha_lower: keys1.append(matrix[i][j]) mx1.append(row) for i in range(middle_i): row = [] for j in range(middle_j, len(matrix[0])): row.append(matrix[i][j]) if matrix[i][j] in alpha_lower: keys2.append(matrix[i][j]) mx2.append(row) for i in range(middle_i, len(matrix)): row = [] for j in range(middle_j): row.append(matrix[i][j]) if matrix[i][j] in alpha_lower: keys3.append(matrix[i][j]) mx3.append(row) for i in range(middle_i, len(matrix)): row = [] for j in range(middle_j, len(matrix[0])): row.append(matrix[i][j]) if matrix[i][j] in alpha_lower: keys4.append(matrix[i][j]) mx4.append(row) return parse_matrix(mx1), keys1, \ parse_matrix(mx2), keys2, \ parse_matrix(mx3), keys3, \ parse_matrix(mx4), keys4 if __name__ == '__main__': print(solve(parse("data2.txt")))

34.123288

116

0.607788

378

2,491

3.902116

0.338624

0.056949

0.065085

0.075932

0.267119

0.255593

0.249492

0

0.031561

0.27499

2,491

72

117

34.597222

0.785161

0.283019

0

0.425532

0

0.009583

0

1

0.042553

false

0

0.021277

0

0.106383

0.021277

0

null

0

null

0

1

0

1183ffbe924869fb7a592b3839706503e0ba51ef

1,029

py

Python

test/test_clamp.py

XiaoxuanZhangCM/igakit

71bc4237a561272f6189d067688c182aa705c5eb

[ "BSD-2-Clause" ]

2

2022-03-21T09:38:55.000Z

2022-03-24T23:33:55.000Z

test/test_clamp.py

XiaoxuanZhangCM/igakit

71bc4237a561272f6189d067688c182aa705c5eb

[ "BSD-2-Clause" ]

1

2022-03-24T22:50:35.000Z

2022-03-27T06:33:16.000Z

test/test_clamp.py

XiaoxuanZhangCM/igakit

71bc4237a561272f6189d067688c182aa705c5eb

[ "BSD-2-Clause" ]

null

import numpy as np from igakit.cad import circle, Pi def make_crv(p,u): c = circle(radius=1, angle=Pi/2) c.rotate(Pi/4) c.elevate(0,p-2) c.refine(0,u) return c def check_crv(c): u0, u1 = c.breaks(0)[[0,-1]] u = np.linspace(u0,u1,100) x, y, z = c(u).T r = np.hypot(x,y) return np.allclose(r, 1) def test_clamp(): for p in range(2,6): for u in ([],[0.5],[1/3.0,2/3.0],[0.1,0.9]): c = make_crv(p,u) check_crv(c) for continuity in range(c.degree[0]): for side in (0, 1, None): cc = c.copy() cc.unclamp(0, continuity=continuity, side=side) check_crv(cc) cc.clamp(0, side=side) check_crv(cc) cc.clamp(0) check_crv(cc) assert np.allclose(cc.knots[0], c.knots[0]) assert np.allclose(cc.array, c.array) if __name__ == '__main__': test_clamp()

27.810811

67

0.477162

163

1,029

2.907975

0.368098

0.084388

0.063291

0.037975

0.109705

0

0.060278

0.371234

1,029

36

68

28.583333

0.672334

0

0.09375

0

0.007775

0

0.0625

1

0.09375

false

0

0.0625

0

0.21875

0

null

0

null

0

1

0

1186952effff0749fbfa4fc2d6b7b1c8935379fb

4,261

py

Python

autoload/vim_ext/vim_opt.py

nielsmadan/venom

87dee312fe8dbaa64fe29fba7a2ccd5f41a55b4a

[ "0BSD" ]

1

2022-01-26T04:57:28.000Z

autoload/vim_ext/vim_opt.py

nielsmadan/venom

87dee312fe8dbaa64fe29fba7a2ccd5f41a55b4a

[ "0BSD" ]

null

autoload/vim_ext/vim_opt.py

nielsmadan/venom

87dee312fe8dbaa64fe29fba7a2ccd5f41a55b4a

[ "0BSD" ]

1

2021-08-19T08:11:58.000Z

import vim _BOOL_OPTS = set(('allowrevins', 'altkeymap', 'antialias', 'autochdir', 'arabic', 'arabicshape', 'autoindent', 'autoread', 'autowrite', 'backup', 'ballooneval', 'binary', 'bioskey', 'bomb', 'buflisted', 'buftype', 'cindent', 'compatible', 'confirm', 'conskey', 'copyindent', 'cscoperelative', 'cscopetag', 'cscopeverbose', 'cursorbind', 'cursorcolumn', 'cursorline', 'delcombine', 'diff', 'digraph', 'edcompatible', 'endofline', 'equalalways', 'equalprg', 'errorbells', 'esckeys', 'expandtab', 'exrc', 'fkmap', 'foldenable', 'fsync', 'gdefault', 'guipty', 'hidden', 'hlsearch', 'hkmap', 'hkmapp', 'icon', 'ignorecase', 'imcmdline', 'imdisable', 'incsearch', 'infercase', 'insertmode', 'joinspaces', 'lazyredraw', 'linebreak', 'lisp', 'list', 'loadplugins', 'macatsui', 'magic', 'modeline', 'modifiable', 'modified', 'more', 'mouse', 'mousefocus', 'mousehide', 'number', 'opendevice', 'paste', 'preserveindent', 'previewwindow', 'prompt', )) _NUM_OPTS = set(('aleph', 'balloondelay', 'cmdheight', 'cmdwinheight', 'columns', 'concellevel', 'cscopepathcomp', 'cscopetagorder', 'foldcolumn', 'foldlevel', 'foldlevelstart', 'foldminlines', 'foldnestmax', 'guiheadroom', 'history', 'iminsert', 'imsearch', 'laststatus', 'lines', 'linespace', 'matchtime', 'maxcombine', 'maxfuncdepth', 'maxmem', 'maxmempattern', 'maxmemtot', 'menuitems', 'modelines', 'mousetime', 'mzquantum', 'numberwidth', 'previewheight', 'pumheight', )) _STR_OPTS = set(('ambiwidth', 'background', 'backspace', 'backupcopy', 'backupdir', 'backupext', 'backupskip', 'balloonexpr', 'breakat', 'browsedir', 'bufhidden', 'casemap', 'cdpath', 'cedit', 'charconvert', 'cinkeys', 'cinoptions', 'cinwords', 'clipboard', 'colorcolumn', 'comments', 'commentstring', 'complete', 'completefunc', 'completeopt', 'concealcursor', 'cpoptions', 'cryptmethod', 'cscopeprg', 'cscopequickfix', 'debug', 'define', 'dictionary', 'diffexpr', 'diffopt', 'directory', 'display', 'eadirection', 'encoding', 'errorfile', 'errorformat', 'eventignore', 'fileencoding', 'fileencodings', 'fileformat', 'fileformats', 'filetype', 'fillchars', 'foldclose', 'foldexpr', 'foldignore', 'foldmarker', 'foldmethod', 'foldopen', 'foldtext', 'formatoptions', 'formatlistpat', 'formatprg', 'formatexpr', 'grepformat', 'grepprg', 'guicursor', 'guifont', 'guifontset', 'guifontwide', 'guioptions', 'guitablabel', 'guitabtooltip', 'helpfile', 'helpheight', 'helplang', 'highlight', 'iconstring', 'imactivatekey', 'include', 'includeexpr', 'indentexpr', 'indentkeys', 'isfname', 'isindent', 'iskeyword', 'isprint', 'key', 'keymap', 'keymodel', 'keywordprg', 'langmap', 'langmenu', 'lispwords', 'listchars', 'makeef', 'makeprg', 'matchpairs', 'mkspellmem', 'mousemodel', 'mouseshape', 'nrformats', 'omnifunc', 'operatorfunc', 'osfiletype', 'paragraphs', 'pastetoggle', 'patchexpr', 'patchmode', 'path', 'printdevice', 'printencoding', 'printexpr', 'printfont', 'printheader', 'printmbcharset', 'printmbfont', 'printoptions', 'quoteescape', )) class _opt(object): def __getattr__(self, name): # print "TRYING TO GET %s" % name if name in _BOOL_OPTS: return vim.eval('&' + name) == '1' elif name in _NUM_OPTS: return int(vim.eval('&' + name), 0) elif name in _STR_OPTS: return vim.eval('&' + name) def __setattr__(self, name, val): # print "TRYING TO SET %s TO %s" % (name, val) if name in _BOOL_OPTS: if val: vim.command('set %s' % name) else: vim.command('set no%s' % name) vim.opt = _opt()

59.180556

106

0.553157

312

4,261

7.477564

0.814103

0.008573

0.014145

0.010287

0.030004

0

0.000642

0.268716

4,261

71

107

60.014085

0.748074

0.017836

0

0.086207

0

0.485892

0

1

0.034483

false

0

0.017241

0

0.12069

0.051724

0

null

0

null

0

1

0

1188f7d771d6606445861a8c6c54d6fe5f5f1785

40,198

py

Python

statestream/utils/shared_memory.py

boschresearch/statestream

3ea93b2e0434cfaf5d546f37b2068dc0a0b8c281

[ "Apache-2.0", "MIT" ]

9

2019-02-21T14:25:26.000Z

2021-07-21T08:14:32.000Z

statestream/utils/shared_memory.py

VolkerFischer/statestream

3ea93b2e0434cfaf5d546f37b2068dc0a0b8c281

[ "Apache-2.0", "MIT" ]

null

statestream/utils/shared_memory.py

VolkerFischer/statestream

3ea93b2e0434cfaf5d546f37b2068dc0a0b8c281

[ "Apache-2.0", "MIT" ]

1

2019-03-04T03:17:00.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2017 - for information on the respective copyright owner # see the NOTICE file and/or the repository https://github.com/boschresearch/statestream # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys import numpy as np import importlib import SharedArray from statestream.utils.helper import is_scalar_shape from statestream.utils.shared_memory_layout import SharedMemoryLayout from statestream.meta.network import get_item_type from statestream.meta.network import S2L from statestream.meta.neuron_pool import np_shm_layout, np_init from statestream.meta.synapse_pool import sp_shm_layout, sp_init def shared_layout(net, param): """Generates shared-memory layout from net and param. """ layout = {} for t in ["np", "sp", "plast", "if"]: for i,I in net[S2L(t)].items(): # Begin with empty layout. layout[i] = {} # Empty tmem layout structure. Rest will be filled during shm creation. layout[i]["tmem"] = [] for tmem in range(len(param["core"]["temporal_memory"])): layout[i]["tmem"].append({"parameter": {}, "variables": {}}) if t == "plast": layout[i]["tmem"][tmem]["updates"] = {} # Get item shm data layout. if t == "np": layout[i].update(np_shm_layout(i, net, param)) elif t == "sp": layout[i].update(sp_shm_layout(i, net, param)) elif t == "plast": plast_shm_layout \ = getattr(importlib.import_module("statestream.meta.plasticities." + I["type"]), "plast_shm_layout") layout[i].update(plast_shm_layout(i, net, param)) layout[i]["updates"] = {} for par in I["parameter"]: if par[1] not in layout[i]["updates"]: layout[i]["updates"][par[1]] = {} shml = layout[par[1]]["parameter"][par[2]] layout[i]["updates"][par[1]][par[2]] = shml elif t == "if": if_shm_layout \ = getattr(importlib.import_module("statestream.interfaces.process_if_" + I["type"]), "if_shm_layout") layout[i].update(if_shm_layout(i, net, param)) return layout class SharedMemory(object): def __init__(self, net, param, session_id=None, force_id=None): self.net = net self.param = param # Get list of all existing shared memory arrays. shm_list = SharedArray.list() shm_list_name = [] for i in range(len(shm_list)): if sys.version[0] == "2": shm_list_name.append(shm_list[i].name) elif sys.version[0] == "3": shm_list_name.append(shm_list[i].name.decode("utf-8")) # Initially start with invalid session id. self.session_id = None # Begin with empty structure holding the entire layout. self.dat = {} # Estimate of bytes reserved in shared memory. self.log_lines = [] self.log_bytes = [] # Build layout. # --------------------------------------------------------------------- self.layout = shared_layout(self.net, self.param) # Dependent on given session_id initialize shared memory. # --------------------------------------------------------------------- if session_id is None: if force_id is None: # Determine next free session id. for tmp_session_id in range(2**10): id_taken = False for i in range(len(shm_list)): if shm_list_name[i].find("statestream." + str(tmp_session_id) + ".") != -1: id_taken = True break # Take the first free session id and break. if not id_taken: self.session_id = tmp_session_id session_name = "statestream." + str(self.session_id) + "." break else: self.session_id = force_id session_name = "statestream." + str(self.session_id) + "." # Allocate all shared memory. # --------------------------------------------------------- for t in ["np", "sp", "plast", "if"]: for i, I in self.net[S2L(t)].items(): # Allocate process identifiers. shm_name = session_name + "core.proc_id." + i SharedArray.create(shm_name, 1, dtype=np.int32) # Allocate shm for neuron pool states. if t == "np": shm_name = session_name + "net." + i + ".state" SharedArray.create(shm_name, self.layout[i]["state"].shape, dtype=self.layout[i]["state"].dtype) # Allocate also tmem for np states. for tmem in range(len(param["core"]["temporal_memory"])): self.layout[i]["tmem"][tmem]["state"] = self.layout[i]["state"] tmem_shm_name = session_name + "net.tmem." + str(tmem) + "." + i + ".state" SharedArray.create(tmem_shm_name, self.layout[i]["state"].shape, dtype=self.layout[i]["state"].dtype) # Allocate parameters and variables (incl. tmem). for T in ["parameter", "variables"]: shm_name = session_name + "net." + i + "." + T for d,d_l in self.layout[i][T].items(): dat_name = shm_name + "." + d if is_scalar_shape(d_l.shape): SharedArray.create(dat_name, 1, dtype=d_l.dtype) else: SharedArray.create(dat_name, d_l.shape, dtype=d_l.dtype) # Allocate also tmem for parameters / variables. for tmem in range(len(param["core"]["temporal_memory"])): self.layout[i]["tmem"][tmem][T][d] = d_l tmem_shm_name = session_name + "net.tmem." + str(tmem) \ + "." + i + "." + T + "." + d if is_scalar_shape(d_l.shape): SharedArray.create(tmem_shm_name, 1, dtype=d_l.dtype) else: SharedArray.create(tmem_shm_name, d_l.shape, dtype=d_l.dtype) # Allocate shm for plasticity updates (incl. tmem). for i,I in self.net["plasticities"].items(): shm_name = session_name + "net." + i + ".updates." for par in I["parameter"]: shml = self.layout[par[1]]["parameter"][par[2]] dat_name = shm_name + par[0] + "." + par[1] + "." + par[2] if is_scalar_shape(shml.shape): SharedArray.create(dat_name, 1, dtype=shml.dtype) else: SharedArray.create(dat_name, shml.shape, dtype=shml.dtype) # Allocate also tmem for updates. for tmem in range(len(param["core"]["temporal_memory"])): if par[1] not in self.layout[i]["tmem"][tmem]["updates"]: self.layout[i]["tmem"][tmem]["updates"][par[1]] = {} self.layout[i]["tmem"][tmem]["updates"][par[1]][par[2]] = shml tmem_shm_name = session_name + "net.tmem." + str(tmem) \ + "." + i + ".updates." \ + par[0] + "." + par[1] + "." + par[2] if is_scalar_shape(shml.shape): SharedArray.create(tmem_shm_name, 1, dtype=shml.dtype) else: SharedArray.create(tmem_shm_name, shml.shape, dtype=shml.dtype) else: # Set session name for shm. session_name = "statestream." + str(session_id) + "." # Check if shared memory for this session_id was already created. for i in range(len(shm_list)): if shm_list_name[i].find(session_name) != -1: self.session_id = session_id break assert (self.session_id != None), \ "Error: SharedMemory() Given session_id was not found: " \ + str(session_id) + " " + session_name # Attach all shared memory. # --------------------------------------------------------------------- self.proc_id = {} for t in ["np", "sp", "plast", "if"]: for i,I in self.net[S2L(t)].items(): self.dat[i] = {} # Begin with empty list of dicts for temporal memory. self.dat[i]["tmem"] = [{} for tmem in range(len(param["core"]["temporal_memory"]))] # Process relevant memory. shm_name = session_name + "core.proc_id." + i self.proc_id[i] = SharedArray.attach(shm_name) # Network data shared memory for neuron pool states. if t == "np": shm_name = session_name + "net." + i + ".state" self.dat[i]["state"] = SharedArray.attach(shm_name) self.log_lines += [str(i) + ".state"] self.log_bytes += [self.dat[i]["state"].nbytes] # Attach also tmem for np states. for tmem in range(len(param["core"]["temporal_memory"])): tmem_dat_name = session_name + "net.tmem." + str(tmem) + "." + i + ".state" self.dat[i]["tmem"][tmem]["state"] = SharedArray.attach(tmem_dat_name) self.log_lines += [str(i) + ".tmem." + str(tmem) + ".state"] self.log_bytes += [self.dat[i]["tmem"][tmem]["state"].nbytes] # Network data shared memory for plasticity updates. if t == "plast": # Begin with empty dict also for temporal memory. self.dat[i]["updates"] = {} for tmem in range(len(param["core"]["temporal_memory"])): self.dat[i]["tmem"][tmem]["updates"] = {} shm_name = session_name + "net." + i + ".updates." for par in I["parameter"]: # First time add parameter for specific item (incl. tmem). if par[1] not in self.dat[i]["updates"]: self.dat[i]["updates"][par[1]] = {} for tmem in range(len(param["core"]["temporal_memory"])): self.dat[i]["tmem"][tmem]["updates"][par[1]] = {} # Specify shm update id. dat_name = shm_name + par[0] + "." + par[1] + "." + par[2] # Attach shm. self.dat[i]["updates"][par[1]][par[2]] = SharedArray.attach(dat_name) self.log_lines += [str(i) + ".updates." + str(par[1]) + "." + str(par[2])] self.log_bytes += [self.dat[i]["updates"][par[1]][par[2]].nbytes] # Attach also tmem for updates. for tmem in range(len(param["core"]["temporal_memory"])): tmem_shm_name = session_name + "net.tmem." + str(tmem) \ + "." + i + ".updates." \ + par[0] + "." + par[1] + "." + par[2] self.dat[i]["tmem"][tmem]["updates"][par[1]][par[2]] \ = SharedArray.attach(tmem_shm_name) self.log_lines += [str(i) + ".tmem." + str(tmem) \ + ".updates." + str(par[1]) + "." + str(par[2])] self.log_bytes += [self.dat[i]["tmem"][tmem]["updates"][par[1]][par[2]].nbytes] # Network data shared memory for variables and parameter. for T in ["parameter", "variables"]: # Begin with empty dict also for temporal memory. self.dat[i][T] = {} for tmem in range(len(param["core"]["temporal_memory"])): self.dat[i]["tmem"][tmem][T] = {} # Determine shm id item "prefix". shm_name = session_name + "net." + i + "." + T # Loop over all vars/pars of this item. for d,d_l in self.layout[i][T].items(): dat_name = shm_name + "." + d self.dat[i][T][d] = SharedArray.attach(dat_name) self.log_lines += [str(i) + "." + str(T) + "." + str(d)] self.log_bytes += [self.dat[i][T][d].nbytes] # Attach also tmem for parameter / variables. for tmem in range(len(param["core"]["temporal_memory"])): tmem_shm_name = session_name + "net.tmem." + str(tmem) \ + "." + i + "." + T + "." + d self.dat[i]["tmem"][tmem][T][d] = SharedArray.attach(tmem_shm_name) self.log_lines += [str(i) + ".tmem." + str(tmem) + "." \ + str(T) + "." + str(d)] self.log_bytes += [self.dat[i]["tmem"][tmem][T][d].nbytes] def delete(self): """Method to free statestream shared memory of the particular session. """ if self.session_id != None: shm_list = SharedArray.list() shm_list_name = [] for i in range(len(shm_list)): if sys.version[0] == "2": shm_list_name.append(shm_list[i].name) elif sys.version[0] == "3": shm_list_name.append(shm_list[i].name.decode("utf-8")) for i in range(len(shm_list)): if shm_list_name[i].find("statestream." + str(self.session_id) + ".") != -1: SharedArray.delete(shm_list_name[i]) def add_sys_client(self, client_param): """Create shared memory for a single system client. """ client_shm_name = 'statestream.' \ + str(self.session_id) + '.' \ + 'sys_clients.' \ + str(client_param['name']) + '.' # Create and attach client specific shared memory. for T in ['parameter', 'variables']: if T in client_param: for pv,PV in client_param[T].items(): shm_name = client_shm_name + T + '.' + pv try: SharedArray.create(shm_name, PV['shape'], dtype=np.float32) except: dat = SharedArray.attach(shm_name) if dat.shape != PV['shape']: print('\nError: Shared memory: Tried to create already existing memory: ' + shm_name) def update_sys_client(self): """Update this instance of shared memory to existing clients. """ # Determine all clients, currently in shared memory. clients = {} shm_list = SharedArray.list() client_shm_name = 'statestream.' \ + str(self.session_id) + '.' \ + 'sys_clients.' for shm_name_raw in shm_list: if sys.version[0] == "2": shm_name = shm_name_raw.name elif sys.version[0] == "3": shm_name = shm_name_raw.name.decode("utf-8") if shm_name.startswith(client_shm_name): shm_name_split = shm_name.split('.') client_name = shm_name_split[3] if client_name not in clients: clients[client_name] = { 'parameter': {}, 'variables': {} } clients[client_name][shm_name_split[4]][shm_name_split[5]] \ = shm_name # Update client shared memroy dat and layout. for c,C in clients.items(): if c not in self.dat: self.dat[c] = { 'parameter': {}, 'variables': {} } self.layout[c] = { 'parameter': {}, 'variables': {} } for t,T in C.items(): for d,D in T.items(): self.dat[c][t][d] = SharedArray.attach(D) self.layout[c][t][d] = SharedMemoryLayout('np', self.dat[c][t][d].shape, self.dat[c][t][d].dtype, 0.0) # Determine all items in dat / layout which are not in shared memory. # Remove deprecated shared memory from layout and dat. remove_items = [] for i,I in self.layout.items(): if i not in clients and i not in self.net['neuron_pools'] \ and i not in self.net['synapse_pools'] \ and i not in self.net['plasticities'] \ and i not in self.net['interfaces']: remove_items.append(i) for i in remove_items: self.dat.pop(i) self.layout.pop(i) def remove_sys_client(self, client_name): """Remove shared memory for system client. """ client_shm_name = 'statestream.' \ + str(self.session_id) + '.' \ + 'sys_clients.' \ + str(client_name) + '.' # Delete shared memory. for T in ['parameter', 'variables']: for d,d_l in self.layout[client_name][T].items(): shm_name = client_shm_name + T + '.' + str(d) try: SharedArray.delete(shm_name) except: print("\nERROR: Unable to delete non-existing shared memory: " + str(shm_name) + "\n") def pprint_list(self, what=""): """Return a list of lines containing shm info about what. """ lines = [] w = what.split(".") if len(w) > 1: if len(w[1]) == 1: if w[1] == "n": i_type = "neuron_pools" elif w[1] == "s": i_type = "synapse_pools" elif w[1] == "p": i_type = "plasticities" elif w[1] == "i": i_type = "interfaces" else: return [] if what in ["shm", "shm."]: lines.append("[n]euron pools") lines.append("[s]ynapse pools") lines.append("[p]lasticities") lines.append("[i]nterfaces") if len(w) == 2: # shm.i_type if w[1] != "": cntr = 0 for i in self.net[i_type]: if cntr == 0: # Append new line. lines.append(" " + i.ljust(18)) else: # Append to existing line. lines[-1] = lines[-1] + i.ljust(18) if cntr < 3: cntr += 1 else: cntr = 0 elif len(w) == 3: # shm.i_type.item_name if w[1] != "": cntr = 0 for i in self.net[i_type]: if i.startswith(w[2]): if cntr == 0: # Append new line. lines.append(" " + i.ljust(18)) else: # Append to existing line. lines[-1] = lines[-1] + i.ljust(18) if cntr < 3: cntr += 1 else: cntr = 0 elif len(w) == 4: # shm.i_type.item_name.data_type if w[1] != "": if w[2] in self.net[i_type]: # Assuming all classes of data begin # with a different letter. if w[3] == "": for e in self.dat[w[2]]: lines.append(" [" + e[0] + "]" + e[1:]) else: dat_type = "x" if w[3][0] in ["v", "p"]: if w[3] == "v": dat_type = "variables" else: dat_type = "parameter" for vp in self.dat[w[2]][dat_type]: lines.append(" " + vp) elif w[3].startswith("s"): if w[3] == "s": lines.append(" shape: " + str(self.layout[w[2]]["state"].shape)) lines.append(" type: " + str(self.layout[w[2]]["state"].dtype)) nbytes = self.dat[w[2]]["state"].nbytes lines.append(" memory: " + str(nbytes) + " B") if w[3].startswith("s[") and w[3][-1] == "]": # Get data. s = eval("self.dat[w[2]]['state']" + w[3][1:]) if len(s.shape) == 0: lines.append(" value: " + str(s)) if len(s.shape) == 1: for i in range(min(s.shape[0], 16)): lines.append(str(i).ljust(4) + " " + str(s[i])) if s.shape[0] >= 16: lines.append("...") elif len(w) == 5: if w[1] != "": if w[2] in self.net[i_type]: dat_type = "x" if w[3][0] in ["v", "p"]: if w[3] == "v": dat_type = "variables" else: dat_type = "parameter" for vp in self.dat[w[2]][dat_type]: if vp.startswith(w[4]) and len(w[4]) < len(vp): lines.append(" " + vp) if vp == w[4]: lines.append(" shape: " + str(self.layout[w[2]][dat_type][vp].shape)) lines.append(" type: " + str(self.layout[w[2]][dat_type][vp].dtype)) nbytes = self.dat[w[2]][dat_type][vp].nbytes lines.append(" memory: " + str(nbytes) + " B") if w[4].startswith(vp) and w[4][-1] == "]" and "[" in w[4]: # Get data. s = eval("self.dat[w[2]][dat_type][vp]" + w[4][len(vp):]) if len(s.shape) == 0: lines.append(" value: " + str(s)) if len(s.shape) == 1: for i in range(min(s.shape[0], 16)): lines.append(str(i).ljust(4) + " " + str(s[i])) if s.shape[0] >= 16: lines.append("...") return lines def init(self, what=[], mode=None): """Method to recusively initialize a subset of the network. what: [] Initialize everything. ["state"] Initialize all states. ["parameter"] Initialize all parameter. ["variables"] Initialize all variables. ["updates"] Initialize all updates. [np_id, "state"] Initialize state of neuron pool np_id. [item_id, Initialize parameter par_id of item item_id. "parameter", par_id] [item_id, Initialize variable var_id of item item_id. "variables", var_id] [plast_id, Initialize updates [tar_id, par_id] of plasticity plast_id. "updates", tar_id, par_id] """ # Do not initialize meta-variables. if len(what) >= 1: if what[0] in self.dat \ and what[0] not in self.net['neuron_pools'] \ and what[0] not in self.net['synapse_pools'] \ and what[0] not in self.net['plasticities'] \ and what[0] not in self.net['interfaces']: return # Adjust mode in some cases. if isinstance(mode, list): if "external_models" in self.net: # In case of external model init, set mode here to none. if mode[0] in self.net["external_models"]: mode = None # Determine item to be set and its type. item_id = None item_type = None if len(what) >= 1: item_id = what[0] if item_id == "state": # Initialize all states. for n in self.net["neuron_pools"]: self.init([n, "state"], mode=mode) # Done with initialization. return None elif item_id in ["parameter", "variables"]: # Initialize all parameters or variables. for i in self.dat: for d, d_l in self.layout[i][item_id].items(): self.init([i, item_id, d], mode=mode) # Done with initialization. return None elif item_id == "updates": # Initialize all updates. for i in self.net["plasticities"]: for target_i in self.dat[i]["updates"]: for target_p in self.dat[i]["updates"][target_i]: self.init([i, "updates", target_i, target_p], mode=mode) # Done with initialization. return None else: # Assume what[0] is an item. # Determine item type. item_type = get_item_type(self.net, item_id) else: # len ought to be zero, so everthing should be set. self.init(["state"], mode=0.0) self.init(["parameter"], mode=mode) self.init(["variables"], mode=0.0) self.init(["updates"], mode=0.0) # Done with initialization. return None # Dependent on len of what, determine what is to be set. set_flag = False if len(what) == 1: # Re-init a single item. if item_type == "np": pass elif item_type == "sp": pass elif item_type == "plast": pass elif item_type == "if": pass # TODO elif len(what) == 2: if what[1] in ["state"]: dat_name = "__state__" dat_layout = self.layout[item_id]["state"] set_flag = True else: raise NameError("SharedMemory.init() inconsistent what parameter for what of length " \ + str(len(what)) + ".") elif len(what) == 3: if what[1] in ["parameter", "variables"]: dat_name = what[2] dat_layout = self.layout[item_id][what[1]][what[2]] set_flag = True else: raise NameError("SharedMemory.init() inconsistent what parameter for what of length " \ + str(len(what)) + ".") elif len(what) == 4: if what[1] == "updates": dat_name = [what[2], [what[3]]] dat_layout = self.layout[item_id]["updates"][what[2]][what[3]] set_flag = True else: raise NameError("SharedMemory.init() inconsistent what parameter for what of length " \ + str(len(what)) + ".") else: raise NameError("SharedMemory.init() Unexpected what of length " + str(len(what)) + ".") # Set if something is to be set. if set_flag: if item_type == "np": value = np_init(self.net, item_id, dat_name, dat_layout, mode=mode) elif item_type == "sp": value = sp_init(self.net, item_id, dat_name, dat_layout, mode=mode) elif item_type == "plast": # Determine plasticity type. plast_type = self.net["plasticities"][item_id]["type"] # Get correct plasticity initializer. try: plast_init \ = getattr(importlib.import_module("statestream.meta.plasticities." + plast_type), "plast_init") value = plast_init(self.net, item_id, dat_name, dat_layout, mode=mode) except: value = None elif item_type == "if": # Determine interface type. if_type = self.net["interfaces"][item_id]["type"] # Get correct plasticity initializer. try: if_init = getattr(importlib.import_module("statestream.interfaces." + if_type), "if_init") value = if_init(self.net, item_id, dat_name, dat_layout, mode=mode) except: value = None # Fallback if invalid value. if value is None: value = self.init_fallback(item_id, dat_name, dat_layout, mode=mode) # Finally set value. self.set_shm(what, value) def init_fallback(self, item_id, dat_name, dat_layout, mode=None): """Fallback to default initialization. """ # Get local dictionary. if item_id in self.net["neuron_pools"]: p = self.net["neuron_pools"][item_id] elif item_id in self.net["synapse_pools"]: p = self.net["synapse_pools"][item_id] elif item_id in self.net["plasticities"]: p = self.net["plasticities"][item_id] elif item_id in self.net["interfaces"]: p = self.net["interfaces"][item_id] # Dependent on scalar or not, try to initialize. if is_scalar_shape(dat_layout.shape): # Scalar values. if mode is None: dat_value = np.array(p.get(dat_name, dat_layout.default), dtype=dat_layout.dtype) else: dat_value = np.array(mode, dtype=dat_layout.dtype) if mode in ["one", 1.0]: dat_value = np.array(1.0, dtype=dat_layout.dtype) else: dat_value = np.array(0.0, dtype=dat_layout.dtype) else: # If mode is None, set to default. if mode is None: dat_value = np.ones(dat_layout.shape, dtype=dat_layout.dtype) try: dat_value *= dat_layout.default except: dat_value *= 0 print("Warning: No valid initialization for " + str(dat_name) \ + " of item " + str(item_id) + ". Set to zero.") else: # Dependent on specified mode set value. if mode in ["zero", 0.0]: dat_value = np.zeros(dat_layout.shape, dtype=dat_layout.dtype) elif mode in ["one", 1.0]: dat_value = np.ones(dat_layout.shape, dtype=dat_layout.dtype) # Return initialized value. return dat_value def set_shm(self, which, value): """Method to set a specific array in shared memory to value. """ if len(which) == 2: if self.layout[which[0]][which[1]].min is not None: value = np.maximum(value, self.layout[which[0]][which[1]].min) if self.layout[which[0]][which[1]].max is not None: value = np.minimum(value, self.layout[which[0]][which[1]].max) shape = self.layout[which[0]][which[1]].shape if is_scalar_shape(shape): self.dat[which[0]][which[1]][0] = value elif value.shape == self.dat[which[0]][which[1]].shape: self.dat[which[0]][which[1]][:] = value else: print("\nError set_shm: incompatible shapes: " \ + str(value.shape) + " " \ + str(self.dat[which[0]][which[1]].shape) \ + " for " + str(which)) elif len(which) == 3: if self.layout[which[0]][which[1]][which[2]].min is not None: value = np.maximum(value, self.layout[which[0]][which[1]][which[2]].min) if self.layout[which[0]][which[1]][which[2]].max is not None: value = np.minimum(value, self.layout[which[0]][which[1]][which[2]].max) shape = self.layout[which[0]][which[1]][which[2]].shape if is_scalar_shape(shape): self.dat[which[0]][which[1]][which[2]][0] = value elif value.shape == self.dat[which[0]][which[1]][which[2]].shape: self.dat[which[0]][which[1]][which[2]][:] = value else: print("\nError set_shm: incompatible shapes: " \ + str(value.shape) + " " \ + str(self.dat[which[0]][which[1]][which[2]].shape) \ + " for " + str(which)) elif len(which) == 4: if self.layout[which[0]][which[1]][which[2]][which[3]].min is not None: value = np.maximum(value, self.layout[which[0]][which[1]][which[2]][which[3]].min) if self.layout[which[0]][which[1]][which[2]][which[3]].max is not None: value = np.minimum(value, self.layout[which[0]][which[1]][which[2]][which[3]].max) shape = self.layout[which[0]][which[1]][which[2]][which[3]].shape if is_scalar_shape(shape): self.dat[which[0]][which[1]][which[2]][which[3]][0] = value elif value.shape == self.dat[which[0]][which[1]][which[2]][which[3]].shape: self.dat[which[0]][which[1]][which[2]][which[3]][:] = value else: print("\nError set_shm: incompatible shapes: " \ + str(value.shape) + " " \ + str(self.dat[which[0]][which[1]][which[2]][which[3]].shape) \ + " for " + str(which)) elif len(which) == 5: if self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].min is not None: value = np.maximum(value, self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].min) if self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].max is not None: value = np.minimum(value, self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].max) shape = self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].shape if is_scalar_shape(shape): self.dat[which[0]][which[1]][which[2]][which[3]][which[4]][0] = value elif value.shape == self.dat[which[0]][which[1]][which[2]][which[3]][which[4]].shape: self.dat[which[0]][which[1]][which[2]][which[3]][which[4]][:] = value else: print("\nError set_shm: incompatible shapes: " \ + str(value.shape) + " " \ + str(self.dat[which[0]][which[1]][which[2]][which[3]][which[4]].shape) \ + " for " + str(which)) else: raise NameError("SharedMemory.set_shm() expected item \ specification of length 2-5, got " + str(len(which))) def get_shm(self, which): """Method to get a specific array in shared memory. """ if len(which) == 2: shape = self.layout[which[0]][which[1]].shape if is_scalar_shape(shape): return self.dat[which[0]][which[1]][0] else: return self.dat[which[0]][which[1]][:] elif len(which) == 3: shape = self.layout[which[0]][which[1]][which[2]].shape if is_scalar_shape(shape): return self.dat[which[0]][which[1]][which[2]][0] else: return self.dat[which[0]][which[1]][which[2]][:] elif len(which) == 4: shape = self.layout[which[0]][which[1]][which[2]][which[3]].shape if is_scalar_shape(shape): return self.dat[which[0]][which[1]][which[2]][which[3]][0] else: return self.dat[which[0]][which[1]][which[2]][which[3]][:] elif len(which) == 5: shape = self.layout[which[0]][which[1]][which[2]][which[3]][which[4]].shape if is_scalar_shape(shape): return self.dat[which[0]][which[1]][which[2]][which[3]][which[4]][0] else: return self.dat[which[0]][which[1]][which[2]][which[3]][which[4]][:] else: raise NameError("SharedMemory.get_shm() expected item \ specification of length 2-5, got " + str(len(which))) def update_net(self, net): """Update the given net (its parameters, etc.) from shared memory. """ # Search all parameters of the network in shared memory. for i in self.dat: # Determine item type. i_type = get_item_type(self.net, i) if i_type is not None: for p in self.dat[i]['parameter']: # TODO: For now update of metas is not done. try: if p in net[S2L(i_type)][i] and is_scalar_shape(self.layout[i]['parameter'][p].shape): net[S2L(i_type)][i][p] = float(self.dat[i]['parameter'][p][0]) except: pass

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40,198

3.933587

0.077818

0.026263

0.030015

0.032744

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0.541186

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null

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null

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1

0

118a041ae6d96652625804e1d8039c4ff86c7d07

3,957

py

Python

core/views.py

MichelAtieno/Rogue-Nation

c4d78b42b5e6312043de2308a591951d0ba297b8

[ "Unlicense" ]

null

core/views.py

MichelAtieno/Rogue-Nation

c4d78b42b5e6312043de2308a591951d0ba297b8

[ "Unlicense" ]

11

2020-06-05T22:42:25.000Z

2022-03-11T23:58:46.000Z

core/views.py

MichelAtieno/Rogue-Nation

c4d78b42b5e6312043de2308a591951d0ba297b8

[ "Unlicense" ]

null

from django.db.models import Count, Q from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from django.shortcuts import render, get_object_or_404 from .models import NewsItem, SignUp, Artist, Athlete,Category # Create your views here. def search(request): queryset = NewsItem.objects.all() query = request.GET.get('q') if query: queryset = queryset.filter( Q(title__icontains=query)| Q(news_story__icontains=query) ).distinct() context = { 'queryset': queryset } return render(request, 'search_results.html', context) def get_category_count(): queryset = NewsItem.objects.values('categories__title').annotate(Count('categories__title')) return queryset def get_category(): queryset = Athlete.objects.values('categories__title') return queryset def home(request): queryset = NewsItem.objects.filter(featured=True) latest = NewsItem.objects.order_by('-date')[0:3] if request.method == "POST": email = request.POST["email"] new_signup = SignUp() new_signup.email = email new_signup.save() context = { 'object_list': queryset, 'latest': latest } return render(request, "home_page.html", context) def news(request): category_count = get_category_count() # print(category_count) most_recent = NewsItem.objects.order_by('-date')[0:6] news = NewsItem.objects.all() paginator = Paginator(news, 6) page_request_var = 'page' page = request.GET.get(page_request_var) try: paginated_queryset = paginator.page(page) except PageNotAnInteger: paginated_queryset = paginator.page(1) except EmptyPage: paginated_queryset = paginator.page(paginator.num_pages) context = { 'queryset': paginated_queryset, 'most_recent': most_recent, 'page_request_var': page_request_var, 'category_count': category_count } return render(request, "news.html", context) def post(request, id): news = get_object_or_404(NewsItem, id=id) context = { 'news': news } return render(request, 'post.html', context) def news_letter(request): return render(request, 'news_letter.html') def get_artist(request): artists = Artist.objects.all() context = { 'artists': artists } return render(request, 'artists.html', context) def artist_profile(request, id): artist = get_object_or_404(Artist, id=id) queryset = NewsItem.objects.all() query = artist.name queryset = queryset.filter( Q(title__icontains=query)| Q(news_story__icontains=query) ).distinct() context = { 'artist': artist, 'queryset': queryset } return render(request, 'artist_profile.html', context) def get_athlete(request): category = get_category() athletes = Athlete.objects.all() all_categories = Category.objects.all() context = { 'athletes':athletes, 'category': category, 'all_categories': all_categories } return render(request, 'athletes.html', context) def athlete_profile(request, id): athlete = get_object_or_404(Athlete, id=id) queryset = NewsItem.objects.all() query = athlete.name queryset = queryset.filter( Q(title__icontains=query)| Q(news_story__icontains=query) ).distinct() context = { 'athlete': athlete, 'queryset': queryset } return render(request, 'athlete_profile.html', context) def category_profile(request, id): one_category = get_object_or_404(Category, id=id) cat_queryset = Athlete.objects.all() cat_query = one_category.title cat_queryset = cat_queryset.filter(Q(categories__title__icontains=cat_query)).distinct() context = { 'one_category': one_category, 'queryset': cat_queryset } return render(request, 'category.html', context)

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452

3,957

5.637168

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3,957

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0.243478

0

null

0

null

0

1

0

118a9196e211cb95ce33a333ef7a6481cddafdd3

2,133

py

Python

process-invoices/update_invoice_statuses.py

MITLibraries/alma-scripts

c312692a71a83dc0b5e60761bc3e7b37d7d42099

[ "Apache-2.0" ]

null

process-invoices/update_invoice_statuses.py

MITLibraries/alma-scripts

c312692a71a83dc0b5e60761bc3e7b37d7d42099

[ "Apache-2.0" ]

16

2021-07-23T20:46:29.000Z

2022-03-10T19:34:10.000Z

process-invoices/update_invoice_statuses.py

MITLibraries/alma-scripts

c312692a71a83dc0b5e60761bc3e7b37d7d42099

[ "Apache-2.0" ]

null

import datetime import glob import sys import requests from defusedxml import ElementTree as ET sys.path.append("..") from llama.alma import Alma_API_Client import llama.config as config TODAY = datetime.date.today() count_total_invoices = 0 count_invoices_updated = 0 count_invoice_errors = 0 # Update empty invoice XML file that gets posted to Alma to use today's date tree = ET.parse("empty_invoice.xml") root = tree.getroot() voucher_date = root.find(".//voucher_date") voucher_date.text = TODAY.strftime("%Y-%m-%dT12:%M:%SZ") tree.write("output-files/empty.xml") # Update invoices status in Alma for all invoice IDs in # output-files/invoice_ids_YYYYMMDDhhmmss.txt and # output-files/invoice_special_YYYYMMDDhhmmss.txt alma_client = Alma_API_Client(config.get_alma_api_key("ALMA_API_ACQ_READ_WRITE_KEY")) alma_client.set_content_headers("application/xml", "application/xml") today_string = TODAY.strftime("%Y%m%d") invoice_files = glob.glob(f"output-files/invoice_ids_{today_string}*.txt") special_invoice_files = glob.glob(f"output-files/invoice_special_{today_string}*.txt") with open(invoice_files[0]) as f: invoice_ids = f.readlines() with open(special_invoice_files[0]) as f: invoice_ids.extend(f.readlines()) for item in invoice_ids: count_total_invoices += 1 invoice_id = item.strip() print("Marking invoice as Paid in Alma") try: paid_xml = alma_client.mark_invoice_paid( invoice_id, "output-files/empty.xml") print(f"Invoice #{invoice_id} marked as Paid in Alma\n") with open(f"output-files/paid_{invoice_id}.xml", "w") as f: f.write(paid_xml) count_invoices_updated += 1 except requests.HTTPError as e: print(f"Error marking invoice #{invoice_id} as paid in Alma") print(f"{e.response.text}\n") print("'update_invoice_statuses' process complete") print("Summary:") print(f" Total invoices processed: {count_total_invoices}") print(f" Invoices marked as paid in Alma: {count_invoices_updated}") print( f" Invoices not successfully marked as paid in Alma: {count_invoice_errors}" )

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0.191489

0

null

0

null

0

1

0

118a963b9511f86ed9bf75aa58347aecde3de782

4,117

py

Python

src/converter/convert_mobilenetv2.py

xiongzhiyao/pytorch-segmentation

a13b1aa1b316d06f050deef29d5be8b6e99460e7

[ "MIT" ]

359

2018-11-22T04:13:19.000Z

2022-03-08T09:05:47.000Z

src/converter/convert_mobilenetv2.py

xiongzhiyao/pytorch-segmentation

a13b1aa1b316d06f050deef29d5be8b6e99460e7

[ "MIT" ]

21

2018-12-07T22:37:09.000Z

2021-01-22T02:18:28.000Z

src/converter/convert_mobilenetv2.py

xiongzhiyao/pytorch-segmentation

a13b1aa1b316d06f050deef29d5be8b6e99460e7

[ "MIT" ]

73

2018-11-22T06:16:54.000Z

2021-03-30T18:26:47.000Z

import argparse from pathlib import Path import tensorflow as tf import torch from models.net import SPPNet def convert_mobilenetv2(ckpt_path, num_classes): def conv_converter(pt_layer, tf_layer_name, depthwise=False, bias=False): if depthwise: pt_layer.weight.data = torch.Tensor( reader.get_tensor(f'{tf_layer_name}/depthwise_weights').transpose(2, 3, 0, 1)) else: pt_layer.weight.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/weights').transpose(3, 2, 0, 1)) if bias: pt_layer.bias.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/biases')) def bn_converter(pt_layer, tf_layer_name): pt_layer.bias.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/beta')) pt_layer.weight.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/gamma')) pt_layer.running_mean.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/moving_mean')) pt_layer.running_var.data = torch.Tensor(reader.get_tensor(f'{tf_layer_name}/moving_variance')) def block_converter(pt_layer, tf_layer_name): if hasattr(pt_layer, 'expand'): conv_converter(pt_layer.expand.conv, f'{tf_layer_name}/expand') bn_converter(pt_layer.expand.bn, f'{tf_layer_name}/expand/BatchNorm') conv_converter(pt_layer.depthwise.conv, f'{tf_layer_name}/depthwise', depthwise=True) bn_converter(pt_layer.depthwise.bn, f'{tf_layer_name}/depthwise/BatchNorm') conv_converter(pt_layer.project.conv, f'{tf_layer_name}/project') bn_converter(pt_layer.project.bn, f'{tf_layer_name}/project/BatchNorm') reader = tf.train.NewCheckpointReader(ckpt_path) model = SPPNet(num_classes, enc_type='mobilenetv2', dec_type='maspp') # MobileNetV2 conv_converter(model.encoder.conv, 'MobilenetV2/Conv') bn_converter(model.encoder.bn, 'MobilenetV2/Conv/BatchNorm') block_converter(model.encoder.block0, 'MobilenetV2/expanded_conv') block_converter(model.encoder.block1, 'MobilenetV2/expanded_conv_1') block_converter(model.encoder.block2, 'MobilenetV2/expanded_conv_2') block_converter(model.encoder.block3, 'MobilenetV2/expanded_conv_3') block_converter(model.encoder.block4, 'MobilenetV2/expanded_conv_4') block_converter(model.encoder.block5, 'MobilenetV2/expanded_conv_5') block_converter(model.encoder.block6, 'MobilenetV2/expanded_conv_6') block_converter(model.encoder.block7, 'MobilenetV2/expanded_conv_7') block_converter(model.encoder.block8, 'MobilenetV2/expanded_conv_8') block_converter(model.encoder.block9, 'MobilenetV2/expanded_conv_9') block_converter(model.encoder.block10, 'MobilenetV2/expanded_conv_10') block_converter(model.encoder.block11, 'MobilenetV2/expanded_conv_11') block_converter(model.encoder.block12, 'MobilenetV2/expanded_conv_12') block_converter(model.encoder.block13, 'MobilenetV2/expanded_conv_13') block_converter(model.encoder.block14, 'MobilenetV2/expanded_conv_14') block_converter(model.encoder.block15, 'MobilenetV2/expanded_conv_15') block_converter(model.encoder.block16, 'MobilenetV2/expanded_conv_16') # SPP conv_converter(model.spp.aspp0.conv, 'aspp0') bn_converter(model.spp.aspp0.bn, 'aspp0/BatchNorm') conv_converter(model.spp.image_pooling.conv, 'image_pooling') bn_converter(model.spp.image_pooling.bn, 'image_pooling/BatchNorm') conv_converter(model.spp.conv, 'concat_projection') bn_converter(model.spp.bn, 'concat_projection/BatchNorm') # Logits conv_converter(model.logits, 'logits/semantic', bias=True) return model if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('ckpt_path') parser.add_argument('num_classes', type=int) parser.add_argument('output_path') args = parser.parse_args() ckpt_path = args.ckpt_path num_classes = args.num_classes output_path = Path(args.output_path) output_path.parent.mkdir() model = convert_mobilenetv2(ckpt_path, num_classes) torch.save(model.state_dict(), output_path)

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4,117

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0.147059

0

null

0

null

0

1

0

118bb9cc42104087368b917dcb655edae791e512

2,624

py

Python

extract-subimages-videos.py

rzaluska/fcnn-conferences

509946a4d342451f29e7b8706b6ff46b0af20f36

[ "MIT" ]

1

2018-04-07T05:55:48.000Z

extract-subimages-videos.py

rzaluska/fcnn-conferences

509946a4d342451f29e7b8706b6ff46b0af20f36

[ "MIT" ]

null

extract-subimages-videos.py

rzaluska/fcnn-conferences

509946a4d342451f29e7b8706b6ff46b0af20f36

[ "MIT" ]

null

# script for extracting patches from video frames suitable for neural network # training from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img from PIL import Image import sys import os import glob from PIL import Image from os.path import basename, splitext import numpy as np def acceptable(a): if np.average(a) > 0.95 * 255: return False return True overlap = 8 source_path = './reducted-conferences-videos-equations/' destination_path = './conferences-videos-equations-samples-512/' for file_name in glob.glob(source_path+ "*.jpg"): name_without_extenstion = splitext(basename(file_name))[0] gt_file_path = source_path + name_without_extenstion + ".gt.jpg" print(file_name) print(gt_file_path) source_image = load_img(file_name, grayscale=False) try: groud_img = load_img(gt_file_path, grayscale=True) except FileNotFoundError: #groud_img = Image.new('RGB', (source_image.size[0], source_image.size[1]), (255, 255, 255)) continue size_list = [512] for size_x in size_list: for size_y in size_list: subimage_size = (size_x, size_y) num_of_subimages_horizontal = source_image.size[0] // (subimage_size[0] // overlap) num_of_subimages_vertical = source_image.size[1] // (subimage_size[1] // overlap) rest_h = source_image.size[0] - num_of_subimages_horizontal * (subimage_size[0] // overlap) rest_v = source_image.size[1] - num_of_subimages_vertical * (subimage_size[1] // overlap) for i in range(num_of_subimages_horizontal): for j in range(num_of_subimages_vertical): x = i * (subimage_size[0] // overlap) y = j * (subimage_size[1] // overlap) w = x + (subimage_size[0]) h = y + (subimage_size[1]) crop_rect = (x,y,w,h) if w > source_image.size[0] or h > source_image.size[1]: continue chunk_file_name = "{dir}{name}-{sizex}-{sizey}-{i}-{j}".format(dir=destination_path, i=i, j=j, name=name_without_extenstion, sizex=size_x, sizey=size_y) gt_sub_image = groud_img.crop(crop_rect) if not acceptable(img_to_array(gt_sub_image)): continue print(chunk_file_name) gt_sub_image.save(chunk_file_name + ".gt.jpg") sub_image = source_image.crop(crop_rect) sub_image.save(chunk_file_name+ ".jpg")

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null

0

null

0

1

0

118d551ca0c80c0e51a840c724d05bb79e776a14

2,113

py

Python

src/extract_cdi/__init__.py

igorgbr/extract_cdi

66d8aa56d0099c04ef491908ab246389b79fdc7e

[ "MIT" ]

null

src/extract_cdi/__init__.py

igorgbr/extract_cdi

66d8aa56d0099c04ef491908ab246389b79fdc7e

[ "MIT" ]

null

src/extract_cdi/__init__.py

igorgbr/extract_cdi

66d8aa56d0099c04ef491908ab246389b79fdc7e

[ "MIT" ]

null

import os import time import json from random import random from datetime import datetime import pandas as pd import seaborn as sns import requests class SearchAndExtractData(object): def __init__(self, file: str, graph_name: str) -> None: self.file = file self.graph_name = graph_name def create_csv(self) -> None: ENDPOINT = "ConsultarTaxaDICetip.aspx" URL = f"https://www2.cetip.com.br/ConsultarTaxaDi/{ENDPOINT}" # Criando a variável data e hora for _ in range(0, 10): data_e_hora = datetime.now() data = datetime.strftime(data_e_hora, "%Y/%m/%d") hora = datetime.strftime(data_e_hora, "%H:%M:%S") # Captando a taxa CDI do site da B3 try: response = requests.get(URL) response.raise_for_status() except requests.HTTPError: print("Dado não encontrado, continuando.") cdi = None except Exception as exc: print("Erro, parando a execução.") raise exc else: dado = json.loads(response.text) cdi = float(dado["taxa"].replace(",", ".")) # Verificando se o arquivo "taxa-cdi.csv" existe if os.path.exists(f"./{self.file}") is False: with open( file=f"./{self.file}", mode="w", encoding="utf8" ) as fp: fp.write("data,hora,taxa\n") # Salvando dados no arquivo "taxa-cdi.csv" with open(file=f"./{self.file}", mode="a", encoding="utf8") as fp: fp.write(f"{data},{hora},{cdi}\n") time.sleep(2 + (random() - 0.5)) print("Sucesso") def create_graph(self) -> None: # Extraindo as colunas hora e taxa df = pd.read_csv(f"./{self.file}") # Salvando no grafico grafico = sns.lineplot(x=df["hora"], y=df["taxa"]) _ = grafico.set_xticklabels(labels=df["hora"], rotation=90) grafico.get_figure().savefig(f"{self.graph_name}.png")

