xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

ccff8fb01ea3f497743ce74c1e9b8975a96ada59

5,544

py

Python

PYTHON/dgemm_omp.py

dbaaha/Kernels

232fc44fc9427dd7b56862cec2d46296c467b4e8

[ "BSD-3-Clause" ]

346

2015-06-07T19:55:15.000Z

2022-03-18T07:55:10.000Z

PYTHON/dgemm_omp.py

dbaaha/Kernels

232fc44fc9427dd7b56862cec2d46296c467b4e8

[ "BSD-3-Clause" ]

202

2015-06-16T15:28:05.000Z

2022-01-06T18:26:13.000Z

PYTHON/dgemm_omp.py

dbaaha/Kernels

232fc44fc9427dd7b56862cec2d46296c467b4e8

[ "BSD-3-Clause" ]

101

2015-06-15T22:06:46.000Z

2022-01-13T02:56:02.000Z

#!/usr/bin/env python3 # # Copyright (c) 2015, Intel Corporation # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Intel Corporation nor the names of its # contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. #******************************************************************* # # NAME: dgemm # # PURPOSE: This program tests the efficiency with which a dense matrix # dense multiplication is carried out # # USAGE: The program takes as input the matrix order, # the number of times the matrix-matrix multiplication # is carried out. # # <progname> <# iterations> <matrix order> # # The output consists of diagnostics to make sure the # algorithm worked, and of timing statistics. # # HISTORY: Written by Rob Van der Wijngaart, February 2009. # Converted to Python by Jeff Hammond, February 2016. # PyOMP support, ave+std_dev by Tim Mattson, May 2021 # ******************************************************************* import sys from numba import njit from numba.openmp import openmp_context as openmp from numba.openmp import omp_set_num_threads, omp_get_thread_num, omp_get_num_threads, omp_get_wtime import numpy as np #from time import process_time as timer #@njit(enable_ssa=False, cache=True) What does "enable_ssa" mean? @njit(fastmath=True) def dgemm(iters,order): # ******************************************************************** # ** Allocate space for the input and transpose matrix # ******************************************************************** print('inside dgemm') A = np.zeros((order,order)) B = np.zeros((order,order)) C = np.zeros((order,order)) for i in range(order): A[:,i] = float(i) B[:,i] = float(i) # print(omp_get_num_threads()) for kiter in range(0,iters+1): if kiter==1: t0 = omp_get_wtime() tSum=0.0 tsqSum=0.0 with openmp("parallel for schedule(static) private(j,k)"): for i in range(order): for k in range(order): for j in range(order): C[i][j] += A[i][k] * B[k][j] if kiter>0: tkiter = omp_get_wtime() t = tkiter - t0 tSum = tSum + t tsqSum = tsqSum+t*t t0 = tkiter dgemmAve = tSum/iters dgemmStdDev = ((tsqSum-iters*dgemmAve*dgemmAve)/(iters-1))**0.5 print('finished with computations') # ******************************************************************** # ** Analyze and output results. # ******************************************************************** checksum = 0.0; for i in range(order): for j in range(order): checksum += C[i][j]; ref_checksum = order*order*order ref_checksum *= 0.25*(order-1.0)*(order-1.0) ref_checksum *= (iters+1) epsilon=1.e-8 if abs((checksum - ref_checksum)/ref_checksum) < epsilon: print('Solution validates') nflops = 2.0*order*order*order recipDiff = (1.0/(dgemmAve-dgemmStdDev) - 1.0/(dgemmAve+dgemmStdDev)) GfStdDev = 1.e-6*nflops*recipDiff/2.0 print('nflops: ',nflops) print('Rate: ',1.e-6*nflops/dgemmAve,' +/- (MF/s): ',GfStdDev) else: print('ERROR: Checksum = ', checksum,', Reference checksum = ', ref_checksum,'\n') # sys.exit("ERROR: solution did not validate") # ******************************************************************** # read and test input parameters # ******************************************************************** print('Parallel Research Kernels version ') #, PRKVERSION print('Python Dense matrix-matrix multiplication: C = A x B') if len(sys.argv) != 3: print('argument count = ', len(sys.argv)) sys.exit("Usage: ./dgemm <# iterations> <matrix order>") itersIn = int(sys.argv[1]) if itersIn < 1: sys.exit("ERROR: iterations must be >= 1") orderIn = int(sys.argv[2]) if orderIn < 1: sys.exit("ERROR: order must be >= 1") print('Number of iterations = ', itersIn) print('Matrix order = ', orderIn) dgemm(itersIn, orderIn)

37.459459

100

0.590729

689

5,544

4.711176

0.391872

0.021565

0.022181

0.015712

0.085952

0.066235

0.05915

0.041898

0

0.014345

0.220418

5,544

147

101

37.714286

0.736696

0.539683

0

0.078125

0

0.167404

0

1

0.015625

false

0

0.078125

0

0.09375

0.171875

0

null

0

null

0

1

0

ccff9565d795674cf4b93f869a86dae2c49d6c9e

1,859

py

Python

DS_Algo/quick_sort.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

DS_Algo/quick_sort.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

DS_Algo/quick_sort.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

# coding:utf-8 # example 17: quick_sort.py import random # def quick_sort(array): # if len(array) <= 1: # return array # pivot_idx = 0 # pivot = array[pivot_idx] # less_part = [num for num in array[pivot_idx + 1:] if num <= pivot] # great_part = [num for num in array[pivot_idx + 1:] if num > pivot] # return quick_sort(less_part) + [pivot] + quick_sort(great_part) # def test_quick_sort(): # import random # array = [random.randint(1, 100) for _ in range(10)] # sorted_array = sorted(array) # my_sorted_array = quick_sort(array) # assert my_sorted_array == sorted_array def partition(array, start, stop): # [start, stop) pivot_idx = start pivot = array[pivot_idx] left = pivot_idx + 1 right = stop - 1 while left <= right: while left <= right and array[left] < pivot: left += 1 while left <= right and pivot <= array[right]: right -= 1 if left < right: array[left], array[right] = array[right], array[left] array[pivot_idx], array[right] = array[right], array[pivot_idx] return right def test_partition(): lst = [3, 1, 4, 2] assert partition(lst, 0, len(lst)) == 2 lst = [1, 2, 3, 4] assert partition(lst, 0, len(lst)) == 0 lst = [4, 3, 2, 1] assert partition(lst, 0, len(lst)) == 3 lst = [3, 5, 4, 3, 6, 7, 2, 3] assert partition(lst, 0, len(lst)) == 1 def quick_sort_inplace(array, start, stop): # [start, stop) if start < stop: pivot = partition(array, start, stop) quick_sort_inplace(array, start, pivot) quick_sort_inplace(array, pivot + 1, stop) def test_quick_sort_inplace(): seq = [random.randint(-100, 100) for _ in range(10)] sorted_seq = sorted(seq) quick_sort_inplace(seq, 0, len(seq)) assert seq == sorted_seq

27.338235

72

0.603012

273

1,859

3.952381

0.179487

0.091752

0.084337

0.070436

0.331789

0.203892

0.072289

0

0.040117

0.262507

1,859

67

73

27.746269

0.7469

0.324906

0

0.147059

1

0.117647

false

0

0.029412

0

0.176471

0

null

0

null

0

1

0

69003cd799998d8973cda2d04b1c36351df5836d

2,304

py

Python

tests/test_zestimation.py

jibanCat/gpy_dla_detection

4d987adec75a417313fdc6601ee41a0ea60a0a2e

[ "MIT" ]

1

2020-07-31T01:31:52.000Z

tests/test_zestimation.py

jibanCat/gpy_dla_detection

4d987adec75a417313fdc6601ee41a0ea60a0a2e

[ "MIT" ]

12

2020-07-20T18:55:15.000Z

2021-09-23T05:08:26.000Z

tests/test_zestimation.py

jibanCat/gpy_dla_detection

4d987adec75a417313fdc6601ee41a0ea60a0a2e

[ "MIT" ]

null

""" A test file for testing zestimation The learned file could be downloaded at [learned_zqso_only_model_outdata_full_dr9q_minus_concordance_norm_1176-1256.mat] (https://drive.google.com/file/d/1SqAU_BXwKUx8Zr38KTaA_nvuvbw-WPQM/view?usp=sharing) """ import os import re import time import numpy as np from .test_selection import filenames, z_qsos from gpy_dla_detection.read_spec import read_spec, retrieve_raw_spec from gpy_dla_detection.zqso_set_parameters import ZParameters from gpy_dla_detection.zqso_samples import ZSamples from gpy_dla_detection.zqso_gp import ZGPMAT def test_zestimation(nspec: int): filename = filenames[nspec] if not os.path.exists(filename): plate, mjd, fiber_id = re.findall( r"spec-([0-9]+)-([0-9]+)-([0-9]+).fits", filename, )[0] retrieve_raw_spec(int(plate), int(mjd), int(fiber_id)) params = ZParameters() z_qso_samples = ZSamples(params) wavelengths, flux, noise_variance, pixel_mask = read_spec(filename) z_qso_gp = ZGPMAT( params, z_qso_samples, learned_file="data/dr12q/processed/learned_zqso_only_model_outdata_full_dr9q_minus_concordance_norm_1176-1256.mat", ) tic = time.time() z_qso_gp.inference_z_qso(wavelengths, flux, noise_variance, pixel_mask) print("Z True : {:.3g}".format(z_qsos[nspec])) toc = time.time() print("spent {} mins; {} seconds".format((toc - tic) // 60, (toc - tic) % 60)) return z_qso_gp.z_map, z_qsos[nspec] def test_batch(num_quasars: int = 100): all_z_diffs = np.zeros((num_quasars,)) for nspec in range(num_quasars): z_map, z_true = test_zestimation(nspec) z_diff = z_map - z_true print("[Info] z_diff = z_map - z_true = {:.8g}".format(z_diff)) all_z_diffs[nspec] = z_diff print("[Info] abs(z_diff) < 0.5 = {:.4g}".format(accuracy(all_z_diffs, 0.5))) print("[Info] abs(z_diff) < 0.05 = {:.4g}".format(accuracy(all_z_diffs, 0.05))) # we got ~99% accuracy in https://arxiv.org/abs/2006.07343 # so at least we need to ensure ~98% here assert accuracy(all_z_diffs, 0.5) > 0.98 def accuracy(z_diff: np.ndarray, z_thresh: float): num_quasars = z_diff.shape[0] corrects = (np.abs(z_diff) < z_thresh).sum() return corrects / num_quasars

29.538462

123

0.69401

359

2,304

4.175487

0.392758

0.03002

0.0507

0.274183

0.228152

0.122749

0.088059

0

0.037546

0.179253

2,304

77

124

29.922078

0.755156

0.147569

0

0.143734

0.069054

0

0.022727

1

0.068182

false

0

0.204545

0

0.318182

0.113636

0

null

0

null

0

1

0

690437062ad9bc153f8073ff84de085091fb62c1

5,249

py

Python

megaman/embedding/base.py

jakevdp/Mmani

681b6cdbd358b207e8b6c4a482262c84bea15bd7

[ "BSD-2-Clause" ]

303

2016-03-03T00:44:37.000Z

2022-03-14T03:43:38.000Z

megaman/embedding/base.py

jakevdp/Mmani

681b6cdbd358b207e8b6c4a482262c84bea15bd7

[ "BSD-2-Clause" ]

52

2016-02-26T21:41:31.000Z

2021-06-27T08:33:51.000Z

megaman/embedding/base.py

jakevdp/Mmani

681b6cdbd358b207e8b6c4a482262c84bea15bd7

[ "BSD-2-Clause" ]

67

2016-03-03T22:38:35.000Z

2022-01-12T08:03:47.000Z

""" base estimator class for megaman """ # Author: James McQueen -- <jmcq@u.washington.edu> # LICENSE: Simplified BSD https://github.com/mmp2/megaman/blob/master/LICENSE import numpy as np from scipy.sparse import isspmatrix from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_array from ..geometry.geometry import Geometry # from sklearn.utils.validation import FLOAT_DTYPES FLOAT_DTYPES = (np.float64, np.float32, np.float16) class BaseEmbedding(BaseEstimator, TransformerMixin): """ Base Class for all megaman embeddings. Inherits BaseEstimator and TransformerMixin from sklearn. BaseEmbedding creates the common interface to the geometry class for all embeddings as well as providing a common .fit_transform(). Parameters ---------- n_components : integer number of coordinates for the manifold. radius : float (optional) radius for adjacency and affinity calculations. Will be overridden if either is set in `geom` geom : dict or megaman.geometry.Geometry object specification of geometry parameters: keys are ["adjacency_method", "adjacency_kwds", "affinity_method", "affinity_kwds", "laplacian_method", "laplacian_kwds"] Attributes ---------- geom_ : a fitted megaman.geometry.Geometry object. """ def __init__(self, n_components=2, radius=None, geom=None): self.n_components = n_components self.radius = radius self.geom = geom def _validate_input(self, X, input_type): if input_type == 'data': sparse_formats = None elif input_type in ['adjacency', 'affinity']: sparse_formats = ['csr', 'coo', 'lil', 'bsr', 'dok', 'dia'] else: raise ValueError("unrecognized input_type: {0}".format(input_type)) return check_array(X, dtype=FLOAT_DTYPES, accept_sparse=sparse_formats) # # The world is not ready for this... # def estimate_radius(self, X, input_type='data', intrinsic_dim=None): # """Estimate a radius based on the data and intrinsic dimensionality # # Parameters # ---------- # X : array_like, [n_samples, n_features] # dataset for which radius is estimated # intrinsic_dim : int (optional) # estimated intrinsic dimensionality of the manifold. If not # specified, then intrinsic_dim = self.n_components # # Returns # ------- # radius : float # The estimated radius for the fit # """ # if input_type == 'affinity': # return None # elif input_type == 'adjacency': # return X.max() # elif input_type == 'data': # if intrinsic_dim is None: # intrinsic_dim = self.n_components # mean_std = np.std(X, axis=0).mean() # n_features = X.shape[1] # return 0.5 * mean_std / n_features ** (1. / (intrinsic_dim + 6)) # else: # raise ValueError("Unrecognized input_type: {0}".format(input_type)) def fit_geometry(self, X=None, input_type='data'): """Inputs self.geom, and produces the fitted geometry self.geom_""" if self.geom is None: self.geom_ = Geometry() elif isinstance(self.geom, Geometry): self.geom_ = self.geom else: try: kwds = dict(**self.geom) except TypeError: raise ValueError("geom must be a Geometry instance or " "a mappable/dictionary") self.geom_ = Geometry(**kwds) if self.radius is not None: self.geom_.set_radius(self.radius, override=False) # if self.radius == 'auto': # if X is not None and input_type != 'affinity': # self.geom_.set_radius(self.estimate_radius(X, input_type), # override=False) # else: # self.geom_.set_radius(self.radius, # override=False) if X is not None: self.geom_.set_matrix(X, input_type) return self def fit_transform(self, X, y=None, input_type='data'): """Fit the model from data in X and transform X. Parameters ---------- input_type : string, one of: 'data', 'distance' or 'affinity'. The values of input data X. (default = 'data') X: array-like, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. If self.input_type is 'distance': X : array-like, shape (n_samples, n_samples), Interpret X as precomputed distance or adjacency graph computed from samples. Returns ------- X_new: array-like, shape (n_samples, n_components) """ self.fit(X, y=y, input_type=input_type) return self.embedding_ def transform(self, X, y=None, input_type='data'): raise NotImplementedError("transform() not implemented. " "Try fit_transform()")

36.451389

81

0.599543

613

5,249

4.986949

0.288744

0.061825

0.025515

0.016683

0.167484

0.117763

0.100752

0.085051

0.064115

0.036637

0

0.004347

0.298724

5,249

143

82

36.706294

0.826134

0.547152

0

0.047619

0

0.086915

0

1

0.119048

false

0

0.119048

0

0.333333

0

null

0

null

0

1

0

690613c67cd63310af621f929b186a79abbe5cd8

1,032

py

Python

ppr-api/migrations/versions/d3f96fb8b8e5_update_user_profile_definition.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

[ "Apache-2.0" ]

4

2020-01-21T21:46:42.000Z

2021-02-24T18:30:24.000Z

ppr-api/migrations/versions/d3f96fb8b8e5_update_user_profile_definition.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

[ "Apache-2.0" ]

1,313

2019-10-18T22:48:16.000Z

2022-03-30T17:42:47.000Z

ppr-api/migrations/versions/d3f96fb8b8e5_update_user_profile_definition.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

[ "Apache-2.0" ]

201

2019-10-18T21:34:41.000Z

2022-03-31T20:07:42.000Z

"""update user profile definition Revision ID: d3f96fb8b8e5 Revises: 2b13f89aa1b3 Create Date: 2021-10-18 15:45:33.906745 """ from alembic import op import sqlalchemy as sa from alembic_utils.pg_function import PGFunction from sqlalchemy import text as sql_text # revision identifiers, used by Alembic. revision = 'd3f96fb8b8e5' down_revision = '2b13f89aa1b3' branch_labels = None depends_on = None # Update user profile to add registrations table and miscellaneous (future) preferences. def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('user_profiles', sa.Column('registrations_table', sa.JSON(), nullable=True)) op.add_column('user_profiles', sa.Column('misc_preferences', sa.JSON(), nullable=True)) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_column('user_profiles', 'misc_preferences') op.drop_column('user_profiles', 'registrations_table') # ### end Alembic commands ###

30.352941

94

0.738372

131

1,032

5.679389

0.48855

0.053763

0.096774

0.061828

0.260753

0.198925

0.11828

0

0.052273

0.147287

1,032

33

95

31.272727

0.793182

0.386628

0

0.245378

0

1

0.142857

false

0

0.285714

0

0.428571

0

null

0

null

0

1

0

6906a12f2953d09ffd721ec5d1611ca70e378fb9

2,948

py

Python

questions/available-captures-for-rook/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

141

2017-12-12T21:45:53.000Z

2022-03-25T07:03:39.000Z

questions/available-captures-for-rook/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

32

2015-10-05T14:09:52.000Z

2021-05-30T10:28:41.000Z

questions/available-captures-for-rook/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

56

2015-09-30T05:23:28.000Z

2022-03-08T07:57:11.000Z

""" On an 8 x 8 chessboard, there is one white rook. There also may be empty squares, white bishops, and black pawns. These are given as characters 'R', '.', 'B', and 'p' respectively. Uppercase characters represent white pieces, and lowercase characters represent black pieces. The rook moves as in the rules of Chess: it chooses one of four cardinal directions (north, east, west, and south), then moves in that direction until it chooses to stop, reaches the edge of the board, or captures an opposite colored pawn by moving to the same square it occupies. Also, rooks cannot move into the same square as other friendly bishops. Return the number of pawns the rook can capture in one move. Example 1: Input: [[".",".",".",".",".",".",".","."],[".",".",".","p",".",".",".","."],[".",".",".","R",".",".",".","p"],[".",".",".",".",".",".",".","."],[".",".",".",".",".",".",".","."],[".",".",".","p",".",".",".","."],[".",".",".",".",".",".",".","."],[".",".",".",".",".",".",".","."]] Output: 3 Explanation: In this example the rook is able to capture all the pawns. Example 2: Input: [[".",".",".",".",".",".",".","."],[".","p","p","p","p","p",".","."],[".","p","p","B","p","p",".","."],[".","p","B","R","B","p",".","."],[".","p","p","B","p","p",".","."],[".","p","p","p","p","p",".","."],[".",".",".",".",".",".",".","."],[".",".",".",".",".",".",".","."]] Output: 0 Explanation: Bishops are blocking the rook to capture any pawn. Example 3: Input: [[".",".",".",".",".",".",".","."],[".",".",".","p",".",".",".","."],[".",".",".","p",".",".",".","."],["p","p",".","R",".","p","B","."],[".",".",".",".",".",".",".","."],[".",".",".","B",".",".",".","."],[".",".",".","p",".",".",".","."],[".",".",".",".",".",".",".","."]] Output: 3 Explanation: The rook can capture the pawns at positions b5, d6 and f5. Note: board.length == board[i].length == 8 board[i][j] is either 'R', '.', 'B', or 'p' There is exactly one cell with board[i][j] == 'R' """ class Solution(object): def numRookCaptures(self, board): """ :type board: List[List[str]] :rtype: int """ ri, rj = 0, 0 found = False for i in xrange(len(board)): for j in xrange(len(board[0])): c = board[i][j] if c == 'R': ri, rj = i, j found = True break if found: break num = 0 dirs = [[1, 0], [-1, 0], [0, 1], [0, -1]] for di, dj in dirs: i, j = ri + di, rj + dj while i >= 0 and i < len(board) and j >= 0 and j < len(board[0]): c = board[i][j] if c == '.': pass elif c == 'p': num += 1 break else: break i += di j += dj return num

39.837838

353

0.402307

341

2,948

3.478006

0.387097

0.033727

0.035413

0.030354

0.065767

0.052277

0.033727

0

0.012897

0.263569

2,948

74

354

39.837838

0.533395

0.678426

0

0.206897

0

0.003363

0

1

0.034483

false

0.034483

0

0.103448

0

null

0

null

0

1

0

6908c59f82b4dce18b0359af8fb11f6688af03cf

3,200

py

Python

test/test_npu/test_network_ops/test_sin.py

Ascend/pytorch

39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc

[ "BSD-3-Clause" ]

1

2021-12-02T03:07:35.000Z

test/test_npu/test_network_ops/test_sin.py

Ascend/pytorch

39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc

[ "BSD-3-Clause" ]

1

2021-11-12T07:23:03.000Z

2021-11-12T08:28:13.000Z

test/test_npu/test_network_ops/test_sin.py

Ascend/pytorch

39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc

[ "BSD-3-Clause" ]

null

# Copyright (c) 2020, Huawei Technologies.All rights reserved. # # Licensed under the BSD 3-Clause License (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://opensource.org/licenses/BSD-3-Clause # # 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 torch import numpy as np import sys import copy from common_utils import TestCase, run_tests from common_device_type import dtypes, instantiate_device_type_tests from util_test import create_common_tensor class TestSin(TestCase): def cpu_op_exec(self, input1): output = torch.sin(input1) output = output.numpy() return output def npu_op_exec(self, input1): output = torch.sin(input1) output = output.to("cpu") output = output.numpy() return output def npu_op_exec_out(self, input1, input2): torch.sin(input1, out=input2) output = input2.to("cpu") output = output.numpy() return output def test_sin_common_shape_format(self, device): shape_format = [ [[np.float32, 0, (5,3)]], ] for item in shape_format: cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10) cpu_output = self.cpu_op_exec(cpu_input1) npu_output = self.npu_op_exec(npu_input1) self.assertRtolEqual(cpu_output, npu_output) def test_sin_out_common_shape_format(self, device): shape_format = [ [[np.float16, -1, (4, 3, 128, 128)], [np.float16, -1, (4, 3, 128, 128)]], [[np.float16, 0, (4, 3, 128, 128)], [np.float16, 0, (10, 3, 64, 128)]], [[np.float16, 0, (4, 3, 128, 128)], [np.float16, 0, (2, 3, 256, 128)]], [[np.float32, 0, (4, 3, 128, 128)], [np.float32, 0, (4, 3, 128, 128)]], [[np.float32, 0, (4, 3, 128, 128)], [np.float32, 0, (8, 3, 64, 128)]], [[np.float32, -1, (4, 3, 128, 128)], [np.float32, -1, (4, 3, 256, 64)]], ] for item in shape_format: cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10) cpu_input2, npu_input2 = create_common_tensor(item[0], -10, 10) cpu_input3, npu_input3 = create_common_tensor(item[1], -10, 10) if cpu_input1.dtype == torch.float16: cpu_input1 = cpu_input1.to(torch.float32) cpu_output = self.cpu_op_exec(cpu_input1) npu_output_out1 = self.npu_op_exec_out(npu_input1, npu_input2) npu_output_out2 = self.npu_op_exec_out(npu_input1, npu_input3) cpu_output = cpu_output.astype(npu_output_out1.dtype) self.assertRtolEqual(cpu_output, npu_output_out1) self.assertRtolEqual(cpu_output, npu_output_out2) instantiate_device_type_tests(TestSin, globals(), except_for='cpu') if __name__ == "__main__": run_tests()

42.105263

89

0.637813

453

3,200

4.284768

0.273731

0.028336

0.020608

0.032973

0.455951

0.381762

0.275631

0.225657

0

0.081675

0.24625

3,200

75

90

42.666667

0.723051

0.179375

0

0.290909

0

0.006508

0

0.054545

1

0.090909

false

0

0.127273

0

0.290909

0

null

0

null

0

1

0

690958b4739c7c62384b219e19362e11eacddb43

968

py

Python

src/simmate/website/core_components/filters/dynamics.py

laurenmm/simmate-1

c06b94c46919b01cda50f78221ad14f75c100a14

[ "BSD-3-Clause" ]

9

2021-12-21T02:58:21.000Z

2022-01-25T14:00:06.000Z

src/simmate/website/core_components/filters/dynamics.py

laurenmm/simmate-1

c06b94c46919b01cda50f78221ad14f75c100a14

[ "BSD-3-Clause" ]

51

2022-01-01T15:59:58.000Z

2022-03-26T21:25:42.000Z

src/simmate/website/core_components/filters/dynamics.py

laurenmm/simmate-1

c06b94c46919b01cda50f78221ad14f75c100a14

[ "BSD-3-Clause" ]

7

2022-01-01T03:44:32.000Z

2022-03-29T19:59:27.000Z

# -*- coding: utf-8 -*- from simmate.website.core_components.filters import ( Structure, Forces, Thermodynamics, Calculation, ) from simmate.database.base_data_types.dynamics import ( DynamicsRun as DynamicsRunTable, DynamicsIonicStep as DynamicsIonicStepTable, ) class DynamicsRun(Structure, Calculation): class Meta: model = DynamicsRunTable fields = dict( temperature_start=["range"], temperature_end=["range"], time_step=["range"], nsteps=["range"], **Structure.get_fields(), **Calculation.get_fields(), ) class DynamicsIonicStep(Structure, Forces, Thermodynamics): class Meta: model = DynamicsIonicStepTable fields = dict( number=["range"], temperature=["range"], **Structure.get_fields(), **Thermodynamics.get_fields(), **Forces.get_fields(), )

25.473684

59

0.599174

79

968

7.202532

0.481013

0.079086

0.101933

0.080844

0

0.001449

0.28719

968

37

60

26.162162

0.823188

0.021694

0

0.193548

0

0.031746

0

1

0

false

0

0.064516

0

0.193548

0

null

0

null

0

1

0

690b4a69f377b976688d836e3b5ab8c8dc9b6884

3,821

py

Python

webproject/taller1/algoritmoCoseno.py

jairocollante/sr

f395c0f9aef804ec0100edcfe1a1c6ccab2494a1

[ "MIT" ]

null

webproject/taller1/algoritmoCoseno.py

jairocollante/sr

f395c0f9aef804ec0100edcfe1a1c6ccab2494a1

[ "MIT" ]

null

webproject/taller1/algoritmoCoseno.py

jairocollante/sr

f395c0f9aef804ec0100edcfe1a1c6ccab2494a1

[ "MIT" ]

null

import numpy as np import pandas as pd from taller1.models import Userid_Timestamp_Count class Coseno(): def recomendacionUsuario(self,usuario_activo): print("Modelo Coseno Usuario") cant = 10 df_mapreduce = Coseno.cargarDatos(self) print("df_mapreduce.shape",df_mapreduce.shape) df_pivot = df_mapreduce.pivot('userid','artist','count').fillna(0) print("Pivot.shape=", df_pivot.shape) lista_coseno_usuario = Coseno.iterarUsuario(self,df_pivot,usuario_activo) print("Termina calculo coseno=",len(lista_coseno_usuario)) lista_coseno_usuario.sort(key=lambda k:k['coseno'], reverse = True) print("Termina ordenar lista coseno") usuario_mas_similar = lista_coseno_usuario[0]['usuario_similar'] print("Usuario mas similar=",usuario_mas_similar) lista_recomendacion = Coseno.artistaMasEscuchadoPorUsuario(self,usuario_mas_similar,cant,df_pivot) resp = {"lista_coseno_usuario":lista_coseno_usuario[:cant], "lista_recomendacion":lista_recomendacion} return resp def cargarDatos(self): #df_mapreduce = pd.read_csv('part-r-00000',sep='\t',names=['userid','artist','count']) df_mapreduce = pd.DataFrame(list(Userid_Timestamp_Count.objects.all().values('userid','artist','count'))) return df_mapreduce.dropna() def iterarUsuario(self,df_pivot,usuario_activo): v_usuario_activo = df_pivot.loc[usuario_activo].values lista_coseno=[] for user_evaluado in df_pivot.index.tolist(): if usuario_activo != user_evaluado: object = {} object['usuario_similar']=user_evaluado v_usuario_evaluado = df_pivot.loc[user_evaluado].values object['coseno']=Coseno.cos_sim(self,v_usuario_activo, v_usuario_evaluado) lista_coseno.append(object) return lista_coseno def valorCoseno(self): return val['coseno'] def artistaMasEscuchadoPorUsuario(self,usuario_evaluado,cant,df_pivot): artistas_escuchados = df_pivot.loc[usuario_evaluado] df_r = pd.DataFrame(artistas_escuchados) df_r = df_r.sort_values(by=[usuario_evaluado], ascending=False).index.tolist() return df_r[:cant] def cos_sim(self,a, b): #Takes 2 vectors a, b and returns the cosine similarity according #to the definition of the dot product dot_product = np.dot(a, b) norm_a = np.linalg.norm(a) norm_b = np.linalg.norm(b) return dot_product / (norm_a * norm_b) def recomendacionItem(self,usuario_activo): print("Modelo Coseno Item") df_mapreduce = Coseno.cargarDatos(self) print("df_mapreduce.shape",df_mapreduce.shape) df_pivotA = df_mapreduce.pivot('userid','artist','count').fillna(0) print("Usuario Pivot.shape=", df_pivotA.shape) artista_activo = Coseno.artistaMasEscuchadoPorUsuario(self,usuario_activo,10,df_pivotA) cant = 10 df_pivot = df_mapreduce.pivot('artist','userid','count').fillna(0) print("Artista Pivot.shape=", df_pivot.shape) lista_coseno_artista = Coseno.iterarArtistas(self,df_pivot,artista_activo[:1]) print("Termina calculo coseno=",len(lista_coseno_artista)) lista_coseno_artista.sort(key=lambda k:k['coseno'], reverse = True) print("Termina ordenar lista coseno") resp = {"lista_coseno_artista":lista_coseno_artista[:cant], "artista_activo":artista_activo} return resp def iterarArtistas(self,df_pivot_artista,artista_activo): v_artista_activo = df_pivot_artista.loc[artista_activo].values lista_coseno=[] for artista_evaluado in df_pivot_artista.index.tolist(): if artista_activo != artista_evaluado: object = {} object['artista_similar']=artista_evaluado v_artista_evaluado = df_pivot_artista.loc[artista_evaluado].values object['coseno']=Coseno.cos_sim(self,v_artista_activo, v_artista_evaluado) lista_coseno.append(object) return lista_coseno

43.420455

108

0.741952

509

3,821

5.298625

0.210216

0.077494

0.046719

0.026696

0.42195

0.35076

0.252874

0.199481

0.163886

0.098628

0

0.0055

0.143418

3,821

88

109

43.420455

0.818515

0.048678

0

0.24

0

0.131134

0

1

0.106667

false

0

0.04

0.013333

0.266667

0.16

0

null

0

null

0

1

0

690bf7914b0e1d6a8fa65445caa5e076e78c0f57

5,646

py

Python

python/dataconversion/shapefile_to_plt.py

Mehrdadj93/handyscripts

5df9a69e17345ca5a3e42dda2424da2da0ab6f12

[ "MIT" ]

66

2018-09-21T22:55:34.000Z

2022-03-22T14:29:57.000Z

python/dataconversion/shapefile_to_plt.py

Mehrdadj93/handyscripts

5df9a69e17345ca5a3e42dda2424da2da0ab6f12

[ "MIT" ]

4

2018-10-04T22:09:01.000Z

2022-03-31T16:18:38.000Z

python/dataconversion/shapefile_to_plt.py

Mehrdadj93/handyscripts

5df9a69e17345ca5a3e42dda2424da2da0ab6f12

[ "MIT" ]

50

2018-09-23T15:50:55.000Z

2022-03-06T06:59:33.000Z

"""Convert Shapefiles to Tecplot plt format usage: > python shapefile_to_plt.py shapefile.shp outfile.plt Necessary modules ----------------- pyshp The Python Shapefile Library (pyshp) reads and writes ESRI Shapefiles in pure Python. https://pypi.python.org/pypi/pyshp https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf Description ----------- This script is used to convert Shapefiles (.shp) to Tecplot plt format. Users will need to answer a few questions about their shapefile to accurately import into Tecplot format. First select a conversion type: Convert to a single zone or one zone per shape. Next select variable names to use: x/y or lon/lat Finally, if using one zone per shape, select the column to name the zones After running the script, append the new plt file to the active frame and match the variable names. """ import sys import os import time import shapefile as sf import tecplot as tp from tecplot.constant import * def create_connectivity_list(shape, element_offset=0): """Use the element indices for each shape to create the connectivity list""" num_points = len(shape.points) num_parts = len(shape.parts) elements = [] for i in range(num_parts): # parts[] returns the point index at the start of each part # These values will define the connectivity list of the line segments p1 = shape.parts[i] # Check to see if we're at the last part so we don't over index the list if i < num_parts - 1: p2 = shape.parts[i + 1] - 1 else: p2 = num_points - 1 p1 += element_offset p2 += element_offset # Create the connectivity list for this part. Each point is connected to the next for i in range(p1, p2): elements.append((i, i + 1)) return elements def convert_to_single_zone(s, zone_name, dataset): """Loop over all the shapes, collecting their point values and generating the FE-Line Segment connectivity list.""" x = [] y = [] elements = [] num_points = 0 for shapeRec in s.shapeRecords(): elements.extend(create_connectivity_list(shapeRec.shape, num_points)) x.extend([n[0] for n in shapeRec.shape.points]) y.extend([n[1] for n in shapeRec.shape.points]) num_points += len(shapeRec.shape.points) # Now that we have the points and connectivity list we add a zone to the dataset zone = dataset.add_fe_zone(ZoneType.FELineSeg, zone_name, num_points, len(elements)) zone.values(0)[:] = x zone.values(1)[:] = y zone.nodemap[:] = elements def convert_to_one_zone_per_shape(s, name_index, dataset): """Create a Tecplot zone for each shape""" for i, shapeRec in enumerate(s.shapeRecords()): # Extract the zone name from the appropriate location in the shape record zone_name = shapeRec.record[name_index] if len(zone_name) == 0: zone_name = 'NONE' num_points = len(shapeRec.shape.points) elements = create_connectivity_list(shapeRec.shape) x = [n[0] for n in shapeRec.shape.points] y = [n[1] for n in shapeRec.shape.points] # Create the Tecplot zone and add the point data as well as the connectivity list zone = dataset.add_fe_zone(ZoneType.FELineSeg, zone_name, num_points, len(elements)) zone.values(0)[:] = x zone.values(1)[:] = y zone.nodemap[:] = elements # Print dots to give the user an indication that something is happening sys.stdout.write('.') sys.stdout.flush() def get_var_names(): """Choose the variable names to use""" print("1 - Use 'x' and 'y'") print("2 - Use 'lon' and 'lat'") var_name_choice = int(input("Enter your choice for variable names: ")) - 1 return var_name_choice def get_name_index(shape_reader): """Displays Shapefile column used to name zones""" first_record = shape_reader.shapeRecords()[0].record # Record is the "column" information for the shape index = 1 for f, r in zip(shape_reader.fields[1:], first_record): print(index, "- ", f[0], ": ", r) index += 1 name_index = int(input("Enter the index to use for zone names: ")) - 1 return name_index def get_conversion_option(shape_records): """Prompts user for conversion options""" print("1 - Convert to a single zone") print("2 - Convert to one zone per shape (%d zones) (this can take a while)" % (len(shape_records))) import_option = int(input("Enter your conversion selection: ")) return import_option def main(shapefilename, outfilename): # define index from record for zone name s = sf.Reader(shapefilename) shape_records = s.shapeRecords() conversion_option = get_conversion_option(shape_records) if get_var_names() == 0: x_var_name = 'x' y_var_name = 'y' else: x_var_name = 'lon' y_var_name = 'lat' dataset = tp.active_frame().create_dataset("Shapefile", [x_var_name, y_var_name]) if conversion_option == 1: # Single Zone start = time.time() convert_to_single_zone(s, os.path.basename(shapefilename), dataset) else: # One Zone per Shape name_index = get_name_index(s) start = time.time() convert_to_one_zone_per_shape(s, name_index, dataset) tp.data.save_tecplot_plt(outfilename) print("Elapsed time: ", time.time() - start) if len(sys.argv) != 3: print("Usage:\nshapefile_to_plt.py shapefile.shp outfile.plt") else: shapefilename = sys.argv[1] outfilename = sys.argv[2] main(shapefilename, outfilename)

33.607143

104

0.669146

825

5,646

4.45697

0.249697

0.039162

0.016318

0.024476

0.214849

0.150122

0.126734

0.11096

0.096274

0.081044

0

0.008972

0.230074

5,646

167

105

33.808383

0.836899

0.333688

0

0.170213

0

0.091939

0.00728

0

1

0.074468

false

0

0.085106

0

0.202128

0.074468

0

null

0

null

0

1

0

690cf46e3a65cc79fc30a8178dbe551901ad1473

2,746

py

Python

mastering_oop/special_methods/card_factory.py

brittainhard/py

aede05530ad05a8319fef7e76b49e4bf3cebebac

[ "MIT" ]

null

mastering_oop/special_methods/card_factory.py

brittainhard/py

aede05530ad05a8319fef7e76b49e4bf3cebebac

[ "MIT" ]

null

mastering_oop/special_methods/card_factory.py

brittainhard/py

aede05530ad05a8319fef7e76b49e4bf3cebebac

[ "MIT" ]

null

"""When creating factory functions, plain functions are good unless you need to inherit from a higher level class. If you don't need to inherit, dont use a class.""" from functools import partial from .suits import * from .cards import * def card(rank, suit): if rank == 1: return AceCard('A', suit) elif 2 <= rank < 11: return NumberCard(str(rank), suit) elif 11 <= rank < 14: name = {11: "J", 12: "Q", 13: "K"}[rank] return FaceCard(name, suit) else: """The else clause is there to make explicit what inputs this function will handle""" raise Exception("Rank out of range.") def card_better_elif(rank, suit): if rank == 1: return AceCard('A', suit) elif 2 <= rank < 11: return NumberCard(str(rank), suit) elif rank == 11: return FaceCard("J", suit) elif rank == 12: return FaceCard("Q", suit) elif rank == 13: return FaceCard("K", suit) else: """The else clause is there to make explicit what inputs this function will handle""" raise Exception("Rank out of range.") def card_mapping(rank, suit): """Get the desired rank. If the rank isnt there by default, return a nubmer card""" class_ = {1: AceCard, 11: FaceCard, 12: FaceCard, 13: FaceCard}.get(rank, NumberCard) return class_(rank, suit) def card_functools_mapping(rank, suit): part_class = { 1: partial(AceCard, 'A'), 11: partial(FaceCard, 'J'), 12: partial(FaceCard, 'Q'), 13: partial(FaceCard, 'K') }.get(rank, partial(NumberCard, str(rank))) return part_class(suit) class CardFactory: """This class is designed to contain a 'fluent api'. That means that one function call happens after the next. In the example, its x.a().b(). This class is returning itself, which the next function uses to generate the card. We are containing this in one object for the sake of simplicity. It seems like the minute we decide to do a different API... I don't know how this woulf be useful exactly. A lot of these are just examples of stuff you can do with collections.""" def rank(self, rank): self.class_, self.rank_str = { 1: (AceCard, 'A'), 11: (FaceCard, 'J'), 12: (FaceCard, 'Q'), 13: (FaceCard, 'K') }.get(rank, (NumberCard, str(rank))) return self def suit(self, suit): return self.class_(self.rank_str, suit) def get_deck(self): return [self.rank(r + 1).suit(s) for r in range(13) for s in (Club, Diamond, Heart, Spade)] factory_functions = [card, card_better_elif, card_mapping, card_functools_mapping]