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2,113

4.376923

0.480769

0.042179

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0

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false

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0.066667

0

null

0

null

0

1

0

118ff67c852ea38f217b5c566e77f4efa9b7fe30

9,368

py

Python

FirmsLocations/Retrieve/density_assignation.py

tgquintela/Firms_locations

476680cbc3eb1308811633d24810049e215101a0

[ "MIT" ]

null

FirmsLocations/Retrieve/density_assignation.py

tgquintela/Firms_locations

476680cbc3eb1308811633d24810049e215101a0

[ "MIT" ]

null

FirmsLocations/Retrieve/density_assignation.py

tgquintela/Firms_locations

476680cbc3eb1308811633d24810049e215101a0

[ "MIT" ]

null

""" Assign geographically density value to a points. """ from scipy.spatial import KDTree from scipy.spatial.distance import cdist from scipy.stats import norm from scipy.optimize import minimize import numpy as np def general_density_assignation(locs, parameters, values=None, locs2=None): "Density assignation function." # Creation of the kdtree for retrieving neighs if locs2 is None: leafsize = int(locs.shape[0]/float(10.)) kdtree = KDTree(locs, leafsize=leafsize) else: leafsize = int(locs2.shape[0]/float(10.)) kdtree = KDTree(locs2, leafsize=leafsize) parameters = preparation_parameters(parameters) M = compute_measure(locs=locs, kdtree=kdtree, values=values, **parameters) ## Recurrent measure (TODO)[better before with the population?] return M # method, params (weitghted count, ...) # method, params (linear, trapezoid,...) ############################################################################### ############################### Compute measure ############################### ############################################################################### def compute_measure(locs, kdtree, max_r, values, method, params): "Retrieve the neighs indices and the neighs descriptors and weights." ## Computation of the measure based in the distances as weights. M = np.zeros(locs.shape[0]) for i in range(locs): neighs, dist = get_self_neighs_i(locs, kdtree, max_r, i) M[i] = compute_measure_i(neighs, dist, values[neighs], method, params) return M def get_self_neighs_i(locs, kdtree, max_r, i): "Retrieving neighs and distance." loc = locs[i, :] neighs = kdtree.query_ball_point(loc, max_r) neighs.remove(i) dist = cdist(locs[i, :], locs[neighs, :]) return neighs, dist def compute_measure_i(neighs, dist, values, method, params): "Swither function between different possible options to compute density." if method == 'weighted_count': measure = compute_measure_wcount(neighs, dist, params) elif method == 'weighted_avg': measure = compute_measure_wavg(neighs, dist, params) return measure def compute_measure_wcount(neighs, dist, params): """Measure to compute density only based on the weighted count of selected elements around the point considered. """ weights = from_distance_to_weights(dist, **params) measure = np.sum(weights) return measure def compute_measure_wavg(neighs, dist, values, params): """Measure to compute density based on the weighted average of selected elements around the point considered. """ weights = from_distance_to_weights(dist, **params) measure = np.sum(weights * values) return measure ############################################################################### ############################# Distance to weights ############################# ############################################################################### def from_distance_to_weights(dist, method, params): "Function which transforms the distance given to weights." if method == 'linear': weights = dist2weights_linear(dist, **params) elif method == 'Trapezoid': weights = dist2weights_trapez(dist, **params) elif method == 'inverse_prop': weights = dist2weights_invers(dist, **params) elif method == 'exponential': weights = dist2weights_exp(dist, **params) elif method == 'gaussian': weights = dist2weights_gauss(dist, **params) elif method == 'surgaussian': weights = dist2weights_surgauss(dist, **params) elif method == 'sigmoid': weights = dist2weights_sigmoid(dist, **params) return weights def dist2weights_linear(dist, max_r, max_w=1, min_w=0): "Linear distance weighting." weights = (max_w - dist)*((max_w-min_w)/float(max_r))+min_w return weights def dist2weights_trapez(dist, max_r, r2, max_w=1, min_w=0): "Trapezoidal distance weighting." if type(dist) == np.ndarray: weights = dist2weights_linear(dist-r2, max_r-r2, max_w, min_w) weights[dist <= r2] = max_w else: if dist <= r2: weights = max_w else: weights = dist2weights_linear(dist-r2, max_r-r2, max_w, min_w) return weights def dist2weights_invers(dist, max_r, max_w=1, min_w=1e-8, rescale=True): "Inverse distance weighting." if min_w == 0: tau = 1. else: tau = (max_w/min_w-1)/max_r if rescale: floor_f = 1./float(1.+tau*max_r) weights = max_w/(1.-floor_f) * (1./float(1.+tau*dist)-floor_f) else: weights = max_w/float(1.+tau*dist) return weights def dist2weights_exp(dist, max_r, max_w=1, min_w=1e-8, rescale=True): "Exponential distanve weighting." if min_w == 0: C = 1. else: C = -np.log(min_w/max_w) if rescale: weights = max_w/(1.-np.exp(-C)) * np.exp(-C*dist/max_r) else: weights = max_w * np.exp(-C*dist/max_r) return weights def dist2weights_gauss(dist, max_r, max_w=1, min_w=1e-3, S=None, rescale=True): "Gaussian distance weighting." if S is None: S = set_scale_surgauss(max_r, max_w, min_w) if rescale: A = max_w/(norm.pdf(0)-norm.pdf(max_r, scale=S)) weights = A*norm.pdf(dist, scale=S) else: A = max_w/norm.pdf(0) weights = A*norm.pdf(dist, scale=S) return weights def dist2weights_surgauss(dist, max_r, max_w=1, min_w=1e-3, S=None, rescale=True): "Survival gaussian distance weighting." if S is None: S = set_scale_surgauss(max_r, max_w, min_w) if rescale: A = max_w/(norm.sf(0, scale=S)-norm.sf(max_r, scale=S)) weights = A*(norm.sf(dist, scale=S)-norm.sf(max_r, scale=S)) else: A = max_w/norm.sf(0) weights = A*norm.sf(dist, scale=S) return weights def dist2weights_sigmoid(dist, max_r, max_w=1, min_w=1e-3, r_char=0, B=None, rescale=True): "Sigmoid-like distance weighting" C = r_char*max_r if B is None: B = set_scale_sigmoid(max_r, max_w, min_w, r_char) sigmoid = lambda x: 1./(1.+B*np.exp(x+C)) if rescale: floor_f = sigmoid(max_r) weights = max_w/(sigmoid(0)-floor_f)*(sigmoid(dist)-floor_f) else: weights = 1./(1.+B*np.exp(dist+C)) return weights ############################################################################### ############################# Set scale functions ############################# ############################################################################### def set_scales_kernel(method, max_r, max_w, min_w, r_char=None): "Switcher function for set scale functions." if method == 'surgaussian': scale = set_scale_surgauss(max_r, max_w, min_w) elif method == 'gaussian': scale = set_scale_gauss(max_r, max_w, min_w) elif method == 'sigmoid': scale = set_scale_sigmoid(max_r, max_w, min_w, r_char) return scale def set_scale_surgauss(max_r, max_w, min_w): "Set the scale factor of the surgauss kernel." A = max_w/norm.sf(0) scale = minimize(lambda x: (A*norm.sf(max_r, scale=x)-min_w)**2, x0=np.array([max_r]), method='BFGS', tol=1e-8, bounds=(0, None)) scale = scale['x'][0] return scale def set_scale_gauss(max_r, max_w, min_w): "Set the scale factor of the gauss kernel." A = max_w/norm.pdf(0) scale = minimize(lambda x: (A*norm.pdf(max_r, scale=x)-min_w)**2, x0=np.array([max_r]), method='BFGS', tol=1e-8, bounds=(0, None)) scale = scale['x'][0] return scale def set_scale_sigmoid(max_r, max_w, min_w, r_char): "Set scale for sigmoidal functions." C = r_char*max_r sigmoid_c = lambda B: 1./(1.+B*np.exp(max_r+C)) - min_w B = minimize((sigmoid_c)**2, x0=np.array([1]), method='BFGS', tol=1e-8, bounds=(0, None)) return B ############################################################################### ############################# Preparation inputs ############################# ############################################################################### def preparation_parameters(parameters): "Function to put into coherence the selected parameters." method = parameters['params']['method'] params = parameters['params']['params'] if method == 'gaussian': bool_scale = 'S' in params if not bool_scale: scale = set_scale_gauss(params['max_r'], params['max_w'], params['min_w']) parameters['params']['params']['S'] = scale elif method == 'surgaussian': bool_scale = 'S' in params if not bool_scale: scale = set_scale_surgauss(params['max_r'], params['max_w'], params['min_w']) parameters['params']['params']['S'] = scale elif method == 'sigmoid': bool_scale = 'B' in params if not bool_scale: scale = set_scale_sigmoid(params['max_r'], params['max_w'], params['min_w'], params['r_char']) parameters['params']['params']['B'] = scale return parameters

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null

0

null

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1

0

1190f1b038385208afbc477445817bdebba87dc5

561

py

Python

nexus/pylon/sources/specific/biorxiv.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

nexus/pylon/sources/specific/biorxiv.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

nexus/pylon/sources/specific/biorxiv.py

leoll2/hyperboria

30a0ae466b290208f690560160ef1f5c16e4a744

[ "Unlicense" ]

null

from typing import AsyncIterable from nexus.pylon.sources.base import ( DoiSource, PreparedRequest, ) class BiorxivSource(DoiSource): base_url = 'https://dx.doi.org' async def resolve(self) -> AsyncIterable[PreparedRequest]: async with self.get_resolve_session() as session: url = f'{self.base_url}/{self.doi}' async with session.get( url, timeout=self.resolve_timeout ) as resp: yield PreparedRequest(method='get', url=str(resp.url) + '.full.pdf')

28.05

84

0.616756

63

561

5.412698

0.52381

0.041056

0

0.27451

561

19

85

29.526316

0.837838

0

0.099822

0.046346

0

1

0

false

0

0.133333

0

0.266667

0

null

0

null

0

1

0

1193d717cb8b9aa0587bf651757ef62435cc6b62

3,645

py

Python

addLatLon.py

amnh-sciviz/amnh-time-machine

c75c75c6bd3ee91d81cb4b0181a292de27eab9c8

[ "MIT" ]

null

addLatLon.py

amnh-sciviz/amnh-time-machine

c75c75c6bd3ee91d81cb4b0181a292de27eab9c8

[ "MIT" ]

null

addLatLon.py

amnh-sciviz/amnh-time-machine

c75c75c6bd3ee91d81cb4b0181a292de27eab9c8

[ "MIT" ]

null

# -*- coding: utf-8 -*- import argparse from difflib import SequenceMatcher import os from pprint import pprint import sys import lib.eac_utils as eac import lib.io_utils as io # input parser = argparse.ArgumentParser() parser.add_argument('-in', dest="INPUT_FILE", default="data/eac_dates.csv", help="File with EAC data (from collectDates.py)") parser.add_argument('-countries', dest="COUNTRIES_FILE", default="data/countries.csv", help="File with countries data") parser.add_argument('-states', dest="STATES_FILE", default="data/states.csv", help="File with states data") parser.add_argument('-keys', dest="KEYS", default="name,dateplace,dateevent", help="List of keys to check in order of priority (first is highest priority)") parser.add_argument('-out', dest="OUTPUT_FILE", default="data/eac_expeditions.csv", help="File for output") a = parser.parse_args() MIN_MATCH_LEN = 4 placeKeys = a.KEYS.strip().split(",") keysToAdd = ["lon", "lat", "match"] # Make sure output dirs exist io.makeDirectories(a.OUTPUT_FILE) _, countries = io.readCsv(a.COUNTRIES_FILE) _, states = io.readCsv(a.STATES_FILE) # https://stackoverflow.com/questions/18715688/find-common-substring-between-two-strings def findLongestCommonSubstring(string1, string2): match = SequenceMatcher(None, string1, string2).find_longest_match(0, len(string1), 0, len(string2)) if match: return string1[match.a: match.a + match.size] else: return None def listIntersection(a, b): return list(set(a).intersection(set(b))) def isValidMatch(candidate, match): if match is None: return False candidate = candidate.lower() match = match.lower() valid = True stopWords = ["and", "the", "to", "of", "united", "american", "island", "islands", "north", "south", "southern", "northern", "east", "west", "eastern", "western", "central", "columbia", "african"] aList = [word.strip('[]()') for word in candidate.split()] bList = [word.strip('[]()') for word in match.split()] intersections = listIntersection(aList, bList) intersections = [word for word in intersections if word not in stopWords and len(word) > 3] if len(intersections) <= 0: valid = False return valid def findPlace(value, pool): value = value.lower() matches = [] for i, candidate in enumerate(pool): match = findLongestCommonSubstring(value, candidate["name"].lower()) if isValidMatch(value, candidate["name"]): matches.append((i,match)) matches = [m for m in matches if len(m[1]) >= MIN_MATCH_LEN] if len(matches) > 0: matches = sorted(matches, key=lambda m:-len(m[1])) place = pool[matches[0][0]] print("%s = %s" % (value, place["name"])) return (place["longitude"], place["latitude"], place["name"]) else: return (None, None, None) # retrieve expeditions expeditions = [] fieldNames, eacData = io.readCsv(a.INPUT_FILE) for key in keysToAdd: if key not in fieldNames: fieldNames.append(key) # eacData = [e for e in eacData if e["type"]=="Expedition"] entryCount = len(eacData) for i, entry in enumerate(eacData): if entry["type"]=="Expedition": for key in placeKeys: lon, lat, match = findPlace(entry[key], countries+states) if match: eacData[i].update({ "lon": lon, "lat": lat, "match": match }) break sys.stdout.write('\r') sys.stdout.write("%s%%" % round(1.0*(i+1)/entryCount*100,2)) sys.stdout.flush() io.writeCsv(a.OUTPUT_FILE, eacData, fieldNames)

35.735294

199

0.652126

471

3,645

4.989384

0.341826

0.019149

0.03617

0.019149

0.015319

0

0.011259

0.195885

3,645

101

200

36.089109

0.790515

0.060631

0

0.051282

0

0.160082

0.014047

0

1

0.051282

false

0

0.089744

0.012821

0.230769

0.025641

0

null

0

null

0

1

0

11958f77466ed28b0ddf34aab10041bc97b2f55d

912

py

Python

Solutions/problem07.py

WalrusCow/euler

b5bfa67c87c7043f521cde32e7212c0fffdbacd9

[ "MIT" ]

null

Solutions/problem07.py

WalrusCow/euler

b5bfa67c87c7043f521cde32e7212c0fffdbacd9

[ "MIT" ]

null

Solutions/problem07.py

WalrusCow/euler

b5bfa67c87c7043f521cde32e7212c0fffdbacd9

[ "MIT" ]

null

# Project Euler Problem 7 # Created on: 2012-06-13 # Created by: William McDonald import math import time # Short list of prime numbers under 20 primeList = [2, 3, 5, 7, 9, 11, 13, 17, 19] # Returns True if n is prime, otherwise False def isPrime(n): prime = True for i in primeList: if n % i == 0: prime = False break if i > math.floor(math.sqrt(n)): break return prime # Returns the nth prime number def getPrime(n): if n < len(primeList): return primeList[n - 1] else: p = primeList[len(primeList) - 1] + 2 while len(primeList) <= n: if isPrime(p): primeList.append(p) p += 2 return primeList[len(primeList) - 1] start = time.time() ans = getPrime(10001) cost = time.time() - start print(ans) print("Time: {}".format(cost))

24

46

0.551535

124

912

4.056452

0.508065

0.095427

0.083499

0.087475

0

0.058043

0.338816

912

38

47

24

0.776119

0.202851

0

0.074074

0

0.011696

0

1

0.074074

false

0

0.074074

0

0.259259

0.074074

0

null

0

null

0

1

0

119622f084f9dfff43411b649a9c89be1e105982

1,820

py

Python

cogs/ban.py

QuentiumYT/QuentiumBot

1673d24d93f13f464b1175424529c4d58abb5c00

[ "MIT" ]

9

2019-11-14T10:12:00.000Z

2021-12-17T13:05:40.000Z

cogs/ban.py

QuentiumYT/QuentiumBot

1673d24d93f13f464b1175424529c4d58abb5c00

[ "MIT" ]

null

cogs/ban.py

QuentiumYT/QuentiumBot

1673d24d93f13f464b1175424529c4d58abb5c00

[ "MIT" ]

4

2020-08-20T21:24:52.000Z

2021-12-17T13:05:17.000Z

import discord from discord.ext import commands from QuentiumBot import HandleData, get_translations # Basic command configs cmd_name = "ban" tran = get_translations() aliases = [] if not tran[cmd_name]["fr"]["aliases"] else tran[cmd_name]["fr"]["aliases"].split("/") class BanAdminRights(commands.Cog): """Ban command in Administration Rights section""" def __init__(self, client): self.client = client @commands.command( name=cmd_name, aliases=aliases, pass_context=True ) @commands.guild_only() async def ban_cmd(self, ctx, *, member: discord.Member = None): # Get specific server data if isinstance(ctx.channel, discord.TextChannel): data = await HandleData.retrieve_data(self, ctx.message.guild) lang_server = data[0] else: lang_server = "en" cmd_tran = tran[cmd_name][lang_server] # Doesn't respond to bots if not ctx.message.author.bot == True: # Check user perms if not ctx.message.author.guild_permissions.ban_members: return await ctx.send(cmd_tran["msg_perm_ban_user"].format(ctx.message.author.name)) # Check bot perms if not ctx.message.guild.me.guild_permissions.ban_members: return await ctx.send(cmd_tran["msg_perm_ban_bot"]) # No member, aborting if not member: return await ctx.send(cmd_tran["msg_mention_user"].format(ctx.message.author.name)) # Ban the member await member.ban() embed = discord.Embed(color=0xFF1111) embed.description = cmd_tran["msg_user_baned"].format(member.name) await ctx.send(embed=embed) def setup(client): client.add_cog(BanAdminRights(client))

36.4

100

0.637912

227

1,820

4.947137

0.365639

0.053428

0.05699

0.040071

0.28228

0.186999

0.133571

0.108638

0

0.004438

0.257143

1,820

49

101

37.142857

0.826183

0.101099

0

0.053571

0

0.004926

0

1

0.057143

false

0.028571

0.085714

0

0.257143

0

null

0

null

0

1

0

119816fffb1e07970d7a5eddf0acda8930b0e4b4

3,057

py

Python

tpp_tensorflow/models/semisparse.py

gfrogat/tpp_tensorflow

711dd8cc0a8155ce6b6e5663afb2331b55748d30

[ "MIT" ]

null

tpp_tensorflow/models/semisparse.py

gfrogat/tpp_tensorflow

711dd8cc0a8155ce6b6e5663afb2331b55748d30

[ "MIT" ]

null

tpp_tensorflow/models/semisparse.py

gfrogat/tpp_tensorflow

711dd8cc0a8155ce6b6e5663afb2331b55748d30

[ "MIT" ]

null

from tensorflow.keras import Model, layers, regularizers class SemiSparseInput(Model): def __init__(self, params): super(SemiSparseInput, self).__init__() # Correctly handle SELU dropout = layers.AlphaDropout if params.activation == "selu" else layers.Dropout kernel_init = ( "lecun_normal" if params.activation == "selu" else params.kernel_init ) kernel_reg = ( regularizers.l2(params.reg_l2_rate) if params.reg_l2_rate is not None else None ) self.input_dropout_maccs_fp = dropout( rate=params.input_dropout_rate, seed=params.input_dropout_seed, name="input_dropout_maccs_fp", ) self.input_maccs_fp = layers.Dense( 256, activation=params.activation, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg, name="dense_maccs_fp", ) self.input_dropout_rdkit_fp = dropout( rate=params.input_dropout_rate, seed=params.input_dropout_seed, name="input_dropout_rdkit_fp", ) self.input_rdkit_fp = layers.Dense( 2048, activation=params.activation, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg, name="dense_rdkit_fp", ) self.input_dropout_pubchem_fp = dropout( rate=params.input_dropout_rate, seed=params.input_dropout_seed, name="input_dropout_pubchem_fp", ) self.input_pubchem_fp = layers.Dense( 1024, activation=params.activation, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg, name="dense_pubchem_fp", ) self.input_shed = layers.Dense( 8, activation=params.activation, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg, name="dense_shed", ) self.input_dropout_cats2d = dropout( rate=params.input_dropout_rate, seed=params.input_dropout_seed, name="input_dropout_cats2d", ) self.input_cats2d = layers.Dense( 32, activation=params.activation, kernel_initializer=kernel_init, kernel_regularizer=kernel_reg, name="dense_cats2d", ) def call(self, features, training=False): x1 = self.input_dropout_maccs_fp(features["maccs_fp"]) x1 = self.input_maccs_fp(x1) x2 = self.input_dropout_rdkit_fp(features["rdkit_fp"]) x2 = self.input_rdkit_fp(x2) x3 = self.input_dropout_pubchem_fp(features["pubchem_fp"]) x3 = self.input_pubchem_fp(x3) x4 = self.input_shed(features["shed"]) x5 = self.input_dropout_cats2d(features["cats2d"]) x5 = self.input_cats2d(x5) x = layers.concatenate([x1, x2, x3, x4, x5], axis=1) return x

31.515464

88

0.59928

330

3,057

5.212121

0.187879

0.139535

0.074419

0.093023

0.543023

0.433721

0

0.020564

0.315996

3,057

96

89

31.84375

0.802009

0.006869

0

0.2875

0

0.069216

0.022413

0

1

0.025

false

0

0.0125

0

0.0625

0

null

0

null

0

1

0

119876ff369ecd32448c59ea7ad56ae2b54cfef2

4,628

py

Python

AT/neo4j_functions.py

seakers/daphne-brain

1d703d468cd503a21395f986dd72e67b6e556451

[ "MIT" ]

null

AT/neo4j_functions.py

seakers/daphne-brain

1d703d468cd503a21395f986dd72e67b6e556451

[ "MIT" ]

null

AT/neo4j_functions.py

seakers/daphne-brain

1d703d468cd503a21395f986dd72e67b6e556451

[ "MIT" ]

null

# Testing for neo4j query functions from neo4j import GraphDatabase, basic_auth # setup neo4j database connection driver = GraphDatabase.driver("bolt://13.58.54.49:7687", auth=basic_auth("neo4j", "goSEAKers!")) session = driver.session() # Function that can take the intersection of multiple symptom queries # Fabricate detection skill output (list of dictionaries) symptom1, symptom2, symptom3 = {}, {}, {} symptom1['measurement'], symptom1['relationship'] = 'ppO2', 'Exceeds_LWL' symptom2['measurement'], symptom2['relationship'] = 'ppCO2', 'Exceeds_UWL' symptom3['measurement'], symptom3['relationship'] = 'Water Level', 'Exceeds_UWL' symptoms = [symptom1, symptom2, symptom3] def diagnose_symptoms_neo4j(symptoms, session): # build the query based on the symptoms list query = '' for id, symp in enumerate(symptoms): query = query + 'MATCH (m' + str(id) + ':Measurement)-[r' + str(id) + ':' + symp['relationship'] + ']->(g:Anomaly) ' query = query + 'WHERE ' for id, symp in enumerate(symptoms): if ((id + 1) < len(symptoms)): query = query + 'm' + str(id) + '.Name=\'' + symp['measurement'] + '\' and ' else: query = query + 'm' + str(id) + '.Name=\'' + symp['measurement'] + '\' RETURN DISTINCT g.Title' print(query) # query the database result = session.run(query) diagnosis = [node[0] for node in result] return diagnosis print(diagnose_symptoms_neo4j(symptoms, session)) # Function that can take an anomaly or list of anomalies (names) and query the related procedures def get_related_procedures(anomaly, session): query = '' if type(anomaly) is list: for id, anom in enumerate(anomaly): query = query + 'MATCH (a' + str(id) + ':Anomaly)-[r' + str(id) + ':Solution]->(p:Procedure) ' query = query + 'WHERE ' for id, anom in enumerate(anomaly): if ((id + 1) < len(anomaly)): query = query + 'a' + str(id) + '.Title=\'' + anom + '\' and ' else: query = query + 'a' + str(id) + '.Title=\'' + anom + '\' RETURN DISTINCT p.Title' else: query = query + 'MATCH (a:Anomaly)-[r:Solution]->(p:Procedure) WHERE a.Title=\'' + str(anomaly) + '\' RETURN DISTINCT p.Title' print(query) result = session.run(query) procedures = [node[0] for node in result] if not procedures: return None else: return procedures print(get_related_procedures('CDRA Failure', session)) print(get_related_procedures(['CDRA Failure', 'Excess CO2 in Cabin'], session)) # Function that can take a specific procedure and return all steps, substeps, and subsubsteps as an ordered list def get_procedure_steps(procedure, session, detail=3): # detail denotes the level of steps to return. (1->steps, 2->steps&substeps, 3->steps&substeps&subsubsteps) if (detail == 1): #Return only highest level steps query1 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' and (s:Step) RETURN s.Title ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' query2 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' and (s:Step) RETURN s.Action ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' elif (detail == 2): #Return Steps and substeps query1 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' and (s:Step OR s:SubStep) RETURN s.Title ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' query2 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' and (s:Step OR s:SubStep) RETURN s.Action ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' else: #Return all steps, substeps, and subsubsteps query1 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' RETURN s.Title ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' query2 = 'MATCH(p:Procedure)-[r:Has]->(s) WHERE p.Title=\'' + procedure + '\' RETURN s.Action ORDER BY s.Step, s.SubStep, s.SubSubStep, s.Note' steps = [] result1 = session.run(query1) step_titles = [node[0] for node in result1] result2 = session.run(query2) step_actions = [node[0] for node in result2] for id, step in enumerate(step_titles): steps.append(step_titles[id] + ' - ' + step_actions[id]) if not steps: return None else: return steps print(get_procedure_steps('Zeolite Filter Swapout', session, 3)) ''' # Function that returns the equipment required for a procedure or list of procedures def get_procedure_equipment(procedure, session): return equipment '''

46.747475

177

0.642178

617

4,628

4.774716

0.226904

0.033944

0.03055

0.032587

0.418873

0.350645

0.250849

0.233876

0.210115

0

0.015808

0.207217

4,628

98

178

47.22449

0.787135

0.143258

0

0.298507

0

0.089552

0.373087

0.072032

0

1

0.044776

false

0

0.014925

0

0.134328

0.089552

0

null

0

null

0

1

0

119a0cbab86f26fb6ab15f22092ddf49f71f6f94

5,486

py

Python

build.py

refaim/wots

dad9918c603293982a598fb5d6c73ade1a6080e1

[ "MIT" ]

2

2018-07-14T19:45:38.000Z

2019-04-21T07:17:20.000Z

build.py

refaim/wots

dad9918c603293982a598fb5d6c73ade1a6080e1

[ "MIT" ]

155

2018-07-07T00:33:31.000Z

2021-08-16T17:55:05.000Z

build.py

refaim/wots

dad9918c603293982a598fb5d6c73ade1a6080e1

[ "MIT" ]

null

import datetime import math import os import sys import PyQt5 import dotenv from PyInstaller.archive.pyz_crypto import PyiBlockCipher from PyInstaller.building.api import PYZ, EXE, COLLECT from PyInstaller.building.build_main import Analysis from app import version from app.core.utils import OsUtils, PathUtils APP_NAME = 'wizard' PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__)) BUILD_DIRECTORY = os.path.join(PROJECT_ROOT, 'build') DISTR_DIRECTORY = os.path.join(PROJECT_ROOT, 'dist', APP_NAME) RESOURCES_DIRECTORY = os.path.join(PROJECT_ROOT, 'res') BINARY_RESOURCE_EXTENSIONS = {'.png'} dotenv.load_dotenv(os.path.join(PROJECT_ROOT, '.env')) extra_path = [] app_version_file = None app_version_ints = None if OsUtils.is_windows(): extra_path.append(os.path.join(os.path.dirname(PyQt5.__file__), 'Qt', 'bin')) extra_path.append(os.path.dirname(sys.executable)) if OsUtils.is_win10(): for program_files_var in ['ProgramFiles', 'ProgramFiles(x86)']: for arch in ['x86', 'x64']: dll_path = os.path.join(os.getenv(program_files_var), 'Windows Kits\\10\\Redist\\ucrt\\DLLs', arch) if os.path.isdir(dll_path): extra_path.append(dll_path) app_version = version.VERSION app_version_ints = [int(x) for x in app_version.split('.')] while len(app_version_ints) < 4: app_version_ints.append(0) app_version_file = os.path.join(BUILD_DIRECTORY, 'exe_version.txt') with open(os.path.join(PROJECT_ROOT, 'exe_version.template.txt')) as version_template_fobj: with open(app_version_file, 'w') as version_fobj: version_fobj.write(version_template_fobj.read().format( version_string=str(app_version), version_tuple=tuple(app_version_ints), current_year=datetime.datetime.today().year)) txt_resources = [] if os.path.exists('.env'): txt_resources.append(('.env', '.')) bin_resources = [] for filename in os.listdir(RESOURCES_DIRECTORY): target = txt_resources if os.path.splitext(filename)[1] in BINARY_RESOURCE_EXTENSIONS: target = bin_resources target.append((os.path.join(RESOURCES_DIRECTORY, filename), os.path.relpath(RESOURCES_DIRECTORY, PROJECT_ROOT))) block_cipher = None cipher_key = os.getenv('PYINSTALLER_CIPHER_KEY') if cipher_key: block_cipher = PyiBlockCipher(key=cipher_key) a = Analysis([os.path.join(PROJECT_ROOT, 'app', 'wizard.py')], pathex=extra_path, binaries=bin_resources, datas=txt_resources, hiddenimports=[], hookspath=[], runtime_hooks=[], excludes=[], win_no_prefer_redirects=True, win_private_assemblies=True, cipher=block_cipher) pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher) exe = EXE(pyz, a.scripts, exclude_binaries=True, name=APP_NAME, debug=False, strip=False, upx=False, console=False, version=app_version_file) COLLECT(exe, a.binaries, a.zipfiles, a.datas, strip=False, upx=False, name=APP_NAME) if OsUtils.is_windows(): nsis_license = os.path.join(BUILD_DIRECTORY, 'license.txt') with open(os.path.join(PROJECT_ROOT, 'LICENSE')) as src_license_fobj: with open(nsis_license, 'w') as dst_license_fobj: dst_license_fobj.write(src_license_fobj.read().replace('\n', '\r\n')) with open(os.path.join(PROJECT_ROOT, 'setup.template.nsi')) as nsis_template_fobj: config = nsis_template_fobj.read() distr_directories = [] distr_files = [] for root, dirs, files in os.walk(DISTR_DIRECTORY): for dir_name in dirs: distr_directories.append(os.path.join(root, dir_name)) for file_name in files: distr_files.append(os.path.join(root, file_name)) def make_inst_path(path: str) -> str: return PathUtils.quote(os.path.join('$INSTDIR', os.path.relpath(path, DISTR_DIRECTORY))) def add_command(commands: list, command: str) -> None: commands.append((' ' * 4) + command) indent = ' ' * 4 install_commands = [] for path in distr_directories: add_command(install_commands, 'CreateDirectory {}'.format(make_inst_path(path))) for path in distr_files: add_command(install_commands, 'File {} {}'.format(PathUtils.quote('/oname={}'.format(os.path.relpath(path, DISTR_DIRECTORY))), PathUtils.quote(path))) uninstall_commands = [] for path in distr_files: add_command(uninstall_commands, 'Delete {}'.format(make_inst_path(path))) for path in reversed(distr_directories): add_command(uninstall_commands, 'RMDir {}'.format(make_inst_path(path))) arch = 'x64' if OsUtils.is_x64() else 'x86' NSIS_VARS = { '%license_file%': os.path.basename(nsis_license), '%version_major%': str(app_version_ints[0]), '%version_minor%': str(app_version_ints[1]), '%version_build%': str(app_version_ints[2]), '%install_size_kb%': str(math.ceil(PathUtils.get_folder_size(DISTR_DIRECTORY) / 1024)), '%program_arch%': arch, '%exe_name%': APP_NAME, '%setup_name%': 'WizardOfTheSearch_v{}_Setup_{}'.format(version.VERSION, arch), '%distr_directory%': DISTR_DIRECTORY, '%install_commands%': '\r\n'.join(install_commands), '%uninstall_commands%': '\r\n'.join(uninstall_commands), } for k, v in NSIS_VARS.items(): config = config.replace(k, v) with open(os.path.join(BUILD_DIRECTORY, 'setup.nsi'), 'w') as nsis_fobj: nsis_fobj.write(config)

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null

0

null

0

1

0

119a349c2ca5822591f4b6677156eec1b27631d0

1,939

py

Python

server/constants.py

chrononyan/ok

1c83e419dd8d5ef64c1e03a7f8a218e65a9fb7cf

[ "Apache-2.0" ]

148

2018-07-03T02:08:30.000Z

2022-03-26T04:03:35.000Z

server/constants.py

chrononyan/ok

1c83e419dd8d5ef64c1e03a7f8a218e65a9fb7cf

[ "Apache-2.0" ]

856

2015-01-10T04:27:20.000Z

2018-06-27T14:43:23.000Z

server/constants.py

chrononyan/ok

1c83e419dd8d5ef64c1e03a7f8a218e65a9fb7cf

[ "Apache-2.0" ]

69

2015-01-26T08:06:55.000Z

2018-06-25T12:46:03.000Z

"""App constants""" import os STUDENT_ROLE = 'student' GRADER_ROLE = 'grader' STAFF_ROLE = 'staff' INSTRUCTOR_ROLE = 'instructor' LAB_ASSISTANT_ROLE = 'lab assistant' ROLE_DISPLAY_NAMES = { STUDENT_ROLE: 'Student', GRADER_ROLE: 'Reader', STAFF_ROLE: 'Teaching Assistant', INSTRUCTOR_ROLE: 'Instructor', LAB_ASSISTANT_ROLE: 'Lab Assistant', } VALID_ROLES = [STUDENT_ROLE, LAB_ASSISTANT_ROLE, GRADER_ROLE, STAFF_ROLE, INSTRUCTOR_ROLE] STAFF_ROLES = [GRADER_ROLE, STAFF_ROLE, INSTRUCTOR_ROLE] SCORE_KINDS = ['composition', 'correctness', 'effort', 'total', 'partner a', 'partner b', 'regrade', 'revision', 'checkpoint 1', 'checkpoint 2', 'private', 'autograder', 'error'] API_PREFIX = '/api' OAUTH_SCOPES = ['all', 'email'] OAUTH_OUT_OF_BAND_URI = 'urn:ietf:wg:oauth:2.0:oob' COMMON_LANGUAGES = ['python', 'java', 'c', 'scheme', 'lisp', 'javascript'] COURSE_ENDPOINT_FORMAT = '^[\w\-]+/[\w\-]+/(fa|sp|su|wi|au|yr)\d\d$' ASSIGNMENT_ENDPOINT_FORMAT = COURSE_ENDPOINT_FORMAT[:-1] + '/\w+$' GRADES_BUCKET = 'ok_grades_bucket' TIMEZONE = 'America/Los_Angeles' ISO_DATETIME_FMT = '%Y-%m-%d %H:%M:%S' APPLICATION_ROOT = os.getenv('APPLICATION_ROOT', '/') # The default autograder url # Each course can configure their own autograder url in course.edit view AUTOGRADER_URL = os.getenv('AUTOGRADER_URL', 'https://autograder.cs61a.org') SENDGRID_KEY = os.getenv("SENDGRID_KEY") FORBIDDEN_ROUTE_NAMES = [ 'about', 'admin', 'api', 'comments', 'login', 'logout', 'oauth', 'rq', 'testing-login', ] FORBIDDEN_ASSIGNMENT_NAMES = [] # Service Providers GOOGLE = "GOOGLE" MICROSOFT = "MICROSOFT" # Maximum file size to show in browser, in characters DIFF_SIZE_LIMIT = 64 * 1024 # 64KB SOURCE_SIZE_LIMIT = 10 * 1024 * 1024 # 10MB MAX_UPLOAD_FILE_SIZE = 25 * 1024 * 1024 # 25MB # Email client format for to field EMAIL_FORMAT = "{name} <{email}>"

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0.020408

0

null

0

null

0

1

0

119efd61f102f9d7b866310597894dc025bd5e5a

466

py

Python

AlgoritimoRandomize.py

falluk/algoritimoDeBuscas

6dbca79ef60f2820f5e81110bc4104bdc46496b1

[ "MIT" ]

1

2021-07-05T13:24:04.000Z

AlgoritimoRandomize.py

falluk/algoritimoDeBuscas

6dbca79ef60f2820f5e81110bc4104bdc46496b1

[ "MIT" ]

null

AlgoritimoRandomize.py

falluk/algoritimoDeBuscas

6dbca79ef60f2820f5e81110bc4104bdc46496b1

[ "MIT" ]

null

#Criado para randomizar uma lsita de 20mil Colaboradores retornando apenas 1000 colaboradores vários cargos distintos. import pandas as pd import random base = pd.read_excel("usuarios - energisa.xlsx", encoding="ISO-8859-1",error_bad_lines=False) sort1 = base.sample(15000) sort2 = sort1.sample(10000) sort3 = sort2.sample(7500) sort4 = sort3.sample(5000) sort5 = sort4.sample(2500) sorteado = sort5.sample(1000) sorteado.to_excel("Lista Randomizada.xlsx")

22.190476

118

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0.2

0

null

0

null

0

1

0

11a158752080d596792054d55693dc41df752af9

7,625

py

Python

app.py

Build-Week-2106FT-AirBnB-3/front-end

0df6f9814387a36002a1aaa8feff1f17fcb30b78

[ "CC0-1.0" ]

null

app.py

Build-Week-2106FT-AirBnB-3/front-end

0df6f9814387a36002a1aaa8feff1f17fcb30b78

[ "CC0-1.0" ]

1

2021-06-24T00:17:40.000Z

2021-06-24T00:18:42.000Z

app.py

Build-Week-2106FT-AirBnB-3/pricing

0df6f9814387a36002a1aaa8feff1f17fcb30b78

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- # https://towardsdatascience.com/build-a-machine-learning-simulation-tool-with-dash-b3f6fd512ad6 # We start with the import of standard ML librairies import pandas as pd import numpy as np from sklearn.datasets import make_regression from sklearn.ensemble import RandomForestRegressor # We add all Plotly and Dash necessary librairies import plotly.graph_objects as go import pickle import dash import dash_core_components as dcc import dash_html_components as html import dash_daq as daq from dash.dependencies import Input, Output from sklearn.pipeline import make_pipeline from sklearn.impute import SimpleImputer from category_encoders import OneHotEncoder from sklearn.model_selection import train_test_split df = pd.read_csv('clean_data.csv') # importing our model # target='price' # X = df.drop(columns=target) # y = df[target] # # Let's split into a test and # X_train, X_t, y_train, y_t = train_test_split(X,y, test_size=.2, random_state=7) # # Let's split our test data into validation and test # X_val, X_test, y_val, y_test = train_test_split(X_t,y_t, test_size=.2, random_state=7) # model = make_pipeline(OneHotEncoder(use_cat_names=True), # SimpleImputer(), # RandomForestRegressor(random_state=70)) # model.fit(X_train,y_train) infile = open('random_forest_model', 'rb') model = pickle.load(infile) infile.close() # # # We create a DataFrame to store the features' importance and their corresponding label f_impor = model.named_steps['randomforestregressor'].feature_importances_ col_names = model.named_steps['onehotencoder'].get_feature_names() df_feature_importances = pd.DataFrame(f_impor, columns=["Importance"], index=col_names) df_feature_importances = df_feature_importances.sort_values("Importance", ascending=False).head(10) # Create the bar chart and limit it to the top 10 features # # We create a Features Importance Bar Chart fig_features_importance = go.Figure() fig_features_importance.add_trace(go.Bar(x=df_feature_importances.index, y=df_feature_importances["Importance"], marker_color='rgb(171, 226, 251)') ) fig_features_importance.update_layout(title_text='<b>Features Importance of the model<b>', title_x=0.5) # The command below can be activated in a standard notebook to display the chart # fig_features_importance.show() # We record the name, min, mean and max of the three most important features dropdown_1_label = df_feature_importances.index[0] dropdown_1_min = round(df[dropdown_1_label].min(),5) dropdown_1_mean = round(df[dropdown_1_label].mean(),5) dropdown_1_max = round(df[dropdown_1_label].max(),5) dropdown_2_label = df_feature_importances.index[1] dropdown_2_min = round(df[dropdown_2_label].min(),5) dropdown_2_mean = round(df[dropdown_2_label].mean(),5) dropdown_2_max = round(df[dropdown_2_label].max(),5) dropdown_3_label = df_feature_importances.index[5] dropdown_3_min = round(df[dropdown_3_label].min(),5) dropdown_3_mean = round(df[dropdown_3_label].mean(),5) dropdown_3_max = round(df[dropdown_3_label].max(),5) ############################################################################### app = dash.Dash() server = app.server # The page structure will be: # Features Importance Chart # <H4> Feature #1 name # Slider to update Feature #1 value # <H4> Feature #2 name # Slider to update Feature #2 value # <H4> Feature #3 name # Slider to update Feature #3 value # <H2> Updated Prediction # Callback fuction with Sliders values as inputs and Prediction as Output # We apply basic HTML formatting to the layout app.layout = html.Div(style={'textAlign': 'center', 'width': '800px', 'font-family': 'Verdana'}, children=[ # The same logic is applied to the following names / sliders html.H1(children="Simulation Tool"), #Dash Graph Component calls the fig_features_importance parameters dcc.Graph(figure=fig_features_importance), # We display the most important feature's name html.H4(children=dropdown_1_label), # The Dash Slider is built according to Feature #1 ranges dcc.Slider( id='X1_slider', min=dropdown_1_min, max=dropdown_1_max, step=0.029311, value=dropdown_1_mean, marks={i: '{}°'.format(i) for i in np.arange(dropdown_1_min, dropdown_1_max)} ), # The same logic is applied to the following names / sliders html.H4(children=dropdown_2_label), dcc.Slider( id='X2_slider', min=dropdown_2_min, max=dropdown_2_max, step=0.080384, value=dropdown_2_mean, marks={i: '{}°'.format(i) for i in np.arange(dropdown_2_min, dropdown_2_max)} ), html.H4(children=dropdown_3_label), dcc.Slider( id='X3_slider', min=dropdown_3_min, max=dropdown_3_max, step=0.6, value=dropdown_3_mean, marks={i: '{}people'.format(i) for i in np.arange(dropdown_2_min, dropdown_2_max)}, ), # The prediction result will be displayed and updated here html.H2(id="prediction_result") ]) # The callback function will provide one "Ouput" in the form of a string (=children) @app.callback(Output(component_id="prediction_result",component_property="children"), # The values correspnding to the three sliders are obtained by calling their id and value property [Input("X1_slider","value"), Input("X2_slider","value"), Input("X3_slider","value")]) # The input variable are set in the same order as the callback Inputs def update_prediction(X1, X2, X3): # We create a NumPy array in the form of the original features # ["Pressure","Viscosity","Particles_size", "Temperature","Inlet_flow", "Rotating_Speed","pH","Color_density"] # Except for the X1, X2 and X3, all other non-influencing parameters are set to their mean input_X = np.array([258668827, 1, 1, 1, X1, X2, X3, df["bedrooms"].mean(), df['beds'].mean(), df['number_of_reviews'].mean(), df["review_scores_rating"].mean(), 1, 1]).reshape(1, -1) # Prediction is calculated based on the input_X array prediction = model.named_steps['randomforestregressor'].predict(input_X) # And retuned to the Output of the callback function return "Prediction in Yen: {}".format(round(prediction[0])) # return 'this is working' if __name__ == "__main__": app.run_server()

39.921466

114

0.6

947

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4.6283

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0.022815

0.145791

0.065024

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0

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7,625

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false

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0.291667

0

null

0

null

0

1

0

11a1d2a8f067924755b1bb004f5652117e69edcd

1,787

py

Python

balloon_learning_environment/env/gym.py

johannah/balloon-learning-environment

cdb2e582f2b03c41f037bf76142d31611f5e0316

[ "Apache-2.0" ]

64

2021-11-09T08:49:02.000Z

2022-03-30T17:33:54.000Z

balloon_learning_environment/env/gym.py

johannah/balloon-learning-environment

cdb2e582f2b03c41f037bf76142d31611f5e0316

[ "Apache-2.0" ]

null

balloon_learning_environment/env/gym.py

johannah/balloon-learning-environment

cdb2e582f2b03c41f037bf76142d31611f5e0316

[ "Apache-2.0" ]

5

2021-11-14T18:56:42.000Z

2022-03-18T16:22:31.000Z

# coding=utf-8 # Copyright 2022 The Balloon Learning Environment Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Balloon Learning Environment gym utilities.""" import contextlib def register_env() -> None: """Register the Gym environment.""" # We need to import Gym's registration module inline or else we'll # get a circular dependency that will result in an error when importing gym from gym.envs import registration # pylint: disable=g-import-not-at-top env_id = 'BalloonLearningEnvironment-v0' env_entry_point = 'balloon_learning_environment.env.balloon_env:BalloonEnv' # We guard registration by checking if our env is already registered # This is necesarry because the plugin system will load our module # which also calls this function. If multiple `register()` calls are # made this will result in a warning to the user. registered = env_id in registration.registry.env_specs if not registered: with contextlib.ExitStack() as stack: # This is a workaround for Gym 0.21 which didn't support # registering into the root namespace with the plugin system. if hasattr(registration, 'namespace'): stack.enter_context(registration.namespace(None)) registration.register(id=env_id, entry_point=env_entry_point)

43.585366

77

0.758254

262

1,787

5.118321

0.538168

0.044743

0.058166

0.023863

0

0.008796

0.172916

1,787

40

78

44.675

0.898512

0.673195

0

0.168784

0.15245

0

1

0.090909

false

0

0.181818

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0.272727

0

null

0

null

0

1

0

11a1e1b730dc4e433d8e1358594ee3d9a8526d1b

15,181

py

Python

models/multimodal_transformer.py

XiaoJake/MTTR

c383c5b151e3c97aeb45cd2fb4bf08719016498b

[ "Apache-2.0" ]

516

2021-11-30T03:22:41.000Z

2022-03-31T19:48:59.000Z

models/multimodal_transformer.py

codwest/MTTR

c383c5b151e3c97aeb45cd2fb4bf08719016498b

[ "Apache-2.0" ]

15

2021-12-07T02:43:24.000Z

2022-03-27T15:59:32.000Z

models/multimodal_transformer.py

codwest/MTTR

c383c5b151e3c97aeb45cd2fb4bf08719016498b

[ "Apache-2.0" ]

57

2021-11-30T08:49:51.000Z

2022-03-25T19:41:08.000Z

""" MTTR Multimodal Transformer class. Modified from DETR https://github.com/facebookresearch/detr """ import copy import os from typing import Optional import torch import torch.nn.functional as F from torch import nn, Tensor from einops import rearrange, repeat from transformers import RobertaModel, RobertaTokenizerFast from models.position_encoding_2d import PositionEmbeddingSine2D os.environ["TOKENIZERS_PARALLELISM"] = "false" # this disables a huggingface tokenizer warning (printed every epoch) class MultimodalTransformer(nn.Module): def __init__(self, num_encoder_layers=3, num_decoder_layers=3, text_encoder_type="roberta-base", freeze_text_encoder=True, **kwargs): super().__init__() self.d_model = kwargs['d_model'] encoder_layer = TransformerEncoderLayer(**kwargs) self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers) decoder_layer = TransformerDecoderLayer(**kwargs) self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, norm=nn.LayerNorm(self.d_model), return_intermediate=True) self.pos_encoder_2d = PositionEmbeddingSine2D() self._reset_parameters() self.text_encoder = RobertaModel.from_pretrained(text_encoder_type) self.text_encoder.pooler = None # this pooler is never used, this is a hack to avoid DDP problems... self.tokenizer = RobertaTokenizerFast.from_pretrained(text_encoder_type) self.freeze_text_encoder = freeze_text_encoder if freeze_text_encoder: for p in self.text_encoder.parameters(): p.requires_grad_(False) self.txt_proj = FeatureResizer( input_feat_size=self.text_encoder.config.hidden_size, output_feat_size=self.d_model, dropout=kwargs['dropout'], ) def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, vid_embeds, vid_pad_mask, text_queries, obj_queries): device = vid_embeds.device t, b, _, h, w = vid_embeds.shape txt_memory, txt_pad_mask = self.forward_text(text_queries, device) # add temporal dim to txt memory & padding mask: txt_memory = repeat(txt_memory, 's b c -> s (t b) c', t=t) txt_pad_mask = repeat(txt_pad_mask, 'b s -> (t b) s', t=t) vid_embeds = rearrange(vid_embeds, 't b c h w -> (h w) (t b) c') # Concat the image & text embeddings on the sequence dimension encoder_src_seq = torch.cat((vid_embeds, txt_memory), dim=0) seq_mask = torch.cat((rearrange(vid_pad_mask, 't b h w -> (t b) (h w)'), txt_pad_mask), dim=1) # vid_pos_embed is: [T*B, H, W, d_model] vid_pos_embed = self.pos_encoder_2d(rearrange(vid_pad_mask, 't b h w -> (t b) h w'), self.d_model) # use zeros in place of pos embeds for the text sequence: pos_embed = torch.cat((rearrange(vid_pos_embed, 't_b h w c -> (h w) t_b c'), torch.zeros_like(txt_memory)), dim=0) memory = self.encoder(encoder_src_seq, src_key_padding_mask=seq_mask, pos=pos_embed) # [S, T*B, C] vid_memory = rearrange(memory[:h*w, :, :], '(h w) (t b) c -> t b c h w', h=h, w=w, t=t, b=b) txt_memory = memory[h*w:, :, :] txt_memory = rearrange(txt_memory, 's t_b c -> t_b s c') txt_memory = [t_mem[~pad_mask] for t_mem, pad_mask in zip(txt_memory, txt_pad_mask)] # remove padding # add T*B dims to query embeds (was: [N, C], where N is the number of object queries): obj_queries = repeat(obj_queries, 'n c -> n (t b) c', t=t, b=b) tgt = torch.zeros_like(obj_queries) # [N, T*B, C] # hs is [L, N, T*B, C] where L is number of layers in the decoder hs = self.decoder(tgt, memory, memory_key_padding_mask=seq_mask, pos=pos_embed, query_pos=obj_queries) hs = rearrange(hs, 'l n (t b) c -> l t b n c', t=t, b=b) return hs, vid_memory, txt_memory def forward_text(self, text_queries, device): tokenized_queries = self.tokenizer.batch_encode_plus(text_queries, padding='longest', return_tensors='pt') tokenized_queries = tokenized_queries.to(device) with torch.inference_mode(mode=self.freeze_text_encoder): encoded_text = self.text_encoder(**tokenized_queries) # Transpose memory because pytorch's attention expects sequence first txt_memory = rearrange(encoded_text.last_hidden_state, 'b s c -> s b c') txt_memory = self.txt_proj(txt_memory) # change text embeddings dim to model dim # Invert attention mask that we get from huggingface because its the opposite in pytorch transformer txt_pad_mask = tokenized_queries.attention_mask.ne(1).bool() # [B, S] return txt_memory, txt_pad_mask def num_parameters(self): return sum(p.numel() for p in self.parameters() if p.requires_grad) class TransformerEncoder(nn.Module): def __init__(self, encoder_layer, num_layers, norm=None): super().__init__() self.layers = _get_clones(encoder_layer, num_layers) self.num_layers = num_layers self.norm = norm def forward(self, src, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): output = src for layer in self.layers: output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos) if self.norm is not None: output = self.norm(output) return output class TransformerDecoder(nn.Module): def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): super().__init__() self.layers = _get_clones(decoder_layer, num_layers) self.num_layers = num_layers self.norm = norm self.return_intermediate = return_intermediate def forward(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): output = tgt intermediate = [] for layer in self.layers: output = layer(output, memory, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask, pos=pos, query_pos=query_pos) if self.return_intermediate: intermediate.append(self.norm(output)) if self.norm is not None: output = self.norm(output) if self.return_intermediate: intermediate.pop() intermediate.append(output) if self.return_intermediate: return torch.stack(intermediate) return output.unsqueeze(0) class TransformerEncoderLayer(nn.Module): def __init__(self, d_model, nheads, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False, **kwargs): super().__init__() self.self_attn = nn.MultiheadAttention(d_model, nheads, dropout=dropout) # Implementation of Feedforward model self.linear1 = nn.Linear(d_model, dim_feedforward) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.activation = _get_activation_fn(activation) self.normalize_before = normalize_before def with_pos_embed(self, tensor, pos: Optional[Tensor]): return tensor if pos is None else tensor + pos def forward_post(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): q = k = self.with_pos_embed(src, pos) src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src = self.norm1(src) src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) src = src + self.dropout2(src2) src = self.norm2(src) return src def forward_pre(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): src2 = self.norm1(src) q = k = self.with_pos_embed(src2, pos) src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src2 = self.norm2(src) src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) src = src + self.dropout2(src2) return src def forward(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): if self.normalize_before: return self.forward_pre(src, src_mask, src_key_padding_mask, pos) return self.forward_post(src, src_mask, src_key_padding_mask, pos) class TransformerDecoderLayer(nn.Module): def __init__(self, d_model, nheads, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False, **kwargs): super().__init__() self.self_attn = nn.MultiheadAttention(d_model, nheads, dropout=dropout) self.multihead_attn = nn.MultiheadAttention(d_model, nheads, dropout=dropout) # Implementation of Feedforward model self.linear1 = nn.Linear(d_model, dim_feedforward) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.norm3 = nn.LayerNorm(d_model) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.dropout3 = nn.Dropout(dropout) self.activation = _get_activation_fn(activation) self.normalize_before = normalize_before def with_pos_embed(self, tensor, pos: Optional[Tensor]): return tensor if pos is None else tensor + pos def forward_post(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): q = k = self.with_pos_embed(tgt, query_pos) tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] tgt = tgt + self.dropout1(tgt2) tgt = self.norm1(tgt) tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), key=self.with_pos_embed(memory, pos), value=memory, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0] tgt = tgt + self.dropout2(tgt2) tgt = self.norm2(tgt) tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) tgt = tgt + self.dropout3(tgt2) tgt = self.norm3(tgt) return tgt def forward_pre(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): tgt2 = self.norm1(tgt) q = k = self.with_pos_embed(tgt2, query_pos) tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] tgt = tgt + self.dropout1(tgt2) tgt2 = self.norm2(tgt) tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), key=self.with_pos_embed(memory, pos), value=memory, attn_mask=memory_mask, key_padding_mask=memory_key_padding_mask)[0] tgt = tgt + self.dropout2(tgt2) tgt2 = self.norm3(tgt) tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) tgt = tgt + self.dropout3(tgt2) return tgt def forward(self, tgt, memory, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): if self.normalize_before: return self.forward_pre(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) return self.forward_post(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) class FeatureResizer(nn.Module): """ This class takes as input a set of embeddings of dimension C1 and outputs a set of embedding of dimension C2, after a linear transformation, dropout and normalization (LN). """ def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): super().__init__() self.do_ln = do_ln # Object feature encoding self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) self.dropout = nn.Dropout(dropout) def forward(self, encoder_features): x = self.fc(encoder_features) if self.do_ln: x = self.layer_norm(x) output = self.dropout(x) return output def _get_activation_fn(activation): """Return an activation function given a string""" if activation == "relu": return F.relu if activation == "gelu": return F.gelu if activation == "glu": return F.glu raise RuntimeError(F"activation should be relu/gelu, not {activation}.")

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122

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null

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0

11a22b195401a97025bc1265b213cb97ff210032

403

py

Python

docker_sdk_api/shared/helpers/get_model_zip.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

20

2021-07-13T13:08:57.000Z

2022-03-29T09:38:00.000Z

docker_sdk_api/shared/helpers/get_model_zip.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

null

docker_sdk_api/shared/helpers/get_model_zip.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

2

2021-07-12T08:42:53.000Z

2022-03-04T18:41:25.000Z

import os from typing import Dict def get_downloadable_zip(folder_path: str) -> Dict[str, str]: servable_models: Dict[str, str] = {} for root, dirs, files in os.walk(folder_path): for directory in dirs: for f in os.listdir(os.path.join(root, directory)): if f.endswith(".zip"): servable_models[f] = directory return servable_models

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63

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55

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0.490909

0.172131

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0

0.272953

403

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0.4

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null

0

null

0

1

0

11a302e0300bce122a82770aa16b84ca6e8d73b5

6,065

py

Python

groups/views.py

3crabs/class-book

f5de12be816aa9be889d8413007be8eb4abdf45f

[ "WTFPL" ]

1

2020-11-19T14:49:41.000Z

groups/views.py

3crabs/class-book

f5de12be816aa9be889d8413007be8eb4abdf45f

[ "WTFPL" ]

null

groups/views.py

3crabs/class-book

f5de12be816aa9be889d8413007be8eb4abdf45f

[ "WTFPL" ]

null

from django.core.mail import EmailMessage from django.http import HttpResponse, HttpResponseRedirect from django.shortcuts import render from accounting.models import Attendance, Result from accounting.templatetags import my_tags from class_book import settings from groups.models import Group, Student from subjects.models import Subject import xlwt def groups(request): if request.POST: item = Group(name=request.POST['name']) item.save() object_list = Group.objects.all().order_by("name") return render(request, 'groups/index.html', locals()) def group(request, pk): group = Group.objects.get(id=pk) if 'delete' in request.POST: group.delete() object_list = Group.objects.all().order_by("name") return render(request, 'groups/index.html', locals()) group = Group.objects.get(id=pk) subject_list = Subject.objects.all().order_by("name") return render(request, 'groups/info.html', locals()) def group_students(request, pk): if request.POST: item = Student( name=request.POST['name'], email=request.POST['email'], group_id=pk, ) item.save() group = Group.objects.get(id=pk) subjects = group.subjects.all() for subject in subjects: for lesson in subject.lesson_set.all(): attendance = Attendance() attendance.student = item attendance.lesson = lesson attendance.save() for task in subject.task_set.all(): result = Result() result.student = item result.task = task result.save() group = Group.objects.get(id=pk) return render(request, 'groups/info.html', locals()) def group_student(request, pk, id): student = Student.objects.get(id=id) if 'delete' in request.POST: student.delete() group = Group.objects.get(id=pk) subject_list = Subject.objects.all().order_by("name") return render(request, 'groups/info.html', locals()) def group_subjects(request, pk): if request.POST: group = Group.objects.get(id=pk) subject = Subject.objects.get(id=request.POST['subject']) group.subjects.add(subject) group.save() group = Group.objects.get(id=pk) for student in group.student_set.all(): for lesson in subject.lesson_set.all(): attendance = Attendance() attendance.student = student attendance.lesson = lesson attendance.save() for task in subject.task_set.all(): result = Result() result.student = student result.task = task result.save() group = Group.objects.get(id=pk) subject_list = Subject.objects.all().order_by("name") return render(request, 'groups/info.html', locals()) def group_subject(request, pk, id): subject = Subject.objects.get(id=id) if 'delete' in request.POST: group = Group.objects.get(id=pk) group.subjects.remove(subject) group.save() group = Group.objects.get(id=pk) subject_list = Subject.objects.all().order_by("name") return render(request, 'groups/info.html', locals()) else: group = Group.objects.get(id=pk) itogs = {} for student in group.student_set.all(): itogs[student.id] = student.id + 1 print(itogs) return render(request, 'accouting/index.html', locals()) def create_xls_(group, subject): book = xlwt.Workbook(encoding="utf-8") sheet = book.add_sheet(group.name) sheet.write(0, 0, "Успеваемость группы " + group.name + " по предмету " + subject.name) row = 1 col = 0 sheet.write(row, col, "Посещаемость") row += 1 sheet.write(row, col, "Студент") col += 1 for lesson in subject.lesson_set.all(): sheet.write(row, col, lesson.name) col += 1 sheet.write(row, col, "Посещаемость") row += 1 col = 0 for student in group.student_set.all(): sheet.write(row, col, student.name) col += 1 for attendance in student.attendance_set.filter(lesson__subject_id=subject.id): sheet.write(row, col, attendance.visit) col += 1 sheet.write(row, col, my_tags.lessons(student, subject)) row += 1 col = 0 sheet.write(row, col, "Результаты") row += 1 sheet.write(row, col, "Студент") col += 1 for task in subject.task_set.all(): sheet.write(row, col, task.name) col += 1 sheet.write(row, col, "Успеваемость") row += 1 col = 0 for student in group.student_set.all(): sheet.write(row, col, student.name) col += 1 for result in student.result_set.filter(task__subject_id=subject.id): sheet.write(row, col, result.rating) col += 1 sheet.write(row, col, my_tags.tasks(student, subject)) row += 1 col = 0 path = "groups/static/docs/spreadsheet-" + str(group.id) + "-" + str(subject.id) + ".xlsx" book.save(path) return path def create_xls(request, pk, id): group = Group.objects.get(id=pk) subject = group.subjects.get(id=id) path = create_xls_(group, subject) file = open(path, 'rb') response = HttpResponse(file, content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') response['Content-Disposition'] = 'attachment; filename=table.xlsx' return response def sending(request, pk, id): group = Group.objects.get(id=pk) students = group.student_set.all() emails = [student.email for student in students] email = EmailMessage( 'Результаты', 'Здравствуй, вот ваша успеваемость', settings.EMAIL_HOST_USER, emails ) path = create_xls_(group, Subject.objects.get(id=id)) email.attach_file(path) email.send() return HttpResponseRedirect(request.META.get('HTTP_REFERER'))

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755

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0.156291

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null

0

null

0

1

0

11a8090bef6d5fb982bc2e421b4aadbc73c27dfc

3,861

py

Python

src/tree/tree_builder.py

rpSebastian/LeducPoker

5bbdf61d885bcb23490410ef871de924c58bbf01

[ "MIT" ]

1

2020-05-22T15:45:22.000Z

src/tree/tree_builder.py

rpSebastian/LeducPoker

5bbdf61d885bcb23490410ef871de924c58bbf01

[ "MIT" ]

null

src/tree/tree_builder.py

rpSebastian/LeducPoker

5bbdf61d885bcb23490410ef871de924c58bbf01

[ "MIT" ]

1

2020-05-31T03:01:42.000Z

from settings import constants from game import bet_sizing, card_tools, card_to_string from base import Node import torch class PokerTreeBuilder(): def __init__(self): pass def build_tree(self, params): root = Node() root.street = params.root_node.street root.bets = params.root_node.bets.clone() root.current_player = params.root_node.current_player root.board = params.root_node.board.clone() root.board_string = params.root_node.board_string self.build_tree_dfs(root) return root def build_tree_dfs(self, current_node): current_node.pot = torch.min(current_node.bets).item() children = self.get_children_nodes(current_node) current_node.children = children for child in children: self.build_tree_dfs(child) def get_children_nodes(self, parent_node): if parent_node.terminal: return [] chance_node = parent_node.current_player == constants.players.chance if chance_node: return self.get_children_chance_nodes(parent_node) else: return self.get_children_player_nodes(parent_node) def get_children_chance_nodes(self, parent_node): children = [] next_boards = card_tools.get_second_round_boards() for board in next_boards: chance_node = Node(parent_node) chance_node.current_player = constants.players.P1 chance_node.street = parent_node.street + 1 chance_node.board = board chance_node.board_string = card_to_string.cards_to_string(board) chance_node.num_bets = 0 chance_node.action = chance_node.board_string children.append(chance_node) return children def get_children_player_nodes(self, parent_node): children = [] # fold action fold_node = Node(parent_node) fold_node.terminal = True fold_node.action = "fold" fold_node.node_type = constants.node_types.terminal_fold children.append(fold_node) # P1 start check action if (parent_node.current_player == constants.players.P1 and parent_node.bets[0] == parent_node.bets[1]): check_node = Node(parent_node) check_node.action = "check" children.append(check_node) # raise -> ( P1 / P2 call ) -> chance # P1 check -> (P2 check ) -> chance elif parent_node.street == 0 and ( parent_node.bets[0] != parent_node.bets[1] or parent_node.bets[0] == parent_node.bets[1] and parent_node.current_player == constants.players.P2 ): chance_node = Node(parent_node) chance_node.current_player = constants.players.chance chance_node.bets[:] = chance_node.bets.max() chance_node.action = "call" if parent_node.bets[0] != parent_node.bets[1] else "check" children.append(chance_node) # call -> terminal else: terminal_call_node = Node(parent_node) terminal_call_node.current_player = 1 - constants.players.P2 terminal_call_node.terminal = True terminal_call_node.node_type = constants.node_types.terminal_call terminal_call_node.bets[:] = terminal_call_node.bets.max() terminal_call_node.action = "call" children.append(terminal_call_node) # raise action possible_bets = bet_sizing.get_possible_bets(parent_node) for possible_bet in possible_bets: raise_node = Node(parent_node) raise_node.bets = possible_bet raise_node.num_bets += 1 raise_node.action = "raise" children.append(raise_node) return children tree_builder = PokerTreeBuilder()

38.227723

98

0.644911

474

3,861

4.934599

0.154008

0.111159

0.047884

0.046174

0.22018

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Python

src/htsql/core/tr/bind.py

sirex/htsql

52275f6a584b412c109822d2ed2a5e69ac522cdf

[ "Apache-2.0" ]

null

src/htsql/core/tr/bind.py

sirex/htsql

52275f6a584b412c109822d2ed2a5e69ac522cdf

[ "Apache-2.0" ]

null

src/htsql/core/tr/bind.py

sirex/htsql

52275f6a584b412c109822d2ed2a5e69ac522cdf

[ "Apache-2.0" ]

null

# # Copyright (c) 2006-2013, Prometheus Research, LLC # """ :mod:`htsql.core.tr.bind` ========================= This module implements the binding process. """ from ..util import maybe, listof, tupleof, similar from ..adapter import Adapter, Protocol, adapt, adapt_many from ..domain import (Domain, BooleanDomain, IntegerDomain, DecimalDomain, FloatDomain, UntypedDomain, EntityDomain, RecordDomain, ListDomain, IdentityDomain, VoidDomain) from ..classify import normalize from ..error import Error, translate_guard, choices_guard, point from ..syn.syntax import (Syntax, CollectSyntax, SelectSyntax, ApplySyntax, FunctionSyntax, PipeSyntax, OperatorSyntax, PrefixSyntax, ProjectSyntax, FilterSyntax, LinkSyntax, DetachSyntax, AttachSyntax, AssignSyntax, ComposeSyntax, LocateSyntax, IdentitySyntax, GroupSyntax, IdentifierSyntax, UnpackSyntax, ReferenceSyntax, LiftSyntax, StringSyntax, LabelSyntax, NumberSyntax, RecordSyntax, DirectSyntax) from .binding import (Binding, WrappingBinding, CollectBinding, RootBinding, HomeBinding, TableBinding, ChainBinding, ColumnBinding, QuotientBinding, KernelBinding, ComplementBinding, LocateBinding, SieveBinding, AttachBinding, SortBinding, CastBinding, IdentityBinding, ImplicitCastBinding, RescopingBinding, AssignmentBinding, DefineBinding, DefineReferenceBinding, DefineCollectionBinding, DefineLiftBinding, SelectionBinding, WildSelectionBinding, DirectionBinding, TitleBinding, RerouteBinding, ReferenceRerouteBinding, AliasBinding, LiteralBinding, FormulaBinding, VoidBinding, Recipe, LiteralRecipe, SelectionRecipe, FreeTableRecipe, AttachedTableRecipe, ColumnRecipe, KernelRecipe, ComplementRecipe, IdentityRecipe, ChainRecipe, SubstitutionRecipe, BindingRecipe, ClosedRecipe, PinnedRecipe, AmbiguousRecipe) from .lookup import (lookup_attribute, lookup_reference, lookup_complement, lookup_attribute_set, lookup_reference_set, expand, direct, guess_tag, identify, unwrap) from .signature import IsEqualSig, AndSig from .coerce import coerce from .decorate import decorate class BindingState(object): def __init__(self, root, environment=None): assert isinstance(root, RootBinding) # The root lookup scope. self.root = root # The current lookup scope. self.scope = root # The stack of previous lookup scopes. self.scope_stack = [] # References in the root scope. self.environment = environment if self.environment is not None: collection = {} for name, recipe in self.environment: name = normalize(name) collection[name] = recipe if collection: self.scope = DefineCollectionBinding( self.scope, collection, True, self.scope.syntax) def push_scope(self, scope): """ Sets the new lookup scope. This function stores the current scope in the stack and makes the given binding the new lookup scope. Use the :attr:`scope` attribute to get the current scope; :meth:`pop_scope` to restore the previous scope. `scope` (:class:`htsql.core.tr.binding.Binding`) The new lookup scope. """ # Sanity check on the argument. assert isinstance(scope, Binding) # Ensure that the root scope was set. assert self.root is not None # Save the current lookup scope. self.scope_stack.append(self.scope) # Assign the new lookup scope. self.scope = scope def pop_scope(self): """ Restores the previous lookup scope. This functions restores the previous lookup scope from the stack. Use the :attr:`scope` attribute to get the current scope; :meth:`push_scope` to change the current scope. """ # Restore the prevous lookup scope from the stack. self.scope = self.scope_stack.pop() def bind(self, syntax, scope=None): """ Binds the given syntax node using the current binding state. Returns a binding node. `syntax` (:class:`htsql.core.tr.syntax.Syntax`) The syntax node to bind. `scope` (:class:`htsql.core.tr.binding.Binding` or ``None``) If set, the lookup scope is set to `scope` when binding the syntax node. """ with translate_guard(syntax): if scope is not None: self.push_scope(scope) binding = Bind.__prepare__(syntax, self)() if scope is not None: self.pop_scope() return binding def use(self, recipe, syntax, scope=None): """ Applies a recipe to produce a binding node. Returns a binding node. `recipe` (:class:`htsql.core.tr.binding.Recipe`) The recipe to apply. `syntax` (:class:`htsql.core.tr.syntax.Syntax`) The syntax node associated with the recipe. `scope` (:class:`htsql.core.tr.binding.Binding` or ``None``) If set, the lookup scope is set to `scope` when binding the syntax node. """ # If passed, set the new lookup scope. if scope is not None: self.push_scope(scope) # Realize and apply `BindByRecipe` adapter. with translate_guard(syntax): binding = BindByRecipe.__invoke__(recipe, syntax, self) # Restore the old lookup scope. if scope is not None: self.pop_scope() # Return the generated binding node. return binding def call(self, syntax, scope=None): """ Binds a global function or a global identifier. Returns a binding node. `syntax` (:class:`htsql.core.tr.syntax.Syntax`) The syntax node to bind. `scope` (:class:`htsql.core.tr.binding.Binding` or ``None``) If set, the lookup context is set to `scope` when binding the syntax node. """ # If passed, set the new lookup scope. if scope is not None: self.push_scope(scope) # Realize and apply `BindByName` protocol. with translate_guard(syntax): binding = BindByName.__invoke__(syntax, self) # Restore the old lookup scope. if scope is not None: self.pop_scope() # Return the generated binding node. return binding class Bind(Adapter): """ Translates a syntax node to a binding node. This is an interface adapter; see subclasses for implementations. The binding process resolves identifiers against database objects, resolves and validates operators and function calls, and determine types of all expression. The :class:`Bind` adapter has the following signature:: Bind: (Syntax, BindingState) -> Binding The adapter is polymorphic on the `Syntax` argument. `syntax` (:class:`htsql.core.tr.syntax.Syntax`) The syntax node to bind. `state` (:class:`BindingState`) The current state of the binding process. """ adapt(Syntax) def __init__(self, syntax, state): assert isinstance(syntax, Syntax) assert isinstance(state, BindingState) self.syntax = syntax self.state = state def __call__(self): # The default implementation raises an error. It is actually # unreachable since we provide an implementation for all syntax nodes. raise Error("Unable to bind a node") def hint_choices(choices): # Generate a hint from a list of choices. assert isinstance(choices, listof(unicode)) if not choices: return None chunks = ["did you mean:"] if len(choices) == 1: chunks.append("'%s'" % choices[0].encode('utf-8')) else: chunks.append(", ".join("'%s'" % choice.encode('utf-8') for choice in choices[:-1])) chunks.append("or") chunks.append("'%s'" % choices[-1].encode('utf-8')) return " ".join(chunks) class BindCollect(Bind): adapt(CollectSyntax) def __call__(self): ## FIXME: an empty segment syntax should not be generated. #if self.syntax.arm is None: # raise Error("output columns are not specified", # self.syntax.mark) # Bind the segment expression. if self.syntax.arm is not None: seed = self.state.bind(self.syntax.arm) if isinstance(seed, AssignmentBinding): with translate_guard(seed): if len(seed.terms) != 1: raise Error("Qualified definition is not allowed" " for an in-segment assignment") if seed.parameters is not None: raise Error("Parameterized definition is not allowed" " for an in-segment assignment") name, is_reference = seed.terms[0] if is_reference: recipe = BindingRecipe(self.state.bind(seed.body)) else: recipe = SubstitutionRecipe(self.state.scope, [], None, seed.body) recipe = ClosedRecipe(recipe) syntax = seed.syntax if isinstance(syntax, AssignSyntax): syntax = syntax.larm seed = self.state.use(recipe, syntax) else: seed = self.state.scope seed = Select.__invoke__(seed, self.state) domain = ListDomain(seed.domain) return CollectBinding(self.state.scope, seed, domain, self.syntax) class Select(Adapter): adapt(Domain) @classmethod def __dispatch__(interface, binding, *args, **kwds): assert isinstance(binding, Binding) return (type(binding.domain),) def __init__(self, binding, state): self.binding = binding self.state = state def __call__(self): domain = coerce(self.binding.domain) if domain is None: # FIXME: separate implementation for VoidDomain with a better error # message. raise Error("Output column must be scalar") return ImplicitCastBinding(self.binding, domain, self.binding.syntax) class SelectRecord(Select): adapt_many(EntityDomain, RecordDomain) def __call__(self): recipes = expand(self.binding, with_syntax=True, with_wild=True, with_class=True) if recipes is None: return super(SelectRecord, self).__call__() elements = [] for syntax, recipe in recipes: element = self.state.use(recipe, syntax, scope=self.binding) element = Select.__invoke__(element, self.state) elements.append(element) fields = [decorate(element) for element in elements] domain = RecordDomain(fields) binding = SelectionBinding(self.binding, elements, domain, self.binding.syntax) return binding class SelectList(Select): adapt(ListDomain) def __call__(self): return self.binding class SelectIdentity(Select): adapt(IdentityDomain) def __call__(self): return self.binding class SelectUntyped(Select): adapt(UntypedDomain) def __call__(self): return self.binding class BindSelect(Bind): adapt(SelectSyntax) def __call__(self): scope = self.state.bind(self.syntax.larm) return self.state.bind(self.syntax.rarm, scope=scope) class BindRecord(Bind): adapt(RecordSyntax) def __call__(self): # Extract selector elements. elements = [] scope = self.state.scope self.state.push_scope(scope) for arm in self.syntax.arms: binding = self.state.bind(arm) # Handle in-selector assignments. if isinstance(binding, AssignmentBinding): with translate_guard(binding): if len(binding.terms) != 1: raise Error("Qualified definition is not allowed" " for an in-selector assignment") if binding.parameters is not None: raise Error("Parameterized definition is not allowed" " for an in-selector assignment") name, is_reference = binding.terms[0] if is_reference: recipe = BindingRecipe(self.state.bind(binding.body)) else: recipe = SubstitutionRecipe(scope, [], None, binding.body) recipe = ClosedRecipe(recipe) syntax = binding.syntax if isinstance(syntax, AssignSyntax): syntax = syntax.larm.larms[0] binding = self.state.use(recipe, syntax) if is_reference: scope = DefineReferenceBinding(scope, name, recipe, scope.syntax) else: scope = DefineBinding(scope, name, None, recipe, scope.syntax) self.state.pop_scope() self.state.push_scope(scope) # Extract nested selectors, if any. bindings = [] recipes = expand(binding, with_wild=True) if recipes is not None: seed = binding for syntax, recipe in recipes: binding = self.state.use(recipe, syntax) binding = RescopingBinding(binding, seed, binding.syntax) bindings.append(binding) else: bindings.append(binding) # Handle in-selector direction decorators. order = [] for binding in bindings: direction = direct(binding) if direction is not None: order.append(binding) if order: scope = SortBinding(scope, order, None, None, scope.syntax) self.state.pop_scope() self.state.push_scope(scope) elements.extend(bindings) self.state.pop_scope() # Generate a selection scope. fields = [decorate(element) for element in elements] domain = RecordDomain(fields) return SelectionBinding(scope, elements, domain, self.syntax) class BindApply(Bind): adapt(ApplySyntax) def __call__(self): # Look for the parameterized attribute in the current local scope. recipe = lookup_attribute(self.state.scope, self.syntax.name, len(self.syntax.arguments)) if recipe is not None: binding = self.state.use(recipe, self.syntax) # If not found, look for a global function. else: binding = self.state.call(self.syntax) return binding class BindOperator(Bind): adapt(OperatorSyntax) def __call__(self): # Look for the operator in the global scope. We skip the local scope # as there is no way to add an operator to a local scope. return self.state.call(self.syntax) class BindProject(Bind): adapt(ProjectSyntax) def __call__(self): # Get the seed of the quotient. seed = self.state.bind(self.syntax.larm) # get the kernel expressions. elements = [] binding = self.state.bind(self.syntax.rarm, scope=seed) recipes = expand(binding, with_syntax=True) if recipes is not None: for syntax, recipe in recipes: element = self.state.use(recipe, syntax, scope=binding) element = RescopingBinding(element, binding, element.syntax) elements.append(element) else: elements.append(binding) # Validate types of the kernel expressions. kernels = [] for element in elements: domain = coerce(element.domain) with translate_guard(element): if domain is None: raise Error("Expected a scalar column") kernel = ImplicitCastBinding(element, domain, element.syntax) kernels.append(kernel) # Generate the quotient scope. quotient = QuotientBinding(self.state.scope, seed, kernels, self.syntax) # Assign names to the kernel and the complement links when possible. binding = quotient name = guess_tag(seed) if name is not None: recipe = ComplementRecipe(quotient) recipe = ClosedRecipe(recipe) binding = DefineBinding(binding, name, None, recipe, self.syntax) for index, kernel in enumerate(kernels): name = guess_tag(kernel) if name is not None: recipe = KernelRecipe(quotient, index) recipe = ClosedRecipe(recipe) binding = DefineBinding(binding, name, None, recipe, self.syntax) return binding class BindFilter(Bind): adapt(FilterSyntax) def __call__(self): # Get the sieve base. base = self.state.bind(self.syntax.larm) # Bind the filter and force the Boolean type on it. filter = self.state.bind(self.syntax.rarm, scope=base) filter = ImplicitCastBinding(filter, coerce(BooleanDomain()), filter.syntax) # Produce a sieve scope. return SieveBinding(base, filter, self.syntax) class BindLink(Bind): adapt(LinkSyntax) def __call__(self): # Bind the origin images. origin_images = [] binding = self.state.bind(self.syntax.larm) recipes = expand(binding, with_syntax=True) if recipes is not None: for syntax, recipe in recipes: element = self.state.use(recipe, syntax) element = RescopingBinding(element, binding, element.syntax) origin_images.append(element) else: origin_images.append(binding) # Bind the target scope. home = HomeBinding(self.state.scope, self.syntax) seed = self.state.bind(self.syntax.rarm, scope=home) # Bind the target images; if not provided, reuse the syntax node # of the origin images. binding = seed target_images = [] recipes = expand(seed, with_syntax=True) if recipes is None: binding = self.state.bind(self.syntax.larm, scope=seed) recipes = expand(binding, with_syntax=True) if recipes is not None: for syntax, recipe in recipes: element = self.state.use(recipe, syntax, scope=seed) element = RescopingBinding(element, binding, element.syntax) target_images.append(element) else: target_images.append(binding) # Correlate origin and target images. if len(origin_images) != len(target_images): raise Error("Found unbalanced origin and target columns") images = [] for origin_image, target_image in zip(origin_images, target_images): domain = coerce(origin_image.domain, target_image.domain) if domain is None: raise Error("Cannot coerce origin and target columns" " to a common type") origin_image = ImplicitCastBinding(origin_image, domain, origin_image.syntax) target_image = ImplicitCastBinding(target_image, domain, target_image.syntax) images.append((origin_image, target_image)) # Generate a link scope. return AttachBinding(self.state.scope, seed, images, None, self.syntax) class BindAttach(Bind): adapt(AttachSyntax) def __call__(self): home = HomeBinding(self.state.scope, self.syntax) seed = self.state.bind(self.syntax.rarm, scope=home) recipe = BindingRecipe(seed) scope = self.state.scope scope = DefineLiftBinding(scope, recipe, self.syntax) name = guess_tag(seed) if name is not None: scope = DefineBinding(scope, name, None, recipe, self.syntax) condition = self.state.bind(self.syntax.larm, scope=scope) condition = ImplicitCastBinding(condition, coerce(BooleanDomain()), condition.syntax) return AttachBinding(self.state.scope, seed, [], condition, self.syntax) class BindDetach(Bind): adapt(DetachSyntax) def __call__(self): # Make the home scope. home = HomeBinding(self.state.scope, self.syntax) # Bind the operand against the home scope. return self.state.bind(self.syntax.arm, scope=home) class BindAssign(Bind): adapt(AssignSyntax) def __call__(self): # Parse the left side of the assignment. It takes one of the forms: # $reference := ... # identifier := ... # identifier(parameter,...) := ... # parent. ... .identifier(parameter,...) := ... # parent. ... .$identifier(parameter,...) := ... # The dot-separated names and reference indicators. terms = [] parameters = None syntax = self.syntax.larm for idx, arm in enumerate(syntax.larms): if isinstance(arm, ReferenceSyntax): with translate_guard(arm): if idx < len(syntax.larms)-1: raise Error("Expected an identifier") terms.append((arm.identifier.name, True)) else: terms.append((arm.name, False)) if syntax.rarms is not None: parameters = [] for arm in syntax.rarms: if isinstance(arm, ReferenceSyntax): parameters.append((arm.identifier.name, True)) else: parameters.append((arm.name, False)) # The right side of the assignment expression. body = self.syntax.rarm # Generate an assignment node. return AssignmentBinding(self.state.scope, terms, parameters, body, self.syntax) class BindCompose(Bind): adapt(ComposeSyntax) def __call__(self): # Expression: # parent . child # evaluates `child` in the scope of `parent`. scope = self.state.bind(self.syntax.larm) binding = self.state.bind(self.syntax.rarm, scope=scope) return binding class BindLocate(Bind): adapt(LocateSyntax) def __call__(self): seed = self.state.bind(self.syntax.larm) recipe = identify(seed) with translate_guard(seed): if recipe is None: raise Error("Cannot determine identity") identity = self.state.use(recipe, self.syntax.rarm, scope=seed) location = self.state.bind(self.syntax.rarm, scope=seed) with translate_guard(self.syntax.rarm): if identity.domain.width != location.width: raise Error("Found ill-formed locator") def convert(identity, elements): assert isinstance(identity, IdentityBinding) images = [] for field in identity.elements: if isinstance(field.domain, IdentityDomain): total_width = 0 items = [] while total_width < field.domain.width: assert elements element = elements.pop(0) if (total_width == 0 and isinstance(element, IdentityBinding) and element.width == field.domain.width): items = element.elements[:] total_width = element.width elif isinstance(element, IdentityBinding): items.append(element) total_width += element.width else: items.append(element) total_width += 1 with translate_guard(self.syntax.rarm): if total_width > field.domain.width: raise Error("Found ill-formed locator") images.extend(convert(field, items)) else: assert elements element = elements.pop(0) with translate_guard(self.syntax.larm): if isinstance(element, IdentityBinding): raise Error("Found ill-formed locator") item = ImplicitCastBinding(element, field.domain, element.syntax) images.append((item, field)) return images elements = location.elements[:] while len(elements) == 1 and isinstance(elements[0], IdentityBinding): elements = elements[0].elements[:] images = convert(identity, elements) return LocateBinding(self.state.scope, seed, images, None, self.syntax) class BindIdentity(Bind): adapt(IdentitySyntax) def __call__(self): elements = [] for arm in self.syntax.arms: element = self.state.bind(arm) identity = unwrap(element, IdentityBinding, is_deep=False) if identity is not None: element = identity elements.append(element) return IdentityBinding(self.state.scope, elements, self.syntax) class BindGroup(Bind): adapt(GroupSyntax) def __call__(self): # Bind the expression in parenthesis, then wrap the result # to attach the original syntax node. binding = self.state.bind(self.syntax.arm) return WrappingBinding(binding, self.syntax) class BindIdentifier(Bind): adapt(IdentifierSyntax) def __call__(self): # Look for the identifier in the current lookup scope. recipe = lookup_attribute(self.state.scope, self.syntax.name) if recipe is not None: binding = self.state.use(recipe, self.syntax) # If not found, try the global scope. else: binding = self.state.call(self.syntax) return binding class BindUnpack(Bind): adapt(UnpackSyntax) def __call__(self): # Get all public columns in the current lookup scope. recipes = expand(self.state.scope, with_syntax=True, with_wild=True, with_class=True, with_link=True) if recipes is None: raise Error("Cannot expand '*' since output columns" " are not defined") # If a position is given, extract a specific element. if self.syntax.index is not None: index = self.syntax.index index -= 1 if not (0 <= index < len(recipes)): raise Error("Expected value in range 1-%s" % len(recipes)) syntax, recipe = recipes[index] syntax = point(syntax, self.syntax) return self.state.use(recipe, syntax) # Otherwise, generate a selection node. elements = [] for syntax, recipe in recipes: syntax = point(syntax, self.syntax) element = self.state.use(recipe, syntax) elements.append(element) fields = [decorate(element) for element in elements] domain = RecordDomain(fields) return WildSelectionBinding(self.state.scope, elements, domain, self.syntax) class BindDirect(Bind): adapt(DirectSyntax) def __call__(self): base = self.state.bind(self.syntax.arm) direction = {u'+': +1, u'-': -1}[self.syntax.symbol] return DirectionBinding(base, direction, self.syntax) class BindReference(Bind): adapt(ReferenceSyntax) def __call__(self): # Look for a reference, complain if not found. recipe = lookup_reference(self.state.scope, self.syntax.identifier.name) if recipe is None: model = self.syntax.identifier.name.lower() names = lookup_reference_set(self.state.scope) choices = [u"$"+name for name in sorted(names) if similar(model, name)] with choices_guard(choices): raise Error("Found unknown reference", self.syntax) return self.state.use(recipe, self.syntax) class BindLift(Bind): adapt(LiftSyntax) def __call__(self): # Look for a complement, complain if not found. recipe = lookup_complement(self.state.scope) if recipe is None: raise Error("'^' could only be used in a quotient scope") return self.state.use(recipe, self.syntax) class BindString(Bind): adapt_many(StringSyntax, LabelSyntax) def __call__(self): # Bind a quoted literal. Note that a quoted literal not necessarily # represents a string value; its initial domain is untyped. binding = LiteralBinding(self.state.scope, self.syntax.text, UntypedDomain(), self.syntax) return binding class BindNumber(Bind): adapt(NumberSyntax) def __call__(self): # Bind an unquoted (numeric) literal. # Create an untyped literal binding. binding = LiteralBinding(self.state.scope, self.syntax.text, UntypedDomain(), self.syntax) # Cast the binding to an appropriate numeric type. if self.syntax.is_float: domain = coerce(FloatDomain()) elif self.syntax.is_decimal: domain = coerce(DecimalDomain()) elif self.syntax.is_integer: domain = coerce(IntegerDomain()) binding = ImplicitCastBinding(binding, domain, self.syntax) return binding class BindByName(Protocol): """ Binds a application node. This is an abstract protocol interface that provides a mechanism for name-based dispatch of application syntax nodes. The :class:`BindByName` interface has the following signature:: BindByName: (ApplicationSyntax, BindingState) -> Binding BindByName: (IdentifierSyntax, BindingState) -> Binding The protocol is polymorphic on the name and the number of arguments of the syntax node. To add an implementation of the interface, define a subclass of :class:`BindByName` and specify its name and expected number of arguments using function :func:`call`. Class attributes: `names` (a list of names or pairs `(name, length)`) List of names the component matches. Here `name` is a non-empty string, `length` is an integer or ``None``, where ``-1`` indicates any number of arguments, ``None`` means no arguments are accepted. """ names = [] @classmethod def __dominates__(component, other): # Determine if the component dominates another component # assuming that they match the same dispatch key. # A component implementing a protocol interface dominates # another component if one of the following two conditions # holds: # (1) The component is a subclass of the other component. if issubclass(component, other): return True # (2) The component and the other component match the # same name, but the former requires a fixed number of # arguments while the latter accepts a node with any # number of arguments. for name in component.__names__: arity = -1 if isinstance(name, tuple): name, arity = name name = name.lower() for other_name in other.__names__: other_arity = -1 if isinstance(other_name, tuple): other_name, other_arity = other_name other_name = other_name.lower() if name == other_name: if arity != -1 and other_arity == -1: return True return False @classmethod def __matches__(component, dispatch_key): # Check if the component matches the given function name # and the number of arguments. assert isinstance(dispatch_key, tupleof(unicode, maybe(int))) # The name and the number of arguments of the call node. key_name, key_arity = dispatch_key # We want to compare names case insensitive. Unfortunately, # we cannot use `normalize` from `htsql.core.tr.lookup` since it # mangles symbols. key_name = key_name.lower() # Check if any of the component names matches the given name. for name in component.__names__: # `name` could be either a string or a pair of a string # and an integer. The former assumes that the component # accepts call nodes with any number of arguments. arity = -1 if isinstance(name, tuple): name, arity = name name = name.lower() # Check if the component name matches the node name. if name == key_name: if ((arity == key_arity) or (arity == -1 and key_arity is not None)): return True # None of the names matched the dispatch key. return False @classmethod def __dispatch__(interface, syntax, *args, **kwds): assert isinstance(syntax, (ApplySyntax, IdentifierSyntax)) # We override `dispatch` since, as opposed to regular protocol # interfaces, we also want to take into account not only the # function name, but also the number of arguments. if isinstance(syntax, ApplySyntax): name = syntax.name arity = len(syntax.arguments) elif isinstance(syntax, IdentifierSyntax): name = syntax.name arity = None return (name, arity) def __init__(self, syntax, state): assert isinstance(syntax, (ApplySyntax, IdentifierSyntax)) assert isinstance(state, BindingState) self.syntax = syntax self.state = state # Extract commonly accessed attributes of the call node. if isinstance(syntax, ApplySyntax): self.name = syntax.name self.arguments = syntax.arguments elif isinstance(syntax, IdentifierSyntax): self.name = syntax.name self.arguments = None def __call__(self): # The default implementation; override in subclasses. # Generate a hint with a list of alternative names. model = self.name.lower() arity = None if self.arguments is not None: arity = len(self.arguments) attributes = lookup_attribute_set(self.state.scope) global_attributes = set() for component_name in BindByName.__catalogue__(): component_arity = -1 if isinstance(component_name, tuple): component_name, component_arity = component_name if isinstance(component_name, str): component_name = component_name.decode('utf-8') component_name = component_name.lower() global_attributes.add((component_name, component_arity)) all_attributes = sorted(attributes|global_attributes) choices = [] if not choices and arity is None: names = lookup_reference_set(self.state.scope) if model in names: choices = ["a reference '$%s'" % model.encode('utf-8')] if not choices and arity is None: if any(model == sample for sample, sample_arity in all_attributes if sample_arity is not None): choices = ["a function '%s'" % model.encode('utf-8')] if not choices and arity is None: choices = [sample for sample, sample_arity in all_attributes if sample_arity is None and sample != model and similar(model, sample)] if not choices and arity is not None \ and not isinstance(self.syntax, OperatorSyntax): arities = [sample_arity for sample, sample_arity in all_attributes if sample == model and sample_arity not in [None, -1, arity]] if arities: required_arity = [] arities.sort() if len(arities) == 1: required_arity.append(str(arities[0])) else: required_arity.append(", ".join(str(sample_arity) for sample_arity in arities[:-1])) required_arity.append("or") required_arity.append(str(arities[-1])) if required_arity[-1] == "1": required_arity.append("argument") else: required_arity.append("arguments") required_arity = " ".join(required_arity) raise Error("Function '%s' requires %s; got %s" % (self.syntax.identifier, required_arity, arity)) if not choices and arity is not None: if any(model == sample for sample, sample_arity in all_attributes if sample_arity is None): choices = ["an attribute '%s'" % model.encode('utf-8')] if not choices and arity is not None: choices = [sample for sample, sample_arity in all_attributes if sample_arity in [-1, arity] and sample != model and similar(model, sample)] scope_name = guess_tag(self.state.scope) if scope_name is not None: scope_name = scope_name.encode('utf-8') with choices_guard(choices): if isinstance(self.syntax, (FunctionSyntax, PipeSyntax)): raise Error("Found unknown function", self.syntax.identifier) if isinstance(self.syntax, OperatorSyntax): raise Error("Found unknown operator", self.syntax.symbol) if isinstance(self.syntax, PrefixSyntax): raise Error("Found unknown unary operator", self.syntax.symbol) if isinstance(self.syntax, IdentifierSyntax): raise Error("Found unknown attribute", "%s.%s" % (scope_name, self.syntax) if scope_name is not None else str(self.syntax)) class BindByRecipe(Adapter): """ Applies a recipe to generate a binding node. This is an abstract adapter that generates new binding nodes from binding recipes. The :class:`BindByRecipe` interface has the following signature:: BindByRecipe: (Recipe, Syntax, BindingState) -> Binding The adapter is polymorphic by the first argument. `recipe` (:class:`htsql.core.tr.binding.Recipe`) A recipe to apply. `syntax` (:class:`htsql.core.tr.syntax.Syntax`) The syntax node associated with the recipe. `state` (:class:`BindingState`) The current binding state. """ adapt(Recipe) def __init__(self, recipe, syntax, state): assert isinstance(recipe, Recipe) assert isinstance(syntax, Syntax) assert isinstance(state, BindingState) self.recipe = recipe self.syntax = syntax self.state = state def __call__(self): # The default implementation should not be reachable. raise Error("unable to bind a node") class BindByLiteral(BindByRecipe): adapt(LiteralRecipe) def __call__(self): return LiteralBinding(self.state.scope, self.recipe.value, self.recipe.domain, self.syntax) class BindBySelection(BindByRecipe): adapt(SelectionRecipe) def __call__(self): elements = [] for recipe in self.recipe.recipes: element = self.state.use(recipe, self.syntax) elements.append(element) fields = [decorate(element) for element in elements] domain = RecordDomain(fields) return SelectionBinding(self.state.scope, elements, domain, self.syntax) class BindByFreeTable(BindByRecipe): adapt(FreeTableRecipe) def __call__(self): # Produce a free table scope. return TableBinding(self.state.scope, self.recipe.table, self.syntax) class BindByAttachedTable(BindByRecipe): adapt(AttachedTableRecipe) def __call__(self): return ChainBinding(self.state.scope, self.recipe.joins, self.syntax) class BindByColumn(BindByRecipe): adapt(ColumnRecipe) def __call__(self): # Generate a link associated with the column. link = None if self.recipe.link is not None: link = self.state.use(self.recipe.link, self.syntax) # Produce a column scope. return ColumnBinding(self.state.scope, self.recipe.column, link, self.syntax) class BindByKernel(BindByRecipe): adapt(KernelRecipe) def __call__(self): # Generate a kernel expression of a quotient scope. return KernelBinding(self.state.scope, self.recipe.quotient, self.recipe.index, self.syntax) class BindByComplement(BindByRecipe): adapt(ComplementRecipe) def __call__(self): # Generate a complement link to a quotient scope. return ComplementBinding(self.state.scope, self.recipe.quotient, self.syntax) class BindByIdentity(BindByRecipe): adapt(IdentityRecipe) def __call__(self): elements = [self.state.use(recipe, self.syntax) for recipe in self.recipe.elements] return IdentityBinding(self.state.scope, elements, self.syntax) class BindBySubstitution(BindByRecipe): adapt(SubstitutionRecipe) def __call__(self): # Bind the given syntax node in place of an identifier # or a function call. # Check if the recipe has a qualifier. if self.recipe.terms: # Find the same identifier in the base scope. assert isinstance(self.syntax, IdentifierSyntax) name, is_reference = self.recipe.terms[0] arity = None if (len(self.recipe.terms) == 1 and self.recipe.parameters is not None): arity = len(self.recipe.parameters) recipe = lookup_attribute(self.recipe.base, self.syntax.name) if recipe is None: raise Error("Found unknown attribute", self.syntax) binding = self.state.use(recipe, self.syntax) # Check if the term is a reference. if is_reference: # Must the the last term in the assignment. assert len(self.recipe.terms) == 1 # Bind the reference against the scope where it is defined. body = self.state.bind(self.recipe.body, scope=binding) recipe = BindingRecipe(body) # Augment the scope with the tail of the recipe. else: recipe = SubstitutionRecipe(binding, self.recipe.terms[1:], self.recipe.parameters, self.recipe.body) recipe = ClosedRecipe(recipe) if is_reference: binding = DefineReferenceBinding(binding, name, recipe, self.syntax) else: binding = DefineBinding(binding, name, arity, recipe, self.syntax) return binding # Otherwise, bind the syntax node associated with the recipe. # Bind against the current scope, but route all lookup requests # to the scope where the recipe was defined. scope = self.state.scope scope = RerouteBinding(scope, self.recipe.base, scope.syntax) # Bind the parameters. if self.recipe.parameters is not None: assert isinstance(self.syntax, ApplySyntax) assert len(self.syntax.arguments) == len(self.recipe.parameters) for (name, is_reference), syntax in zip(self.recipe.parameters, self.syntax.arguments): binding = self.state.bind(syntax) recipe = BindingRecipe(binding) recipe = ClosedRecipe(recipe) if is_reference: scope = DefineReferenceBinding(scope, name, recipe, scope.syntax) else: scope = DefineBinding(scope, name, None, recipe, scope.syntax) # Bind the syntax node associated with the recipe. binding = self.state.bind(self.recipe.body, scope=scope) # Hide all referenced defined there. binding = ReferenceRerouteBinding(binding, self.state.scope, binding.syntax) return binding class BindByBinding(BindByRecipe): adapt(BindingRecipe) def __call__(self): return self.recipe.binding class BindByClosed(BindByRecipe): adapt(ClosedRecipe) def __call__(self): # Generate a binding from the given recipe. binding = self.state.use(self.recipe.recipe, self.syntax) # Force the current syntax node to the binding. return AliasBinding(binding, self.syntax) class BindByChain(BindByRecipe): adapt(ChainRecipe) def __call__(self): binding = self.state.scope for recipe in self.recipe.recipes: binding = self.state.use(recipe, self.syntax, scope=binding) return binding class BindByPinned(BindByRecipe): adapt(PinnedRecipe) def __call__(self): # Bind the given recipe in the specified scope. binding = self.state.use(self.recipe.recipe, self.syntax, scope=self.recipe.scope) return binding class BindByAmbiguous(BindByRecipe): adapt(AmbiguousRecipe) def __call__(self): syntax = self.syntax if isinstance(self.syntax, (FunctionSyntax, PipeSyntax)): syntax = self.syntax.identifier int = None choices = [] if self.recipe.alternatives: choices = [str(alternative) for alternative in self.recipe.alternatives] with choices_guard(choices): raise Error("Found ambiguous name", syntax) def bind(syntax, environment=None): recipes = [] if environment is not None: for name in sorted(environment): value = environment[name] if value.data is None: recipe = LiteralRecipe(value.data, value.domain) elif isinstance(value.domain, ListDomain): item_recipes = [LiteralRecipe(item, value.domain.item_domain) for item in value.data] recipe = SelectionRecipe(item_recipes) elif isinstance(value.domain, RecordDomain): item_recipes = [LiteralRecipe(item, profile.domain) for item, profile in zip(value.data, value.domain.fields)] recipe = SelectionRecipe(item_recipes) elif isinstance(value.domain, IdentityDomain): def convert(domain, data): items = [] for element, item in zip(domain.labels, data): if isinstance(element, IdentityDomain): item = convert(element, item) else: item = LiteralRecipe(item, element) items.append(item) return IdentityRecipe(items) recipe = convert(value.domain, value.data) else: recipe = LiteralRecipe(value.data, value.domain) recipes.append((name, recipe)) root = RootBinding(syntax) state = BindingState(root, recipes) if isinstance(syntax, AssignSyntax): specifier = syntax.larm with translate_guard(specifier): if specifier.identifier is None: raise Error("Expected an identifier") identifier = specifier.larms[0] binding = state.bind(syntax.rarm) binding = Select.__invoke__(binding, state) binding = TitleBinding(binding, identifier, binding.syntax) else: binding = state.bind(syntax) binding = Select.__invoke__(binding, state) return binding

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null

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11ad8fe6bba3193be56826f292aa054b4c5199e3

2,226

py

Python

locuszoom_plotting_service/gwas/tests/factories.py

statgen/locuszoom-hosted

ecfcc5f48fefe2869ab277202a661c2575af6abb

[ "MIT" ]

null

locuszoom_plotting_service/gwas/tests/factories.py

statgen/locuszoom-hosted

ecfcc5f48fefe2869ab277202a661c2575af6abb

[ "MIT" ]

14

2021-01-01T17:16:23.000Z

2022-02-28T19:37:28.000Z

locuszoom_plotting_service/gwas/tests/factories.py

statgen/locuszoom-hosted

ecfcc5f48fefe2869ab277202a661c2575af6abb

[ "MIT" ]

null

import os import random from django.db.models import signals from django.utils import timezone import factory from factory.django import DjangoModelFactory from locuszoom_plotting_service.users.tests.factories import UserFactory from .. import constants as lz_constants from .. import models as lz_models def choose_genome_build() -> str: return random.choice(lz_constants.GENOME_BUILDS)[0] def choose_consortium() -> str: return random.choice(['LocusZoom JS', 'LocusZoom Standalone', 'LocusZoom Hosted', 'LocusZoom.org']) @factory.django.mute_signals(signals.post_save) class AnalysisFilesetFactory(DjangoModelFactory): raw_gwas_file = None # Only create temp files if has_data trait is True ingest_status = 0 # pending (most tests don't run celery tasks, and therefore are "pending" processing) ingest_complete = None parser_options = factory.Dict({ # Parser options for standard gwas format 'chrom_col': 1, 'pos_col': 2, 'ref_col': 3, 'alt_col': 4, 'pvalue_col': 5, 'is_neg_log_pvalue': False }) class Meta: model = lz_models.AnalysisFileset class Params: # Most samples will be fine with a 0B file. Only provide actual data if explicitly requested. has_data = factory.Trait( raw_gwas_file=factory.django.FileField( from_path=os.path.join(os.path.dirname(__file__), 'fixtures/placeholder.txt')) ) has_completed = factory.Trait( # Marks pipeline complete (without actually running it) ingest_complete=timezone.now(), ingest_status=2 ) class AnalysisInfoFactory(DjangoModelFactory): owner = factory.SubFactory(UserFactory) label = factory.Faker('sentence', nb_words=2) study_name = factory.LazyFunction(choose_consortium) files = factory.SubFactory(AnalysisFilesetFactory) build = factory.LazyFunction(choose_genome_build) is_public = False class Meta: model = lz_models.AnalysisInfo class ViewLinkFactory(DjangoModelFactory): label = factory.Faker('sentence', nb_words=2) gwas = factory.SubFactory(AnalysisInfoFactory) class Meta: model = lz_models.ViewLink

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null

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null

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1

0

11b627ad398f9ae3625b734210d1a5d1347b9bf2

1,700

py

Python

pantofola_search/management/commands/_private.py

phingage/pantofola.io

f41036d2e568a45f328e2a7ca81d76a27cd134dc

[ "WTFPL" ]

1

2018-06-09T22:20:00.000Z

pantofola_search/management/commands/_private.py

phingage/pantofola.io

f41036d2e568a45f328e2a7ca81d76a27cd134dc

[ "WTFPL" ]

4

2020-02-11T22:01:16.000Z

2021-06-10T17:38:56.000Z

pantofola_search/management/commands/_private.py

phingage/pantofola.io

f41036d2e568a45f328e2a7ca81d76a27cd134dc

[ "WTFPL" ]

null

from pantofola_search.models import * from pantofola_search.tools.imdb_fetcher import ImdbFetcher def update_new_movie_info(clean_title, imdb_id, torrent, is_imdb=False): my_imdb = ImdbFetcher() if not Movie.objects.filter(pk=imdb_id).exists(): # #[imdb_id,year,max_ratio,[titles[1]]] movie_info = my_imdb.query_movie_info(imdb_id, clean_title) movie = Movie(imdb_id=movie_info[0], year=movie_info[1], original_title=movie_info[3][0]) movie.save() for aka in movie_info[3][1]: movie.title_set.create(title=aka) for forg in movie_info[3][2]: movie.foreigntitle_set.create(title=forg[0], language=forg[1]) max_ratio = movie_info[2] # print movie_info, tags, lang_tag else: movie = Movie.objects.get(pk=imdb_id) score_title = [movie.original_title] for aka_q in movie.title_set.all(): score_title.append(aka_q.title) max_ratio = my_imdb.compute_score(clean_title, score_title) alarm_ratio = False if float(max_ratio) < 0.5 and not is_imdb: alarm_ratio = True torrent.movie = movie torrent.score = max_ratio torrent.broken = alarm_ratio # torrent.ready_to_recheck = False if is_imdb: #torrent.sanitized_name = movie.original_title torrent.score = 1 torrent.broken = False torrent.save() def check_for_title_in_db(clean_title): t_e = Torrent.objects.filter(sanitized_name__exact=clean_title, broken=False, ready_to_recheck=False).first() if t_e: return t_e.movie.imdb_id else: return None

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0

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false

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0.157895

0

null

0

null

0

1

0

11b6a22d0d9d730ae6441343ec296d67f55adf10

7,663

py

Python

ArcLint.py

namur007/ArcLint

b17b39cf7fdfeff144339b6f3494d9120eafde90

[ "MIT" ]

null

ArcLint.py

namur007/ArcLint

b17b39cf7fdfeff144339b6f3494d9120eafde90

[ "MIT" ]

4

2020-07-17T18:11:54.000Z

2020-07-26T12:34:57.000Z

ArcLint.py

namur007/ArcLint

b17b39cf7fdfeff144339b6f3494d9120eafde90

[ "MIT" ]

null

import json import re import datetime import os import arcpy regex_flag_dict = { # 'ASCII' re.A, # this is py3 only so wont work in arcgis desktop 'IGNORECASE': re.I, 'LOCALE': re.L, "MULTILINE": re.M, "DOTMATCH": re.S, "UNICODE": re.U, "VERBOSE": re.X, } def main(json_path, feature, output_location=None, output_file_name=None): output_file = format_output_file(output_location, output_file_name) start_time = datetime.datetime.now() start_str = start_time.strftime("%Y-%m-%d %H:%M:%S") json_obj = read_json(json_path) rule_data = compile_rules(json_obj) results = _arc_process(rule_data, feature) save_json(format_results(results, start_str), output_file) def format_output_file(output_location, output_file_name): if output_location is None: output_location = "" if output_file_name is None: output_file_name = "results.json" if not output_file_name.endswith(".json"): output_file_name += ".json" return os.path.join(output_location, output_file_name) def read_json(_json_path): js = None with open(_json_path, 'r') as fl: js = json.loads(fl.read()) return js def save_json(_json_data, output_file): with open(output_file, 'w') as fl: fl.write(json.dumps(_json_data)) def format_results(rule_data, _datetime_str): # format fields # format groups out_fields = {} out_groups = {} for field in rule_data['Fields']: field_result = [] for rule in rule_data['Fields'][field]: if not rule['output']: continue field_result.append({ "ruleName": rule['ruleName'], "errorIDs": rule['result'] }) if len(field_result) == 0: continue out_fields[field] = field_result for group in rule_data['Groups']: out_groups[group] = { "errorIDs": rule_data['Groups'][group]['result'], 'description': rule_data['Groups'][group]['description'] } result = { "run_datetime": _datetime_str, "fields": out_fields, "groups": out_groups, } return result def _arc_process(rule_data, feature): """ impure function as i am modifying the rule_data input = { "Rules": rule_dict, "Fields": field_dict, "Groups": group_dict } returns dictionary of the rules""" fields = [field for field in rule_data['Fields']] with arcpy.da.SearchCursor(feature, ["OID@"] + fields) as sc: for row in sc: _id = row[0] for ix, value in enumerate(row[1:]): field_rules = rule_data['Fields'][fields[ix]] # append ID to each rule if they test = False [rule['result'].append(_id) for rule in field_rules if rule['rule'](value)] for group_name in rule_data['Groups']: group = rule_data['Groups'][group_name] group_func = any if group.get('match') == 'any' else all group_result = group_func([True if _id in r['result'] else False for r in group['rules']]) if group_result == True: group['result'].append(_id) return rule_data # region Linters def regex_lint(value, _regex): # if regex is good, return true, else return false if len(_regex.findall(str(value))) > 0: return True else: return False def range_lint(value, firstValue, secondValue, outside): lv = min(firstValue, secondValue) mx = max(firstValue, secondValue) result = True if value >= lv and value <= mx else False result = not result if outside else result return result # region builders def compile_rules(json_obj): rule_dict = _compile_global_rules(json_obj) field_dict = _compile_field_rules(json_obj, rule_dict) group_dict = _compile_group_rules(json_obj, field_dict) return { "Rules": rule_dict, "Fields": field_dict, "Groups": group_dict } def _compile_global_rules(json_obj): """ returns rule name is either global_RULENAME for global or fieldname_RULENAME for field specific ones { rule_name: rule_function > str: function } """ rule_dict = {} for rule in json_obj.get('globalRules', []): rule_name = rule.get('ruleName', '').upper() nm = 'global_{}'.format(rule_name) f = _parse_rule(rule) rule_dict[nm] = f return rule_dict def _compile_field_rules(json_obj, rule_dict): """ returns: { FieldName > str: { 'result': [] > str: list, 'ruleName': ruleName > str: str, 'rule': rule_dict[fieldname_rule_name] > str: function, } } """ field_dict = {} for field in json_obj.get('fields', []): field_rules = [] field_name = field.get('fieldName') for rule in field.get('rules', []): rule_name = rule.get('ruleName', '').upper() rule_type = rule.get('type') output_rule = rule.get('output', True) nm = None if rule_type is None and 'global_{}'.format(rule_name) in rule_dict: nm = 'global_{}'.format(rule_name) else: nm = '{}_{}'.format(field_name, rule_name) rule_dict[nm] = _parse_rule(rule) field_rules.append({ 'result': [], 'ruleName': rule_name, 'rule': rule_dict[nm], 'output': output_rule }) field_dict[field_name] = field_rules return field_dict def _compile_group_rules(json_obj, field_dict): """ rules are the address to the rule from the field dictionary. when updating the result in the field results, should be available here returns { group_name: { "result": [], # array of ids with errors, "match": "all" or "any", # type of match to test for "rules": [group_rules], # array of the rules for this group } } """ group_dict = {} for group in json_obj.get("ruleGroups", []): group_name = group.get("groupName", "") match_type = group.get("match", "") group_rules = [] for rule in group.get("rules", []): f = rule.get("fieldName") rn = rule.get("ruleName","").upper() group_rules += [r for r in field_dict[f] if r['ruleName']==rn] group_dict[group_name] = { "result": [], "match": match_type, "rules": group_rules, "description": group.get('description', '') } return group_dict def _parse_rule(rule): func_dct = { 'regex': _parse_regex, 'range': _parse_range } return func_dct[rule.get('type')](rule) # region parse rules def _parse_regex(rule): _pattern = rule.get('pattern') flags = rule.get('flags', []) pat_flags = 0 for f in flags: if f is None: continue pat_flags |= regex_flag_dict.get(f.upper(), 0) _regex = re.compile(_pattern, pat_flags) def f(x): return regex_lint(x, _regex) return f def _parse_range(rule): f_value = rule.get('fromValue') s_value = rule.get('toValue') outside = rule.get('outside', False) def f(x): return range_lint(x, f_value, s_value, outside) return f if __name__ == "__main__": feat = r"C:\Users\scody\Desktop\ArcPro Model\AllPipes2020\Data\ModelNetwork.gdb\facility_junction" main('facil_jct.json', feat)

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1

0

11b7d8f84ea9074863867abdbc15c4a61c060614

1,710

py

Python

files/persona_dao.py

DaletWolff/Curso_postgresql

a9d716236b1a840f104c98a4982eab9b1ad641ba

[ "Unlicense" ]

null

files/persona_dao.py

DaletWolff/Curso_postgresql

a9d716236b1a840f104c98a4982eab9b1ad641ba

[ "Unlicense" ]

null

files/persona_dao.py

DaletWolff/Curso_postgresql

a9d716236b1a840f104c98a4982eab9b1ad641ba

[ "Unlicense" ]

null

from persona import Persona from logger_base import log from cursor import Cursor class PersonaDAO: _SELECCIONAR = 'SELECT * FROM persona ORDER BY id_persona' _INSERTAR = 'INSERT INTO persona(nombre, apellido, email) VALUES(%s, %s, %s)' _ACTUALIZAR = 'UPDATE persona SET nombre=%s, apellido=%s, email=%s WHERE id_persona=%s' _ELIMINAR = 'DELETE FROM persona WHERE id_persona=%s' @classmethod def seleccionar(cls): with Cursor() as cursor: cursor.execute(cls._SELECCIONAR) registros = cursor.fetchall() personas = [] for registro in registros: persona = Persona(registro[0], registro[1], registro[2], registro[3]) personas.append(persona) return personas @classmethod def insertar(cls, persona): with Cursor() as cursor: valores = (persona.nombre, persona.apellido, persona.email) cursor.execute(cls._INSERTAR, valores) log.debug(f"Persona insertada: {persona}") return cursor.rowcount @classmethod def actualizar(cls, persona): with Cursor() as cursor: valores = (persona.nombre, persona.apellido, persona.email, persona.id_persona) cursor.execute(cls._ACTUALIZAR, valores) log.debug(f'Persona actualizada: {persona}') return cursor.rowcount @classmethod def eliminar(cls, persona): with Cursor() as cursor: valores = (persona.id_persona,) cursor.execute(cls._ELIMINAR, valores) log.debug(f'Persona eliminada: {persona}') return cursor.rowcount