31.563218

89

0.617626

393

2,746

4.264631

0.335878

0.033413

0.040573

0.016706

0.243437

0.21957

0

0.023928

0.269483

2,746

86

90

31.930233

0.811565

0.257101

0

0.226415

0

0.028729

0

1

0.132075

false

0

0.056604

0.037736

0.45283

0

null

0

null

0

1

0

690eeeccad42026d1f4e91d779a9b52f4b5eb52e

847

py

Python

Python3/1311-Get-Watched-Videos-by-Your-Friends/soln.py

wyaadarsh/LeetCode-Solutions

3719f5cb059eefd66b83eb8ae990652f4b7fd124

[ "MIT" ]

5

2020-07-24T17:48:59.000Z

2020-12-21T05:56:00.000Z

Python3/1311-Get-Watched-Videos-by-Your-Friends/soln.py

zhangyaqi1989/LeetCode-Solutions

2655a1ffc8678ad1de6c24295071308a18c5dc6e

[ "MIT" ]

null

Python3/1311-Get-Watched-Videos-by-Your-Friends/soln.py

zhangyaqi1989/LeetCode-Solutions

2655a1ffc8678ad1de6c24295071308a18c5dc6e

[ "MIT" ]

2

2020-07-24T17:49:01.000Z

2020-08-31T19:57:35.000Z

class Solution: def watchedVideosByFriends(self, watchedVideos: List[List[str]], friends: List[List[int]], ID: int, level: int) -> List[str]: n = len(friends) # BFS frontier = [ID] levels = {ID : 0} nsteps = 0 while frontier: if level == 0: break level -= 1 next_level = [] for u in frontier: for v in friends[u]: if v not in levels: levels[v] = nsteps + 1 next_level.append(v) frontier = next_level nsteps += 1 counter = collections.Counter() for ID in frontier: for video in watchedVideos[ID]: counter[video] += 1 return sorted(counter, key=lambda x : (counter[x], x))

33.88

129

0.472255

91

847

4.362637

0.417582

0.06801

0.050378

0

0.014523

0.430933

847

24

130

35.291667

0.809129

0.003542

0

1

0.043478

false

0

0.130435

0

null

0

null

0

1

0

690f42d3fe72fbc49129a7da627897da2c12d466

2,614

py

Python

tests/fixtures/client.py

radiac/mara

413f1f9f4c7117839a8c03d72733d6f75494ddd3

[ "BSD-3-Clause" ]

16

2015-11-22T13:12:46.000Z

2020-09-04T06:42:55.000Z

tests/fixtures/client.py

radiac/mara

413f1f9f4c7117839a8c03d72733d6f75494ddd3

[ "BSD-3-Clause" ]

8

2016-01-09T23:32:46.000Z

2019-09-30T23:30:49.000Z

tests/fixtures/client.py

radiac/mara

413f1f9f4c7117839a8c03d72733d6f75494ddd3

[ "BSD-3-Clause" ]

7

2016-07-19T04:39:31.000Z

2020-09-04T06:43:06.000Z

from __future__ import annotations import logging import socket import pytest from .constants import TEST_HOST, TEST_PORT logger = logging.getLogger("tests.fixtures.client") class BaseClient: """ Blocking test client to connect to an app server """ name: str def __init__(self, name: str): self.name = name def __str__(self): return self.name class SocketClient(BaseClient): socket: socket.socket | None buffer: bytes def __init__(self, name: str): super().__init__(name) self.buffer = b"" def connect(self, host: str, port: int): logger.debug(f"Socket client {self} connecting") self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.socket.connect((host, port)) logger.debug(f"Socket client {self} connected") def write(self, raw: bytes): if not self.socket: raise ValueError("Socket not open") logger.debug(f"Socket client {self} writing {raw!r}") self.socket.sendall(raw) def read(self, len: int = 1024) -> bytes: if not self.socket: raise ValueError("Socket not open") raw: bytes = self.socket.recv(len) logger.debug(f"Socket client {self} received {raw!r}") return raw def read_line(self, len: int = 1024) -> bytes: if b"\r\n" not in self.buffer: self.buffer += self.read(len) if b"\r\n" not in self.buffer: raise ValueError("Line not found") line, self.buffer = self.buffer.split(b"\r\n", 1) return line def close(self): if not self.socket: raise ValueError("Socket not open") logger.debug(f"Socket client {self} closing") self.socket.close() logger.debug(f"Socket client {self} closed") @pytest.fixture def socket_client_factory(request: pytest.FixtureRequest): """ Socket client factory fixture Usage:: def test_client(app_harness, socket_client_factory): app_harness(myapp) client = socket_client_factory() client.write(b'hello') assert client.read() == b'hello' """ clients = [] def connect(name: str | None = None, host: str = TEST_HOST, port: int = TEST_PORT): client_name = request.node.name if name is not None: client_name = f"{client_name}:{name}" client = SocketClient(client_name) client.connect(host, port) clients.append(client) return client yield connect for client in clients: client.close()

25.627451

87

0.613236

331

2,614

4.722054

0.253776

0.076775

0.046065

0.069098

0.266155

0.243122

0.150352

0.12476

0

0.004762

0.27697

2,614

101

88

25.881188

0.822222

0.109028

0

0.163934

0

0.132366

0.009235

0

1

0.163934

false

0

0.081967

0.016393

0.393443

0

null

0

null

0

1

0

690f93b1369267fc52a8a48543ba3893e460a7b1

7,768

py

Python

pi.py

przemyslawgarlinski/pi_iowrap

3eef421ebe718dd3bfe1723d1ede6b6a7bc76599

[ "MIT" ]

1

2017-11-26T22:12:16.000Z

pi.py

przemyslawgarlinski/pi_iowrap

3eef421ebe718dd3bfe1723d1ede6b6a7bc76599

[ "MIT" ]

null

pi.py

przemyslawgarlinski/pi_iowrap

3eef421ebe718dd3bfe1723d1ede6b6a7bc76599

[ "MIT" ]

null

"""Standard raspberry GPIO access layer. It defines abstract layer that extends InOutInterface to access all standard ports on rapsberry pi. It uses RPi.GPIO under the hood. Thanks to that you have a standardized way of accessing these ports, as well as any others implementing InOutInterface. """ import logging from base import InOutInterface from base import get_gpio from base import Settings from base import PortListener from exceptions import InvalidPortNumberError from port import Port class PiInterface(InOutInterface): """Standard GPIO interface abstraction layer. Some examples of raw calls to ports using RPi.GPIO GPIO.setmode(GPIO.BOARD) // set usual port numbering GPIO.setup(7, GPIO.OUT) GPIO.output(7, GPIO.HIGH) GPIO.output(7, GPIO.LOW) GPIO.cleanup() """ _GROUND = (6, 9, 14, 20, 25, 30, 34, 39) _POWER_5V = (2, 4) _POWER_3V3 = (1, 17) _I2C = (3, 5, 27, 28) _FORBIDDEN = _GROUND + _POWER_5V + _POWER_3V3 + _I2C PULL_UP = 'pull_up' PULL_DOWN = 'pull_down' def __init__(self): super(PiInterface, self).__init__(40) for number in range(1, 41): if number not in self._FORBIDDEN: self._ports[number] = Port(self, number) # Defines the pull up or pull down rezistor for inputs. # Possible values are: # 1. self.PULL_UP # 2. self.PULL_DOWN # 3. None (input fluctuating by default) self.pull_up_down_rezistor = self.PULL_UP self._port_listeners = {} self._initialize_ports() def __str__(self): return 'Raspberry PI GPIO' def _validate_port_number(self, port_number): super(PiInterface, self)._validate_port_number(port_number) if port_number in self._GROUND: raise InvalidPortNumberError( 'This port number(%d) is reserved for GROUND.', port_number) if port_number in self._POWER_3V3: raise InvalidPortNumberError( 'This port number(%d) is reserved for 3.3V POWER.', port_number) if port_number in self._POWER_5V: raise InvalidPortNumberError( 'This port number(%d) is reserved for 5V POWER.', port_number) if port_number in self._I2C: raise InvalidPortNumberError( 'This port number(%d) is reserved for I2c.', port_number) if port_number in self._FORBIDDEN: raise InvalidPortNumberError( 'This port number(%d) is forbidden to take.', port_number) def _gpio_setup(self, port_number, gpio_attr_name): self._validate_port_number(port_number) if Settings.IS_NO_HARDWARE_MODE: logging.warning('No hardware mode, no value written') else: gpio = get_gpio() if gpio_attr_name == 'IN': # Special case for settings port as input. # Pullup or pulldown rezistor should be set here. kwargs = {} if self.pull_up_down_rezistor == self.PULL_UP: kwargs['pull_up_down'] = gpio.PUD_UP elif self.pull_up_down_rezistor == self.PULL_DOWN: kwargs['pull_up_down'] = gpio.PUD_DOWN gpio.setup( port_number, getattr(gpio, gpio_attr_name), **kwargs) else: gpio.setup(port_number, getattr(gpio, gpio_attr_name)) def _gpio_output(self, port_number, value): self._validate_port_number(port_number) if Settings.IS_NO_HARDWARE_MODE: logging.warning('No hardware mode, no value written') else: gpio = get_gpio() gpio.output( port_number, gpio.HIGH if value == self.HIGH else gpio.LOW ) def get_value(self, port_number): self._validate_port_number(port_number) value = self._check_no_hardware_port_value(port_number) if value is not None: return value else: gpio = get_gpio() value = gpio.input(port_number) # logging.debug( # 'Read gpio port value (%s): %s', # self.get_port(port_number), # value) return self.HIGH if value == gpio.HIGH else self.LOW def set_as_input(self, port_number): self._gpio_setup(port_number, 'IN') self._in_out_registry[port_number] = self._INPUT return self def set_as_output(self, port_number): self._gpio_setup(port_number, 'OUT') self._in_out_registry[port_number] = self._OUTPUT return self def set_high(self, port_number): self._validate_port_number(port_number) self._validate_write_port_number(port_number) self._gpio_output(port_number, self.HIGH) return self def set_low(self, port_number): self._validate_port_number(port_number) self._validate_write_port_number(port_number) self._gpio_output(port_number, self.LOW) return self def add_event( self, port_number, on_rising_callback=None, on_falling_callback=None): """Adds listening event on given port. In this case 2nd argument passed to a callback is a value read during callback invocation, which in theory might not be the one that actually cause triggering the event. """ if Settings.IS_NO_HARDWARE_MODE: logging.warning('No hardware mode, adding read event failed.') else: port_listener = self._port_listeners.get(port_number) if not port_listener: port_listener = _PiPortListener(self.get_port(port_number)) gpio = get_gpio() gpio.add_event_detect( port_number, gpio.BOTH, callback=port_listener.trigger_callbacks, bouncetime=Settings.READ_SWITCH_DEBOUNCE) self._port_listeners[port_number] = port_listener if on_rising_callback: logging.debug( 'Adding rising callback for interface (%s) on port %d', self, port_number) port_listener.add_rising_callback(on_rising_callback) if on_falling_callback: logging.debug( 'Adding falling callback for interface (%s) on port %d', self, port_number) port_listener.add_falling_callback(on_falling_callback) def clear_read_events(self, port_number): if not Settings.IS_NO_HARDWARE_MODE: get_gpio().remove_event_detect(port_number) if port_number in self._port_listeners: del self._port_listeners[port_number] class _PiPortListener(PortListener): def get_callbacks_to_trigger(self): if not self._rising_callbacks and not self._falling_callbacks: return [] to_trigger = [] port_value = self.port.value if (port_value == InOutInterface.HIGH): to_trigger.extend(self._rising_callbacks) logging.debug( 'Event detected on interface (%s) on port (%d). ' 'Type: RISING.', self.port.interface, self.port.number) elif (port_value == InOutInterface.LOW): to_trigger.extend(self._falling_callbacks) logging.debug( 'Event detected on interface (%s) on port (%d). ' 'Type: FALLING.', self.port.interface, self.port.number) return to_trigger

36.469484

80

0.608651

938

7,768

4.76226

0.204691

0.145512

0.043877

0.035818

0.40206

0.375196

0.351019

0.296844

0.242668

0.164316

0

0.009972

0.315783

7,768

213

81

36.469484

0.83048

0.137873

0

0.304636

0

0.094242

0

1

0.086093

false

0

0.046358

0.006623

0.251656

0

null

0

null

0

1

0

6910edd2d74b03e0e705138a62b8524d93b325d6

4,190

py

Python

cogs/ments.py

NastyHub/yewon

62e7666a6be8c970871d15af4dfbbcd3ff0a97fd

[ "MIT" ]

null

cogs/ments.py

NastyHub/yewon

62e7666a6be8c970871d15af4dfbbcd3ff0a97fd

[ "MIT" ]

null

cogs/ments.py

NastyHub/yewon

62e7666a6be8c970871d15af4dfbbcd3ff0a97fd

[ "MIT" ]

null

import discord import os import json from discord.ext import commands, tasks import time import asyncio import random from discord.utils import MAX_ASYNCIO_SECONDS ########################################################################## #generalrole = discord.utils.get(ctx.guild.roles, id=661454256251076613) #logchannel = discord.utils.get(client.get_all_channels(), id = 753619980548833401) #SERVER INFO ownerid = 631441731350691850 chanwoo = 631441731350691850 yewon = 819734468465786891 saji = 785135229894524959 donggu = 543680309661663233 hanjae = 406822771524501516 mintchocolate = 434328592739074048 csticker = 864745666580316170 dohyun = 652531481767444498 ########################################################################## #USEFUL FUNCTIONS ########################################################################## def checkidentity(supposeid): if int(supposeid) == chanwoo: return "chanwoo" elif int(supposeid) == yewon: return "yewon" elif int(supposeid) == saji: return "saji" elif int(supposeid) == donggu: return "donggu" elif int(supposeid) == hanjae: return "hanjae" elif int(supposeid) == mintchocolate: return "mint" elif int(supposeid) == csticker: return "csticker" elif int(supposeid) == dohyun: return "dohyun" else: return None def sendrandom(providedlist, min, max): howmuchtosend = random.randint(min, max) sizeoflist = len(providedlist) i = 1 returnlist = [] while i <= howmuchtosend: i += 1 thingtoadd = providedlist[random.randrange(0, sizeoflist)] returnlist.append(thingtoadd) return returnlist def getlist(sendid): sendid = str(sendid) path = "ments/ments.json" with open(path) as f: jsondata = json.load(f) f.close() try: mylist = jsondata[sendid] except: mylist = None return mylist class ments(commands.Cog): def __init__(self, client): self.client = client @commands.command(aliases=["테스트"]) async def test(self, ctx): checkme = checkidentity(ctx.author.id) #await ctx.message.delete() if ctx.author.id == 434328592739074048: await ctx.send('...나는 모구모구') await ctx.send(file=discord.File('image/mogumogu.jpg')) else: grablist = getlist(ctx.author.id) if grablist == None: await ctx.send("아직 너는 잘 모르겠는데..") else: herelist = sendrandom(grablist, 1, 1) for i in herelist: await ctx.send(i) @commands.command() async def joinvc(self, ctx): if ctx.author.id == ownerid: await ctx.message.delete() channel = ctx.author.voice.channel await channel.connect() @commands.command() async def leavevc(self, ctx): if ctx.author.id == ownerid: await ctx.message.delete() await ctx.voice_client.disconnect() @commands.command() async def sendjson(self, ctx): if ctx.author.id == ownerid: await ctx.author.send(file=discord.File('ments/ments.json')) @commands.command(aliases=["전송"]) async def dm(self, ctx, target: discord.Member, *, message): try: await ctx.message.delete() except: await ctx.send("이 명령어는 서버에서 사용해 주세요") embed = discord.Embed( title = f"📨 메세지가 도착했습니다!", description = f"```{message}```\n\n답장해도 보내지지 않으니 직접 그 사람에게 말하세용\n명령어: `?전송 @유저 메세지 내용`", color = discord.Color.from_rgb(255,105,180) ) embed.set_footer(text=f"{ctx.author.name}님이 보낸 메세지") try: await target.send(embed=embed) except: await ctx.send(f"{target.mention}, 도착한 메세지가 있었지만 디엠 수신 기능이 꺼져있어 보내지 못하였습니다.") #find a channel with an id 879895499338039301 from all the servers the bot is in channel = discord.utils.get(self.client.get_all_channels(), id = 879895499338039301) await channel.send(embed=embed) def setup(client): client.add_cog(ments(client))

28.310811

100

0.587351

458

4,190

5.344978

0.393013

0.039216

0.045752

0.034314

0.071487

0.053513

0.039216

0

0.085613

0.258473

4,190

148

101

28.310811

0.701963

0.068019

0

0.165138

0

0.009174

0.085101

0.006253

0

1

0.045872

false

0

0.073395

0

0.229358

0

null

0

null

0

1

0

691177c77ccb4ef9f4c89444502885d55b50a94c

5,007

py

Python

data/process_data.py

MitraG/Disaster-Response-Project

179d875f9d16aba08cca14d9517531fb29b28041

[ "OLDAP-2.4" ]

null

data/process_data.py

MitraG/Disaster-Response-Project

179d875f9d16aba08cca14d9517531fb29b28041

[ "OLDAP-2.4" ]

null

data/process_data.py

MitraG/Disaster-Response-Project

179d875f9d16aba08cca14d9517531fb29b28041

[ "OLDAP-2.4" ]

null

#First, we import the relevant libraries import sys import pandas as pd from sqlalchemy import create_engine def load_data(messages_filepath, categories_filepath): '''This function will load the messages and categories datasets. Then, this function will merge the datasets by left join using the common id and then return a pandas dataframe. If the input is invalid or the data does not exist, this function will raise an error. INPUT: messages_filepath --> location of messages data file from the project root categories_filepath --> location of the categories data file from the project root OUTPUT: df --> a DataFrame containing the merged dataset ''' #load the messages dataset messages = pd.read_csv(messages_filepath) #load the categories dataset categories = pd.read_csv(categories_filepath) #merge the two datasets df = pd.merge(messages, categories, on='id', how = 'left') return df def clean_data(df): ''' This function will clean and prepare the merged data to make it more efficient to work with. The steps this function will take to clean and prepare the data are: - Split the categories into separate category columns - Rename every column to its corresponding category - Convert category values to a boolean format (0 and 1) - Replace the original categories column in the merged dataframe with the new category columns - Drop any dulplicates in the newly merged dataset If the input is invalid or the data does not exist, this function will raise an error. INPUT: df --> a Pandas DataFrame with the merged data OUTPUT: df --> a new Pandas Dataframe with each category as a column and its entries as 0/1 indicators. This is to flag if a message is classified under each category column. ''' #Split the categories into 36 individual category columns and create a dataframe cat_cols = df["categories"].str.split(";", expand=True) #Rename every column to its corresponding category ##First, calling the first row of cat_cols to extract a new list of new column names ##Using a lambda function that takes everything ##up to the second to last character of each string with slicing row = cat_cols.iloc[0] string_slicer = lambda x: x[:-2] cat_colnames = [string_slicer(i) for i in list(row)] cat_cols.columns = cat_colnames #Convert category values to a boolean format (0 and 1) #Iterating through the category columns in df to keep only the last character of each string (the 1 or 0) ##Then convert the string into a numeric value ##Using the slicing method once again int_slicer = lambda x: int(x[-1]) for column in cat_cols: cat_cols[column] = [int_slicer(i) for i in list(cat_cols[column])] #Replace the original categories column in the merged dataframe with the new category columns df = df.drop(['categories'], axis=1) df = pd.merge(df, cat_cols, left_index=True, right_index=True) df['related'] = df['related'].astype('str').str.replace('2', '1') df['related'] = df['related'].astype('int') #Drop any dulplicates in the newly merged dataset df = df.drop_duplicates() return df def save_data(df, database_filename): ''' This function will load the prepared data into a SQLite database file. If the input is invalid or the data does not exist, this function will raise an error. INPUT: df --> a Pandas DataFrame containing the prepared data DisasterResponse.db --> database to store data for model ingestion ''' engine = create_engine('sqlite:///DisasterResponse.db') df.to_sql('categorised_messages', engine, index=False, if_exists='replace') def main(): ''' This is the mail ETL function that extracts, transforms and loads the data. ''' 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()

37.931818

117

0.675654

694

5,007

4.792507

0.279539

0.028864

0.038485

0.04089

0.312688

0.251052

0.19994

0.174083

0.149429

0

0.004791

0.24965

5,007

132

118

37.931818

0.88049

0.496105

0

0.043478

0

0.243035

0.03129

0

1

0.086957

false

0

0.065217

0

0.195652

0.108696

0

null

0

null

0

1

0

691235d4163755651c608d7db64917c20c45cfde

1,568

py

Python

randomseq/modules/make_random.py

andreagrioni/special-couscous

17b8dcd0bcafab2f6952ddf3b38cd1292f62cee7

[ "MIT" ]

null

randomseq/modules/make_random.py

andreagrioni/special-couscous

17b8dcd0bcafab2f6952ddf3b38cd1292f62cee7

[ "MIT" ]

1

2021-08-17T12:17:29.000Z

randomseq/modules/make_random.py

andreagrioni/special-couscous

17b8dcd0bcafab2f6952ddf3b38cd1292f62cee7

[ "MIT" ]

null

import pandas as pd from modules import bedtools from modules import intervals def generator(ARGUMENTS): if not ARGUMENTS.input_bed and not ARGUMENTS.gtf_anno: print(f"get random intervals from genome {ARGUMENTS.reference}") RANDOM_BED = bedtools.random_interval( ARGUMENTS.reference, ARGUMENTS.int_size, ARGUMENTS.N ) elif ARGUMENTS.gtf_anno: print(f"get intervals from annotation file {ARGUMENTS.gtf_anno}") RANDOM_BED = intervals.gtf_to_bed( file_name=ARGUMENTS.gtf_anno, feature=ARGUMENTS.feature, int_size=ARGUMENTS.int_size, N=ARGUMENTS.N, ) elif ARGUMENTS.input_bed: print(f"load input bed file {ARGUMENTS.input_bed}") RANDOM_BED = ARGUMENTS.input_bed else: print("nothing to do") if ARGUMENTS.avoid_int and RANDOM_BED: print("removing positive intervals") RANDOM_BED = bedtools.intersect( RANDOM_BED, ARGUMENTS.avoid_int, opt=ARGUMENTS.intersect_opt ) return RANDOM_BED def make_set(ARGUMENTS): df_list = list() tmp_size = 0 while tmp_size < ARGUMENTS.N: RANDOM_BED = generator(ARGUMENTS) tmp_df = pd.read_csv(RANDOM_BED, sep="\t", header=None) tmp_size += tmp_df.shape[0] df_list.append(tmp_df) if df_list: merge_df = pd.concat(df_list, axis=0).sample(n=ARGUMENTS.N) merge_df.to_csv(RANDOM_BED, sep="\t", header=False, index=False) return RANDOM_BED if __name__ == "__main__": pass

28

73

0.658163

207

1,568

4.7343

0.323672

0.10102

0.069388

0.042857

0.095918

0

0.002562

0.253189

1,568

55

74

28.509091

0.83433

0

0.047619

0

0.128827

0.026786

0

1

0.047619

false

0.02381

0.071429

0

0.166667

0.119048

0

null

0

null

0

1

0

69146b024afb8e8179d7794495072111c92f9cf1

532

py

Python

Modulo3/aula18.py

Werberty/Curso-em-Video-Python3

24c0299edd635fb9c2db2ecbaf8532d292f92d49

[ "MIT" ]

1

2022-03-06T11:37:47.000Z

Modulo3/aula18.py

Werberty/Curso-em-Video-Python3

24c0299edd635fb9c2db2ecbaf8532d292f92d49

[ "MIT" ]

null

Modulo3/aula18.py

Werberty/Curso-em-Video-Python3

24c0299edd635fb9c2db2ecbaf8532d292f92d49

[ "MIT" ]

null

test = list() test.append('Werberty') test.append(21) galera = list() galera.append(test[:]) test[0] = 'Maria' test[1] = 22 galera.append(test[:]) print(galera) pessoal = [['joão', 19], ['Ana', 33], ['Joaquim', 13], ['Maria', 45]] print(pessoal[1]) print(pessoal[2][1]) for p in pessoal: print(f'{p[0]} tem {p[1]} anos de idade.') galerinha = list() dado = list() for c in range(0, 3): dado.append(str(input('Nome: '))) dado.append(int(input('idade: '))) galerinha.append(dado[:]) dado.clear() print(galerinha)

22.166667

69

0.614662

81

532

4.037037

0.469136

0.061162

0.097859

0

0.046256

0.146617

532

24

70

22.166667

0.674009

0

0.090909

0

0.144465

0

1

0

false

0

0.227273

0

null

0

null

0

1

0

6914bfcaeef4fb7954973326e68cefa7ccd0e8c9

4,667

py

Python

summariser/ngram_vector/vector_generator.py

UKPLab/ijcai2019-relis

8a40762dcfa90c075a4f6591cbdceb468026ef17

[ "MIT" ]

5

2019-06-30T14:45:12.000Z

2020-07-26T12:59:36.000Z

summariser/ngram_vector/vector_generator.py

UKPLab/ijcai2019-relis

8a40762dcfa90c075a4f6591cbdceb468026ef17

[ "MIT" ]

1

2020-07-11T10:47:57.000Z

2020-09-16T10:53:36.000Z

summariser/ngram_vector/vector_generator.py

UKPLab/ijcai2019-relis

8a40762dcfa90c075a4f6591cbdceb468026ef17

[ "MIT" ]

2

2019-12-24T02:10:42.000Z

2020-04-27T05:39:49.000Z

from summariser.ngram_vector.base import Sentence from summariser.utils.data_helpers import * from nltk.stem.porter import PorterStemmer from summariser.ngram_vector.state_type import * import random class Vectoriser: def __init__(self,docs,sum_len=100,no_stop_words=True,stem=True,block=1,base=200,lang='english'): self.docs = docs self.without_stopwords = no_stop_words self.stem = stem self.block_num = block self.base_length = base self.language = lang self.sum_token_length = sum_len self.stemmer = PorterStemmer() self.stoplist = set(stopwords.words(self.language)) self.sim_scores = {} self.stemmed_sentences_list = [] self.load_data() def sampleRandomReviews(self,num,heuristic_reward=True,rouge_reward=True,models=None): heuristic_list = [] rouge_list = [] act_list = [] for ii in range(num): state = State(self.sum_token_length, self.base_length, len(self.sentences), self.block_num, self.language) while state.available_sents != [0]: new_id = random.choice(state.available_sents) if new_id == 0: continue if new_id > 0 and len(self.sentences[new_id-1].untokenized_form.split(' ')) > self.sum_token_length: continue state.updateState(new_id-1,self.sentences) actions = state.historical_actions act_list.append(actions) if heuristic_reward: rew = state.getTerminalReward(self.sentences,self.stemmed_sentences_list,self.sent2tokens,self.sim_scores) heuristic_list.append(rew) if rouge_reward: assert models is not None r_dic = {} for model in models: model_name = model[0].split('/')[-1].strip() rew = state.getOptimalTerminalRougeScores(model) r_dic[model_name] = rew rouge_list.append(r_dic) return act_list, heuristic_list, rouge_list def getSummaryVectors(self,summary_acts_list): vector_list = [] for act_list in summary_acts_list: state = State(self.sum_token_length, self.base_length, len(self.sentences), self.block_num, self.language) for i, act in enumerate(act_list): state.updateState(act, self.sentences, read=True) vector = state.getSelfVector(self.top_ngrams_list, self.sentences) vector_list.append(vector) return vector_list def sent2tokens(self, sent_str): if self.without_stopwords and self.stem: return sent2stokens_wostop(sent_str, self.stemmer, self.stoplist, self.language) elif self.without_stopwords == False and self.stem: return sent2stokens(sent_str, self.stemmer, self.language) elif self.without_stopwords and self.stem == False: return sent2tokens_wostop(sent_str, self.stoplist, self.language) else: # both false return sent2tokens(sent_str, self.language) def load_data(self): self.sentences = [] for doc_id, doc in enumerate(self.docs): doc_name, doc_sents = doc doc_tokens_list = [] for sent_id, sent_text in enumerate(doc_sents): token_sent = word_tokenize(sent_text, self.language) current_sent = Sentence(token_sent, doc_id, sent_id + 1) untokenized_form = untokenize(token_sent) current_sent.untokenized_form = untokenized_form current_sent.length = len(untokenized_form.split(' ')) self.sentences.append(current_sent) sent_tokens = self.sent2tokens(untokenized_form) doc_tokens_list.extend(sent_tokens) stemmed_form = ' '.join(sent_tokens) self.stemmed_sentences_list.append(stemmed_form) #print('total sentence num: ' + str(len(self.sentences))) self.state_length_computer = StateLengthComputer(self.block_num, self.base_length, len(self.sentences)) self.top_ngrams_num = self.state_length_computer.getStatesLength(self.block_num) self.vec_length = self.state_length_computer.getTotalLength() sent_list = [] for sent in self.sentences: sent_list.append(sent.untokenized_form) self.top_ngrams_list = getTopNgrams(sent_list, self.stemmer, self.language, self.stoplist, 2, self.top_ngrams_num)

42.045045

122

0.630812

548

4,667

5.120438

0.229927

0.055595

0.021383

0.025659

0.162153

0.11119

0.070563

0.058446

0

0.006876

0.283265

4,667

110

123

42.427273

0.831988

0.014142

0

0.022472

0

0.002393

0

0.011236

1

0.05618

false

0

0.05618

0

0.191011

0

null

0

null

0

1

0

69212d7ab58acd86f0a6a7a1366fa6f42c3e9584

1,296

py

Python

src/openprocurement/tender/openua/procedure/models/award.py

ProzorroUKR/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

10

2020-02-18T01:56:21.000Z

2022-03-28T00:32:57.000Z

src/openprocurement/tender/openua/procedure/models/award.py

quintagroup/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

26

2018-07-16T09:30:44.000Z

2021-02-02T17:51:30.000Z

src/openprocurement/tender/openua/procedure/models/award.py

ProzorroUKR/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

15

2019-08-08T10:50:47.000Z

2022-02-05T14:13:36.000Z

from schematics.types import StringType, BooleanType, MD5Type, BaseType from schematics.exceptions import ValidationError from schematics.types.compound import ModelType from openprocurement.api.models import ListType from openprocurement.tender.core.procedure.models.award import ( Award as BaseAward, PatchAward as BasePatchAward, PostAward as BasePostAward, ) from openprocurement.tender.core.procedure.models.milestone import QualificationMilestoneListMixin from openprocurement.tender.openua.procedure.models.item import Item class Award(QualificationMilestoneListMixin, BaseAward): complaints = BaseType() items = ListType(ModelType(Item, required=True)) qualified = BooleanType(default=False) eligible = BooleanType(default=False) def validate_qualified(self, data, qualified): if data["status"] == "active" and not qualified: raise ValidationError("This field is required.") def validate_eligible(self, data, eligible): if data["status"] == "active" and not eligible: raise ValidationError("This field is required.") class PatchAward(BasePatchAward): items = ListType(ModelType(Item, required=True)) qualified = BooleanType() eligible = BooleanType() class PostAward(BasePostAward): pass

35.027027

98

0.759259

135

1,296

7.274074

0.392593

0.077393

0.076375

0.059063

0.336049

0.118126

0

0.000916

0.157407

1,296

36

99

36

0.898352

0

0.142857

0

0.054012

0

1

0.071429

false

0.035714

0.25

0

0.678571

0

null

0

null

0

1

0

6922080e0ef28e36cb9344b32948cabc43d991c9

4,448

py

Python

tests/test_exch_uniform.py

computationalmodelling/fidimag

07a275c897a44ad1e0d7e8ef563f10345fdc2a6e

[ "BSD-2-Clause" ]

53

2016-02-27T09:40:21.000Z

2022-01-19T21:37:44.000Z

tests/test_exch_uniform.py

computationalmodelling/fidimag

07a275c897a44ad1e0d7e8ef563f10345fdc2a6e

[ "BSD-2-Clause" ]

132

2016-02-26T13:18:58.000Z

2021-12-01T21:52:42.000Z

tests/test_exch_uniform.py

computationalmodelling/fidimag

07a275c897a44ad1e0d7e8ef563f10345fdc2a6e

[ "BSD-2-Clause" ]

32

2016-02-26T13:21:40.000Z

2022-03-08T08:54:51.000Z

from __future__ import print_function import numpy as np from fidimag.atomistic import Sim from fidimag.common import CuboidMesh from fidimag.atomistic import UniformExchange def init_m(pos): x, y, z = pos return (x - 0.5, y - 0.5, z - 0.5) def test_exch_1d(): """ Test the x component of the exchange field in a 1D mesh, with the spin ordering: 0 1 2 3 4 5 """ mesh = CuboidMesh(nx=5, ny=1, nz=1) sim = Sim(mesh) exch = UniformExchange(1) sim.add(exch) sim.set_m(init_m, normalise=False) field = exch.compute_field() assert field[0] == 1 assert field[1 * 3] == 2 assert field[2 * 3] == 4 assert field[3 * 3] == 6 assert field[4 * 3] == 3 assert np.max(field[2::3]) == 0 assert np.max(field[1::3]) == 0 def test_exch_1d_pbc(): mesh = CuboidMesh(nx=5, ny=1, nz=1, periodicity=(True, False, False)) sim = Sim(mesh) exch = UniformExchange(1) sim.add(exch) sim.set_m(init_m, normalise=False) field = exch.compute_field() assert field[0] == 1 + 4 assert field[3] == 2 assert field[6] == 4 assert field[9] == 6 assert field[12] == 3 + 0 assert np.max(field[2::3]) == 0 assert np.max(field[1::3]) == 0 def test_exch_2d(): mesh = CuboidMesh(nx=5, ny=2, nz=1) sim = Sim(mesh) exch = UniformExchange(1) sim.add(exch) sim.set_m(init_m, normalise=False) field = exch.compute_field() assert np.max(field[2::3]) == 0 assert field[0] == 1 assert field[3] == 2 + 1 assert field[6] == 1 + 2 + 3 assert field[9] == 2 + 3 + 4 assert field[12] == 3 + 4 def test_exch_2d_pbc2d(): """ Test the exchange field components in a 2D mesh with PBCs The mesh sites: 3 4 5 --> (0,1,0) (1,1,0) (2,1,0) y ^ 0 1 2 (0,0,0) (1,0,0) (2,0,0) | x --> The expected components are in increasing order along x """ mesh = CuboidMesh(nx=3, ny=2, nz=1, periodicity=(True, True, False)) print(mesh.neighbours) sim = Sim(mesh) exch = UniformExchange(1) sim.add(exch) sim.set_m(init_m, normalise=False) field = exch.compute_field() expected_x = np.array([3, 4, 5, 3, 4, 5]) expected_y = np.array([2, 2, 2, 2, 2, 2]) # Since the field ordering is now: fx1 fy1 fz1 fx2 ... # We extract the x components jumping in steps of 3 assert np.max(abs(field[::3] - expected_x)) == 0 # For the y component is similar, now we start at the 1th # entry and jump in steps of 3 assert np.max(abs(field[1::3] - expected_y)) == 0 # Similar fot he z component assert np.max(field[2::3]) == 0 def test_exch_3d(): """ Test the exchange field of the spins in this 3D mesh: bottom layer: 8 9 10 11 4 5 6 7 x 2 0 1 2 3 Assertions are according to the mx component of the spins, since J is set to 1 Spin components are given according to the (i, j) index position in the lattice: i lattice site [[ 0. 0. 0.] --> 0 j=0 [ 1. 0. 0.] --> 1 [ 2. 0. 0.] --> 2 [ 3. 0. 0.] --> 3 [ 0. 1. 0.] --> 4 j=1 [ 1. 1. 0.] ... Remember the field ordering: fx0, fy0, fz0, fx1, ... """ mesh = CuboidMesh(nx=4, ny=3, nz=2) sim = Sim(mesh) exch = UniformExchange(1) sim.add(exch) sim.set_m(init_m, normalise=False) field = exch.compute_field() # print field # Exchange from 0th spin assert field[0] == 1 # Exchange from 1st spin # spin: 2 0 5 13 # mx: 2 0 1 1 assert field[3] == 2 + 0 + 1 + 1 # Exchange from 2nd spin # spin: 3 1 6 14 # mx: 3 1 2 2 assert field[6] == 3 + 1 + 2 + 2 # ... assert field[9] == 2 + 3 + 3 assert field[4 * 3] == 1 assert field[5 * 3] == 5 assert field[6 * 3] == 10 assert field[7 * 3] == 11 def test_exch_energy_1d(): mesh = CuboidMesh(nx=2, ny=1, nz=1) sim = Sim(mesh) exch = UniformExchange(1.23) sim.add(exch) sim.set_m((0, 0, 1)) energy = exch.compute_energy() assert energy == -1.23 if __name__ == '__main__': # test_exch_1d() # test_exch_1d_pbc() # test_exch_2d() test_exch_2d_pbc2d() # test_exch_3d() # test_exch_energy_1d()

23.046632

77

0.546088

717

4,448

3.297071

0.175732

0.107022

0.037225

0.035533

0.409052

0.348562

0.315567

0.30753

0.282572

0.258037

0

0.088206

0.319469

4,448

192

78

23.166667

0.692765

0.317221

0

0.413793

0

0.002787

0

0.367816

1

0.08046

false

0

0.057471

0

0.149425

0.022989

0

null

0

null

0

1

0

6928610238b2e0cae526f79687ff41ce7a474164

4,882

py

Python

Competing_Algorithm/demo.py

ZhiQiu976/project-Indian-Buffet-Process

a4817550f2ca1778333066fa03ec6bb5b9cb4240

[ "MIT" ]

null

Competing_Algorithm/demo.py

ZhiQiu976/project-Indian-Buffet-Process

a4817550f2ca1778333066fa03ec6bb5b9cb4240

[ "MIT" ]

null

Competing_Algorithm/demo.py

ZhiQiu976/project-Indian-Buffet-Process

a4817550f2ca1778333066fa03ec6bb5b9cb4240

[ "MIT" ]