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1,710

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null

0

null

0

1

0

11c058f314fcdf27f630e4e67e934c957629b5a4

1,000

py

Python

pype9/cmd/convert.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

[ "MIT" ]

null

pype9/cmd/convert.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

[ "MIT" ]

null

pype9/cmd/convert.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

[ "MIT" ]

1

2021-04-08T12:46:21.000Z

""" Tool to convert 9ML files between different supported formats (e.g. XML_, JSON_, YAML_) and 9ML versions. """ from argparse import ArgumentParser from pype9.utils.arguments import nineml_document from pype9.utils.logging import logger def argparser(): parser = ArgumentParser(prog='pype9 convert', description=__doc__) parser.add_argument('in_file', type=nineml_document, help="9ML file to be converted") parser.add_argument('out_file', help="Converted filename") parser.add_argument('--nineml_version', '-v', type=str, default=None, help="The version of nineml to output") return parser def run(argv): args = argparser().parse_args(argv) doc = args.in_file.clone() kwargs = {} if args.nineml_version is not None: kwargs['version'] = args.nineml_version doc.write(args.out_file, **kwargs) logger.info("Converted '{}' to '{}'".format(args.in_file, args.out_file))

33.333333

77

0.664

127

1,000

5.055118

0.503937

0.042056

0.079439

0

0.007673

0.218

1,000

29

78

34.482759

0.813299

0.105

0

0.166855

0

1

0.1

false

0

0.15

0

0.3

0

null

0

null

0

1

0

11c365d4ccc71a94837656d754364a0fe60f8958

3,615

py

Python

Tools/MakeHDF.py

Kadantte/VideoSuperResolution

4c86e49d81c7a9bea1fe0780d651afc126768df3

[ "MIT" ]

1,447

2018-06-04T08:44:07.000Z

2022-03-29T06:19:10.000Z

Tools/MakeHDF.py

Evergreengyq/VideoSuperResolution

1d0c54fafaf7a02f0d69408502f90c55f0f76536

[ "MIT" ]

96

2018-08-29T01:02:45.000Z

2022-01-12T06:00:01.000Z

Tools/MakeHDF.py

Evergreengyq/VideoSuperResolution

1d0c54fafaf7a02f0d69408502f90c55f0f76536

[ "MIT" ]

307

2018-06-26T13:35:54.000Z

2022-01-21T09:01:54.000Z

# Copyright (c): Wenyi Tang 2017-2019. # Author: Wenyi Tang # Email: wenyi.tang@intel.com # Update Date: 2019/4/3 下午5:03 import argparse import time from pathlib import Path import h5py import numpy as np import tqdm from PIL import Image __all__ = ["gather_videos_vqp", "gather_videos", "print_dataset"] parser = argparse.ArgumentParser(description="Make HDF5 datasets") parser.add_argument("input_dir", help="path of the input root folder.") parser.add_argument("-o", "--output", help="output hdf file path.") parser.add_argument("-a", "--append", action='store_true') parser.add_argument("-t", "--task_name", choices=__all__, help="task name") parser.add_argument("--compression", type=int, default=None) parser.add_argument("--glob", help="glob pattern to gather files inside input." "For recursively glob, use **/*.") parser.add_argument("--data_format", choices=('channels_first', 'channels_last'), default='channels_first', help="data format (default: CHW)") FLAGS, args = parser.parse_known_args() def make_hdf_header(): if FLAGS.output: if FLAGS.append: fd = h5py.File(FLAGS.output, 'a') else: fd = h5py.File(FLAGS.output, 'w') fd.attrs['author'] = 'LoSealL' fd.attrs['email'] = 'wenyi.tang@intel.com' fd.attrs['date'] = time.strftime("%Y-%m-%d") fd.attrs['data_format'] = FLAGS.data_format return fd def flush_hdf(fd: h5py.File): if isinstance(fd, h5py.File): fd.close() def gather_videos_vqp(fd: h5py.File): """Specified for VQP""" root = Path(FLAGS.input_dir) glob = FLAGS.glob or '*' inputs = sorted(root.glob(glob)) candidates = set(i.parent for i in filter(lambda f: f.is_file(), inputs)) frames_info = {} for p in tqdm.tqdm(candidates): seq = [Image.open(f) for f in filter(lambda f: f.is_file(), sorted(p.rglob('*')))] cube = np.stack(seq) if FLAGS.data_format == 'channels_first': cube = cube.transpose([0, 3, 1, 2]) cube = np.expand_dims(cube, 0) path = p.relative_to(root) # ugly path = path.parent / path.stem.split('_')[0] key = str(path.as_posix()) if not key in fd: fd.create_dataset(key, data=cube, maxshape=(52,) + cube.shape[1:], compression=FLAGS.compression) frames_info[key] = len(seq) else: d = fd[key] cnt = d.shape[0] + 1 d.resize(cnt, 0) d[-1] = cube del cube def gather_videos(fd: h5py.File): """Gather videos. Video is defined in a folder containing sequential images.""" root = Path(FLAGS.input_dir) glob = FLAGS.glob or '*' inputs = sorted(root.glob(glob)) candidates = set(i.parent for i in filter(lambda f: f.is_file(), inputs)) frames_info = {} for p in tqdm.tqdm(candidates): seq = [Image.open(f) for f in filter(lambda f: f.is_file(), sorted(p.rglob('*')))] cube = np.stack(seq) if FLAGS.data_format == 'channels_first': cube = cube.transpose([0, 3, 1, 2]) path = p.relative_to(root) key = str(path.as_posix()) fd.create_dataset(key, data=cube, compression=FLAGS.compression) frames_info[key] = len(seq) del cube fd.attrs['frames_info'] = list(frames_info.items()) def print_dataset(*args): def _print(name, obj): print(f"key: [{name}], shape: {obj.shape}") fd = Path(FLAGS.input_dir) if fd.exists(): with h5py.File(str(fd), 'r') as fd: fd.visititems(_print) def main(): fd = make_hdf_header() globals()[FLAGS.task_name](fd) flush_hdf(fd) if __name__ == '__main__': main()

30.125

81

0.634302

526

3,615

4.214829

0.302281

0.028417

0.053676

0.027064

0.372576

0.301308

0.277853

0.236355

0

0.015009

0.207469

3,615

119

82

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0.758813

0.059198

0

0.347826

0

0.145895

0

1

0.076087

false

0

0.076087

0

0.163043

0.054348

0

null

0

null

0

1

0

11c45856fc39f00ce8b427bda4629a69a7f9c3b7

1,480

py

Python

modules/ddg_appwv_cookies.py

ItWasDNS/DDG-Parser

fd63099df7b93a603b9fe2ae4259c232f0555a65

[ "MIT" ]

null

modules/ddg_appwv_cookies.py

ItWasDNS/DDG-Parser

fd63099df7b93a603b9fe2ae4259c232f0555a65

[ "MIT" ]

null

modules/ddg_appwv_cookies.py

ItWasDNS/DDG-Parser

fd63099df7b93a603b9fe2ae4259c232f0555a65

[ "MIT" ]

null

""" Process 'com.duckduckgo.mobile.android/app_webview/Cookies' """ import os import sqlite3 from modules.helpers.ddg_path_handler import process_directory_paths query_cookies = """ SELECT host_key, path, name, value, creation_utc, last_access_utc, expires_utc, secure, httponly, persistent, encrypted_value FROM cookies; """ cookies_template = """-- Host: %s Path: %s Cookie Name: %s Cookie Value: %s Cookie Creation: %s Cookie Expiration: %s """ def process_appwv_cookies(duckduckgo_path, output_path): """ Process DDG 'Cookies' database """ with open(os.path.join(output_path, 'appwv_cookies_output.txt'), 'w') as o: o.write("Processed: 'com.duckduckgo.mobile.android/app_webview/Cookies'\n") try: conn = sqlite3.connect(duckduckgo_path + 'app_webview/Cookies') answer = conn.execute(query_cookies).fetchall() conn.close() except sqlite3.OperationalError as e: o.write("Error: %s" % str(e)) return None if len(answer) == 0: o.write("No Cookies Found in app_webview/Cookies") return None for result in answer: o.write(cookies_template % (result[0], result[1], result[2], result[3], result[4], result[5])) if __name__ == '__main__': # Set DDG application data path for testing ddg_path, out_path = process_directory_paths() # Process artifacts process_appwv_cookies(ddg_path, out_path)

25.964912

107

0.663514

190

1,480

4.947368

0.452632

0.042553

0.07234

0.055319

0.091489

0

0.008651

0.218919

1,480

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false

0

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0.136364

0

null

0

null

0

1

0

11c4b04fb594071b02b7ee34e2b0b343fa536a12

3,382

py

Python

scripts/redis_performance_test.py

Robbybp/IDAES-CLC

5498aeab070afe5f3dc57be4cd198250f0f88ff9

[ "MIT" ]

null

scripts/redis_performance_test.py

Robbybp/IDAES-CLC

5498aeab070afe5f3dc57be4cd198250f0f88ff9

[ "MIT" ]

1

2021-06-01T23:42:14.000Z

scripts/redis_performance_test.py

Robbybp/IDAES-CLC

5498aeab070afe5f3dc57be4cd198250f0f88ff9

[ "MIT" ]

null

""" A simple and short Redis performance test. """ __author__ = 'Dan Gunter <dkgunter@lbl.gov>' __date__ = '8/8/16' import argparse import logging import os import redis import subprocess import sys import time _log = logging.getLogger(__name__) _h = logging.StreamHandler() _h.setFormatter(logging.Formatter('%(asctime)s %(levelname)10s - %(message)s')) _log.addHandler(_h) def run_server(binpath=None): _log.info("Run Redis server") if binpath: server_cmd = os.path.join(binpath, 'redis-server') else: server_cmd = 'redis-server' retcode = subprocess.Popen([server_cmd]) return retcode def run_performance_test(cmd='set', num_items=1000, list_len=5): _log.info("Run Performance test") r = redis.StrictRedis(host='localhost', port=6379, db=0) data = ['bar'] * list_len if cmd == 'set': t0 = time.time() t1 = redis_set(r, num_items, data) elif cmd == 'get': redis_set(r, num_items, data) t0 = time.time() t1 = redis_get(r, num_items) elif cmd == 'mix': t0 = time.time() t1 = redis_getset(r, num_items, data) else: _log.error('Bad command: {}'.format(cmd)) return report_timing(True, cmd, t1 - t0, num_items, ['list-length'], ['{}'.format(list_len)]) def redis_set(r, num_items, data): i = 0 while i < num_items: key = 'foo' + str(i) r.set(key, data) i += 1 return time.time() def redis_get(r, num_items): i = 0 while i < num_items: key = 'foo' + str(i) data = r.get(key) i += 1 return time.time() def redis_getset(r, num_items, data): i = 0 while i < num_items: key = 'foo' + str(i) r.set(key, data) data2 = r.get(key) i += 1 return time.time() def report_timing(readable, mode, dt, n, info_hdr, info): rate = 1. * n / dt gap = 1. * dt / n if readable: kvp = ', '.join(['{}={}'.format(k, v) for k, v in zip(info_hdr, info)]) print("{}: Processed {:d} items in {:.3f} seconds: {:.1f} items/sec <-> {:.6f} seconds/item. {}" .format(mode, n, dt, rate, gap, kvp)) else: print('blah') def verbose_add(parser): """Add a verbosity argument to an ArgumentParser. """ parser.add_argument('-v', '--verbose', dest='vb', action='count', default=0) def verbose_set_log(vb, log): """Set logging level from verbosity level. """ if vb >= 2: log.setLevel(logging.DEBUG) elif vb >= 1: log.setLevel(logging.INFO) else: log.setLevel(logging.WARN) def main(): parser = argparse.ArgumentParser() parser.add_argument('-m', '--mode', dest='mode', help='mode: get, set, mix') parser.add_argument('-n', '--count', dest='count', type=int, help='iterations', default=1000) parser.add_argument('-z', '--length', dest='len', type=int, help='list length', default=5) parser.add_argument('-s', '--server', dest='server', action='store_true') verbose_add(parser) args = parser.parse_args() verbose_set_log(args.vb, _log) if args.server: retcode = run_server() _log.info("Redis server stopped") return retcode else: run_performance_test(cmd=args.mode, num_items=args.count, list_len=args.len) if __name__ == '__main__': sys.exit(main())

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104

0.59521

469

3,382

4.121535

0.302772

0.053802

0.032592

0.033626

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0.107087

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0.065184

0

0.017221

0.24453

3,382

117

105

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0.739335

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0.092784

false

0

0.072165

0

0.226804

0.020619

0

null

0

null

0

1

0

11c756cc812aa8aa64b2f69c97b3ae507b530f8b

1,323

py

Python

Question-1.py

sowmyamanojna/CS6910-Deep-Learning-Assignment-1

e46d3a82bdfb61d7527ed3daf9250bb4ce228854

[ "MIT" ]

null

Question-1.py

sowmyamanojna/CS6910-Deep-Learning-Assignment-1

e46d3a82bdfb61d7527ed3daf9250bb4ce228854

[ "MIT" ]

null

Question-1.py

sowmyamanojna/CS6910-Deep-Learning-Assignment-1

e46d3a82bdfb61d7527ed3daf9250bb4ce228854

[ "MIT" ]

null

print("Importing packages... ", end="") ############################################################################## import wandb import numpy as np from keras.datasets import fashion_mnist import matplotlib.pyplot as plt wandb.init(project="trail-1") print("Done!") ############################################################################## print("Loading data... ", end="") # Load the dataset [(x_train, y_train), (x_test, y_test)] = fashion_mnist.load_data() # Get the number of classes and their name mappings num_classes = 10 class_mapping = {0: "T-shirt/top", 1: "Trouser", 2: "Pullover", 3: "Dress", 4: "Coat", 5: "Sandal", 6: "Shirt", 7: "Sneaker", 8: "Bag", 9: "Ankle boot"} print("Done!") ############################################################################## # Plotting a figure from each class plt.figure(figsize=[12, 5]) img_list = [] class_list = [] for i in range(num_classes): position = np.argmax(y_train==i) image = x_train[position,:,:] plt.subplot(2, 5, i+1) plt.imshow(image) plt.title(class_mapping[i]) img_list.append(image) class_list.append(class_mapping[i]) wandb.log({"Question 1": [wandb.Image(img, caption=caption) for img, caption in zip(img_list, class_list)]}) ##############################################################################

35.756757

152

0.517763

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4.18239

0.54717

0.054135

0.03609

0.04812

0

0.017109

0.116402

1,323

36

153

36.75

0.551754

0.075586

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0.083333

0

0.144432

0

1

0

false

0

0.208333

0

0.208333

0.166667

0

null

0

null

0

1

0

11c77e0e125890c44783034eeeb3c9b9a0ff0a7d

1,386

py

Python

app/api/v1/task.py

coder-yuan/vue-template-api

135f13d7c32b4a2830366fc0b79a1e2a1eda6923

[ "MIT" ]

null

app/api/v1/task.py

coder-yuan/vue-template-api

135f13d7c32b4a2830366fc0b79a1e2a1eda6923

[ "MIT" ]

null

app/api/v1/task.py

coder-yuan/vue-template-api

135f13d7c32b4a2830366fc0b79a1e2a1eda6923

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Project : icode_flask_be # @Package : task # @Author : jackeroo # @Time : 2019/11/29 5:25 下午 # @File : task.py # @Contact : # @Software : PyCharm # @Desc : from app.extensions import celery from flask_jwt_extended import jwt_required from app.helper.HttpHelper import HttpHelper from app.libs.redprint import RedPrint api = RedPrint('task') @api.route('/<task_id>', methods=['GET']) @jwt_required def get_task_result(task_id): task = celery.AsyncResult(task_id) if task.state == 'PENDING': # job did not start yet response = { 'state': task.state, 'current': 0, 'total': 1, 'status': 'Pending...' } elif task.state != 'FAILURE': response = { 'state': task.state, 'current': task.info.get('current', 0), 'total': task.info.get('total', 1), 'status': task.info.get('status', '') } if 'result' in task.info: response['result'] = task.info['result'] else: # something went wrong in the background job response = { 'state': task.state, 'current': 1, 'total': 1, 'status': str(task.info), # this is the exception raised } return HttpHelper.normal_handler(response)

27.72

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0.063492

0.06746

0.087302

0.115079

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1,386

49

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0.764767

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false

0

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0.181818

0.030303

0

null

0

null

0

1

0

11cc4762ea46108968ee8aa2c98fc1627da5eca3

981

py

Python

pypy/jit/codegen/ppc/test/test_rgenop.py

camillobruni/pygirl

ddbd442d53061d6ff4af831c1eab153bcc771b5a

[ "MIT" ]

12

2016-01-06T07:10:28.000Z

2021-05-13T23:02:02.000Z

pypy/jit/codegen/ppc/test/test_rgenop.py

camillobruni/pygirl

ddbd442d53061d6ff4af831c1eab153bcc771b5a

[ "MIT" ]

null

pypy/jit/codegen/ppc/test/test_rgenop.py

camillobruni/pygirl

ddbd442d53061d6ff4af831c1eab153bcc771b5a

[ "MIT" ]

2

2016-07-29T07:09:50.000Z

2016-10-16T08:50:26.000Z

import py from pypy.jit.codegen.ppc.rgenop import RPPCGenOp from pypy.rpython.lltypesystem import lltype from pypy.jit.codegen.test.rgenop_tests import AbstractRGenOpTests, FUNC, FUNC2 from ctypes import cast, c_int, c_void_p, CFUNCTYPE from pypy.jit.codegen.ppc import instruction as insn # for the individual tests see # ====> ../../test/rgenop_tests.py class FewRegisters(RPPCGenOp): freeregs = { insn.GP_REGISTER:insn.gprs[3:6], insn.FP_REGISTER:insn.fprs, insn.CR_FIELD:insn.crfs[:1], insn.CT_REGISTER:[insn.ctr]} class FewRegistersAndScribble(FewRegisters): DEBUG_SCRIBBLE = True class TestRPPCGenop(AbstractRGenOpTests): RGenOp = RPPCGenOp class TestRPPCGenopNoRegs(TestRPPCGenop): RGenOp = FewRegisters def compile(self, runner, argtypes): py.test.skip("Skip compiled tests w/ restricted register allocator") class TestRPPCGenopNoRegsAndScribble(TestRPPCGenopNoRegs): RGenOp = FewRegistersAndScribble

29.727273

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981

6.194915

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0.073871

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981

32

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false

0

0.272727

0

0.772727

0

null

0

null

0

1

0

11cd2cba6c6fa6a758300d6008e0f69f4e32d609

996

py

Python

app/someapp/views.py

artas728/monitoring-example-prometheus-grafana

2d72f29c19e8a280eca82ca1f25a7fa88453559c

[ "MIT" ]

null

app/someapp/views.py

artas728/monitoring-example-prometheus-grafana

2d72f29c19e8a280eca82ca1f25a7fa88453559c

[ "MIT" ]

null

app/someapp/views.py

artas728/monitoring-example-prometheus-grafana

2d72f29c19e8a280eca82ca1f25a7fa88453559c

[ "MIT" ]

null

from django.shortcuts import render from django.views.decorators.csrf import csrf_exempt from django.http import JsonResponse from .models import TestModel import json import redis import time redis_cli = redis.Redis(host='127.0.0.1', port=6379, db=0) @csrf_exempt def save_to_redis(request): data = json.loads(request.body.decode()) for key, value in data.items(): redis_cli.rpush(key, value) return JsonResponse({'success': True, 'data': 'Saved in Redis'}) @csrf_exempt def endpoint(request): time.sleep(0.1) return JsonResponse({'success': True, 'data': 'Request processed'}) @csrf_exempt def write_to_db(request): data = json.loads(request.body.decode()) for row in data: TestModel.objects.create(key1=row['key1'], key2=row['key2'], key3=row['key3'], key4=row['key4']) return JsonResponse({'success': True, 'data': 'Data has been saved'})

29.294118

73

0.638554

130

996

4.815385

0.423077

0.063898

0.0623

0.138978

0.285942

0.127796

0

0.027487

0.232932

996

33

74

30.181818

0.791885

0

0.185185

0

0.108652

0

1

0.111111

false

0

0.259259

0

0.481481

0

null

0

null

0

1

0

11d1192c076a5c79df7f15736899d5d72fa6cb5f

1,401

py

Python

NewEventReporter/blockmanager/blockmanager.py

Deofex/GETNFTBOTV3

0b8f1a77925b8f87224b2eaae93560e154b881b8

[ "MIT" ]

null

NewEventReporter/blockmanager/blockmanager.py

Deofex/GETNFTBOTV3

0b8f1a77925b8f87224b2eaae93560e154b881b8

[ "MIT" ]

null

NewEventReporter/blockmanager/blockmanager.py

Deofex/GETNFTBOTV3

0b8f1a77925b8f87224b2eaae93560e154b881b8

[ "MIT" ]

null

import logging import json import os # Initialize logger logger = logging.getLogger(__name__) class BlockManager(): def __init__(self, config, processedblock=0): logger.info('Initialize Block Manager') self.processedblock = int(processedblock) self.config = config if os.path.isfile(self.config): self.load_config() def set_processedblock(self,processedblock): if int(processedblock) <= self.processedblock: logger.warning('Block will not be set because block is ' 'lower or equal than the previous block') return logger.info('Set processed block on: {}'.format(processedblock)) self.processedblock = int(processedblock) blockconfig = { 'processedblock': processedblock } with open(self.config, 'w') as config: json.dump(blockconfig, config) def load_config(self): logger.info('Loading config') with open(self.config, 'r') as config: loadconfig = json.load(config) self.set_processedblock(loadconfig['processedblock']) def get_processedblock(self): return self.processedblock if __name__ == '__main__': blockprocessedconfig = './config/blockprocessed.json' bm = BlockManager(blockprocessedconfig,300000) bm.set_processedblock(242443) print(bm.get_processedblock())

31.133333

72

0.660243

145

1,401

6.22069

0.4

0.055432

0.10643

0.077605

0

0.012241

0.24197

1,401

44

73

31.840909

0.8371

0.012134

0

0.057143

0

0.149783

0.02026

0

1

0.114286

false

0

0.085714

0.028571

0.285714

0.028571

0

null

0

null

0

1

0

11d3d683bc5376ecd600cfbd620489e72ca787ca

5,299

py

Python

nmf_eval.py

logan-wright/INMF

611ccdfd4608ec37629975d04e013ab97e05ff31

[ "Apache-2.0" ]

2

2017-06-16T19:18:53.000Z

2019-04-18T02:11:45.000Z

nmf_eval.py

logan-wright/INMF

611ccdfd4608ec37629975d04e013ab97e05ff31

[ "Apache-2.0" ]

null

nmf_eval.py

logan-wright/INMF

611ccdfd4608ec37629975d04e013ab97e05ff31

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 23 20:35:49 2017 @author: wrightad """ import numpy as N import matplotlib.pyplot as plt def rmse(v1,v2): ''' rmse(v1,v2) - Calculates the root mean square error between two vectors Version 1.0 Created On: Apr 17, 2017 Last Modified: Jun 14, 2017 Author: Logan Wright, logan.wright@colorado.edu Description: - Calculates the Root-Mean-Square-Error between two vectors - Vectors must be the same length Inputs: v1 - Numpy 1-dimensional array of arbitrary length v2 - Numpy 1-dimensional array with a length equal to that of v1 Output: rmse, the rmse value for the comparison of the two vectors ''' dims1 = v1.shape dims2 = v2.shape if dims1 == dims2: diff = v1 - v2 err = N.sum(diff**2)/dims1[0] rms = N.sqrt(err) else: print('Dimension Mismatch: v1.shape ~= v2.shape!') rms = None return rms def sid(v1,v2): ''' sid(v1,v2) - Calculates the spectral information divergence (SID) between two vectors Version 1.0 Created On: Apr 17, 2017 Last Modified: Jun 14, 2017 Author: Logan Wright, logan.wright@colorado.edu Description: - Calculates the Spectral Information Divergence between two vectors - Vectors must be the same length Reference: Chang, C.-I. (2000), An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis, Inf. Theory, IEEE Trans., 46(5), 1927–1932, doi:10.1109/18.857802. Inputs: v1 - Numpy 1-dimensional array of arbitrary length v2 - Numpy 1-dimensional array with a length equal to that of v1 Output: SID, the SID value for the comparison of the two vectors ''' p = v1 / N.sum(v1) q = v2 / N.sum(v2) D1 = N.sum(p * N.log(p / q)) D2 = N.sum(q * N.log(q / p)) D_sum = D1 + D2 return D_sum def scattering_fit(data, function, sigma = 1e-9): ''' Linear least-squares fit for a function of the form y = a * f(x) Version 1.0 Created On: Apr 17, 2017 Last Modified: Apr 17, 2017 Author: Logan Wright, logan.wright@colorado.edu Description: Reference: Inputs: wvl, wavelength in NANOMETERS, must be same length as data and function data, the y data that the function is to be fit to. Should be a vector (N,) or a 2D array with one single dimension. function, the function to be scaled with a linear factor to fit the data. Again it should be a vector (N,) or a 2D array with one single dimension. data and function must be the same length. OPTIONAL: sigma, the value small value that determines when iteration stops Output: a, a single scalar describing the best-fit value of "a" ''' # Initialize parametrs, including change and the initial minimum change = 100 # Arbitrary value greater than sigma minval = N.sum((data - function) ** 2) # Initial Min # Calculate the intial multiplicative factor between the data and function, # and use to set range for calculating minimums Amin = 0 Amax = (data/function).max() # Iterate while change > sigma: # Create Array of Amplitudes for the fit Arr = N.linspace(Amin,Amax,100) Test = N.matmul(N.reshape(Arr,(-1,1)),function) # Calculate the square difference between the data and the fit guess diff = Test - N.matlib.repmat(N.reshape(data,(1,-1)),100,1) # Find Minimum, and calculate the change and difference. val = N.sum(diff ** 2, axis = 1) vali = N.argmin(val) change = minval - val.min() minval = val.min() # Calculate New range of "a" for next iteration Amin = Arr[max(vali-2,0)] Amax = Arr[min(vali+2,len(Arr)-1)] result = N.squeeze(Arr[vali] * function) return result def bodhaine(wvl): ''' bodhaine(wvl) - Calculates the Bodhaine aproximation of rayleigh optical depth Version 1.0 Created On: Apr 17, 2017 Last Modified: June 14, 2017 Author: Logan Wright, logan.wright@colorado.edu Description: Reference: Bodhaine, B. A., N. B. Wood, E. G. Dutton, and J. R. Slusser (1999), On Rayleigh optical depth calculations, J. Atmos. Ocean. Technol., 16(11 PART 2), 1854–1861, doi:10.1175/1520-0426(1999)016<1854:ORODC>2.0.CO;2. Inputs: wvl - a vector of wavelengths at which to calculate the rayleigh optical depth. Wavelength sould be in MICROMETERS Output: tr - vector of rayleigh optical depths corresponding to wavelengths from the input vectora single scalar describing the best-fit value of "a" ''' s = 0.0021520 a = 1.0455996 b = 341.29061 c = 0.90230850 d = 0.0027059889 e = 85.968563 tr = s * (a - b * wvl ** -2 - c * wvl ** 2)/(1 + d * wvl ** -2 - e * wvl ** 2) return tr

32.509202

150

0.60351

749

5,299

4.268358

0.344459

0.027526

0.014076

0.020019

0.357523

0.329371

0.30685

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0

0.072618

0.308738

5,299

163

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32.509202

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0.647481

0

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1

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false

0

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0.217391

0.021739

0

null

0

null

0

1

0

11d8b360dafd771af3d50fb23f126c256bc27cc5

423

py

Python

recieve.py

RyuYamamoto/inter-process-communication-py

377c73833f230ba1132006c2cda86decd3580a5b

[ "MIT" ]

null

recieve.py

RyuYamamoto/inter-process-communication-py

377c73833f230ba1132006c2cda86decd3580a5b

[ "MIT" ]

null

recieve.py

RyuYamamoto/inter-process-communication-py

377c73833f230ba1132006c2cda86decd3580a5b

[ "MIT" ]

null

import socket with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(('127.0.0.1', 50007)) s.listen(1) while True: conn, addr = s.accept() with conn: while True: data = conn.recv(1024) if not data: break print('data: {}, add: {}'.format(data, addr)) conn.sendall(b'Recieved: ' + data)

28.2

61

0.486998

52

423

3.923077

0.615385

0.117647

0

0.061069

0.380615

423

14

62

30.214286

0.717557

0

0.153846

0

0.085106

0

1

0

false

0

0.076923

0

0.076923

0

null

0

null

0

1

0

11dc5601e32f2a14e2e6dbd6c443d6cb0fdbc322

4,503

py

Python

utils.py

bbpp222006/elec_nose_plus

d79faa47d3fbb63c697501dd521e834bcc8e4814

[ "MIT" ]

1

2021-04-08T04:17:04.000Z

utils.py

bbpp222006/elec_nose_plus

d79faa47d3fbb63c697501dd521e834bcc8e4814

[ "MIT" ]

null

utils.py

bbpp222006/elec_nose_plus

d79faa47d3fbb63c697501dd521e834bcc8e4814

[ "MIT" ]

null

#!/usr/bin/python # encoding: utf-8 #!/usr/bin/python # encoding: utf-8 import torch import torch.nn as nn from torch.autograd import Variable import collections from tqdm import tqdm import numpy as np import cv2 import os import random from sklearn.cluster import KMeans import matplotlib.pyplot as plt class strLabelConverter(object): """Convert between str and label. NOTE: Insert `blank` to the alphabet for CTC. Args: alphabet (list): set of the possible characters. ignore_case (bool, default=True): whether or not to ignore all of the case. """ def __init__(self, alphabet): alphabet.remove('基线') self.dict = {} for i, char in enumerate(alphabet): # NOTE: 0 is reserved for 'blank' required by ctc self.dict[char] = i + 1 self.dict['基线'] = 0 self.dict_reverse = {value: key for key, value in self.dict.items()} def encode(self, text): """Support batch or single str. Args: text (str or list of str): texts to convert. Returns: torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts. torch.IntTensor [n]: length of each text. """ length = [] result = [] for char in text: length.append(1) index = self.dict[char] result.append(index) text = result return (torch.IntTensor(text), torch.IntTensor(length)) def decode(self, t, length=1, raw=False): """Decode encoded texts back into strs. Args: torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts. torch.IntTensor [n]: length of each text. Raises: AssertionError: when the texts and its length does not match. Returns: text (str or list of str): texts to convert. """ t = t.numpy() result = [] for value in t: index = self.dict_reverse[value] result.append(index) texts = result return texts class averager(object): """Compute average for `torch.Variable` and `torch.Tensor`. """ def __init__(self): self.reset() def add(self, v): if isinstance(v, Variable): count = v.data.numel() v = v.data.sum() elif isinstance(v, torch.Tensor): count = v.numel() v = v.sum() self.n_count += count self.sum += v def reset(self): self.n_count = 0 self.sum = 0 def val(self): res = 0 if self.n_count != 0: res = self.sum / float(self.n_count) return res def props_to_onehot(props): if isinstance(props, list): props = np.array(props) a = np.argmax(props, axis=1) b = np.zeros((len(a), props.shape[1])) b[np.arange(len(a)), a] = 1 return b def onehot_to_num(onehot): if isinstance(onehot, list): onehot = np.array(onehot) b = np.zeros((onehot.shape[0], 1)) for i, h in enumerate(onehot): b[i, 0] = np.argwhere(onehot[i] == 1) return b def draw(preds, x_train_batch,x_label,ax): predsnp = preds.cpu().detach().numpy() x_train_batchnp = x_train_batch.cpu().detach().numpy() x_label = x_label.cpu().detach().numpy() # print(predsnp.shape, x_train_batchnp.shape) # (2000,6) predsnp = props_to_onehot(predsnp) # print(predsnp) predsnp = onehot_to_num(predsnp) # print(max(predsnp)) #对原数据进行kmeans分类 estimator = KMeans(n_clusters=2) # 构造聚类器 estimator.fit(x_train_batchnp) # 聚类 label_pred = estimator.labels_ # 获取聚类标签 # 绘制k-means结果 if label_pred[0]==1: label_pred = 1-label_pred # plt.plot(np.argwhere(label_pred == 0), np.zeros(len(np.argwhere(label_pred == 0)))*x_label,'go-') # plt.plot(np.argwhere(label_pred == 1), np.ones(len(np.argwhere(label_pred == 1))) * x_label,'go-') ax.scatter(np.argwhere(label_pred == 0), np.zeros(len(np.argwhere(label_pred == 0)))*x_label, c="green", marker='o',s = 10, label='kmeans') ax.scatter(np.argwhere(label_pred == 1), np.ones(len(np.argwhere(label_pred == 1)))*x_label, c="green", marker='o',s = 10, label='kmeans') for i in range(int(max(predsnp))+1): x= np.argwhere(predsnp == i)[:,0] y = np.ones(len(x))*i # plt.plot(x, y, c = "red") ax.scatter(x, y, c = "red", marker='.', label='pred',s = 5)

27.457317

143

0.584055

628

4,503

4.092357

0.281847

0.045525

0.046693

0.059144

0.224903

0.193774

0.168872

0

0.015105

0.279591

4,503

163

144

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0.777127

0.282034

0

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0

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0

1

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false

0

0.126437

0

0.321839

0

null

0

null

0

1

0

11dc7d7484bc78800544b03df7488f722be7a5ea

2,729

py

Python

down.py

pcahan1/CellNet_Cloud

a228953946b81ccb304fbd068e33766e134103b6

[ "MIT" ]

1

2020-11-13T10:53:27.000Z

down.py

pcahan1/CellNet_Cloud

a228953946b81ccb304fbd068e33766e134103b6

[ "MIT" ]

2

2020-06-28T18:17:59.000Z

2020-12-18T14:11:29.000Z

down.py

pcahan1/CellNet_Cloud

a228953946b81ccb304fbd068e33766e134103b6

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import division import random import argparse import os parser = argparse.ArgumentParser() parser.add_argument("input", help="input FASTQ Directory") parser.add_argument("-n", "--number", type=int, help="number of reads to sample") args = parser.parse_args() random.seed(12) if not args.number: print("No sample size specified. Defaulting to five million reads.") args.number = 5000000 # LIST FILES TO BE DOWN-SAMPLED fastq_files = os.listdir(args.input) if int(len(fastq_files)) <= 0: print("No files in listed directory") exit() # CREATE OUTPUT DIRECTORY output_dir = "subset_"+args.input os.mkdir(output_dir) for fastq in fastq_files: print("\tcounting records....") with open(args.input+"/"+fastq) as inRead: num_lines = sum([1 for line in inRead]) print("Num lines:" + str(num_lines) ) if int(num_lines % 4) != 0: print("FILE " + fastq + " CORRUPTED: Number of lines in FASTQ file not divisible by 4. Is file decompressed?") exit() total_records = int(num_lines / 4) number_to_sample = args.number print("\tsampling " + str(number_to_sample) + " out of " + str(total_records) + " records") try: records_to_keep = set(random.sample(range(total_records), number_to_sample)) record_number = 0 with open(args.input+"/"+fastq) as inFile: with open(output_dir+"/"+"subset_"+fastq, "w") as output: for tag in inFile: bases = next(inFile) sign = next(inFile) quality = next(inFile) if record_number in records_to_keep: output.write(tag) output.write(bases) output.write(sign) output.write(quality) record_number += 1 except ValueError as e: if str(e) == "Sample larger than population or is negative": print("Desired number of reads is greater than number of reads in original file.") print("No down-sampling is necessary.") elif str(e) == "sample larger than population": print("Desired number of reads is greater than number of reads in original file.") print("No down-sampling is necessary.") else: raise print("Compressing downsampled reads") os.system("COPYFILE_DISABLE=1 tar cvfz compressed_reads.tgz "+output_dir) if os.path.getsize("compressed_reads.tgz") >= 4000000000: print("WARNING: Your archive contains too many FASTQ files. Max size is 4GB.") else: print("Archive file size is ~"+str(os.path.getsize("compressed_reads.tgz")/1000000000)+"GB")

35.907895

118

0.629901

357

2,729

4.703081

0.366947

0.028588

0.038714

0.02025

0.214413

0.113163

0

0.019307

0.259802

2,729

75

119

36.386667

0.811881

0.027116

0

0.135593

0

0.30279

0

1

0

false

0

0.067797

0

0.067797

0.220339

0

null

0

null

0

1

0

11deda09dc4cd77f3a703e78c0ad5fb515e8de96

3,507

py

Python

CSR/utility.py

MoreNiceJay/CAmanager_web

29c6e35b9b1b9e8d851b2825df18e34699f6c5d2

[ "bzip2-1.0.6" ]

null

CSR/utility.py

MoreNiceJay/CAmanager_web

29c6e35b9b1b9e8d851b2825df18e34699f6c5d2

[ "bzip2-1.0.6" ]

3

2020-02-11T23:59:34.000Z

2021-06-10T21:19:16.000Z

CSR/utility.py

MoreNiceJay/CAmanager_web

29c6e35b9b1b9e8d851b2825df18e34699f6c5d2

[ "bzip2-1.0.6" ]

null

from django.shortcuts import render import sys, json, random, hashlib, calendar,time, datetime, os, random import ast from cryptography.fernet import Fernet from django.shortcuts import redirect from django.http import Http404, HttpResponse import json from cryptography.hazmat.primitives.serialization import Encoding, PrivateFormat, NoEncryption,load_pem_private_key from cryptography import x509 from cryptography.x509.oid import NameOID from cryptography.hazmat.primitives import serialization,hashes from cryptography.x509 import load_pem_x509_csr from cryptography.hazmat.primitives.asymmetric import rsa, ec from cryptography.hazmat.backends import default_backend def generate_private_key(algorithm): private_key = None if algorithm == "RSA_2048": private_key = generate_RSA_private_key(2048) elif algorithm == "RSA_4096": private_key = generate_RSA_private_key(4096) elif algorithm == "ECDSA_P256": private_key = generate_ECP256_private_key() elif algorithm == "ECDSA_P384": private_key = generate_ECP384_private_key() else: raise AlgorithmMismatchError return private_key def generate_RSA_private_key(KEY_SIZE): private_key = rsa.generate_private_key( public_exponent=65537, key_size=KEY_SIZE, backend=default_backend() ) return private_key def generate_ECP384_private_key(): private_key = ec.generate_private_key( ec.SECP384R1(), default_backend() ) return private_key def generate_ECP256_private_key(): private_key = ec.generate_private_key( ec.SECP384R1(), default_backend() ) return private_key def generate_pub_key(private_key): public_key = private_key.public_key() return public_key def encode_private_key_pem_format(private_key): pem = private_key.private_bytes( encoding=serialization.Encoding.PEM, format=serialization.PrivateFormat.TraditionalOpenSSL, encryption_algorithm=serialization.NoEncryption() ) return pem def encode_public_key_pem_format(public_key): pem = public_key.public_bytes( encoding=serialization.Encoding.PEM, format=serialization.PublicFormat.SubjectPublicKeyInfo, ) return pem def encode_in_Base64(key_in_pem_format): with open("base64.key", "rb") as f: key = f.read() f = Fernet(key) token = f.encrypt(key_in_pem_format) return token.decode() def decode_Base64(encrypted_key_token): with open("base64.key", "rb") as f: key = f.read() f = Fernet(key) return f.decrypt(encrypted_key_token.encode()) def generate_CSR(country,state,locality,organization,common_name,domain,private_key): csr = x509.CertificateSigningRequestBuilder().subject_name(x509.Name([ x509.NameAttribute(NameOID.COUNTRY_NAME, country), x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, state), x509.NameAttribute(NameOID.LOCALITY_NAME, locality), x509.NameAttribute(NameOID.ORGANIZATION_NAME, organization), x509.NameAttribute(NameOID.COMMON_NAME, common_name), ])).add_extension( x509.SubjectAlternativeName([ # Describe what sites we want this certificate for. x509.DNSName(domain), ]), critical=False, # Sign the CSR with our private key. ).sign(private_key, hashes.SHA256(), default_backend()) return csr def encode_CSR_in_pem_format(temp_csr): return temp_csr.public_bytes(serialization.Encoding.PEM).decode()

34.722772

115

0.741374

430

3,507

5.776744

0.267442

0.128824

0.048309

0.030596

0.220209

0.192834

0.167874

0.10628

0

0.03459

0.175649

3,507

101

116

34.722772

0.824628

0.023952

0

0.206897

0

0.017539

0

1

0.126437

false

0

0.16092

0.011494

0.413793

0

null

0

null

0

1

0

11df93a40b853400f38b4c489077ebc7674cd549

51,584

py

Python

uctp_ufabc/src/uctp.py

luizfmgarcia/uctp_ufabc

2342f5431e258a4feffdf4e7931344a9d03a8f9c

[ "MIT" ]

null

uctp_ufabc/src/uctp.py

luizfmgarcia/uctp_ufabc

2342f5431e258a4feffdf4e7931344a9d03a8f9c

[ "MIT" ]

6

2018-10-30T00:37:20.000Z

2019-07-23T00:23:18.000Z

uctp_ufabc/src/uctp.py

luizfmgarcia/uctp_ufabc

2342f5431e258a4feffdf4e7931344a9d03a8f9c

[ "MIT" ]

1

2019-06-06T00:54:13.000Z

# UCTP Main Methods import objects import ioData import random # Set '1' to allow, during the run, the print on terminal of some steps printSteps = 0 #============================================================================================================== # Create the first generation of solutions def start(solutionsNoPop, subjList, profList, init): if(printSteps == 1): print("Creating first generation...", end='') for _ in range(init): solutionsNoPop.addCand(newCandRand(subjList, profList)) if(printSteps == 1): print("Created first generation!") #------------------------------------------------------- # Create new Candidate Full-Random def newCandRand(subjList, profList): candidate = objects.Candidate() # Follow the subjects in 'subjList', in order, and for each one, choose a professor randomly for sub in subjList: candidate.addRelation(sub, profList[random.randrange(len(profList))]) return candidate #============================================================================================================== # Extracts info about what Subj appears in which Prof PrefList def extractSubjIsPref(subjList, profList): # Lists for each Prof, where it is '1' if Subj in respective index is on Prof List of Pref but not same Quadri # '2' if same quadri subjIsPrefList = [[0 for _ in range(len(subjList))] for _ in range(len(profList))] # Counting the occurrences, filling the vectors for pIndex in range(len(profList)): # Getting data of current Prof prefSubjLists = [i for i in profList[pIndex].getPrefSubjLists()] # All Relations of one Prof for sIndex in range(len(subjList)): # Getting data of current Subj sName = subjList[sIndex].getName() sQuadri = subjList[sIndex].getQuadri() # For each quadri for i in range(3): # Finding the Subject 'sName' in "pPrefSubjQXList+pPrefSubjLimList" list sumList = prefSubjLists[i] + prefSubjLists[3] # Checking if the List is not empty if(len(sumList) > 0): try: index_value = sumList.index(sName) except ValueError: index_value = -1 # If the Subj name appears in the list if(index_value != -1): # If current Subject in analysis is on current Quadri if(str(i+1) in sQuadri): # Informing that the Subj appears on respective Prof-QuadriPrefList subjIsPrefList[pIndex][sIndex] = 2 # Informing that the Subj appears on other Prof-QuadriPrefList that is not same Quadri else: # Granting that do not decrease a value 2 already set if(subjIsPrefList[pIndex][sIndex] == 0): subjIsPrefList[pIndex][sIndex] = 1 return subjIsPrefList #============================================================================================================== # Separation of solutions into 2 populations def twoPop(solutionsNoPop, infPool, feaPool, profList, subjList, weightsList, numInfWeights): # Granting that the Lists will be empty to receive new Solutions infPool.resetCandList() feaPool.resetCandList() for cand in solutionsNoPop.getCandList(): # Classification by checking feasibility pop = checkFeasibility(cand, profList, subjList, weightsList, numInfWeights) if(pop == "feasible"): feaPool.addCand(cand) elif(pop == "infeasible"): infPool.addCand(cand) # Granting that the List will be empty to next operations solutionsNoPop.resetCandList() if(printSteps == 1): print("Checked Feasibility (new Candidates)/", end='') #============================================================================================================== # Detect the violation of a Restriction into a candidate def checkFeasibility(candidate, profList, subjList, weightsList, numInfWeights): # As part of the Candidate's Prof-Subj relations (with both Feasible and the Infeasible) will be traversed to check they Feasibility here, # instead of re-pass an entire Infeasible Candidate again in the 'calc_fitInfeas', the calculation of its Fitness will already be done # only one time here. Only the Feasible ones will have to pass through 'calc_fitFeas' later. fit = -1 fit = calc_fitInfeas(candidate, profList, subjList, weightsList[:numInfWeights]) if(fit < 0): candidate.setFitness(fit) return "infeasible" return "feasible" #============================================================================================================== # Calculate the Fitness of the candidate def calcFit(infeasibles, feasibles, profList, subjList, weightsList, numInfWeights, subjIsPrefList): # All Infeasible Candidates - is here this code only for the representation of the default/original algorithm`s work # The Inf. Fitness calc was already done in 'checkFeasibility()' method # Check if the Infeasible pop. is empty if(len(infeasibles.getCandList()) != 0): for cand in infeasibles.getCandList(): if(cand.getFitness() == 0.0): # Setting the Fitness with the return of calc_fitInfeas() method cand.setFitness(calc_fitInfeas(cand, profList, subjList, weightsList[:numInfWeights])) if(printSteps == 1): print("Fitness of all Inf./", end='') # All Feasible Candidates # Check if the Feasible pop. is empty if(len(feasibles.getCandList()) != 0): for cand in feasibles.getCandList(): if(cand.getFitness() == 0.0): # Setting the Fitness with the return of calc_fitFeas() method cand.setFitness(calc_fitFeas(cand, profList, subjList, weightsList[numInfWeights:], subjIsPrefList)) if(printSteps == 1): print("Fitness of all Feas./", end='') #============================================================================================================== # Calculate Fitness of Infeasible Candidates def calc_fitInfeas(candidate, profList, subjList, weightsList): # Getting information about the Candidate prof_relationsList = calc_i1(candidate, profList, subjList) i2_conflictsList, i3_conflictsList = calc_i2_i3(prof_relationsList, subjList) # Setting found variables candidate.setInfVariables(prof_relationsList, i2_conflictsList, i3_conflictsList) # Checking if occurred violations of restrictions on the Candidate # If there are violated restrictions, this Candidate is Infeasible and then will calculate and return a negative Fitness, # if not, is Feasible, will return 1.0 as Fitness if(prof_relationsList.count([]) != 0 or i2_conflictsList.count([]) != len(i2_conflictsList) or i3_conflictsList.count([]) != len(i3_conflictsList)): # Calculating main variables i1 = float(prof_relationsList.count([]) / (len(profList) - 1.0)) i2 = float(sum([len(i) for i in i2_conflictsList]) / len(subjList)) i3 = float(sum([len(i) for i in i3_conflictsList]) / len(subjList)) i = [i1, i2, i3] # Final Infeasible Function Fitness Calc Fi = -1.0 * sum([i[j] * weightsList[j] for j in range(len(i))]) / sum([w for w in weightsList]) # Returning the calculated result return Fi # If all Relations Prof-Subj in this Candidate passed through the restrictions) return 1.0 #------------------------------------------------------- # i1: penalty to how many Professors does not have at least one relation with a Subject def calc_i1(candidate, profList, subjList): # List of lists of Subjects that are related to the same Professor, where the position in this list is the same of the same professor in 'profList' list # Empty list in this list means that some Professor (p) does not exists on the Candidate prof_relationsList = [[] for _ in range(len(profList))] # Filling the list according to the candidate for s, p in candidate.getRelationsList(): indexp = profList.index(p) indexs = subjList.index(s) prof_relationsList[indexp].append(indexs) return prof_relationsList #------------------------------------------------------- # i2: penalty to how many Subjects, related to the same Professor, are teach in the same day, hour and quadri # i3: penalty to how many Subjects, related to the same Professor, are teach in the same day and quadri but in different campus def calc_i2_i3(prof_relationsList, subjList): # List of the subjects that have a conflict between them - always the two conflicts are added, that is, # there can be repetitions of subjects i2_conflictsList, i3_conflictsList = [[] for _ in range(len(prof_relationsList))], [[] for _ in range(len(prof_relationsList))] # Searching, in each professor (one at a time), conflicts of schedules between subjects related to it for list_subj in prof_relationsList: # Current Prof in analysis profIndex = prof_relationsList.index(list_subj) # Check if the professor has more than 1 relation Prof-Subj to analyze if(len(list_subj) > 1): # Getting the data of all Subjects related to current Professor in analysis timetableList_List = [subjList[i].getTimeTableList() for i in list_subj] quadri_List = [subjList[i].getQuadri() for i in list_subj] campus_List = [subjList[i].getCampus() for i in list_subj] period_List = [subjList[i].getPeriod() for i in list_subj] # Comparing the data of one Subject (i) with all next subjects listed, and do the same with next ones i = 0 for timeTable in timetableList_List: # all [day/hour/frequency] of the Timetable of the Subject (i) in 'timetableList_List' i_day = [j[0] for j in timeTable] i_hour = [j[1] for j in timeTable] i_frequency = [j[2] for j in timeTable] # Now, comparing current (i) subject data with next ones (k), one at a time k = i + 1 rest = timetableList_List[k:] # repeat this 'len(rest)' times for nextK in rest: # Already check if both Subj (i, k) is on same Quadri if(quadri_List[i] == quadri_List[k]): # Variables that flags if a conflict was already detected (do not count 2 or more times same 2 subjects in conflict) verified_i2, verified_i3 = False, False # all [day/hour/frequency] of the Timetable of the Subject (k) in 'timetableList_List' inext_day = [j[0] for j in nextK] inext_hour = [j[1] for j in nextK] inext_frequency = [j[2] for j in nextK] # Finally comparing one-to-one timetables - between i and k subjects for a in i_day: for b in inext_day: if(a == b): # There is, at least, two subjects teach in the same day and quadri, but in different campus if(campus_List[i] != campus_List[k]): if(verified_i3 == False): i3_conflictsList[profIndex].append(list_subj[i]) i3_conflictsList[profIndex].append(list_subj[k]) verified_i3 = True # There is, at least, two subjects teach in the same day, hour and quadri # First check if they have the same Period if(period_List[i] == period_List[k] and i_hour[i_day.index(a)] == inext_hour[inext_day.index(b)]): # if one 'frequency' is "QUINZENAL I" and the other is "QUINZENAL II" then DO NOT count if('SEMANAL' in i_frequency[i_day.index(a)] or 'SEMANAL' in inext_frequency[inext_day.index(b)]): if(verified_i2 == False): i2_conflictsList[profIndex].append(list_subj[i]) i2_conflictsList[profIndex].append(list_subj[k]) #print(subjList[list_subj[i]].get(), subjList[list_subj[k]].get(), '\n') verified_i2 = True elif('QUINZENAL I' in i_frequency[i_day.index(a)] and 'QUINZENAL I' in inext_frequency[inext_day.index(b)]): if(verified_i2 == False): i2_conflictsList[profIndex].append(list_subj[i]) i2_conflictsList[profIndex].append(list_subj[k]) #print(subjList[list_subj[i]].get(), subjList[list_subj[k]].get(), '\n') verified_i2 = True elif('QUINZENAL II' in i_frequency[i_day.index(a)] and 'QUINZENAL II' in inext_frequency[inext_day.index(b)]): if(verified_i2 == False): i2_conflictsList[profIndex].append(list_subj[i]) i2_conflictsList[profIndex].append(list_subj[k]) #print(subjList[list_subj[i]].get(), subjList[list_subj[k]].get(), '\n') verified_i2 = True # Going to the next Subject (k+1) to compare with the same, current, main, Subject (i) k = k + 1 # Going to the next Subject (i+1) related to the same Professor i = i + 1 # Removing from 'i2_conflictsList' and 'i3_conflictsList' duplicates final_i2 = [[] for _ in range(len(prof_relationsList))] final_i3 = [[] for _ in range(len(prof_relationsList))] for i in range(len(prof_relationsList)): for j in i2_conflictsList[i]: if(final_i2[i].count(j) == 0): final_i2[i].append(j) for j in i3_conflictsList[i]: if(final_i3.count(j) == 0): final_i3[i].append(j) return final_i2, final_i3 #============================================================================================================== # Calculate Fitness of Feasible Candidates def calc_fitFeas(candidate, profList, subjList, weightsList, subjIsPrefList): prof_relationsList, _, _, _, _, _ = candidate.getFeaVariables() # Looking for good Relations into the Candidate using "Quality Amplifiers" # Getting information about the Candidate sum_chargesRelative, difChargeList = calc_f1(subjList, profList, prof_relationsList) sum_Satisfaction, numSubjPrefList = calc_f2(subjList, profList, prof_relationsList, subjIsPrefList) sum_quadSabbNotPref, quadSabbNotPrefList = calc_f3(subjList, profList, prof_relationsList) sum_periodPref, periodPrefList = calc_f4(subjList, profList, prof_relationsList) sum_campusPref, campPrefList = calc_f5(subjList, profList, prof_relationsList) sum_relationsRelative, _ = calc_f6(subjList, profList, prof_relationsList) sum_qualityRelative, _ = calc_f7(subjList, profList, prof_relationsList, subjIsPrefList) # Setting found variables candidate.setFeaVariables(prof_relationsList, numSubjPrefList, periodPrefList, quadSabbNotPrefList, campPrefList, difChargeList) # Calculating main variables f1 = 1.0 - float(sum_chargesRelative / len(profList)) f2 = float(sum_Satisfaction / len(profList)) f3 = float(sum_quadSabbNotPref / len(subjList)) f4 = float(sum_periodPref / len(subjList)) f5 = float(sum_campusPref / len(subjList)) f6 = 1.0 - float(sum_relationsRelative / len(profList)) f7 = float(sum_qualityRelative / len(profList)) f = [f1, f2, f3, f4, f5, f6, f7] # Final Feasible Function Fitness Calc Ff = sum([f[j] * weightsList[j] for j in range(len(f))]) / sum([w for w in weightsList]) # Returning the result calculated return Ff #------------------------------------------------------- # f1: how balanced is the distribution of Subjects, considering the "Charge" of each Professor and its Subj related def calc_f1(subjList, profList, prof_relationsList): # List of all 'Effective Charges', that is, the sum of the charges of all the subjects related to the professor charges_eachProfRelations = [0 for _ in range(len(profList))] # List of requested charges of each professor charges_EachProf = [profList[i].getCharge() for i in range(len(profList))] # Counting the occurrences, filling the vectors for i in range(len(prof_relationsList)): # Summing all chargers of all relations of this Prof charges_eachProfRelations[i] = sum([subjList[sIndex].getCharge() for sIndex in prof_relationsList[i]]) # Difference of Prof Charge and the sum of all of its Subj-Relations difChargeList = [charges_EachProf[i] - charges_eachProfRelations[i] for i in range(len(profList))] # Relative weigh of excess or missing charge for each Prof - based on the absolute credit difference # between the credits requested by the Prof and the sum off all Subj related to it charges_relative = [float(abs(difChargeList[i]) / charges_EachProf[i]) for i in range(len(profList))] # Making a simple adjust on the value charges_relativeFinal = [charge if charge < 1.0 else 1.0 for charge in charges_relative] # The sum of charge discrepancies of all professors sum_chargesRelative = sum([charge for charge in charges_relativeFinal]) return sum_chargesRelative, difChargeList #------------------------------------------------------- # f2: how many and which Subjects are the professors preference, considering "prefSubj..." Lists def calc_f2(subjList, profList, prof_relationsList, subjIsPrefList): # These are Lists (each quadri - 3) of Lists (each professor) of Lists (each PrefList+LimList) # In each List (inside the List inside the List) we have 1 if the same index Subject (from same Quadri X Pref List + Lim Pref List) is related to Same Prof # or we have 0 if it is not related qX_relations = [[[] for _ in range(len(profList))] for _ in range(3)] # List with the number of subjects that are on respective Prof's List of Preferences numSubjPrefList = [0 for _ in range(len(profList))] # Counting the occurrences, filling the vectors for relations in prof_relationsList: # Setting Index of current Prof pIndex = prof_relationsList.index(relations) # Getting data of current Prof prefSubjLists = [i for i in profList[pIndex].getPrefSubjLists()] # For each Quadri - Filling QX Lists of current Prof # in each one appends "pPrefSubjQXList" with "pPrefSubjLimList" to have the length of the subList for i in range(3): qX_relations[i][pIndex] = [0 for _ in range(len(prefSubjLists[i]) + len(prefSubjLists[3]))] # All Relations of one Prof for sIndex in relations: # Getting data of current Subj sName = subjList[sIndex].getName() sQuadri = subjList[sIndex].getQuadri() # For each quadri for i in range(3): # Looking for only in the list of respective quadri of current Subject in analysis if(str(i+1) in sQuadri): # Finding the Subject 'sName' in "pPrefSubjQXList+pPrefSubjLimList" list sumList = prefSubjLists[i] + prefSubjLists[3] # Checking if the List is not empty if(len(sumList) > 0): try: index_value = sumList.index(sName) except ValueError: index_value = -1 # If the Subj name appears in the list if(index_value != -1): # Putting '1' in same position found 'index_value' in the subList (which this one, is in same position of profList) qX_relations[i][pIndex][index_value] = 1 # Adding the Subj that is on Prof Pref List numSubjPrefList[pIndex] = numSubjPrefList[pIndex] + 1 # Calculating intermediate variables # Lists of the calculation of "satisfaction" based on the order of Subjects choose by a Professor (index = 0 has more weight) finalQX = [[0.0 for _ in range(len(profList))] for _ in range(3)] # For each Qaudri for i in range(3): # Calculating the Satisfaction from QX relations for each Professor for list_choice_relation in qX_relations[i]: # Setting current Prof Index and current List Relations-Preference prof_index = qX_relations[i].index(list_choice_relation) len_current_list = len(list_choice_relation) # Initializing current position and total weight that will be calculated next total_weight = 0 # Checking if the Relations-Preference List is empty if(len_current_list == 0): finalQX[i][prof_index] = 1.0 # If is needed to be calculated (is not empty) else: # QX Relations of each Professor for h in list_choice_relation: # Setting current Subject Preference Position pref_index = list_choice_relation.index(h) # Summing the Total Weight of this list of preferences to normalize later (+1 because first index is 0) total_weight = total_weight + pref_index + 1 # If the current Subj, in this specific position on the Preference List of current Prof, is related to it if(h == 1): # Summing the respective weight the Subj has in the Prof List of Preferences finalQX[i][prof_index] = finalQX[i][prof_index] + (len_current_list - pref_index + 1) # Calculate the final value of "Satisfaction" normalized, after obtained and summed all weights from Subjects related to current professor finalQX[i][prof_index] = float(finalQX[i][prof_index] / total_weight) # Calculate the final value of a Prof "satisfaction" summing all 3 values (from finalQ1, finalQ2 and finalQ3 lists) and normalizing it final_Satisf = [float((finalQX[0][i] + finalQX[1][i] + finalQX[2][i]) / 3.0) for i in range(len(finalQX[0]))] # Finally, calculating all Professors Satisfaction summing all final values sum_Satisfaction = sum([value for value in final_Satisf]) return sum_Satisfaction, numSubjPrefList #------------------------------------------------------- # f3: how many Subjects are teach in a "Quadri" that is not the same of Professors 'quadriSabbath' def calc_f3(subjList, profList, prof_relationsList): # List of Subjs related to a Prof that is on different Quadri of prof's QuadSabb quadSabbNotPrefList = [[] for _ in range(len(profList))] # Getting the occurrences, filling the vector for i in range(len(prof_relationsList)): # Getting data of current Prof pQuadriSabbath = profList[i].getQuadriSabbath() # All Relations of one Prof for sIndex in prof_relationsList[i]: # Getting data of current Subj sQuadri = subjList[sIndex].getQuadri() # Adding to count if the Subj is not in the same 'pQuadriSabbath' (if Prof choose 'nenhum' he does not have a 'pQuadriSabbath') if('NENHUM' in pQuadriSabbath or sQuadri != pQuadriSabbath): quadSabbNotPrefList[i].append(sIndex) # Calculating intermediate variable sum_quadSabbNotPref = sum([len(listSubj) for listSubj in quadSabbNotPrefList]) return sum_quadSabbNotPref, quadSabbNotPrefList #------------------------------------------------------- # f4: how many Subjects are teach in the same "Period" of the Professor preference "pPeriod" def calc_f4(subjList, profList, prof_relationsList): # List of Subjs related to a Prof that is on same Period of prof's Period periodPrefList = [[] for _ in range(len(profList))] # Getting the occurrences, filling the vector for i in range(len(prof_relationsList)): # Getting data of current Prof pPeriod = profList[i].getPeriod() # All Relations of one Prof for sIndex in prof_relationsList[i]: # Getting data of current Subj sPeriod = subjList[sIndex].getPeriod() # Adding to count if the Subj is in the same 'pPeriod' or if Prof do not care about 'pPeriod' equal to 'NEGOCIAVEL' if('NEGOCI' in pPeriod or sPeriod == pPeriod): periodPrefList[i].append(sIndex) # Calculating intermediate variable sum_periodPref = sum([len(listSubj) for listSubj in periodPrefList]) return sum_periodPref, periodPrefList #------------------------------------------------------- # f5: how many Subjects are teach in the same "Campus" of the Professor preference "prefCampus" def calc_f5(subjList, profList, prof_relationsList): # List of Subjs related to a Prof that is on same Campus of prof's Campus campPrefList = [[] for _ in range(len(profList))] # Getting the occurrences, filling the vector for i in range(len(prof_relationsList)): # Getting data of current Prof pPrefCampus = profList[i].getPrefCampus() # All Relations of one Prof for sIndex in prof_relationsList[i]: # Getting data of current Subj sCampus = subjList[sIndex].getCampus() # Adding to count if the Subj is in the same 'pPrefCampus' if(sCampus == pPrefCampus): campPrefList[i].append(sIndex) # Calculating intermediate variable sum_campusPref = sum([len(listSubj) for listSubj in campPrefList]) return sum_campusPref, campPrefList #------------------------------------------------------- # f6: average of relations between profs def calc_f6(subjList, profList, prof_relationsList): # Number of Subjs ideal for each professor avgSubjperProf = float(len(subjList)/len(profList)) # Difference between num of relations of each prof and the average difNumRel = [len(relations) - avgSubjperProf for relations in prof_relationsList] # Relative weigh of excess or missing relations for each Prof - based on the absolute relations difference relations_relative = [float(abs(difNumRel[i]) / avgSubjperProf) for i in range(len(prof_relationsList))] # Making a simple adjust on the values relations_relativeFinal = [value if value < 1.0 else 1.0 for value in relations_relative] # The sum of relations discrepancies of all professors sum_relationsRelative = sum([charge for charge in relations_relativeFinal]) return sum_relationsRelative, difNumRel #------------------------------------------------------- # f7: quality of relations (subj appears in some list of pref or/and same quadri) def calc_f7(subjList, profList, prof_relationsList, subjIsPrefList): # Summing, for each professor, its relations qualities sumRelationsQuality = [sum([subjIsPrefList[i][pos] for pos in prof_relationsList[i]]) for i in range(len(prof_relationsList))] # Relative value of quality of all relations for each Prof (2 is the max value of quality - same quadri of pref list) qualityRelative = [float(sumRelationsQuality[i] / (2 * len(prof_relationsList[i]))) for i in range(len(prof_relationsList))] # The sum of relative qualities of all professors sum_qualityRelative = sum([value for value in qualityRelative]) return sum_qualityRelative, qualityRelative #============================================================================================================== # Generate new solutions from the current Infeasible population def offspringI(solutionsNoPop, solutionsI, profList, subjList, subjIsPrefList, mutWithRand): # Check if the Infeasible pop. is empty if(len(solutionsI.getCandList()) != 0): # Make a Mutation for each candidate, trying to repair a restriction problem maker for cand in solutionsI.getCandList(): newCand = mutationI(cand, profList, subjList, subjIsPrefList, mutWithRand) # Adding the new Candidate generated by Mutation to 'solutionsNoPop' solutionsNoPop.addCand(newCand) if(printSteps == 1): print("Inf. Offspring/", end='') #============================================================================================================== # Generate new solutions from the current Feasible population def offspringF(solutionsNoPop, solutionsF, profList, subjList, subjIsPrefList, maxNumCand_perPop, pctParentsCross, reposCross, twoPointsCross, mutWithRand): # Check if the Feasible pop. is empty if(len(solutionsF.getCandList()) != 0): # 'objectiveNum': number of solutions to become parents - based on 'pctParentsCross' objectiveNum = int(pctParentsCross * len(solutionsF.getCandList()) / 100) # Turning 'objectiveNum' to Even if it is Odd -> summing +1 to it only if the new 'objectiveNum' is not bigger then len(solutionsF) if(objectiveNum % 2 != 0): if((objectiveNum + 1) <= len(solutionsF.getCandList())): objectiveNum = objectiveNum + 1 else: objectiveNum = objectiveNum - 1 # Granting that are solutions enough to became fathers (more than or equal 2) if(objectiveNum < 2): # If have at most 1 solution (insufficient to make any crossover) - then all solutions will generate a child through a mutation for cand in solutionsF.getCandList(): solutionsNoPop.addCand(mutationF(cand, profList, subjList, subjIsPrefList,mutWithRand)) # If we have at least 2 solutions else: # Roulette Wheel to choose solutions to become Parents fitnessList = [cand.getFitness() for cand in solutionsF.getCandList()] parentsSolFeas, notParents_objectsList, _ = rouletteWheel(solutionsF.getCandList(), fitnessList, objectiveNum, reposCross) # Solutions 'children' created by crossover childSolFeas = [] # Make a Crossover (create two new candidates) for each pair of parents candidates randomly choose # Granting the number of children is equal of parents while(len(childSolFeas) != objectiveNum): # If there are only 2 parents, make a crossover between them if(len(parentsSolFeas) <= 2): parent1, parent2 = 0, 1 # If there are more then 2, choosing the parents Randomly else: parent1, parent2 = random.randrange(len(parentsSolFeas)), random.randrange(len(parentsSolFeas)) # Granting the second parent is not the same of first one while(parent1 == parent2): parent2 = random.randrange(len(parentsSolFeas)) # Making the Crossover with the selected parents newCand1, newCand2 = crossover(parentsSolFeas[parent1], parentsSolFeas[parent2], twoPointsCross) # Removing used parents to make a new selection of Parents parent2 = parentsSolFeas[parent2] parentsSolFeas.remove(parentsSolFeas[parent1]) parentsSolFeas.remove(parent2) # adding the new candidates generated to childSolFeas childSolFeas.append(newCand1) childSolFeas.append(newCand2) # Adding the child generated by crossover to 'solutionsNoPop' for cand in childSolFeas: solutionsNoPop.addCand(cand) # Make Mutation with all the candidates that were not chosen to be Parents right before for cand in notParents_objectsList: # Making a not random mutation newCand = mutationF(cand, profList, subjList, subjIsPrefList,mutWithRand) # Adding the child not generated by crossover to 'solutionsNoPop' solutionsNoPop.addCand(newCand) if(printSteps == 1): print("Feas. Offspring/", end='') #============================================================================================================== # Make a mutation into a infeasible candidate def mutationI(candidate, profList, subjList, subjIsPrefList, mutWithRand=1): # Getting data to work with relations = candidate.getRelationsList()[:] prof_relationsList, i2_conflictsList, i3_conflictsList = candidate.getInfVariables() # This While ensures that 'problemType' will choose Randomly one 'restriction repair' flag_work_done = False while(flag_work_done == False): # Choosing one type of restriction to repair if(mutWithRand == 0): problemType = random.randrange(1,4) if(mutWithRand == 1): problemType = random.randrange(0,4) if(mutWithRand == 2): problemType = 0 # (0) No repair -> Random Change if(problemType == 0): flag_work_done, newCand = mutationRand(candidate, profList) # (1) Prof without relations (with no Subjects) in 'prof_relationsList' elif(problemType == 1): # Granting that the 'problemType' do not change good relations without restrictions to repair if(prof_relationsList.count([]) != 0): flag_work_done, newCand = mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, prof_relationsList) else: # (2) 2 or more Subjects (related to the same Prof) with same 'quadri', 'day' and 'hour' in 'i2_conflictsList' if(problemType == 2): iX_conflictsList = i2_conflictsList # (3) 2 or more Subjects (related to the same Prof) with same 'day' and 'quadri' but different 'campus' in 'i3_conflictsList' if(problemType == 3): iX_conflictsList = i3_conflictsList # Granting that the 'problemType' do not change good relations without restrictions to repair if(len(iX_conflictsList) != 0 and iX_conflictsList.count([]) != len(iX_conflictsList)): flag_work_done, newCand = mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, iX_conflictsList) return newCand #============================================================================================================== # Make a mutation into a feasible candidate def mutationF(candidate, profList, subjList, subjIsPrefList, mutWithRand=1): # Getting data to work with relations = candidate.getRelationsList()[:] prof_relationsList, _, periodPrefList, quadSabbNotPrefList, campPrefList, _ = candidate.getFeaVariables() # This While ensures that 'adjustType' will choose Randomly one 'Improvement work' flag_work_done = False while(flag_work_done == False): # Choosing one type of 'Improvement work' if(mutWithRand == 0): adjustType = random.randrange(1,6) if(mutWithRand == 1): adjustType = random.randrange(0,6) if(mutWithRand == 2): adjustType = 0 # (0) No 'Improvement work' -> Random Change if(adjustType == 0): flag_work_done, newCand = mutationRand(candidate, profList) # (1) Improving number of Relations elif(adjustType == 1): flag_work_done, newCand = mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, prof_relationsList) # (2) Improving number of Subj Preferences elif(adjustType == 2): # Building a list with relations that is NOT Pref notPrefList = [[subjIndex for subjIndex in prof_relationsList[i] if subjIsPrefList[i][subjIndex] == 0] for i in range(len(prof_relationsList))] # Granting that the 'adjustType' do not change good relations without Problems to improve if(notPrefList.count([]) != len(notPrefList)): flag_work_done, newCand = mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, prof_relationsList) else: # (3) Improving number of Periods if(adjustType == 3): XPref = periodPrefList # (4) Improving number of QuadSabb if(adjustType == 4): XPref = quadSabbNotPrefList # (5) Improving number of Campus if(adjustType == 5): XPref = campPrefList if(len(XPref) != 0): # Building a list with relations that is NOT Pref notPrefList = [[subjIndex for subjIndex in prof_relationsList[i] if [i].count(subjIndex) == 0] for i in range(len(prof_relationsList))] # Granting that the 'adjustType' do not change good relations without Problems to improve if(notPrefList.count([]) != len(notPrefList)): flag_work_done, newCand = mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, notPrefList) return newCand #============================================================================================================== # Make a selection of the solutions from all Infeasible Pop.('infPool' and 'solutionsI') def selectionI(infPool, solutionsI, maxNumCand_perPop, reposSelInf): # Check if the Infeasible pop. is empty if(len(solutionsI.getCandList()) != 0 or len(infPool.getCandList()) != 0): # Gathering both lists (infPool and solutionsI) infeasibles_List = solutionsI.getCandList() + infPool.getCandList() # Check if is needed to make a selection process if(len(infeasibles_List) > maxNumCand_perPop): # Roulette Wheel Selection # Since the value of Fitness is in the range of '-1' and '0' it is needed to be modified to a range of '0' and '1' fitnessList = [1.0 + cand.getFitness() for cand in infeasibles_List] infeasibles_List, _, _ = rouletteWheel(infeasibles_List, fitnessList, maxNumCand_perPop, reposSelInf) # Updating the (new) 'solutionsI' list to the next generation solutionsI.setCandList(infeasibles_List) if(printSteps == 1): print("Inf. Selection/", end='') #============================================================================================================== # Make a Selection of the best solutions from Feasible Pop. def selectionF(feaPool, solutionsF, maxNumCand_perPop, pctElitism, reposSelFea): # Check if the Feasible pop. is empty if(len(solutionsF.getCandList()) != 0 or len(feaPool.getCandList()) != 0): # Gathering both lists (feaPool and solutions) feasibles_List = solutionsF.getCandList() + feaPool.getCandList() # Check if is needed to make a selection process if(len(feasibles_List) > maxNumCand_perPop): # Defining the division of number of candidates between selections process elitismNum = maxNumCand_perPop * pctElitism / 100.0 if(elitismNum > 0.0 and elitismNum < 1.0): elitismNum = 1 else: elitismNum = int(elitismNum) roulNum = maxNumCand_perPop - elitismNum # Elitism and Roulette Selection listFit = [cand.getFitness() for cand in feasibles_List] maxFeasibles_List, rest_objectsList, rest_valuesList = elitismSelection(feasibles_List, listFit, elitismNum) selectedObj, _, _ = rouletteWheel(rest_objectsList, rest_valuesList, roulNum, reposSelFea) feasibles_List = maxFeasibles_List + selectedObj # Updating the (new) 'solutionsF' list to the next generation solutionsF.setCandList(feasibles_List) if(printSteps == 1): print("Feas. Selection/", end='') #============================================================================================================== # Make a rand mutation into a solution def mutationRand(candidate, profList): # Getting all relations from Candidate relations = candidate.getRelationsList()[:] # Choosing randomly a relation to be modified original = random.randrange(len(relations)) # Recording the Original Relation subj, oldProf = relations[original] # Granting that the 'newProf' is different from the 'oldProf' newProf = oldProf while(oldProf == newProf): # Finding randomly a new Prof change = random.randrange(len(profList)) newProf = profList[change] # Setting the new Relation modified, creating and setting a new Candidate relations[original]=[subj,newProf] newCand = objects.Candidate() newCand.setRelationsList(relations) # Setting the flag to finish the while flag_work_done = True # Returning the new Candidate generated return flag_work_done, newCand #============================================================================================================== # Make some deterministic type of adjustment changing some 'bad' relation def mutationDeterm(profList, prof_relationsList, relations, subjIsPrefList, problemList): # Choosing a professor to lose a relation # Roulette Wheel - more 'bad' relations -> more weight weightList = [len(i) for i in problemList] problemSubList_selected, _, _ = rouletteWheel(problemList, weightList, objectiveNum=1, repos=0) profLost_Index = problemList.index(problemSubList_selected[0]) # Choosing the relation to be modified # Roulette Wheel - less preference -> more weight lessPrefValue = [2 - subjIsPrefList[profLost_Index][subjIndex] for subjIndex in problemList[profLost_Index]] will_change_index, _, _ = rouletteWheel(problemSubList_selected[0], lessPrefValue, objectiveNum=1, repos=0) relation_will_change_index = will_change_index[0] # Recording original relation that will be modified subjList, oldProf = relations[relation_will_change_index] # Choosing new Prof to be in the selected relation # Granting that the new Prof is different from the old one newProf = oldProf while(oldProf == newProf): # Roulette Wheel - more preference AND less relations -> more weight SubjPrefValuesList = [subjIsPref_subList[relation_will_change_index] for subjIsPref_subList in subjIsPrefList] # Removing possible Zeros to make the division prof_relations_final = [len(i) if len(i) != 0 else 0.5 for i in prof_relationsList] # Getting the weights values morePrefValueList = [float(SubjPrefValuesList[i] / prof_relations_final[i]) for i in range(len(profList))] # If there is only one Prof with value != 0.0 if(morePrefValueList.count(0.0) == len(morePrefValueList) - 1): indexNotZero = [i for i in range(len(profList)) if morePrefValueList[i] != 0.0] # If is the same of the old one - random choice if(oldProf == profList[indexNotZero[0]]): newProf = profList[random.randrange(len(profList))] # If not else: newProf = profList[indexNotZero[0]] # If there are more then 1 Prof to chose else: newProf, _, _ = rouletteWheel(profList, morePrefValueList, objectiveNum=1, repos=0) newProf = newProf[0] # Setting the new relation, creating new Candidate and returning it relations[relation_will_change_index]=[subjList, newProf] # Setting the flag to finish the while flag_work_done = True # Generating a new candidate newCand = objects.Candidate() newCand.setRelationsList(relations) return flag_work_done, newCand #============================================================================================================== # Make a crossover between two solutions def crossover(cand1, cand2, twoPointsCross=-1): # The number of changes between parents will always be equal (same crossover segment size), never same size of Num of Parents Relations # twoPointsCross = False -> its chosen only one point, will have changes from the 0 relation till the chosed point # What is equal '-1' will be a random choice if(twoPointsCross == -1): twoPointsCross = random.choice([True, False]) # Getting all relations from Candidates to work with relations1 = cand1.getRelationsList()[:] relations2 = cand2.getRelationsList()[:] # OnePoint type: if(not twoPointsCross): point1 = 0 # Default initial point ('first-half') - if we make changes on 'second-half' the result woud be the same point2 = random.randrange(len(relations1)) # Randomly choosing other point that can be equal to 'point1' # Granting that not occur only a copy of parents - the chosen point is not the last relation while(point2 == len(relations1)-1): point2 = random.randrange(len(relations1)) # twoPointsCross Type else: # Generating, randomly two numbers to create a patch - can be a single modification (when p1=p2) point1, point2 = random.randrange(len(relations1)), random.randrange(len(relations1)) # Granting that 'point2' is bigger than 'point1' if(point2 < point1): p = point1 point1 = point2 point2 = p # Granting that the crossover do not only copy all relations of one Cand to the another while(point2 - point1 == len(relations1) - 1): # Generating, randomly two numbers to create a patch - can be a single modification (when p1==p2) point1, point2 = random.randrange(len(relations1)), random.randrange(len(relations1)) # Granting that 'point2' is bigger than 'point1' if(point2 < point1): p = point1 point1 = point2 point2 = p # Passing through the relations between Parents making all changes while (point1 <= point2): # Recording the original relations s1, p1 = relations1[point1] s2, p2 = relations2[point1] # Making the exchange of relations (changing only professors) relations1[point1] = s1, p2 relations2[point1] = s2, p1 # Next relation point1 = point1 + 1 # Creating and setting the two new Candidates newCand1, newCand2 = objects.Candidate(), objects.Candidate() newCand1.setRelationsList(relations1) newCand2.setRelationsList(relations2) # Returning the new Candidates return newCand1, newCand2 #============================================================================================================== # Selection by elitism def elitismSelection(objectsList, valuesList, objectiveNum): selectedObj = [] # List with the selected Objects objectsList = objectsList[:] valuesList = valuesList[:] # Getting the maximal Value Solutions while(len(selectedObj) < objectiveNum): # Finding the maximal value in the list and its respective index maxValue = max(valuesList) maxIndex = valuesList.index(maxValue) # Adding selected object to list selectedObj.append(objectsList[maxIndex]) # Removing maximal Value/Object to next selection valuesList.pop(maxIndex) objectsList.pop(maxIndex) return selectedObj, objectsList, valuesList #============================================================================================================== # Make selection of objects by Roulette Wheel def rouletteWheel(objectsList, valuesList, objectiveNum, repos=0): # objectiveNum: Num of objects will be selected # repos: Type of wheel (with reposition) # Making a copy of the original lists to work with objectsList = objectsList[:] valuesList = valuesList[:] # List with the selected Objects selectedObj = [] # Flag that allows to make all important calcs at least one time when the Roulette is configured to have Reposition reCalc = True while(len(selectedObj) < objectiveNum): # Allow the Updating of the data for the next Roulette Round without the object that was recent selected on past round if(reCalc): # When the Roulette process does have reposition of objects if(repos): reCalc = False # Find the total Value of the Objects totalValue = sum([value for value in valuesList]) # If all values are Zero if(totalValue == 0.0): valuesList = [1.0 for _ in valuesList] totalValue = len(valuesList) # Calculate the prob. of a selection for each object probObj = [float(value / totalValue) for value in valuesList] # Calculate a cumulative prob. for each object cumulative = 0.0 cumulativeProbObj = [] for q in probObj: qNew = q + cumulative cumulativeProbObj.append(qNew) cumulative = qNew # MAIN Roulette Wheel Selection process (one round) probPrev = 0.0 r = float(random.randrange(101) / 100.0) #r = float(random.randrange(0, 1, 0.001)) for i in range(len(cumulativeProbObj)): if(probPrev < r and r <= cumulativeProbObj[i]): # Adding the selected Object to 'selectedObj' selectedObj.append(objectsList[i]) if(not repos): # Removing the selected object/value from 'valuesList' to do next roulette process valuesList.pop(i) objectsList.pop(i) break probPrev = cumulativeProbObj[i] # Removing from 'objectsList' the selected objects (not removed before because of the reposition) # If there are repeated objects, (objectsList + selectedObj) will be larger then original objectsList size if(repos): for i in selectedObj: try: index = objectsList.index(i) objectsList.pop(index) valuesList.pop(index) except ValueError: index = -1 return selectedObj, objectsList, valuesList #============================================================================================================== # Detect the stop condition def stop(asks, curr_Iter, maxNum_Iter, lastMaxFit_Iter, convergDetect, maxFitFea): if(curr_Iter == maxNum_Iter): return (True if asks == 0 else ioData.askStop()) # Reached max num of iterations if(convergDetect != 0 and curr_Iter - lastMaxFit_Iter == convergDetect): return (True if asks == 0 else ioData.askStop()) # Reached convergence num of iterations return False # Continues the run with same num of iterations #==============================================================================================================

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11e388ebd565f092940b5ad2ddba87b868dac5de

3,171

py

Python

HyperV/WS2012R2/stress/StorVSCIOZoneTest.py

microsoft/FreeBSD-Test-Automation

e96a84054d771ece83908299d37e3c02a19f98b3

[ "Apache-2.0" ]

1

2020-01-16T08:45:59.000Z

HyperV/WS2012R2/stress/StorVSCIOZoneTest.py

LIS/FreeBSD-Test-Automation

e96a84054d771ece83908299d37e3c02a19f98b3

[ "Apache-2.0" ]

null

HyperV/WS2012R2/stress/StorVSCIOZoneTest.py

LIS/FreeBSD-Test-Automation

e96a84054d771ece83908299d37e3c02a19f98b3

[ "Apache-2.0" ]

1

2021-08-03T00:22:40.000Z

#!/usr/bin/env python import sys import os import time import test_class import subprocess class StorVSCIOZoneTest(test_class.TestClass): def _set_up_vm(self, vm_name, args): # this piece of code will be executed first thing after the VM is # booted up args['working_dir'] = self._test_param(None)['working_dir'] test_class._run_on_vm(self, vm_name, "install_iozone", args) test_class._run_on_vm(self, vm_name, "format_drive", args) def _set_up_host(self, host_name, args): # BEFORE the VM boots up, this function will be called to prepare # the host. # Tasks could include creating VM, configuring VM and install host # software. pass def format_drive(self, args): DEFAULT_SCSI_DRIVE = '/dev/da1' if os.path.exists(DEFAULT_SCSI_DRIVE + 'p1'): # delete the partition subprocess.call(["gpart", "delete", "-i", "1", DEFAULT_SCSI_DRIVE]) subprocess.call(["gpart", "destroy", DEFAULT_SCSI_DRIVE]) time.sleep(2) subprocess.call(["gpart", "create", "-s", "GPT", DEFAULT_SCSI_DRIVE]) subprocess.call(["gpart", "add", "-t", "freebsd-ufs", DEFAULT_SCSI_DRIVE]) subprocess.call(["newfs", DEFAULT_SCSI_DRIVE + "p1"]) time.sleep(5) subprocess.call(["mount", DEFAULT_SCSI_DRIVE + "p1", args['working_dir']]) def install_iozone(self, args): logfile = open('install-iozone.log', 'w') p = subprocess.Popen(["pkg", "install", "-y" , "iozone"], stdout = logfile, stderr = logfile) p.wait() logfile.close() def run_iozone(self, args): # remember to copy the logs logfile = open('iozone.log', 'w') # make IOZone run on a separate drive os.chdir(args['working_dir']) p = subprocess.Popen(["iozone", "-a", "-z", "-g10g", "-Vshostc"], stdout=logfile, stderr=logfile) p.wait() logfile.close() def _run(self, args): # get a host... # yes I know it's ugly host_one = self._machines[0]['host'] # get a VM vm_one = self._machines[0]['vms'][0]['name'] args['working_dir'] = self._test_param(None)['working_dir'] test_class._run_on_vm(self, vm_one, "run_iozone", args) def _tear_down(self, args): pass def _request_machines(self): # EXAMPLE: requesting machines from pool # the size of the request array will be the number of hosts # required, and each array element indicates how many VMs are # required on that host. # only 1 VM on 1 host is required request = {'pool': 'stress', 'desc': 'storvsc_IOZone', 'req': [1] } return request def _test_param(self, args): param = { 'multi-threaded': True, 'snapshot': 'ICABase', 'remote_path': '/root/', 'working_dir': '/mnt/test' } return param

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null

0

null

0

1

0

11e3f9c5f47a0f678f4c4be381a8ca3e9eaec6d2

16,809

py

Python

LDDMM_Python/lddmm_python/lib/plotly/colors.py

tt6746690/lddmm-ot

98e45d44969221b0fc8206560d9b7a655ef7e137

[ "MIT" ]

48

2017-08-04T03:30:22.000Z

2022-03-09T03:24:11.000Z

LDDMM_Python/lddmm_python/lib/plotly/colors.py

hushunbo/lddmm-ot

5af26fe32ae440c598ed403ce2876e98d6e1c692

[ "MIT" ]

null

LDDMM_Python/lddmm_python/lib/plotly/colors.py

hushunbo/lddmm-ot

5af26fe32ae440c598ed403ce2876e98d6e1c692

[ "MIT" ]

15

2017-09-30T18:55:48.000Z

2021-04-27T18:27:55.000Z

""" colors ===== Functions that manipulate colors and arrays of colors There are three basic types of color types: rgb, hex and tuple: rgb - An rgb color is a string of the form 'rgb(a,b,c)' where a, b and c are floats between 0 and 255 inclusive. hex - A hex color is a string of the form '#xxxxxx' where each x is a character that belongs to the set [0,1,2,3,4,5,6,7,8,9,a,b,c,d,e,f]. This is just the list of characters used in the hexadecimal numeric system. tuple - A tuple color is a 3-tuple of the form (a,b,c) where a, b and c are floats between 0 and 1 inclusive. """ from __future__ import absolute_import from plotly import exceptions from numbers import Number DEFAULT_PLOTLY_COLORS = ['rgb(31, 119, 180)', 'rgb(255, 127, 14)', 'rgb(44, 160, 44)', 'rgb(214, 39, 40)', 'rgb(148, 103, 189)', 'rgb(140, 86, 75)', 'rgb(227, 119, 194)', 'rgb(127, 127, 127)', 'rgb(188, 189, 34)', 'rgb(23, 190, 207)'] PLOTLY_SCALES = { 'Greys': [ [0, 'rgb(0,0,0)'], [1, 'rgb(255,255,255)'] ], 'YlGnBu': [ [0, 'rgb(8,29,88)'], [0.125, 'rgb(37,52,148)'], [0.25, 'rgb(34,94,168)'], [0.375, 'rgb(29,145,192)'], [0.5, 'rgb(65,182,196)'], [0.625, 'rgb(127,205,187)'], [0.75, 'rgb(199,233,180)'], [0.875, 'rgb(237,248,217)'], [1, 'rgb(255,255,217)'] ], 'Greens': [ [0, 'rgb(0,68,27)'], [0.125, 'rgb(0,109,44)'], [0.25, 'rgb(35,139,69)'], [0.375, 'rgb(65,171,93)'], [0.5, 'rgb(116,196,118)'], [0.625, 'rgb(161,217,155)'], [0.75, 'rgb(199,233,192)'], [0.875, 'rgb(229,245,224)'], [1, 'rgb(247,252,245)'] ], 'YlOrRd': [ [0, 'rgb(128,0,38)'], [0.125, 'rgb(189,0,38)'], [0.25, 'rgb(227,26,28)'], [0.375, 'rgb(252,78,42)'], [0.5, 'rgb(253,141,60)'], [0.625, 'rgb(254,178,76)'], [0.75, 'rgb(254,217,118)'], [0.875, 'rgb(255,237,160)'], [1, 'rgb(255,255,204)'] ], 'Bluered': [ [0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)'] ], # modified RdBu based on # www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf 'RdBu': [ [0, 'rgb(5,10,172)'], [0.35, 'rgb(106,137,247)'], [0.5, 'rgb(190,190,190)'], [0.6, 'rgb(220,170,132)'], [0.7, 'rgb(230,145,90)'], [1, 'rgb(178,10,28)'] ], # Scale for non-negative numeric values 'Reds': [ [0, 'rgb(220,220,220)'], [0.2, 'rgb(245,195,157)'], [0.4, 'rgb(245,160,105)'], [1, 'rgb(178,10,28)'] ], # Scale for non-positive numeric values 'Blues': [ [0, 'rgb(5,10,172)'], [0.35, 'rgb(40,60,190)'], [0.5, 'rgb(70,100,245)'], [0.6, 'rgb(90,120,245)'], [0.7, 'rgb(106,137,247)'], [1, 'rgb(220,220,220)'] ], 'Picnic': [ [0, 'rgb(0,0,255)'], [0.1, 'rgb(51,153,255)'], [0.2, 'rgb(102,204,255)'], [0.3, 'rgb(153,204,255)'], [0.4, 'rgb(204,204,255)'], [0.5, 'rgb(255,255,255)'], [0.6, 'rgb(255,204,255)'], [0.7, 'rgb(255,153,255)'], [0.8, 'rgb(255,102,204)'], [0.9, 'rgb(255,102,102)'], [1, 'rgb(255,0,0)'] ], 'Rainbow': [ [0, 'rgb(150,0,90)'], [0.125, 'rgb(0,0,200)'], [0.25, 'rgb(0,25,255)'], [0.375, 'rgb(0,152,255)'], [0.5, 'rgb(44,255,150)'], [0.625, 'rgb(151,255,0)'], [0.75, 'rgb(255,234,0)'], [0.875, 'rgb(255,111,0)'], [1, 'rgb(255,0,0)'] ], 'Portland': [ [0, 'rgb(12,51,131)'], [0.25, 'rgb(10,136,186)'], [0.5, 'rgb(242,211,56)'], [0.75, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)'] ], 'Jet': [ [0, 'rgb(0,0,131)'], [0.125, 'rgb(0,60,170)'], [0.375, 'rgb(5,255,255)'], [0.625, 'rgb(255,255,0)'], [0.875, 'rgb(250,0,0)'], [1, 'rgb(128,0,0)'] ], 'Hot': [ [0, 'rgb(0,0,0)'], [0.3, 'rgb(230,0,0)'], [0.6, 'rgb(255,210,0)'], [1, 'rgb(255,255,255)'] ], 'Blackbody': [ [0, 'rgb(0,0,0)'], [0.2, 'rgb(230,0,0)'], [0.4, 'rgb(230,210,0)'], [0.7, 'rgb(255,255,255)'], [1, 'rgb(160,200,255)'] ], 'Earth': [ [0, 'rgb(0,0,130)'], [0.1, 'rgb(0,180,180)'], [0.2, 'rgb(40,210,40)'], [0.4, 'rgb(230,230,50)'], [0.6, 'rgb(120,70,20)'], [1, 'rgb(255,255,255)'] ], 'Electric': [ [0, 'rgb(0,0,0)'], [0.15, 'rgb(30,0,100)'], [0.4, 'rgb(120,0,100)'], [0.6, 'rgb(160,90,0)'], [0.8, 'rgb(230,200,0)'], [1, 'rgb(255,250,220)'] ], 'Viridis': [ [0, '#440154'], [0.06274509803921569, '#48186a'], [0.12549019607843137, '#472d7b'], [0.18823529411764706, '#424086'], [0.25098039215686274, '#3b528b'], [0.3137254901960784, '#33638d'], [0.3764705882352941, '#2c728e'], [0.4392156862745098, '#26828e'], [0.5019607843137255, '#21918c'], [0.5647058823529412, '#1fa088'], [0.6274509803921569, '#28ae80'], [0.6901960784313725, '#3fbc73'], [0.7529411764705882, '#5ec962'], [0.8156862745098039, '#84d44b'], [0.8784313725490196, '#addc30'], [0.9411764705882353, '#d8e219'], [1, '#fde725'] ] } def color_parser(colors, function): """ Takes color(s) and a function and applies the function on the color(s) In particular, this function identifies whether the given color object is an iterable or not and applies the given color-parsing function to the color or iterable of colors. If given an iterable, it will only be able to work with it if all items in the iterable are of the same type - rgb string, hex string or tuple """ if isinstance(colors, str): return function(colors) if isinstance(colors, tuple) and isinstance(colors[0], Number): return function(colors) if hasattr(colors, '__iter__'): if isinstance(colors, tuple): new_color_tuple = tuple(function(item) for item in colors) return new_color_tuple else: new_color_list = [function(item) for item in colors] return new_color_list def validate_colors(colors): """ Validates color(s) and returns an error for invalid colors """ colors_list = [] if isinstance(colors, str): if colors in PLOTLY_SCALES: return elif 'rgb' in colors or '#' in colors: colors_list = [colors] else: raise exceptions.PlotlyError( "If your colors variable is a string, it must be a " "Plotly scale, an rgb color or a hex color." ) elif isinstance(colors, tuple): if isinstance(colors[0], Number): colors_list = [colors] else: colors_list = list(colors) if isinstance(colors, dict): colors_list.extend(colors.values()) elif isinstance(colors, list): colors_list = colors # Validate colors in colors_list for j, each_color in enumerate(colors_list): if 'rgb' in each_color: each_color = color_parser( each_color, unlabel_rgb ) for value in each_color: if value > 255.0: raise exceptions.PlotlyError( "Whoops! The elements in your rgb colors " "tuples cannot exceed 255.0." ) elif '#' in each_color: each_color = color_parser( each_color, hex_to_rgb ) elif isinstance(each_color, tuple): for value in each_color: if value > 1.0: raise exceptions.PlotlyError( "Whoops! The elements in your colors tuples " "cannot exceed 1.0." ) return colors def convert_colors_to_same_type(colors, colortype='rgb'): """ Converts color(s) to the specified color type Takes a single color or an iterable of colors and outputs a list of the color(s) converted all to an rgb or tuple color type. If colors is a Plotly Scale name then the cooresponding colorscale will be outputted and colortype will not be applicable """ colors_list = [] if isinstance(colors, str): if colors in PLOTLY_SCALES: return PLOTLY_SCALES[colors] elif 'rgb' in colors or '#' in colors: colors_list = [colors] else: raise exceptions.PlotlyError( "If your colors variable is a string, it must be a Plotly " "scale, an rgb color or a hex color.") elif isinstance(colors, tuple): if isinstance(colors[0], Number): colors_list = [colors] else: colors_list = list(colors) elif isinstance(colors, list): colors_list = colors # convert all colors to rgb for j, each_color in enumerate(colors_list): if '#' in each_color: each_color = color_parser( each_color, hex_to_rgb ) each_color = color_parser( each_color, label_rgb ) colors_list[j] = each_color elif isinstance(each_color, tuple): each_color = color_parser( each_color, convert_to_RGB_255 ) each_color = color_parser( each_color, label_rgb ) colors_list[j] = each_color if colortype == 'rgb': return colors_list elif colortype == 'tuple': for j, each_color in enumerate(colors_list): each_color = color_parser( each_color, unlabel_rgb ) each_color = color_parser( each_color, unconvert_from_RGB_255 ) colors_list[j] = each_color return colors_list else: raise exceptions.PlotlyError("You must select either rgb or tuple " "for your colortype variable.") def convert_dict_colors_to_same_type(colors, colortype='rgb'): """ Converts color(s) to the specified color type Takes a single color or an iterable of colors and outputs a list of the color(s) converted all to an rgb or tuple color type. If colors is a Plotly Scale name then the cooresponding colorscale will be outputted """ for key in colors: if '#' in colors[key]: colors[key] = color_parser( colors[key], hex_to_rgb ) colors[key] = color_parser( colors[key], label_rgb ) elif isinstance(colors[key], tuple): colors[key] = color_parser( colors[key], convert_to_RGB_255 ) colors[key] = color_parser( colors[key], label_rgb ) if colortype == 'rgb': return colors elif colortype == 'tuple': for key in colors: colors[key] = color_parser( colors[key], unlabel_rgb ) colors[key] = color_parser( colors[key], unconvert_from_RGB_255 ) return colors else: raise exceptions.PlotlyError("You must select either rgb or tuple " "for your colortype variable.") def make_colorscale(colors, scale=None): """ Makes a colorscale from a list of colors and a scale Takes a list of colors and scales and constructs a colorscale based on the colors in sequential order. If 'scale' is left empty, a linear- interpolated colorscale will be generated. If 'scale' is a specificed list, it must be the same legnth as colors and must contain all floats For documentation regarding to the form of the output, see https://plot.ly/python/reference/#mesh3d-colorscale """ colorscale = [] # validate minimum colors length of 2 if len(colors) < 2: raise exceptions.PlotlyError("You must input a list of colors that " "has at least two colors.") if not scale: scale_incr = 1./(len(colors) - 1) return [[i * scale_incr, color] for i, color in enumerate(colors)] else: # validate scale if len(colors) != len(scale): raise exceptions.PlotlyError("The length of colors and scale " "must be the same.") if (scale[0] != 0) or (scale[-1] != 1): raise exceptions.PlotlyError( "The first and last number in scale must be 0.0 and 1.0 " "respectively." ) for j in range(1, len(scale)): if scale[j] <= scale[j-1]: raise exceptions.PlotlyError( "'scale' must be a list that contains an increasing " "sequence of numbers where the first and last number are" "0.0 and 1.0 respectively." ) colorscale = [list(tup) for tup in zip(scale, colors)] return colorscale def find_intermediate_color(lowcolor, highcolor, intermed): """ Returns the color at a given distance between two colors This function takes two color tuples, where each element is between 0 and 1, along with a value 0 < intermed < 1 and returns a color that is intermed-percent from lowcolor to highcolor """ diff_0 = float(highcolor[0] - lowcolor[0]) diff_1 = float(highcolor[1] - lowcolor[1]) diff_2 = float(highcolor[2] - lowcolor[2]) inter_colors = (lowcolor[0] + intermed * diff_0, lowcolor[1] + intermed * diff_1, lowcolor[2] + intermed * diff_2) return inter_colors def unconvert_from_RGB_255(colors): """ Return a tuple where each element gets divided by 255 Takes a (list of) color tuple(s) where each element is between 0 and 255. Returns the same tuples where each tuple element is normalized to a value between 0 and 1 """ un_rgb_color = (colors[0]/(255.0), colors[1]/(255.0), colors[2]/(255.0)) return un_rgb_color def convert_to_RGB_255(colors): """ Multiplies each element of a triplet by 255 """ return (colors[0]*255.0, colors[1]*255.0, colors[2]*255.0) def n_colors(lowcolor, highcolor, n_colors): """ Splits a low and high color into a list of n_colors colors in it Accepts two color tuples and returns a list of n_colors colors which form the intermediate colors between lowcolor and highcolor from linearly interpolating through RGB space """ diff_0 = float(highcolor[0] - lowcolor[0]) incr_0 = diff_0/(n_colors - 1) diff_1 = float(highcolor[1] - lowcolor[1]) incr_1 = diff_1/(n_colors - 1) diff_2 = float(highcolor[2] - lowcolor[2]) incr_2 = diff_2/(n_colors - 1) color_tuples = [] for index in range(n_colors): new_tuple = (lowcolor[0] + (index * incr_0), lowcolor[1] + (index * incr_1), lowcolor[2] + (index * incr_2)) color_tuples.append(new_tuple) return color_tuples def label_rgb(colors): """ Takes tuple (a, b, c) and returns an rgb color 'rgb(a, b, c)' """ return ('rgb(%s, %s, %s)' % (colors[0], colors[1], colors[2])) def unlabel_rgb(colors): """ Takes rgb color(s) 'rgb(a, b, c)' and returns tuple(s) (a, b, c) This function takes either an 'rgb(a, b, c)' color or a list of such colors and returns the color tuples in tuple(s) (a, b, c) """ str_vals = '' for index in range(len(colors)): try: float(colors[index]) str_vals = str_vals + colors[index] except ValueError: if colors[index] == ',' or colors[index] == '.': str_vals = str_vals + colors[index] str_vals = str_vals + ',' numbers = [] str_num = '' for char in str_vals: if char != ',': str_num = str_num + char else: numbers.append(float(str_num)) str_num = '' return (numbers[0], numbers[1], numbers[2]) def hex_to_rgb(value): """ Calculates rgb values from a hex color code. :param (string) value: Hex color string :rtype (tuple) (r_value, g_value, b_value): tuple of rgb values """ value = value.lstrip('#') hex_total_length = len(value) rgb_section_length = hex_total_length // 3 return tuple(int(value[i:i + rgb_section_length], 16) for i in range(0, hex_total_length, rgb_section_length)) def colorscale_to_colors(colorscale): """ Converts a colorscale into a list of colors """ color_list = [] for color in colorscale: color_list.append(color[1]) return color_list

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11e3feaa8eddda799c32e0dc2f9c36ee4b41ba9c

420

py

Python

nonebot/consts.py

he0119/nonebot2

bd7ee0a1bafc0ea7a7501ba37541349d4a81b73e

[ "MIT" ]

1

2022-01-26T12:52:33.000Z

nonebot/consts.py

he0119/nonebot2

bd7ee0a1bafc0ea7a7501ba37541349d4a81b73e

[ "MIT" ]

null

nonebot/consts.py

he0119/nonebot2

bd7ee0a1bafc0ea7a7501ba37541349d4a81b73e

[ "MIT" ]

null

# used by Matcher RECEIVE_KEY = "_receive_{id}" LAST_RECEIVE_KEY = "_last_receive" ARG_KEY = "{key}" REJECT_TARGET = "_current_target" REJECT_CACHE_TARGET = "_next_target" # used by Rule PREFIX_KEY = "_prefix" CMD_KEY = "command" RAW_CMD_KEY = "raw_command" CMD_ARG_KEY = "command_arg" SHELL_ARGS = "_args" SHELL_ARGV = "_argv" REGEX_MATCHED = "_matched" REGEX_GROUP = "_matched_groups" REGEX_DICT = "_matched_dict"

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null

0

1

0

11ed16385a989b7c743480e1ee477feb796f62cc

9,845

py

Python

iaso/tests/api/test_token.py

ekhalilbsq/iaso

e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee

[ "MIT" ]

29

2020-12-26T07:22:19.000Z

2022-03-07T13:40:09.000Z

iaso/tests/api/test_token.py

ekhalilbsq/iaso

e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee

[ "MIT" ]

150

2020-11-09T15:03:27.000Z

2022-03-07T15:36:07.000Z

iaso/tests/api/test_token.py

ekhalilbsq/iaso

e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee

[ "MIT" ]

4

2020-11-09T10:38:13.000Z

2021-10-04T09:42:47.000Z

from django.test import tag from django.core.files import File from unittest import mock from iaso import models as m from iaso.test import APITestCase class TokenAPITestCase(APITestCase): @classmethod def setUpTestData(cls): data_source = m.DataSource.objects.create(name="counsil") version = m.SourceVersion.objects.create(data_source=data_source, number=1) star_wars = m.Account.objects.create(name="Star Wars", default_version=version) cls.yoda = cls.create_user_with_profile(username="yoda", account=star_wars) cls.yoda.set_password("IMomLove") cls.yoda.save() cls.jedi_council = m.OrgUnitType.objects.create(name="Jedi Council", short_name="Cnc") cls.jedi_council_corruscant = m.OrgUnit.objects.create(name="Corruscant Jedi Council") cls.project = m.Project.objects.create( name="Hydroponic gardens", app_id="stars.empire.agriculture.hydroponics", account=star_wars, needs_authentication=True, ) cls.form_1 = m.Form.objects.create(name="Hydroponics study", period_type=m.MONTH, single_per_period=True) cls.form_2 = m.Form.objects.create( name="Hydroponic public survey", form_id="sample2", device_field="deviceid", location_field="geoloc", period_type="QUARTER", single_per_period=True, ) form_2_file_mock = mock.MagicMock(spec=File) form_2_file_mock.name = "test.xml" cls.form_2.form_versions.create(file=form_2_file_mock, version_id="2020022401") cls.form_2.org_unit_types.add(cls.jedi_council) cls.create_form_instance(form=cls.form_2, period="202001", org_unit=cls.jedi_council_corruscant) cls.form_2.save() cls.project.unit_types.add(cls.jedi_council) cls.project.forms.add(cls.form_1) cls.project.forms.add(cls.form_2) cls.project.save() def authenticate_using_token(self): response = self.client.post(f"/api/token/", data={"username": "yoda", "password": "IMomLove"}, format="json") self.assertJSONResponse(response, 200) response_data = response.json() access_token = response_data.get("access") self.client.credentials(HTTP_AUTHORIZATION=f"Bearer {access_token}") return response_data def test_acquire_token_and_authenticate(self): """Test token authentication""" self.authenticate_using_token() response = self.client.get("/api/forms/?app_id=stars.empire.agriculture.hydroponics") self.assertJSONResponse(response, 200) response_data = response.json() form_ids = [f["id"] for f in response_data["forms"]] self.assertTrue(self.form_2.id in form_ids) def test_acquire_token_and_post_instance(self): """Test upload to a project that requires authentication""" # Unauthenticated case is already tested in test_api self.authenticate_using_token() uuid = "4b7c3954-f69a-4b99-83b1-df73957b32E1" instance_body = [ { "id": uuid, "latitude": 4.4, "created_at": 1565258153704, "updated_at": 1565258153704, "orgUnitId": self.jedi_council_corruscant.id, "formId": self.form_2.id, "longitude": 4.4, "accuracy": 10, "altitude": 100, "file": "\/storage\/emulated\/0\/odk\/instances\/RDC Collecte Data DPS_2_2019-08-08_11-54-46\/RDC Collecte Data DPS_2_2019-08-08_11-54-46.xml", "name": "the name", } ] response = self.client.post( "/api/instances/?app_id=stars.empire.agriculture.hydroponics", data=instance_body, format="json" ) self.assertEqual(response.status_code, 200) self.assertTrue(m.Instance.objects.filter(uuid=uuid).first() is not None) def test_unauthenticated_post_instance(self): """Test unauthenticated upload to a project that requires authentication""" # Unauthenticated case is already tested in test_api uuid = "4b7c3954-f69a-4b99-83b1-df73957b32E2" instance_body = [ { "id": uuid, "latitude": 4.4, "created_at": 1565258153704, "updated_at": 1565258153704, "orgUnitId": self.jedi_council_corruscant.id, "formId": self.form_2.id, "longitude": 4.4, "accuracy": 10, "altitude": 100, "file": "\/storage\/emulated\/0\/odk\/instances\/RDC Collecte Data DPS_2_2019-08-08_11-54-46\/RDC Collecte Data DPS_2_2019-08-08_11-54-46.xml", "name": "the name", } ] response = self.client.post( "/api/instances/?app_id=stars.empire.agriculture.hydroponics", data=instance_body, format="json" ) self.assertEqual(response.status_code, 200) self.assertIsNone(m.Instance.objects.filter(uuid=uuid).first()) # The result is that the instance is not created, even though the api sent back a 200 # this is normal: we want the api to accept all creations requests to be able to debug on the server # and not have data stuck on a mobile phone. # An APIImport record with has_problem set to True should be created self.assertAPIImport( "instance", request_body=instance_body, has_problems=True, exception_contains_string="Could not find project for user", ) def test_refresh(self): """Test refreshing authentication token""" # Unauthenticated case is already tested in test_api response_data = self.authenticate_using_token() refresh_token = response_data.get("refresh") response = self.client.post(f"/api/token/refresh/", data={"refresh": refresh_token}, format="json") self.assertJSONResponse(response, 200) response_data = response.json() access_token_2 = response_data.get("access") self.client.credentials(HTTP_AUTHORIZATION=f"Bearer {access_token_2}") # test an endpoint that requires authentication response = self.client.get("/api/orgunits/?app_id=stars.empire.agriculture.hydroponics") self.assertJSONResponse(response, 200) def test_no_token(self): """Test invalid authentication tokens""" # Unauthenticated case is already tested in test_api self.client.credentials(HTTP_AUTHORIZATION=f"Bearer ") # test an endpoint that requires authentication response = self.client.get("/api/groups/?app_id=stars.empire.agriculture.hydroponics") self.assertJSONResponse(response, 403) self.client.credentials(HTTP_AUTHORIZATION=f"Bearer WRONG") # test an endpoint that requires authentication response = self.client.get("/api/groups/?app_id=stars.empire.agriculture.hydroponics") self.assertJSONResponse(response, 403) def test_acquire_token_and_post_org_unit(self): """Test upload to a project that requires authentication""" # Unauthenticated case is already tested in test_api self.authenticate_using_token() uuid = "r5dx2671-bb59-4fb2-a4a0-4af80573e2de" name = "Kashyyyk Wookies Council" unit_body = [ { "id": uuid, "latitude": 0, "created_at": 1565194077692, "updated_at": 1565194077693, "org_unit_type_id": self.jedi_council.id, "parent_id": None, "longitude": 0, "accuracy": 0, "altitude": 0, "time": 0, "name": name, } ] response = self.client.post( "/api/orgunits/?app_id=stars.empire.agriculture.hydroponics", data=unit_body, format="json" ) self.assertEqual(response.status_code, 200) self.assertTrue(m.OrgUnit.objects.filter(uuid=uuid).first() is not None) self.assertAPIImport("orgUnit", request_body=unit_body, has_problems=False, check_auth_header=True) def test_unauthenticated_post_org_unit(self): """Test upload to a project that requires authentication without token""" # Unauthenticated case is already tested in test_api uuid = "s5dx2671-ac59-4fb2-a4a0-4af80573e2de" name = "Antar 4 Council" unit_body = [ { "id": uuid, "latitude": 0, "created_at": 1565194077692, "updated_at": 1565194077693, "org_unit_type_id": self.jedi_council.id, "parent_id": None, "longitude": 0, "accuracy": 0, "altitude": 0, "time": 0, "name": name, } ] response = self.client.post( "/api/orgunits/?app_id=stars.empire.agriculture.hydroponics", data=unit_body, format="json" ) self.assertEqual(response.status_code, 200) self.assertIsNone(m.OrgUnit.objects.filter(uuid=uuid).first()) # The result is that the org unit is not created, even though the api sent back a 200 # this is normal: we want the api to accept all creations requests to be able to debug on the server # and not have data stuck on a mobile phone. # An APIImport record with has_problem set to True should be created self.assertAPIImport( "orgUnit", request_body=unit_body, has_problems=True, exception_contains_string="Could not find project for user", )