1

2020-04-30T17:26:26.000Z

"""Demo for latent factor model""" from __future__ import division import numpy as np import numpy.random as nr import matplotlib.pyplot as plt from IBPFM import IBPFM from utils.tracePlot import trace from utils.scaledimage import scaledimage N = 100 chain = 1000 K_finite = 6 # # read the keyboard input for the number of images # N = raw_input("Enter the number of noisy images for learning features: ") # try: # N = int(N) # except ValueError: # print "Not a number" # sys.exit('Try again') # # read the keyboard input for the number of MCMC chain # chain = raw_input("Enter the number of MCMC chain: ") # try: # chain = int(chain) # except ValueError: # print "Not a number" # sys.exit('Try again') # # read the keyboard input for the number of finite K # K_finite = raw_input("Enter the finite number (upper bound) of features K: ") # try: # K_finite = int(K_finite) # except ValueError: # print "Not a number" # sys.exit('Try again') # ------------------------------------------------------------------------------ # Model parameter (alpha, alpha_a, alpha_b) = (1., 1., 1.) (sigma_x, sigma_xa, sigma_xb) = (.5, 1., 1.) (sigma_a, sigma_aa, sigma_ab) = (1., 1., 1.) # ------------------------------------------------------------------------------ # Generate image data from the known features feature1 = np.array([[0,1,0,0,0,0],[1,1,1,0,0,0],[0,1,0,0,0,0],\ [0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0]]) feature2 = np.array([[0,0,0,1,1,1],[0,0,0,1,0,1],[0,0,0,1,1,1],\ [0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0]]) feature3 = np.array([[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],\ [1,0,0,0,0,0],[1,1,0,0,0,0],[1,1,1,0,0,0]]) feature4 = np.array([[0,0,0,0,0,0],[0,0,0,0,0,0],[0,0,0,0,0,0],\ [0,0,0,1,1,1],[0,0,0,0,1,0],[0,0,0,0,1,0]]) D = 36 f1 = feature1.reshape(D) f2 = feature2.reshape(D) f3 = feature3.reshape(D) f4 = feature4.reshape(D) trueWeights = np.vstack((f1, f2, f3, f4)) # ------------------------------------------------------------------------------ # Generate noisy image data K = 4 sig_x_true = 0.5 A = np.vstack((f1, f2, f3, f4)).astype(np.float) Z_orig = nr.binomial(1, 0.5, (N, K)).astype(np.float) V_orig = nr.normal(0, 1, size=(N, K)) # V_orig = nr.exponential(1, size=(N, K)) Z_orig = np.multiply(Z_orig, V_orig) X = np.dot(Z_orig, A) noise = nr.normal(0, sig_x_true, (N, D)) X += noise # ------------------------------------------------------------------------------ # Return MCMC result (K_save, alpha_save, sigma_x_save, sigma_a_save, loglikelihood_save, Z_save, A_save) = \ IBPFM(iteration=chain, data=X, upperbound_K=K_finite, alpha=(alpha, alpha_a, alpha_b), sigma_x=(sigma_x, sigma_xa, sigma_xb), sigma_a=(sigma_a, sigma_aa, sigma_ab), realvaluedZ=True, proposeNewfeature=True, updateAlpha=True, updateSigma_x=True, updateSigma_a=True, initZ=None, stdData=False) # Save trace plots trace(K_save, alpha_save, sigma_x_save, sigma_a_save, loglikelihood_save) # Save true latent feature plot (orig, sub) = plt.subplots(1, 4) for sa in sub.flatten(): sa.set_visible(False) orig.suptitle('True Latent Features') for (i, true) in enumerate(trueWeights): ax = sub[i] ax.set_visible(True) scaledimage(true.reshape(6, 6), pixwidth=3, ax=ax) orig.set_size_inches(13, 3) orig.savefig('Original_Latent_Features.png') plt.close() # Save some of example figures from data X examples = X[0:4, :] (ex, sub) = plt.subplots(1, 4) for sa in sub.flatten(): sa.set_visible(False) ex.suptitle('Image Examples') for (i, true) in enumerate(examples): ax = sub[i] ax.set_visible(True) scaledimage(true.reshape(6, 6), pixwidth=3, ax=ax) ex.set_size_inches(13, 3) ex.savefig('Image_Examples.png') plt.close() # Show and save result lastZ = Z_save[:, :, chain] mcount = (lastZ != 0).astype(np.int).sum(axis=0) index = np.where(mcount > 0) lastK = K_save[chain].astype(np.int) lastA = A_save[index, :, chain] A = lastA.reshape(len(index[0]), D) A_row = A.shape[0] for i in range(A_row): cur_row = A[i, :].tolist() abs_row = [abs(j) for j in cur_row] max_index = abs_row.index(max(abs_row)) if cur_row[max_index] < 0: A[i, :] = -np.array(cur_row) K = max(len(trueWeights), len(A)) (fig, subaxes) = plt.subplots(2, K) for sa in subaxes.flatten(): sa.set_visible(False) fig.suptitle('Ground truth (top) vs learned factors (bottom)') for (idx, trueFactor) in enumerate(trueWeights): ax = subaxes[0, idx] ax.set_visible(True) scaledimage(trueFactor.reshape(6, 6), pixwidth=3, ax=ax) for (idx, learnedFactor) in enumerate(A): ax = subaxes[1, idx] scaledimage(learnedFactor.reshape(6, 6), pixwidth=3, ax=ax) ax.set_visible(True) #fig.savefig("IBP_meanA.png") plt.show()

30.135802

88

0.599549

820

4,882

3.460976

0.213415

0.072586

0.09408

0.105708

0.388302

0.32382

0.26709

0.251233

0.239253

0.236082

0

0.055968

0.17288

4,882

161

89

30.322981

0.646855

0.262392

0

0.195876

0

0.035413

0.00787

0

1

0

false

0

0.072165

0

0.072165

0

null

0

null

0

1

0

6928d12a9070c21f90e40630002456e946b09b39

1,391

py

Python

ex3_1_dnn_mnist_cl.py

yoongon/keraspp

4950e2e78bfd19095b88fd3a1ca74ffedba819a5

[ "MIT" ]

null

ex3_1_dnn_mnist_cl.py

yoongon/keraspp

4950e2e78bfd19095b88fd3a1ca74ffedba819a5

[ "MIT" ]

null

ex3_1_dnn_mnist_cl.py

yoongon/keraspp

4950e2e78bfd19095b88fd3a1ca74ffedba819a5

[ "MIT" ]

null

# 기본 파라미터 설정 ######################### Nin = 784 Nh_l = [100, 50] number_of_class = 10 Nout = number_of_class # 분류 DNN 모델 구현 ######################## from keras import layers, models class DNN(models.Sequential): def __init__(self, Nin, Nh_l, Nout): super().__init__() self.add(layers.Dense(Nh_l[0], activation='relu', input_shape=(Nin,), name='Hidden-1')) self.add(layers.Dense(Nh_l[1], activation='relu', name='Hidden-2')) self.add(layers.Dense(Nout, activation='softmax')) self.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 데이터 준비 ############################## import numpy as np from keras import datasets from keras.utils import np_utils (X_train, y_train), (X_test, y_test) = datasets.mnist.load_data() Y_train = np_utils.to_categorical(y_train) Y_test = np_utils.to_categorical(y_test) L, W, H = X_train.shape X_train = X_train.reshape(-1, W * H) X_test = X_test.reshape(-1, W * H) X_train = X_train / 255.0 X_test = X_test / 255.0 # 분류 DNN 학습 및 테스팅 #################### model = DNN(Nin, Nh_l, Nout) history = model.fit(X_train, Y_train, epochs=5, batch_size=100, validation_split=0.2) performace_test = model.evaluate(X_test, Y_test, batch_size=100) print('Test Loss and Accuracy ->', performace_test)

33.119048

95

0.608914

205

1,391

3.882927

0.404878

0.052764

0.048995

0.067839

0.133166

0.052764

0

0.029623

0.199137

1,391

41

96

33.926829

0.684919

0.035226

0

0.074373

0.019402

0

1

0.034483

false

0

0.137931

0

0.206897

0.034483

0

null

0

null

0

1

0

69301dd6f35763c4b558452e2a75490a7e95b5cb

1,176

py

Python

ChestnutPatcher/inject-lib.py

chestnut-sandbox/Chestnut

b42b9eb902e0928e8b549339788f83bb009290c1

[ "Zlib" ]

7

2020-12-08T02:00:14.000Z

2021-05-10T13:12:35.000Z

ChestnutPatcher/inject-lib.py

cc0x1f/Chestnut

b42b9eb902e0928e8b549339788f83bb009290c1

[ "Zlib" ]

2

2022-01-03T13:51:48.000Z

2022-01-26T15:42:44.000Z

ChestnutPatcher/inject-lib.py

cc0x1f/Chestnut

b42b9eb902e0928e8b549339788f83bb009290c1

[ "Zlib" ]

2

2021-05-15T03:06:07.000Z

2021-08-06T18:11:35.000Z

import sys import lief import json import struct import os def filter_file(fname): f = fname.replace("/", "_") + ".json" if f[0] == ".": f = f[1:] return f def main(fname): # load filter ffname = "policy_%s" % filter_file(fname) filters = None try: filters = json.loads(open(ffname).read()) except: print("[-] Could not load filter file %s" % ffname) return 1 print("[+] Allowed syscalls: %d" % len(filters["syscalls"])) # inject sandboxing library binary = lief.parse(fname) binary.add_library("libchestnut.so") # add seccomp library as well binary.add_library("libseccomp.so.2") binary.write("%s_patched" % fname) with open("%s_patched" % fname, "ab") as elf: filter_data = json.dumps(filters).encode() elf.write(filter_data) elf.write(struct.pack("I", len(filter_data))) os.chmod("%s_patched" % fname, 0o755); #print(binary) print("[+] Saved patched binary as %s_patched" % fname) if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: %s <binary>" % sys.argv[0]) else: main(sys.argv[1])

25.021277

64

0.590986

154

1,176

4.376623

0.422078

0.047478

0.077151

0

0.012514

0.252551

1,176

46

65

25.565217

0.754266

0.066327

0

0.190302

0

1

0.058824

false

0

0.147059

0

0.264706

0.117647

0

null

0

null

0

1

0

69331d5c6bc354bd17e9f9d5696da9f4cbff069b

5,826

py

Python

scoff/parsers/generic.py

brunosmmm/scoff

e1a0b5f98dd9e60f41f3f7cfcda9038ffd80e138

[ "MIT" ]

null

scoff/parsers/generic.py

brunosmmm/scoff

e1a0b5f98dd9e60f41f3f7cfcda9038ffd80e138

[ "MIT" ]

1

2020-03-20T13:57:52.000Z

2021-03-11T17:25:25.000Z

scoff/parsers/generic.py

brunosmmm/scoff

e1a0b5f98dd9e60f41f3f7cfcda9038ffd80e138

[ "MIT" ]

null

"""Generic regex-based parser.""" import re from collections import deque from typing import Union, Any, List, Deque, Tuple, Dict, Callable from scoff.parsers.linematch import MatcherError, LineMatcher EMPTY_LINE = re.compile(b"\s*$") class ParserError(Exception): """Parser error.""" class DataParser: """Simple data parser. Tokens are regular expression-based """ def __init__( self, initial_state: Union[str, int, None] = None, consume_spaces: bool = False, **kwargs, ): """Initialize. :param initial_state: Initial state of the parser :param consume_spaces: Consume stray space characters """ self._state_hooks = {} super().__init__(**kwargs) self._state_stack: Deque[Union[str, int, None]] = deque() self._state = initial_state self._consume = consume_spaces self._current_position = 1 self._current_line = 1 self._data = None self._abort = False @property def state(self): """Get current state.""" return self._state def add_state_hook(self, state: Union[str, int], hook: Callable): """Add state hook (callback). A callback will be called when the parser reaches a specified state. :param state: The parser state to add a callback to :param hook: The callback to be added """ if not callable(hook): raise TypeError("hook must be callable") if state not in self.states: print(self.states) raise ParserError(f"unknown state '{state}'") if state not in self._state_hooks: self._state_hooks[state] = {hook} else: self._state_hooks[state] |= {hook} def _handle_match(self, candidate): """Handle candidate match.""" def _handle_options(self, **options: Any): """Handle candidate options.""" def _try_parse( self, candidates: List[LineMatcher], position: int ) -> Tuple[int, LineMatcher, Dict[str, str]]: if self._consume: m = EMPTY_LINE.match(self._data, position) if m is not None: # an empty line, consume return (m.span()[1], None, None) for candidate in candidates: try: if not isinstance(candidate, LineMatcher): raise TypeError("candidate must be LineMatcher object") size, fields = candidate.parse_first(self._data, position) except MatcherError: continue options = candidate.options.copy() change_state = options.pop("change_state", None) push_state = options.pop("push_state", None) pop_state = options.pop("pop_state", None) if change_state is not None: self._change_state(change_state) elif push_state is not None: self._push_state(push_state) elif pop_state is not None: self._pop_state(pop_state) # handle other options self._handle_options(**options) # handle other custom options self._handle_match(candidate) # advance position self._current_position += size # advance line self._current_line += ( self._data.count(b"\n", position, position + size) + 1 ) return (size, candidate, fields) raise ParserError("could not parse data") def _current_state_function(self, position: int) -> int: if not hasattr(self, "_state_{}".format(self._state)): raise RuntimeError(f"in unknown state: {self._state}") size, stmt, fields = getattr(self, "_state_{}".format(self._state))( position ) # call hooks if self._state in self._state_hooks: for hook in self._state_hooks[self._state]: hook(self._state, stmt, fields) return size def _abort_parser(self): """Stop parsing.""" self._abort = True @property def current_pos(self): """Get current position.""" return self._current_position @property def current_line(self): """Get current line.""" return self._current_line @property def states(self): """Get possible states.""" return [ attr_name.split("_")[2] for attr_name in dir(self) if attr_name.startswith("_state") ] def parse(self, data: str) -> int: """Parse data. :param data: Textual data to be parsed :return: Current position in data """ self._data = data.encode() self._current_position = 1 self._current_line = 1 current_pos = 0 while current_pos < len(data): if self._abort is True: break size = self._current_state_function(current_pos) # consume data current_pos += size + 1 return current_pos def _state_change_handler(self, old_state, new_state): """State change handler.""" def _change_state(self, new_state): """Change state.""" old_state = self._state self._state = new_state # call state change handler self._state_change_handler(old_state, new_state) def _push_state(self, new_state): """Push into state stack and change state.""" self._state_stack.append(self._state) self._change_state(new_state) def _pop_state(self, count): """Pop from state stack and change state.""" for num in range(count): state = self._state_stack.popleft() self._change_state(state)

31.15508

76

0.583934

667

5,826

4.874063

0.230885

0.066441

0.025838

0.014765

0.103045

0.037527

0.022147

0

0.002265

0.317885

5,826

186

77

31.322581

0.815803

0.154652

0

0.068376

0

0.040717

0

1

0.136752

false

0

0.034188

0

0.25641

0.008547

0

null

0

null

0

1

0

6933f6dc14a4268b062d6716b8b6fce13e8b8ff9

34,279

py

Python

cfg/model/CfgModel.py

sdnellen/ordt-config-tool

30cc7342c5bc0f574b2a4a8d207230e1fa527615

[ "Apache-2.0" ]

1

2019-12-06T19:11:28.000Z

utils/cfgtool/cfg/model/CfgModel.py

mytoys/open-register-design-tool

5d6dea268f77546a9a786a16603f50e974d87050

[ "Apache-2.0" ]

null

utils/cfgtool/cfg/model/CfgModel.py

mytoys/open-register-design-tool

5d6dea268f77546a9a786a16603f50e974d87050

[ "Apache-2.0" ]

null

#!/usr/bin/python3 ''' @author: snellenbach Config sequence model ''' from enum import Enum, unique import re from cfg.model.RegModelWrapper import RegModelWrapper from cfg.model.Utils import MsgUtils from cfg.output.OutBuilder import OutBuilder as ob # ------- config model node classes class BaseCfgNode: _nodeStack = [] # stack of active config nodes for auto-add _outBuilder = None def __init__(self, sourceAstNode=None, comment=''): self.sourceAstNode = sourceAstNode self.comment = comment self.children = [] self.parent = None self.allowedTags = set() # set of allowed versions for this level (parser allows currently allows in class, method) # add this node to parent (top of stack) if __class__._nodeStack: self.parent = __class__._nodeStack[-1] self.parent.addChild(self) def addChild(self, child): self.children.append(child) def popChild(self): ''' pop last added child from this node ''' if self.children: self.children.pop() def display(self, indent = 0): ''' display config model node info recursively ''' print(' '*indent + 'base:') for child in self.children: child.display(indent+1) @staticmethod def finishNode(omit): ''' Pop current node from the active model stack. Optionally, remove this node if omit is set. ''' __class__.popNode() if omit: parent = __class__._nodeStack[-1] parent.popChild() @staticmethod def popNode(): ''' pop cfg node from top of the stack ''' return __class__._nodeStack.pop() @staticmethod def peekNode(): ''' return cfg node at top of the stack ''' return __class__._nodeStack[-1] def hierDisplay(self, indent, s): ''' display config model node info recursively ''' print(' '*indent + s) for child in self.children: child.display(indent+1) def resolvePaths(self): ''' resolve all paths in this config model node info recursively ''' for child in self.children: child.resolvePaths() def setOutBuilder(self, outBuilder): ''' set specified output builder ''' #print(f'BaseCfgNode setOutBuilder: called in {type(self)}, outBuilder type={type(outBuilder)}') BaseCfgNode._outBuilder = outBuilder def generateOutput(self): ''' generate specified output for this config model recursively ''' #print(f'BaseCfgNode generateOutput: called in {type(self)}') for child in self.children: child.generateOutput() class HierCfgNode(BaseCfgNode): ''' hierarchical node (pushed to node stack on create) ''' def __init__(self, sourceAstNode = None, comment=''): BaseCfgNode.__init__(self, sourceAstNode, comment) # append this node to the stack __class__._nodeStack.append(self) self.vars = {} # dict of vars defined in this node scope def whatami(self): return 'unspecified hierarchy' def findVar(self, varName, allowInputs = True): ''' find a variable by name traversing from current node thru ancestors ''' if self.vars.__contains__(varName): retVar = self.vars[varName] if allowInputs or (type(retVar) is not CfgInputVariable): return retVar MsgUtils.errorExit('input variable ' + varName + ' can not be assigned a value.') return None elif self.parent is None: return None else: return self.parent.findVar(varName) def getInputList(self): return {k: v for k, v in self.vars.items() if type(v) is CfgInputVariable} def verifyInputParms(self, inputListStr, callingNode): ''' check that a list of call parameter strings matches inputs for this hier and return the list of resolved inputs ''' if type(inputListStr) is not str: MsgUtils.errorExit(f'misformed input list found when in call of {self.whatami()} {self.name}') inputList = [] if not inputListStr else inputListStr.split(',') inputCount = len(inputList) inputParms = self.getInputList() inputParmCount = len(inputParms) #print(f"HierCfgNode verifyInputParms: inputList={inputList}, in len={inputCount}, vars=({', '.join(str(e) for e in inputParms.values())}), parm len={inputParmCount}, callNode type={type(callingNode)}") if inputCount != inputParmCount: MsgUtils.errorExit(f'incorrect number of input parameters (found {inputCount}, expected {inputParmCount}) in call of {self.whatami()} {self.name}') # loop and resolve inputs CfgVariable.resolveRhsExpression(className, CfgClassNode, False, True) resolvedInputList = [] for inVal, inParm in zip(inputList, inputParms.values()): resolvedInputList.append(CfgVariable.resolveRhsExpression(inVal, inParm.vartype, True, True)) return resolvedInputList class CfgClassNode(HierCfgNode): _classes = {} _current = None def __init__(self, name, sourceAstNode = None, comment=''): HierCfgNode.__init__(self, sourceAstNode, comment) self.name = name self.methods = {} __class__._classes[self.name] = self __class__._current = self #print('creating class node, name=', self.name) def whatami(self): return 'class' @staticmethod def getCurrent(): ''' return last created CfgClassNode ''' return __class__._current @staticmethod def findClass(className): ''' return a CfgClassNode by name ''' return None if className not in __class__._classes else __class__._classes[className] def findMethod(self, methodName): ''' return a CfgMethodNode in this class by name ''' return None if methodName not in self.methods else self.methods[methodName] def display(self, indent = 0): inParms = self.getInputList() self.hierDisplay(indent, f"class: {self.name}, vars=({', '.join(str(e) for e in self.vars.values())}), inputs=({', '.join(str(e) for e in inParms.values())}), allowed versions='{self.allowedTags}") def generateOutput(self): ''' generate specified output for this class node ''' #print(f'CfgClassNode generateOutput: called in {type(self)}') BaseCfgNode._outBuilder.enterClass(self) for child in self.children: child.generateOutput() BaseCfgNode._outBuilder.exitClass(self) class CfgMethodNode(HierCfgNode): def __init__(self, name, sourceAstNode = None, comment=''): HierCfgNode.__init__(self, sourceAstNode, comment) self.name = name self.args = [] # add method to dict in current class scope parent = BaseCfgNode._nodeStack[-2] parent.methods[self.name] = self #print('creating method node, name=', self.name) def whatami(self): return 'method' def display(self, indent = 0): inParms = self.getInputList() self.hierDisplay(indent, f"method: {self.name}, vars=({', '.join(str(e) for e in self.vars.values())}), inputs=({', '.join(str(e) for e in inParms.values())})") def generateOutput(self): ''' generate specified output for this method node ''' #print(f'CfgMethodNode generateOutput: called in {type(self)}') BaseCfgNode._outBuilder.enterMethod(self) for child in self.children: child.generateOutput() BaseCfgNode._outBuilder.exitMethod(self) @unique class ConfigAssignType(Enum): UNSUPPORTED = 0 EQ = 1 def isSupported(self): return type(self) is not ConfigAssignType.UNSUPPORTED @staticmethod def resolve(opStr): ''' convert a string to ConfigAssignType ''' if type(opStr) is ConfigAssignType: # if type is already correct, just return input return opStr if opStr == '=': return ConfigAssignType.EQ else: return ConfigAssignType.UNSUPPORTED class CfgAssign(BaseCfgNode): def __init__(self, left=None, op=ConfigAssignType.UNSUPPORTED, right=None, sourceAstNode = None): BaseCfgNode.__init__(self, sourceAstNode) self.op = ConfigAssignType.resolve(op) self.left = left # TODO - resolve here and remove checks from builder or allow default var create? self.right = right # maybe pass target type into assign? or verify type match? def isValid(self): if self.op.isSupported() and (self.left is not None) and (self.right is not None): return True return False def isRead(self): ''' return True if assign involves a reg read ''' return (type(self.right) is CfgReadNode) def display(self, indent = 0): self.hierDisplay(indent, f'assign: {self.left} {self.op.name} {self.right}') def resolvePaths(self): if self.isRead(): self.right.resolvePaths() class CfgMethodCall(BaseCfgNode): def __init__(self, className, methodName, parmList, sourceAstNode = None): BaseCfgNode.__init__(self, sourceAstNode) # if className specified in call path resolve class as a variable, else use current class if className: cfgClassVar = CfgVariable.resolveRhsExpression(className, CfgClassNode, False, True) # find the class variable #self.cfgClass = CfgClassNode.getCurrent() # TODO add findVar option for non-none className self.cfgClass = CfgClassNode.findClass(cfgClassVar.val[0].name) # TODO - saved call name structure shoul be fixed else: self.cfgClass = CfgClassNode.getCurrent() #if not cfgClass: # MsgUtils.errorExit('unable to resolve cfgClass ' + str(className) + ' in call of method ' + methodName) self.cfgMethod = self.cfgClass.findMethod(methodName) if not self.cfgMethod: MsgUtils.errorExit(f'unable to resolve method {methodName} in cfgClass {self.cfgClass.name}') self.parmList = self.cfgMethod.verifyInputParms(parmList, self.parent) def display(self, indent = 0): self.hierDisplay(indent, f'call: cfgClass={self.cfgClass.name}, method={self.cfgMethod.name}, parms={self.parmList}') class CfgCaseNode(HierCfgNode): def __init__(self, selectVar, sourceAstNode = None): HierCfgNode.__init__(self, sourceAstNode) self.selectVar = HierCfgNode.findVar(self, selectVar) #print('creating case node, select var=' + str(self.selectVar)) def display(self, indent = 0): self.hierDisplay(indent, f'case: select var={self.selectVar}') class CfgCaseBlockNode(HierCfgNode): _currentChoices = set() # init current choice set def __init__(self, sourceAstNode = None): HierCfgNode.__init__(self, sourceAstNode) self.selectVals = set(__class__._currentChoices) # copy current set of choices __class__._currentChoices.clear() # clear current choices #print('creating case block node, choices=' + str(self.selectVals)) def display(self, indent = 0): self.hierDisplay(indent, f'case block: choices={self.selectVals}') @staticmethod def addChoice(choiceName): __class__._currentChoices.add(choiceName) class CfgNumericForNode(HierCfgNode): def __init__(self, name, rangeStart, rangeEnd, sourceAstNode = None): HierCfgNode.__init__(self, sourceAstNode) self.forVar = CfgVariable(name, CfgNumDataType) self.rangeStart = CfgVariable.resolveRhsExpression(rangeStart, CfgNumDataType) self.rangeEnd = CfgVariable.resolveRhsExpression(rangeEnd, CfgNumDataType) #print('creating numeric for loop node, iterator var=' + str(self.forVar) + ' rangeStart=' + str(self.rangeStart) + ' rangeEnd=' + str(self.rangeEnd)) def display(self, indent = 0): self.hierDisplay(indent, f'for (numeric): iterator={self.forVar} rangeStart={self.rangeStart} rangeEnd={self.rangeEnd}') class CfgPathForNode(HierCfgNode): def __init__(self, name, path, sourceAstNode = None): HierCfgNode.__init__(self, sourceAstNode) self.forVar = CfgVariable(name, CfgPathDataType) self.path = CfgVariable.resolveRhsExpression(path, CfgPathDataType) # create path range self.forVar.val = self.path # assign path to loop var so full path prefix can be extracted recursively using var #print('creating path for loop node, iterator var=' + str(self.forVar) + ' path=' + str(self.path)) def display(self, indent = 0): self.hierDisplay(indent, f'for (path): iterator={self.forVar}, range={self.path}') def resolvePaths(self): ''' resolve paths in this for node ''' print(f'resolve CfgPathForNode path: {self.path}') # TODO if type(self.path) is CfgPathDataType: self.path.resolvePath(self.allowedTags) #TODO - any checks for a var?, how is version resolve handled? # resolve paths in child nodes for child in self.children: child.resolvePaths() class CfgPrintNode(BaseCfgNode): def __init__(self, form, form_vars, sourceAstNode = None): BaseCfgNode.__init__(self, sourceAstNode) self.form = form # form can also be a list of comma separated args self.form_vars = form_vars #print('creating display node, form=', self.form, 'form_vars=', self.form_vars) def display(self, indent = 0): self.hierDisplay(indent, 'print: ' + str(self.form) + ', vars=' + str(self.form_vars)) class CfgWaitNode(BaseCfgNode): def __init__(self, time, sourceAstNode = None): BaseCfgNode.__init__(self, sourceAstNode) self.time = time # time in ms #print('creating wait node, time=', self.time) def display(self, indent = 0): self.hierDisplay(indent, 'wait: ' + str(self.time)) class CfgWriteNode(BaseCfgNode): def __init__(self, path, value, wtype, isRmw = False, sourceAstNode = None): BaseCfgNode.__init__(self, sourceAstNode) self.path = CfgVariable.resolveRhsExpression(path, CfgPathDataType) self.wtype = CfgPathHierType.resolve(wtype) self.value = CfgVariable.resolveRhsExpression(value, CfgNumDataType) self.isRmw = isRmw #print('creating write node, path=', str(self.path), 'value=', str(self.value)) def display(self, indent = 0): self.hierDisplay(indent, 'write: ' + str(self.path) + ', wtype=' + str(self.wtype) + ', value=' + str(self.value) + ', rmw=' + str(self.isRmw)) pass def resolvePaths(self): ''' resolve paths in this write node ''' print(f'resolve CfgWriteNode path: {self.path}, wtype: {self.wtype}, rwm: {self.isRmw} --- self.path type={type(self.path)}') # TODO if type(self.path) is CfgPathDataType: self.path.resolvePath(self.allowedTags, self.wtype) #TODO - any checks for a var?, how is version resolve handled? def generateOutput(self): ''' generate specified output for this write node ''' #print(f'CfgWriteNode generateOutput: called in {type(self)}') if self.wtype.isReg(): BaseCfgNode._outBuilder.doRegWrite(self) else: BaseCfgNode._outBuilder.doFieldWrite(self) class CfgWhileNode(HierCfgNode): def __init__(self, compare, delay = 1, timeout = None, sourceAstNode = None): HierCfgNode.__init__(self, sourceAstNode) self.compare = compare self.delay = delay self.timeout = timeout #print('creating poll node, compare=', self.compare, 'delay=', self.delay) CfgWaitNode(self.delay) def display(self, indent = 0): prefix = 'poll ' if self.compare.isPoll() else '' self.hierDisplay(indent, prefix + 'while: ' + str(self.compare) + ' timeout=' + str(self.timeout)) def isPoll(self): ''' return True if compare involves a reg read ''' return self.compare.isPoll() def resolvePaths(self): ''' resolve paths in this for while node ''' if self.isPoll(): self.compare.resolvePaths() for child in self.children: child.resolvePaths() # ------- config model support classes (not BaseCfgNode children) @unique class CfgPathHierType(Enum): UNKNOWN = 0 REGSET = 1 REG = 2 FIELDSET = 3 FIELD = 4 @staticmethod def resolve(hierStr): ''' convert a string to CfgPathHierType ''' if type(hierStr) is CfgPathHierType: # if type is already correct, just return input return hierStr if 'RegSet' in hierStr: return CfgPathHierType.REGSET elif 'FieldSet' in hierStr: return CfgPathHierType.FIELDSET elif 'Reg' in hierStr: return CfgPathHierType.REG elif 'Field' in hierStr: return CfgPathHierType.FIELD else: return CfgPathHierType.UNKNOWN def isReg(self): return self is CfgPathHierType.REG def matchesRegModelType(self, regModType): if self is CfgPathHierType.UNKNOWN: return True #print(f' -> CfgPathHierType matchesRegModelType: self type={self.name}, regModType={regModType.name}') # TODO if self.name == regModType.name: return True return False class CfgReadNode(): def __init__(self, path, rtype = CfgPathHierType.UNKNOWN, sourceAstNode = None): self.path = CfgVariable.resolveRhsExpression(path, CfgPathDataType) self.rtype = CfgPathHierType.resolve(rtype) self.sourceAstNode = sourceAstNode # TODO - change to srcInfo #print('creating read node, path=', self.path) def __str__(self): return f'read {self.path}, rtype={self.rtype}' def resolvePaths(self): ''' resolve paths in this read ''' print(f'resolve CfgReadNode path: {self.path}, rtype={self.rtype}') # TODO if type(self.path) is CfgPathDataType: self.path.resolvePath(set(), self.rtype) # read node has no allowed tag override, TODO - any checks for a var?, how is version resolve handled? # ------- config model data classes class CfgDataType(): def __init__(self): pass def isValid(self): return hasattr(self, 'val') and (self.val is not None) class CfgBoolDataType(CfgDataType): def __init__(self): pass class CfgNumDataType(CfgDataType): def __init__(self, s): self.size = None self.hasSize = False intval = __class__.strToInt(s) if intval is not None: self.val = intval @staticmethod def strToInt(s): ''' convert str to int if possible, else return None ''' try: out = int(s, 0) return out except ValueError: return None def __str__(self): return str(self.val) + (('(size=' + str(self.size) + ')') if self.size else '') if self.isValid() else 'invalid num' def needsSize(self): return self.hasSize and self.size is None class CfgEnumDataType(CfgNumDataType): # FIXME use separate type def __init__(self): pass class CfgPathDataElement(): def __init__(self, pelemstr): self.name = None # invalid if name is None self.start = None self.end = None self.isIndexed = False self.hasRange = False # is element indexed with start not equal to end self.annotations = {} if '[' in pelemstr: # detect an array self.isIndexed = True pat = re.compile('(\\w+)\\s*\\[(.*)\\]') mat = pat.match(pelemstr) if mat: self.name = mat.group(1) arraystr = mat.group(2) if ':' in arraystr: self.hasRange = True pat = re.compile('(\\w+|\\*)\\s*:\\s*(\\w+|\\*)') mat = pat.match(arraystr) if mat: leftstr = mat.group(1) rightstr = mat.group(2) if leftstr == '*': self.hasRange = True else: self.start = leftstr if rightstr == '*': self.hasRange = True else: self.end = rightstr #else: # print('CfgPathDataElement array match failed for s=' + arraystr) elif '*' in arraystr: # detect full range wildcard self.hasRange = True else: self.start = arraystr # single index case self.end = arraystr else: self.name = pelemstr # scalar, so just save the name def isVar(self): ''' return true if this path element is a path variable ''' return hasattr(self, 'baseVar') def isRootVar(self): ''' return true if this path element is a path variable representing root of the reg model ''' return self.isVar() and (self.name == 'root') def needsResolution(self): return self.isIndexed and ((self.start is None) or (self.end is None)) def getElementString(self, unrollBase, leftIdx, rightIdx=None): if unrollBase and self.isVar() and not self.isRootVar(): return self.baseVar.val.genFullPathStr() if not self.isIndexed: return self.name if not rightIdx or (rightIdx == leftIdx): return f'{self.name}[{leftIdx}]' return f'{self.name}[{leftIdx}:{rightIdx}]' def getFullElementString(self): # TODO ''' return full element string ''' startStr = str(self.start) if self.start else '*' endStr = str(self.end) if self.end else '*' return self.getElementString(True, startStr, endStr) def getRawElementString(self): ''' return raw element string ''' startStr = str(self.start) if self.start else '*' endStr = str(self.end) if self.end else '*' return self.getElementString(False, startStr, endStr) def getSampleElementString(self): ''' return sample element string for model lookup with indices set to 0 ''' return self.getElementString(True, 0) def __str__(self): return self.getRawElementString() class CfgPathDataType(CfgDataType): def __init__(self, pathstr): self.htype = CfgPathHierType.UNKNOWN # resolved path type is unknown by default self.call = None # default to no call basepathstr = '' if '(' in pathstr: # detect a call and remove from path pat = re.compile('(.*)\\.(\\w+)') mat = pat.match(pathstr) if mat: basepathstr = mat.group(1) self.call = mat.group(2) #print(f'found call match path={self.val}, call={self.call}') else: basepathstr = pathstr # TODO - store as path elem tuples? also TODO allow range wildcards # create a list of path elements self.val = [] newlist = basepathstr.split('.') for elemstr in newlist: elem = CfgPathDataElement(elemstr) self.val.append(elem) # check for valid path var extract if not self.val: MsgUtils.errorExit(f'unable create path from string={pathstr}') firstPathElement = self.getBasePathElem() # check for valid path base variable baseVar = CfgVariable.resolveLhsExpression(firstPathElement.name, CfgPathDataType, False, False) # check for existing base path variable if not baseVar: MsgUtils.errorExit(f'unable to resolve root of path {pathstr}') firstPathElement.baseVar = baseVar # save the referenced path variable in first element def genFullPathStr(self): ''' return path with base var unrolled ''' return '.'.join([ elem.getFullElementString() for elem in self.getPathList() ]) def genRawPathStr(self): ''' return raw path (no base var unroll) ''' return '.'.join([ elem.getRawElementString() for elem in self.getPathList() ]) def genSamplePathStr(self): ''' return sample path for model lookup with all indices set to 0 ''' return '.'.join([ elem.getSampleElementString() for elem in self.getPathList() ]) def hasCall(self): return self.call is not None def setRegset(self): self.htype = CfgPathHierType.REGSET def setReg(self): self.htype = CfgPathHierType.REG def setFieldset(self): self.htype = CfgPathHierType.FIELDSET def setField(self): self.htype = CfgPathHierType.FIELD def getBasePathElem(self): ''' return the base path element ''' return self.getPathList()[0] def getBasePathVar(self): ''' return the base path variable ''' return self.getBasePathElem().baseVar def needsResolution(self): if not self.getBasePathVar(): # or self.getBasePath().needsResolution(): # TODO - need variable needsResolution method? return True for elem in self.getPathList(): # check to see if any path elems are unresolved if elem.needsResolution(): return True return False def isMultiPath(self): for elem in self.getPathList(): # check to see if any path elems have more than single element range if elem.hasRange: return True return False def resolvePath(self, allowedTags, targetType=CfgPathHierType.UNKNOWN): # TODO also pass in allowedTags ''' resolve path type and any path index wildcards by referencing the regmodel ''' print(f' -> resolvePath CfgPathDataType raw path: {self} full path: {self.genFullPathStr()} sample path: {self.genSamplePathStr()}') # TODO regModel = RegModelWrapper.getRegModelRoot() if not regModel: if self.needsResolution(): MsgUtils.errorExit(f'Path {self} has unresolved info, but no register model is defined.') return # if no model and resolved we're done # extract valid version tags and annotate path elements for each validTags = RegModelWrapper.getValidTags(allowedTags) print(f' -> resolvePath CfgPathDataType: allowedTags={allowedTags}, regmod tags: {RegModelWrapper.getRegModelTags()} valid tags: {validTags}') # TODO for tag in validTags: plist = regModel.get_path_instance_list(tag, self.genSamplePathStr()) if 'error' in plist: MsgUtils.errorExit(f'Path {self.genRawPathStr()} was not found in register model using tag="{tag}".') if not targetType.matchesRegModelType(plist['type']): # check that path type returned from model matches target MsgUtils.errorExit(f'Expected type of path {self.genRawPathStr()} ({targetType}) does not match returned register model type ({plist["type"]}).') # TODO - check that MultPath elems are allowed self.annotatePath(tag, plist['instances']) #print(f' -> resolvePath CfgPathDataType model returns: {plist}') def annotatePath(self, tag, regModelPath): # extract the full path by expanding lead path vars expandedPath = self.getExpandedPathList() #print(f' -> CfgPathDataType annotatePath: this path len={len(self.getPathList())}, expanded path len={len(expandedPath)}, regmod path len={len(regModelPath)}, path={regModelPath}') if len(expandedPath) != len(regModelPath): MsgUtils.errorExit(f'Path {self.genRawPathStr()} does not match form of returned register model path.') # now loop and append regmodel info to local (non expanded) path elements localIndex = len(expandedPath) - len(self.getPathList()) for pathElem, regModElem in zip(self.getPathList(), regModelPath[localIndex:]): # only annotate local path elements print(f' -> CfgPathDataType annotatePath: element annotation, tag={tag}, elem={pathElem.name}, mod elem type={type(regModElem)}') annotation = RegModelWrapper.createAnnotation(regModElem) pathElem.annotations[tag] = annotation # annotate pathElem by tag def getPathList(self): ''' return non-expanded path list ''' return self.val def getExpandedPathList(self): ''' generate full path list by unrolling base path variable ''' if self.getBasePathElem().isRootVar(): return self.getPathList() else: if len(self.getPathList()) > 1: return self.getBasePathElem().baseVar.val.getExpandedPathList() + self.getPathList()[1:] # remove lead element and append remainder else: return self.getBasePathElem().val.getExpandedPathList() def __str__(self): return f'ptype={self.htype.name}, path={self.genRawPathStr()}, needsResolution={self.needsResolution()}' # ------- variable classes class CfgVariable: def __init__(self, name, vartype = CfgNumDataType): self.name = name self.vartype = vartype self.val = None # add var in current scope parent = BaseCfgNode._nodeStack[-1] if parent.findVar(self.name): MsgUtils.errorExit('variable ' + self.name + ' is already defined.') if not name.isalnum(): MsgUtils.errorExit('variable name ' + self.name + ' is not valid.') parent.vars[self.name] = self #print (f'--- cfg_model CfgVariable: adding var {self.name}, parent type is {type(parent)}') def __str__(self): return self.vartype.__name__ + ' ' + self.name @staticmethod def resolveRhsExpression(inVal, targetVarType, allowInstCreate = True, exitOnFail = True): # targetVarType is valid CfgDataType ''' given an unknown rhs expression, return an existing variable or instance (new from str or existing) of specified target data type ''' if type(inVal) is targetVarType: # already target type so done return inVal if (type(inVal) is CfgVariable) and (inVal.vartype is targetVarType): # already a variable so done return inVal if type(inVal) is str: # try to find an existing variable foundVar = HierCfgNode.peekNode().findVar(inVal) if (foundVar is not None) and (foundVar.vartype is targetVarType): return foundVar # else try creating new target instance if allowInstCreate: newVal = targetVarType(inVal) if newVal.isValid(): return newVal if exitOnFail: MsgUtils.errorExit('unable to resolve rhs expression ' + str(inVal) + ' to a value or variable.') @staticmethod def resolveLhsExpression(inVar, targetVarType, allowVarCreate = True, exitOnFail = True): # targetVarType is valid CfgDataType ''' given an unknown lhs expression, return an existing variable or create a new variable of specified target data type from str ''' if (type(inVar) is CfgVariable) and (inVar.vartype is targetVarType): # already a variable so done return inVar if type(inVar) is str: # try to find an existing (non-input) variable foundVar = HierCfgNode.peekNode().findVar(inVar, False) # input variables are not allowed on lhs if (foundVar is not None) and (foundVar.vartype is targetVarType): return foundVar # else create a new var of target type if allowVarCreate: return CfgVariable(inVar, targetVarType) if exitOnFail: MsgUtils.errorExit('unable to resolve lhs expression ' + str(inVar) + ' to a variable.') class CfgInputVariable(CfgVariable): def __str__(self): return 'input ' + self.vartype.__name__ + ' ' + self.name # ------- config model compare class @unique class ConfigCompareType(Enum): UNSUPPORTED = 0 EQ = 1 NE = 2 GT = 3 LT = 4 GE = 5 LE = 6 def isSupported(self): return type(self) is not ConfigCompareType.UNSUPPORTED @staticmethod def resolve(opStr): ''' convert a string to ConfigCompareType ''' if type(opStr) is ConfigCompareType: # if type is already correct, just return input return opStr if opStr == '==': return ConfigCompareType.EQ elif opStr == '!=': return ConfigCompareType.NE elif opStr == '>': return ConfigCompareType.GT elif opStr == '<': return ConfigCompareType.LT elif opStr == '>=': return ConfigCompareType.GE elif opStr == '<=': return ConfigCompareType.LE else : return ConfigCompareType.UNSUPPORTED class CfgCompare(): def __init__(self, left=None, op=ConfigCompareType.UNSUPPORTED, right=None): self.op = op if type(op) is ConfigCompareType else ConfigCompareType.resolve(op) self.left = left if type(left) is CfgReadNode else left # TODO - extract into val or variable self.right = right if type(right) is CfgReadNode else right # TODO - extract into val or variable def isValid(self): if self.op.isSupported() and (self.left is not None) and (self.right is not None): return True return False def leftIsPoll(self): return type(self.left) is CfgReadNode def rightIsPoll(self): return type(self.right) is CfgReadNode def isPoll(self): ''' return True if compare involves a reg read ''' return self.leftIsPoll() or self.rightIsPoll() def __str__(self): return f'l=({self.left}) op={self.op.name} r=({self.right})' def resolvePaths(self): ''' resolve paths in this compare node ''' if self.leftIsPoll(): self.left.resolvePaths() if self.rightIsPoll(): self.right.resolvePaths() # ------ config model visitor TODO