40.020325

159

0.622854

1,154

9,845

5.146447

0.205373

0.023573

0.030308

0.024247

0.703991

0.676376

0.653982

0.614245

0.59707

0.558512

0

0.048238

0.27354

9,845

245

160

40.183673

0.782159

0.139563

0

0.451977

0

0.011299

0.205606

0.100131

0

0.101695

1

0.050847

false

0.011299

0.045198

0

0.107345

0

null

0

null

0

1

0

11f229b9297d3ad1a65bef9c394df841a9ccc992

6,552

py

Python

interpro.py

TAMU-CPT/blast-db-download

53261f08d1f9193c4f538fa90983a465502190a9

[ "BSD-3-Clause" ]

null

interpro.py

TAMU-CPT/blast-db-download

53261f08d1f9193c4f538fa90983a465502190a9

[ "BSD-3-Clause" ]

3

2017-09-15T18:58:21.000Z

2020-03-24T19:11:16.000Z

interpro.py

TAMU-CPT/blast-db-download

53261f08d1f9193c4f538fa90983a465502190a9

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import os import sys import time import datetime import logging import subprocess logging.basicConfig(level=logging.INFO) log = logging.getLogger('dl') NOW = datetime.datetime.now() SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) DOWNLOAD_ROOT = os.getcwd() VERSION = '5.22-61.0' PANTHER_VERSION = '11.1' class Timer: def __enter__(self): self.start = time.time() return self def __exit__(self, *args): self.end = time.time() self.interval = self.end - self.start class XUnitReportBuilder(object): XUNIT_TPL = """<?xml version="1.0" encoding="UTF-8"?> <testsuite name="{suite_name}" tests="{total}" errors="{errors}" failures="{failures}" skip="{skips}"> {test_cases} </testsuite> """ TESTCASE_TPL = """ <testcase classname="{classname}" name="{name}" {time}> {error} </testcase>""" ERROR_TPL = """ <error type="{test_name}" message="{errorMessage}">{errorDetails} </error>""" def __init__(self, suite_name): self.xunit_data = { 'total': 0, 'errors': 0, 'failures': 0, 'skips': 0 } self.test_cases = [] self.suite_name = suite_name def ok(self, classname, test_name, time=0): log.info("OK: [%s] %s", classname, test_name) self.xunit_data['total'] += 1 self.__add_test(test_name, classname, errors="", time=time) def error(self, classname, test_name, errorMessage, errorDetails="", time=0): log.info("ERROR: [%s] %s", classname, test_name) self.xunit_data['total'] += 1 self.__add_test(test_name, classname, errors=self.ERROR_TPL.format( errorMessage=errorMessage, errorDetails=errorDetails, test_name=test_name), time=time) def failure(self, classname, test_name, errorMessage, errorDetails="", time=0): log.info("FAIL: [%s] %s", classname, test_name) self.xunit_data['total'] += 1 self.__add_test(test_name, classname, errors=self.ERROR_TPL.format( errorMessage=errorMessage, errorDetails=errorDetails, test_name=test_name), time=time) def skip(self, classname, test_name, time=0): log.info("SKIP: [%s] %s", classname, test_name) self.xunit_data['skips'] += 1 self.xunit_data['total'] += 1 self.__add_test(test_name, classname, errors=" <skipped />", time=time) def __add_test(self, name, classname, errors, time=0): t = 'time="%s"' % time self.test_cases.append( self.TESTCASE_TPL.format(name=name, error=errors, classname=classname, time=t)) def serialize(self): self.xunit_data['test_cases'] = '\n'.join(self.test_cases) self.xunit_data['suite_name'] = self.suite_name return self.XUNIT_TPL.format(**self.xunit_data) xunit = XUnitReportBuilder('interpro_installer') def timedCommand(classname, testname, errormessage, test_file, command, shell=False, cwd=None): if os.path.exists(test_file): xunit.skip(classname, testname) else: try: if not cwd: cwd = DOWNLOAD_ROOT with Timer() as t: # If it's a shell command we automatically join things # to make our timedCommand calls completely uniform log.info('cd %s && ' % cwd + ' '.join(command)) if shell: command = ' '.join(command) subprocess.check_call(command, shell=shell, cwd=cwd) xunit.ok(classname, testname, time=t.interval) except subprocess.CalledProcessError as cpe: xunit.failure(classname, testname, errormessage, errorDetails=str(cpe), time=t.interval) raise Exception("Cannot continute") def interpro(): classname = 'interpro' extracted_dir = os.path.join(DOWNLOAD_ROOT, 'interproscan-' + VERSION) data_dir = os.path.join(extracted_dir, 'data') tarball = 'interproscan-%s-64-bit.tar.gz' % VERSION panther_tarball = 'panther-data-%s.tar.gz' % PANTHER_VERSION panther_tarball_md5 = panther_tarball + '.md5' base_data_url = 'ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/data/' # wget md5sum = tarball + '.md5' base_url = 'ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/%s/' % VERSION timedCommand(classname, 'download.tarball', 'Download failed', tarball, [ 'wget', base_url + tarball, '-O', tarball, ]) timedCommand(classname, 'download.md5sum', 'Download failed', md5sum, [ 'wget', base_url + md5sum, '-O', md5sum, ]) timedCommand(classname, 'contents.verify', 'MD5SUM failed to validate', os.path.join(extracted_dir, 'interproscan.sh'), [ 'md5sum', '-c', md5sum ]) timedCommand(classname, 'contents.extract', 'Failed to extract', os.path.join(extracted_dir, 'interproscan.sh'), [ 'tar', 'xvfz', tarball ]) timedCommand(classname, 'setup.phobius', 'Failed to install phobius', os.path.join(extracted_dir, 'bin', 'phobius', '1.01', 'phobius.pl'), [ 'tar', 'xvfz', os.path.join(os.path.pardir, 'phobius.tgz') ], cwd=extracted_dir) timedCommand(classname, 'setup.signalp', 'Failed to install signalp', os.path.join(extracted_dir, 'bin', 'signalp', '4.1', 'signalp'), [ 'tar', 'xvfz', os.path.join(os.path.pardir, 'signalp.tgz') ], cwd=extracted_dir) timedCommand(classname, 'panther.download_tarball', 'Download failed', os.path.join(extracted_dir, 'data', panther_tarball), [ 'wget', base_data_url + panther_tarball, '-O', os.path.join(extracted_dir, 'data', panther_tarball), ]) timedCommand(classname, 'panther.download_md5sum', 'Download failed', os.path.join(extracted_dir, 'data', panther_tarball_md5), [ 'wget', base_data_url + panther_tarball_md5, '-O', os.path.join(extracted_dir, 'data', panther_tarball_md5), ]) timedCommand(classname, 'panther.verify', 'MD5SUM failed to validate', os.path.join(extracted_dir, 'data', 'panther'), [ 'md5sum', '-c', panther_tarball_md5 ], cwd=data_dir) timedCommand(classname, 'panther.extract', 'Failed to extract', os.path.join(extracted_dir, 'data', 'panther'), [ 'tar', 'xvfz', panther_tarball ], cwd=data_dir) if __name__ == '__main__': try: interpro() except Exception: pass finally: # Write out the report with open(sys.argv[1], 'w') as handle: handle.write(xunit.serialize())

36.808989

144

0.6308

793

6,552

5.039092

0.218159

0.028529

0.035035

0.052302

0.368118

0.362613

0.30956

0.297047

0.258008

0.213964

0

0.010557

0.219322

6,552

177

145

37.016949

0.770674

0.022741

0

0.175182

0

0.021898

0.20944

0.044545

0

1

0.080292

false

0.007299

0.043796

0

0.175182

0

null

0

null

0

1

0

11f3026c5b723ebaca4c3ade5e133a02d8fccef0

6,423

py

Python

Developing.../main01.py

MuhikaThomas/Pro-forma

da97d9a6581f4dfbd06fe4a0db1128ebb7472d81

[ "MIT" ]

null

Developing.../main01.py

MuhikaThomas/Pro-forma

da97d9a6581f4dfbd06fe4a0db1128ebb7472d81

[ "MIT" ]

null

Developing.../main01.py

MuhikaThomas/Pro-forma

da97d9a6581f4dfbd06fe4a0db1128ebb7472d81

[ "MIT" ]

null

import kivy from kivy.app import App from kivy.uix.tabbedpanel import TabbedPanelHeader from kivy.uix.tabbedpanel import TabbedPanel from kivy.uix.floatlayout import FloatLayout from kivy.uix.scrollview import ScrollView from kivy.uix.gridlayout import GridLayout from kivy.uix.textinput import TextInput from kivy.uix.slider import Slider from kivy.uix.button import Button from kivy.uix.label import Label from kivy.lang import Builder Builder.load_string(""" """) class Proforma(App): def build(self): #*******ROOTWIDGET******* layout = GridLayout(rows=2) #*******SUB-WIDGETS******* layoutTop = GridLayout(cols=3,rows=1)#SUB-WIDGET-1 layoutTop.size_hint = (1, 0.1) layoutMid = GridLayout(cols=1, size_hint_x=1)#SUB-WIDGET-2 #*******CONTENT-OF-SUB-WIDGET-1******* backbtn = Button() title = Label(text = 'Pro-Forma App', font_size = '20sp', pos = (0,300), size_hint_y = None,size_hint_x=None, width=200, halign ='right', valign='middle') title.size_hint = (None, 0.1) dropbtn = Button() #*******CONTENT-OF-SUB-WIDGET-2******* tp_panel = TabbedPanel() tp_panel.default_tab_text = "Login Tab" #*******TAB1******* th_tab1 = TabbedPanelHeader(text = 'Pro-Forma') #*******MAIN-LAYOUT-FOR-TAB1******* mainlayout = GridLayout(cols=1, spacing=10) #*******LAYOUT-FOR-PROPERTY-INFORMATION******* layouttab1 = GridLayout(cols=2,rows=6, pos_hint ={'center_x': .5, 'center_y': .5},row_force_default=True, row_default_height=40) #*******LAYOUT-FOR-UNIT-MIX******* layoutmix = GridLayout(cols=4, pos_hint ={'center_x': .5, 'center_y': .5},row_force_default=True, row_default_height=40) #*******LAYOUT-FOR-EXPENSES******* layoutexpense = GridLayout(cols=2) #*******LAYOUT-FOR-ACCOUNTS******* #*******CONTENT1******* mainlayout.add_widget(Label(text='Property Information',size_hint_y=None, height=50)) #*******CONTENT2******* layouttab1.add_widget(Label(text= 'Property Name', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) layouttab1.add_widget(Label(text= 'Property Address', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) layouttab1.add_widget(Label(text= 'Town/City', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) layouttab1.add_widget(Label(text= 'Asking Price', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) layouttab1.add_widget(Label(text= 'Total Units', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) layouttab1.add_widget(Label(text= 'Square Footage', size_hint_x=None, width=200,size_hint_y=None, height=50, font_size='20sp')) layouttab1.add_widget(TextInput(text='input', font_size=15, halign ='left', valign='middle')) mainlayout.add_widget(layouttab1) #*******CONTENT3******* mainlayout.add_widget(Label(text='Unit Mix',size_hint_x=None, width=200, size_hint_y=None, height=50)) #*******CONTENT4******* layoutmix.add_widget(Label(text='# of Units')) layoutmix.add_widget(Label(text='Unit Type')) layoutmix.add_widget(Label(text='SquareFeet')) layoutmix.add_widget(Label(text='Monthly Rent')) layoutmix.add_widget(TextInput(text='Input', font_size=15)) layoutmix.add_widget(TextInput(text='Input', font_size=15)) layoutmix.add_widget(TextInput(text='Input', font_size=15)) layoutmix.add_widget(TextInput(text='Input', font_size=15)) mainlayout.add_widget(layoutmix) #*******CONTENT5******* mainlayout.add_widget(Label(text='Expenses',size_hint_x=None, width=200, size_hint_y=None, height=50)) #*******CONTENT6******* layoutexpense.add_widget(Label(text='Accounting')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Advertising')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Bank Charges')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Electricity')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Gas')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Security')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='All insurance')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Permits and fees')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Maintenance')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='Trash Pick-up')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) layoutexpense.add_widget(Label(text='All other')) layoutexpense.add_widget(TextInput(text='Input', font_size=15)) mainlayout.add_widget(layoutexpense) #*******CONTENT7******* mainlayout.add_widget(Label(text='Accounts')) #*******CONTENT7******* #*******SCOLLABILITY******* #*******CALLING-MAINLAYOUT-IN-TAB1******* th_tab1.content = mainlayout #___*******TAB2*******___# th_tab2 = TabbedPanelHeader(text = 'Info. Tab') #___*******TAB3*******___# th_tab3 = TabbedPanelHeader(text = 'Due Deligence') #___*******TAB4*******___# th_tab4 = TabbedPanelHeader(text = 'Saved Reports') #*******CALLING-TABS-TO-tp_panel******* tp_panel.add_widget(th_tab1) tp_panel.add_widget(th_tab2) tp_panel.add_widget(th_tab3) tp_panel.add_widget(th_tab4) #*******ADDING-CONTENTS-OF-SUB-WIDGETS******* layoutTop.add_widget(backbtn) layoutTop.add_widget(title) layoutTop.add_widget(dropbtn) layoutMid.add_widget(tp_panel) #*******ADDING-CONTENTS-OF-ROOT-WIDGET******* layout.add_widget(layoutTop) layout.add_widget(layoutMid) #*******CALLING-THE-ROOT-WIDGET******* return layout if __name__ == '__main__': Proforma().run()

47.932836

156

0.717422

867

6,423

5.096886

0.175317

0.120163

0.079203

0.101833

0.603983

0.514596

0.495135

0.492193

0

0.029226

0.089055

6,423

133

157

48.293233

0.72603

0.127978

0

0.216495

0

0.105603

0

1

0.010309

false

0

0.123711

0

0.154639

0

null

0

null

0

1

0

11f30bdb0ea58245a57190b0de64ce5ae30b036d

1,943

py

Python

day8/day8.py

jwhitex/AdventOfCode2018

e552185f7d6413ccdad824911c66a6590e8de9bb

[ "MIT" ]

null

day8/day8.py

jwhitex/AdventOfCode2018

e552185f7d6413ccdad824911c66a6590e8de9bb

[ "MIT" ]

null

day8/day8.py

jwhitex/AdventOfCode2018

e552185f7d6413ccdad824911c66a6590e8de9bb

[ "MIT" ]

null

import itertools from io import StringIO from queue import LifoQueue inputs = "2 3 0 3 10 11 12 1 1 0 1 99 2 1 1 2" #data = [int(v) for v in StringIO(inputs).read().split(' ')] data = [int(v) for v in open("day8.input").read().split(' ')] def parse_packet(idata, lifoq_children, tc_metadata): if not lifoq_children.empty(): c_childn_level = q.get() else: c_childn_level = 1 for i in range(0, c_childn_level): c_childn = next(idata, None) if c_childn is None: break # know iter not empty c_metad = next(idata) # know has value if c_childn > 0: lifoq_children.put(c_childn) tc_metadata += parse_packet(idata, lifoq_children, 0) for i in range(0, c_metad): md = next(idata) tc_metadata += md return tc_metadata # idata = iter(data) # q = LifoQueue() # tc_metadata = parse_packet(idata, q, 0) # print(tc_metadata) # pt2 def parse_packet_pt2(idata, lifoq_children): level_values = [] if not lifoq_children.empty(): c_childn_level = q.get() else: c_childn_level = 1 for i in range(0, c_childn_level): child_values = [] tc_metadata = 0 c_childn = next(idata, None) if c_childn is None: break # know iter not empty c_metad = next(idata) if c_childn > 0: lifoq_children.put(c_childn) # list of values from children child_values = parse_packet_pt2(idata, lifoq_children) for i in range(0, c_metad): md = next(idata) if c_childn == 0: tc_metadata += md else: if md == 0: continue if len(child_values) >= md: tc_metadata += child_values[md-1] level_values += [tc_metadata] return level_values idata = iter(data) q = LifoQueue() tc_metadata = parse_packet_pt2(idata, q)[0] print(tc_metadata)

30.359375

66

0.5965

284

1,943

3.866197

0.228873

0.095628

0.065574

0.040073

0.663024

0.601093

0.468124

0.327869

0

0.029608

0.304683

1,943

63

67

30.84127

0.783124

0.124035

0

0.5

0

0.027187

0

1

0.041667

false

0

0.0625

0

0.145833

0.020833

0

null

0

null

0

1

0

11f3952caf0eac585e166a957bfe31975eafdc39

2,971

py

Python

dataset_utils/roi.py

kocurvik/retinanet_traffic_3D

592ceac767750c65bb3d6678b36e6880a7bb0403

[ "Apache-2.0" ]

12

2021-04-06T00:50:41.000Z

2022-03-23T03:27:02.000Z

dataset_utils/roi.py

kocurvik/retinanet_traffic_3D

592ceac767750c65bb3d6678b36e6880a7bb0403

[ "Apache-2.0" ]

7

2021-07-13T12:47:41.000Z

2022-03-05T15:08:51.000Z

dataset_utils/roi.py

kocurvik/retinanet_traffic_3D

592ceac767750c65bb3d6678b36e6880a7bb0403

[ "Apache-2.0" ]

4

2021-07-15T12:22:06.000Z

2022-03-01T03:12:36.000Z

import json import os import cv2 import numpy as np from dataset_utils.geometry import computeCameraCalibration def line_to_point(p1, p2, p3): return np.abs(np.cross(p2 - p1, p3 - p1, axis=2) / np.linalg.norm(p2 - p1, axis=2)) def get_pts(vid_dir, json_path): video_path = os.path.join(vid_dir, 'video.avi') mask_path = os.path.join(vid_dir, 'video_mask.png') with open(json_path, 'r+') as file: # with open(os.path.join(os.path.dirname(json_path), 'system_retinanet_first.json'), 'r+') as file: structure = json.load(file) camera_calibration = structure['camera_calibration'] vp0, vp1, vp2, _, _, _ = computeCameraCalibration(camera_calibration["vp1"], camera_calibration["vp2"], camera_calibration["pp"]) vp0 = vp0[:-1] / vp0[-1] vp1 = vp1[:-1] / vp1[-1] vp2 = vp2[:-1] / vp2[-1] cap = cv2.VideoCapture(video_path) ret, frame = cap.read() frame = cv2.resize(frame, (640, 360)) vp0 = vp0 / 3 prvs = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) hsv = np.zeros_like(frame) accumulator = np.zeros([360, 640]) hsv[..., 1] = 255 y, x = np.mgrid[0:360, 0:640] yx = np.stack([x, y], axis=2) cnt = 0 while (cap.isOpened(), cnt < 10000): ret, frame2 = cap.read() frame2 = cv2.resize(frame2, (640, 360)) next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY) flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 7, 1.5, 0) d = line_to_point(yx, yx + flow, vp0) # for y in range(360): # for x in range(640): # p1 = np.array([x,y]) # d[y,x] = line_to_point(p1, p1 + flow[y,x], vp0) accepted = np.zeros_like(d) accepted[d < 3] = 1 n = np.linalg.norm(flow, axis=2) accepted[n < 1] = 0 accepted[flow[:, :, 1] < 0] = 0 accumulator = accumulator + accepted # mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1]) # hsv[..., 0] = ang * 180 / np.pi / 2 # hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) # bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) # cv2.imshow('frame2', bgr) cv2.imshow('accepted', accepted) cv2.imshow('frame', frame2) final = np.zeros_like(accumulator) final[accumulator > 0.01 * np.max(accumulator)] = 1 cv2.imshow('accumulator', accumulator / np.max(accumulator)) cv2.imshow('norm', n / np.max(n)) cv2.imshow('final', final) k = cv2.waitKey(30) & 0xff if k == 27: break prvs = next cv2.destroyAllWindows() cap.release() if __name__ == '__main__': vid_dir = 'D:/Skola/PhD/data/2016-ITS-BrnoCompSpeed/dataset/session5_left' result_path = 'D:/Skola/PhD/data/2016-ITS-BrnoCompSpeed/results/session5_left/system_SochorCVIU_Edgelets_BBScale_Reg.json' get_pts(vid_dir, result_path)

32.648352

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2,971

4.062954

0.324455

0.032181

0.019666

0.015495

0.06913

0

0.073838

0.261528

2,971

90

127

33.011111

0.690975

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0

0.017241

0.103668

0.066986

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1

0.034483

false

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0.137931

0

null

0

null

0

1

0

11f661d7ecc4156688dc11d7e9f3988ffd85ee03

1,292

py

Python

src/ansible_remote_checks/modules/check_process.py

davidvoit/ansible_remote_checks

491f31855c96297e5466b238e648fa57c1e646d0

[ "MIT" ]

null

src/ansible_remote_checks/modules/check_process.py

davidvoit/ansible_remote_checks

491f31855c96297e5466b238e648fa57c1e646d0

[ "MIT" ]

null

src/ansible_remote_checks/modules/check_process.py

davidvoit/ansible_remote_checks

491f31855c96297e5466b238e648fa57c1e646d0

[ "MIT" ]

1

2019-08-20T13:19:16.000Z

#!/usr/bin/python2 import re import subprocess from ansible.module_utils.basic import AnsibleModule def get_procs(process_regex, cmdline_regex): cmd=["ps","-hax","-o","comm pid args"] process = subprocess.Popen(cmd, stdout=subprocess.PIPE) output, error = process.communicate() lines = output.splitlines() processes = [] # Python3 reads the output as byte and needs decoding try: output = output.decode() except (UnicodeDecodeError, AttributeError): pass for line in lines: process = line.split()[0] cmdline = ' '.join(line.split()[2:]) if (not process_regex or re.findall(process_regex, process)) and (not cmdline_regex or re.findall(cmdline_regex, cmdline)): processes.append(line) return { "processes": processes } def main(): module_args= dict( process = dict(type='str', required=True), cmdline = dict(type='str') ) module = AnsibleModule( argument_spec=module_args, supports_check_mode=True ) process_regex = module.params['process'] cmdline_regex = module.params['cmdline'] procs = get_procs(process_regex, cmdline_regex) try: result = dict(procs = procs) except Exception as ex: module.fail_json(msg=str(ex)) module.exit_json(**result) if __name__ == '__main__': main()

23.490909

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1,292

5.280488

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0.034642

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0.073903

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0.003784

0.181889

1,292

54

128

23.925926

0.815516

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0.05

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0.05

false

0.025

0.075

0

0.15

0

null

0

null

0

1

0

11f7ea214def9b4195dd57f26ec40b4d4be26bb2

972

py

Python

RESSPyLab/modified_cholesky.py

ioannis-vm/RESSPyLab

306fc24d5f8ece8f2f2de274b56b80ba2019f605

[ "MIT" ]

7

2019-10-15T09:16:41.000Z

2021-09-24T11:28:45.000Z

RESSPyLab/modified_cholesky.py

ioannis-vm/RESSPyLab

306fc24d5f8ece8f2f2de274b56b80ba2019f605

[ "MIT" ]

3

2020-10-22T14:27:22.000Z

2021-11-15T17:46:49.000Z

RESSPyLab/modified_cholesky.py

ioannis-vm/RESSPyLab

306fc24d5f8ece8f2f2de274b56b80ba2019f605

[ "MIT" ]

6

2019-07-22T05:47:10.000Z

2021-10-24T02:06:26.000Z

"""@package modified_cholesky Function to perform the modified Cholesky decomposition. """ import numpy as np import numpy.linalg as la def modified_cholesky(a): """ Returns the matrix A if A is positive definite, or returns a modified A that is positive definite. :param np.array a: (n, n) The symmetric matrix, A. :return list: [np.array (n, n), float] Positive definite matrix, and the factor required to do so. See Bierlaire (2015) Alg. 11.7, pg. 278. """ iteration = 0 maximum_iterations = 10 identity = np.identity(len(a)) a_mod = a * 1.0 identity_factor = 0. successful = False while not successful and iteration < maximum_iterations: try: la.cholesky(a_mod) successful = True except la.LinAlgError: identity_factor = np.max([2 * identity_factor, 0.5 * la.norm(a, 'fro')]) a_mod = a + identity_factor * identity return [a_mod, identity_factor]

31.354839

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0.057878

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32.4

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false

0

0.117647

0

0.235294

0

null

0

null

0

1

0

11f9627891295b2fef341d114f820b8acfae0f4d

1,713

py

Python

estudo/bingo/bingo.py

PedroMoreira87/python

7f8ed2d17ba12a8089618477b2738e3b1c809e74

[ "MIT" ]

null

estudo/bingo/bingo.py

PedroMoreira87/python

7f8ed2d17ba12a8089618477b2738e3b1c809e74

[ "MIT" ]

null

estudo/bingo/bingo.py

PedroMoreira87/python

7f8ed2d17ba12a8089618477b2738e3b1c809e74

[ "MIT" ]

null

# Entregar arquivo com o código da função teste_cartela # # Verificador de cartela de bingo # # CRIAR UMA FUNÇÃO DO TIPO: # # def teste_cartela(numeros_bilhete,numeros_sorteados): #numeros_bilhete e numeros_sorteados tipo lista com valores inteiros # # ... # # return([bingo,n_acertos,p_acertos,[numeros_acertados],[numeros_faltantes]]) #retorno tipo lista # # ps: a função deve suportar qualquer tamanho das listas # # exemplo1: # # bilhete=[1,2,3,4,6] # # sorteados=[1,2,3,4,5,6,7,8,9,10] # # x=teste_cartela(bilhete,sorteados) # # print(x) # # [true,5,100.0,[1,2,3,4,6],[]] # # print(x[1]) # # 5 # # exemplo2: # bilhete=[1,4,7,13,20,22] # # sorteados=[11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] # # x=teste_cartela(bilhete,sorteados) # # print(x) # # [False,3,50.0,[13,20,22],[1,4,7]] # # print(x[3]) # # [13,20,22] bilhete1 = [1, 2, 3, 4, 6] sorteados1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] bilhete2 = [1, 4, 7, 13, 20, 22] sorteados2 = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30] def teste_cartela(numeros_bilhete, numeros_sorteados): bingo = False n_acertos = 0 numeros_acertados = [] for element in numeros_sorteados: # outro modo de fazer list(set(sorteados).intersection(bilhete)) if element in numeros_bilhete: numeros_acertados.append(element) n_acertos += 1 numeros_faltantes = list(set(numeros_bilhete) - set(numeros_sorteados)) if numeros_bilhete == numeros_acertados: bingo = True p_acertos = len(numeros_acertados) * 100 / len(numeros_bilhete) return [bingo, n_acertos, p_acertos, numeros_acertados, numeros_faltantes] print(teste_cartela(bilhete1, sorteados1))

23.148649

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0.669002

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1,713

4.07326

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0.08813

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0.017986

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0.357914

0.341727

0.197842

0

0.123153

0.170461

1,713

73

125

23.465753

0.659395

0.472271

0

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false

0

0.111111

0.055556

0

null

0

null

0

1

0

11fc76302eb18d7762bad32d8a7fb8d4acc13c44

3,033

py

Python

word_breakdown.py

imjeffhi4/word-breakdown

7edf823fbc49ac56a5dc356067938d3828edc014

[ "MIT" ]

null

word_breakdown.py

imjeffhi4/word-breakdown

7edf823fbc49ac56a5dc356067938d3828edc014

[ "MIT" ]

null

word_breakdown.py

imjeffhi4/word-breakdown

7edf823fbc49ac56a5dc356067938d3828edc014

[ "MIT" ]

null

from transformers import GPTNeoForCausalLM, GPT2Tokenizer from fastapi import FastAPI import re import json from pydantic import BaseModel from typing import Optional import torch app = FastAPI() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") morph_path = './Model' morph_tokenizer = GPT2Tokenizer.from_pretrained(morph_path) special_tokens = {'bos_token': '<|startoftext|>', 'pad_token': '<PAD>', 'additional_special_tokens':['<DEF>', '<SYLLABLES>', '<NULL>', '<ETY>', '<MORPH>']} morph_tokenizer.add_special_tokens(special_tokens) morph_model = GPTNeoForCausalLM.from_pretrained(morph_path).to(device) class UserInput(BaseModel): word: str definition: Optional[str] = None # returning WikiMorph output @app.post('/') async def main(x: UserInput): return get_morpheme_output(x.word, x.definition) def get_etymology(ety_txt): """Parses text to return a list of dict containing the etymology compound and definitions""" etys = re.findall('<ETY>.+?(?=<ETY>|$)', ety_txt) for ety in etys: compound = re.findall("<ETY>(.+?)(?=<DEF>|$)", ety)[0].strip() if "<NULL>" not in compound: ety_dict = { "Etymology Compound": re.findall("<ETY>(.+?)(?=<DEF>)", ety)[0].strip(), "Compound Meaning": re.findall("<DEF>(.+)", ety)[0].strip() } yield ety_dict else: yield {"Etymology Compound": None, "Compound Meaning": None} def parse_morphemes(morph_txt): """Parses text to return a list of affixes and a definition for each affix""" morphs = re.findall('<MORPH>.+?(?=<MORPH>|$)', morph_txt) for morph in morphs: yield { "Morpheme": re.findall("<MORPH>(.+?)(?=<DEF>)", morph)[0].strip(), "Definition": re.findall("<DEF>(.+?)(?=<ETY>)", morph)[0].strip(), "Etymology Compounds": list(get_etymology(re.findall("(<ETY>.+?)$", morph)[0].strip())) } def to_dict(generated_txt): """Returns a dictionary containing desired items""" return { "Word": re.findall('<\|startoftext\|> (.+?)(?= \w )', generated_txt)[0].strip().replace(' ', ''), "Definition": re.findall("<DEF>(.+?)(?=<SYLLABLES>)", generated_txt)[0].strip(), "Syllables": re.findall("<SYLLABLES> (.+?)(?=<MORPH>)", generated_txt)[0].strip().split(), "Morphemes": list(parse_morphemes(re.findall("(<MORPH>.+?)(?=<\|endoftext\|>)", generated_txt)[0].strip())) } def get_morpheme_output(word, definition): """Calls the GPT-based model to generated morphemes""" split_word = ' '.join(word) if definition: word_def = f'<|startoftext|> {word} {split_word} <DEF> {definition} <SYLLABLES>' else: word_def = f'<|startoftext|> {word} {split_word} <DEF> ' tokenized_string = morph_tokenizer.encode(word_def, return_tensors='pt').to(device) output = morph_model.generate(tokenized_string, max_length=400) generated_txt = morph_tokenizer.decode(output[0]) return to_dict(generated_txt)

40.986486

155

0.636334

362

3,033

5.185083

0.298343

0.057539

0.025573

0.038359

0.101225

0

0.006811

0.177052

3,033

73

156

41.547945

0.745192

0.092648

0

0.035088

0

0.22922

0.05346

0

1

0.070175

false

0

0.122807

0

0.298246

0

null

0

null

0

1

0

f506803cc0725d8f77786e4264a390f804bf912b

447

py

Python

ping_pong.py

kpbochenek/codewarz

20f600623bddd269fb845d06b1826c9e50b49594

[ "Apache-2.0" ]

null

ping_pong.py

kpbochenek/codewarz

20f600623bddd269fb845d06b1826c9e50b49594

[ "Apache-2.0" ]

null

ping_pong.py

kpbochenek/codewarz

20f600623bddd269fb845d06b1826c9e50b49594

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import sys import requests ping = sys.argv[1] pong = sys.argv[2] word = sys.argv[3] if not ping.startswith('http'): ping = 'http://' + ping if not pong.startswith('http'): pong = 'http://' + pong while True: r = requests.post(ping, data={'food': word}) answer = r.text if 'serving' not in answer: print(answer, end='') break word = answer.split()[2] ping, pong = pong, ping

17.88

48

0.592841

65

447

4.076923

0.492308

0.079245

0

0.014706

0.239374

447

24

49

18.625

0.764706

0.04698

0

0.077647

0

1

0

false

0

0.117647

0

0.117647

0.058824

0

null

0

null

0

1

0

f506a97a368ef7e32d2a9750ae1f1a3c19762e70

437

py

Python

fenixstroy/shop/forms.py

wiky-avis/fenixstroy_shop

9e5ed0425e8fc5bcd77b7a0a640484a87c2f888c

[ "MIT" ]

null

fenixstroy/shop/forms.py

wiky-avis/fenixstroy_shop

9e5ed0425e8fc5bcd77b7a0a640484a87c2f888c

[ "MIT" ]

3

2021-09-22T18:44:30.000Z

2022-03-12T00:58:02.000Z

fenixstroy/shop/forms.py

wiky-avis/fenixstroy_shop

9e5ed0425e8fc5bcd77b7a0a640484a87c2f888c

[ "MIT" ]

null

from django import forms from .models import Comment, Rating, RatingStar class RatingForm(forms.ModelForm): star = forms.ModelChoiceField( queryset=RatingStar.objects.all(), widget=forms.RadioSelect(), empty_label=None ) class Meta: model = Rating fields = ('star',) class CommentForm(forms.ModelForm): class Meta: model = Comment fields = ['author', 'text']

19.863636

47

0.631579

44

437

6.25

0.613636

0.101818

0

0.265446

437

21

48

20.809524

0.856698

0

0.133333

0

0.032037

0

1

0

false

0

0.133333

0

0.466667

0

null

0

null

0

1

0

ee921704bb61e5ef659b3c250a5774e67e1fc9fd

3,433

py

Python

lib/aquilon/consistency/checks/branch.py

ned21/aquilon

6562ea0f224cda33b72a6f7664f48d65f96bd41a

[ "Apache-2.0" ]

7

2015-07-31T05:57:30.000Z

2021-09-07T15:18:56.000Z

lib/aquilon/consistency/checks/branch.py

ned21/aquilon

6562ea0f224cda33b72a6f7664f48d65f96bd41a

[ "Apache-2.0" ]

115

2015-03-03T13:11:46.000Z

2021-09-20T12:42:24.000Z

lib/aquilon/consistency/checks/branch.py

ned21/aquilon

6562ea0f224cda33b72a6f7664f48d65f96bd41a

[ "Apache-2.0" ]

13

2015-03-03T11:17:59.000Z

2021-09-09T09:16:41.000Z

#!/usr/bin/env python # -*- cpy-indent-level: 4; indent-tabs-mode: nil -*- # ex: set expandtab softtabstop=4 shiftwidth=4: # # Copyright (C) 2013,2014,2017 Contributor # # 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 logging from aquilon.consistency.checker import ConsistencyChecker from aquilon.aqdb.model.branch import Branch from aquilon.worker.processes import run_git from aquilon.worker.dbwrappers.branch import merge_into_trash class BranchChecker(ConsistencyChecker): """ Branch Consistency Checker This module performs validation that is common for all branches (both domains and sandboxes) in template-king. """ def check(self, repair=False): # Find all of the branches that are listed in the database db_branches = {} for branch in self.session.query(Branch): db_branches[branch.name] = branch # Find all of the branches that are in the template king, this # includes both domains and sandbox's kingdir = self.config.get("broker", "kingdir") out = run_git(['for-each-ref', '--format=%(refname:short)', 'refs/heads'], path=kingdir, loglevel=logging.DEBUG) git_branches = set(out.splitlines()) # The trash branch is special if self.config.has_option("broker", "trash_branch"): git_branches.remove(self.config.get("broker", "trash_branch")) # Branches in the database and not in the template-king for branch in set(db_branches.keys()).difference(git_branches): self.failure(branch, format(db_branches[branch]), "found in the database but not in template-king") # No repair mode. We consider AQDB more canonical than # template-king, so we should not delete the DB object, and we don't # have any information how to restore the branch in template-king. # Branches in the template-king and not in the database for branch in git_branches.difference(db_branches.keys()): if repair: self.logger.info("Deleting branch %s", branch) merge_msg = [] merge_msg.append("Delete orphaned branch %s" % branch) merge_msg.append("") merge_msg.append("The consistency checker found this branch to be ") merge_msg.append("orphaned.") if self.config.has_option("broker", "trash_branch"): merge_into_trash(self.config, self.logger, branch, "\n".join(merge_msg), loglevel=logging.DEBUG) run_git(['branch', '-D', branch], path=kingdir, loglevel=logging.DEBUG) else: self.failure(branch, "Branch %s" % branch, "found in template-king but not in the database")

42.9125

84

0.637635

437

3,433

4.947368

0.416476

0.044403

0.030065

0.023589

0.108233

0.06013

0.035153

0

0.007603

0.272065

3,433

79

85

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0.857543

0.377221

0

0.111111

0

0.155577

0.011967

0

1

0.027778

false

0

0.138889

0

0.194444

0

null

0

null

0

1

0

ee92be80023074621572bda99d5be62e1b63d427

1,418

py

Python

server.py

aoii103/magicworld

cad0df6aa872cd5dcd4142f83ea9fde821652551

[ "MIT" ]

7

2018-02-05T03:14:08.000Z

2019-07-28T18:49:41.000Z

server.py

aoii103/magicworld

cad0df6aa872cd5dcd4142f83ea9fde821652551

[ "MIT" ]

null

server.py

aoii103/magicworld

cad0df6aa872cd5dcd4142f83ea9fde821652551

[ "MIT" ]

3

2019-05-21T08:58:32.000Z

2019-12-26T17:03:07.000Z

import json import os from extra import MainStart import threading import moment from jinja2 import Environment, PackageLoader from sanic import Sanic, response from sanic.log import logger from termcolor import colored from conf import config from spider import bot env = Environment(loader=PackageLoader(__name__, './template')) app = Sanic(__name__) app.static('static_path',config.static) @app.route('/') async def handle_request(request): return response.text('') idList = list(os.walk("./img"))[0][1] logger.info(colored(f'{max(idList)}','red')) if idList: return response.redirect(f"/{max(idList)}") @app.route('/<docid>') async def handle_request(request, docid): datapath = f"{config.static}/{docid}/data.json" logger.info(colored(f'load {datapath}', 'yellow')) if os.path.exists(datapath): try: with open(datapath, "r") as f: template = env.get_template('index.html') return response.html(template.render(data=json.loads(f.read()))) except Exception as e: logger.error(e) return response.html('',status=404) def run_bot(): spider = bot() spider.start() if __name__ == '__main__': SPY = threading.Thread(target=MainStart, args=(run_bot, None, config.delay)) SPY.start() app.run(host=config.host,port=config.port)

26.754717

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180

1,418

4.983333

0.45

0.06243

0.031215

0.046823

0.06243

0

0.005391

0.215092

1,418

52

82

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0

0.4

0

null

0

null

0

1

0

ee9c514425fe52fb6f66f62ee9d6108d08382363

5,332

py

Python

solutions/solution_14.py

claudiobierig/adventofcode19

40dabd7c780ab1cd8bad4292550cd9dd1d178365

[ "MIT" ]

null

solutions/solution_14.py

claudiobierig/adventofcode19

40dabd7c780ab1cd8bad4292550cd9dd1d178365

[ "MIT" ]

null

solutions/solution_14.py

claudiobierig/adventofcode19

40dabd7c780ab1cd8bad4292550cd9dd1d178365

[ "MIT" ]

null

#!/usr/bin/env python import math def read_input(path): with open(path) as file: reactions = [line.strip().split('=>') for line in file.readlines()] reactions2 = [[r[0].strip().split(","), r[1].strip()] for r in reactions] result = {} for reaction in reactions2: goal = reaction[1].strip().split() goal_resource = goal[1] goal_amount = int(goal[0]) start = [r.strip().split() for r in reaction[0]] start2 = [[int(s[0]), s[1]] for s in start] result[goal_resource] = [goal_amount, start2] return result def get_amount(resource, amount_needed, reactions, leftovers): reaction = reactions[resource] leftover = leftovers.get(resource, 0) number_of_reactions = math.ceil((amount_needed - leftover)/reaction[0]) leftovers[resource] = leftover + number_of_reactions*reaction[0] - amount_needed return [[number_of_reactions*r[0], r[1]] for r in reaction[1]] def need_reaction(required_resources): for resource in required_resources: if resource[1] != "ORE": return True return False def reduce_leftovers(leftovers, reactions): """ >>> reactions = read_input("input/14.txt") >>> leftovers = {"DWBL": 10} >>> reduce_leftovers(leftovers, reactions) >>> leftovers {'DWBL': 1, 'ORE': 149} >>> leftovers = {"ZKZHV": 9} >>> reduce_leftovers(leftovers, reactions) >>> leftovers {'ZKZHV': 1, 'KFKWH': 0, 'DWBL': 2, 'ORE': 532} >>> reduce_leftovers(leftovers, reactions) >>> leftovers {'ZKZHV': 1, 'KFKWH': 0, 'DWBL': 2, 'ORE': 532} >>> leftovers = {'FUEL': 0, 'BRTX': 1, 'CFBP': 1, 'HJPD': 3, 'HDRMK': 1, 'LWGNJ': 2, 'JVGRC': 2, 'CVZLJ': 2, 'PZRSQ': 2, 'LQBJP': 1, 'DVRS': 4, 'TNRGW': 2, 'QGVJV': 0, 'NSWDH': 6, 'XMHN': 0, 'PDKZ': 1, 'NDNP': 3, 'DBKL': 1, 'RLKDF': 0, 'DQPX': 0, 'BWHKF': 0, 'QMQB': 0, 'QZMZ': 3, 'HJFV': 0, 'SLQN': 2, 'XHKG': 6, 'KXHQW': 3, 'GHNG': 1, 'CSNS': 1, 'JVRQ': 0, 'PHBMP': 6, 'LZWR': 1, 'JKRZH': 0, 'WKFTZ': 2, 'GFDP': 3, 'ZKZHV': 0, 'XJFQR': 3, 'JQFM': 0, 'WQCT': 0, 'QMTMN': 0, 'QDJD': 0, 'FRTK': 2, 'MLJN': 8, 'LHXN': 2, 'DWBL': 1, 'MCWF': 2, 'VCMPS': 0, 'SVTK': 7, 'XNGTQ': 2, 'MXQF': 2, 'XCMJ': 3, 'NHVQD': 6, 'WGLN': 1, 'KFKWH': 0, 'VMDSG': 2, 'BMSNV': 0, 'WCMV': 4, 'ZJKB': 2, 'TDPN': 0} >>> reduce_leftovers(leftovers, reactions) >>> leftovers {'FUEL': 0, 'BRTX': 1, 'CFBP': 1, 'HJPD': 3, 'HDRMK': 1, 'LWGNJ': 2, 'JVGRC': 2, 'CVZLJ': 2, 'PZRSQ': 2, 'LQBJP': 1, 'DVRS': 4, 'TNRGW': 2, 'QGVJV': 0, 'NSWDH': 6, 'XMHN': 0, 'PDKZ': 1, 'NDNP': 3, 'DBKL': 1, 'RLKDF': 0, 'DQPX': 0, 'BWHKF': 0, 'QMQB': 0, 'QZMZ': 3, 'HJFV': 0, 'SLQN': 2, 'XHKG': 6, 'KXHQW': 3, 'GHNG': 1, 'CSNS': 1, 'JVRQ': 0, 'PHBMP': 6, 'LZWR': 1, 'JKRZH': 0, 'WKFTZ': 2, 'GFDP': 3, 'ZKZHV': 0, 'XJFQR': 3, 'JQFM': 0, 'WQCT': 0, 'QMTMN': 0, 'QDJD': 0, 'FRTK': 2, 'MLJN': 8, 'LHXN': 2, 'DWBL': 1, 'MCWF': 2, 'VCMPS': 0, 'SVTK': 7, 'XNGTQ': 2, 'MXQF': 2, 'XCMJ': 3, 'NHVQD': 6, 'WGLN': 1, 'KFKWH': 0, 'VMDSG': 2, 'BMSNV': 0, 'WCMV': 4, 'ZJKB': 2, 'TDPN': 0} >>> leftovers = {"ZKZHV": 8, 'DWBL': 7} >>> reduce_leftovers(leftovers, reactions) >>> leftovers {'ZKZHV': 0, 'DWBL': 0, 'KFKWH': 0, 'ORE': 681} """ can_reduce = True while can_reduce: can_reduce = False to_add = {} for key in leftovers.keys(): if key == "ORE": continue if reactions[key][0] <= leftovers[key]: times = int(leftovers[key]/reactions[key][0]) can_reduce = True leftovers[key] -= times*reactions[key][0] for r in reactions[key][1]: to_add[r[1]] = to_add.get(r[1], 0) + times*r[0] for key, value in to_add.items(): leftovers[key] = leftovers.get(key, 0) + value if __name__ == "__main__": input = read_input("input/14.txt") leftovers = {} required_resources = get_amount("FUEL", 1, input, leftovers) while need_reaction(required_resources): i = 0 while required_resources[i][1] == "ORE": i += 1 required_resources += get_amount(required_resources[i][1], required_resources[i][0], input, leftovers) required_resources.pop(i) required_ore = 0 for r in required_resources: required_ore += r[0] print("Solution1") print(required_ore) max_ore = 1000000000000 without_problems = int(max_ore/required_ore) leftovers2 = {k:without_problems*leftovers[k] for k in leftovers.keys()} ore = required_ore*without_problems fuel = without_problems reduce_leftovers(leftovers2, input) ore -= leftovers2.get("ORE", 0) leftovers2["ORE"] = 0 while without_problems > 0: without_problems = int((max_ore-ore)/required_ore) for key, value in leftovers.items(): leftovers2[key] = leftovers2.get(key, 0) + value*without_problems ore += required_ore*without_problems fuel += without_problems reduce_leftovers(leftovers2, input) ore -= leftovers2.get("ORE", 0) leftovers2["ORE"] = 0 leftovers2["FUEL"] = 1 reduce_leftovers(leftovers2, input) ore -= leftovers2.get("ORE", 0) if ore<=max_ore: fuel += 1 print("Solution 2") print(fuel)

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0.067439

0.438011

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false

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0.05

0

null

0

null

0

1

0

ee9daa8c3f24ee0e5956c82c505b318b5493b1d6

471

py

Python

src/actions/action_sleep.py

JohnVillalovos/webhook-proxy

fbb2df31b10a0c3ffb9572a0abde4df7e1ad2ef3

[ "MIT" ]

null

src/actions/action_sleep.py

JohnVillalovos/webhook-proxy

fbb2df31b10a0c3ffb9572a0abde4df7e1ad2ef3

[ "MIT" ]

null

src/actions/action_sleep.py

JohnVillalovos/webhook-proxy

fbb2df31b10a0c3ffb9572a0abde4df7e1ad2ef3

[ "MIT" ]

null

import time from actions import Action, action @action("sleep") class SleepAction(Action): def __init__( self, seconds, output="Waiting {{ seconds }} seconds before continuing ..." ): self.seconds = seconds self.output_format = output def _run(self): seconds = float(self._render_with_template(str(self.seconds))) print(self._render_with_template(self.output_format, seconds=seconds)) time.sleep(seconds)

23.55

83

0.673036

54

471

5.62963

0.444444

0.144737

0.105263

0.144737

0

0.218684

471

19

84

24.789474

0.826087

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0.118896

0

1

0.153846

false

0

0.153846

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0.384615

0.076923

0

null

0

null

0

1

0

ee9fab028e33102060e656a46df7bd6afed90358

1,262

py

Python

a1d05eba1/special_fields/choice_filter.py

dorey/a1d05eba1

eb6f66a946f3c417ab6bf9047ba9715be071967c

[ "0BSD" ]

null

a1d05eba1/special_fields/choice_filter.py

dorey/a1d05eba1

eb6f66a946f3c417ab6bf9047ba9715be071967c

[ "0BSD" ]

28

2020-06-23T19:00:58.000Z

2021-03-26T22:13:07.000Z

a1d05eba1/special_fields/choice_filter.py

dorey/a1d05eba1

eb6f66a946f3c417ab6bf9047ba9715be071967c

[ "0BSD" ]

null

from ..utils.kfrozendict import kfrozendict from ..utils.kfrozendict import kassertfrozen class ChoiceFilter: ROW_KEYS = { '1': ['choice_filter'], '2': ['choice_filter'], } EXPORT_KEY = 'choice_filter' @classmethod def in_row(kls, row, schema): return 'choice_filter' in row @classmethod def pull_from_row(kls, row, content): schema = content.schema_version if schema == '2': cfdata = row.get('choice_filter') if not cfdata: return assert 'raw' in cfdata yield ChoiceFilter(content=content, val=cfdata) elif schema == '1': cfdata = {'raw': row['choice_filter']} yield ChoiceFilter(content=content, val=cfdata) def __init__(self, content, val): self.content = content self.key = 'choice_filter' self.val = val self._string = val.get('raw') def dict_key_vals_old(self, renames=None): # print(('choice_filter', self._string,)) yield ('choice_filter', self._string,) @kassertfrozen def dict_key_vals_new(self, renames=None): return ( 'choice_filter', kfrozendict(raw=self.val.get('raw')), )

28.044444

59

0.585578

140

1,262

5.071429

0.307143

0.169014

0.067606

0.073239

0.112676

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0.004515

0.29794

1,262

44

60

28.681818

0.79684

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0.111111

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0.108927

0

0.083333

1

0.138889

false

0

0.055556

0.361111

0

null

0

null

0

1

0

ee9ff38e8ac3eaab8a58f8de6b4ed70735c17d0f

3,878

py

Python

hamster_control_test_version.py

iamnotmarcel/HamsterModell

ce8391e8e120e2cf957f9d49e812be3c4f757f75

[ "MIT" ]

null

hamster_control_test_version.py

iamnotmarcel/HamsterModell

ce8391e8e120e2cf957f9d49e812be3c4f757f75

[ "MIT" ]

1

2022-03-26T17:27:30.000Z

hamster_control_test_version.py

iamnotmarcel/HamsterModell

ce8391e8e120e2cf957f9d49e812be3c4f757f75

[ "MIT" ]

null

''' Author: Marcel Miljak Klasse: 5aHEL - HTL Anichstraße Diplomarbeit: Entwicklung eines Hamster Roboters Jahrgang: 2021/22 ''' import time from time import sleep import RPi.GPIO as GPIO DIR_2 = 18 # Direction-Pin vom 2ten Modul DIR_1 = 24 # Direction-pin vom 1sten Modul STEP_1 = 25 # Step-Pin vom 1sten Modul STEP_2 = 23 # Step-Pin vom 2ten Modul CW = 1 # Clockwise Rotation CCW = 0 # Counterclockwise Rotation SENS_TRIG = 6 # Trigger-Pin HC-SR04 SENS_ECHO = 5 # Echo-Pin HC-SR04 whole_cycle = 300 # ganze Umdrehung (360 / 7.5) was aber foisch is cycle_left = 548 # Viertel Umdrehung delay = 0.005 def setup(): GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) GPIO.setup(DIR_2, GPIO.OUT) GPIO.setup(STEP_1, GPIO.OUT) GPIO.setup(STEP_2, GPIO.OUT) GPIO.setup(DIR_1, GPIO.OUT) GPIO.setup(SENS_TRIG, GPIO.OUT) GPIO.setup(SENS_ECHO, GPIO.IN) def vor(): ''' lässt den Hamster eine ganze Motor-Umdrehung nach vorne fahren (360°) ''' setup() GPIO.output(DIR_1, CW) GPIO.output(DIR_2, CW) print("Vorwärts...") for i in range(3): dist = vornFrei() if dist < 20.0: print("Achtung - Hinderniss voraus!") stop() time.sleep(delay) linksUm() time.sleep(delay) break else: for i in range (100): GPIO.output(STEP_1, GPIO.HIGH) GPIO.output(STEP_2, GPIO.HIGH) sleep(delay) GPIO.output(STEP_1, GPIO.LOW) GPIO.output(STEP_2, GPIO.LOW) sleep(delay) def linksUm(): ''' Dreht sich um 90° nach links ''' setup() GPIO.output(DIR_1, CW) GPIO.output(DIR_2, CCW) print("Ausrichtung nach links...") for i in range(298): GPIO.output(STEP_1, GPIO.HIGH) GPIO.output(STEP_2, GPIO.LOW) sleep(delay) GPIO.output(STEP_1, GPIO.LOW) GPIO.output(STEP_2, GPIO.HIGH) sleep(delay) def rechtsUm(): ''' Nur als Test angesehen, ob Hamster auch wirklich nach rechts ausrichtet ''' setup() print("Ausrichtung nach rechts...") linksUm() linksUm() linksUm() GPIO.cleanup() def vornFrei(): ''' liefert true, wenn sich keine Mauer vor dem Hamster befindet. Kommt gemeinsam mit Obstacle-Avoidance-Sensor in Einsatz. ''' setup() GPIO.output(SENS_TRIG,1) time.sleep(0.00001) GPIO.output(SENS_TRIG,0) while GPIO.input(SENS_ECHO) == 0: pass start = time.time() timer = 0 while (GPIO.input(SENS_ECHO) == 1 and timer <= 12): timer +=1 time.sleep(0.0001) stop = time.time() return (stop-start) * 34300 / 2 def stop(): ''' Wenn sich eine Mauer vor dem Hamster befindet, soll diese Funktion die Motoren stoppen. ''' setup() print("Stop des Hamsters...") GPIO.output(DIR_1, GPIO.LOW) GPIO.output(DIR_2, GPIO.LOW) GPIO.output(STEP_1, GPIO.LOW) GPIO.output(STEP_2, GPIO.LOW) ''' def kornDa(): liefert true, wenn sich auf dem Feld, auf der der Hamster steht, sich mindestens ein Korn befindet. setup() print("Check ob Korn auf Feld vorhanden...") korn_indicator = GPIO.input(SENS_Korn) if korn_indicator == 0: print("Es befindet sich ein Korn auf dem Feld") return True else: return False ''' def nimm(): ''' nimmt von dem Feld, auf dem er gerade steht, ein Korn auf ''' pass def gib(): ''' lege auf dem Feld, auf dem er gerade steht, ein Korn aus seinem Maul ab. ''' pass def maulLeer(): ''' liefert true, wenn der Hamster keinen Körner im Maul hat. ''' pass

22.678363

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null

0

null

0

1

0

eea2f57d28acf6796635f1259b4f5d6adad79071

7,980

py

Python

codeball/tests/test_models.py

metrica-sports/codeball

60bfe54b7898bed87cbbbae9dfc0f3bc49d31025

[ "MIT" ]

54

2020-09-16T13:09:03.000Z

2022-03-28T12:32:19.000Z

codeball/tests/test_models.py

metrica-sports/codeball

60bfe54b7898bed87cbbbae9dfc0f3bc49d31025

[ "MIT" ]

null

codeball/tests/test_models.py

metrica-sports/codeball

60bfe54b7898bed87cbbbae9dfc0f3bc49d31025

[ "MIT" ]