47.675939

210

0.632603

3,825

34,279

5.59451

0.131242

0.013459

0.011823

0.01215

0.308472

0.261788

0.22258

0.168513

0.130427

0.110846

0

0.001985

0.265177

34,279

718

211

47.74234

0.847553

0.223956

0

0.29432

0

0.017212

0.10098

0.026361

0

0.001393

0

1

0.194492

false

0.006885

0.008606

0.034423

0.437177

0.015491

0

null

0

null

0

1

0

69357d4b61dce4eb42c2cf196abf2f132d27de5f

2,061

py

Python

tests/test_dict_qtable.py

fgka/reinforcement-learning-py

e4c582d192b36a270efce5e1512596b72466c8f7

[ "MIT" ]

null

tests/test_dict_qtable.py

fgka/reinforcement-learning-py

e4c582d192b36a270efce5e1512596b72466c8f7

[ "MIT" ]

null

tests/test_dict_qtable.py

fgka/reinforcement-learning-py

e4c582d192b36a270efce5e1512596b72466c8f7

[ "MIT" ]

null

#!/usr/bin/env python # vim: ai:sw=4:ts=4:sta:et:fo=croql # coding=utf-8 import pytest # Uncomment to run test in debug mode # import pudb; pudb.set_trace() from reinforcement_learning.dict_qtable import DictQTable from test_qaction import QActionTest from test_qstate import QStateTest """ DictQTable """ @pytest.mark.incremental class TestDictQTable(object): action_a = QActionTest(3) action_b = QActionTest(4) action_c = QActionTest(5) state_a = QStateTest([action_a, action_b]) state_b = QStateTest([action_c]) value_a = 123.1 value_b = 234.5 def test_set_value(self): # given obj = DictQTable() obj.set_value(self.state_a, self.action_a, self.value_a) # when stored_states = obj.get_all_stored_states() # then assert stored_states is not None, 'Table: {}'.format(obj) assert len(stored_states) is 1, 'Table: {}'.format(obj) assert stored_states[0] is self.state_a, 'Table: {}'.format(obj) value = obj.get_value(self.state_a, self.action_a) assert value is not None, 'Table: {}'.format(obj) assert value is self.value_a, 'Table: {}'.format(obj) def test_get_stored_action_values(self): # given obj = DictQTable() obj.set_value(self.state_a, self.action_a, self.value_a) obj.set_value(self.state_a, self.action_b, self.value_b) # when stored_action_values = obj.get_stored_action_values(self.state_a) # then assert stored_action_values is not None, 'Table: {}'.format(obj) assert len(stored_action_values) is 2, 'Table: {}'.format(obj) assert self.action_a in stored_action_values.keys(), \ 'Table: {}'.format(obj) assert stored_action_values[self.action_a] is self.value_a, \ 'Table: {}'.format(obj) assert self.action_b in stored_action_values.keys(), \ 'Table: {}'.format(obj) assert stored_action_values[self.action_b] is self.value_b, \ 'Table: {}'.format(obj)

33.786885

73

0.654051

291

2,061

4.395189

0.257732

0.094605

0.120407

0.125098

0.472244

0.41595

0.379984

0.296325

0.272088

0.212666

0

0.010665

0.226589

2,061

60

74

34.35

0.791719

0.080058

0

0.210526

0

0.053026

0

0.289474

1

0.052632

false

0

0.105263

0

0.368421

0

null

0

null

0

1

0

693580d8911168f775041c87c4274d3d07d8d2de

2,851

py

Python

handlers/acceptmember.py

micjerry/groupservice

807e5d53533897ac36d9bf1cce30aee09979ea9f

[ "Apache-2.0" ]

1

2015-12-14T08:31:30.000Z

handlers/acceptmember.py

micjerry/groupservice

807e5d53533897ac36d9bf1cce30aee09979ea9f

[ "Apache-2.0" ]

null

handlers/acceptmember.py

micjerry/groupservice

807e5d53533897ac36d9bf1cce30aee09979ea9f

[ "Apache-2.0" ]

null

import tornado.web import tornado.gen import json import io import logging import motor from bson.objectid import ObjectId import mickey.userfetcher from mickey.basehandler import BaseHandler class AcceptMemberHandler(BaseHandler): @tornado.web.asynchronous @tornado.gen.coroutine def post(self): coll = self.application.db.groups publish = self.application.publish token = self.request.headers.get("Authorization", "") data = json.loads(self.request.body.decode("utf-8")) groupid = data.get("groupid", "") inviteid = data.get("invite_id", self.p_userid) members = data.get("members", []) logging.info("begin to add members to group %s" % groupid) if not groupid or not members: logging.error("invalid request") self.set_status(403) self.finish() return result = yield coll.find_one({"_id":ObjectId(groupid)}) if not result: logging.error("group %s does not exist" % groupid) self.set_status(404) self.finish() return if result.get("owner", "") != self.p_userid: logging.error("%s are not the owner" % self.p_userid) self.set_status(403) self.finish() return; #get exist members exist_ids = [x.get("id", "") for x in result.get("members", [])] # get members and the receivers add_members = list(filter(lambda x: x not in exist_ids, [x.get("id", "") for x in members])) notify = {} notify["name"] = "mx.group.authgroup_invited" notify["pub_type"] = "any" notify["nty_type"] = "device" notify["msg_type"] = "other" notify["groupid"] = groupid notify["groupname"] = result.get("name", "") notify["userid"] = inviteid opter_info = yield mickey.userfetcher.getcontact(inviteid, token) if opter_info: notify["username"] = opter_info.get("name", "") else: logging.error("get user info failed %s" % inviteid) adddb_members = list(filter(lambda x: x.get("id", "") in add_members, members)) append_result = yield coll.find_and_modify({"_id":ObjectId(groupid)}, { "$addToSet":{"appendings":{"$each": adddb_members}}, "$unset": {"garbage": 1} }) if append_result: self.set_status(200) publish.publish_multi(add_members, notify) else: self.set_status(500) logging.error("add user failed %s" % groupid) return self.finish()

33.541176

105

0.54437

308

2,851

4.938312

0.357143

0.039448

0.042735

0.021039

0.101249

0.068376

0.026298

0

0.008976

0.335672

2,851

84

106

33.940476

0.794087

0.016485

0

0.166667

0

0.121028

0.009282

0

1

0.015152

false

0

0.136364

0

0.212121

0

null

0

null

0

1

0

693bf1abd077feaa155858e8c51deee32624b50d

14,767

py

Python

example/hermes_bot.py

azalio/python-icq-bot

b5ab8306d2abf8c259da71db1a3195c842d51110

[ "MIT" ]

null

example/hermes_bot.py

azalio/python-icq-bot

b5ab8306d2abf8c259da71db1a3195c842d51110

[ "MIT" ]

null

example/hermes_bot.py

azalio/python-icq-bot

b5ab8306d2abf8c259da71db1a3195c842d51110

[ "MIT" ]

null

import logging.config import random import re from collections import defaultdict from datetime import datetime from enum import Enum import requests from example.util import log_call from icq.bot import ICQBot, FileNotFoundException from icq.constant import TypingStatus from icq.filter import MessageFilter from icq.handler import MessageHandler try: from urllib import parse except ImportError: import urlparse as parse logging.config.fileConfig("logging.ini") log = logging.getLogger(__name__) NAME = "Hermes Bot" VERSION = "0.0.2" TOKEN = "000.0000000000.0000000000:000000000" PHRASES = ( "Sweet lion of Zion!", "Sweet manatee of Galilee!", "Sweet llamas of the Bahamas!", "Sweet something... of... someplace...", "Great cow of Moscow!", "Sweet giant anteater of Santa Anita!", "Sweet ghost of Babylon!", "Sacred boa of West and Eastern Samoa!", "Sacred hog of Prague!", "Cursed bacteria of Liberia!", "Sweet guinea pig of Winnipeg!", "Great bonda of Uganda!", "Sweet three-toed sloth of the ice planet Hoth!", "Sweet honey bee of infinity!", "Sweet yeti of the Serengeti!", "Sweet bongo of the Congo!", "Sweet squid of Madrid!", "Sweet kookaburra of Edinburgh!", "Sweet topology of cosmology!", "Sweet coincidence of Port-au-Prince!", "Sweet orca of Mallorca!", "Sweet candelabra of Le Havre, LaBarbara!" ) def logging_iterator(name, iterable): for item in iterable: log.debug("Processing line ({name}): '{item}'.".format(name=name, item=item)) yield item class HTTPMethod(Enum): GET = "GET" POST = "POST" HEAD = "HEAD" OPTIONS = "OPTIONS" PUT = "PUT" DELETE = "DELETE" TRACE = "TRACE" CONNECT = "CONNECT" PATCH = "PATCH" class HTTPRequest(object): pattern = re.compile(r"^Connected to (?P<host>\S+) $(?P<ip>[^)]+)$ port (?P<port>\d+) $#\d+$$", re.IGNORECASE) _pattern_request_line = re.compile( r"^(?P<method>" + "|".join(m.value for m in HTTPMethod) + r")\s(?P<uri>/\S*)\sHTTP/(?P<version>\d\.\d)$", flags=re.IGNORECASE ) _pattern_http_header = re.compile( r"^\s*(?P<name>X-[^:]*?|Host|User-Agent|Accept|Accept-Encoding|Connection|Content-Length|Content-Type|Expect|If" r"-None-Match)\s*:\s*(?P<value>.*?)\s*$", flags=re.IGNORECASE ) @log_call def __init__(self, ip, method, url, version, headers, data): super(HTTPRequest, self).__init__() self.ip = ip self.method = method self.url = url self.version = version self.headers = headers self.data = data @staticmethod @log_call def parse(match, lines): for line in lines: request_line_match = HTTPRequest._pattern_request_line.search(line) if request_line_match: log.debug("Line matched with 'HTTPRequest._pattern_request_line' pattern.") break else: raise ParseException("Can't find request line!") headers = defaultdict(list) for line in lines: header_match = re.search(HTTPRequest._pattern_http_header, line) if header_match: headers[header_match.group("name")].append(header_match.group("value")) else: break method = HTTPMethod(request_line_match.group("method")) # Crutch for handling "Expect" request. if "Expect" in headers: if len(headers["Expect"]) != 1 and headers["Expect"][0] != "100-continue": raise ParseException("Unknown 'Expect' request header value ('{}')!".format(headers["Expect"])) line = next(lines) if line != "HTTP/1.1 100 Continue": raise ParseException("Unknown status line ('{}') for 'Expect' response!".format(line)) line = next(lines) if line == "We are completely uploaded and fine": # No data, seems like client logging bug. data = None else: data = line else: if method is HTTPMethod.GET: data = None elif method is HTTPMethod.POST: data = next(lines) else: raise ParseException("Unsupported HTTP method ('{}')!".format(method)) return HTTPRequest( ip=match.group("ip"), method=method, url=parse.urlparse("{scheme}://{host}{uri}".format( scheme={80: "HTTP", 443: "HTTPS"}[int(match.group("port"))], host=match.group("host"), uri=request_line_match.group("uri") )), version=request_line_match.group("version"), headers=headers, data=data ) def __repr__(self): return ( "HTTPRequest(method='{self.method}', url='{self.url}', version='{self.version}', headers='{self.headers}', " "data='{self.data}')".format(self=self) ) class HTTPResponse(object): pattern = re.compile(r"^HTTP/(?P<version>\d\.\d)\s(?P<status_code>\d{3})\s(?P<reason_phrase>.+)$", re.IGNORECASE) _pattern_http_header = re.compile( r"^\s*(?P<name>X-[^:]*?|Server|Date|Content-Type|Content-Length|Content-Encoding|Connection|Keep-Alive|Access-C" r"ontrol-Allow-Origin|Transfer-Encoding|Pragma|Cache-Control|ETag|Strict-Transport-Security|Set-Cookie)\s*:\s*(" r"?P<value>.*?)\s*$", re.IGNORECASE ) _pattern_elapsed = re.compile(r"^Completed in (?P<elapsed>\d+) ms$", re.IGNORECASE) @log_call def __init__(self, version, status_code, reason_phrase, headers, data, elapsed): super(HTTPResponse, self).__init__() self.version = version self.status_code = status_code self.reason_phrase = reason_phrase self.headers = headers self.data = data self.elapsed = elapsed @staticmethod @log_call def parse(match, lines): headers = defaultdict(list) for line in lines: (key, value) = map(lambda s: s.strip(), line.split(":", 1)) headers[key].append(value) data = next(lines) for line in lines: elapsed_match = re.search(HTTPResponse._pattern_elapsed, line) if elapsed_match: log.debug("Line matched with 'HTTPResponse._pattern_elapsed' pattern.") elapsed = elapsed_match.group("elapsed") break else: raise ParseException("Can't find elapsed time!") return HTTPResponse( version=match.group("version"), status_code=match.group("status_code"), reason_phrase=match.group("reason_phrase"), headers=headers, data=data, elapsed=elapsed ) def __repr__(self): return ( "HTTPResponse(version='{self.version}', status_code='{self.status_code}', reason_phrase='{self.reason_phras" "e}', headers='{self.headers}', data='{self.data}', elapsed='{self.elapsed}')".format(self=self) ) class LogRecord(object): pattern = re.compile( r"^\[(?P<week_day>Sun|Mon|Tue|Wed|Thu|Fri|Sat)\s(?P<month>Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s{1," r"2}(?P<day>\d{1,2})\s(?P<hour>\d{2}):(?P<minute>\d{2}):(?P<second>\d{2})\s(?P<year>\d+)\.(?P<microsecond>\d{1," r"3})\]\.\[(?:0x)?[0-9a-fA-F]+\]\s*$", re.IGNORECASE ) @log_call def __init__(self, date_time, request=None, response=None): super(LogRecord, self).__init__() self.date_time = date_time self.request = request self.response = response @staticmethod @log_call def parse(match, lines): date_time = datetime( year=int(match.group("year")), month=int(datetime.strptime(match.group("month"), "%b").month), day=int(match.group("day")), hour=int(match.group("hour")), minute=int(match.group("minute")), second=int(match.group("second")), microsecond=int(match.group("microsecond")) * 1000, ) for line in lines: request_match = HTTPRequest.pattern.search(line) if request_match: log.debug("Line matched with 'HTTPRequest.pattern' pattern.") buffer = [] # noinspection PyAssignmentToLoopOrWithParameter for line in lines: response_match = re.search(HTTPResponse.pattern, line) if response_match: log.debug("Line matched with 'HTTPResponse.pattern' pattern.") return LogRecord( date_time=date_time, request=HTTPRequest.parse(request_match, logging_iterator(HTTPRequest.__name__, buffer)), response=HTTPResponse.parse( response_match, logging_iterator(HTTPResponse.__name__, list(lines)) ) ) else: buffer.append(line) return LogRecord(date_time=date_time) def fix_log(lines): status_line_regexp = re.compile(r"^(?P<body>.*)(?P<status_line>HTTP/\d\.\d\s\d{3}\s.+)$", re.IGNORECASE) connection_left_regexp = re.compile(r"^.*Connection #\d+ to host \S+ left intact$", re.IGNORECASE) upload_sent_regexp = re.compile(r"^.*upload completely sent off: \d+ out of \d+ bytes$", re.IGNORECASE) prev_line = None for line in lines: log.debug("Processing line: '{}'.".format(line)) if prev_line == "HTTP/1.1 100 Continue": match = re.search(status_line_regexp, line) if match: log.debug("Fixing '100-continue' problem line.") yield match.group("body") yield match.group("status_line") elif re.search(connection_left_regexp, line): log.debug("Fixing 'Connection blah-blah left intact' problem line.") # yield re.split(connection_left_split_regexp, line)[0] elif re.search(upload_sent_regexp, line): log.debug("Fixing 'Upload completely sent blah-blah' problem line.") # result = re.split(upload_sent_split_regexp, line)[0] else: yield line prev_line = line def iterate_log(lines): buffer = [] match = None for line in lines: m = re.search(LogRecord.pattern, line) if m: log.debug("Line matched with 'LogRecord.pattern' pattern.") if buffer and match: yield LogRecord.parse(match, logging_iterator(LogRecord.__name__, buffer)) buffer = [] match = m else: buffer.append(line) def file_callback(bot, event): source_uin = event.data["source"]["aimId"] message = event.data["message"] try: bot.set_typing(target=source_uin, typing_status=TypingStatus.TYPING) # Getting info for file in message. path = parse.urlsplit(message.strip()).path file_id = path.rsplit("/", 1).pop() file_info_response = bot.get_file_info(file_id=file_id) if file_info_response.status_code == requests.codes.not_found: raise FileNotFoundException url = file_info_response.json()["file_list"].pop()["dlink"] # Starting file download. file_response = bot.http_session.get(url, stream=True) if file_response.encoding is None: file_response.encoding = "utf-8" # Downloading file and calculating stats. stats = defaultdict(int) status_codes = defaultdict(int) for log_record in iterate_log(fix_log( line for line in file_response.iter_lines(chunk_size=1024, decode_unicode=True) if line )): if log_record.request: stats["requests_count"] += 1 if log_record.request.url.path == "/aim/startSession": stats["start_session_count"] += 1 if log_record.request.url.path == "/genToken": stats["gen_token_count"] += 1 if log_record.response: key = log_record.response.status_code + " " + log_record.response.reason_phrase status_codes[key] += 1 else: stats["no_response_count"] += 1 bot.send_im( target=source_uin, message=( "Total requests: {requests_count}\n /aim/startSession: {start_session_count}\n /genToken: {gen_to" "ken_count}\n\nResponse count by status code:\n{status_codes}\n\nFound problems:\n{problems}\n\n{phrase" "}" ).format( requests_count=stats["requests_count"], start_session_count=stats["start_session_count"], gen_token_count=stats["gen_token_count"], status_codes="\n".join([ " {code}: {count}".format( code=code, count=count ) for (code, count) in sorted(status_codes.items()) ]), problems=" Requests without response: {no_response_count}".format( no_response_count=stats["no_response_count"] ), phrase=random.choice(PHRASES) ) ) except FileNotFoundException: bot.send_im(target=source_uin, message=random.choice(PHRASES) + " Give me your log right now!") except ParseException as e: bot.send_im( target=source_uin, message="{phrase} Log format is not supported! Error: '{error}'.".format( phrase=random.choice(PHRASES), error=e ) ) raise except Exception: bot.send_im(target=source_uin, message=random.choice(PHRASES) + " Something has gone wrong!") raise finally: bot.set_typing(target=source_uin, typing_status=TypingStatus.NONE) class ParseException(Exception): pass def main(): # Creating a new bot instance. bot = ICQBot(token=TOKEN, name=NAME, version=VERSION) # Registering message handlers. bot.dispatcher.add_handler(MessageHandler( callback=file_callback, filters=MessageFilter.file & ~(MessageFilter.image | MessageFilter.video | MessageFilter.audio) )) # Starting a polling thread watching for new events from server. This is a non-blocking call. bot.start_polling() # Blocking the current thread while the bot is working until SIGINT, SIGTERM or SIGABRT is received. bot.idle() if __name__ == "__main__": main()

36.825436

120

0.590709

1,720

14,767

4.926744

0.237209

0.024782

0.011801

0.013217

0.187397

0.138423

0.11199

0.06396

0.034694

0.022658

0

0.008487

0.281845

14,767

400

121

36.9175

0.79057

0.039209

0

0.214511

0

0.0347

0.251146

0.091499

0

1

0.041009

false

0.003155

0.047319

0.006309

0.173502

0

null

0

null

0

1

0

693d787b8fc4cb803c97288d52b6f488a6db0a75

2,920

py

Python

qstat_live.py

romeromig/qstat_live

dde8ceb956dc0689a1c40c06ff20d58990488765

[ "MIT" ]

null

qstat_live.py

romeromig/qstat_live

dde8ceb956dc0689a1c40c06ff20d58990488765

[ "MIT" ]

null

qstat_live.py

romeromig/qstat_live

dde8ceb956dc0689a1c40c06ff20d58990488765

[ "MIT" ]

null

#! /usr/bin/python3 import curses import sys import subprocess def main_menu(stdscr): k = 0 cursor_x = 0 cursor_y = 0 # Start colors in curses curses.start_color() curses.init_pair(1, curses.COLOR_CYAN, curses.COLOR_BLACK) curses.init_pair(2, curses.COLOR_RED, curses.COLOR_BLACK) curses.init_pair(3, curses.COLOR_BLACK, curses.COLOR_WHITE) # Set mode switch = 0 # Loop where k is the last character pressed while True: if k == ord('q'): sys.exit() # Respond if the switch was pressed if k == ord('.'): if switch == 0: switch = 1 else: switch = 0 k = -1 # Initialization curses.curs_set(False) stdscr.nodelay(True) stdscr.clear() height, width = stdscr.getmaxyx() # Call qstat if switch == 0: process = subprocess.Popen("qstat -u '*'", stdout=subprocess.PIPE, shell=True) else: process = subprocess.Popen('qstat', stdout=subprocess.PIPE) stdout, stderr = process.communicate() qstat = str(stdout)[2:-1].split('\\n')[:-1] # Strings statusbarstr = " github.com/miferg | '.' to toggle all or user | 'q' to exit " if switch == 0: title = " qstat all users, {} jobs".format(len(qstat)-2) title_empty = " qstat all users, no jobs" if switch == 1: title = " qstat current user, {} jobs".format(len(qstat)-2) title_empty = " qstat current user, no jobs" # Centering calculations start_x_title = int((width // 2) - (len(title) // 2) - len(title) % 2) # Render status bar stdscr.attron(curses.color_pair(3)) stdscr.addstr(height-1, 0, statusbarstr) stdscr.addstr(height-1, len(statusbarstr), " " * (width - len(statusbarstr) - 1)) stdscr.attroff(curses.color_pair(3)) # Rendering title stdscr.attron(curses.color_pair(3)) stdscr.attron(curses.A_BOLD) if len(qstat)-2 == -2: stdscr.addstr(0, 0, title_empty) stdscr.addstr(0, len(title_empty), " " * (width - len(title) - 1)) else: stdscr.addstr(0, 0, title) stdscr.addstr(0, len(title), " " * (width - len(title) - 1)) stdscr.attroff(curses.color_pair(3)) # Turning off attributes for title stdscr.attroff(curses.color_pair(2)) stdscr.attroff(curses.A_BOLD) # Print the qstat report, line by line until the screen is filled for i in range(0, min(len(qstat),height-3)): stdscr.addstr(i+1, 0, qstat[i]) # Refresh the screen stdscr.refresh() curses.napms(100) # Wait for next input k = stdscr.getch() def main(): curses.wrapper(main_menu) if __name__ == "__main__": main()

28.627451

90

0.564726

367

2,920

4.395095

0.332425

0.075016

0.046497

0.039678

0.225666

0.15871

0.121513

0.042157

0

0.024354

0.310959

2,920

101

91

28.910891

0.777336

0.121233

0

0.190476

0

0.078431

0

1

0.031746

false

0

0.047619

0

0.079365

0

null

0

null

0

1

0

693fc462e0aaf1cceaf2297cb92e001c5129520c

2,458

py

Python

nwbwidgets/utils/plotly.py

NeurodataWithoutBorders/nwb-jupyter-widgets

0d11e5d7b193c53d744b13c6404186ac84f4a5c1

[ "BSD-3-Clause-LBNL" ]

35

2019-03-10T23:39:17.000Z

2021-11-16T11:50:33.000Z

nwbwidgets/utils/plotly.py

catalystneuro/nwb-jupyter-widgets

0d11e5d7b193c53d744b13c6404186ac84f4a5c1

[ "BSD-3-Clause-LBNL" ]

158

2019-03-12T21:40:24.000Z

2022-03-16T14:35:55.000Z

nwbwidgets/utils/plotly.py

catalystneuro/nwb-jupyter-widgets

0d11e5d7b193c53d744b13c6404186ac84f4a5c1

[ "BSD-3-Clause-LBNL" ]

20

2019-03-08T14:30:27.000Z

2021-11-08T16:31:26.000Z

import plotly.graph_objects as go import numpy as np def multi_trace(x, y, color, label=None, fig=None, insert_nans=False): """Create multiple traces that are associated with a single legend label Parameters ---------- x: array-like y: array-like color: str label: str, optional fig: go.FigureWidget Returns ------- """ if fig is None: fig = go.FigureWidget() if insert_nans: y_nans = [] x_nans = [] for xx,yy in zip(x,y): y_nans.append(np.append(yy,np.nan)) x_nans.append(np.append(xx, np.nan)) y_plot = np.concatenate(y_nans,axis=0) x_plot = np.concatenate(x_nans, axis=0) fig.add_scattergl( x=x_plot, y=y_plot, name=label, line={"color": color}, ) return fig else: for i, yy in enumerate(y): if label is not None and i: showlegend = False else: showlegend = True fig.add_scattergl( x=x, y=yy, legendgroup=label, name=label, showlegend=showlegend, line={"color": color}, ) return fig def event_group( times_list, offset=0, color="Black", label=None, fig=None, marker=None, line_width=None, ): """Create an event raster that are all associated with a single legend label Parameters ---------- times_list: list of array-like offset: float, optional label: str, optional fig: go.FigureWidget optional, passed to go.Scatter.marker: marker: str line_width: str color: str default: Black Returns ------- """ if fig is None: fig = go.FigureWidget() if label is not None: showlegend = True else: showlegend = False for i, times in enumerate(times_list): if len(times): fig.add_scattergl( x=times, y=np.ones_like(times) * (i + offset), marker=dict( color=color, line_width=line_width, symbol=marker, line_color=color ), legendgroup=str(label), name=label, showlegend=showlegend, mode="markers", ) showlegend = False return fig

22.345455

87

0.513832

284

2,458

4.352113

0.302817

0.022654

0.055016

0.038835

0.325243

0.18123

0.127832

0.059871

0

0.001989

0.386493

2,458

109

88

22.550459

0.817639

0.203417

0

0.384615

0

0.011911

0

1

0.030769

false

0

0.030769

0

0.107692

0

null

0

null

0

1

0

69417b7d7a3a4bf350d3fdf5bf9bebda6e608488

5,997

py

Python

django_python3_saml/saml_settings.py

IronCountySchoolDistrict/django-python3-saml

06d6198ed6c2b9ebfbfe4d6782715d91b6a468d8

[ "BSD-3-Clause" ]

6

2018-04-16T16:38:59.000Z

2022-02-10T09:02:11.000Z

django_python3_saml/saml_settings.py

IronCountySchoolDistrict/django-python3-saml

06d6198ed6c2b9ebfbfe4d6782715d91b6a468d8

[ "BSD-3-Clause" ]

1

2018-10-18T20:59:11.000Z

2018-10-19T13:42:43.000Z

django_python3_saml/saml_settings.py

IronCountySchoolDistrict/django-python3-saml

06d6198ed6c2b9ebfbfe4d6782715d91b6a468d8

[ "BSD-3-Clause" ]

6

2018-04-16T17:06:12.000Z

2020-05-06T11:32:39.000Z

from django.conf import settings class SAMLServiceProviderSettings(object): contact_info = { # Contact information template, it is recommended to suply a # technical and support contacts. "technical": { "givenName": settings.SAML['CONTACT_INFO']['TECHNICAL']['GIVEN_NAME'], "emailAddress": settings.SAML['CONTACT_INFO']['TECHNICAL']['EMAIL'], }, "support": { "givenName": settings.SAML['CONTACT_INFO']['SUPPORT']['GIVEN_NAME'], "emailAddress": settings.SAML['CONTACT_INFO']['SUPPORT']['EMAIL'], } } organization_info = { # Organization information template, the info in en_US lang is # recommended, add more if required. "en-US": { "name": settings.SAML['ORGANIZATION_INFO']['EN_US']['NAME'], "displayname": settings.SAML['ORGANIZATION_INFO']['EN_US']['DISPLAY_NAME'], "url": settings.SAML['ORGANIZATION_INFO']['EN_US']['URL'], } } def __init__(self, debug=False, strict=True, sp_metadata_url=None, sp_login_url=None, sp_logout_url=None, sp_x509cert=None, sp_private_key=None, # Service provider settings (e.g. us) idp_metadata_url=None, idp_sso_url=None, idp_slo_url=None, idp_x509cert=None, idp_x509_fingerprint=None, # Identify provider settings (e.g. onelogin) ): super(SAMLServiceProviderSettings, self).__init__() self.settings = default_settings = { # If strict is True, then the Python Toolkit will reject unsigned # or unencrypted messages if it expects them to be signed or encrypted. # Also it will reject the messages if the SAML standard is not strictly # followed. Destination, NameId, Conditions ... are validated too. "strict": strict, # Enable debug mode (outputs errors). "debug": debug, # Service Provider Data that we are deploying. "sp": { # Identifier of the SP entity (must be a URI) "entityId": sp_metadata_url, # Specifies info about where and how the <AuthnResponse> message MUST be # returned to the requester, in this case our SP. "assertionConsumerService": { # URL Location where the <Response> from the IdP will be returned "url": sp_login_url, # SAML protocol binding to be used when returning the <Response> # message. OneLogin Toolkit supports this endpoint for the # HTTP-POST binding only. "binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-POST" }, # Specifies info about where and how the <Logout Response> message MUST be # returned to the requester, in this case our SP. "singleLogoutService": { # URL Location where the <Response> from the IdP will be returned "url": sp_logout_url, # SAML protocol binding to be used when returning the <Response> # message. OneLogin Toolkit supports the HTTP-Redirect binding # only for this endpoint. "binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect" }, # Specifies the constraints on the name identifier to be used to # represent the requested subject. # Take a look on src/onelogin/saml2/constants.py to see the NameIdFormat that are supported. "NameIDFormat": "urn:oasis:names:tc:SAML:2.0:nameid-format:unspecified", # Usually x509cert and privateKey of the SP are provided by files placed at # the certs folder. But we can also provide them with the following parameters 'x509cert': sp_x509cert, 'privateKey': sp_private_key }, # Identity Provider Data that we want connected with our SP. "idp": { # Identifier of the IdP entity (must be a URI) "entityId": idp_metadata_url, # SSO endpoint info of the IdP. (Authentication Request protocol) "singleSignOnService": { # URL Target of the IdP where the Authentication Request Message # will be sent. "url": idp_sso_url, # SAML protocol binding to be used when returning the <Response> # message. OneLogin Toolkit supports the HTTP-Redirect binding # only for this endpoint. "binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect" }, # SLO endpoint info of the IdP. "singleLogoutService": { # URL Location of the IdP where SLO Request will be sent. "url": idp_slo_url, # SAML protocol binding to be used when returning the <Response> # message. OneLogin Toolkit supports the HTTP-Redirect binding # only for this endpoint. "binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect" }, # Public x509 certificate of the IdP "x509cert": idp_x509cert, # Instead of use the whole x509cert you can use a fingerprint # (openssl x509 -noout -fingerprint -in "idp.crt" to generate it) "certFingerprint": idp_x509_fingerprint }, "organization": self.organization_info, 'contactPerson': self.contact_info, } if not idp_x509cert: del self.settings['idp']['x509cert'] if not idp_x509_fingerprint: del self.settings['idp']['certFingerprint']

51.698276

167

0.571786

651

5,997

5.165899

0.298003

0.011894

0.014273

0.022302

0.398454

0.348498

0.305679

0.254237

0

0.01426

0.345173

5,997

115

168

52.147826

0.842119

0.408371

0

0.079365

0

0.224222

0.077978

0

0.015873

1

0.015873

false

0

0.015873

0

0.079365

0.063492

0

null

0

null

0

1

0

6943f352d6732b6ea4e8c626dd8012e42b34ad09

25,972

py

Python

heat/engine/parser.py

citrix-openstack-build/heat

fa31873529481472e037e3ce157b87f8057fe622

[ "Apache-2.0" ]

null

heat/engine/parser.py

citrix-openstack-build/heat

fa31873529481472e037e3ce157b87f8057fe622

[ "Apache-2.0" ]

null

heat/engine/parser.py

citrix-openstack-build/heat

fa31873529481472e037e3ce157b87f8057fe622

[ "Apache-2.0" ]

null

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # # 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 functools import re from oslo.config import cfg from heat.engine import environment from heat.common import exception from heat.engine import dependencies from heat.common import identifier from heat.engine import resource from heat.engine import resources from heat.engine import scheduler from heat.engine import template from heat.engine import timestamp from heat.engine import update from heat.engine.parameters import Parameters from heat.engine.template import Template from heat.engine.clients import Clients from heat.db import api as db_api from heat.openstack.common import log as logging from heat.openstack.common.gettextutils import _ from heat.common.exception import StackValidationFailed logger = logging.getLogger(__name__) (PARAM_STACK_NAME, PARAM_REGION) = ('AWS::StackName', 'AWS::Region') class Stack(object): ACTIONS = (CREATE, DELETE, UPDATE, ROLLBACK, SUSPEND, RESUME ) = ('CREATE', 'DELETE', 'UPDATE', 'ROLLBACK', 'SUSPEND', 'RESUME') STATUSES = (IN_PROGRESS, FAILED, COMPLETE ) = ('IN_PROGRESS', 'FAILED', 'COMPLETE') created_time = timestamp.Timestamp(functools.partial(db_api.stack_get, show_deleted=True), 'created_at') updated_time = timestamp.Timestamp(functools.partial(db_api.stack_get, show_deleted=True), 'updated_at') _zones = None def __init__(self, context, stack_name, tmpl, env=None, stack_id=None, action=None, status=None, status_reason='', timeout_mins=60, resolve_data=True, disable_rollback=True, parent_resource=None, owner_id=None): ''' Initialise from a context, name, Template object and (optionally) Environment object. The database ID may also be initialised, if the stack is already in the database. ''' if owner_id is None: if re.match("[a-zA-Z][a-zA-Z0-9_.-]*$", stack_name) is None: raise ValueError(_('Invalid stack name %s' ' must contain only alphanumeric or ' '\"_-.\" characters, must start with alpha' ) % stack_name) self.id = stack_id self.owner_id = owner_id self.context = context self.clients = Clients(context) self.t = tmpl self.name = stack_name self.action = action self.status = status self.status_reason = status_reason self.timeout_mins = timeout_mins self.disable_rollback = disable_rollback self.parent_resource = parent_resource self._resources = None self._dependencies = None resources.initialise() self.env = env or environment.Environment({}) self.parameters = Parameters(self.name, self.t, user_params=self.env.params) self._set_param_stackid() if resolve_data: self.outputs = self.resolve_static_data(self.t[template.OUTPUTS]) else: self.outputs = {} @property def resources(self): if self._resources is None: template_resources = self.t[template.RESOURCES] self._resources = dict((name, resource.Resource(name, data, self)) for (name, data) in template_resources.items()) return self._resources @property def dependencies(self): if self._dependencies is None: self._dependencies = self._get_dependencies( self.resources.itervalues()) return self._dependencies def reset_dependencies(self): self._dependencies = None @property def root_stack(self): ''' Return the root stack if this is nested (otherwise return self). ''' if (self.parent_resource and self.parent_resource.stack): return self.parent_resource.stack.root_stack return self def total_resources(self): ''' Total number of resources in a stack, including nested stacks below. ''' total = 0 for res in iter(self.resources.values()): if hasattr(res, 'nested') and res.nested(): total += res.nested().total_resources() total += 1 return total def _set_param_stackid(self): ''' Update self.parameters with the current ARN which is then provided via the Parameters class as the AWS::StackId pseudo parameter ''' # This can fail if constructor called without a valid context, # as it is in many tests try: stack_arn = self.identifier().arn() except (AttributeError, ValueError, TypeError): logger.warning("Unable to set parameters StackId identifier") else: self.parameters.set_stack_id(stack_arn) @staticmethod def _get_dependencies(resources): '''Return the dependency graph for a list of resources.''' deps = dependencies.Dependencies() for resource in resources: resource.add_dependencies(deps) return deps @classmethod def load(cls, context, stack_id=None, stack=None, resolve_data=True, parent_resource=None, show_deleted=True): '''Retrieve a Stack from the database.''' if stack is None: stack = db_api.stack_get(context, stack_id, show_deleted=show_deleted) if stack is None: message = 'No stack exists with id "%s"' % str(stack_id) raise exception.NotFound(message) template = Template.load(context, stack.raw_template_id) env = environment.Environment(stack.parameters) stack = cls(context, stack.name, template, env, stack.id, stack.action, stack.status, stack.status_reason, stack.timeout, resolve_data, stack.disable_rollback, parent_resource, owner_id=stack.owner_id) return stack def store(self, backup=False): ''' Store the stack in the database and return its ID If self.id is set, we update the existing stack ''' s = { 'name': self._backup_name() if backup else self.name, 'raw_template_id': self.t.store(self.context), 'parameters': self.env.user_env_as_dict(), 'owner_id': self.owner_id, 'username': self.context.username, 'tenant': self.context.tenant_id, 'action': self.action, 'status': self.status, 'status_reason': self.status_reason, 'timeout': self.timeout_mins, 'disable_rollback': self.disable_rollback, } if self.id: db_api.stack_update(self.context, self.id, s) else: # Create a context containing a trust_id and trustor_user_id # if trusts are enabled if cfg.CONF.deferred_auth_method == 'trusts': trust_context = self.clients.keystone().create_trust_context() new_creds = db_api.user_creds_create(trust_context) else: new_creds = db_api.user_creds_create(self.context) s['user_creds_id'] = new_creds.id new_s = db_api.stack_create(self.context, s) self.id = new_s.id self._set_param_stackid() return self.id def _backup_name(self): return '%s*' % self.name def identifier(self): ''' Return an identifier for this stack. ''' return identifier.HeatIdentifier(self.context.tenant_id, self.name, self.id) def __iter__(self): ''' Return an iterator over this template's resources in the order that they should be started. ''' return iter(self.dependencies) def __reversed__(self): ''' Return an iterator over this template's resources in the order that they should be stopped. ''' return reversed(self.dependencies) def __len__(self): '''Return the number of resources.''' return len(self.resources) def __getitem__(self, key): '''Get the resource with the specified name.''' return self.resources[key] def __setitem__(self, key, value): '''Set the resource with the specified name to a specific value.''' self.resources[key] = value def __contains__(self, key): '''Determine whether the stack contains the specified resource.''' return key in self.resources def keys(self): '''Return a list of resource keys for the stack.''' return self.resources.keys() def __str__(self): '''Return a human-readable string representation of the stack.''' return 'Stack "%s"' % self.name def resource_by_refid(self, refid): ''' Return the resource in this stack with the specified refid, or None if not found ''' for r in self.resources.values(): if r.state in ( (r.CREATE, r.IN_PROGRESS), (r.CREATE, r.COMPLETE), (r.RESUME, r.IN_PROGRESS), (r.RESUME, r.COMPLETE), (r.UPDATE, r.IN_PROGRESS), (r.UPDATE, r.COMPLETE)) and r.FnGetRefId() == refid: return r def validate(self): ''' http://docs.amazonwebservices.com/AWSCloudFormation/latest/\ APIReference/API_ValidateTemplate.html ''' # TODO(sdake) Should return line number of invalid reference # Check duplicate names between parameters and resources dup_names = set(self.parameters.keys()) & set(self.resources.keys()) if dup_names: logger.debug("Duplicate names %s" % dup_names) raise StackValidationFailed(message="Duplicate names %s" % dup_names) for res in self: try: result = res.validate() except exception.Error as ex: logger.exception(ex) raise ex except Exception as ex: logger.exception(ex) raise StackValidationFailed(message=str(ex)) if result: raise StackValidationFailed(message=result) def requires_deferred_auth(self): ''' Returns whether this stack may need to perform API requests during its lifecycle using the configured deferred authentication method. ''' return any(res.requires_deferred_auth for res in self) def state_set(self, action, status, reason): '''Update the stack state in the database.''' if action not in self.ACTIONS: raise ValueError("Invalid action %s" % action) if status not in self.STATUSES: raise ValueError("Invalid status %s" % status) self.action = action self.status = status self.status_reason = reason if self.id is None: return stack = db_api.stack_get(self.context, self.id) stack.update_and_save({'action': action, 'status': status, 'status_reason': reason}) @property def state(self): '''Returns state, tuple of action, status.''' return (self.action, self.status) def timeout_secs(self): ''' Return the stack creation timeout in seconds, or None if no timeout should be used. ''' if self.timeout_mins is None: return None return self.timeout_mins * 60 def create(self): ''' Create the stack and all of the resources. ''' def rollback(): if not self.disable_rollback and self.state == (self.CREATE, self.FAILED): self.delete(action=self.ROLLBACK) creator = scheduler.TaskRunner(self.stack_task, action=self.CREATE, reverse=False, post_func=rollback) creator(timeout=self.timeout_secs()) @scheduler.wrappertask def stack_task(self, action, reverse=False, post_func=None): ''' A task to perform an action on the stack and all of the resources in forward or reverse dependency order as specfifed by reverse ''' self.state_set(action, self.IN_PROGRESS, 'Stack %s started' % action) stack_status = self.COMPLETE reason = 'Stack %s completed successfully' % action.lower() res = None def resource_action(r): # Find e.g resource.create and call it action_l = action.lower() handle = getattr(r, '%s' % action_l) return handle() action_task = scheduler.DependencyTaskGroup(self.dependencies, resource_action, reverse) try: yield action_task() except exception.ResourceFailure as ex: stack_status = self.FAILED reason = 'Resource %s failed: %s' % (action.lower(), str(ex)) except scheduler.Timeout: stack_status = self.FAILED reason = '%s timed out' % action.title() self.state_set(action, stack_status, reason) if callable(post_func): post_func() def _backup_stack(self, create_if_missing=True): ''' Get a Stack containing any in-progress resources from the previous stack state prior to an update. ''' s = db_api.stack_get_by_name(self.context, self._backup_name(), owner_id=self.id) if s is not None: logger.debug('Loaded existing backup stack') return self.load(self.context, stack=s) elif create_if_missing: prev = type(self)(self.context, self.name, self.t, self.env, owner_id=self.id) prev.store(backup=True) logger.debug('Created new backup stack') return prev else: return None def update(self, newstack): ''' Compare the current stack with newstack, and where necessary create/update/delete the resources until this stack aligns with newstack. Note update of existing stack resources depends on update being implemented in the underlying resource types Update will fail if it exceeds the specified timeout. The default is 60 minutes, set in the constructor ''' updater = scheduler.TaskRunner(self.update_task, newstack) updater() @scheduler.wrappertask def update_task(self, newstack, action=UPDATE): if action not in (self.UPDATE, self.ROLLBACK): logger.error("Unexpected action %s passed to update!" % action) self.state_set(self.UPDATE, self.FAILED, "Invalid action %s" % action) return if self.status != self.COMPLETE: if (action == self.ROLLBACK and self.state == (self.UPDATE, self.IN_PROGRESS)): logger.debug("Starting update rollback for %s" % self.name) else: self.state_set(action, self.FAILED, 'State invalid for %s' % action) return self.state_set(self.UPDATE, self.IN_PROGRESS, 'Stack %s started' % action) oldstack = Stack(self.context, self.name, self.t, self.env) backup_stack = self._backup_stack() try: update_task = update.StackUpdate(self, newstack, backup_stack, rollback=action == self.ROLLBACK) updater = scheduler.TaskRunner(update_task) self.env = newstack.env self.parameters = newstack.parameters try: updater.start(timeout=self.timeout_secs()) yield while not updater.step(): yield finally: self.reset_dependencies() if action == self.UPDATE: reason = 'Stack successfully updated' else: reason = 'Stack rollback completed' stack_status = self.COMPLETE except scheduler.Timeout: stack_status = self.FAILED reason = 'Timed out' except exception.ResourceFailure as e: reason = str(e) stack_status = self.FAILED if action == self.UPDATE: # If rollback is enabled, we do another update, with the # existing template, so we roll back to the original state if not self.disable_rollback: yield self.update_task(oldstack, action=self.ROLLBACK) return else: logger.debug('Deleting backup stack') backup_stack.delete() self.state_set(action, stack_status, reason) # flip the template to the newstack values # Note we do this on success and failure, so the current # stack resources are stored, even if one is in a failed # state (otherwise we won't remove them on delete) self.t = newstack.t template_outputs = self.t[template.OUTPUTS] self.outputs = self.resolve_static_data(template_outputs) self.store() def delete(self, action=DELETE): ''' Delete all of the resources, and then the stack itself. The action parameter is used to differentiate between a user initiated delete and an automatic stack rollback after a failed create, which amount to the same thing, but the states are recorded differently. ''' if action not in (self.DELETE, self.ROLLBACK): logger.error("Unexpected action %s passed to delete!" % action) self.state_set(self.DELETE, self.FAILED, "Invalid action %s" % action) return stack_status = self.COMPLETE reason = 'Stack %s completed successfully' % action.lower() self.state_set(action, self.IN_PROGRESS, 'Stack %s started' % action) backup_stack = self._backup_stack(False) if backup_stack is not None: backup_stack.delete() if backup_stack.status != backup_stack.COMPLETE: errs = backup_stack.status_reason failure = 'Error deleting backup resources: %s' % errs self.state_set(action, self.FAILED, 'Failed to %s : %s' % (action, failure)) return action_task = scheduler.DependencyTaskGroup(self.dependencies, resource.Resource.destroy, reverse=True) try: scheduler.TaskRunner(action_task)(timeout=self.timeout_secs()) except exception.ResourceFailure as ex: stack_status = self.FAILED reason = 'Resource %s failed: %s' % (action.lower(), str(ex)) except scheduler.Timeout: stack_status = self.FAILED reason = '%s timed out' % action.title() self.state_set(action, stack_status, reason) if stack_status != self.FAILED: # If we created a trust, delete it stack = db_api.stack_get(self.context, self.id) user_creds = db_api.user_creds_get(stack.user_creds_id) trust_id = user_creds.get('trust_id') if trust_id: self.clients.keystone().delete_trust(trust_id) # delete the stack db_api.stack_delete(self.context, self.id) self.id = None def suspend(self): ''' Suspend the stack, which invokes handle_suspend for all stack resources waits for all resources to become SUSPEND_COMPLETE then declares the stack SUSPEND_COMPLETE. Note the default implementation for all resources is to do nothing other than move to SUSPEND_COMPLETE, so the resources must implement handle_suspend for this to have any effect. ''' sus_task = scheduler.TaskRunner(self.stack_task, action=self.SUSPEND, reverse=True) sus_task(timeout=self.timeout_secs()) def resume(self): ''' Resume the stack, which invokes handle_resume for all stack resources waits for all resources to become RESUME_COMPLETE then declares the stack RESUME_COMPLETE. Note the default implementation for all resources is to do nothing other than move to RESUME_COMPLETE, so the resources must implement handle_resume for this to have any effect. ''' sus_task = scheduler.TaskRunner(self.stack_task, action=self.RESUME, reverse=False) sus_task(timeout=self.timeout_secs()) def output(self, key): ''' Get the value of the specified stack output. ''' value = self.outputs[key].get('Value', '') return self.resolve_runtime_data(value) def restart_resource(self, resource_name): ''' stop resource_name and all that depend on it start resource_name and all that depend on it ''' deps = self.dependencies[self[resource_name]] failed = False for res in reversed(deps): try: scheduler.TaskRunner(res.destroy)() except exception.ResourceFailure as ex: failed = True logger.error('delete: %s' % str(ex)) for res in deps: if not failed: try: res.state_reset() scheduler.TaskRunner(res.create)() except exception.ResourceFailure as ex: logger.exception('create') failed = True else: res.state_set(res.CREATE, res.FAILED, 'Resource restart aborted') # TODO(asalkeld) if any of this fails we Should # restart the whole stack def get_availability_zones(self): if self._zones is None: self._zones = [ zone.zoneName for zone in self.clients.nova().availability_zones.list(detailed=False)] return self._zones def resolve_static_data(self, snippet): return resolve_static_data(self.t, self, self.parameters, snippet) def resolve_runtime_data(self, snippet): return resolve_runtime_data(self.t, self.resources, snippet) def resolve_static_data(template, stack, parameters, snippet): ''' Resolve static parameters, map lookups, etc. in a template. Example: >>> from heat.common import template_format >>> template_str = '# JSON or YAML encoded template' >>> template = Template(template_format.parse(template_str)) >>> parameters = Parameters('stack', template, {'KeyName': 'my_key'}) >>> resolve_static_data(template, None, parameters, {'Ref': 'KeyName'}) 'my_key' ''' return transform(snippet, [functools.partial(template.resolve_param_refs, parameters=parameters), functools.partial(template.resolve_availability_zones, stack=stack), functools.partial(template.resolve_resource_facade, stack=stack), template.resolve_find_in_map, template.reduce_joins]) def resolve_runtime_data(template, resources, snippet): return transform(snippet, [functools.partial(template.resolve_resource_refs, resources=resources), functools.partial(template.resolve_attributes, resources=resources), template.resolve_split, template.resolve_member_list_to_map, template.resolve_select, template.resolve_joins, template.resolve_replace, template.resolve_base64]) def transform(data, transformations): ''' Apply each of the transformation functions in the supplied list to the data in turn. ''' for t in transformations: data = t(data) return data

37.262554

79

0.578354

2,867

25,972

5.106034

0.161842

0.012979

0.01052

0.01093

0.240317

0.189152

0.164834

0.119066

0.107111

0.089214

0

0.001111

0.341522

25,972

696

80

37.316092

0.854871

0.197713

0

0.23341

0

0.0557

0.001203

0

0.002874

0

1

0.100687

false

0.004577

0.045767

0.009153

0.249428

0

null

0

null

0

1

0

69453942628ce1c37639781c43bed0432a313dc3

1,547

py

Python

src/puzzle_1_you_will_all_conform/my/please_conform_squared.py

foryourselfand/mit_6_S095_programming_for_the_puzzled

88371bd8461709011acbed6066ac4f40c5cde29e

[ "MIT" ]

null

src/puzzle_1_you_will_all_conform/my/please_conform_squared.py

foryourselfand/mit_6_S095_programming_for_the_puzzled

88371bd8461709011acbed6066ac4f40c5cde29e

[ "MIT" ]

null

src/puzzle_1_you_will_all_conform/my/please_conform_squared.py

foryourselfand/mit_6_S095_programming_for_the_puzzled

88371bd8461709011acbed6066ac4f40c5cde29e

[ "MIT" ]

null

from typing import List from please_conform import PleaseConform from structures import Interval class PleaseConformSquared(PleaseConform): def please_conform(self, caps: List[str]) -> List[Interval]: if len(caps) == 0: return list() caps: List[str] = caps.copy() caps.append('end') interval_inputs: List[Interval] = list() count_forward: int = 0 count_backward: int = 0 index_previous: int = 0 for index_current in range(1, len(caps)): cap_current = caps[index_current] cap_previous = caps[index_previous] if cap_current != cap_previous: interval_input = Interval(start=index_previous, end=index_current - 1, cap_type=cap_previous) interval_inputs.append(interval_input) if cap_previous == 'F': count_forward += 1 else: count_backward += 1 index_previous = index_current cap_to_flip: str if count_forward < count_backward: cap_to_flip = 'F' else: cap_to_flip = 'B' interval_results: List[Interval] = list() for interval_input in interval_inputs: if interval_input.cap_type == cap_to_flip: interval_result: Interval = interval_input interval_results.append(interval_result) return interval_results

30.333333

64

0.564318

163

1,547

5.079755

0.276074

0.078502

0.043478

0

0.008147

0.365223

1,547

50

65

30.94

0.835031

0

0.054054

0

0.003878

0

1

0.027027

false

0

0.081081

0

0.189189

0

null

0

null

0

1

0

694611c01663b1d27e4ebc26f84bb2603c45ff7c

5,784

py

Python

apps/almoxarifado/apps/cont/forms.py

mequetrefe-do-subtroco/web_constel

57b5626fb17b4fefc740cbe1ac95fd4ab90147bc

[ "MIT" ]

1

2020-06-18T09:03:53.000Z

apps/almoxarifado/apps/cont/forms.py

gabrielhjs/web_constel

57b5626fb17b4fefc740cbe1ac95fd4ab90147bc

[ "MIT" ]

33

2020-06-16T18:59:33.000Z

2021-08-12T21:33:17.000Z

apps/almoxarifado/apps/cont/forms.py

gabrielhjs/web_constel

57b5626fb17b4fefc740cbe1ac95fd4ab90147bc

[ "MIT" ]

null

from django import forms from .models import * class FormCadastraModelo(forms.ModelForm): class Meta: model = Modelo fields = ['nome', 'descricao', ] def __init__(self, *args, **kwargs): super(FormCadastraModelo, self).__init__(*args, **kwargs) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class FormCadastraSecao(forms.ModelForm): class Meta: model = Secao fields = ['nome', 'descricao', ] def __init__(self, *args, **kwargs): super(FormCadastraSecao, self).__init__(*args, **kwargs) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class FormEntradaOnt1(forms.Form): modelo = forms.ChoiceField() secao = forms.ChoiceField() def __init__(self, *args, **kwargs): super(FormEntradaOnt1, self).__init__(*args, **kwargs) modelos = Modelo.objects.all().order_by('nome') modelos_name = [(i.id, i.nome.upper()) for i in modelos] self.fields['modelo'] = forms.ChoiceField( choices=modelos_name, label='Modelo', help_text='Modelo das ONT\'s a serem inseridas', ) secoes = Secao.objects.all().order_by('nome') secoes_name = [(i.id, i.nome.upper()) for i in secoes] self.fields['secao'] = forms.ChoiceField( choices=secoes_name, label='Seção', help_text='Atividade de destino das ONT\'s a serem inseridas', ) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class NonstickyCharfield(forms.TextInput): """Custom text input widget that's "non-sticky" (i.e. does not remember submitted values). """ def get_context(self, name, value, attrs): value = None # Clear the submitted value. return super().get_context(name, value, attrs) class FormEntradaOnt2(forms.Form): serial = forms.CharField(required=True, widget=NonstickyCharfield()) def __init__(self, *args, **kwargs): super(FormEntradaOnt2, self).__init__(*args, **kwargs) self.fields['serial'].widget.attrs.update( {'autofocus': 'autofocus', 'required': 'required'} ) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) def clean(self): form_data = super().clean() serial = form_data['serial'].upper() if serial.find('4857544', 0, 7) >= 0: if len(serial) != 16: self.errors['serial'] = ['Serial de Ont Huawei inválido'] return form_data elif serial.find('ZNTS', 0, 5) >= 0: if len(serial) != 12: self.errors['serial'] = ['Serial de Ont Zhone inválido'] return form_data else: self.errors['serial'] = ['Serial de Ont inválido'] return form_data class FormOntFechamento(forms.Form): serial = forms.CharField(required=True, widget=NonstickyCharfield()) def __init__(self, *args, **kwargs): super(FormOntFechamento, self).__init__(*args, **kwargs) self.fields['serial'].widget.attrs.update( {'autofocus': 'autofocus', 'required': 'required'} ) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) def clean(self): form_data = super().clean() serial = form_data['serial'].upper() if Ont.objects.filter(codigo=serial).exists(): form_data['serial'] = Ont.objects.get(codigo=serial) else: self.errors['serial'] = ['Ont não cadastrada no sistema, cadastre-a para registrá-la como com defeito'] return form_data class FormOntManutencao1(forms.Form): modelo = forms.ChoiceField() def __init__(self, *args, **kwargs): super(FormOntManutencao1, self).__init__(*args, **kwargs) modelos = Modelo.objects.all().order_by('nome') modelos_name = [(i.id, i.nome.upper()) for i in modelos] self.fields['modelo'] = forms.ChoiceField( choices=modelos_name, label='Modelo', ) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class FormPswLogin(forms.Form): """ Formulário de login de usuário no psw """ username = forms.CharField(max_length=150, label='Chave da Copel') password = forms.CharField(widget=forms.PasswordInput) widgets = { 'password': forms.PasswordInput(), } def __init__(self, *args, **kwargs): super(FormPswLogin, self).__init__(*args, **kwargs) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class FormPswContrato(forms.Form): """ Formulário de busca de contrato no psw """ contratos = forms.CharField( label='Contratos', widget=forms.TextInput( attrs={'placeholder': 'Ex: 1234567,1234568, 1234569'} ) ) def __init__(self, *args, **kwargs): super(FormPswContrato, self).__init__(*args, **kwargs) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'}) class FormSerial(forms.Form): """ Formulário de busca de serial """ serial = forms.CharField(label='Serial', required=False) def __init__(self, *args, **kwargs): super(FormSerial, self).__init__(*args, **kwargs) for key in self.fields.keys(): self.fields[key].widget.attrs.update({'class': 'form-control'})

28.492611

115

0.602006

651

5,784

5.202765

0.219662

0.067907

0.055211

0.039858

0.613227

0.576912

0.500443

0.475642

0.442279

0

0.010862

0.251902

5,784

202

116

28.633663

0.771897

0.038382

0

0.5

0

0.114867

0

1

0.098361

false

0.016393

0

0.336066

0

null

0

null

0

1

0

69469bc9f4f19c9f16e8cc58a6b94958c3abce9d

1,980

py

Python

DMD/pyDMD.py

yusovm/GEMSEC

d9abd43d27e05607e7b1ea8c99fcc736abd204fd

[ "MIT" ]

null

DMD/pyDMD.py

yusovm/GEMSEC

d9abd43d27e05607e7b1ea8c99fcc736abd204fd

[ "MIT" ]

null

DMD/pyDMD.py

yusovm/GEMSEC

d9abd43d27e05607e7b1ea8c99fcc736abd204fd

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Mar 2 16:43:45 2020 @author: micha """ import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from MD_Analysis import Angle_Calc from pydmd import DMD pdb="pdbs/WT_295K_200ns_50ps_0_run.pdb" #Extract phi, psi angles AC=Angle_Calc(pdb) Angle_DF=AC.get_phi_psi() def cossin(data): cols = data.columns data = data.to_numpy() coss = np.cos(data/180.*np.pi) sins = np.sin(data/180.*np.pi) res=pd.DataFrame() for i in range(len(cols)): res[cols[i]+"_cos"] = coss[:,i] res[cols[i]+"_sin"] = sins[:,i] return res def halftime(data): dropindex = [1+2*i for i in (range(int(data.shape[0]/2)))] return data.drop(dropindex) #half = halftime(Angle_DF) angle_cossin = cossin(Angle_DF) angle_cossin_full = angle_cossin.copy() angle_cossin_full.drop(angle_cossin_full.tail(1).index,inplace=True) f=angle_cossin_full.to_numpy() dt=50*(10**-12) xi=np.linspace(np.min(f),np.max(f),f.shape[0]) t=np.linspace(0,f.shape[0],f.shape[1])*dt #+200*10**-9 Xgrid,T=np.meshgrid(xi,t) dmd = DMD(svd_rank=40) dmd.fit(f.T) xl=np.linspace(0,4000*dt,2000) yl=range(40) xlabel,ylabel=np.meshgrid(xl,yl) #Actual fig = plt.figure(figsize=(17,6)) plt.pcolor(xl, yl, f.real.T) plt.yticks([]) plt.title('Actual Data') plt.colorbar() plt.show() fig.savefig("PyDMD Actual Data.png") #Reconstructed fig2 = plt.figure(figsize=(17,6)) plt.pcolor(xl, yl, dmd.reconstructed_data.real) plt.yticks([]) plt.title('Reconstructed Data') plt.colorbar() plt.show() fig2.savefig("PyDMD Reconstructed Data.png") #Error fig3 = plt.figure(figsize=(17,6)) plt.pcolor(xl, yl, (np.sqrt(f.T-dmd.reconstructed_data)**2).real) plt.yticks([]) plt.title('RMSE Error') plt.colorbar() plt.show() fig3.savefig("PyDMD Error.png") #Eigenvalues dmd.plot_eigs(show_axes=True, show_unit_circle=True)

21.758242

69

0.663636

332

1,980

3.855422

0.391566

0.051563

0.046875

0.042188

0.142188

0.075

0

0.0454

0.165657

1,980

90

70

22

0.729419

0.084848

0

0.157895

0

0.084557

0.019378

0

1

0.035088

false

0

0.105263

0

0.175439

0

null

0

null

0

1

0

69496f7144482f3f124f969247fa77f335fc5db1

695

py

Python

models/t_compensate_event_definition.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

models/t_compensate_event_definition.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

models/t_compensate_event_definition.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

from dataclasses import dataclass, field from typing import Optional from xml.etree.ElementTree import QName from .t_event_definition import TEventDefinition __NAMESPACE__ = "http://www.omg.org/spec/BPMN/20100524/MODEL" @dataclass class TCompensateEventDefinition(TEventDefinition): class Meta: name = "tCompensateEventDefinition" wait_for_completion: Optional[bool] = field( default=None, metadata={ "name": "waitForCompletion", "type": "Attribute", } ) activity_ref: Optional[QName] = field( default=None, metadata={ "name": "activityRef", "type": "Attribute", } )

24.821429

61

0.640288

63

695

6.920635

0.650794

0.055046

0.073395

0.110092

0.12844

0

0.015534

0.258993

695

27

62

25.740741

0.831068

0

0.26087

0

0.188489

0.03741

0

1

0

false

0

0.173913

0

0.347826

0

null

0

null

0

1

0

694980cd79dd13058b0c41b67c32eb322a3674e4

822

py

Python

plugins.py

ddfabbro/translatorbot

a14a442ec840d81e3d8bbc6faa15e52f68145655

[ "Unlicense" ]

null

plugins.py

ddfabbro/translatorbot

a14a442ec840d81e3d8bbc6faa15e52f68145655

[ "Unlicense" ]

null

plugins.py

ddfabbro/translatorbot

a14a442ec840d81e3d8bbc6faa15e52f68145655

[ "Unlicense" ]

null

import html from googletrans import Translator from slackbot.bot import default_reply, respond_to, listen_to translator = Translator() def translate(message): msg_in = html.unescape(message.body["text"]) if msg_in != "": if translator.detect(msg_in).lang == "en": text = translator.translate(msg_in, dest = "ja").text else: text = translator.translate(msg_in, dest = "en").text msg_out = "```{}```".format(text) if message.thread_ts == message.body["event_ts"]: message.send(msg_out) else: message.reply(msg_out) @default_reply def my_default_handler(message): translate(message) @respond_to(".*") def all_replies(message): translate(message) @listen_to(".*") def all_messages(message): translate(message)

24.176471

65

0.647202

101

822

5.069307

0.376238

0.048828

0.134766

0.101563

0.125

0

0.217762

822

33

66

24.909091

0.796268

0

0.2

0

0.036496

0

1

0.16

false

0

0.12

0

0.28

0

null

0

null

0

1

0

694a62087121f0e7a903137a5cded3d86b3d17e4

1,324

py

Python

app.py

muelletm/search

3087dcfd26861b1386c38575b53cb026cb1045f8

[ "Apache-2.0" ]

1

2022-03-25T19:14:53.000Z

app.py

muelletm/search

3087dcfd26861b1386c38575b53cb026cb1045f8

[ "Apache-2.0" ]

null

app.py

muelletm/search

3087dcfd26861b1386c38575b53cb026cb1045f8

[ "Apache-2.0" ]

4

2022-03-10T18:40:44.000Z

2022-03-10T19:20:30.000Z

import collections import os from pathlib import Path from typing import List import streamlit as st from sentence_transformers import SentenceTransformer from search.engine import Engine, Result from search.model import load_minilm_model from search.utils import get_memory_usage os.environ["TOKENIZERS_PARALLELISM"] = "false" _DATA_DIR = os.environ.get("DATA_DIR", "data/people_pm_minilm") st.set_page_config(page_title="Search Engine", layout="wide") st.markdown( """ <style> .big-font { font-size:20px; } </style> """, unsafe_allow_html=True, ) @st.cache(allow_output_mutation=True) def load_engine() -> Engine: engine = Engine( data_dir=Path(_DATA_DIR), ) return engine @st.cache(allow_output_mutation=True) def load_model() -> SentenceTransformer: return load_minilm_model() engine = load_engine() model = load_model() st.error("Create a text input for the query.") st.error("Create a slider with the number of results to retrieve.") with st.spinner("Querying index ..."): st.error("Get query embedding.") st.error("Search results (engine.search).") # Show the results. # You can use st.markdown to render markdown. # e.g. st.markdown("**text**") will add text in bold font. st.error("Render results") st.markdown(f"**Mem Usage**: {get_memory_usage()}MB")

21.354839

67

0.728852

191

1,324

4.890052

0.450262

0.037473

0.03212

0.038544

0.079229

0

0.00177

0.146526

1,324

61

68

21.704918

0.824779

0.089124

0

0.058824

0

0.246504

0.056818

0

1

0.058824

false

0

0.264706

0.029412

0.382353

0

null

0

null

0

1

0

694b3b51b65fa886685be715d3c914e309e0c1fe

1,596

py

Python

interface/exemplos1/04.py

ell3a/estudos-python

09808a462aa3e73ad433501acb11f62217548af8

[ "MIT" ]

null

interface/exemplos1/04.py

ell3a/estudos-python

09808a462aa3e73ad433501acb11f62217548af8

[ "MIT" ]

null

interface/exemplos1/04.py

ell3a/estudos-python

09808a462aa3e73ad433501acb11f62217548af8

[ "MIT" ]

null

from tkinter import * class EditBoxWindow: def __init__(self, parent = None): if parent == None: parent = Tk() self.myParent = parent self.top_frame = Frame(parent) # Criando a barra de rolagem scrollbar = Scrollbar(self.top_frame) self.editbox = Text(self.top_frame, yscrollcommand=scrollbar.set) scrollbar.pack(side=RIGHT, fill=Y) scrollbar.config(command=self.editbox.yview) # Área do texto self.editbox.pack(anchor=CENTER, fill=BOTH) self.top_frame.pack(side=TOP) # Texto a procurar self.bottom_left_frame = Frame(parent) self.textfield = Entry(self.bottom_left_frame) self.textfield.pack(side=LEFT, fill=X, expand=1) # Botão Find buttonSearch = Button(self.bottom_left_frame, text='Find', command=self.find) buttonSearch.pack(side=RIGHT) self.bottom_left_frame.pack(side=LEFT, expand=1) self.bottom_right_frame = Frame(parent) def find(self): self.editbox.tag_remove('found', '1.0', END) s = self.textfield.get() if s: idx = '1.0' while True: idx =self.editbox.search(s, idx, nocase=1, stopindex=END) if not idx: break lastidx = '%s+%dc' % (idx, len(s)) self.editbox.tag_add('found', idx, lastidx) idx = lastidx self.editbox.tag_config('found', foreground='red') if __name__=="__main__": root = Tk() myapp = EditBoxWindow(root)

32.571429

85

0.58396

193

1,596

4.678756

0.393782

0.085271

0.053156

0.084164

0

0.006289

0.302632

1,596

49

86

32.571429

0.805031

0.042607

0

0.027559

0

1

0.055556

false

0

0.027778

0

0.111111

0

null

0

null

0

1

0

694cb06a76643cd64ded70df62959d8318b7af93

426

py

Python

app/app.py

cagriozkurt/EksiSansur

071f5e136d58f7fdd5ba32c8387904b2710d04a5

[ "MIT" ]

null

app/app.py

cagriozkurt/EksiSansur

071f5e136d58f7fdd5ba32c8387904b2710d04a5

[ "MIT" ]

null

app/app.py

cagriozkurt/EksiSansur

071f5e136d58f7fdd5ba32c8387904b2710d04a5

[ "MIT" ]

1

2022-03-22T13:50:41.000Z

import psycopg from flask import Flask, render_template from flask_compress import Compress app = Flask(__name__) DATABASE_URL = "" Compress(app) @app.route("/") def index(): with psycopg.connect(DATABASE_URL, sslmode="require") as conn: with conn.cursor() as cur: cur.execute("SELECT * FROM topics;") items = cur.fetchall() return render_template("index.html", items=items)

25.058824

66

0.671362

53

426

5.226415

0.54717

0.064982

0

0.213615

426

16

67

26.625

0.826866

0

0.091549

0

1

0.076923

false

0

0.230769

0

0.384615

0

null

0

null

0

1

0

69512ed9252aea21d648e173c9d6e12c14061403

1,404

py

Python

2020/07/ape.py

notxenonbox/adventofcode

82cd8fafdf21c988bd7383f2b6d71cec04282e65

[ "Unlicense" ]

null

2020/07/ape.py

notxenonbox/adventofcode

82cd8fafdf21c988bd7383f2b6d71cec04282e65

[ "Unlicense" ]

null

2020/07/ape.py

notxenonbox/adventofcode

82cd8fafdf21c988bd7383f2b6d71cec04282e65

[ "Unlicense" ]

null

import re class Bag: def __init__(self, _name, _contents): self.name = _name self.contents = _contents self.c_cache = None self.has_cache = {} def hasBagType(self, _name, bags): try: return self.has_cache[_name] except: if _name != self.name: for i in self.contents: if bags[i[1]].hasBagType(_name, bags): break else: return False return True else: return True def children_count(self): if self.c_cache != None: return self.c_cache count = 0 for i in self.contents: count += i[0] + (i[0] * bags[i[1]].children_count()) self.c_cache = count return count input_lines = [] with open('input.txt') as f: input_lines = f.readlines() input_lines = list(filter(None, input_lines)) bags = {} for i in input_lines: bag, contents = re.search(r'^((?:[\w]+ ){2})bags contain ([\S\s]+)', i).groups() if contents.strip() == "no other bags.": bag = bag.strip() bags[bag] = Bag(bag, []) continue contents = contents.split(', ') contents = list(map(lambda x: re.search(r'(\d)+ ((?:[\w]+ ){2})', x).groups(), contents)) # cleaning up contents = list(map(lambda x: (int(x[0]), x[1].strip()), contents)) bag = bag.strip() bags[bag] = Bag(bag, contents) part1 = -1 for i in bags.values(): if i.hasBagType("shiny gold", bags): part1 += 1 print(f'part 1: {part1}') print(f'part 2: {bags["shiny gold"].children_count()}')

22.285714

90

0.623219

215

1,404

3.948837

0.302326

0.042403

0.047114

0.03298

0.150766

0.056537

0

0.014184

0.196581

1,404

63

91

22.285714

0.738475

0.007835

0

0.16

0

0.110632

0.017241

0

1

0.06

false

0

0.02

0

0.22

0.04

0

null

0

null

0

1

0

69517bd1fdcbc759ae3114b27d1f3038e73dc9c5

3,209

py

Python

src/drive.py

Matej-Chmel/pydrive-chat

551504335bcebbeed239f1961b7bffa3f45d220d

[ "Apache-2.0", "CC0-1.0" ]

null

src/drive.py

Matej-Chmel/pydrive-chat

551504335bcebbeed239f1961b7bffa3f45d220d

[ "Apache-2.0", "CC0-1.0" ]

null

src/drive.py

Matej-Chmel/pydrive-chat

551504335bcebbeed239f1961b7bffa3f45d220d

[ "Apache-2.0", "CC0-1.0" ]

null

from datetime import datetime, timedelta from io import BytesIO from pathlib import Path from time import altzone, daylight, localtime, timezone from pydrive.auth import GoogleAuth, AuthenticationRejected from pydrive.drive import GoogleDrive as Drive, GoogleDriveFile as File from requests import patch from .auth import gauth from ._this import ENDL, res_ CHAT_LOG: File = None FILE_TYPE = 'application/vnd.google-apps.file' FOLDER_TYPE = 'application/vnd.google-apps.folder' LAST_READ: datetime = None UTC_OFFSET_SECS = -(altzone if daylight and localtime().tm_isdst > 0 else timezone) drive: Drive = None def setup_gauth(): path = res_('client_secrets.json') if not Path(path).is_file(): raise FileNotFoundError GoogleAuth.DEFAULT_SETTINGS['client_config_file'] = path def empty_contents_of_(file): patch( f"https://www.googleapis.com/upload/drive/v3/files/{file['id']}?uploadType=multipart", headers={'Authorization': f"Bearer {gauth.credentials.token_response['access_token']}"}, files={ 'data': ('metadata', '{}', 'application/json'), 'file': BytesIO() } ) def ensure_item(title: str, mime_type=None, parents=None, trashed=False): query = f"title='{title}'" if mime_type: query += f" and mimeType='{mime_type}'" if parents: query += f""" and { ' and '.join(f"'{item['id']}' in parents" for item in parents) }""" if type(parents) is list else f" and '{parents['id']}' in parents" if trashed is not None: query += f' and trashed={str(trashed).lower()}' try: return drive.ListFile({'q': query}).GetList()[0] except IndexError: metadata = {'title': title} if mime_type: metadata['mimeType'] = mime_type if parents: metadata['parents'] = [ {'id': item['id']} for item in parents ] if type(parents) is list else [{'id': parents['id']}] file = drive.CreateFile(metadata) file.Upload() return file def log_into_drive(): creds_path = res_('creds.json') if Path(creds_path).is_file(): gauth.LoadCredentialsFile(creds_path) else: try: gauth.LocalWebserverAuth() gauth.SaveCredentialsFile(creds_path) except: return None return Drive(gauth) def login_and_init(): global CHAT_LOG, drive drive = log_into_drive() if drive is None: return False app_data = ensure_item('AppData', FOLDER_TYPE) app_folder = ensure_item('pydrive-chat', FOLDER_TYPE, app_data) CHAT_LOG = ensure_item('chat_log.txt', parents=app_folder) return True def append_to_log(text): CHAT_LOG.SetContentString(f'{CHAT_LOG.GetContentString()}{text}{ENDL}') CHAT_LOG.Upload() def overwrite_log(text=None): if not text: empty_contents_of_(CHAT_LOG) CHAT_LOG.Upload() CHAT_LOG.SetContentString('') else: CHAT_LOG.SetContentString(text) CHAT_LOG.Upload() def read_log(): return CHAT_LOG.GetContentString() def read_if_modified(): global LAST_READ, LINES_READ def was_modified(): modified_at = when_modified() if LAST_READ < modified_at: LAST_READ = modified_at return True return False if LAST_READ is None or was_modified(): return CHAT_LOG.GetContentString() return None def when_modified(): return datetime.strptime(CHAT_LOG['modifiedDate'], '%Y-%m-%dT%H:%M:%S.%fZ') + timedelta(seconds=UTC_OFFSET_SECS)

26.966387

113

0.727641

454

3,209

4.955947

0.306167

0.046667

0.012

0.021333

0.099556

0.034667

0

0.001087

0.139607

3,209

118

114

27.194915

0.813836

0

0.193878

0

0.010204

0.192895

0.078841

0

1

0.112245

false

0

0.091837

0.020408

0.326531

0

null

0

null

0

1

0

69550175d4982933c72091480c87edac34bffafc

1,967

py

Python

tests/test_yaml_files.py

graeme-winter/data

6e359b169c35d1a6569fd316f7b7ab19fa5812b8

[ "BSD-3-Clause" ]

null

tests/test_yaml_files.py

graeme-winter/data

6e359b169c35d1a6569fd316f7b7ab19fa5812b8

[ "BSD-3-Clause" ]

null

tests/test_yaml_files.py

graeme-winter/data

6e359b169c35d1a6569fd316f7b7ab19fa5812b8

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import pkg_resources import pytest import string import yaml definition_yamls = { fn for fn in pkg_resources.resource_listdir("dials_data", "definitions") if fn.endswith(".yml") } hashinfo_yamls = { fn for fn in pkg_resources.resource_listdir("dials_data", "hashinfo") if fn.endswith(".yml") } def is_valid_name(filename): if not filename.endswith(".yml") or len(filename) <= 4: return False allowed_characters = frozenset(string.ascii_letters + string.digits + "_") return all(c in allowed_characters for c in filename[:-4]) @pytest.mark.parametrize("yaml_file", definition_yamls) def test_yaml_file_is_valid_definition(yaml_file): assert is_valid_name(yaml_file) definition = yaml.safe_load( pkg_resources.resource_stream("dials_data", "definitions/" + yaml_file).read() ) fields = set(definition) required = {"name", "data", "description"} optional = {"license", "url", "author"} assert fields >= required, "Required fields missing: " + str( sorted(required - fields) ) assert fields <= (required | optional), "Unknown fields present: " + str( sorted(fields - required - optional) ) @pytest.mark.parametrize("yaml_file", hashinfo_yamls) def test_yaml_file_is_valid_hashinfo(yaml_file): assert is_valid_name(yaml_file) assert ( yaml_file in definition_yamls ), "hashinfo file present without corresponding definition file" hashinfo = yaml.safe_load( pkg_resources.resource_stream("dials_data", "hashinfo/" + yaml_file).read() ) fields = set(hashinfo) required = {"definition", "formatversion", "verify"} assert fields >= required, "Required fields missing: " + str( sorted(required - fields) ) assert fields <= required, "Unknown fields present: " + str( sorted(fields - required) )