9

2021-03-28T13:02:57.000Z

2022-03-24T11:19:06.000Z

import os import pandas as pd from kloppy import ( load_epts_tracking_data, to_pandas, load_metrica_json_event_data, load_xml_code_data, ) from codeball import ( GameDataset, DataType, TrackingFrame, EventsFrame, CodesFrame, PossessionsFrame, BaseFrame, Zones, Area, PatternEvent, Pattern, PatternsSet, ) import codeball.visualizations as vizs class TestModels: def test_pattern_event(self): xy = [0.3, 0.6] viz = vizs.Players( start_time=500, end_time=700, players=[], options=[] ) pattern_event = PatternEvent( pattern_code="MET_001", start_time=400, event_time=500, end_time=800, coordinates=[xy, xy], visualizations=[viz, viz], tags=["T001"], ) assert pattern_event.end_time == 800 assert pattern_event.coordinates[0][0] == 0.3 assert pattern_event.visualizations[0].start_time == 500 def test_pattern(self): class pattern_class(Pattern): def __init__( self, name: str, code: str, in_time: int = 0, out_time: int = 0, parameters: dict = None, game_dataset: GameDataset = None, ): super().__init__( name, code, in_time, out_time, parameters, game_dataset ) def run(self): return True def build_pattern_event(self): pass test_pattern = pattern_class( name="Test Pattern", code="MET_001", in_time=3, out_time=2, parameters=None, game_dataset=None, ) assert test_pattern.in_time == 3 assert test_pattern.run() is True def test_game_dataset(self): base_dir = os.path.dirname(__file__) game_dataset = GameDataset( tracking_metadata_file=f"{base_dir}/files/metadata.xml", tracking_data_file=f"{base_dir}/files/tracking.txt", events_metadata_file=f"{base_dir}/files/metadata.xml", events_data_file=f"{base_dir}/files/events.json", ) assert game_dataset.tracking.data_type == DataType.TRACKING assert game_dataset.events.data_type == DataType.EVENT def test_tracking_game_dataset(self): base_dir = os.path.dirname(__file__) game_dataset = GameDataset( tracking_metadata_file=f"{base_dir}/files/metadata.xml", tracking_data_file=f"{base_dir}/files/tracking.txt", ) assert game_dataset.tracking.data_type == DataType.TRACKING assert game_dataset.has_event_data is False def test_codes_only_game_dataset(self): base_dir = os.path.dirname(__file__) game_dataset = GameDataset( codes_files=f"{base_dir}/files/code_xml.xml", ) assert game_dataset.codes[0].data_type == DataType.CODE assert game_dataset.has_event_data is False def test_pattern_set(self): base_dir = os.path.dirname(__file__) game_dataset = GameDataset( tracking_metadata_file=f"{base_dir}/files/metadata.xml", tracking_data_file=f"{base_dir}/files/tracking.txt", events_metadata_file=f"{base_dir}/files/metadata.xml", events_data_file=f"{base_dir}/files/events.json", ) class pattern_class(Pattern): def __init__( self, name: str, code: str, in_time: int = 0, out_time: int = 0, parameters: dict = None, game_dataset: GameDataset = None, ): super().__init__( name, code, in_time, out_time, parameters, game_dataset ) def run(self): return True def build_pattern_event(self): pass test_pattern = pattern_class( name="Test Pattern", code="MET_001", in_time=3, out_time=2, parameters=None, game_dataset=game_dataset, ) patterns_set = PatternsSet(game_dataset=game_dataset) patterns_set.patterns = [test_pattern, test_pattern] assert patterns_set.game_dataset.events.data_type == DataType.EVENT assert len(patterns_set.patterns) == 2 def test_base_data_frame(self): data = { "player1_x": [1, 2, 3, 4], "player2_x": [5, 6, 7, 8], "player3_x": [9, 10, 11, 12], } base_df = BaseFrame(data) base_df.metadata = "metadata" base_df.records = [1, 2, 3, 4] base_df.data_type = "test" assert isinstance(base_df, BaseFrame) assert hasattr(base_df, "metadata") assert hasattr(base_df, "records") assert isinstance(base_df[["player1_x", "player2_x"]], BaseFrame) assert hasattr(base_df[["player1_x", "player2_x"]], "metadata") assert not hasattr(base_df[["player1_x", "player2_x"]], "records") def test_tracking_data_frame(self): base_dir = os.path.dirname(__file__) tracking_dataset = load_epts_tracking_data( metadata_filename=f"{base_dir}/files/metadata.xml", raw_data_filename=f"{base_dir}/files/tracking.txt", ) tracking = TrackingFrame(to_pandas(tracking_dataset)) tracking.data_type = DataType.TRACKING tracking.metadata = tracking_dataset.metadata tracking.records = tracking_dataset.records assert tracking.get_team_by_id("FIFATMA").team_id == "FIFATMA" assert tracking.get_period_by_id(1).id == 1 assert tracking.get_other_team_id("FIFATMA") == "FIFATMB" assert tracking.team("FIFATMA").shape[1] == 22 assert tracking.dimension("x").shape[1] == 23 assert tracking.players().shape[1] == 44 assert tracking.players("field").shape[1] == 40 assert sum(tracking.phase(defending_team_id="FIFATMA")) == 0 assert sum(tracking.team("FIFATMA").stretched(90)) == 863 def test_events_data_frame(self): base_dir = os.path.dirname(__file__) events_dataset = load_metrica_json_event_data( metadata_filename=f"{base_dir}/files/metadata.xml", raw_data_filename=f"{base_dir}/files/events.json", ) events = EventsFrame(to_pandas(events_dataset)) events.data_type = DataType.EVENT events.metadata = events_dataset.metadata events.records = events_dataset.records assert events.type("PASS").shape[0] == 26 assert events.result("COMPLETE").shape[0] == 45 assert events.into(Zones.OPPONENT_BOX).shape[0] == 1 assert events.starts_inside(Zones.OPPONENT_BOX).shape[0] == 2 assert events.ends_inside(Zones.OPPONENT_BOX).shape[0] == 2 assert events.ends_outside(Zones.OPPONENT_BOX).shape[0] == 43 # Test diferent ways to input Zones and areas custom_area = Area((0.25, 0.2), (0.75, 0.8)) assert ( events.ends_outside(Zones.OPPONENT_BOX, Zones.OWN_BOX).shape[0] == 45 ) assert ( events.ends_inside(Zones.OPPONENT_BOX, custom_area).shape[0] == 14 ) assert events.ends_inside(custom_area, custom_area).shape[0] == 12 def test_codes_data_frame(self): base_dir = os.path.dirname(__file__) codes_dataset = load_xml_code_data( xml_filename=f"{base_dir}/files/code_xml.xml", ) codes = CodesFrame(to_pandas(codes_dataset)) codes.data_type = DataType.CODE codes.metadata = codes_dataset.metadata codes.records = codes_dataset.records assert len(codes.records) == 3

30.930233

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0

null

0

null

0

1

0

eea44ef30a81ba67ad14a68694b3cdcb38fe067e

1,686

py

Python

cv_workshops/6-day/2-clazz.py

afterloe/opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

5

2020-03-13T07:34:30.000Z

2021-10-01T03:03:05.000Z

cv_workshops/6-day/2-clazz.py

afterloe/Opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

null

cv_workshops/6-day/2-clazz.py

afterloe/Opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

1

2020-03-01T13:21:43.000Z

#!/usr/bin/env python3 # -*- coding=utf-8 -*- import cv2 as cv import numpy as np """ 使用几何矩计算轮廓中心与横纵波比对过滤对二值图像的各个轮廓进行计算获得对应的几何矩，根据几何矩计算轮廓点的中心位置。 cv.moments(contours, binaryImage) - contours: 轮廓点集 - binaryImage: bool, default False；二值图返回 """ def main(): src = cv.imread("../../pic/money.jpg") cv.namedWindow("src", cv.WINDOW_KEEPRATIO) cv.namedWindow("dst", cv.WINDOW_KEEPRATIO) cv.imshow("src", src) t = 80 binary = cv.Canny(src, t, t * 2) k = np.ones((3, 3), dtype=np.uint8) binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k) contours, _ = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) for index in range(len(contours)): contour = contours[index] rect = cv.minAreaRect(contour) # cx, cy = rect[0] ww, hh = rect[1] ratio = np.minimum(ww, hh) / np.maximum(ww, hh) print("ratio is ", ratio) mm = cv.moments(contour) m00 = mm["m00"] m10 = mm["m10"] m01 = mm["m01"] cx = np.int(m10 / m00) cy = np.int(m01 / m00) box = np.int0(cv.boxPoints(rect)) if 0.9 < ratio: cv.drawContours(src, [box], 0, (255, 0, 0), 2, cv.LINE_8) cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (0, 0, 255), 2, cv.LINE_8) if 0.5 > ratio: cv.drawContours(src, [box], 0, (255, 255, 0), 2, cv.LINE_8) cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (0, 255, 0), 2, cv.LINE_8) cv.imshow("dst", src) cv.waitKey(0) cv.destroyAllWindows() if "__main__" == __name__: main()

31.811321

87

0.541518

231

1,686

3.87013

0.402597

0.035794

0.03132

0.035794

0.173378

0.089485

0

0.066554

0.295967

1,686

52

88

32.423077

0.686605

0.034994

0

0.041697

0

1

0.027778

false

0

0.055556

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0.083333

0.027778

0

null

0

null

0

1

0

eea747f6a5f58fa9f7cb6e82312ed9dadca75ac3

1,967

py

Python

war.py

Eduardojvr/Space_Atack_Game

f37e1891bf00af71f3c1758a0288a6b0b830bb9e

[ "MIT" ]

null

war.py

Eduardojvr/Space_Atack_Game

f37e1891bf00af71f3c1758a0288a6b0b830bb9e

[ "MIT" ]

null

war.py

Eduardojvr/Space_Atack_Game

f37e1891bf00af71f3c1758a0288a6b0b830bb9e

[ "MIT" ]

null

from settings import Settings from ship import Ship import pygame import sys from trap import Trap from time import clock from random import randint def run_game(): tela1 = Settings() screen = pygame.display.set_mode((tela1.altura, tela1.largura)) background = Settings() pygame.display.set_caption("Space War") nave = Ship(screen) #pygame.mouse.set_visible(0) trap = [Trap(screen,randint(0,1200)), Trap(screen,randint(0,1200)), Trap(screen,randint(0,1200))] while True: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() elif event.type == pygame.KEYDOWN or event.type == pygame.KEYUP: if event.key == pygame.K_RIGHT: nave.rect.centerx +=30 elif event.key == pygame.K_LEFT: nave.rect.centerx -=30 elif event.key == pygame.K_UP: nave.rect.bottom -=30 elif event.key == pygame.K_DOWN: nave.rect.bottom +=30 elif event.key == pygame.K_SPACE: nave.moveMissile() for i in trap: i.rect.bottom += 30 if (i.rect.colliderect(nave.rect)): nave.vida = nave.vida-1 if (nave.vida < 1): background.bg_image = pygame.image.load('imagens/gameover.bmp') screen.fill(tela1.bg_color) screen.blit(background.bg_image, (0,0)) nave.blitme() nave.blitmemissile() for i in trap: i.blitme() for i in trap: if i.rect.centery > Settings().altura: i.rect.centery = 0 i.rect.centerx = randint(0,1200) i.rect.centery = randint(0,200) pygame.display.flip() ################################ Main ################################ run_game()

32.245902

101

0.516523

229

1,967

4.379913

0.323144

0.02991

0.069791

0.074776

0.229312

0.207378

0.065803

0

0.034215

0.346213

1,967

60

102

32.783333

0.745723

0.016268

0

0.0625

0

0.015533

0

1

0.020833

false

0

0.145833

0

0.166667

0

null

0

null

0

1

0

eea8fc748971275806d47350049795a3a98b474a

1,463

py

Python

Project-2/doc/contingency_mat_parser.py

TooSchoolForCool/EE219-Larger-Scale-Data-Mining

9a42c88169ace88f9b652d0e174c7f641fcc522e

[ "Apache-2.0" ]

null

Project-2/doc/contingency_mat_parser.py

TooSchoolForCool/EE219-Larger-Scale-Data-Mining

9a42c88169ace88f9b652d0e174c7f641fcc522e

[ "Apache-2.0" ]

12

2020-01-28T22:09:15.000Z

2022-03-11T23:16:26.000Z

Project-2/doc/contingency_mat_parser.py

TooSchoolForCool/EE219-Larger-Scale-Data-Mining

9a42c88169ace88f9b652d0e174c7f641fcc522e

[ "Apache-2.0" ]

null

import sys import argparse def read_in(file_path): try: file = open(file_path, 'r') except: sys.stderr.write("[ERROR] read_in(): Cannot open file '%s'\n" % file_path) exit(1) file_content = [] for line in file: file_content.append(line) i = 0 while i < len(file_content): line = file_content[i] title = line[5:-6] print("\t%s" % title) line = file_content[i + 7].strip('\n').strip(' ').strip('\t') line = [l.strip('\t') for l in line.split(' ') if l] for item in line: print("\t%s" % item.replace("cluster_", "c")), print("") for j in range(8, 28): line = file_content[i + j].strip('\n').strip(' ').strip('\t') line = [l.strip('\t') for l in line.split(' ') if l] for item in line: print("%s\t" % item), print("") i += 28 return file_content def add_parser(): parser = argparse.ArgumentParser(prog='Compare Evaluation Result') parser.add_argument("-s", "--src", dest = "src", help = "source file path", required = True ) parser.add_argument("-d", "--dest", dest = "dest", help = "destination file path", ) return parser def main(): parser = add_parser() args = parser.parse_args() src_file = read_in(args.src) if __name__ == '__main__': main()

21.835821

82

0.514012

189

1,463

3.830688

0.354497

0.106354

0.062155

0.066298

0.176796

0

0.010101

0.323308

1,463

67

83

21.835821

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0

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0

0.117486

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1

0.06383

false

0

0.042553

0

0.148936

0.106383

0

null

0

null

0

1

0

eea9326c5e16b9ddd8185aff0917cab86602e465

5,426

py

Python

voldemort_client/helper.py

mirko-lelansky/voldemort-client

a2839a0cc50ca4fdc5bdb36b2df3a3cf7f7d9db9

[ "Apache-2.0" ]

null

voldemort_client/helper.py

mirko-lelansky/voldemort-client

a2839a0cc50ca4fdc5bdb36b2df3a3cf7f7d9db9

[ "Apache-2.0" ]

null

voldemort_client/helper.py

mirko-lelansky/voldemort-client

a2839a0cc50ca4fdc5bdb36b2df3a3cf7f7d9db9

[ "Apache-2.0" ]

null

# Copyright 2017 Mirko Lelansky <mlelansky@mail.de> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This module contains some helper methods for building parts of http requests. """ from datetime import datetime import simplejson as json from voldemort_client.exception import VoldemortError def create_vector_clock(node_id, timeout): """This method builds the initial vector clock for a new key. Parameters ---------- node_id : int the id of one node in the cluster timeout : int the expire timeout of the key Returns ------- dict the vector clock as dictonary """ if node_id is not None and timeout is not None: return { "versions": [{"nodeId": node_id, "version": 1}], "timestamp": timeout } else: raise ValueError("You must gave the node id and the timeout.") def merge_vector_clock(vector_clock, node_id, timeout=None): """This method merges an existing vector clock with the new values. Parameters ---------- vector_clock : dict the vector clock which should be updated node_id : int the node id to use timeout : int the expire timeout of the key Returns ------- dict the update vector clock as dictionary """ if vector_clock is not None and node_id is not None: versions = vector_clock["versions"] version_map_list_node = [version_map for version_map in versions if version_map["nodeId"] == node_id] if version_map_list_node == []: versions.append({"nodeId": node_id, "version": 1}) elif len(version_map_list_node) == 1: old_map = version_map_list_node[0] new_map = old_map new_map["version"] = new_map["version"] + 1 versions.remove(old_map) versions.append(new_map) else: raise VoldemortError("Only one version map per node is allowed.") vector_clock["versions"] = versions if timeout is not None: vector_clock["timestamp"] = timeout return vector_clock else: raise ValueError("You need the vector clock, timeout and the node id.") def build_get_headers(request_timeout): """This method builds the request headers for get requests like receving keys. Parameters ---------- request_timeout : int the time where the request should be done in milli seconds Returns ------- dict the headers as dictonary """ timestamp = datetime.now().timestamp() return { "X-VOLD-Request-Timeout-ms": str(int(request_timeout)), "X-VOLD-Request-Origin-Time-ms": str(int(timestamp)) } def build_delete_headers(request_timeout, vector_clock): """This method builds the request headers for the delete requests. Parameters ---------- request_timeout : int the time where the request should be done in milli seconds vector_clock : dict the vector clock which represents the version which should be delete Returns ------- dict the headers as dictionary """ delete_headers = build_get_headers(request_timeout) delete_headers["X-VOLD-Vector-Clock"] = json.dumps(vector_clock) return delete_headers def build_set_headers(request_timeout, vector_clock, content_type="text/plain"): """This method builds the request headers for the set requests. Parameters ---------- request_timeout : int the time where the request should be done in milli seconds vector_clock : dict the vector clock which represents the version which should be create or update content_type : str the content type of the value Returns ------- dict the headers as dictionary """ set_headers = build_delete_headers(request_timeout, vector_clock) set_headers["Content-Type"] = content_type return set_headers def build_version_headers(request_timeout): """This method builds the request headers for the version requests. Parameters ---------- request_timeout : int the time where the request should be done in milli seconds Returns -------- dict the headers as dictionary """ version_headers = build_get_headers(request_timeout) version_headers["X-VOLD-Get-Version"] = "" return version_headers def build_url(url, store_name, key): """This method combine the different parts of the urls to build the url to acces the REST-API. Parameters ---------- url : str the base url store_name : str the name of the voldemort store key : str the url part which represents the key or keys Returns ------- str the combined url of the REST-API """ return "%s/%s/%s" % (url, store_name, key)

29.32973

82

0.651493

712

5,426

4.837079

0.247191

0.076655

0.042683

0.027584

0.360046

0.308653

0.265679

0.230836

0.207317

0

0.003255

0.263914

5,426

184

83

29.48913

0.859039

0.490232

0

0.096154

0

0.144118

0.022689

0

1

0.134615

false

0

0.057692

0

0.326923

0

null

0

null

0

1

0

eea9c161475ffd63195c5ca94c42455b4deb9625

1,581

py

Python

src/reddack/exceptions.py

diatomicDisaster/Reddit-Slackbot

4f22af110e72eab19d9162a4428800a1895303f3

[ "MIT" ]

null

src/reddack/exceptions.py

diatomicDisaster/Reddit-Slackbot

4f22af110e72eab19d9162a4428800a1895303f3

[ "MIT" ]

10

2022-02-21T01:11:20.000Z

2022-02-22T18:13:00.000Z

src/reddack/exceptions.py

diatomicDisaster/redack

4f22af110e72eab19d9162a4428800a1895303f3

[ "MIT" ]

null

from __future__ import ( annotations, ) class ModFromSlackError(Exception): """Base class for modfromslack errors""" def __init__( self, message: str, *, preamble: str | None = None, afterword: str | None = None ) -> None: if preamble is not None: message = f"{preamble} {message}" if afterword is not None: message = f"{message}\n\n{afterword}" super().__init__(message) class MsgSendError(ModFromSlackError): """Failed to send Slack message.""" class SequenceError(ModFromSlackError): """Something has happened in the wrong order.""" def __init__( self, should_be_first, should_be_second, *, preamble: str | None = None, afterword: str | None = None ) -> None: message = f"Expected {should_be_first} before {should_be_second}" super().__init__( message, preamble = preamble, afterword = afterword ) class ActionSequenceError(SequenceError): """App thinks action came before its parent message.""" def __init__( self, parentmsg_ts, action_ts, *, afterword=None ) -> None: _preamble=f"'message_ts' {parentmsg_ts} is later than 'action_ts' {action_ts}" super().__init__( "parent message", "action", preamble=_preamble, afterword=afterword ) class ConfigError(ModFromSlackError): """Error in config file format."""

26.79661

86

0.573688

153

1,581

5.640523

0.392157

0.06489

0.050985

0.044032

0.229432

0.099652

0

0.324478

1,581

58

87

27.258621

0.808052

0.117647

0

0.416667

0

0.132213

0.017531

0

1

0.0625

false

0

0.020833

0

0.1875

0

null

0

null

0

1

0

eeaa2be76b33b3286d73455fcb963e240ddf8af4

7,276

py

Python

cid/cli/cli_generator.py

zeljko-bal/CID

52ecc445c441ec63386c9f092b226090588a3789

[ "MIT" ]

1

2017-09-15T06:14:54.000Z

cid/cli/cli_generator.py

zeljko-bal/CID

52ecc445c441ec63386c9f092b226090588a3789

[ "MIT" ]

null

cid/cli/cli_generator.py

zeljko-bal/CID

52ecc445c441ec63386c9f092b226090588a3789

[ "MIT" ]

null

from collections import defaultdict from os import makedirs from os.path import realpath, join, dirname, isdir, exists from shutil import copy from jinja2 import Environment, FileSystemLoader from cid.cli.cli_model_specs import CliModelSpecs from cid.cli import cli_post_processing from cid.parser.cid_parser import parse from cid.common.cid_model_processor import CidModelProcessor from cid.common.utils import * _cli_templates_path = join(dirname(realpath(__file__)), 'templates') _cli_framework_path = join(dirname(realpath(__file__)), 'framework') # ------------------------------- JINJA FILTERS ------------------------------- def parameter_model_filter(parameter): def print_list(lst): return str(lst) if len(lst) > 1 else "'{}'".format(lst[0]) if parameter.type == 'Bool': positives = [p.positive for p in parameter.all_patterns if p.positive] negatives = [p.negative for p in parameter.all_patterns if p.negative] positives_str = ", positives={prefixes}".format(prefixes=print_list(positives)) if positives else '' negatives_str = ", negatives={prefixes}".format(prefixes=print_list(negatives)) if negatives else '' return "BooleanNonpositional('{name}'{positives}{negatives})".format( name=parameter.name, positives=positives_str, negatives=negatives_str) else: ret = [] classified = defaultdict(lambda: defaultdict(set)) for pattern in parameter.all_patterns: if pattern.white_space: if pattern.count: count_str = ", count={count}".format(count=pattern.count) elif pattern.count_many: count_str = ", count='*'" else: count_str = '' classified['MultiArgNonpositional'][count_str].add(pattern) else: if pattern.count_many: if pattern.separator: separator_str = ", '{}'".format(pattern.separator) else: separator_str = '' classified['SeparatedNonpositional'][separator_str].add(pattern) elif pattern.count_char: classified['CounterNonpositional'][pattern.count_char].add(pattern) else: classified['BasicNonpositional']['_'].add(pattern) if classified['MultiArgNonpositional']: for count_str, patterns in classified['MultiArgNonpositional'].items(): prefixes = [p.prefix for p in patterns] ret.append("MultiArgNonpositional('{name}', {prefixes}{count_str})".format(name=parameter.name, prefixes=print_list(prefixes), count_str=count_str)) if classified['SeparatedNonpositional']: for separator_str, patterns in classified['SeparatedNonpositional'].items(): prefixes = [p.prefix for p in patterns] ret.append("SeparatedNonpositional('{name}', {prefixes}{separator_str})".format(name=parameter.name, prefixes=print_list(prefixes), separator_str=separator_str)) if classified['CounterNonpositional']: for count_char, patterns in classified['CounterNonpositional'].items(): prefixes = [p.prefix for p in patterns] ret.append("CounterNonpositional('{name}', {prefixes}, '{count_char}')".format(name=parameter.name, prefixes=print_list(prefixes), count_char=count_char)) if classified['BasicNonpositional']: for _, patterns in classified['BasicNonpositional'].items(): prefixes = [p.prefix for p in patterns] ret.append("BasicNonpositional('{name}', {prefixes})".format(name=parameter.name, prefixes=print_list(prefixes))) return ', '.join(ret) def have_sub_commands_filter(commands): return any([c.sub_commands for c in commands]) # ------------------------------- GENERATOR FUNCTIONS ------------------------------- def process_model(model): for visitor in cli_post_processing.model_visitors: CidModelProcessor(visitor).process_model(model) CidModelProcessor(CliModelSpecs().visitor).process_model(model) def render_cli_code(model, root_command_name, cli_app_path): # EXTRACT DATA --------------------- model_extractor = ElementExtractor() CidModelProcessor(model_extractor.visitor).process_model(model) all_commands = model_extractor.all_commands all_parameters = model_extractor.all_parameters # RENDER CLI PARSER --------------------- env = Environment(loader=FileSystemLoader(_cli_templates_path)) env.filters['parameter_model'] = parameter_model_filter env.filters['element_type'] = element_type env.filters['tab_indent'] = tab_indent_filter env.filters['stringify'] = stringify_filter env.filters['have_sub_commands'] = have_sub_commands_filter env.globals['raise'] = raise_exception_helper parser_template = env.get_template('cli_parser.template') parser_rendered = parser_template.render(root_command_name=root_command_name, root_command_id=element_id(root_command_name), commands=all_commands, parameters=all_parameters) with open(join(cli_app_path, root_command_name + "_cli_parser.py"), "w") as text_file: text_file.write(parser_rendered) # RENDER CLI COMMAND --------------------- command_file_path = join(cli_app_path, root_command_name + '_cli.py') if not exists(command_file_path): command_template = env.get_template('cli_command.template') command_rendered = command_template.render(root_command_name=root_command_name) with open(command_file_path, "w") as text_file: text_file.write(command_rendered) def copy_framework(cli_app_path): if not isdir(cli_app_path): makedirs(cli_app_path) copy(join(_cli_framework_path, "generic_cli_parser.py"), cli_app_path) copy(join(_cli_framework_path, "js_date.py"), cli_app_path) def render_runner_script(root_command_name, dest_path): env = Environment(loader=FileSystemLoader(_cli_templates_path)) template = env.get_template('windows_cli_py_runner.template') rendered = template.render(command_path=join(root_command_name + '_cli', root_command_name + "_cli.py")) with open(join(dest_path, root_command_name + ".bat"), "w") as text_file: text_file.write(rendered) def is_root_command_defined(model, root_command_name): model_extractor = ElementExtractor() CidModelProcessor(model_extractor.visitor).process_model(model) return root_command_name in [command.name for command in model_extractor.all_commands] def generate_cli(cid_file, root_command_name, dest_path): cli_app_path = join(dest_path, root_command_name + "_cli") model = parse(cid_file) if not is_root_command_defined(model, root_command_name): print("Error: The specified root command is not defined.") return process_model(model) copy_framework(cli_app_path) render_cli_code(model, root_command_name, cli_app_path) render_runner_script(root_command_name, dest_path) print("Generated cli successfully.")

42.8

177

0.67276

827

7,276

5.625151

0.170496

0.054385

0.061264

0.027085

0.344153

0.280739

0.269132

0.231298

0.126397

0.092003

0

0.000518

0.204233

7,276

169

178

43.053254

0.802936

0.03807

0

0.128205

0

0.128414

0.057343

0

1

0.076923

false

0

0.08547

0.017094

0.213675

0.076923

0

null

0

null

0

1

0

eeaa72a12bf7e9c9d8b1d3537dc9a129425ee115

2,037

py

Python

container/sample-inf1/inf1_mx.py

yunma10/neo-ai-dlr

1f5c65d9bf7155c016e5d2f78d273755760a4f2a

[ "Apache-2.0" ]

446

2019-01-24T02:04:17.000Z

2022-03-16T13:45:32.000Z

container/sample-inf1/inf1_mx.py

yunma10/neo-ai-dlr

1f5c65d9bf7155c016e5d2f78d273755760a4f2a

[ "Apache-2.0" ]

179

2019-01-24T10:03:34.000Z

2022-03-19T02:06:56.000Z

container/sample-inf1/inf1_mx.py

yunma10/neo-ai-dlr

1f5c65d9bf7155c016e5d2f78d273755760a4f2a

[ "Apache-2.0" ]

111

2019-01-24T20:51:45.000Z

2022-02-18T06:22:40.000Z

import mxnet as mx #import neomxnet import os import json import numpy as np from collections import namedtuple import os dtype='float32' Batch = namedtuple('Batch', ['data']) ctx = mx.neuron() is_gpu = False def model_fn(model_dir): print("param {}".format(os.environ.get('MODEL_NAME_CUSTOM'))) print("ctx {}".format(ctx)) sym, arg_params, aux_params = mx.model.load_checkpoint(os.path.join(model_dir, os.environ.get('MODEL_NAME_CUSTOM')), 0) mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None) for arg in arg_params: arg_params[arg] = arg_params[arg].astype(dtype) for arg in aux_params: aux_params[arg] = aux_params[arg].astype(dtype) exe = mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], label_shapes=mod._label_shapes) mod.set_params(arg_params, aux_params, allow_missing=True) return mod def transform_fn(mod, img, input_content_type, output_content_type): ''' stream = os.popen('/opt/aws/neuron/bin/neuron-cli list-model') output = stream.read() print(output) stream = os.popen('/opt/aws/neuron/bin/neuron-cli list-ncg') output = stream.read() print(output) ''' image = mx.image.imdecode(img) resized = mx.image.resize_short(image, 224) # minimum 224x224 images cropped, crop_info = mx.image.center_crop(resized, (224, 224)) normalized = mx.image.color_normalize(cropped.astype(np.float32) / 255, mean=mx.nd.array([0.485, 0.456, 0.406]), std=mx.nd.array([0.229, 0.224, 0.225])) # the network expect batches of the form (N,3,224,224) transposed = normalized.transpose((2, 0, 1)) # Transposing from (224, 224, 3) to (3, 224, 224) batchified = transposed.expand_dims(axis=0) # change the shape from (3, 224, 224) to (1, 3, 224, 224) image = batchified.astype(dtype='float32') mod.forward(Batch([image])) prob = mod.get_outputs()[0].asnumpy().tolist() prob_json = json.dumps(prob) return prob_json, output_content_type

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null

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null

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0

eeab90972c87f9c41713b77c4809b4a9c645a33d

4,040

py

Python

data/process_data.py

KCKhoo/disaster_response_dashboard

ee337125121664503675bfb5bf01af85c7c1a8ca

[ "FTL", "CNRI-Python", "blessing" ]

null

data/process_data.py

KCKhoo/disaster_response_dashboard

ee337125121664503675bfb5bf01af85c7c1a8ca

[ "FTL", "CNRI-Python", "blessing" ]

null

data/process_data.py

KCKhoo/disaster_response_dashboard

ee337125121664503675bfb5bf01af85c7c1a8ca

[ "FTL", "CNRI-Python", "blessing" ]

null

import sys import pandas as pd from sqlalchemy import create_engine def load_data(messages_filepath, categories_filepath): ''' Load and merge two CSV files - one containing messages and the other containing categories Args: messages_filepath (str): Path to the CSV file containing messages categories_filepath (str): Path to the CSV file containing categories of each message Returns: df (DataFrame): A merged DataFrame containing messages and categories ''' # Load messages dataset messages = pd.read_csv(messages_filepath) # load categories dataset categories = pd.read_csv(categories_filepath) # Merge datasets df = messages.merge(categories, on='id') return df def clean_data(df): ''' Clean the data for machine learning model. Cleaning processes include: 1) Split 'categories' column in the dataframe into separate category columns. 2) Convert category values to just numbers 0 or 1 by removing the texts. 3) Replace 'categories' column in df with new category columns created in Step 1. 4) Remove duplicates. 5) Remove rows with 2 in 'related' category column. Args: df (DataFrame): A DataFrame Returns: df_clean (DataFrame): clean DataFrame ''' # Make a copy of df df_clean = df.copy() # Create a dataframe of the 36 individual category columns categories = df_clean['categories'].str.strip().str.split(';', expand=True) # Select the first row of the categories dataframe row = categories.iloc[0, :] # Use this row to extract a list of new column names for categories. category_colnames = row.apply(lambda x: x[:-2]) # Rename the columns of `categories` categories.columns = category_colnames # Convert category values to just numbers 0 or 1. for column in categories: # Set each value to be the last character of the string categories[column] = categories[column].str.split('-').str[-1] # Convert column from string to numeric categories[column] = pd.to_numeric(categories[column]) # Drop the original categories column from 'df' df_clean = df_clean.drop(columns=['categories']) # Concatenate the original dataframe with the new 'categories' dataframe df_clean = pd.concat([df_clean, categories], axis=1) # Drop duplicates df_clean = df_clean.drop_duplicates() # Drop rows with 2 in 'related' column df_clean = df_clean[df_clean['related'] != 2].reset_index(drop=True) return df_clean def save_data(df, database_filename): ''' Save clean dataset to a SQLite database Args: df (DataFrame): Clean dataframe database_filename (string): Path at which database will be stored Returns: None ''' engine = create_engine('sqlite:///' + database_filename) df.to_sql('DisasterMessages', engine, index=False) def main(): if len(sys.argv) == 4: messages_filepath, categories_filepath, database_filepath = sys.argv[1:] print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}' .format(messages_filepath, categories_filepath)) df = load_data(messages_filepath, categories_filepath) print('Cleaning data...') df = clean_data(df) print('Saving data...\n DATABASE: {}'.format(database_filepath)) save_data(df, database_filepath) print('Cleaned data saved to database!') else: print('Please provide the filepaths of the messages and categories '\ 'datasets as the first and second argument respectively, as '\ 'well as the filepath of the database to save the cleaned data '\ 'to as the third argument. \n\nExample: python process_data.py '\ 'disaster_messages.csv disaster_categories.csv '\ 'DisasterResponse.db') if __name__ == '__main__': main()

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0.02922

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0.257673

4,040

119

99

33.94958

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0

0.218709

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1

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false

0

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0.2

0.111111

0

null

0

null

0

1

0

eeacff18635731300c340b2e253ce1bf7ee2b4e0

3,432

py

Python

pycle/bicycle-scrapes/bike-data-scrape/scraperMulti.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

pycle/bicycle-scrapes/bike-data-scrape/scraperMulti.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

pycle/bicycle-scrapes/bike-data-scrape/scraperMulti.py

fusuyfusuy/School-Projects

8e38f19da90f63ac9c9ec91e550fc5aaab3d0234

[ "MIT" ]

null

from bs4 import BeautifulSoup import os import csv bicycles = [] basepath = 'HTMLFiles/' outputFile = open('scraped.py','a') outputFile.write("list=[") len1 = len(os.listdir(basepath)) counter1 = 0 for entry in os.listdir(basepath): counter2 = 0 len2 = len(os.listdir(basepath+'/'+entry)) for folder in os.listdir(basepath+'/'+entry): listFile = open(basepath+entry+'/'+folder,"r") try: parsed = BeautifulSoup(listFile, "html.parser") except: print('bs4 error in '+basepath+entry+'/'+folder) break bicycle = { 'Brand': '-', 'Model': '-', 'Weight': '-', 'Released on the market': '-', 'For women': '-', 'For kids': '-', 'Frame material': '-', 'Frame type': '-', 'Collapsible frame': '-', 'Color': '-', 'Fork type': '-', 'Shock absorber type': '-', 'Shock absorber pressure': '-', 'Fork name': '-', 'Wheel drive': '-', 'Drive type': '-', 'Transmission type': '-', 'Number of speeds': '-', 'System name': '-', 'Cassette name': '-', 'Front derailleur gears name': '-', 'Rear derailleur gears name': '-', 'Shifters type': '-', 'Shifters name': '-', 'Front brakes': '-', 'Front brakes name': '-', 'Rear brakes': '-', 'Number of wheels': '-', 'Wheels diameter': '-', 'Double rim': '-', 'Rim material': '-', 'Rims name': '-', 'Tyres pattern': '-', 'Tyres name': '-', 'Handlebar type': '-', 'Handlebar name': '-', 'Seat type': '-', 'Seat suspension': '-', 'Seat name': '-', 'Pedals type': '-', 'Pedals name': '-', 'Front panel': '-', 'Rear panel panel': '-', 'Trunk': '-', 'Rearview mirror': '-', 'Horn': '-', 'Basket': '-' } tableRows = parsed.findAll('tr') for row in tableRows: tableData = row.findAll('td') try: key = tableData[0].text.strip() value = tableData[1].text.strip() except: print('error in '+basepath+entry+'/'+folder) break else: bicycle[key] = value if(bicycle['Brand']!='-'): bicycles.append(bicycle) outputFile.write(str(bicycle)+',\n') counter2+=1 print("parsing "+str(counter2)+" of "+str(len2)+" ", end='\r') counter1+=1 print("\nFOLDER parsing "+str(counter1)+" of "+str(len1)+" \n", end='\r') # keys = bicycles[0].keys() # with open('bicycles.csv', 'w', newline='') as output_file: # dict_writer = csv.DictWriter(output_file, keys) # dict_writer.writeheader() # dict_writer.writerows(bicycles) outputFile.write(']') toWrite = """ import csv keys = list[0].keys() with open('bicycles.csv', 'w', newline='') as output_file: dict_writer = csv.DictWriter(output_file, keys) dict_writer.writeheader() dict_writer.writerows(list) """ outputFile.write(toWrite)

28.840336

106

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3,432

5.183333

0.4

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0.04373

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0.195498

0.155627

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0.36451

3,432

119

107

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0.703806

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0.315447

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1

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false

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0.041667

0

0.041667

0

null

0

null

0

1

0

eeb69df1582f775092e1af736d2173a50d2365bb

484

py

Python

tests/test_lines_count.py

MacHu-GWU/single_file_module-project

01f7a6b250853bebfd73de275895bf274325cfc1

[ "MIT" ]

3

2017-02-27T05:07:46.000Z

2022-01-17T06:46:20.000Z

tests/test_lines_count.py

MacHu-GWU/single_file_module-project

01f7a6b250853bebfd73de275895bf274325cfc1

[ "MIT" ]

null

tests/test_lines_count.py

MacHu-GWU/single_file_module-project

01f7a6b250853bebfd73de275895bf274325cfc1

[ "MIT" ]

1

2017-09-05T14:05:55.000Z

# -*- coding: utf-8 -*- import os import pytest from sfm import lines_count def test_lines_count(): assert lines_count.count_lines(__file__) >= 22 def test_lines_stats(): n_files, n_lines = lines_count.lines_stats( os.path.dirname(__file__), lines_count.filter_python_script) assert n_files >= 17 assert n_lines >= 1096 if __name__ == "__main__": import os basename = os.path.basename(__file__) pytest.main([basename, "-s", "--tb=native"])

20.166667

68

0.688017

68

484

4.367647

0.470588

0.16835

0.080808

0

0.022901

0.188017

484

23

69

21.043478

0.732824

0.043388

0

0.142857

0

0.045553

0

0.214286

1

0.142857

false

0

0.285714

0

0.428571

0

null

0

null

0

1

0

eebdcac25970fd8db9e1b4ca1a89af16a4e7a240

803

py

Python

slushtools/string/__init__.py

ZackPaceCoder/slushtools

32bfee028d30fd8fd88e332bdd744a71e51d6dcc

[ "MIT" ]

null

slushtools/string/__init__.py

ZackPaceCoder/slushtools

32bfee028d30fd8fd88e332bdd744a71e51d6dcc

[ "MIT" ]

null

slushtools/string/__init__.py

ZackPaceCoder/slushtools

32bfee028d30fd8fd88e332bdd744a71e51d6dcc

[ "MIT" ]

null

# Slush Tools STRING Module class String: bu = None dat = None def __init__(str): if str == None: print("String argument required.") exit() else: dat = str bu = str return dat def reset(): dat = bu return dat def format(type="custom",args={}): if type == "custom": for i,v in args: x = dat.split("$" + i) v.join(v) dat = x return dat elif type == "py": x = dat.format(*args) dat = x return dat else: print("Unknown format type.") def append(str): dat = dat + str return dat def endswith(str): if dat[len(dat)-len(str):len(str)] == str: return True else: return False def simple(delimiter): return dat.split(delimiter)

20.075

48

0.523039

106

803

3.924528

0.377358

0.129808

0.086538

0.072115

0

0.354919

803

39

49

20.589744

0.803089

0.031133

0

0.27027

0

0.07732

0

1

0.162162

false

0

0.027027

0.459459

0.054054

0

null

0

null

0

1

0

eebe3ee2689c486643e9c66684f0834e67a050c1

2,001

py

Python

lib/gams/general_utils.py

zzzace2000/nodegam

79c8675e65d75237f2e853ae55bbc40ae7124ee9

[ "MIT" ]

7

2021-11-06T14:26:07.000Z

2022-03-17T10:27:17.000Z

lib/gams/general_utils.py

zzzace2000/node

4501233177173ee9b246a5a5e462afd3b1d51bbb

[ "MIT" ]

1

2022-03-22T01:08:27.000Z

2022-03-22T17:19:50.000Z

lib/gams/general_utils.py

zzzace2000/node

4501233177173ee9b246a5a5e462afd3b1d51bbb

[ "MIT" ]

1

2021-11-06T14:27:05.000Z

import time, os import numpy as np import json class Timer: def __init__(self, name, remove_start_msg=True): self.name = name self.remove_start_msg = remove_start_msg def __enter__(self): self.start_time = time.time() print('Run "%s".........' % self.name, end='\r' if self.remove_start_msg else '\n') def __exit__(self, exc_type, exc_val, exc_tb): time_diff = float(time.time() - self.start_time) time_str = '{:.1f}s'.format(time_diff) if time_diff >= 1 else '{:.0f}ms'.format(time_diff * 1000) print('Finish "{}" in {}'.format(self.name, time_str)) def output_csv(the_path, data_dict, order=None, delimiter=','): if the_path.endswith('.tsv'): delimiter = '\t' is_file_exists = os.path.exists(the_path) with open(the_path, 'a+') as op: keys = list(data_dict.keys()) if order is not None: keys = order + [k for k in keys if k not in order] col_title = delimiter.join([str(k) for k in keys]) if not is_file_exists: print(col_title, file=op) else: old_col_title = open(the_path, 'r').readline().strip() if col_title != old_col_title: old_order = old_col_title.split(delimiter) additional_keys = [k for k in keys if k not in old_order] if len(additional_keys) > 0: print('WARNING! The data_dict has following additional keys %s' % (str(additional_keys))) no_key = [k for k in old_order if k not in keys] if len(no_key) > 0: raise(RuntimeError('The data_dict does not have the following old keys: %s' % str(no_key))) keys = old_order + additional_keys print(delimiter.join([str(data_dict[k]) for k in keys]), file=op) def vector_in(vec, names): is_kept = (vec == names[0]) for m_name in names[1:]: is_kept = (is_kept | (vec == m_name)) return is_kept

35.105263

111

0.592704

300

2,001

3.71

0.303333

0.043127

0.022462

0.031447

0.055705

0.045822

0.034142

0

0.00766

0.282359

2,001

56

112

35.732143

0.767409

0

0.085957

0

1

0.119048

false

0

0.071429

0

0.238095

0.119048

0

null

0

null

0

1

0

eec036acad92775b225df98eed2eda788c78e178

32,553

py

Python

mindaffectBCI/decoder/utils.py

rohitvk1/pymindaffectBCI

0348784d9b0fbd9d595e31ae46d2e74632399507

[ "MIT" ]

44

2020-02-07T15:01:47.000Z

2022-03-21T14:36:15.000Z

mindaffectBCI/decoder/utils.py

CkiChen/pymindaffectBCI

0119145a8b280c776f4c4e6cd776fed0f0156404

[ "MIT" ]

17

2020-02-07T17:11:23.000Z

2022-02-20T18:01:42.000Z

mindaffectBCI/decoder/utils.py

CkiChen/pymindaffectBCI

0119145a8b280c776f4c4e6cd776fed0f0156404

[ "MIT" ]