31.222222

86

0.68785

234

1,967

5.547009

0.316239

0.067797

0.061633

0.01849

0.514638

0.437596

0.329738

0.278891

0.206471

0

0.001895

0.195221

1,967

62

87

31.725806

0.818067

0.010676

0

0.192308

0

0.170782

0

0.134615

1

0.057692

false

0

0.096154

0

0.192308

0.019231

0

null

0

null

0

1

0

695579af900de69250d3b0d15ec3f825c2990c7f

18,839

py

Python

PacELF_Phase3/scripts/csvColumnToXMLFile.py

pacelf/pacelf

cd9f3608843eaf7d9dff6e20e06ee4bf773467e3

[ "MIT" ]

null

PacELF_Phase3/scripts/csvColumnToXMLFile.py

pacelf/pacelf

cd9f3608843eaf7d9dff6e20e06ee4bf773467e3

[ "MIT" ]

2

2021-10-06T01:58:48.000Z

2022-02-18T04:52:34.000Z

PacELF_Phase3/scripts/csvColumnToXMLFile.py

pacelf/pacelf

cd9f3608843eaf7d9dff6e20e06ee4bf773467e3

[ "MIT" ]

null

# -*- coding: utf-8 -*- # This program is designed to create XML sidecar files for harvesting metadata into Mediaflux. # # Author: Jay van Schyndel # Date: 02 May 2017. # # Significant modifications done by: Daniel Baird # Date: 2018 and early 2019 # # Scenario. Metadata is stored in an MS Excel file in various columns. # Excel has been used to create a new column representing the metadata in the required XML format. # The file is then saved as a CSV. # This program will open the CSV file, read the appropriate column and save the XML into a sidecar file based on the name of the data file. # Note the program assumes there is a header row in the CSV file. It skips processesing the first row. # # new example: python ./scripts/csvColumnToXMLFile.py "./rawdata/excel/PacELF Phases 1_2_3 13Dec2018.csv" "/Users/pvrdwb/projects/PacELFDocs/PacELFphase3/" ../docs --location="HardcopyLocation2018" # # old example: python csvColumnToXMLFile.py rawSpreadsheet/PacELF_Phase_1_AND_2.csv ~/projects/PacELFDocs/PacELF\ PDFs ./docs # import os import re import sys import csv import shutil import argparse parser = argparse.ArgumentParser(description="Create XML sidecar files from a CSV file") parser.add_argument( "metadata_csv", metavar="metadataCSV", help="CSV file containing the XML" ) parser.add_argument( "src_folder", metavar="sourceFolder", help="directory containing the source files" ) parser.add_argument( "dest_folder", metavar="destinationFolder", help="Path of the destination folder" ) parser.add_argument( "--title", metavar="titleColumn", help="Column containing the title", default="Title", ) parser.add_argument( "--xml", metavar="xmlColumn", help="Column containing the XML", default="XML" ) parser.add_argument( "--access", metavar="accessColumn", help="Column containing the Access Rights", default="Access Rights", ) parser.add_argument( "--type", metavar="accessColumn", help="Column containing the Type", default="Type" ) parser.add_argument( "--file", metavar="fileColumn", help="Column containing the data file name", default="PDF", ) parser.add_argument( "--location", metavar="primaryLocationColumn", help="Column containing the primary hardcopy location", default="Hardcopy Locations", ) try: args = parser.parse_args() except: sys.exit(0) print("Processing CSV file: ", args.metadata_csv) # this is all the location typos we've found loc_replacements = {} loc_replacements[r"JCU WHOCC Ichimori collectoin"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHOCC Ichimori Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHOCC ICHIMORI Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHO Ichimori Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHO Ichimori collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHO CC Ichimori Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCUWHOCC Ichimori collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCUWHOCC Ichimori Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"Ichimori Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHOCC Nagasaki Collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"JCU WHOCC Nagasaki collection"] = r"JCU WHOCC Ichimori collection" loc_replacements[r"WHO DPS Suva"] = r"WHO DPS Fiji" loc_replacements[r"WHO HQ Geneva"] = r"WHO Geneva" # ----------------------------------------------------------------------------- def clean_hc_location(loc): if loc in loc_replacements: return loc_replacements[loc] else: return loc # ----------------------------------------------------------------------------- def clean_xml_content(xml_string): """ Given some xml in string form that we got right from the spreadsheet, clean it up """ for old_loc in loc_replacements: old = r"<hardcopy_location>([^<]*)" + old_loc + r"([^<]*)</hardcopy_location>" new = ( r"<hardcopy_location>\1" + loc_replacements[old_loc] + r"\2</hardcopy_location>" ) xml_string = re.sub(old, new, xml_string) return xml_string # ----------------------------------------------------------------------------- def get_location_info(location): locations = {} locations[ "JCU WHOCC Ichimori collection" ] = "James Cook University, Bldg 41 Rm 207, Townsville, Queensland 4811, Australia" locations[ "JCU WHOCC" ] = "James Cook University, Bldg 41 Rm 207, Townsville, Queensland 4811, Australia" locations[ "JCU Cairns (PMG)" ] = "James Cook University, Bldg E1 Rm 003C, Cairns, Queensland 4870, Australia" locations[ "WHO DPS Fiji" ] = "World Health Organization, Level 4, Provident Plaza One, Downtown Boulevard, 33 Ellery Street, Suva, Fiji" locations[ "WHO WPRO Manila" ] = "P.O. Box 2932, United Nations Ave. cor. Taft Ave, 1000 Manila, Philippines" locations["WHO Geneva"] = "Avenue Appia 20, 1202 Geneva, Switzerland" locations[ "JCU library" ] = "James Cook University, Eddie Koiko Mabo library, Bldg 18, Townsville, Queensland 4811, Australia" return locations[location] # ----------------------------------------------------------------------------- with open(args.metadata_csv, "rb") as csvfile: metadataReader = csv.DictReader(csvfile, delimiter=",") counts = { "rows": 0, "docs": 0, "restrict": 0, "hc": 0, "restrict_hc": 0, "write_err": 0, "copy_err": 0, "sidecar_err": 0, "no_doc": 0, "doc_missing": 0, "sidecars": 0, } for row in metadataReader: counts["rows"] += 1 # Skipping first row as it contains the header row. if counts["rows"] > 1: real_file = row[args.file] xml_content = row[args.xml] # clean the XML (this part is special to the specific data we're getting) xml_content = clean_xml_content(xml_content) doc_access = row[args.access] doc_type = row[args.type] hc_location = ( row[args.location].split(";")[0].strip() ) # semicolon separated list -- get the first one hc_location = clean_hc_location(hc_location) doc_title = row[args.title] # bail if there's no title if doc_title == "": continue else: # print("LOOKING: " + doc_title) counts["docs"] += 1 pass # destination for the xml file flat_file_name, file_ext = os.path.splitext(real_file) # maybe there are subdirs in the file name, we'll flatten those out flat_file_name = flat_file_name.replace("/", "#") # copy the file there # maybe we have to fake up the content coz it's restricted or something fake_content = False if doc_access == "Restricted" and doc_type == "Hardcopy" and hc_location: # it's a restricted hardcopy with a location counts["restrict_hc"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is unavailable due to data sensitivity, publisher restrictions or is not digitised. ', "Please e-mail pacelf@jcu.edu.au or write to:\n\n ", get_location_info(hc_location), "\n\nto negotiate gaining access to this item.", ] ) elif ( doc_access == "Restricted" and doc_type == "Hardcopy" and not hc_location ): # it's a restricted hardcopy with no location counts["restrict_hc"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is unavailable due to data sensitivity, publisher restrictions or is not digitised. ', "Please e-mail pacelf@jcu.edu.au to negotiate gaining access to this item.", ] ) elif doc_access != "Restricted" and doc_type == "Hardcopy" and hc_location: # it's an unrestricted hardcopy with a location counts["hc"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is not available in digital format. ', "A copy is held at:\n\n ", get_location_info(hc_location), "\n\nplease write or email pacelf@jcu.edu.au to request a copy.", ] ) elif ( doc_access != "Restricted" and doc_type == "Hardcopy" and not hc_location ): # it's an unrestricted hardcopy with no location counts["hc"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is not available in digital format. ', "Please e-mail pacelf@jcu.edu.au to request a copy.", ] ) elif doc_access == "Restricted" and doc_type != "Hardcopy": # it's a restricted PDF counts["restrict"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is unavailable due to data sensitivity, publisher restrictions or is not digitised. ', "Please e-mail pacelf@jcu.edu.au to negotiate gaining access to this item.", ] ) elif flat_file_name == "": # any other situation where there's no doc counts["no_doc"] += 1 fake_content = "".join( [ 'The document "', doc_title, '" is not available in digital format. ', "Please e-mail pacelf@jcu.edu.au to discuss access.", ] ) if flat_file_name == "": flat_file_name = "PacELF_Phase2_" + str(counts["rows"]) # # by now have fake content to use, or we expect the doc to be available. # # destination for the real file real_dest_file = os.path.join(args.dest_folder, flat_file_name + file_ext) # destination for the proxy document (.txt extension) fake_dest_path = os.path.join(args.dest_folder, flat_file_name + ".txt") if fake_content: # write the fake content, if we have it try: file = open(fake_dest_path, "w") file.write(fake_content) file.close() # print(unicode('PROXIED: ') + unicode(doc_title)) except ValueError as e: counts["write_err"] += 1 print("Couldn't write content to: " + real_dest_file) print(e) else: # we didn't have fake content, so use the real doc/pdf real_file_path = os.path.join(args.src_folder, real_file) if real_file == "": print('No doc file specified for "' + doc_title + '"') counts["no_doc"] += 1 continue # try to copy the file -------- # first let's get some common error versions of the filename fn_to_try = [real_file_path] fn_to_try.append( re.sub(r"\.pdf$", r" .pdf", real_file_path) ) # space before the pdf fn_to_try.append(re.sub(r"\\", r"/", real_file_path)) # other slashes fn_to_try.append(re.sub(r"$", r".pdf", real_file_path)) # add .pdf fn_to_try.append( re.sub( r"Multicountry Pacific", r"multicountry pacific", real_file_path ) ) # upper case fn_to_try.append( re.sub( r"Mulitcountry Pacific", r"multicountry pacific", real_file_path ) ) # typo & upper case # some straight fixes fn_to_try.append( re.sub( r"\\\.pdf$", r"PDF version\.pdf", re.sub( r"Mulitcountry Pacific", r"multicountry pacific", real_file_path, ), ) ) # two fixes fn_to_try.append( re.sub( r"PacELF_102", r"PacELF_102 Jarno et al 2006", real_file_path ) ) # add author fn_to_try.append( re.sub( r"PacELF_448", r"PacELF_448 Andrews et al 2012 PLOS PATHOGENS ", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_493", r"PacELF_493 Brelsfoard et al 2008 PLOS NTDs Interspecific hybridization South Pacific filariasis vectors", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_508", r"PacELF_508 Burkot et al 2013 MAL J Barrier screens", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_314", r"PacELF_314 Stolk et al 2013 PLOS NTDs", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_317", r"PacELF_317 Debrah et al 2006 PLOS PATHOGENS Doxycycline reduces VGF and improves pathology LF", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_319", r"PacELF_319 Hooper et al 2014 PLOS NTDs Asseesing progress in reducing at risk population after 13 years", real_file_path, ) ) fn_to_try.append( re.sub( r"\\2001-05 PRG Fiji May-Jun 2011\\", r"/2011-05 PRG Fiji May-Jun 2011/", real_file_path, ) ) fn_to_try.append( re.sub( r"PacELF_414 WPRO PMM 2011 report_2011 Oct 31\.pdf", r"PacELF_414 WPRO PMM 2011 report_2011 Oct 31 PDF version.pdf", real_file_path, ) ) fn_to_try.append( re.sub( r"Multicountry Pacific/PacELF_585", r"French Polynesia/PacELF_585", real_file_path, ) ) fn_to_try.append( re.sub( r"Manson-Bahr 1912 FIlariasis and elephantiasis in Fiji LSHTM b21356658", r"Manson-Bahr 1912 FIlariasis and elephantiasis in Fiji LSHTM b21356658", real_file_path, ) ) # find the first that is a file for pth in fn_to_try: if os.path.isfile(pth): break # try copying that if os.path.isfile(pth): try: shutil.copyfile(pth, real_dest_file) # print(' COPIED: ' + doc_title) except shutil.Error as e: counts["copy_err"] += 1 print("Could not copy doc: " + pth) print(e) else: print( "Could not find doc file for title: '" + doc_title + "', file: " + pth ) counts["doc_missing"] += 1 # # Now we've got content there, make the xml sidecar file # xml_dest_file = flat_file_name + ".xml" xml_dest_path = args.dest_folder + "/" + xml_dest_file try: file = open(xml_dest_path, "w") file.write(xml_content) file.close() counts["sidecars"] += 1 except ValueError as e: counts["sidecar_err"] += 1 print("Oops, this one is dodgy: " + xml_dest_path) print("ValueError: ", e) print("\nSummary:") print( "".join( [ " ", str(counts["rows"]), " rows read: ", str(counts["docs"]), " documents processed, ", str(counts["sidecars"]), " metadata sidecars produced;", "\n ", str(counts["hc"]), " hard copies, ", str(counts["restrict"]), " restricted docs, ", str(counts["restrict_hc"]), " restricted hard copies;", "\n ", str(counts["copy_err"]), " copy errors, ", str(counts["write_err"]), " write errors, ", str(counts["sidecar_err"]), " sidecar errors, ", str(counts["doc_missing"]), " docs not locatable, ", str(counts["no_doc"]), " docs not listed.", "\n", ] ) )

37.157791

197

0.49578

1,970

18,839

4.600508

0.228426

0.021185

0.025157

0.024385

0.388613

0.366435

0.344147

0.339181

0.310493

0.29626

0

0.022933

0.400499

18,839

506

198

37.231225

0.779529

0.144116

0

0.3257

0

0.002545

0.294829

0.007297

0

1

0.007634

false

0.002545

0.015267

0

0.033079

0.02799

0

null

0

null

0

1

0

69566d12c06529c48fecd538d7a1ad6d03fffd43

1,147

py

Python

authenticationCwProject/authenticationCwApp/views.py

cs-fullstack-2019-spring/django-authentication-cw-bettyjware11

0afa675b0b6602a89ecfff9ee29a62c95f677de6

[ "Apache-2.0" ]

null

authenticationCwProject/authenticationCwApp/views.py

cs-fullstack-2019-spring/django-authentication-cw-bettyjware11

0afa675b0b6602a89ecfff9ee29a62c95f677de6

[ "Apache-2.0" ]

null

authenticationCwProject/authenticationCwApp/views.py

cs-fullstack-2019-spring/django-authentication-cw-bettyjware11

0afa675b0b6602a89ecfff9ee29a62c95f677de6

[ "Apache-2.0" ]

null

from django.shortcuts import render from django.shortcuts import HttpResponse from .forms import FoodFitnessForm from django.contrib.auth.models import User # function to test with def index(request): return HttpResponse("You made it.") # function to create new user def createUser(request): form = FoodFitnessForm(request.POST or None) context = { "form": form } if request.method == "POST": print(request.POST) User.objects.create_user(request.POST["username"], request.POST["calories"], request.POST["date"]) return render(request, "authenticationCwApp/confirmUser.html") return render(request, 'authenticationCwApp/createUser.html', context) # function to confirm new user def confirmUser(request): form = FoodFitnessForm(request.GET or None) context = { "form": form } if request.method == 'GET': User.objects.create_user(request.GET["username"], "", request.GET["calories"], request.GET["date"]) form.save() return HttpResponse("New Food Calorie Tracker Created!!!!!") return render(request, "authenticationCwApp/confirmUser.html", context)

28.675

107

0.70619

133

1,147

6.075188

0.360902

0.068069

0.070545

0.141089

0.289604

0.220297

0.089109

0

0.173496

1,147

39

108

29.410256

0.852321

0.068003

0

0.153846

0

0.198122

0.100469

0

1

0.115385

false

0

0.153846

0.038462

0.461538

0.038462

0

null

0

null

0

1

0

695934996d2e79a5c72ef6834b5be0584eb22c99

4,763

py

Python

csv-manipulation/csv-merge.py

bohnacker/data-manipulation

a46cdfdeca8d242038b118509c20b1eb39ea5b36

[ "MIT" ]

2

2020-06-05T15:57:50.000Z

2020-06-30T12:59:00.000Z

csv-manipulation/csv-merge.py

bohnacker/data-manipulation

a46cdfdeca8d242038b118509c20b1eb39ea5b36

[ "MIT" ]

null

csv-manipulation/csv-merge.py

bohnacker/data-manipulation

a46cdfdeca8d242038b118509c20b1eb39ea5b36

[ "MIT" ]

null

from __future__ import print_function # A script to help you with manipulating CSV-files. This is especially necessary when dealing with # CSVs that have more than 65536 lines because those can not (yet) be opened in Excel or Numbers. # This script works with two files from ourworldindata.org: # https://ourworldindata.org/age-structure and https://ourworldindata.org/gender-ratio # This script MERGES two CSVs. # # Usage: # - Adjust filenames and delimiters. # - Variable matchColumns: names of the matching columns in the first CSV # - Variable withColumns: names of the matching columns in the second CSV # - Variable copyColumns: which columns from the second CSV should be copied # to the first. If copyColumns is [], it copies all cloumns except # what's defined in the variable 'withColumn' # Examples: # copyColums = ['latitude', 'longitude'] Will copy those two columns # copyColums = [] Will copy all columns # --------------------------------------------- # Change the parameters according to your task: # Give the name of the CSV file where you want to add columns readFileName1 = 'worlddata-median-age.csv' # <--- Adjust here # What delimiter is used in this CSV? Usually ',' or ';' readDelimiter1 = ',' # <--- Adjust here (have a look in your source CSV) # Give the name of the CSV file that gives additional values readFileName2 = 'worlddata-share-population-female.csv' # <--- Adjust here # What delimiter is used in this CSV? Usually ',' or ';' readDelimiter2 = ',' # <--- Adjust here (have a look in your source CSV) # The result will be a new CSV file: writeFileName = 'worlddata_merged.csv' # <--- Adjust here (has to be different than readFileName1) # You can give a different delimiter for the result. writeDelimiter = ',' # <--- Adjust here (';' is usually good) matchColumns = ['Code', 'Year'] # <--- Adjust here withColumns = ['Code', 'Year'] # <--- Adjust here copyColumns = ['PercentFemale'] # <--- Adjust here # # Second example for merging longitude/latitude data to a file with countries # readFileName1 = 'wintergames_winners.csv' # readDelimiter1 = ';' # readFileName2 = 'longitude-latitude.csv' # readDelimiter2 = ',' # writeFileName = 'wintergame_winners_merged.csv' # writeDelimiter = ';' # matchColumns = ['NOC'] # withColumns = ['IOC'] # copyColumns = ['latitude', 'longitude'] # ---------------------------------------------- # No need to change anything from here on ... import csv from collections import OrderedDict readFile1 = open(readFileName1) reader1 = csv.DictReader(readFile1, delimiter=readDelimiter1) rows1 = list(reader1) readFile2 = open(readFileName2) reader2 = csv.DictReader(readFile2, delimiter=readDelimiter2) rows2 = list(reader2) writeFile = open(writeFileName, 'w') writer = csv.writer(writeFile, delimiter=writeDelimiter) # This writes the field names to the result.csv headings1 = list(reader1.fieldnames) if copyColumns == []: copyColumns = list(filter(lambda x: x != withColumn, reader.fieldnames)) writer.writerow(headings1 + copyColumns) # create dict from second csv to speed up finding stuff print('Preparing merge') print('----------------------') dic = {} unique = True for row in rows2: key = tuple(row[x] for x in withColumns) # for col in withColumns: # key = key + row[col] + '__' if key != '': if key in dic: unique = False else: dic[key] = row if (not unique): print('Warning: The columns "%s" in the second CSV has duplicate values which could result in incorrect matching.' % withColumns) print('----------------------') print('Merging') failed = [] numRows = 0 perc = 0 for i, row in enumerate(rows1): if float(i) / len(rows1) > perc: print('#', end='') perc = perc + 0.01 values = [] val = tuple(row[x] for x in matchColumns) # for col in matchColumns: # val = val + row[col] + '__' for key in headings1: values.append(row[key]) for key in copyColumns: try: values.append(dic[val][key]) except: if (not val in failed): failed.append(val) writer.writerow(values) print('\n----------------------') print('%d value(s) could not be found in the second CSV, so matching was not possible for every row.' % len(failed)) print("These values couldn't be matched:") print(failed[:100]) if (len(failed) > 100): print('... and %d more' % (len(failed) - 100))

33.780142

131

0.618728

567

4,763

5.174603

0.365079

0.030675

0.01636

0.014315

0.103613

0.093388

0.05726

0.034083

0

0.013611

0.244174

4,763

140

132

34.021429

0.801389

0.4789

0

0.031746

0

0.031746

0.186392

0.053196

0

1

0

false

0

0.047619

0

0.047619

0.190476

0

null

0

null

0

1

0

695b6b62546eaa04ce79f4261d88a6eb1b8a86ea

16,047

py

Python

script/python/assoc_sclrt_kernels_spliceai_eval_top_hits.py

HealthML/faatpipe

8292df4f34c99f035756a1acbd2c79055f652958

[ "Apache-2.0" ]

2

2021-12-06T09:00:52.000Z

2022-03-03T15:03:51.000Z

script/python/assoc_sclrt_kernels_spliceai_eval_top_hits.py

HealthML/faatpipe

8292df4f34c99f035756a1acbd2c79055f652958

[ "Apache-2.0" ]

null

script/python/assoc_sclrt_kernels_spliceai_eval_top_hits.py

HealthML/faatpipe

8292df4f34c99f035756a1acbd2c79055f652958

[ "Apache-2.0" ]

null

import os # os.environ["OMP_NUM_THREADS"] = "16" import logging logging.basicConfig(filename=snakemake.log[0], level=logging.INFO) import pandas as pd import numpy as np # seak imports from seak.data_loaders import intersect_ids, EnsemblVEPLoader, VariantLoaderSnpReader, CovariatesLoaderCSV from seak.scoretest import ScoretestNoK from seak.lrt import LRTnoK, pv_chi2mixture, fit_chi2mixture from pysnptools.snpreader import Bed import pickle import sys from util.association import BurdenLoaderHDF5 from util import Timer class GotNone(Exception): pass # set up the covariatesloader covariatesloader = CovariatesLoaderCSV(snakemake.params.phenotype, snakemake.input.covariates_tsv, snakemake.params.covariate_column_names, sep='\t', path_to_phenotypes=snakemake.input.phenotypes_tsv) # initialize the null models Y, X = covariatesloader.get_one_hot_covariates_and_phenotype('noK') null_model_score = ScoretestNoK(Y, X) null_model_lrt = LRTnoK(X, Y) # set up function to filter variants: def maf_filter(mac_report): # load the MAC report, keep only observed variants with MAF below threshold mac_report = pd.read_csv(mac_report, sep='\t', usecols=['SNP', 'MAF', 'Minor', 'alt_greater_ref']) if snakemake.params.filter_highconfidence: vids = mac_report.SNP[(mac_report.MAF < snakemake.params.max_maf) & (mac_report.Minor > 0) & ~(mac_report.alt_greater_ref.astype(bool)) & (mac_report.hiconf_reg.astype(bool))] else: vids = mac_report.SNP[(mac_report.MAF < snakemake.params.max_maf) & (mac_report.Minor > 0) & ~(mac_report.alt_greater_ref.astype(bool))] # this has already been done in filter_variants.py # load the variant annotation, keep only variants in high-confidece regions # anno = pd.read_csv(anno_tsv, sep='\t', usecols=['Name', 'hiconf_reg']) # vids_highconf = anno.Name[anno.hiconf_reg.astype(bool).values] # vids = np.intersect1d(vids, vids_highconf) return mac_report.set_index('SNP').loc[vids] def get_regions(): # load the results, keep those below a certain p-value results = pd.read_csv(snakemake.input.results_tsv, sep='\t') kern = snakemake.params.kernels if isinstance(kern, str): kern = [kern] pvcols_score = ['pv_score_' + k for k in kern ] pvcols_lrt = ['pv_lrt_' + k for k in kern] statcols = ['lrtstat_' + k for k in kern] results = results[['gene', 'n_snp', 'cumMAC', 'nCarrier'] + statcols + pvcols_score + pvcols_lrt] # get genes below threshold genes = [results.gene[results[k] < 1e-7].values for k in pvcols_score + pvcols_lrt ] genes = np.unique(np.concatenate(genes)) if len(genes) == 0: return None # set up the regions to loop over for the chromosome regions = pd.read_csv(snakemake.input.regions_bed, sep='\t', header=None, usecols=[0 ,1 ,2 ,3, 5], dtype={0 :str, 1: np.int32, 2 :np.int32, 3 :str, 5:str}) regions.columns = ['chrom', 'start', 'end', 'name', 'strand'] regions['strand'] = regions.strand.map({'+': 'plus', '-': 'minus'}) regions = regions.set_index('name').loc[genes] regions = regions.join(results.set_index('gene'), how='left').reset_index() return regions # genotype path, vep-path: assert len(snakemake.params.ids) == len (snakemake.input.bed), 'Error: length of chromosome IDs does not match length of genotype files' geno_vep = zip(snakemake.params.ids, snakemake.input.bed, snakemake.input.vep_tsv, snakemake.input.ensembl_vep_tsv, snakemake.input.mac_report, snakemake.input.h5_lof, snakemake.input.iid_lof, snakemake.input.gid_lof) # get the top hits regions_all = get_regions() if regions_all is None: logging.info('No genes pass significance threshold, exiting.') sys.exit(0) # where we store the results stats = [] i_gene = 0 # enter the chromosome loop: timer = Timer() for i, (chromosome, bed, vep_tsv, ensembl_vep_tsv, mac_report, h5_lof, iid_lof, gid_lof) in enumerate(geno_vep): if chromosome.replace('chr','') not in regions_all.chrom.unique(): continue # set up the ensembl vep loader for the chromosome spliceaidf = pd.read_csv(vep_tsv, sep='\t', usecols=['name', 'chrom', 'end', 'gene', 'max_effect', 'DS_AG', 'DS_AL', 'DS_DG', 'DS_DL', 'DP_AG', 'DP_AL', 'DP_DG', 'DP_DL'], index_col='name') # get set of variants for the chromosome: mac_report = maf_filter(mac_report) filter_vids = mac_report.index.values # filter by MAF keep = intersect_ids(filter_vids, spliceaidf.index.values) spliceaidf = spliceaidf.loc[keep] spliceaidf.reset_index(inplace=True) # filter by impact: spliceaidf = spliceaidf[spliceaidf.max_effect >= snakemake.params.min_impact] # set up the regions to loop over for the chromosome regions = regions_all.copy() # discard all genes for which we don't have annotations gene_ids = regions.name.str.split('_', expand=True) # table with two columns, ensembl-id and gene-name regions['gene'] = gene_ids[1] # this is the gene name regions['ensembl_id'] = gene_ids[0] regions.set_index('gene', inplace=True) genes = intersect_ids(np.unique(regions.index.values), np.unique(spliceaidf.gene)) # intersection of gene names regions = regions.loc[genes].reset_index() # subsetting regions = regions.sort_values(['chrom', 'start', 'end']) # check if the variants are protein LOF variants, load the protein LOF variants: ensemblvepdf = pd.read_csv(ensembl_vep_tsv, sep='\t', usecols=['Uploaded_variation', 'Gene']) # this column will contain the gene names: genes = intersect_ids(np.unique(ensemblvepdf.Gene.values), regions.ensembl_id) # intersection of ensembl gene ids ensemblvepdf = ensemblvepdf.set_index('Gene').loc[genes].reset_index() ensemblvepdf['gene'] = gene_ids.set_index(0).loc[ensemblvepdf.Gene.values].values # set up the merge ensemblvepdf.drop(columns=['Gene'], inplace=True) # get rid of the ensembl ids, will use gene names instead ensemblvepdf.rename(columns={'Uploaded_variation': 'name'}, inplace=True) ensemblvepdf['is_plof'] = 1. ensemblvepdf = ensemblvepdf[~ensemblvepdf.duplicated()] # if multiple ensembl gene ids map to the same gene names, this prevents a crash. # we add a column to the dataframe indicating whether the variant is already annotated as protein loss of function by the ensembl variant effect predictor spliceaidf = pd.merge(spliceaidf, ensemblvepdf, on=['name', 'gene'], how='left', validate='one_to_one') spliceaidf['is_plof'] = spliceaidf['is_plof'].fillna(0.).astype(bool) # initialize the loader # Note: we use "end" here because the start + 1 = end, and we need 1-based coordiantes (this would break if we had indels) eveploader = EnsemblVEPLoader(spliceaidf['name'], spliceaidf['chrom'].astype('str') + ':' + spliceaidf['end'].astype('str'), spliceaidf['gene'], data=spliceaidf[['max_effect', 'is_plof', 'DS_AG', 'DS_AL', 'DS_DG', 'DS_DL', 'DP_AG', 'DP_AL', 'DP_DG', 'DP_DL']].values) # set up the variant loader (splice variants) for the chromosome plinkloader = VariantLoaderSnpReader(Bed(bed, count_A1=True, num_threads=4)) plinkloader.update_variants(eveploader.get_vids()) plinkloader.update_individuals(covariatesloader.get_iids()) # set up the protein LOF burden loader bloader_lof = BurdenLoaderHDF5(h5_lof, iid_lof, gid_lof) bloader_lof.update_individuals(covariatesloader.get_iids()) # set up the splice genotype + vep loading function def get_splice(interval): try: V1 = eveploader.anno_by_interval(interval, gene=interval['name'].split('_')[1]) except KeyError: raise GotNone if V1.index.empty: raise GotNone vids = V1.index.get_level_values('vid') V1 = V1.droplevel(['gene']) temp_genotypes, temp_vids = plinkloader.genotypes_by_id(vids, return_pos=False) temp_genotypes -= np.nanmean(temp_genotypes, axis=0) G1 = np.ma.masked_invalid(temp_genotypes).filled(0.) ncarrier = np.sum(G1 > 0.5, axis=0) cummac = mac_report.loc[vids].Minor # spliceAI max score weights = V1[0].values.astype(np.float64) is_plof = V1[1].values.astype(bool) splice_preds_all = V1.iloc[:,2:] splice_preds_all.columns = ['DS_AG', 'DS_AL', 'DS_DG', 'DS_DL', 'DP_AG', 'DP_AL', 'DP_DG', 'DP_DL'] # "standardized" positions -> codon start positions # pos = V1[0].values.astype(np.int32) return G1, vids, weights, ncarrier, cummac, is_plof, splice_preds_all # set up the protein-LOF loading function def get_plof(interval): try: G2 = bloader_lof.genotypes_by_id(interval['name']).astype(np.float) except KeyError: G2 = None return G2 # set up the test-function for a single gene def test_gene(interval, seed): pval_dict = {} pval_dict['gene'] = interval['name'] called = [] def pv_score(GV): pv = null_model_score.pv_alt_model(GV) if pv < 0.: pv = null_model_score.pv_alt_model(GV, method='saddle') return pv def call_score(GV, name, vids=None): if name not in pval_dict: pval_dict[name] = {} called.append(name) pval_dict[name] = {} # single-marker p-values pval_dict[name]['pv_score'] = np.array([pv_score(GV[:,i,np.newaxis]) for i in range(GV.shape[1])]) # single-marker coefficients beta = [ null_model_score.coef(GV[:,i,np.newaxis]) for i in range(GV.shape[1]) ] pval_dict[name]['beta'] = np.array([x['beta'][0,0] for x in beta]) pval_dict[name]['betaSd'] = np.array([np.sqrt(x['var_beta'][0,0]) for x in beta]) if vids is not None: pval_dict[name]['vid'] = vids def call_lrt(GV, name, vids=None): if name not in pval_dict: pval_dict[name] = {} called.append(name) # get gene parameters, test statistics and and single-marker regression weights lik = null_model_lrt.altmodel(GV) pval_dict[name]['nLL'] = lik['nLL'] pval_dict[name]['sigma2'] = lik['sigma2'] pval_dict[name]['lrtstat'] = lik['stat'] pval_dict[name]['h2'] = lik['h2'] logdelta = null_model_lrt.model1.find_log_delta(GV.shape[1]) pval_dict[name]['log_delta'] = logdelta['log_delta'] pval_dict[name]['coef_random'] = null_model_lrt.model1.getPosteriorWeights(logdelta['beta'], logdelta=logdelta['log_delta']) if vids is not None: pval_dict[name]['vid'] = vids # load splice variants G1, vids, weights, ncarrier, cummac, is_plof, splice_preds_all = get_splice(interval) # keep indicates which variants are NOT "protein LOF" variants, i.e. variants already identified by the ensembl VEP keep = ~is_plof # these are common to all kernels pval_dict['vid'] = vids pval_dict['weights'] = weights pval_dict['MAC'] = cummac pval_dict['nCarrier'] = ncarrier pval_dict['not_LOF'] = keep for col in splice_preds_all.columns: pval_dict[col] = splice_preds_all[col].values.astype(np.float32) # single-variant p-values: call_score(G1, 'variant_pvals') # single variant p-values and coefficients estimated independently call_lrt(G1.dot(np.diag(np.sqrt(weights), k=0)), 'variant_pvals') # single variant coefficients estimated *jointly* after weighting # sanity checks assert len(vids) == interval['n_snp'], 'Error: number of variants does not match! expected: {} got: {}'.format(interval['n_snp'], len(vids)) assert cummac.sum() == interval['cumMAC'], 'Error: cumMAC does not match! expeced: {}, got: {}'.format(interval['cumMAC'], cummac.sum()) # do a score burden test (max weighted), this is different than the baseline! G1_burden = np.max(np.where(G1 > 0.5, np.sqrt(weights), 0.), axis=1, keepdims=True) call_score(G1_burden, 'linwb') call_lrt(G1_burden, 'linwb') # linear weighted kernel G1 = G1.dot(np.diag(np.sqrt(weights), k=0)) # do a score test (linear weighted) call_score(G1, 'linw', vids=vids) call_lrt(G1, 'linw') # load plof burden G2 = get_plof(interval) if G2 is not None: call_score(G2, 'LOF') call_lrt(G2, 'LOF') if np.any(keep): # merged (single variable) G1_burden_mrg = np.maximum(G2, G1_burden) call_score(G1_burden_mrg, 'linwb_mrgLOF') call_lrt(G1_burden_mrg, 'linwb_mrgLOF') # concatenated ( >= 2 variables) # we separate out the ones that are already part of the protein LOF variants! G1 = np.concatenate([G1[:, keep], G2], axis=1) call_score(G1, 'linw_cLOF', vids=np.array(vids[keep].tolist() + [-1])) call_lrt(G1, 'linw_cLOF') else: logging.info('All Splice-AI variants for gene {} where already identified by the Ensembl variant effect predictor'.format(interval['name'])) return pval_dict, called logging.info('loaders for chromosome {} initialized in {:.1f} seconds.'.format(chromosome, timer.check())) # run tests for all genes on the chromosome for _, region in regions.iterrows(): try: gene_stats, called = test_gene(region, i_gene) except GotNone: continue # build the single-variant datafame single_var_columns = ['gene', 'vid', 'weights', 'MAC', 'nCarrier', 'not_LOF', 'DS_AG', 'DS_AL', 'DS_DG', 'DS_DL', 'DP_AG', 'DP_AL', 'DP_DG', 'DP_DL'] sv_df = pd.DataFrame.from_dict({k: gene_stats[k] for k in single_var_columns}) sv_df['pv_score'] = gene_stats['variant_pvals']['pv_score'] # single-variant p-values estimated independently sv_df['coef_random'] = gene_stats['variant_pvals']['coef_random'] # single-variant coefficients estimated jointly after weighting sv_df['beta'] = gene_stats['variant_pvals']['beta'] # single-variant coeffcients estimated independently *without* weighting sv_df['betaSd'] = gene_stats['variant_pvals']['betaSd'] # standard errors for the single-variant coefficients estimated independently *without* weighting sv_df['pheno'] = snakemake.params.phenotype out_dir = os.path.join(snakemake.params.out_dir_stats, region['name']) os.makedirs(out_dir, exist_ok=True) sv_df.to_csv(out_dir + '/variants.tsv.gz', sep='\t', index=False) for k in called: if k == 'variant_pvals': continue results_dict = gene_stats[k] df_cols = ['pv_score', 'coef_random', 'beta', 'betaSd', 'vid'] # parts of the dict that have lenght > 1 df = pd.DataFrame.from_dict(data={k: results_dict[k] for k in df_cols if k in results_dict}) df['gene'] = gene_stats['gene'] df['pheno'] = snakemake.params.phenotype df.to_csv(out_dir + '/{}.tsv.gz'.format(k), sep='\t', index=False) # other cols ['nLL', 'sigma2', 'lrtstat', 'h2', 'log_delta'] other_cols = {k: v for k, v in results_dict.items() if k not in df_cols} other_cols['gene'] = gene_stats['gene'] other_cols['pheno'] = snakemake.params.phenotype pickle.dump(other_cols, open(out_dir + '/{}_stats.pkl'.format(k), 'wb')) i_gene += 1 logging.info('tested {} genes...'.format(i_gene)) timer.reset()

42.228947

271

0.645541

2,173

16,047

4.598251

0.202945

0.020016

0.016813

0.003503

0.179243

0.132706

0.110889

0.096677

0.081465

0.076261

0

0.010103

0.228952

16,047

380

272

42.228947

0.797462

0.200287

0

0.114155

0

0.114707

0

0.013699

1

0.03653

false

0.009132

0.054795

0

0.127854

0

null

0

null

0

1

0

695bd5a3034969d8612674c32efea1218548dce0

4,741

py

Python

animations/dafP1.py

TristanCacqueray/demo-render

4c8403e684165e5e75c046ee023c1f794a6650a8

[ "Apache-2.0" ]

9

2018-02-19T14:17:12.000Z

2021-03-27T14:46:28.000Z

animations/dafP1.py

TristanCacqueray/demo-render

4c8403e684165e5e75c046ee023c1f794a6650a8

[ "Apache-2.0" ]

null

animations/dafP1.py

TristanCacqueray/demo-render

4c8403e684165e5e75c046ee023c1f794a6650a8

[ "Apache-2.0" ]

null

#!/bin/env python # 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 yaml from utils.animation import Animation, run_main from utils.audio import SpectroGram, AudioMod p = """ formula: | z.imag = fabs(z.imag); z = cdouble_powr(z, mod); z = cdouble_add(z, c); z = cdouble_log(z); kernel: mean-distance kernel_params: "double mod" kernel_params_mod: - mod mod: 1 xyinverted: True gradient: render_data/Solankii Gradients for Gimp/Gradient-#21.ggr c_imag: -0.13422671142194348 c_real: 0.298544669649099 i_step: 0.012438114469344182 julia: true map_center_imag: 0.298544669649099 map_center_real: -0.1217885969525993 map_radius: 0.12438114469344182 r_step: 0.012438114469344182 radius: 51.16156978094776 """ class Demo(Animation): def __init__(self): self.scenes = [ [4000, None], [3500, self.ending], [3286, self.zoom], [2526, self.verse4], [2025, self.verse3], [1770, self.verse2], [1520, self.tr1], [754, self.verse1], [0, self.intro], ] super().__init__(yaml.load(p)) def setAudio(self, audio): self.audio = audio self.spectre = SpectroGram(audio.audio_frame_size) self.audio_events = { "low": AudioMod((0, 12), "max", decay=10), "mid": AudioMod((152, 483), "max", decay=5), "hgh": AudioMod((12, 456), "avg"), } def ending(self, frame): self.params["c_imag"] -= 4e-5 * self.low + 1e-4 * self.mid + 1e-5 self.params["grad_freq"] += 2e-1 * self.hgh def zoom(self, frame): if self.scene_init: self.imag_mod = self.logspace(self.params["c_imag"], 0.9187686207968877) self.rad_mod = self.logspace(self.params["radius"], 0.03) self.freq_mod = self.logspace(self.params["grad_freq"], 0.20) self.params["grad_freq"] = self.freq_mod[self.scene_pos] self.params["radius"] = self.rad_mod[self.scene_pos] if frame < 3400: self.params["c_imag"] = self.imag_mod[self.scene_pos] else: self.params["c_imag"] -= 4e-5 * self.low + 1e-4 * self.mid def verse4(self, frame): if self.scene_init: self.rad_mod = self.logspace(self.params["radius"], 3606) self.params["radius"] = self.rad_mod[self.scene_pos] self.params["c_imag"] += 5e-6 * self.low self.params["c_real"] -= 5e-6 * self.mid def verse3(self, frame): if self.scene_init: self.rad_mod = self.logspace(self.params["radius"], 556) self.params["radius"] = self.rad_mod[self.scene_pos] self.params["c_imag"] += 8e-5 * self.mid self.params["c_real"] -= 1e-5 * self.low self.params["grad_freq"] += 1e-2 * self.hgh def verse2(self, frame): if self.scene_init: self.base_real = self.params["c_real"] self.params["c_imag"] -= 8e-5 * self.low self.params["grad_freq"] += 1e-2 * self.mid # self.params["c_real"] += 1e-4 * self.mid # self.params["c_real"] += 1e-4 * self.mid def tr1(self, frame): if self.scene_init: self.rad_mod = self.logspace(self.params["radius"], 129) self.params["radius"] = self.rad_mod[self.scene_pos] self.params["grad_freq"] -= 1e-2 * self.low self.params["c_imag"] += 1e-4 * self.mid def verse1(self, frame): if self.scene_init: self.rad_mod = self.linspace(self.params["radius"], 0.1) self.params["radius"] = self.rad_mod[self.scene_pos] self.params["c_imag"] += 4e-5 * self.low self.params["c_real"] += 1e-4 * self.mid self.params["grad_freq"] += 2e-2 * self.hgh def intro(self, frame): if self.scene_init: self.base_real = self.params["c_real"] self.rad_mod = self.linspace(self.params["radius"], 0.08) self.params["radius"] = self.rad_mod[self.scene_pos] self.params["c_imag"] += 4e-5 * self.low self.params["c_real"] = self.base_real + 2e-4 * self.hgh self.params["grad_freq"] += 3e-3 * self.mid if __name__ == "__main__": run_main(Demo())

34.605839

75

0.607467

670

4,741

4.153731

0.274627

0.136543

0.071146

0.060367

0.440532

0.398491

0.385196

0.355372

0.341718

0.306504

0

0.08228

0.248893

4,741

136

76

34.860294

0.699242

0.133938

0

0.163462

0

0.195748

0.005132

0

1

0.096154

false

0

0.028846

0

0.134615

0

null

0

null

0

1

0

695cb03fad71e52d66b52655147eb22d30993c0d

366

py

Python

backup/server/__init__.py

TheSithPadawan/CSE222A-CourseProject

ded7232fa76a8c57e2355a6559573f71e6e63871

[ "MIT" ]

1

2019-02-17T06:41:59.000Z

backup/server/__init__.py

TheSithPadawan/CSE222A-CourseProject

ded7232fa76a8c57e2355a6559573f71e6e63871

[ "MIT" ]

1

2019-02-21T05:19:31.000Z

2019-03-02T06:38:33.000Z

backup/server/__init__.py

TheSithPadawan/CSE222A-CourseProject

ded7232fa76a8c57e2355a6559573f71e6e63871

[ "MIT" ]

1

2022-02-12T05:18:49.000Z

from flask import Flask from flask_sqlalchemy import SQLAlchemy db_url = 'postgresql://postgres:postgres@localhost:5432/postgres' db = SQLAlchemy() def create_app(): app = Flask(__name__) app.config["SQLALCHEMY_DATABASE_URI"] = db_url app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False # initialize database db.init_app(app) return app

22.875

65

0.743169

46

366

5.630435

0.5

0.069498

0.146718

0

0.013029

0.161202

366

15

66

24.4

0.830619

0.051913

0

0.311047

0

1

0.1

false

0

0.2

0

0.4

0

null

0

null

0

1

0

696b0fe7fb16a5ee74d9c65d62711192a825d49b

942

py

Python

loop_fix_sample_rate.py

Crystalwarrior/AO2-Scripts

acbabc64706374362bd93662d9ce1e2cf8eb35fb

[ "MIT" ]

1

2020-11-21T14:27:27.000Z

loop_fix_sample_rate.py

Crystalwarrior/AO2-Scripts

acbabc64706374362bd93662d9ce1e2cf8eb35fb

[ "MIT" ]

null

loop_fix_sample_rate.py

Crystalwarrior/AO2-Scripts

acbabc64706374362bd93662d9ce1e2cf8eb35fb

[ "MIT" ]

1

2020-08-14T02:44:46.000Z

import os from os import path old_sample_rate = float(input("What was the original sample rate? ")) new_sample_rate = float(input("What is the new sample rate? ")) for file in os.listdir(os.getcwd()): name = file.rsplit(".",1)[0] if file.rsplit(".",1)[-1] == "opus" and path.exists(name + ".opus.txt"): print('\n') print(name) f = open(name + ".opus.txt", "r") lines = f.readlines() f.close() new_lines = [] for line in lines: args = line.split('=') command = args[0].strip() samples = int(args[1].strip()) new_samples = int(samples * (new_sample_rate / old_sample_rate)) new_line = f'{command}={new_samples}' print(f'Converting {line.strip()} to {new_line.strip()}') new_lines.append(new_line) f = open(name + ".opus.txt", "w") f.write('\n'.join(new_lines)) f.close()

36.230769

76

0.549894

129

942

3.891473

0.387597

0.119522

0.077689

0.079681

0.159363

0

0.008837

0.279193

942

26

77

36.230769

0.730486

0

0.083333

0

0.184518

0.02439

0

1

0

false

0

0.083333

0

0.083333

0.125

0

null

0

null

0

1

0

696b8fef41b6b27dc44ddf7473367dc19aba29de

2,221

py

Python

reviewboard/extensions/templatetags/rb_extensions.py

Khan/reviewboard

51ec4261e67b8bf4e2cfa9a0894a97b16509ad33

[ "MIT" ]

1

2015-09-11T15:50:17.000Z

reviewboard/extensions/templatetags/rb_extensions.py

Khan/reviewboard

51ec4261e67b8bf4e2cfa9a0894a97b16509ad33

[ "MIT" ]

null

reviewboard/extensions/templatetags/rb_extensions.py

Khan/reviewboard

51ec4261e67b8bf4e2cfa9a0894a97b16509ad33

[ "MIT" ]

null

from django import template from django.conf import settings from django.template.loader import render_to_string from djblets.util.decorators import basictag from reviewboard.extensions.hooks import DiffViewerActionHook, \ NavigationBarHook, \ ReviewRequestActionHook, \ ReviewRequestDropdownActionHook register = template.Library() def action_hooks(context, hookcls, action_key="action", template_name="extensions/action.html"): """Displays all registered action hooks from the specified ActionHook.""" s = "" for hook in hookcls.hooks: for actions in hook.get_actions(context): if actions: new_context = { action_key: actions } context.update(new_context) s += render_to_string(template_name, new_context) return s @register.tag @basictag(takes_context=True) def diffviewer_action_hooks(context): """Displays all registered action hooks for the diff viewer.""" return action_hooks(context, DiffViewerActionHook) @register.tag @basictag(takes_context=True) def review_request_action_hooks(context): """Displays all registered action hooks for review requests.""" return action_hooks(context, ReviewRequestActionHook) @register.tag @basictag(takes_context=True) def review_request_dropdown_action_hooks(context): """Displays all registered action hooks for review requests.""" return action_hooks(context, ReviewRequestDropdownActionHook, "actions", "extensions/action_dropdown.html") @register.tag @basictag(takes_context=True) def navigation_bar_hooks(context): """Displays all registered navigation bar entries.""" s = "" for hook in NavigationBarHook.hooks: for nav_info in hook.get_entries(context): if nav_info: context.push() context['entry'] = nav_info s += render_to_string("extensions/navbar_entry.html", context) context.pop() return s

30.847222

78

0.641603

225

2,221

6.16

0.288889

0.087302

0.090909

0.077922

0.344877

0.29798

0.243146

0.131313

0

0.281405

2,221

71

79

31.28169

0.868421

0.130122

0

0.25

0

0.051941

0.042497

0

1

0.104167

false

0

0.104167

0

0.3125

0

null

0

null

0

1

0

696c68c8ad6875cf4824c538720ffa0e9657eb27

8,671

py

Python

opengnn/models/model.py

CoderPat/OpenGNN

bc54328ad4aa034098073c72153eed361b4266ce

[ "MIT" ]

32

2019-01-28T13:38:21.000Z

2022-03-29T08:39:00.000Z

opengnn/models/model.py

CoderPat/OpenGNN

bc54328ad4aa034098073c72153eed361b4266ce

[ "MIT" ]

1

2020-01-16T03:09:18.000Z

2020-01-16T03:44:20.000Z

opengnn/models/model.py

CoderPat/OpenGNN

bc54328ad4aa034098073c72153eed361b4266ce

[ "MIT" ]

7

2019-03-07T14:13:15.000Z

2022-03-15T10:40:41.000Z

from abc import ABC, abstractmethod from typing import Dict, Any import tensorflow as tf import numpy as np from opengnn.utils.data import diverse_batch, batch_and_bucket_by_size from opengnn.utils.data import filter_examples_by_size, truncate_examples_by_size def optimize(loss: tf.Tensor, params: Dict[str, Any]): global_step = tf.train.get_or_create_global_step() optimizer = params.get('optimizer', 'Adam') if optimizer != 'Adam': optimizer_class = getattr(tf.train, optimizer, None) if optimizer_class is None: raise ValueError("Unsupported optimizer %s" % optimizer) optimizer_params = params.get("optimizer_params", {}) def optimizer(lr): return optimizer_class(lr, **optimizer_params) learning_rate = params['learning_rate'] if params.get('decay_rate') is not None: learning_rate = tf.train.exponential_decay( learning_rate, global_step, decay_steps=params.get('decay_steps', 1), decay_rate=params['decay_rate'], staircase=True) return tf.contrib.layers.optimize_loss( loss=loss, global_step=global_step, learning_rate=learning_rate, clip_gradients=params['clip_gradients'], summaries=[ "learning_rate", "global_gradient_norm", ], optimizer=optimizer, name="optimizer") class Model(ABC): def __init__(self, name: str, features_inputter=None, labels_inputter=None) -> None: self.name = name self.features_inputter = features_inputter self.labels_inputter = labels_inputter def model_fn(self): def _model_fn(features, labels, mode, params, config=None): if mode == tf.estimator.ModeKeys.TRAIN: with tf.variable_scope(self.name): # build models graph outputs, predictions = self.__call__( features, labels, mode, params, config) # compute loss, tb_loss and train_op loss, tb_loss = self.compute_loss( features, labels, outputs, params, mode) train_op = optimize(loss, params) return tf.estimator.EstimatorSpec( mode, loss=tb_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: with tf.variable_scope(self.name): # build models graph outputs, predictions = self.__call__( features, labels, mode, params, config) # compute loss, tb_loss and metric ops loss, tb_loss = self.compute_loss( features, labels, outputs, params, mode) metrics = self.compute_metrics( features, labels, predictions) # TODO: this assumes that the loss across validation can be # calculated as the average over the loss of the minibatch # which is not always the case (cross entropy averaged over time an batch) # but if minibatch a correctly shuffled, this is a good aproximation for now return tf.estimator.EstimatorSpec( mode, loss=tb_loss, eval_metric_ops=metrics) elif mode == tf.estimator.ModeKeys.PREDICT: with tf.variable_scope(self.name): # build models graph _, predictions = self.__call__( features, labels, mode, params, config) return tf.estimator.EstimatorSpec( mode, predictions=predictions) return _model_fn def input_fn(self, mode: tf.estimator.ModeKeys, batch_size: int, metadata, features_file, labels_file=None, sample_buffer_size=None, maximum_features_size=None, maximum_labels_size=None, features_bucket_width=None, labels_bucket_width=None, num_threads=None): assert not (mode != tf.estimator.ModeKeys.PREDICT and labels_file is None) # the function returned def _input_fn(): self.initialize(metadata) feat_dataset, feat_process_fn, feat_batch_fn, features_size_fn =\ self.get_features_builder(features_file, mode) if labels_file is not None: labels_dataset, labels_process_fn, \ labels_batch_fn, labels_size_fn = \ self.get_labels_builder(labels_file, mode) dataset = tf.data.Dataset.zip((feat_dataset, labels_dataset)) def process_fn(features, labels): return feat_process_fn(features), labels_process_fn(labels, features) def batch_fn(dataset, batch_size): return diverse_batch( dataset, batch_size, (feat_batch_fn, labels_batch_fn)) example_size_fns = [features_size_fn, labels_size_fn] bucket_widths = [features_bucket_width, labels_bucket_width] maximum_example_size = (maximum_features_size, maximum_labels_size) else: dataset = feat_dataset process_fn = feat_process_fn batch_fn = feat_batch_fn example_size_fns = features_size_fn bucket_widths = features_bucket_width maximum_example_size = maximum_features_size # shuffle, process batch and allow repetition # TODO: Fix derived seed (bug in tensorflow) seed = np.random.randint(np.iinfo(np.int64).max) if sample_buffer_size is not None: dataset = dataset.shuffle( sample_buffer_size, reshuffle_each_iteration=False, seed=seed) dataset = dataset.map(process_fn, num_parallel_calls=num_threads or 4) dataset = dataset.apply(filter_examples_by_size( example_size_fns=example_size_fns, maximum_example_sizes=maximum_example_size)) dataset = dataset.apply(batch_and_bucket_by_size( batch_size=batch_size, batch_fn=batch_fn, bucket_widths=bucket_widths, example_size_fns=example_size_fns)) if mode == tf.estimator.ModeKeys.TRAIN: dataset = dataset.repeat() return dataset.prefetch(None) return _input_fn def initialize(self, metadata): """ Runs model specific initialization (e.g. vocabularies loading). Args: metadata: A dictionary containing additional metadata set by the user. """ if self.features_inputter is not None: self.features_inputter.initialize(metadata) if self.labels_inputter is not None: self.labels_inputter.initialize(metadata) @abstractmethod def __call__(self, features, labels, mode, params, config=None): raise NotImplementedError() @abstractmethod def compute_loss(self, features, labels, outputs, params, mode): raise NotImplementedError() @abstractmethod def compute_metrics(self, features, labels, predictions): raise NotImplementedError() def get_features_builder(self, features_file, mode): if self.features_inputter is None: raise NotImplementedError() dataset = self.features_inputter.make_dataset(features_file, mode) process_fn = self.features_inputter.process batch_fn = self.features_inputter.batch size_fn = self.features_inputter.get_example_size return dataset, process_fn, batch_fn, size_fn def get_labels_builder(self, labels_file, mode): if self.labels_inputter is None: raise NotImplementedError() dataset = self.labels_inputter.make_dataset(labels_file, mode) process_fn = self.labels_inputter.process batch_fn = self.labels_inputter.batch size_fn = self.labels_inputter.get_example_size return dataset, process_fn, batch_fn, size_fn

39.958525

93

0.587937

913

8,671

5.297919

0.20701

0.018813

0.033078

0.02853

0.357039

0.237544

0.199504

0.167459

0.104817

0.095927

0

0.000705

0.345404

8,671

216

94

40.143519

0.85148

0.075539

0

0.170886

0

0.020282

0

0.00463

0.006329

1

0.094937

false

0

0.037975

0.018987

0.208861

0

null

0

null

0

1

0

696cdcbe00ab21ab20807832ccc7573329262037

894

py

Python

pyopendds/dev/itl2py/Output.py

jwillemsen/pyopendds

fd025416a02433dd42eeaf1ae449b6c1e19d177e

[ "MIT" ]

19

2020-03-10T22:23:00.000Z

2022-03-30T01:18:56.000Z

pyopendds/dev/itl2py/Output.py

jwillemsen/pyopendds

fd025416a02433dd42eeaf1ae449b6c1e19d177e

[ "MIT" ]

28

2020-02-15T18:07:08.000Z

2022-03-31T18:38:57.000Z

pyopendds/dev/itl2py/Output.py

jwillemsen/pyopendds

fd025416a02433dd42eeaf1ae449b6c1e19d177e

[ "MIT" ]

6

2021-04-29T07:39:11.000Z

2022-01-21T13:38:13.000Z

from pathlib import Path from .ast import NodeVisitor class Output(NodeVisitor): def __init__(self, context: dict, path: Path, templates: dict): self.context = context self.path = path self.templates = {} for filename, template in templates.items(): self.templates[path / filename] = context['jinja'].get_template(template) def write(self): if self.context['dry_run']: print('######################################## Create Dir', self.path) else: self.path.mkdir(exist_ok=True) for path, template in self.templates.items(): content = template.render(self.context) if self.context['dry_run']: print('======================================== Write file', path) print(content) else: path.write_text(content)

33.111111

85

0.530201

91

894

5.10989

0.406593

0.11828

0.055914

0.068817

0.103226

0

0.282998

894

26

86

34.384615

0.725429

0

0.190476

0

0.135347

0.089485

0

1

0.095238

false

0

0.095238

0

0.238095

0.142857

0

null

0

null

0

1

0

696f6073b8eafcb697b67113138ad0cec4dabea2

3,423

py

Python

Lab7/Q3.py

fancent/PHY407

38ce8badb9537060becc255ec64e6de2968ca73c

[ "MIT" ]

1

2020-12-20T17:30:06.000Z

Lab7/Q3.py

fancent/PHY407

38ce8badb9537060becc255ec64e6de2968ca73c

[ "MIT" ]

null

Lab7/Q3.py

fancent/PHY407

38ce8badb9537060becc255ec64e6de2968ca73c

[ "MIT" ]

1

2021-06-12T14:21:13.000Z

""" This file aims to simulate the Belousov–Zhabotinsky reaction, is a chemical mixture which, when heated, undergoes a series of reactions that cause the chemical concentrations in the mixture to oscillate between two extremes (x, y). """ import numpy as np import matplotlib.pyplot as plt ## Constants a = 1 b = 3 x0 = 0 # x concentration level (M) y0 = 0 # y concentration level (M) targetAcc = 10 ** -10 # target accuracy for BS method start = 0 # start time (s) end = 20 # end time (s) def f(r): """ This function calculates the equations for the BZ reaction """ x = r[0] y = r[1] dxdt = 1 - ((b + 1) * x) + (a * (x ** 2) * y) dydt = (b * x) - (a * (x ** 2) * y) return np.array([dxdt, dydt], float) def midpoint(r, n, H): """ This function calculates the modified mid-point method given in the textbook """ r2 = np.copy(r) h = H / n r1 = r + 0.5 * h * f(r) r2 += h * f(r1) for _ in range(n - 1): r1 += h * f(r2) r2 += h * f(r1) return 0.5 * (r1 + r2 + 0.5 * h * f(r2)) def BZ_reaction(): """ This function simulates the entire Belousov–Zhabotinsky reaction from start time to end time with the given constants at the beginning of the file using the Bulirsch–Stoer method with recursion instead of a while loop. """ r = np.array([x0, y0], float) tpoints = [start] xpoints = [r[0]] ypoints = [r[1]] def BS(r, t, H): """ This function is just a shell for the following recursive function if n, the number of recursive calls, exceeds 8. Then we will redo the calculation with a smaller H. """ def BS_row(R1, n): """ This function calculates the row of extrapolation estimates. Then it calculates the error and check if it falls under our desired accuracy. If not, it will recurse on itself with a larger n. If yes, then it will update the list of variables. """ if n > 8: r1 = BS(r, t, H / 2) return BS(r1, t + H / 2, H / 2) else: R2 = [midpoint(r, n, H)] for m in range(1, n): R2.append(R2[m - 1] + (R2[m - 1] - R1[m - 1]) / ((n / (n - 1)) ** (2 * (m)) - 1)) R2 = np.array(R2, float) error_vector = (R2[n - 2] - R1[n - 2]) / ((n / (n - 1)) ** (2 * (n - 1)) - 1) error = np.sqrt(error_vector[0] ** 2 + error_vector[1] ** 2) target_accuracy = H * targetAcc if error < target_accuracy: tpoints.append(t + H) xpoints.append(R2[n - 1][0]) ypoints.append(R2[n - 1][1]) return R2[n - 1] else: return BS_row(R2, n + 1) return BS_row(np.array([midpoint(r, 1, H)], float), 2) BS(r, start, end - start) return tpoints, xpoints, ypoints #plotting our results t, x, y = BZ_reaction() fig, graph = plt.subplots() graph.plot(t, x, 'r', label="x") graph.plot(t, y, 'b', label="y") graph.plot(t, x, 'r.') graph.plot(t, y, 'b.') graph.set(xlabel='time (s)', ylabel='concentration level (M)', title='Belousov–Zhabotinsky concentration level over time') graph.grid() graph.legend() fig.savefig("q3.png") plt.show()

31.990654

101

0.532282

512

3,423

3.544922

0.3125

0.008815

0.041322

0.031956

0

0.038698

0.33567

3,423

107

102

31.990654

0.757696

0.334502

0

0.0625

0

0.045564

0

1

0.078125

false

0

0.03125

0

0.21875

0

null

0

null

0

1

0

69708664ff337e44ffae2d3817c35e032d51bec6

2,747

py

Python

tests/solr_tests/tests/management_commands.py

kmgroup/django-haystack-1.2.x-for-d1.8

b9368f169eec06e78131e81d6968cc6580d6ddee

[ "BSD-3-Clause" ]

1

2016-02-24T19:40:05.000Z

tests/solr_tests/tests/management_commands.py

kmgroup/django-haystack-1.2.x-for-d1.8

b9368f169eec06e78131e81d6968cc6580d6ddee

[ "BSD-3-Clause" ]

null

tests/solr_tests/tests/management_commands.py

kmgroup/django-haystack-1.2.x-for-d1.8

b9368f169eec06e78131e81d6968cc6580d6ddee

[ "BSD-3-Clause" ]

null

import pysolr from django.conf import settings from django.core.management import call_command from django.test import TestCase from haystack import indexes from haystack.sites import SearchSite from core.models import MockModel class SolrMockSearchIndex(indexes.SearchIndex): text = indexes.CharField(document=True, use_template=True) name = indexes.CharField(model_attr='author', faceted=True) pub_date = indexes.DateField(model_attr='pub_date') class ManagementCommandTestCase(TestCase): fixtures = ['bulk_data.json'] def setUp(self): super(ManagementCommandTestCase, self).setUp() self.solr = pysolr.Solr(settings.HAYSTACK_SOLR_URL) self.site = SearchSite() self.site.register(MockModel, SolrMockSearchIndex) # Stow. import haystack self.old_site = haystack.site haystack.site = self.site def tearDown(self): import haystack haystack.site = self.old_site super(ManagementCommandTestCase, self).tearDown() def test_basic_commands(self): call_command('clear_index', interactive=False, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 0) call_command('update_index', verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 23) call_command('clear_index', interactive=False, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 0) call_command('rebuild_index', interactive=False, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 23) def test_remove(self): call_command('clear_index', interactive=False, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 0) call_command('update_index', verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 23) # Remove a model instance. MockModel.objects.get(pk=1).delete() self.assertEqual(self.solr.search('*:*').hits, 23) # Plain ``update_index`` doesn't fix it. call_command('update_index', verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 23) # With the remove flag, it's gone. call_command('update_index', remove=True, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 22) def test_multiprocessing(self): call_command('clear_index', interactive=False, verbosity=0) self.assertEqual(self.solr.search('*:*').hits, 0) # Watch the output, make sure there are multiple pids. call_command('update_index', verbosity=2, workers=2, batchsize=5) self.assertEqual(self.solr.search('*:*').hits, 23)

36.626667

73

0.650528

316

2,747

5.541139

0.306962

0.054826

0.11936

0.144489

0.432895

0.415191

0.375214

0.350657

0.349515

0

0.014513

0.222424

2,747

74

37.121622

0.805243

0.056425

0

0.38

0

0.068832

0

0.22

1

0.1

false

0

0.18

0

0.4

0

null

0

null

0

1

0

6971294bf9fe140162bcbb44e819402a23fefea8

15,633

py

Python

cons3rt/models/user.py

cons3rt/cons3rt-python-sdk

f0bcb295735ac55bbe47448fcbd95d2c7beb3ec0

[ "RSA-MD" ]

null

cons3rt/models/user.py

cons3rt/cons3rt-python-sdk

f0bcb295735ac55bbe47448fcbd95d2c7beb3ec0

[ "RSA-MD" ]

null

cons3rt/models/user.py

cons3rt/cons3rt-python-sdk

f0bcb295735ac55bbe47448fcbd95d2c7beb3ec0

[ "RSA-MD" ]

null

""" Copyright 2020 Jackpine Technologies Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ # coding: utf-8 """ cons3rt - Copyright Jackpine Technologies Corp. NOTE: This file is auto-generated. Do not edit the file manually. """ import pprint import re # noqa: F401 import six from cons3rt.configuration import Configuration __author__ = 'Jackpine Technologies Corporation' __copyright__ = 'Copyright 2020, Jackpine Technologies Corporation' __license__ = 'Apache 2.0', __version__ = '1.0.0' __maintainer__ = 'API Support' __email__ = 'support@cons3rt.com' class User(object): """NOTE: This class is auto-generated. Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'created_at': 'int', 'updated_at': 'int', 'administered_clouds': 'list[Cloud]', 'administered_virt_realms': 'list[VirtualizationRealm]', 'certificates': 'list[Certificate]', 'comment': 'str', 'default_project': 'Project', 'email': 'str', 'firstname': 'str', 'id': 'int', 'lastname': 'str', 'log_entries': 'list[LogEntry]', 'organization': 'str', 'project_count': 'int', 'state': 'str', 'terms_of_service_accepted': 'bool', 'username': 'str' } attribute_map = { 'created_at': 'createdAt', 'updated_at': 'updatedAt', 'administered_clouds': 'administeredClouds', 'administered_virt_realms': 'administeredVirtRealms', 'certificates': 'certificates', 'comment': 'comment', 'default_project': 'defaultProject', 'email': 'email', 'firstname': 'firstname', 'id': 'id', 'lastname': 'lastname', 'log_entries': 'logEntries', 'organization': 'organization', 'project_count': 'projectCount', 'state': 'state', 'terms_of_service_accepted': 'termsOfServiceAccepted', 'username': 'username' } def __init__(self, created_at=None, updated_at=None, administered_clouds=None, administered_virt_realms=None, certificates=None, comment=None, default_project=None, email=None, firstname=None, id=None, lastname=None, log_entries=None, organization=None, project_count=None, state=None, terms_of_service_accepted=None, username=None, local_vars_configuration=None): # noqa: E501 """User - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._created_at = None self._updated_at = None self._administered_clouds = None self._administered_virt_realms = None self._certificates = None self._comment = None self._default_project = None self._email = None self._firstname = None self._id = None self._lastname = None self._log_entries = None self._organization = None self._project_count = None self._state = None self._terms_of_service_accepted = None self._username = None self.discriminator = None if created_at is not None: self.created_at = created_at if updated_at is not None: self.updated_at = updated_at if administered_clouds is not None: self.administered_clouds = administered_clouds if administered_virt_realms is not None: self.administered_virt_realms = administered_virt_realms if certificates is not None: self.certificates = certificates if comment is not None: self.comment = comment if default_project is not None: self.default_project = default_project if email is not None: self.email = email if firstname is not None: self.firstname = firstname if id is not None: self.id = id if lastname is not None: self.lastname = lastname if log_entries is not None: self.log_entries = log_entries if organization is not None: self.organization = organization if project_count is not None: self.project_count = project_count if state is not None: self.state = state if terms_of_service_accepted is not None: self.terms_of_service_accepted = terms_of_service_accepted if username is not None: self.username = username @property def created_at(self): """Gets the created_at of this User. # noqa: E501 :return: The created_at of this User. # noqa: E501 :rtype: int """ return self._created_at @created_at.setter def created_at(self, created_at): """Sets the created_at of this User. :param created_at: The created_at of this User. # noqa: E501 :type: int """ self._created_at = created_at @property def updated_at(self): """Gets the updated_at of this User. # noqa: E501 :return: The updated_at of this User. # noqa: E501 :rtype: int """ return self._updated_at @updated_at.setter def updated_at(self, updated_at): """Sets the updated_at of this User. :param updated_at: The updated_at of this User. # noqa: E501 :type: int """ self._updated_at = updated_at @property def administered_clouds(self): """Gets the administered_clouds of this User. # noqa: E501 :return: The administered_clouds of this User. # noqa: E501 :rtype: list[Cloud] """ return self._administered_clouds @administered_clouds.setter def administered_clouds(self, administered_clouds): """Sets the administered_clouds of this User. :param administered_clouds: The administered_clouds of this User. # noqa: E501 :type: list[Cloud] """ self._administered_clouds = administered_clouds @property def administered_virt_realms(self): """Gets the administered_virt_realms of this User. # noqa: E501 :return: The administered_virt_realms of this User. # noqa: E501 :rtype: list[VirtualizationRealm] """ return self._administered_virt_realms @administered_virt_realms.setter def administered_virt_realms(self, administered_virt_realms): """Sets the administered_virt_realms of this User. :param administered_virt_realms: The administered_virt_realms of this User. # noqa: E501 :type: list[VirtualizationRealm] """ self._administered_virt_realms = administered_virt_realms @property def certificates(self): """Gets the certificates of this User. # noqa: E501 :return: The certificates of this User. # noqa: E501 :rtype: list[Certificate] """ return self._certificates @certificates.setter def certificates(self, certificates): """Sets the certificates of this User. :param certificates: The certificates of this User. # noqa: E501 :type: list[Certificate] """ self._certificates = certificates @property def comment(self): """Gets the comment of this User. # noqa: E501 :return: The comment of this User. # noqa: E501 :rtype: str """ return self._comment @comment.setter def comment(self, comment): """Sets the comment of this User. :param comment: The comment of this User. # noqa: E501 :type: str """ self._comment = comment @property def default_project(self): """Gets the default_project of this User. # noqa: E501 :return: The default_project of this User. # noqa: E501 :rtype: Project """ return self._default_project @default_project.setter def default_project(self, default_project): """Sets the default_project of this User. :param default_project: The default_project of this User. # noqa: E501 :type: Project """ self._default_project = default_project @property def email(self): """Gets the email of this User. # noqa: E501 :return: The email of this User. # noqa: E501 :rtype: str """ return self._email @email.setter def email(self, email): """Sets the email of this User. :param email: The email of this User. # noqa: E501 :type: str """ self._email = email @property def firstname(self): """Gets the firstname of this User. # noqa: E501 :return: The firstname of this User. # noqa: E501 :rtype: str """ return self._firstname @firstname.setter def firstname(self, firstname): """Sets the firstname of this User. :param firstname: The firstname of this User. # noqa: E501 :type: str """ self._firstname = firstname @property def id(self): """Gets the id of this User. # noqa: E501 :return: The id of this User. # noqa: E501 :rtype: int """ return self._id @id.setter def id(self, id): """Sets the id of this User. :param id: The id of this User. # noqa: E501 :type: int """ self._id = id @property def lastname(self): """Gets the lastname of this User. # noqa: E501 :return: The lastname of this User. # noqa: E501 :rtype: str """ return self._lastname @lastname.setter def lastname(self, lastname): """Sets the lastname of this User. :param lastname: The lastname of this User. # noqa: E501 :type: str """ self._lastname = lastname @property def log_entries(self): """Gets the log_entries of this User. # noqa: E501 :return: The log_entries of this User. # noqa: E501 :rtype: list[LogEntry] """ return self._log_entries @log_entries.setter def log_entries(self, log_entries): """Sets the log_entries of this User. :param log_entries: The log_entries of this User. # noqa: E501 :type: list[LogEntry] """ self._log_entries = log_entries @property def organization(self): """Gets the organization of this User. # noqa: E501 :return: The organization of this User. # noqa: E501 :rtype: str """ return self._organization @organization.setter def organization(self, organization): """Sets the organization of this User. :param organization: The organization of this User. # noqa: E501 :type: str """ self._organization = organization @property def project_count(self): """Gets the project_count of this User. # noqa: E501 :return: The project_count of this User. # noqa: E501 :rtype: int """ return self._project_count @project_count.setter def project_count(self, project_count): """Sets the project_count of this User. :param project_count: The project_count of this User. # noqa: E501 :type: int """ self._project_count = project_count @property def state(self): """Gets the state of this User. # noqa: E501 :return: The state of this User. # noqa: E501 :rtype: str """ return self._state @state.setter def state(self, state): """Sets the state of this User. :param state: The state of this User. # noqa: E501 :type: str """ allowed_values = ["REQUESTED", "ACTIVE", "INACTIVE"] # noqa: E501 if self.local_vars_configuration.client_side_validation and state not in allowed_values: # noqa: E501 raise ValueError( "Invalid value for `state` ({0}), must be one of {1}" # noqa: E501 .format(state, allowed_values) ) self._state = state @property def terms_of_service_accepted(self): """Gets the terms_of_service_accepted of this User. # noqa: E501 :return: The terms_of_service_accepted of this User. # noqa: E501 :rtype: bool """ return self._terms_of_service_accepted @terms_of_service_accepted.setter def terms_of_service_accepted(self, terms_of_service_accepted): """Sets the terms_of_service_accepted of this User. :param terms_of_service_accepted: The terms_of_service_accepted of this User. # noqa: E501 :type: bool """ self._terms_of_service_accepted = terms_of_service_accepted @property def username(self): """Gets the username of this User. # noqa: E501 :return: The username of this User. # noqa: E501 :rtype: str """ return self._username @username.setter def username(self, username): """Sets the username of this User. :param username: The username of this User. # noqa: E501 :type: str """ self._username = username def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, User): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, User): return True return self.to_dict() != other.to_dict()

27.966011

382

0.602827

1,820

15,633

4.992857

0.112088

0.044899

0.074832

0.078574

0.443381

0.335864

0.307252

0.195994

0.103444

0.030923

0

0.018182

0.306915

15,633

558

383

28.016129

0.820489

0.319197

0

0.085714

0

0.09905

0.018237

0

1

0.163265

false

0

0.016327

0

0.289796

0.008163

0

null

0

null

0

1

0

69716928cf6a5af476069ffd69aec9842674f683

1,703

py

Python

Topics/6_Compute/files/lambda_keepsecret.py

nsvijay04b1/AWS_Certified_Solutions_Architect_Professional

f141b9a64b652ffa9496ff47d527e7eaf4df006d

[ "Apache-2.0" ]

1

2022-03-10T18:38:54.000Z

Topics/6_Compute/files/lambda_keepsecret.py

nsvijay04b1/AWS_Certified_Solutions_Architect_Professional

f141b9a64b652ffa9496ff47d527e7eaf4df006d

[ "Apache-2.0" ]

null

Topics/6_Compute/files/lambda_keepsecret.py

nsvijay04b1/AWS_Certified_Solutions_Architect_Professional

f141b9a64b652ffa9496ff47d527e7eaf4df006d

[ "Apache-2.0" ]

1

2022-03-10T18:38:56.000Z

from __future__ import print_function import json import boto3 print('Loading function') s3 = boto3.client('s3') bucket_of_interest = "secretcatpics" # For a PutObjectAcl API Event, gets the bucket and key name from the event # If the object is not private, then it makes the object private by making a # PutObjectAcl call. def lambda_handler(event, context): # Get bucket name from the event bucket = event['Records'][0]['s3']['bucket']['name'] if (bucket != bucket_of_interest): print("Doing nothing for bucket = " + bucket) return # Get key name from the event key = event['Records'][0]['s3']['object']['key'] # If object is not private then make it private if not (is_private(bucket, key)): print("Object with key=" + key + " in bucket=" + bucket + " is not private!") make_private(bucket, key) else: print("Object with key=" + key + " in bucket=" + bucket + " is already private.") # Checks an object with given bucket and key is private def is_private(bucket, key): # Get the object ACL from S3 acl = s3.get_object_acl(Bucket=bucket, Key=key) # Private object should have only one grant which is the owner of the object if (len(acl['Grants']) > 1): return False # If canonical owner and grantee ids do no match, then conclude that the object # is not private owner_id = acl['Owner']['ID'] grantee_id = acl['Grants'][0]['Grantee']['ID'] if (owner_id != grantee_id): return False return True # Makes an object with given bucket and key private by calling the PutObjectAcl API. def make_private(bucket, key): s3.put_object_acl(Bucket=bucket, Key=key, ACL="private") print("Object with key=" + key + " in bucket=" + bucket + " is marked as private.")