19

2020-02-07T17:13:22.000Z

2022-03-17T01:22:35.000Z

# Copyright (c) 2019 MindAffect B.V. # Author: Jason Farquhar <jason@mindaffect.nl> # This file is part of pymindaffectBCI <https://github.com/mindaffect/pymindaffectBCI>. # # pymindaffectBCI is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # pymindaffectBCI is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with pymindaffectBCI. If not, see <http://www.gnu.org/licenses/> import numpy as np # time-series tests def window_axis(a, winsz, axis=0, step=1, prependwindowdim=False): ''' efficient view-based slicing of equal-sized equally-spaced windows along a selected axis of a numpy nd-array ''' if axis < 0: # no negative axis indices axis = len(a.shape)+axis # compute the shape/strides for the windowed view of a if prependwindowdim: # window dim before axis shape = a.shape[:axis] + (winsz, int((a.shape[axis]-winsz)/step)+1) + a.shape[(axis+1):] strides = a.strides[:axis] + (a.strides[axis], a.strides[axis]*step) + a.strides[(axis+1):] else: # window dim after axis shape = a.shape[:axis] + (int((a.shape[axis]-winsz)/step)+1, winsz) + a.shape[(axis+1):] strides = a.strides[:axis] + (a.strides[axis]*step, a.strides[axis]) + a.strides[(axis+1):] #print("a={}".format(a.shape)) #print("shape={} stride={}".format(shape,strides)) # return the computed view return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) def equals_subarray(a, pat, axis=-1, match=-1): ''' efficiently find matches of a 1-d sub-array along axis within an nd-array ''' if axis < 0: # no negative dims axis = a.ndim+axis # reshape to match dims of a if not isinstance(pat, np.ndarray): pat = np.array(pat) # ensure is numpy pshape = np.ones(a.ndim+1, dtype=int); pshape[axis+1] = pat.size pat = np.array(pat.ravel(),dtype=a.dtype).reshape(pshape) # [ ... x l x...] # window a into pat-len pieces aw = window_axis(a, pat.size, axis=axis, step=1) # [ ... x t-l x l x ...] # do the match F = np.all(np.equal(aw, pat), axis=axis+1) # [... x t-l x ...] # pad to make the same shape as input padshape = list(a.shape); padshape[axis] = a.shape[axis]-F.shape[axis] if match == -1: # match at end of pattern -> pad before F = np.append(np.zeros(padshape, dtype=F.dtype), F, axis) else: # match at start of pattern -> pad after F = np.append(F, np.zeros(padshape, dtype=F.dtype), axis) return F class RingBuffer: ''' time efficient linear ring-buffer for storing packed data, e.g. continguous np-arrays ''' def __init__(self, maxsize, shape, dtype=np.float32): self.elementshape = shape self.bufshape = (int(maxsize), )+shape self.buffer = np.zeros((2*int(maxsize), np.prod(shape)), dtype=dtype) # store as 2d # position for the -1 element. N.B. start maxsize so pos-maxsize is always valid self.pos = int(maxsize) self.n = 0 # count of total number elements added to the buffer self.copypos = 0 # position of the last element copied to the 1st half self.copysize = 0 # number entries to copy as a block def clear(self): '''empty the ring-buffer and reset to empty''' self.pos=int(self.bufshape[0]) self.n =0 self.copypos=0 self.copysize=0 def append(self, x): '''add single element to the ring buffer''' return self.extend(x[np.newaxis, ...]) def extend(self, x): '''add a group of elements to the ring buffer''' # TODO[] : incremental copy to the 1st half, to spread the copy cost? nx = x.shape[0] if self.pos+nx >= self.buffer.shape[0]: flippos = self.buffer.shape[0]//2 # flippos-nx to 1st half self.buffer[:(flippos-nx), :] = self.buffer[(self.pos-(flippos-nx)):self.pos, :] # move cursor to end 1st half self.pos = flippos-nx # insert in the buffer self.buffer[self.pos:self.pos+nx, :] = x.reshape((nx, self.buffer.shape[1])) # move the cursor self.pos = self.pos+nx # update the count self.n = self.n + nx return self @property def shape(self): return (min(self.n,self.bufshape[0]),)+self.bufshape[1:] def unwrap(self): '''get a view on the valid portion of the ring buffer''' return self.buffer[self.pos-min(self.n,self.bufshape[0]):self.pos, :].reshape(self.shape) def __getitem__(self, item): return self.unwrap()[item] def __iter__(self): return iter(self.unwrap()) def extract_ringbuffer_segment(rb, bgn_ts, end_ts=None): ''' extract the data between start/end time stamps, from time-stamps contained in the last channel of a nd matrix''' # get the data / msgs from the ringbuffers X = rb.unwrap() # (nsamp,nch+1) X_ts = X[:, -1] # last channel is timestamps # TODO: binary-search to make these searches more efficient! # search backwards for trial-start time-stamp # TODO[X] : use a bracketing test.. (better with wrap-arround) bgn_samp = np.flatnonzero(np.logical_and(X_ts[:-1] < bgn_ts, bgn_ts <= X_ts[1:])) # get the index of this timestamp, guarding for after last sample if len(bgn_samp) == 0 : bgn_samp = 0 if bgn_ts <= X_ts[0] else len(X_ts)+1 else: bgn_samp = bgn_samp[0] # and just to be sure the trial-end timestamp if end_ts is not None: end_samp = np.flatnonzero(np.logical_and(X_ts[:-1] < end_ts, end_ts <= X_ts[1:])) # get index of this timestamp, guarding for after last data sample end_samp = end_samp[-1] if len(end_samp) > 0 else len(X_ts) else: # until now end_samp = len(X_ts) # extract the trial data, and make copy (just to be sure) X = X[bgn_samp:end_samp+1, :].copy() return X def unwrap(x,range=None): ''' unwrap a list of numbers to correct for truncation due to limited bit-resolution, e.g. time-stamps stored in 24bit integers''' if range is None: range = 1<< int(np.ceil(np.log2(max(x)))) wrap_ind = np.diff(x) < -range/2 unwrap = np.zeros(x.shape) unwrap[np.flatnonzero(wrap_ind)+1]=range unwrap=np.cumsum(unwrap) x = x + unwrap return x def unwrap_test(): x = np.cumsum(np.random.rand(6000,1)) xw = x%(1<<10) xuw = unwrap(x) import matplotlib.pyplot as plt plt.plot(x,label='x') plt.plot(xw,label='x (wrapped)') plt.plot(xuw,label='x (unwrapped') plt.legend() def search_directories_for_file(f,*args): """search a given set of directories for given filename, return 1st match Args: f (str): filename to search for (or a pattern) *args (): set for directory names to look in Returns: f (str): the *first* full path to where f is found, or f if not found. """ import os import glob f = os.path.expanduser(f) if os.path.exists(f) or len(glob.glob(f))>0: return f for d in args: #print('Searching dir: {}'.format(d)) df = os.path.join(d,f) if os.path.exists(df) or len(glob.glob(df))>0: f = df break return f # toy data generation #@function def randomSummaryStats(d=10, nE=2, tau=10, nY=1): import numpy as np # pure random test-case Cxx = np.random.standard_normal((d, d)) Cxy = np.random.standard_normal((nY, nE, tau, d)) Cyy = np.random.standard_normal((nY, nE, tau, nE, tau)) return (Cxx, Cxy, Cyy) def testNoSignal(d=10, nE=2, nY=1, isi=5, tau=None, nSamp=10000, nTrl=1): # Simple test-problem -- no real signal if tau is None: tau = 10*isi X = np.random.standard_normal((nTrl, nSamp, d)) stimTimes_samp = np.arange(0, X.shape[-2] - tau, isi) Me = np.random.standard_normal((nTrl, len(stimTimes_samp), nY, nE))>1 Y = np.zeros((nTrl, X.shape[-2], nY, nE)) Y[:, stimTimes_samp, :, :] = Me return (X, Y, stimTimes_samp) def testSignal(nTrl=1, d=5, nE=2, nY=30, isi=5, tau=None, offset=0, nSamp=10000, stimthresh=.6, noise2signal=1, irf=None): #simple test problem, with overlapping response import numpy as np if tau is None: tau = 10 if irf is None else len(irf) nEp = int((nSamp-tau)/isi) cb = np.random.standard_normal((nEp, nY, nE)) > stimthresh # codebook = per-epoch stimulus activity E = cb # (nEp, nY, nE) # per-epoch stimulus activity # up-sample to sample rate stimTimes_samp = np.arange(0, nSamp-tau, isi) # (nEp) Y = np.zeros((nSamp, nY, E.shape[-1])) Y[stimTimes_samp, :, :] = E[:len(stimTimes_samp), :, :] #per-sample stimulus activity (nSamp, nY, nE) [nE x nY x nSamp] Y = np.tile(Y,(nTrl,1,1,1)) # replicate for the trials # generate the brain source A = np.random.standard_normal((nE, d)) # spatial-pattern for the source signal if irf is None: B = np.zeros((tau), dtype=np.float32) B[-3] = 1; # true response filter (shift by 10 samples) else: B = np.array(irf, dtype=np.float32) Ytrue = Y[..., 0, :] # (nTrl, nSamp, nE) if True: # convolve with the impulse response - manually using window_axis # zero pad before for the sliding window Ys = np.zeros(Ytrue.shape[:-2]+(Ytrue.shape[-2]+tau-1,)+Ytrue.shape[-1:]) Ys[..., tau-1+offset:Ytrue.shape[-2]+tau-1+offset, :] = Ytrue # zero-pad at front + include the offset. Yse = window_axis(Ys, winsz=len(B), axis=-2) # (nTr,nSamp,tau,nE) YtruecB = np.einsum("Tste,t->Tse", Yse, B[::-1]) # N.B. time-reverse irf (nTr,nSamp,nE) else: # use the np convolve function, N.B. implicitly time reverses B (like we want) YtruecB = np.array([np.convolve(Ytrue[:, ei], B, 'full') for ei in range(Ytrue.shape[-1])]).T #(nSamp+pad, nE) [nE x nSamp] YtruecB = YtruecB[:Ytrue.shape[0], :] # trim the padding #import matplotlib.pyplot as plt; plt.clf(); plt.plot(Ytrue[:100,0],'b*',label='Y'); plt.plot(YtruecB[:100,0],'g*',label='Y*B'); plt.plot(B,'k',label='B'); plt.legend() #print("Ytrue={}".format(Ytrue.shape)) #print("YtruecB={}".format(YtruecB.shape)) S = YtruecB # (nTr, nSamp, nE) true response, i.e. filtered Y N = np.random.standard_normal(S.shape[:-1]+(d,)) # EEG noise (nTr, nSamp, d) X = np.einsum("tse,ed->tsd", S, A) + noise2signal*N # simulated data.. true source mapped through spatial pattern (nSamp, d) #[d x nSamp] return (X, Y, stimTimes_samp, A, B) def testtestSignal(): import matplotlib.pyplot as plt plt.clf() # shift by 5 offset=0; irf=(0,0,0,0,0,1,0,0,0,0) X,Y,st,W,R = testSignal(nTrl=1,nSamp=500,d=1,nE=1,nY=1,isi=10,tau=10,offset=offset,irf=irf,noise2signal=0) plt.subplot(311);plt.plot(X[0,:,0],label='X');plt.plot(Y[0,:,0,0],label='Y');plt.title("offset={}, irf={}".format(offset,irf));plt.legend() # back-shift-by-5 -> 0 shift offset=-5 X,Y,st,W,R = testSignal(nTrl=1,nSamp=500,d=1,nE=1,nY=1,isi=10,tau=10,offset=offset,irf=(0,0,0,0,0,1,0,0,0,0),noise2signal=0) plt.subplot(312);plt.plot(X[0,:,0],label='X');plt.plot(Y[0,:,0,0],label='Y');plt.title("offset={}, irf={}".format(offset,irf));plt.legend() # back-shift-by-10 -> -5 shift offset=-9 X,Y,st,W,R = testSignal(nTrl=1,nSamp=500,d=1,nE=1,nY=1,isi=10,tau=10,offset=offset,irf=(0,0,0,0,0,1,0,0,0,0),noise2signal=0) plt.subplot(313);plt.plot(X[0,:,0],label='X');plt.plot(Y[0,:,0,0],label='Y');plt.title("offset={}, irf={}".format(offset,irf));plt.legend() def sliceData(X, stimTimes_samp, tau=10): # make a sliced version dst = np.diff(stimTimes_samp) if np.all(dst == dst[0]) and stimTimes_samp[0] == 0: # fast path equaly spaced stimTimes Xe = window_axis(X, winsz=tau, axis=-2, step=int(dst[0]), prependwindowdim=False) # (nTrl, nEp, tau, d) #d x tau x ep x trl else: Xe = np.zeros(X.shape[:-2] + (len(stimTimes_samp), tau, X.shape[-1])) # (nTrl, nEp, tau, d) [ d x tau x nEp x nTrl ] for ei, si in enumerate(stimTimes_samp): idx = range(si, si+tau) Xe[:, ei, :, :] = X[:, idx, :] if X.ndim > 2 else X[idx, :] return Xe def sliceY(Y, stimTimes_samp, featdim=True): ''' Y = (nTrl, nSamp, nY, nE) if featdim=True OR Y=(nTrl, nSamp, nY) if featdim=False #(nE x nY x nSamp x nTrl) ''' # make a sliced version si = np.array(stimTimes_samp, dtype=int) if featdim: return Y[:, si, :, :] if Y.ndim > 3 else Y[si, :, :] else: return Y[:, si, :] if Y.ndim > 2 else Y[si, :] def block_randomize(true_target, npermute, axis=-3, block_size=None): ''' make a block random permutaton of the input array Inputs: npermute: int - number permutations to make true_target: (..., nEp, nY, e): true target value for nTrl trials of length nEp flashes axis : int the axis along which to permute true_target''' if true_target.ndim < 3: raise ValueError("true target info must be at least 3d") if not (axis == -3 or axis == true_target.ndim-2): raise NotImplementedError("Only implementated for axis=-2 currently") # estimate the number of blocks to use if block_size is None: block_size = max(1, true_target.shape[axis]/2/npermute) nblk = int(np.ceil(true_target.shape[axis]/block_size)) blk_lims = np.linspace(0, true_target.shape[axis], nblk, dtype=int) # convert to start/end index for each block blk_lims = [(blk_lims[i], blk_lims[i+1]) for i in range(len(blk_lims)-1)] cb = np.zeros(true_target.shape[:axis+1] + (npermute, true_target.shape[-1])) for ti in range(cb.shape[axis+1]): for di, dest_blk in enumerate(blk_lims): yi = np.random.randint(true_target.shape[axis+1]) si = np.random.randint(len(blk_lims)) # ensure can't be the same block if si == di: si = si+1 if si < len(blk_lims)-1 else si-1 src_blk = blk_lims[si] # guard for different lengths for source/dest blocks dest_len = dest_blk[1] - dest_blk[0] if dest_len > src_blk[1]-src_blk[0]: if src_blk[0]+dest_len < true_target.shape[axis]: # enlarge the src src_blk = (src_blk[0], src_blk[0]+dest_len) elif src_blk[1]-dest_len > 0: src_blk = (src_blk[1]-dest_len, src_blk[1]) else: raise ValueError("can't fit source and dest") elif dest_len < src_blk[1]-src_blk[0]: src_blk = (src_blk[0], src_blk[0]+dest_len) cb[..., dest_blk[0]:dest_blk[1], ti, :] = true_target[..., src_blk[0]:src_blk[1], yi, :] return cb def upsample_codebook(trlen, cb, ep_idx, stim_dur_samp, offset_samp=(0, 0)): ''' upsample a codebook definition to sample rate Inputs: trlen : (int) length after up-sampling cb : (nTr, nEp, ...) the codebook ep_idx : (nTr, nEp) the indices of the codebook entries stim_dur_samp: (int) the amount of time the cb entry is held for offset_samp : (2,):int the offset for the stimulus in the upsampled trlen data Outputs: Y : ( nTrl, trlen, ...) the up-sampled codebook ''' if ep_idx is not None: if not np.all(cb.shape[:ep_idx.ndim] == ep_idx.shape): raise ValueError("codebook and epoch indices must has same shape") trl_idx = ep_idx[:, 0] # start each trial else: # make dummy ep_idx with 0 for every trial! ep_idx = np.zeros((cb.shape[0],1),dtype=int) trl_idx = ep_idx Y = np.zeros((cb.shape[0], trlen)+ cb.shape[2:], dtype='float32') # (nTr, nSamp, ...) for ti, trl_start_idx in enumerate(trl_idx): for ei, epidx in enumerate(ep_idx[ti, :]): if ei > 0 and epidx == 0: # zero indicates end of variable length trials break # start index for this epoch in this *trial*, including the 0-offset ep_start_idx = -int(offset_samp[0])+int(epidx-trl_start_idx) Y[ti, ep_start_idx:(ep_start_idx+int(stim_dur_samp)), ...] = cb[ti, ei, ...] return Y def lab2ind(lab,lab2class=None): ''' convert a list of labels (as integers) to a class indicator matrix''' if lab2class is None: lab2class = [ (l,) for l in set(lab) ] # N.B. list of lists if not isinstance(lab,np.ndarray): lab=np.array(lab) Y = np.zeros(lab.shape+(len(lab2class),),dtype=bool) for li,ls in enumerate(lab2class): for l in ls: Y[lab == l, li]=True return (Y,lab2class) def zero_outliers(X, Y, badEpThresh=4, badEpChThresh=None, verbosity=0): '''identify and zero-out bad/outlying data Inputs: X = (nTrl, nSamp, d) Y = (nTrl, nSamp, nY, nE) OR (nTrl, nSamp, nE) nE=#event-types nY=#possible-outputs nEpoch=#stimulus events to process ''' # remove whole bad epochs first if badEpThresh > 0: bad_ep, _ = idOutliers(X, badEpThresh, axis=(-2, -1)) # ave over time,ch if np.any(bad_ep): if verbosity > 0: print("{} badEp".format(np.sum(bad_ep.ravel()))) # copy X,Y so don't modify in place! X = X.copy() Y = Y.copy() X[bad_ep[..., 0, 0], ...] = 0 #print("Y={}, Ybad={}".format(Y.shape, Y[bad_ep[..., 0, 0], ...].shape)) # zero out Y also, so don't try to 'fit' the bad zeroed data Y[bad_ep[..., 0, 0], ...] = 0 # Remove bad individual channels next if badEpChThresh is None: badEpChThresh = badEpThresh*2 if badEpChThresh > 0: bad_epch, _ = idOutliers(X, badEpChThresh, axis=-2) # ave over time if np.any(bad_epch): if verbosity > 0: print("{} badEpCh".format(np.sum(bad_epch.ravel()))) # make index expression to zero out the bad entries badidx = list(np.nonzero(bad_epch)) # convert to linear indices badidx[-2] = slice(X.shape[-2]) # broadcast over the accumulated dimensions if not np.any(bad_ep): # copy so don't update in place X = X.copy() X[tuple(badidx)] = 0 return (X, Y) def idOutliers(X, thresh=4, axis=-2, verbosity=0): ''' identify outliers with excessively high power in the input data Inputs: X:float the data to identify outliers in axis:int (-2) axis of X to sum to get power thresh(float): threshold standard deviation for outlier detection verbosity(int): verbosity level Returns: badEp:bool (X.shape axis==1) indicator for outlying elements epPower:float (X.shape axis==1) power used to identify bad ''' #print("X={} ax={}".format(X.shape,axis)) power = np.sqrt(np.sum(X**2, axis=axis, keepdims=True)) #print("power={}".format(power.shape)) good = np.ones(power.shape, dtype=bool) for _ in range(4): mu = np.mean(power[good]) sigma = np.sqrt(np.mean((power[good] - mu) ** 2)) badThresh = mu + thresh*sigma good[power > badThresh] = False good = good.reshape(power.shape) # (nTrl, nEp) #print("good={}".format(good.shape)) bad = ~good if verbosity > 1: print("%d bad" % (np.sum(bad.ravel()))) return (bad, power) def robust_mean(X,thresh=(3,3)): """Compute robust mean of values in X, using gaussian outlier criteria Args: X (the data): the data thresh (2,): lower and upper threshold in standard deviations Returns: mu (): the robust mean good (): the indices of the 'good' data in X """ good = np.ones(X.shape, dtype=bool) for _ in range(4): mu = np.mean(X[good]) sigma = np.sqrt(np.mean((X[good] - mu) ** 2)) # re-compute outlier list good[:]=True if thresh[0] is not None: badThresh = mu + thresh[0]*sigma good[X > badThresh] = False if thresh[1] is not None: badThresh = mu - thresh[0]*sigma good[X < badThresh] = False mu = np.mean(X[good]) return (mu, good) try: from scipy.signal import butter, bessel, sosfilt, sosfilt_zi except: #if True: # use the pure-python fallbacks def sosfilt(sos,X,axis,zi): return sosfilt_2d_py(sos,X,axis=axis,zi=zi) def sosfilt_zi(sos): return sosfilt_zi_py(sos) def butter(order,freq,btype,output): return butter_py(order,freq,btype,output) def sosfilt_zi_warmup(zi, X, axis=-1, sos=None): '''Use some initial data to "warmup" a second-order-sections filter to reduce startup artifacts. Args: zi (np.ndarray): the sos filter, state X ([type]): the warmup data axis (int, optional): The filter axis in X. Defaults to -1. sos ([type], optional): the sos filter coefficients. Defaults to None. Returns: [np.ndarray]: the warmed up filter coefficients ''' if axis < 0: # no neg axis axis = X.ndim+axis # zi => (order,...,2,...) zi = np.reshape(zi, (zi.shape[0],) + (1,)*(axis) + (zi.shape[1],) + (1,)*(X.ndim-axis-1)) # make a programattic index expression to support arbitary axis idx = [slice(None)]*X.ndim # get the index to start the warmup warmupidx = 0 if sos is None else min(sos.size*3,X.shape[axis]-1) # center on 1st warmup value idx[axis] = slice(warmupidx,warmupidx+1) zi = zi * X[tuple(idx)] # run the filter on the rest of the warmup values if not sos is None and warmupidx>3: idx[axis] = slice(warmupidx,1,-1) _, zi = sosfilt(sos, X[tuple(idx)], axis=axis, zi=zi) return zi def iir_sosfilt_sos(stopband, fs, order=4, ftype='butter', passband=None, verb=0): ''' given a set of filter cutoffs return butterworth or bessel sos coefficients ''' # convert to normalized frequency, Note: not to close to 0/1 if stopband is None: return np.array(()) if not hasattr(stopband[0],'__iter__'): stopband=(stopband,) sos=[] for sb in stopband: btype = None if type(sb[-1]) is str: btype = sb[-1] sb = sb[:-1] # convert to normalize frequency sb = np.array(sb,dtype=np.float32) sb[sb<0] = (fs/2)+sb[sb<0]+1 # neg freq count back from nyquist Wn = sb/(fs/2) if Wn[1] < .0001 or .9999 < Wn[0]: # no filter continue # identify type from frequencies used, cliping if end of frequency range if Wn[0] < .0001: Wn = Wn[1] btype = 'highpass' if btype is None or btype == 'bandstop' else 'lowpass' elif .9999 < Wn[1]: Wn = Wn[0] btype = 'lowpass' if btype is None or btype == 'bandstop' else 'highpass' elif btype is None: # .001 < Wn[0] and Wn[1] < .999: btype = 'bandstop' if verb>0: print("{}={}={}".format(btype,sb,Wn)) if ftype == 'butter': sosi = butter(order, Wn, btype=btype, output='sos') elif ftype == 'bessel': sosi = bessel(order, Wn, btype=btype, output='sos', norm='phase') else: raise ValueError("Unrecognised filter type") sos.append(sosi) # single big filter cascade sos = np.concatenate(sos,axis=0) return sos def butter_sosfilt(X, stopband, fs:float, order:int=6, axis:int=-2, zi=None, verb=True, ftype='butter'): """use a (cascade of) butterworth SOS filter(s) filter X along axis Args: X (np.ndarray): the data to be filtered stopband ([type]): the filter band specifications in Hz, as a list of lists of stopbands (given as (low-pass,high-pass)) or pass bands (given as (low-cut,high-cut,'bandpass')) fs (float): the sampling rate of X order (int, optional): the desired filter order. Defaults to 6. axis (int, optional): the axis of X to filter along. Defaults to -2. zi ([type], optional): the internal filter state -- propogate between calls for incremental filtering. Defaults to None. verb (bool, optional): Verbosity level for logging. Defaults to True. ftype (str, optional): The type of filter to make, one-of: 'butter', 'bessel'. Defaults to 'butter'. Returns: X [np.ndarray]: the filtered version of X sos (np.ndarray): the designed filter coefficients zi (np.ndarray): the filter state for propogation between calls """ ''' ''' if stopband is None: # deal with no filter case return (X,None,None) if axis < 0: # no neg axis axis = X.ndim+axis # TODO []: auto-order determination? sos = iir_sosfilt_sos(stopband, fs, order, ftype=ftype) sos = sos.astype(X.dtype) # keep as single precision if axis == X.ndim-2 and zi is None: zi = sosfilt_zi(sos) # (order,2) zi = zi.astype(X.dtype) zi = sosfilt_zi_warmup(zi, X, axis, sos) else: zi = None print("Warning: not warming up...") # Apply the warmed up filter to the input data #print("zi={}".format(zi.shape)) if not zi is None: #print("filt:zi X{} axis={}".format(X.shape,axis)) X, zi = sosfilt(sos, X, axis=axis, zi=zi) else: print("filt:no-zi") X = sosfilt(sos, X, axis=axis) # zi=zi) # return filtered data, filter-coefficients, filter-state return (X, sos, zi) def save_butter_sosfilt_coeff(filename=None, stopband=((45,65),(5.5,25,'bandpass')), fs=200, order=6, ftype='butter'): ''' design a butterworth sos filter cascade and save the coefficients ''' import pickle sos = iir_sosfilt_sos(stopband, fs, order, passband=None, ftype=ftype) zi = sosfilt_zi(sos) if filename is None: # auto-generate descriptive filename filename = "{}_stopband{}_fs{}.pk".format(ftype,stopband,fs) print("Saving to: {}\n".format(filename)) with open(filename,'wb') as f: pickle.dump(sos,f) pickle.dump(zi,f) f.close() def test_butter_sosfilt(): fs= 100 X = np.random.randn(fs*10,2) X = np.cumsum(X,0) X = X + np.random.randn(1,X.shape[1])*100 # include start shift import matplotlib.pyplot as plt plt.clf();plt.subplot(511);plt.plot(X); pbs=(((0,1),(40,-1)),(10,-1),((5,10),(15,20),(45,50))) for i,pb in enumerate(pbs): Xf,_,_ = butter_sosfilt(X,pb,fs) plt.subplot(5,1,i+2);plt.plot(Xf);plt.title("{}".format(pb)) # test incremental application pb=pbs[0] sos=None zi =None Xf=[] for i in range(0,X.shape[0],fs): if sos is None: # init filter and do 1st block Xfi,sos,zi = butter_sosfilt(X[i:i+fs,:],pb,fs,axis=-2) else: # incremenally apply Xfi,zi = sosfilt(sos,X[i:i+fs,:],axis=-2,zi=zi) Xf.append(Xfi) Xf = np.concatenate(Xf,axis=0) plt.subplot(5,1,5);plt.plot(Xf);plt.title("{} - incremental".format(pb)) plt.show() # test diff specifications pb = ((0,1),(40,-1)) # pair stops Xf0,_,_ = butter_sosfilt(X,pb,fs,axis=-2) plt.subplot(3,1,1);plt.plot(Xf0);plt.title("{}".format(pb)) pb = (1,40,'bandpass') # single pass Xfi,_,_ = butter_sosfilt(X,pb,fs,axis=-2) plt.subplot(3,1,2);plt.plot(Xfi);plt.title("{}".format(pb)) pb = (1,40,'bandpass') # single pass Xfi,_,_ = butter_sosfilt(X,pb,fs,axis=-2,ftype='bessel') plt.subplot(3,1,3);plt.plot(Xfi);plt.title("{} - bessel".format(pb)) plt.show() # TODO[] : cythonize? # TODO[X] : vectorize over d? ---- NO. 2.5x *slower* def sosfilt_2d_py(sos,X,axis=-2,zi=None): ''' pure python fallback for second-order-sections filter in case scipy isn't available ''' X = np.asarray(X) sos = np.asarray(sos) if zi is None: returnzi = False zi = np.zeros((sos.shape[0],2,X.shape[-1]),dtype=X.dtype) else: returnzi = True zi = np.asarray(zi) Xshape = X.shape if not X.ndim == 2: print("Warning: X>2d.... treating as 2d...") X = X.reshape((-1,Xshape[-1])) if axis < 0: axis = X.ndim + axis if not axis == X.ndim-2: raise ValueError("Only for time in dim 0/-2") if sos.ndim != 2 or sos.shape[1] != 6: raise ValueError('sos must be shape (n_sections, 6)') if zi.ndim != 3 or zi.shape[1] != 2 or zi.shape[2] != X.shape[1]: raise ValueError('zi must be shape (n_sections, 2, dim)') # pre-normalize sos if needed for j in range(sos.shape[0]): if sos[j,3] != 1.0: sos[j,:] = sos[j,:]/sos[j,3] n_signals = X.shape[1] n_samples = X.shape[0] n_sections = sos.shape[0] # extract the a/b b = sos[:,:3] a = sos[:,4:] # loop over outputs x_n = 0 for i in range(n_signals): for n in range(n_samples): for s in range(n_sections): x_n = X[n, i] # use direct II transposed structure X[n, i] = b[s, 0] * x_n + zi[s, 0, i] zi[s, 0, i] = b[s, 1] * x_n - a[s, 0] * X[n, i] + zi[s, 1, i] zi[s, 1, i] = b[s, 2] * x_n - a[s, 1] * X[n, i] # back to input shape if not len(Xshape) == 2: X = X.reshape(Xshape) # match sosfilt, only return zi if given zi if returnzi : return X, zi else: return X def sosfilt_zi_py(sos): ''' compute an initial state for a second-order section filter ''' sos = np.asarray(sos) if sos.ndim != 2 or sos.shape[1] != 6: raise ValueError('sos must be shape (n_sections, 6)') n_sections = sos.shape[0] zi = np.empty((n_sections, 2)) scale = 1.0 for section in range(n_sections): b = sos[section, :3] a = sos[section, 3:] if a[0] != 1.0: # Normalize the coefficients so a[0] == 1. b = b / a[0] a = a / a[0] IminusA = np.eye(n_sections - 1) - np.linalg.companion(a).T B = b[1:] - a[1:] * b[0] # Solve zi = A*zi + B lfilter_zi = np.linalg.solve(IminusA, B) zi[section] = scale * lfilter_zi scale *= b.sum() / a.sum() return zi def test_sosfilt_py(): import pickle with open('butter_stopband((0, 5), (25, -1))_fs200.pk','rb') as f: sos = pickle.load(f) zi = pickle.load(f) X = np.random.randn(10000,3) print("X={} sos={}".format(X.shape,sos.shape)) Xsci = sosfilt(sos,X.copy(),-2) Xpy = sosfilt_2d_py(sos,X.copy(),-2) import matplotlib.pyplot as plt plt.clf() plt.subplot(411);plt.plot(X[:500,:]);plt.title('X') plt.subplot(412);plt.plot(Xsci[:500,:]);plt.title('Xscipy') plt.subplot(413);plt.plot(Xpy[:500,:]);plt.title('Xpy') plt.subplot(414);plt.plot(Xsci-Xpy);plt.title('Xsci - Xpy') # def butter_py(order,fc,fs,btype,output): # ''' pure python butterworth filter synthesis ''' # if fc>=fs/2: # error('fc must be less than fs/2') # # I. Find poles of analog filter # k= np.arange(order) # theta= (2*k -1)*np.pi/(2*order); # pa= -sin(theta) + j*cos(theta); # poles of filter with cutoff = 1 rad/s # # # # II. scale poles in frequency # Fc= fs/np.pi * tan(np.pi*fc/fs); # continuous pre-warped frequency # pa= pa*2*np.pi*Fc; # scale poles by 2*pi*Fc # # # # III. Find coeffs of digital filter # # poles and zeros in the z plane # p= (1 + pa/(2*fs))/(1 - pa/(2*fs)) # poles by bilinear transform # q= -np.ones((1,N)); # zeros # # # # convert poles and zeros to polynomial coeffs # a= poly(p); # convert poles to polynomial coeffs a # a= real(a); # b= poly(q); # convert zeros to polynomial coeffs b # K= sum(a)/sum(b); # amplitude scale factor # b= K*b; if __name__=='__main__': save_butter_sosfilt_coeff("sos_filter_coeff.pk") #test_butter_sosfilt()

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0.018634

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null

0

null

0

1

0

eec118b9402f1ab3d9a333bb53d8180c1858ff75

2,100

py

Python

model/test.py

yacoubb/lang-classifier

d39a342cf8ad64b191ea235f9af3f833033f254a

[ "MIT" ]

1

2019-07-03T11:28:55.000Z

model/test.py

yacoubb/lang-classifier

d39a342cf8ad64b191ea235f9af3f833033f254a

[ "MIT" ]

null

model/test.py

yacoubb/lang-classifier

d39a342cf8ad64b191ea235f9af3f833033f254a

[ "MIT" ]

null

from tensorflow import keras import os import numpy as np import sys import json sys.path.append("/".join(os.path.abspath(__file__).split("/")[:-2])) from model.dataset import utils, test_sampler def estimate_model_accuracy(model): def predict(word): word = utils.total_conversion(word) word = word[: utils.max_word_length] vector_word = utils.vectorize_word_2d(word) vector_word = np.array([vector_word]) result = model.predict(vector_word) return utils.vector_to_language(result, languages) languages = [] with open("./RMS_model/metadata.json", "r") as metadata_file: metadata = json.load(metadata_file) languages = metadata["languages"] print("starting sampler worker...") test_sampler.get_sample(1000, languages) test_words = {} with open("./dataset/test_words.json", "r") as test_word_file: test_words = json.load(test_word_file) print("=" * 20 + " doing predictions " + "=" * 20) results = [] word_predictions = [] for key in test_words: print(key) correct = 0.0 total = 0.0 for word in test_words[key]: total += 1.0 prediction = predict(word) word_predictions.append((word, prediction)) if predict(word) == key: correct += 1.0 results.append((key, correct * 100.0 / total)) from tabulate import tabulate summary = "" summary += tabulate(results, headers=["language", "accuracy"]) summary += "\n" summary += "overall accuracy: {:2f}".format( sum(map(lambda x: x[1], results)) / len(list(filter(lambda x: x[1] > 0, results))) ) summary += "\n" return summary, word_predictions summary, all_predictions = estimate_model_accuracy( keras.models.load_model("./RMS_model/model.h5") ) print(summary) with open("./RMS_model/testing.txt", "w+") as test_file: test_file.write(summary) test_file.write("=" * 20 + "\n") for word, pred in all_predictions: test_file.write(word + ", " + pred + "\n")

28.378378

68

0.623333

263

2,100

4.802281

0.334601

0.035629

0.030879

0.025337

0

0.01817

0.24

2,100

73

69

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0.070175

0

null

0

null

0

1

0

eec22817edf6f5ff4caafda2c75d1273cb9edbb8

2,102

py

Python

crawler/crawler2.py

labcontext/image-inpainting-oldpaper

da4683a2c58d662e443ea24ab93fd9d8fcb96bda

[ "Apache-2.0" ]

null

crawler/crawler2.py

labcontext/image-inpainting-oldpaper

da4683a2c58d662e443ea24ab93fd9d8fcb96bda

[ "Apache-2.0" ]

3

2021-03-19T11:16:57.000Z

2022-01-13T02:18:17.000Z

crawler/crawler2.py

labcontext/image-inpainting-oldpaper

da4683a2c58d662e443ea24ab93fd9d8fcb96bda

[ "Apache-2.0" ]

null

import requests import urllib.request import os import pickle import argparse # file read folder path = 'http://db.itkc.or.kr//data/imagedb/BOOK/ITKC_{0}/ITKC_{0}_{1}A/ITKC_{0}_{1}A_{2}{5}_{3}{4}.JPG' # Manual label = ['BT', 'MO'] middle = 1400 last = ['A', 'V'] # A ~400 V ~009 num = 10 num1 = 400 fin = ['A', 'B', 'H', 'L'] # file path, save path # pad for number def pad(num, width): return '%0{}d'.format(width) % num def save_picture(file_name, save_dir): return urllib.request.urlretrieve(file_name, save_dir) def main(): parser = argparse.ArgumentParser() parser.add_argument('-l', '--label', default='BT', type=str, help='BT, MO') parser.add_argument('-f', '--fin', default='A', type=str, help='A,B,H,L') opt = parser.parse_args() # make directory if not os.path.exists('oldDB'): os.mkdir('oldDB') if opt.label == 'BT': for i in range(0, middle+1): for k in range(num + 1): for j in range(num1 + 1): try: p = path.format(opt.label, pad(i, 4), 'V', pad(j, 3), opt.fin, pad(k, 3)) print(p) save_picture(p, './oldDB/{0}_{1}_{2}_{3}_{4}.jpg'.format( opt.label, i, 'V', j, opt.fin)) except Exception as e: print(str(e)) continue elif opt.label == 'MO': for i in range(0, middle+1): for k in range(num1 + 1): for j in range(num1 + 1): try: p = path.format(opt.label, pad(i, 4), 'A', pad(j, 3), opt.fin, pad(k, 3)) print(p) save_picture(p, './oldDB/{0}_{1}_{2}_{3}_{4}.jpg'.format( opt.label, i, 'A', j, opt.fin)) except Exception as e: print(str(e)) continue if __name__ == '__main__': main()

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2,102

3.335714

0.325

0.051392

0.059957

0.038544

0.381156

0

0.040738

0.381066

2,102

79

104

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0.677171

0.041865

0

0.352941

0

0.058824

0.114101

0.030892

0

1

0.058824

false

0

0.098039

0.039216

0.196078

0.078431

0

null

0

null

0

1

0

eeca3c40e6643d64e2cc7861e9484fa8ec9bd6f8

9,415

py

Python

main.py

Arnav-Ghatti/Tkinter-Money-Tracker

365dcafc78522d03062a8f062fa8167b9c015583

[ "MIT" ]

null

main.py

Arnav-Ghatti/Tkinter-Money-Tracker

365dcafc78522d03062a8f062fa8167b9c015583

[ "MIT" ]

null

main.py

Arnav-Ghatti/Tkinter-Money-Tracker

365dcafc78522d03062a8f062fa8167b9c015583

[ "MIT" ]

null

import tkinter as tk from tkinter import messagebox import json # Constants FONT_NAME = "Open Sans" BG_COLOR = "#f9f7f7" FONT_COLOR = "#112d4e" ACCENT = "#dbe2ef" root = tk.Tk() root.title("Money Tracker") root.config(bg=BG_COLOR) root.resizable(0, 0) root.iconbitmap("C:\\Users\\ASUA\\Desktop\\Tests\\MoneyTransactionsOriginal\\money.ico") transactions_history = {} transactions = [] def set_listbox(): """Refreshes the listbox""" global listbox listbox.delete(0, tk.END) for item in transactions: listbox.insert(tk.END, f"{item[0]} to {item[1]}, {clicked.get()}{item[2]}, {item[3]}") def save_json(data): """Saves the date to C:\\Users\\ASUA\\Desktop\\Tests\\MoneyTransactionsOriginal\\history.json file""" with open("C:\\Users\\ASUA\\Desktop\\Tests\\MoneyTransactionsOriginal\\history.json", "w") as file: json.dump(transactions_history, file, indent=4) def check_fields(): if sender_input.get() == "" or reciever_input.get() == "" or desc_input.get("1.0", tk.END) == "": return False return True def clear_fields(): sender_input.delete(0, tk.END) reciever_input.delete(0, tk.END) amount_input.delete(0, tk.END) desc_input.delete("1.0", tk.END) def add_transactions(): """Adds transactios to the listbox""" try: check_int = int(amount_input.get()) except ValueError: messagebox.showwarning(title="❌ Error ❌", message="Please enter only numbers in amount field") return if check_fields(): transactions.append([sender_input.get(), reciever_input.get(), amount_input.get(), desc_input.get("1.0", tk.END)]) transactions_history["Transactions"] = transactions clear_fields() save_json(transactions_history) set_listbox() else: messagebox.showwarning(title="❌ Error ❌", message="Please do not leave any fields empty") def delete_transaction(): """Deletes transactions from the listbox""" try: del transactions[listbox.curselection()[0]] except IndexError: messagebox.showwarning(title="❌ Error ❌", message="Please select any item") else: transactions_history["Transactions"] = transactions save_json(transactions_history) set_listbox() def load_transactions(): """Loads data of transactions from the selected item in the listbox""" try: selected_idx = listbox.curselection()[0] selected_item = transactions[selected_idx] except IndexError: messagebox.showwarning(title="❌ Error ❌", message="Please select any item") else: sender_var.set(selected_item[0]) reciever_var.set(selected_item[1]) amount_var.set(selected_item[2]) desc_input.delete("1.0", tk.END) desc_input.insert(tk.END, selected_item[3]) def update_transactions(): """Updates selected transaction to the details newly entered""" if check_fields(): try: transactions[listbox.curselection()[0]] = [sender_var.get(), reciever_var.get(), amount_var.get(), desc_input.get("1.0", tk.END)] except IndexError: messagebox.showwarning(title="❌ Error ❌", message="Please select any item") else: transactions_history["Transactions"] = transactions save_json(transactions_history) set_listbox() else: messagebox.showwarning(title="❌ Error ❌", message="Please do not leave any fields empty") # Title title = tk.Label(root, text="Money Tracker", font=(FONT_NAME, 15, "bold"), bg=BG_COLOR, highlightthickness=0, fg=FONT_COLOR) title.grid(row=0, column=0, columnspan=2, pady=3) # ---------------------------- ENTRIES AND LABELS ------------------------------- # input_frame = tk.Frame(root, bg=BG_COLOR, highlightthickness=0) input_frame.grid(row=1, column=0, sticky="N", padx=5) # Sender sender_label = tk.Label(input_frame, text="Sender: ", font=(FONT_NAME, 12, "normal"), bg=BG_COLOR, fg=FONT_COLOR, highlightthickness=0) sender_label.grid(row=0, column=0, sticky="W", pady=5) sender_var = tk.StringVar() sender_input = tk.Entry(input_frame, textvariable=sender_var, width=36, font=(FONT_NAME, 12, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) sender_input.focus() sender_input.grid(row=0, column=1, sticky="W", pady=5, padx=10, columnspan=2) # Reciever reciever_label = tk.Label(input_frame, text="Reciever: ", font=(FONT_NAME, 12, "normal"), bg=BG_COLOR, fg=FONT_COLOR, highlightthickness=0) reciever_label.grid(row=1, column=0, sticky="W", pady=5) reciever_var = tk.StringVar() reciever_input = tk.Entry(input_frame, textvariable=reciever_var, width=36, font=(FONT_NAME, 12, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) reciever_input.grid(row=1, column=1, sticky="W", pady=5, padx=10, columnspan=2) # Amount amount_label = tk.Label(input_frame, text="Amount: ", font=(FONT_NAME, 12, "normal"), bg=BG_COLOR, fg=FONT_COLOR, highlightthickness=0) amount_label.grid(row=2, column=0, sticky="W", pady=5) amount_var = tk.StringVar() amount_input = tk.Entry(input_frame, textvariable=amount_var, width=27, font=(FONT_NAME, 12, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) amount_input.grid(row=2, column=1, sticky="W", pady=5, padx=10) # Description desc_label = tk.Label(input_frame, text="Description: ", font=(FONT_NAME, 12, "normal"), bg=BG_COLOR, fg=FONT_COLOR, highlightthickness=0, bd=0) desc_label.grid(row=3, column=0, sticky="N", pady=5) desc_input = tk.Text(input_frame, width=36, height=12, font=(FONT_NAME, 12, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) desc_input.grid(row=3, column=1, sticky="W", pady=5, padx=10, columnspan=2) currencies = [ "$", "₹", "€", "£", "¥" ] clicked = tk.StringVar() clicked.set("$") currency = tk.OptionMenu(input_frame, clicked, *currencies) currency.config(bg=ACCENT, fg=FONT_COLOR, bd=0, highlightthickness=0, font=(FONT_NAME, 10, "normal")) currency["menu"].config(bg=ACCENT, fg=FONT_COLOR, bd=0, font=(FONT_NAME, 10, "normal")) currency.grid(row=2, column=2) # ---------------------------- BUTTONS ------------------------------- # btn_frame = tk.Frame(root, bg=BG_COLOR, highlightthickness=0) btn_frame.grid(row=2, column=0, padx=5, pady=5, sticky="N") # Add add_btn= tk.Button(btn_frame, text=" Add ", command=add_transactions, font=(FONT_NAME, 11, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) add_btn.pack(side=tk.LEFT, padx=5, pady=5) # Update update_btn = tk.Button(btn_frame, text=" Update ", command=update_transactions, font=(FONT_NAME, 11, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) update_btn.pack(side=tk.LEFT, padx=5, pady=5) # Delete del_btn = tk.Button(btn_frame, text=" Delete ", command=delete_transaction, font=(FONT_NAME, 11, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) del_btn.pack(side=tk.LEFT, padx=5, pady=5) # Load load_btn = tk.Button(btn_frame, text=" Load ", command=load_transactions, font=(FONT_NAME, 11, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) load_btn.pack(side=tk.LEFT, padx=5, pady=5) # Refresh refresh_btn = tk.Button(btn_frame, text=" Refresh ", command=set_listbox, font=(FONT_NAME, 11, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) refresh_btn.pack(side=tk.LEFT, padx=5, pady=5) # ---------------------------- LISTBOX ------------------------------- # data_frame = tk.Frame(root, bg=ACCENT, highlightthickness=0) data_frame.grid(row=1, column=1, rowspan=2) # Scroll Bars scroll_bar_y = tk.Scrollbar(data_frame, orient=tk.VERTICAL) scroll_bar_x = tk.Scrollbar(data_frame, orient=tk.HORIZONTAL) # Listbox listbox = tk.Listbox(data_frame, height=18, width=50, yscrollcommand=scroll_bar_y.set, xscrollcommand=scroll_bar_x.set, font=(FONT_NAME, 12, "normal"), bg=ACCENT, fg=FONT_COLOR, highlightthickness=0, bd=0) # Scroll Bars scroll_bar_y.config(command=listbox.yview) scroll_bar_y.pack(side=tk.RIGHT, fill=tk.Y) scroll_bar_x.config(command=listbox.xview) scroll_bar_x.pack(side=tk.BOTTOM, fill=tk.X) listbox.pack(side=tk.LEFT, fill=tk.BOTH, expand=1) # ---------------------------- STATUS BAR ------------------------------- # status_frame = tk.LabelFrame(root, bd=0, relief=tk.SUNKEN, bg="#3f72af", highlightthickness=0) status_frame.grid(sticky=tk.N+tk.S+tk.E+tk.W, columnspan=2) # Made By made_by = tk.Label(status_frame, text="Made By Arnav Ghatti", anchor=tk.E, font=(FONT_NAME, 9, "normal"), bg="#3f72af", highlightthickness=0, fg=BG_COLOR) made_by.pack(side=tk.RIGHT, fill=tk.BOTH, expand=1) # Version version_label = tk.Label(status_frame, text="Version: 2.5.3", anchor=tk.W, font=(FONT_NAME, 9, "normal"), bg="#3f72af", highlightthickness=0, fg=BG_COLOR) version_label.pack(side=tk.LEFT, fill=tk.BOTH, expand=1) def load_data(): """Loads data from the C:\\Users\\ASUA\\Desktop\\Tests\\MoneyTransactionsOriginal\\history.json file to the listbox""" global transactions, listbox with open("C:\\Users\\ASUA\\Desktop\\Tests\\MoneyTransactionsOriginal\\history.json", "r") as file: transaction_history = json.load(file) transactions = transaction_history["Transactions"] listbox.delete(0, tk.END) for item in transactions: listbox.insert(tk.END, f"{item[0]} to {item[1]}, ${item[2]}, {item[3]}") load_data() root.mainloop()

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0.098039

0

null

0

null

0

1

0

eeca73f0a33396739525615f94801665b147bf27

12,725

py

Python

empire_cellular_automaton/dataset_processing.py

ThomasMiller01/ProofOfConcept

021bf29743309224628682d0f82b0be80ae83c95

[ "MIT" ]

1

2019-12-18T13:49:22.000Z

empire_cellular_automaton/dataset_processing.py

ThomasMiller01/Experiments

021bf29743309224628682d0f82b0be80ae83c95

[ "MIT" ]

null

empire_cellular_automaton/dataset_processing.py

ThomasMiller01/Experiments

021bf29743309224628682d0f82b0be80ae83c95

[ "MIT" ]

1

2021-08-29T09:22:52.000Z

import json import matplotlib import matplotlib.pyplot as plt import numpy as np import os import time def people_distribution_map(data, file): unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) generations = list(zip(unique, indices, counts)) plt_size_x = int(np.ceil(np.sqrt(len(generations)))) plt_size_y = int(np.ceil(np.sqrt(len(generations)) - 0.5)) fig, axs = plt.subplots(plt_size_x, plt_size_y, figsize=(10, 10)) fig.suptitle("people distribution", fontsize=10) fig.tight_layout(pad=3.0) i = 0 s_all = () s_mapped_all = None for ax_s in axs: for ax in ax_s: if i < len(generations): gen = generations[i] minified_data = data[gen[1]:gen[1] + gen[2]] all_people = np.zeros((0, 2)).astype('int') for day in minified_data[:, 2]: for person in day: if person[5] != 0: all_people = np.append(all_people, np.asarray( [[person[6], person[7]]]), axis=0) unique, counts = np.unique( all_people, return_counts=True, axis=0) x, y = zip(*unique) if not s_all: s_all = (counts.min(), counts.max()) s_mapped_all = np.interp( counts, (s_all[0], s_all[1]), (0, 100)) s_mapped = np.interp( counts, (counts.min(), counts.max()), (0, 100)) color_palett = [ '#d3ae1b', '#de6e3b', '#b54d47', '#8e321e', '#522a1a'] color_ranges = np.arange( s_mapped_all.min(), s_mapped_all.max(), (s_mapped_all.max() - s_mapped_all.min()) / len(color_palett)) color_indices = [np.where(n < color_ranges)[0] for n in s_mapped] colors = [color_palett[c[0]] if c.size != 0 else color_palett[len( color_palett) - 1] for c in color_indices] img = plt.imread("map.jpg") ax.scatter(x, y, s=s_mapped, c=colors) ax_xlim = ax.get_xlim() ax_ylim = ax.get_ylim() ax.imshow(img, origin="lower") ax.set_xlim(ax_xlim) ax.set_ylim(ax_ylim[::-1]) ax.set(title="gen " + str(gen[0])) i += 1 plt.savefig(file) plt.close(fig=fig) def kind_of_disease_per_generation(data, file): unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) generations = list(zip(unique, indices, counts)) plt_size_x = int(np.ceil(np.sqrt(len(generations)))) plt_size_y = int(np.ceil(np.sqrt(len(generations)) - 0.5)) fig, axs = plt.subplots(plt_size_x, plt_size_y, figsize=(10, 10)) fig.suptitle("kind of disease", fontsize=16) fig.tight_layout(pad=3.0) i = 0 for ax_s in axs: for ax in ax_s: if i < len(generations): gen = generations[i] minified_data = data[gen[1]:gen[1] + gen[2]] all_diseased_people = np.zeros((0, 2)).astype('int') for day in minified_data[:, 2]: for person in day: if person[5] != 0: all_diseased_people = np.append(all_diseased_people, np.asarray( [[person[0], person[5]]]), axis=0) disease_all = np.zeros((0, 2)).astype('int') for disease_kind in np.unique(all_diseased_people[:, 1]): people_disease_kind = all_diseased_people[np.where( all_diseased_people[:, 1] == disease_kind)[0]] unique_disease_kind, counts_disease_kind = np.unique( all_diseased_people, return_counts=True) disease_all = np.append(disease_all, np.asarray( [[disease_kind, len(unique_disease_kind)]]), axis=0) x = np.arange(0, len(disease_all)) y = disease_all[:, 1] ax.bar(x, y) ax.set_xticks(x) ax.set_yticks(y) ax.set_xticklabels(disease_all[:, 0]) ax.set(title="gen " + str(gen[0])) i += 1 plt.savefig(file) plt.close(fig=fig) def strength_distribution_per_generation(data, file): unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) generations = list(zip(unique, indices, counts)) plt_size_x = int(np.ceil(np.sqrt(len(generations)))) plt_size_y = int(np.ceil(np.sqrt(len(generations)) - 0.5)) fig, axs = plt.subplots(plt_size_x, plt_size_y, figsize=(10, 10)) fig.tight_layout(pad=3.0) fig.suptitle("strength distribution", fontsize=12) i = 0 for ax_s in axs: for ax in ax_s: if i < len(generations): gen = generations[i] minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, 100) y = np.zeros(100) for strength in minified_data[:, 2][len(minified_data) - 1][:, 3]: y[int(np.ceil(strength)) - 1] += 1 coeffs = np.polyfit(x, y, 3) poly_eqn = np.poly1d(coeffs) y_hat = poly_eqn(x) ax.plot(x, y) ax.plot(x, y_hat, label="average", c='r') ax.set(xlabel='strength', ylabel='people', title="gen " + str(gen[0])) ax.grid() i += 1 plt.savefig(file) plt.close(fig=fig) def age_distribution_per_generation(data, file): unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) generations = list(zip(unique, indices, counts)) plt_size_x = int(np.ceil(np.sqrt(len(generations)))) plt_size_y = int(np.ceil(np.sqrt(len(generations)) - 0.5)) fig, axs = plt.subplots(plt_size_x, plt_size_y, figsize=(10, 10)) fig.tight_layout(pad=3.0) fig.suptitle("age distribution", fontsize=16) i = 0 for ax_s in axs: for ax in ax_s: if i < len(unique): gen = generations[i] minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, 100) y = np.zeros(100) for age in minified_data[:, 2][len(minified_data) - 1][:, 2]: if age > 100: age = 100 y[int(np.ceil(age)) - 1] += 1 coeffs = np.polyfit(x, y, 3) poly_eqn = np.poly1d(coeffs) y_hat = poly_eqn(x) ax.plot(x, y) ax.plot(x, y_hat, label="average", c='r') ax.set(xlabel='age', ylabel='people', title="gen " + str(gen[0])) ax.grid() i += 1 plt.savefig(file) plt.close(fig=fig) def disease_over_time(data, file): fig, ax = plt.subplots() unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) for gen in zip(unique, indices, counts): minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, len(minified_data)) y = np.asarray([np.sum(x) for x in [a[:, 5] for a in minified_data[:, 2]]]) ax.plot(x, y, label=gen[0]) ax.set(xlabel='days', ylabel='disease', title='disease over time') ax.grid() plt.legend(loc="best", title="generation") plt.savefig(file) plt.close(fig=fig) def avg_reproductionValue_over_time(data, file, settings): fig, ax = plt.subplots() unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) for gen in zip(unique, indices, counts): minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, len(minified_data)) y = np.asarray(np.asarray([np.average(a[:, 4]) for a in minified_data[:, 2]])) ax.plot(x, y, label=gen[0]) ax.axhline(settings['p_reproductionThreshold'], c='r', linestyle=':', label='rT') ax.set(xlabel='days', ylabel='reproductionValue', title='avg reproductionValue over time') ax.grid() plt.legend(loc="best", title="generation") plt.savefig(file) plt.close(fig=fig) def avg_age_over_time(data, file): fig, ax = plt.subplots() unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) for gen in zip(unique, indices, counts): minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, len(minified_data)) y = np.asarray(np.asarray([np.average(a[:, 2]) for a in minified_data[:, 2]])) ax.plot(x, y, label=gen[0]) ax.set(xlabel='days', ylabel='age', title='avg age over time') ax.grid() plt.legend(loc="best", title="generation") plt.savefig(file) plt.close(fig=fig) def population_over_time(data, file): fig, ax = plt.subplots() unique, indices, counts = np.unique( data[:, 0], return_index=True, return_counts=True) for gen in zip(unique, indices, counts): minified_data = data[gen[1]:gen[1] + gen[2]] x = np.arange(0, len(minified_data)) y = np.asarray([len(a) for a in minified_data[:, 2]]) ax.plot(x, y, label=gen[0]) ax.set(xlabel='days', ylabel='population', title='population over time') ax.grid() plt.legend(loc="best", title="generation") plt.savefig(file) plt.close(fig=fig) def save_figs(dataset_name): start_all = time.time() print("------") print("saving " + dataset_name) file_name = './datasets/' + dataset_name print("loading data ...") start = time.time() # load data with open(file_name + '/' + dataset_name + '_settings.json') as json_file: settings = json.load(json_file) data = np.load(file_name + '/' + dataset_name + '_data.npy', allow_pickle=True) end = time.time() print("data loaded in " + str(round(end - start, 2)) + "s") print("***") start = time.time() print("saving pdfs ...") # save as pdf try: os.mkdir(file_name + "/pdf") except: pass population_over_time(data, file_name + "/pdf/population_over_time.pdf") avg_age_over_time(data, file_name + "/pdf/avg_age_over_time.pdf") avg_reproductionValue_over_time( data, file_name + "/pdf/avg_reproductionValue_over_time.pdf", settings) disease_over_time(data, file_name + "/pdf/disease_over_time.pdf") age_distribution_per_generation( data, file_name + "/pdf/age_distribution_per_generation.pdf") strength_distribution_per_generation( data, file_name + "/pdf/strength_distribution_per_generation.pdf") kind_of_disease_per_generation( data, file_name + "/pdf/kind_of_disease.pdf") people_distribution_map( data, file_name + "/pdf/people_distribution_map.pdf") end = time.time() print("pdfs saved in " + str(round(end - start, 2)) + "s") print("***") print("saving pngs ...") start = time.time() # save as png try: os.mkdir(file_name + "/png") except: pass population_over_time(data, file_name + "/png/population_over_time.png") avg_age_over_time(data, file_name + "/png/avg_age_over_time.png") avg_reproductionValue_over_time( data, file_name + "/png/avg_reproductionValue_over_time.png", settings) disease_over_time(data, file_name + "/png/disease_over_time.png") age_distribution_per_generation( data, file_name + "/png/age_distribution_per_generation.png") strength_distribution_per_generation( data, file_name + "/png/strength_distribution_per_generation.png") kind_of_disease_per_generation( data, file_name + "/png/kind_of_disease.png") people_distribution_map( data, file_name + "/png/people_distribution_map.png") end = time.time() print("pngs saved in " + str(round(end - start, 2)) + "s") print("***") end_all = time.time() print("- " + dataset_name + " saved") print("- time elapsed: " + str(round(end_all - start_all, 2)) + "s") print("------") if __name__ == "__main__": for directory in os.listdir('./datasets'): if "example" not in directory: save_figs(directory) print("creating statistics done")

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122

0.558428

1,697

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0.10607

0.028294

0.044798

0.028294

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0

0.051724

0

null

0

null

0

1

0

eed48753201aaf2076987680b987b0334df7af1f

4,653

py

Python

cliff/lister.py

tivaliy/cliff

a04a48f4f7dc72b1bcc95a5c6a550c7650e35ab3

[ "Apache-2.0" ]

187

2015-01-13T04:07:41.000Z

2022-03-10T14:12:27.000Z

cliff/lister.py

tivaliy/cliff

a04a48f4f7dc72b1bcc95a5c6a550c7650e35ab3

[ "Apache-2.0" ]

3

2016-01-05T20:52:55.000Z

2020-10-01T06:16:58.000Z

cliff/lister.py

tivaliy/cliff

a04a48f4f7dc72b1bcc95a5c6a550c7650e35ab3

[ "Apache-2.0" ]

69

2015-02-01T01:28:37.000Z

2021-11-15T08:28:53.000Z

# 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. """Application base class for providing a list of data as output.""" import abc import logging from . import display class Lister(display.DisplayCommandBase, metaclass=abc.ABCMeta): """Command base class for providing a list of data as output.""" log = logging.getLogger(__name__) @property def formatter_namespace(self): return 'cliff.formatter.list' @property def formatter_default(self): return 'table' @property def need_sort_by_cliff(self): """Whether sort procedure is performed by cliff itself. Should be overridden (return False) when there is a need to implement custom sorting procedure or data is already sorted. """ return True @abc.abstractmethod def take_action(self, parsed_args): """Run command. Return a tuple containing the column names and an iterable containing the data to be listed. """ def get_parser(self, prog_name): parser = super(Lister, self).get_parser(prog_name) group = self._formatter_group group.add_argument( '--sort-column', action='append', default=[], dest='sort_columns', metavar='SORT_COLUMN', help=( 'specify the column(s) to sort the data (columns specified ' 'first have a priority, non-existing columns are ignored), ' 'can be repeated' ), ) sort_dir_group = group.add_mutually_exclusive_group() sort_dir_group.add_argument( '--sort-ascending', action='store_const', dest='sort_direction', const='asc', help=('sort the column(s) in ascending order'), ) sort_dir_group.add_argument( '--sort-descending', action='store_const', dest='sort_direction', const='desc', help=('sort the column(s) in descending order'), ) return parser def produce_output(self, parsed_args, column_names, data): if parsed_args.sort_columns and self.need_sort_by_cliff: indexes = [ column_names.index(c) for c in parsed_args.sort_columns if c in column_names ] reverse = parsed_args.sort_direction == 'desc' for index in indexes[::-1]: try: # We need to handle unset values (i.e. None) so we sort on # multiple conditions: the first comparing the results of # an 'is None' type check and the second comparing the # actual value. The second condition will only be checked # if the first returns True, which only happens if the # returns from the 'is None' check on the two values are # the same, i.e. both None or both not-None data = sorted( data, key=lambda k: (k[index] is None, k[index]), reverse=reverse, ) except TypeError: # Simply log and then ignore this; sorting is best effort self.log.warning( "Could not sort on field '%s'; unsortable types", parsed_args.sort_columns[index], ) columns_to_include, selector = self._generate_columns_and_selector( parsed_args, column_names, ) if selector: # Generator expression to only return the parts of a row # of data that the user has expressed interest in # seeing. We have to convert the compress() output to a # list so the table formatter can ask for its length. data = ( list(self._compress_iterable(row, selector)) for row in data ) self.formatter.emit_list( columns_to_include, data, self.app.stdout, parsed_args, ) return 0

36.637795

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4,653

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0.397849

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0.094056

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0.030098

0

0.001959

0.341715

4,653

126

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0.865818

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0

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0

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0

1

0.078947

false

0

0.039474

0.026316

0.210526

0

null

0

null

0

1

0

eed5699e06d3cac61b4a945b53a1004046c608f3

1,026

py

Python

task3/task3.py

ksmirenko/ml-homework

a5e558352ffc332ad5e40526dda21f205718a203

[ "MIT" ]

1

2020-08-05T08:06:33.000Z

task3/task3.py

ksmirenko/ml-homework

a5e558352ffc332ad5e40526dda21f205718a203

[ "MIT" ]

null

task3/task3.py

ksmirenko/ml-homework

a5e558352ffc332ad5e40526dda21f205718a203

[ "MIT" ]

null

from PIL import Image import numpy as np # Works when launched from terminal # noinspection PyUnresolvedReferences from k_means import k_means input_image_file = 'lena.jpg' output_image_prefix = 'out_lena' n_clusters = [2, 3, 5] max_iterations = 100 launch_count = 3 def main(): # Read input image image = np.array(Image.open(input_image_file)) X = image.reshape((image.shape[0] * image.shape[1], image.shape[2])) for k in n_clusters: print(f"{k} clusters") # 'Compress' image using K-means centroids, clustered = k_means(X, k=k, max_iterations=max_iterations, launch_count=launch_count) new_X = np.array([centroids[cluster_index] for cluster_index in clustered]) new_X = new_X.astype(np.uint8) # Write output image new_image = new_X.reshape(image.shape) output_image_name = f"{output_image_prefix}_{k}.jpg" Image.fromarray(new_image).save(output_image_name) print(f"Saved {output_image_name}") print("Done.") main()

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4.470199

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36

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1

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false

0

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0.181818

0.136364

0

null

0

null

0

1

0

eed63ef06321c79002e85fdaeb08205c4299ea39

3,389

py

Python

dcrnn_train.py

syin3/cs224w-traffic

284836b49404bfd38ae23b31f89f8e617548e286

[ "MIT" ]

9

2019-03-20T01:02:07.000Z

2020-11-25T06:45:30.000Z

dcrnn_train.py

syin3/cs224w-traffic

284836b49404bfd38ae23b31f89f8e617548e286

[ "MIT" ]

null

dcrnn_train.py

syin3/cs224w-traffic

284836b49404bfd38ae23b31f89f8e617548e286

[ "MIT" ]

2

2020-09-24T07:03:58.000Z

2020-11-09T04:43:03.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import tensorflow as tf import yaml from model.dcrnn_supervisor import DCRNNSupervisor def main(args): with open(args.config_filename) as f: supervisor_config = yaml.load(f) tf_config = tf.ConfigProto() # if args.use_cpu_only: # tf_config = tf.ConfigProto(device_count={'GPU': 0}) tf_config.gpu_options.allow_growth = True with tf.Session(config=tf_config) as sess: supervisor = DCRNNSupervisor(**supervisor_config) supervisor.train(sess=sess) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config_filename', required=True, default=None, type=str, help='Configuration filename for restoring the model.') parser.add_argument('--use_cpu_only', default=False, type=bool, help='Set true to only use cpu.') # adjacent and distance-weighted parser.add_argument('--weightType', required=True, choices=['a', 'd'], help='w/ or w/o distance pre-processing') parser.add_argument('--att', dest='attention', action='store_true', help='Call this command to raise attention mechanism in the training.') parser.add_argument('--no-att', dest='attention', action='store_false', help='Call this command not to raise attention mechanism in the training.') parser.set_defaults(attention=False) subparsers = parser.add_subparsers() fullyConnectParser = subparsers.add_parser('fc', help='In fully connect mode, choose embed file') fullyConnectParser.add_argument('--gEmbedFile', required=True, default='LA-n2v-14-0.1-1', help='Embedding file for n2v, should add up-directory when calling') fullyConnectParser.add_argument('--network', nargs='?', const='fc', default='fc', help='To store the choice of fully connected') graphConvParser = subparsers.add_parser('graphConv', help='In graph conv mode, choose W matrix form') graphConvParser.add_argument('--hop', required=True, type=int, default=2, help='k-hop neighbors, default is 2 for distance-processed matrix; but must be one for binary matrix') graphConvParser.add_argument('--network', nargs='?', const='gconv', default='gconv', help='To store the choice of gconv') args = parser.parse_args() with open(args.config_filename) as f: doc = yaml.load(f) # default batch sizes to 64, in training, validation and in testing doc['data']['batch_size'] = 64 doc['data']['test_batch_size'] = 64 doc['data']['val_batch_size'] = 64 # set matrix to adjacency or distance-weighted if args.weightType == 'd': doc['data']['graph_pkl_filename'] = "data/sensor_graph/adj_mx_la.pkl" else: doc['data']['graph_pkl_filename'] = "data/sensor_graph/adj_bin_la.pkl" # record necessary info to log doc['model']['weightMatrix'] = args.weightType doc['model']['attention'] = args.attention doc['model']['network'] = args.network if 'gEmbedFile' in vars(args): doc['model']['graphEmbedFile'] = args.gEmbedFile doc['model']['max_diffusion_step'] = 0 if 'hop' in vars(args): doc['model']['max_diffusion_step'] = args.hop # save the info with open(args.config_filename, 'w') as f: yaml.dump(doc, f) main(args)

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eed698cee32da7af7d7cb366130b591986c4feae

1,035

py

Python

train.py

k2sebeom/DeepLOLCourt

630f1eee1729c06f686abc7c2a7ecbdfe66803b3

[ "MIT" ]

null

train.py

k2sebeom/DeepLOLCourt

630f1eee1729c06f686abc7c2a7ecbdfe66803b3

[ "MIT" ]

null

train.py

k2sebeom/DeepLOLCourt

630f1eee1729c06f686abc7c2a7ecbdfe66803b3

[ "MIT" ]

null

import torch.optim as optim from torch import nn from data.match_dataset import MatchDataset from torch.utils.data import DataLoader from models.lol_result_model import LOLResultModel import torch if __name__ == '__main__': EPOCH = 50 BATCH_SIZE = 32 loader = DataLoader(MatchDataset('dataset/train_data.csv'), BATCH_SIZE) print("Dataset Loaded") loss_criterion = nn.BCELoss() device = torch.device('cuda:0') model = LOLResultModel(190) print("Model created") optimizer = optim.Adam(model.parameters(), lr=0.0001) model.to(device) for epoch in range(EPOCH): loss_data = 0 for i, data in enumerate(loader): output = model(data['x'].to(device)) loss = loss_criterion(output, data['y'].unsqueeze(1).float().to(device)) optimizer.zero_grad() loss.backward() optimizer.step() loss_data = loss.data print(f'Epoch {epoch}: {loss_data}') torch.save(model.state_dict(), 'checkpoints/model.pth')

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