32.132075

84

0.704052

266

1,703

4.424812

0.296992

0.071368

0.040782

0.279524

0.189465

0.143585

0.094308

0

0.009279

0.177334

1,703

52

85

32.75

0.830835

0.354668

0

0.068966

0

0.247698

0

1

0.103448

false

0

0.103448

0

0.344828

0.206897

0

null

0

null

0

1

0

15c53f273bb229773a9dd501a2ed7fb628ef13eb

399

py

Python

solutions/dog_muffin.py

attawesome/Computer-Vision-with-OpenCV

1198bfee8683f6b5c415e07e085fa528e00ecd0a

[ "MIT" ]

6

2020-04-30T21:46:13.000Z

2021-02-15T21:32:50.000Z

solutions/dog_muffin.py

attawesome/Computer-Vision-with-OpenCV

1198bfee8683f6b5c415e07e085fa528e00ecd0a

[ "MIT" ]

null

solutions/dog_muffin.py

attawesome/Computer-Vision-with-OpenCV

1198bfee8683f6b5c415e07e085fa528e00ecd0a

[ "MIT" ]

6

2020-04-27T17:59:50.000Z

2020-12-13T16:10:09.000Z

import cv2 import numpy as np import matplotlib.pyplot as plt image = np.flip(cv2.imread('../img/dog_muffin.jpg'), axis=2) mask = np.zeros(image.shape[:2], dtype="uint8") cv2.rectangle(mask, (90, 120), (160, 190), 255, -1) masked = cv2.bitwise_and(image, image, mask=mask) plt.figure(figsize=(20, 10)) plt.imshow(np.flip(masked, axis =2)) plt.title('Masked Image'), plt.xticks([]), plt.yticks([])

30.692308

60

0.691729

67

399

4.089552

0.597015

0.043796

0

0.075419

0.102757

399

12

61

33.25

0.689944

0

0.095238

0.052632

0

1

0

false

0

0.3

0

0.3

0

null

0

null

0

1

0

15c8d860e73304bf40bd4b99e9d98b1c9921b456

2,676

py

Python

jetavator/schema_registry/VaultObject.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

null

jetavator/schema_registry/VaultObject.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

86

2020-04-11T18:03:32.000Z

2021-06-15T14:48:45.000Z

jetavator/schema_registry/VaultObject.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

null

from __future__ import annotations from typing import Any, Dict, List from abc import ABC, abstractmethod from datetime import datetime from collections import namedtuple from lazy_property import LazyProperty from .sqlalchemy_tables import ObjectDefinition import wysdom from jetavator.services import ComputeServiceABC from .ProjectABC import ProjectABC VaultObjectKey = namedtuple('VaultObjectKey', ['type', 'name']) HubKeyColumn = namedtuple('HubKeyColumn', ['name', 'source']) class VaultObject(wysdom.UserObject, wysdom.RegistersSubclasses, ABC): name: str = wysdom.UserProperty(str) type: str = wysdom.UserProperty(str) optional_yaml_properties = [] def __init__( self, project: ProjectABC, sqlalchemy_object: ObjectDefinition ) -> None: self.project = project self._sqlalchemy_object = sqlalchemy_object super().__init__(self.definition) def __repr__(self) -> str: class_name = type(self).__name__ return f'{class_name}({self.name})' @classmethod def subclass_instance( cls, project: ProjectABC, definition: ObjectDefinition ) -> VaultObject: return cls.registered_subclass_instance( definition.type, project, definition ) @LazyProperty def key(self) -> VaultObjectKey: return VaultObjectKey(self.type, self.name) @property def definition(self) -> Dict[str, Any]: return self._sqlalchemy_object.definition def export_sqlalchemy_object(self) -> ObjectDefinition: if self._sqlalchemy_object.version != str(self.project.version): raise ValueError( "ObjectDefinition version must match project version " "and cannot be updated." ) self._sqlalchemy_object.deploy_dt = str(datetime.now()) return self._sqlalchemy_object @abstractmethod def validate(self) -> None: pass @property def compute_service(self) -> ComputeServiceABC: return self.project.compute_service @property def full_name(self) -> str: return f'{self.type}_{self.name}' @property def checksum(self) -> str: return str(self._sqlalchemy_object.checksum) @property def dependent_satellites(self) -> List[VaultObject]: return [ satellite for satellite in self.project.satellites.values() if any( dependency.type == self.type and dependency.name == self.name for dependency in satellite.pipeline.dependencies ) ]

26.49505

72

0.65284

264

2,676

6.424242

0.318182

0.084906

0.070755

0.028302

0.03184

0

0.2642

2,676

100

73

26.76

0.861351

0

0.093333

0

0.062079

0.017951

0

1

0.146667

false

0.013333

0.133333

0.093333

0.453333

0

null

0

null

0

1

0

15ced209f13f0b2a1de3e15ddd92542a87b54d79

29,172

py

Python

ginga/canvas/types/plots.py

kyraikeda/ginga

e0ce979de4a87e12ba7a90eec0517a0be05d14bc

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

76

2015-01-05T14:46:14.000Z

2022-03-23T04:10:54.000Z

ginga/canvas/types/plots.py

kyraikeda/ginga

e0ce979de4a87e12ba7a90eec0517a0be05d14bc

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

858

2015-01-17T01:55:12.000Z

2022-03-08T20:20:31.000Z

ginga/canvas/types/plots.py

kyraikeda/ginga

e0ce979de4a87e12ba7a90eec0517a0be05d14bc

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

60

2015-01-14T21:59:07.000Z

2022-02-13T03:38:49.000Z

# # plots.py -- classes for plots added to Ginga canvases. # # This is open-source software licensed under a BSD license. # Please see the file LICENSE.txt for details. # import sys import numpy as np from ginga.canvas.CanvasObject import (CanvasObjectBase, _color, register_canvas_types, colors_plus_none) from ginga.misc import Bunch from ginga.canvas.types.layer import CompoundObject from .basic import Path from ginga.misc.ParamSet import Param class XYPlot(CanvasObjectBase): """ Plotable object that defines a single path representing an X/Y line plot. Like a Path, but has some optimization to reduce the actual numbers of points in the path, depending on the scale and pan of the viewer. """ @classmethod def get_params_metadata(cls): return [ Param(name='linewidth', type=int, default=2, min=0, max=20, widget='spinbutton', incr=1, description="Width of outline"), Param(name='linestyle', type=str, default='solid', valid=['solid', 'dash'], description="Style of outline (default: solid)"), Param(name='color', valid=colors_plus_none, type=_color, default='black', description="Color of text"), Param(name='alpha', type=float, default=1.0, min=0.0, max=1.0, widget='spinfloat', incr=0.05, description="Opacity of outline"), ] def __init__(self, name=None, color='black', linewidth=1, linestyle='solid', alpha=1.0, x_acc=None, y_acc=None, **kwargs): super(XYPlot, self).__init__(color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha, **kwargs) self.name = name self.kind = 'xyplot' self.x_func = None nul_arr = np.array([]) if x_acc is not None: self.x_func = lambda arr: nul_arr if arr.size == 0 else x_acc(arr) if y_acc is None: y_acc = np.mean self.y_func = lambda arr: nul_arr if arr.size == 0 else y_acc(arr) self.points = np.copy(nul_arr) self.limits = np.array([(0.0, 0.0), (0.0, 0.0)]) self.plot_xlim = (None, None) self.path = Path([], color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha, coord='data') self.path.get_cpoints = self.get_cpoints def plot_xy(self, xpts, ypts): """Convenience function for plotting X and Y points that are in separate arrays. """ self.plot(np.asarray((xpts, ypts)).T) def plot(self, points, limits=None): """Plot `points`, a list, tuple or array of (x, y) points. Parameter --------- points : array-like list, tuple or array of (x, y) points limits : array-like, optional array of (xmin, ymin), (xmax, ymax) Limits will be calculated if not passed in. """ self.points = np.asarray(points) self.plot_xlim = (None, None) # set or calculate limits if limits is not None: # passing limits saves costly min/max calculation self.limits = np.asarray(limits) else: self._calc_limits(self.points) def _calc_limits(self, points): """Internal routine to calculate the limits of `points`. """ # TODO: what should limits be if there are no points? if len(points) == 0: self.limits = np.array([[0.0, 0.0], [0.0, 0.0]]) else: x_vals, y_vals = points.T self.limits = np.array([(x_vals.min(), y_vals.min()), (x_vals.max(), y_vals.max())]) def calc_points(self, viewer, start_x, stop_x): """Called when recalculating our path's points. """ # in case X axis is flipped start_x, stop_x = min(start_x, stop_x), max(start_x, stop_x) new_xlim = (start_x, stop_x) if new_xlim == self.plot_xlim: # X limits are the same, no need to recalculate points return self.plot_xlim = new_xlim points = self.get_data_points(points=self.points) if len(points) == 0: self.path.points = points return x_data, y_data = points.T # if we can determine the visible region shown on the plot # limit the points to those within the region if np.all(np.isfinite([start_x, stop_x])): idx = np.logical_and(x_data >= start_x, x_data <= stop_x) points = points[idx] if self.x_func is not None: # now find all points position in canvas X coord cpoints = self.get_cpoints(viewer, points=points) cx, cy = cpoints.T # Reduce each group of Y points that map to a unique X via a # function that reduces to a single value. The desirable function # will depend on the function of the plot, but mean() would be a # sensible default _, i = np.unique(cx, return_index=True) gr_pts = np.split(points, i) x_data = np.array([self.x_func(a.T[0]) for a in gr_pts if len(a) > 0]) y_data = np.array([self.y_func(a.T[1]) for a in gr_pts if len(a) > 0]) assert len(x_data) == len(y_data) points = np.array((x_data, y_data)).T self.path.points = points def recalc(self, viewer): """Called when recalculating our path's points. """ # select only points within range of the current pan/zoom bbox = viewer.get_pan_rect() if bbox is None: self.path.points = [] return start_x, stop_x = bbox[0][0], bbox[2][0] self.calc_points(viewer, start_x, stop_x) def get_cpoints(self, viewer, points=None, no_rotate=False): """Mostly internal routine used to calculate the native positions to draw the plot. """ # If points are passed, they are assumed to be in data space if points is None: points = self.path.get_points() return viewer.tform['data_to_plot'].to_(points) def update_resize(self, viewer, dims): """Called when the viewer is resized.""" self.recalc(viewer) def get_latest(self): """Get the latest (last) point on the plot. Returns None if there are no points. """ if len(self.points) == 0: return None return self.points[-1] def get_limits(self, lim_type): """Get the limits of the data or the visible part of the plot. If `lim_type` == 'data' returns the limits of all the data points. Otherwise returns the limits of the visibly plotted area. Limits are returned in the form ((xmin, ymin), (xmax, ymax)), as an array. """ if lim_type == 'data': # data limits return np.asarray(self.limits) # plot limits self.path.crdmap = self.crdmap if len(self.path.points) > 0: llur = self.path.get_llur() llur = [llur[0:2], llur[2:4]] else: llur = [(0.0, 0.0), (0.0, 0.0)] return np.asarray(llur) def draw(self, viewer): """Draw the plot. Normally not called by the user, but by the viewer as needed. """ self.path.crdmap = self.crdmap self.recalc(viewer) if len(self.path.points) > 0: self.path.draw(viewer) class Axis(CompoundObject): """ Base class for axis plotables. """ def __init__(self, title=None, num_labels=4, font='sans', fontsize=10.0): super(Axis, self).__init__() self.aide = None self.num_labels = num_labels self.title = title self.font = font self.fontsize = fontsize self.grid_alpha = 1.0 self.format_value = self._format_value def register_decor(self, aide): self.aide = aide def _format_value(self, v): """Default formatter for XAxis labels. """ return "%.4g" % v def set_grid_alpha(self, alpha): """Set the transparency (alpha) of the XAxis grid lines. `alpha` should be between 0.0 and 1.0 """ for i in range(self.num_labels): grid = self.grid[i] grid.alpha = alpha def get_data_xy(self, viewer, pt): arr_pts = np.asarray(pt) x, y = viewer.tform['data_to_plot'].from_(arr_pts).T[:2] flips = viewer.get_transforms() if flips[2]: x, y = y, x return (x, y) def get_title(self): titles_d = self.aide.get_axes_titles() return titles_d[self.kind] def add_plot(self, viewer, plot_src): # Axis objects typically do not need to do anything when a # plot is added--they recalculate labels in update_elements() pass def delete_plot(self, viewer, plot_src): # Axis objects typically do not need to do anything when a # plot is deleted--they recalculate labels in update_elements() pass class XAxis(Axis): """ Plotable object that defines X axis labels and grid lines. """ def __init__(self, title=None, num_labels=4, font='sans', fontsize=10.0): super(XAxis, self).__init__(title=title, num_labels=num_labels, font=font, fontsize=fontsize) self.kind = 'axis_x' self.txt_ht = 0 self.title_wd = 0 self.pad_px = 5 def register_decor(self, aide): self.aide = aide # add X grid self.grid = Bunch.Bunch() for i in range(self.num_labels): self.grid[i] = aide.dc.Line(0, 0, 0, 0, color=aide.grid_fg, linestyle='dash', linewidth=1, alpha=self.grid_alpha, coord='window') self.objects.append(self.grid[i]) self.axis_bg = aide.dc.Rectangle(0, 0, 100, 100, color=aide.norm_bg, fill=True, fillcolor=aide.axis_bg, coord='window') self.objects.append(self.axis_bg) self.lbls = Bunch.Bunch() for i in range(self.num_labels): self.lbls[i] = aide.dc.Text(0, 0, text='', color='black', font=self.font, fontsize=self.fontsize, coord='window') self.objects.append(self.lbls[i]) self._title = aide.dc.Text(0, 0, text='', color='black', font=self.font, fontsize=self.fontsize, alpha=0.0, coord='window') self.objects.append(self._title) def update_elements(self, viewer): """This method is called if the plot is set with new points, or is scaled or panned with existing points. Update the XAxis labels to reflect the new values and/or pan/scale. """ for i in range(self.num_labels): lbl = self.lbls[i] # get data coord equivalents x, y = self.get_data_xy(viewer, (lbl.x, lbl.y)) # format according to user's preference lbl.text = self.format_value(x) def update_bbox(self, viewer, dims): """This method is called if the viewer's window is resized. Update all the XAxis elements to reflect the new dimensions. """ title = self.get_title() self._title.text = title if title is not None else '555.55' self.title_wd, self.txt_ht = viewer.renderer.get_dimensions(self._title) wd, ht = dims[:2] y_hi = ht if title is not None: # remove Y space for X axis title y_hi -= self.txt_ht + 4 # remove Y space for X axis labels y_hi -= self.txt_ht + self.pad_px self.aide.update_plot_bbox(y_hi=y_hi) def update_resize(self, viewer, dims, xy_lim): """This method is called if the viewer's window is resized. Update all the XAxis elements to reflect the new dimensions. """ x_lo, y_lo, x_hi, y_hi = xy_lim wd, ht = dims[:2] # position axis title title = self.get_title() cx, cy = wd // 2 - self.title_wd // 2, ht - 4 if title is not None: self._title.x = cx self._title.y = cy self._title.alpha = 1.0 cy = cy - self.txt_ht else: self._title.alpha = 0.0 # set X labels/grid as needed # calculate evenly spaced interval on X axis in window coords a = (x_hi - x_lo) // (self.num_labels - 1) cx = x_lo for i in range(self.num_labels): lbl = self.lbls[i] lbl.x, lbl.y = cx, cy # get data coord equivalents x, y = self.get_data_xy(viewer, (cx, cy)) # convert to formatted label lbl.text = self.format_value(x) grid = self.grid[i] grid.x1 = grid.x2 = cx grid.y1, grid.y2 = y_lo, y_hi cx += a self.axis_bg.x1, self.axis_bg.x2 = 0, wd self.axis_bg.y1, self.axis_bg.y2 = y_hi, ht class YAxis(Axis): """ Plotable object that defines Y axis labels and grid lines. """ def __init__(self, title=None, num_labels=4, font='sans', fontsize=10.0): super(YAxis, self).__init__(title=title, num_labels=num_labels, font=font, fontsize=fontsize) self.kind = 'axis_y' self.title_wd = 0 self.txt_wd = 0 self.txt_ht = 0 self.pad_px = 4 def register_decor(self, aide): self.aide = aide # add Y grid self.grid = Bunch.Bunch() for i in range(self.num_labels): self.grid[i] = aide.dc.Line(0, 0, 0, 0, color=aide.grid_fg, linestyle='dash', linewidth=1, alpha=self.grid_alpha, coord='window') self.objects.append(self.grid[i]) # bg for RHS Y axis labels self.axis_bg = aide.dc.Rectangle(0, 0, 100, 100, color=aide.norm_bg, fill=True, fillcolor=aide.axis_bg, coord='window') self.objects.append(self.axis_bg) # bg for LHS Y axis title self.axis_bg2 = aide.dc.Rectangle(0, 0, 100, 100, color=aide.norm_bg, fill=True, fillcolor=aide.axis_bg, coord='window') self.objects.append(self.axis_bg2) # Y grid (tick) labels self.lbls = Bunch.Bunch() for i in range(self.num_labels): self.lbls[i] = aide.dc.Text(0, 0, text='', color='black', font=self.font, fontsize=self.fontsize, coord='window') self.objects.append(self.lbls[i]) # Y title self._title = aide.dc.Text(0, 0, text=self.title, color='black', font=self.font, fontsize=self.fontsize, alpha=0.0, rot_deg=90.0, coord='window') self.objects.append(self._title) def update_elements(self, viewer): """This method is called if the plot is set with new points, or is scaled or panned with existing points. Update the YAxis labels to reflect the new values and/or pan/scale. """ # set Y labels/grid as needed for i in range(self.num_labels): lbl = self.lbls[i] # get data coord equivalents x, y = self.get_data_xy(viewer, (lbl.x, lbl.y)) lbl.text = self.format_value(y) def update_bbox(self, viewer, dims): """This method is called if the viewer's window is resized. Update all the YAxis elements to reflect the new dimensions. """ title = self.get_title() self._title.text = title if title is not None else '555.55' wd, ht = dims[:2] self.title_wd, self.txt_ht = viewer.renderer.get_dimensions(self._title) # TODO: not sure this will give us the maximum length of number text = self.format_value(sys.float_info.max) t = self.aide.dc.Text(0, 0, text=text, fontsize=self.fontsize, font=self.font) self.txt_wd, _ = viewer.renderer.get_dimensions(t) if title is not None: x_lo = self.txt_ht + 2 + self.pad_px else: x_lo = 0 x_hi = wd - (self.txt_wd + 4) - self.pad_px self.aide.update_plot_bbox(x_lo=x_lo, x_hi=x_hi) def update_resize(self, viewer, dims, xy_lim): """This method is called if the viewer's window is resized. Update all the YAxis elements to reflect the new dimensions. """ x_lo, y_lo, x_hi, y_hi = xy_lim wd, ht = dims[:2] # position axis title title = self.get_title() cx = self.txt_ht + 2 cy = ht // 2 + self.title_wd // 2 if title is not None: self._title.x = cx self._title.y = cy self._title.alpha = 1.0 else: self._title.alpha = 0.0 cx = x_hi + self.pad_px cy = y_hi # set Y labels/grid as needed a = (y_hi - y_lo) // (self.num_labels - 1) for i in range(self.num_labels): lbl = self.lbls[i] # calculate evenly spaced interval on Y axis in window coords lbl.x, lbl.y = cx, cy # get data coord equivalents x, y = self.get_data_xy(viewer, (cx, cy)) lbl.text = self.format_value(y) grid = self.grid[i] grid.x1, grid.x2 = x_lo, x_hi grid.y1 = grid.y2 = cy cy -= a self.axis_bg.x1, self.axis_bg.x2 = x_hi, wd self.axis_bg.y1, self.axis_bg.y2 = y_lo, y_hi self.axis_bg2.x1, self.axis_bg2.x2 = 0, x_lo self.axis_bg2.y1, self.axis_bg2.y2 = y_lo, y_hi class PlotBG(CompoundObject): """ Plotable object that defines the plot background. Can include a warning line and an alert line. If the last Y value plotted exceeds the warning line then the background changes color. For example, you might be plotting detector values and want to set a warning if a certain threshold is crossed and an alert if the detector has saturated (alerts are higher than warnings). """ def __init__(self, warn_y=None, alert_y=None, linewidth=1): super(PlotBG, self).__init__() self.y_lbl_info = [warn_y, alert_y] self.warn_y = warn_y self.alert_y = alert_y self.linewidth = linewidth # default warning check self.check_warning = self._check_warning self.norm_bg = 'white' self.warn_bg = 'lightyellow' self.alert_bg = 'mistyrose2' self.kind = 'plot_bg' self.pickable = True self.opaque = True def register_decor(self, aide): self.aide = aide # add a backdrop that we can change color for visual warnings self.bg = aide.dc.Rectangle(0, 0, 100, 100, color=aide.norm_bg, fill=True, fillcolor=aide.norm_bg, fillalpha=1.0, coord='window') self.objects.append(self.bg) # add warning and alert lines self.ln_warn = aide.dc.Line(0, self.warn_y, 1, self.warn_y, color='gold3', linewidth=self.linewidth, alpha=0.0, coord='window') self.objects.append(self.ln_warn) self.ln_alert = aide.dc.Line(0, self.alert_y, 1, self.alert_y, color='red', linewidth=self.linewidth, alpha=0.0, coord='window') self.objects.append(self.ln_alert) def warning(self): self.bg.fillcolor = self.warn_bg def alert(self): self.bg.fillcolor = self.alert_bg def normal(self): self.bg.fillcolor = self.norm_bg def _check_warning(self): max_y = None for i, plot_src in enumerate(self.aide.plots.values()): limits = plot_src.get_limits('data') y = limits[1][1] max_y = y if max_y is None else max(max_y, y) if max_y is not None: if self.alert_y is not None and max_y > self.alert_y: self.alert() elif self.warn_y is not None and max_y > self.warn_y: self.warning() else: self.normal() def update_elements(self, viewer): """This method is called if the plot is set with new points, or is scaled or panned with existing points. Update the XAxis labels to reflect the new values and/or pan/scale. """ y_lo, y_hi = self.aide.bbox.T[1].min(), self.aide.bbox.T[1].max() # adjust warning/alert lines if self.warn_y is not None: x, y = self.get_canvas_xy(viewer, (0, self.warn_y)) if y_lo <= y <= y_hi: self.ln_warn.alpha = 1.0 else: # y out of range of plot area, so make it invisible self.ln_warn.alpha = 0.0 self.ln_warn.y1 = self.ln_warn.y2 = y if self.alert_y is not None: x, y = self.get_canvas_xy(viewer, (0, self.alert_y)) if y_lo <= y <= y_hi: self.ln_alert.alpha = 1.0 else: # y out of range of plot area, so make it invisible self.ln_alert.alpha = 0.0 self.ln_alert.y1 = self.ln_alert.y2 = y self.check_warning() def update_bbox(self, viewer, dims): # this object does not adjust the plot bbox at all pass def update_resize(self, viewer, dims, xy_lim): """This method is called if the viewer's window is resized. Update all the PlotBG elements to reflect the new dimensions. """ # adjust bg to window size, in case it changed x_lo, y_lo, x_hi, y_hi = xy_lim wd, ht = dims[:2] self.bg.x1, self.bg.y1 = x_lo, y_lo self.bg.x2, self.bg.y2 = x_hi, y_hi # adjust warning/alert lines if self.warn_y is not None: x, y = self.get_canvas_xy(viewer, (0, self.warn_y)) self.ln_warn.x1, self.ln_warn.x2 = x_lo, x_hi self.ln_warn.y1 = self.ln_warn.y2 = y if self.alert_y is not None: x, y = self.get_canvas_xy(viewer, (0, self.alert_y)) self.ln_alert.x1, self.ln_alert.x2 = x_lo, x_hi self.ln_alert.y1 = self.ln_alert.y2 = y def add_plot(self, viewer, plot_src): pass def delete_plot(self, viewer, plot_src): pass def get_canvas_xy(self, viewer, pt): arr_pts = np.asarray(pt) return viewer.tform['data_to_plot'].to_(arr_pts).T[:2] class PlotTitle(CompoundObject): """ Plotable object that defines the plot title and keys. """ def __init__(self, title='', font='sans', fontsize=12.0): super(PlotTitle, self).__init__() self.font = font self.fontsize = fontsize self.title = title self.txt_ht = 0 self.kind = 'plot_title' self.format_label = self._format_label self.pad_px = 5 def register_decor(self, aide): self.aide = aide self.title_bg = aide.dc.Rectangle(0, 0, 100, 100, color=aide.norm_bg, fill=True, fillcolor=aide.axis_bg, coord='window') self.objects.append(self.title_bg) self.lbls = dict() self.lbls[0] = aide.dc.Text(0, 0, text=self.title, color='black', font=self.font, fontsize=self.fontsize, coord='window') self.objects.append(self.lbls[0]) def _format_label(self, lbl, plot_src): """Default formatter for PlotTitle labels. """ lbl.text = "{0:}".format(plot_src.name) def update_elements(self, viewer): """This method is called if the plot is set with new points, or is scaled or panned with existing points. Update the PlotTitle labels to reflect the new values. """ for i, plot_src in enumerate(self.aide.plots.values()): lbl = self.lbls[plot_src] self.format_label(lbl, plot_src) def update_bbox(self, viewer, dims): """This method is called if the viewer's window is resized. Update all the PlotTitle elements to reflect the new dimensions. """ wd, ht = dims[:2] if self.txt_ht == 0: _, self.txt_ht = viewer.renderer.get_dimensions(self.lbls[0]) y_lo = self.txt_ht + self.pad_px self.aide.update_plot_bbox(y_lo=y_lo) def update_resize(self, viewer, dims, xy_lim): """This method is called if the viewer's window is resized. Update all the PlotTitle elements to reflect the new dimensions. """ x_lo, y_lo, x_hi, y_hi = xy_lim wd, ht = dims[:2] nplots = len(list(self.aide.plots.keys())) + 1 # set title labels as needed a = wd // (nplots + 1) cx, cy = 4, self.txt_ht lbl = self.lbls[0] lbl.x, lbl.y = cx, cy for i, plot_src in enumerate(self.aide.plots.values()): cx += a lbl = self.lbls[plot_src] lbl.x, lbl.y = cx, cy self.format_label(lbl, plot_src) self.title_bg.x1, self.title_bg.x2 = 0, wd self.title_bg.y1, self.title_bg.y2 = 0, y_lo def add_plot(self, viewer, plot_src): text = plot_src.name color = plot_src.color lbl = self.aide.dc.Text(0, 0, text=text, color=color, font=self.font, fontsize=self.fontsize, coord='window') self.lbls[plot_src] = lbl self.objects.append(lbl) lbl.crdmap = self.lbls[0].crdmap self.format_label(lbl, plot_src) # reorder and place labels dims = viewer.get_window_size() self.update_resize(viewer, dims, self.aide.llur) def delete_plot(self, viewer, plot_src): lbl = self.lbls[plot_src] del self.lbls[plot_src] self.objects.remove(lbl) # reorder and place labels dims = viewer.get_window_size() self.update_resize(viewer, dims, self.aide.llur) class CalcPlot(XYPlot): def __init__(self, name=None, x_fn=None, y_fn=None, color='black', linewidth=1, linestyle='solid', alpha=1.0, **kwdargs): super(CalcPlot, self).__init__(name=name, color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha, **kwdargs) self.kind = 'calcplot' if x_fn is None: x_fn = lambda x: x # noqa self.x_fn = x_fn if y_fn is None: y_fn = lambda y: y # noqa self.y_fn = y_fn def plot(self, y_fn, x_fn=None): if x_fn is not None: self.x_fn = x_fn self.y_fn = y_fn self.plot_xlim = (None, None) def calc_points(self, viewer, start_x, stop_x): # in case X axis is flipped start_x, stop_x = min(start_x, stop_x), max(start_x, stop_x) new_xlim = (start_x, stop_x) if new_xlim == self.plot_xlim: # X limits are the same, no need to recalculate points return self.plot_xlim = new_xlim wd, ht = self.viewer.get_window_size() x_pts = self.x_fn(np.linspace(start_x, stop_x, wd, dtype=np.float)) y_pts = self.y_fn(x_pts) points = np.array((x_pts, y_pts)).T self.path.points = points def get_limits(self, lim_type): try: llur = self.path.get_llur() limits = [llur[0:2], llur[2:4]] return np.array(limits) except Exception: return np.array(((0.0, 0.0), (0.0, 0.0))) # register our types register_canvas_types(dict(xyplot=XYPlot, calcplot=CalcPlot))

35.189385

80

0.547614

4,021

29,172

3.828152

0.106441

0.007406

0.005457

0.005717

0.583902

0.526148

0.487429

0.444358

0.409277

0.381472

0

0.016214

0.350953

29,172

828

81

35.231884

0.796768

0.199095

0

0.479371

0

0.01986

0

0.002415

0.001965

1

0.10609

false

0.009823

0.013752

0.001965

0.165029

0

null

0

null

0

1

0

15d0460d3097f204a84b42c16fe07a92ea6eb177

8,305

py

Python

process_disqualified_directors_data.py

Global-Witness/uk-companies-house-parsers-public

e33a3a7791d6565638e2f3c0fd1233a909d983f2

[ "MIT" ]

2

2019-08-03T16:58:43.000Z

2022-01-23T16:57:52.000Z

process_disqualified_directors_data.py

Global-Witness/uk-companies-house-parsers-public

e33a3a7791d6565638e2f3c0fd1233a909d983f2

[ "MIT" ]

1

2019-06-18T10:10:20.000Z

process_disqualified_directors_data.py

Global-Witness/uk-companies-house-parsers-public

e33a3a7791d6565638e2f3c0fd1233a909d983f2

[ "MIT" ]

1

2020-11-01T10:21:03.000Z

import csv import os import sys from collections import defaultdict PERSONS_OUTPUT_FILENAME_TEMPLATE = "persons_data_%s.csv" DISQUALIFICATIONS_FILENAME_TEMPLATE = "disqualifications_data_%s.csv" EXEMPTIONS_FILENAME_TEMPLATE = 'exemptions_data_%s.csv' SNAPSHOT_HEADER_IDENTIFIER = "DISQUALS" TRAILER_RECORD_IDENTIFIER = "DISQUALS" PERSON_RECORD_TYPE = '1' DISQUALIFICATION_RECORD_TYPE = '2' EXEMPTION_RECORD_TYPE = '3' def process_header_row(row): header_identifier = row[0:8] print(header_identifier) run_number = row[8:12] production_date = row[12:20] if header_identifier != SNAPSHOT_HEADER_IDENTIFIER: print( "Unsuported file type from header: '%s'. Expecting a snapshot header: '%s'" % (header_identifier, SNAPSHOT_HEADER_IDENTIFIER)) sys.exit(1) print("Processing snapshot file with run number %s from date %s" % (run_number, production_date)) def process_person_row(row, output_writer): record_type = row[0] person_number = str(row[1:13]) person_dob = row[13:21] person_postcode = row[21:29] person_variable_ind = int(row[29:33]) person_details = row[33:33 + person_variable_ind] person_details = person_details.split('<') title = person_details[0] forenames = person_details[1] surname = person_details[2] honours = person_details[3] address_line_1 = person_details[4] address_line_2 = person_details[5] posttown = person_details[6] county = person_details[7] country = person_details[8] nationality = person_details[9] corporate_number = person_details[10] country_registration = person_details[11] output_writer.writerow([ record_type, person_number, person_dob, person_postcode, person_details, title, forenames, surname, honours, address_line_1, address_line_2, posttown, county, country, nationality, corporate_number, country_registration ]) def process_disqualification_row(row, output_writer): record_type = row[0] person_number = str(row[1:13]) disqual_start_date = row[13:21] disqual_end_date = row[21:29] section_of_act = row[29:49] disqual_type = row[49:79] disqual_order_date = row[79:87] case_number = row[87:117] company_name = row[117:277] court_name_variable_ind = int(row[277:281]) court_name = row[281:281 + court_name_variable_ind] output_writer.writerow([ record_type, person_number, disqual_start_date, disqual_end_date, section_of_act, disqual_type, disqual_order_date, case_number, company_name, court_name ]) def process_exemption_row(row, output_writer): record_type = row[0] person_number = str(row[1:9]) exemption_start_date = row[13:21] exemption_end_date = row[21:29] exemption_purpose = int(row[29:39]) exemption_purpose_dict = defaultdict( lambda: '', { 1: 'Promotion', 2: 'Formation', 3: 'Directorships or other participation in management of a company', 4: 'Designated member/member or other participation in management of an LLP', 5: 'Receivership in relation to a company or LLP' }) exemption_purpose = exemption_purpose_dict[exemption_purpose] exemption_company_name_ind = int(row[39:43]) exemption_company_name = row[43:43 + exemption_company_name_ind] output_writer.writerow([ record_type, person_number, exemption_start_date, exemption_end_date, exemption_purpose, exemption_company_name ]) def init_person_output_file(filename): output_persons_file = open(filename, 'w') persons_writer = csv.writer(output_persons_file, delimiter=",") persons_writer.writerow([ "record_type", "person_number", "person_dob", "person_postcode", "person_details", 'title', 'forenames', 'surname', 'honours', 'address_line_1', 'address_line_2', 'posttown', 'county', 'country', 'nationality', 'corporate_number', 'country_registration' ]) return output_persons_file, persons_writer def init_disquals_output_file(filename): output_disquals_file = open(filename, 'w') disqauls_writer = csv.writer(output_disquals_file, delimiter=",") disqauls_writer.writerow([ "record_type", "person_number", "disqual_start_date", "disqual_end_date", "section_of_act", "disqual_type", "disqual_order_date", "case_number", "company_name", "court_name" ]) return output_disquals_file, disqauls_writer def init_exemptions_output_file(filename): output_exemptions_file = open(filename, 'w') exemptions_writer = csv.writer(output_exemptions_file, delimiter=",") exemptions_writer.writerow([ "record_type", "person_number", "exemption_start_date", "exemption_end_date", "exemption_purpose", "exemption_company_name" ]) return output_exemptions_file, exemptions_writer def init_input_files(output_folder, base_input_name): persons_output_filename = os.path.join( output_folder, PERSONS_OUTPUT_FILENAME_TEMPLATE % (base_input_name)) disquals_output_filename = os.path.join( output_folder, DISQUALIFICATIONS_FILENAME_TEMPLATE % (base_input_name)) exemptions_output_filename = os.path.join( output_folder, EXEMPTIONS_FILENAME_TEMPLATE % (base_input_name)) print("Saving companies data to %s" % persons_output_filename) print("Saving persons data to %s" % disquals_output_filename) print("Saving persons data to %s" % exemptions_output_filename) output_persons_file, output_persons_writer = init_person_output_file( persons_output_filename) output_disquals_file, output_disquals_writer = init_disquals_output_file( disquals_output_filename) output_exemptions_file, output_exemptions_writer = init_exemptions_output_file( exemptions_output_filename) return output_persons_file, output_persons_writer, output_disquals_file, output_disquals_writer, output_exemptions_file, output_exemptions_writer def process_company_appointments_data(input_file, output_folder, base_input_name): persons_processed = 0 disquals_processed = 0 exemptions_processed = 0 output_persons_file, output_persons_writer, output_disquals_file, output_disquals_writer, output_exemptions_file, output_exemptions_writer = init_input_files( output_folder, base_input_name) for row_num, row in enumerate(input_file): if row_num == 0: process_header_row(row) elif row[0:8] == TRAILER_RECORD_IDENTIFIER: # End of file record_count = int(row[45:53]) print( "Reached end of file. Processed %s == %s records: %s persons, %s disquals, %s exemptions." % (record_count, persons_processed + disquals_processed + exemptions_processed, persons_processed, disquals_processed, exemptions_processed)) output_persons_file.close() output_disquals_file.close() output_exemptions_file.close() sys.exit(0) elif row[0] == PERSON_RECORD_TYPE: process_person_row(row, output_persons_writer) persons_processed += 1 elif row[0] == DISQUALIFICATION_RECORD_TYPE: process_disqualification_row(row, output_disquals_writer) disquals_processed += 1 elif row[0] == EXEMPTION_RECORD_TYPE: process_exemption_row(row, output_exemptions_writer) exemptions_processed += 1 if __name__ == '__main__': if len(sys.argv) < 3: print( 'Usage: python process_disqualified_directors_data.py input_file output_folder\n', 'E.g. python process_disqualified_directors_data.py Prod195_1111_ni_sample.dat ./output/' ) sys.exit(1) input_filename = sys.argv[1] output_folder = sys.argv[2] input_file = open(input_filename, 'r') base_input_name = os.path.basename(input_filename) # Do not include the extension in the base input name base_input_name = os.path.splitext(base_input_name)[0] process_company_appointments_data(input_file, output_folder, base_input_name)

41.318408

162

0.706562

1,021

8,305

5.354554

0.164545

0.040424

0.026157

0.02634

0.462777

0.375526

0.313335

0.284068

0.252241

0

0.02382

0.206382

8,305

201

163

41.318408

0.805644

0.007586

0

0.106742

0

0.005618

0.143811

0.021238

0

1

0.050562

false

0

0.022472

0

0.095506

0.044944

0

null

0

null

0

1

0

15d05a5b381b6c77c32c1817ba56017e005be78f

2,875

py

Python

myPackage/tools.py

marcgonzmont/ML_Challenge

ed9767689064a941c2db7642fc499828d0ad1326

[ "CC0-1.0" ]

null

myPackage/tools.py

marcgonzmont/ML_Challenge

ed9767689064a941c2db7642fc499828d0ad1326

[ "CC0-1.0" ]

null

myPackage/tools.py

marcgonzmont/ML_Challenge

ed9767689064a941c2db7642fc499828d0ad1326

[ "CC0-1.0" ]

null

from os import makedirs, errno from os.path import exists, join import numpy as np from matplotlib import pyplot as plt import itertools from sklearn import preprocessing def makeDir(path): ''' To create output path if doesn't exist see: https://stackoverflow.com/questions/273192/how-can-i-create-a-directory-if-it-does-not-exist :param path: path to be created :return: none ''' try: if not exists(path): makedirs(path) print("\nCreated '{}' folder\n".format(path)) except OSError as e: if e.errno != errno.EEXIST: raise def split_train_test(data, test_ratio): shuffled_indices = np.random.permutation(len(data)) test_set_size = int(len(data) * test_ratio) test_indices = shuffled_indices[:test_set_size] train_indices = shuffled_indices[test_set_size:] train_set = [data[i] for i in train_indices] test_set = [data[i] for i in test_indices] return np.asarray(train_set), np.asarray(test_set) def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Greens): ''' This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. :param cm: confusion matrix :param classes: array of classes' names :param normalize: boolean :param title: plot title :param cmap: colour of matrix background :return: plot confusion matrix ''' # plt_name = altsep.join((plot_path,"".join((title,".png")))) if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) print('\nSum of main diagonal') print(np.trace(cm)) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label', labelpad=0) # plt.savefig(plt_name) plt.show() def normalize(data): ''' Normalize input data [0, 1] :param data: input data :return: normalized data ''' scaler = preprocessing.MinMaxScaler() data_min_max = scaler.fit_transform(data) return data_min_max

31.593407

102

0.619826

371

2,875

4.708895

0.425876

0.060103

0.01889

0.029765

0.059531

0.043503

0

0.007583

0.266087

2,875

90

103

31.944444

0.820379

0.224

0

0.089932

0

1

0.074074

false

0

0.111111

0

0.222222

0.111111

0

null

0

null

0

1

0

15d0b4957b2e9a69a93ba4e15d527f629984be60

9,802

py

Python

maskrcnn_benchmark/data/datasets/kitti.py

pwllr/IDA-3D

f5c8acbcf90a7c351499249a24754ad311da375e

[ "MIT" ]

78

2020-03-02T12:00:57.000Z

2022-02-21T09:48:08.000Z

maskrcnn_benchmark/data/datasets/kitti.py

pwllr/IDA-3D

f5c8acbcf90a7c351499249a24754ad311da375e

[ "MIT" ]

19

2020-03-02T12:07:18.000Z

2022-03-30T08:04:06.000Z

maskrcnn_benchmark/data/datasets/kitti.py

pwllr/IDA-3D

f5c8acbcf90a7c351499249a24754ad311da375e

[ "MIT" ]

14

2020-06-14T03:29:47.000Z

2021-07-21T06:30:08.000Z

import os import torch import torch.utils.data from PIL import Image import sys import numpy as np if sys.version_info[0] == 2: import xml.etree.cElementTree as ET else: import xml.etree.ElementTree as ET from maskrcnn_benchmark.structures.bounding_box import BoxList from maskrcnn_benchmark.structures.bounding_box import ObjectList def read_calib(calib_file_path): data = {} with open(calib_file_path, 'r') as f: for line in f.readlines(): line = line.rstrip() if len(line)==0: continue key, value = line.split(':', 1) data[key] = np.array([float(x) for x in value.split()]) return data class KittiDataset(torch.utils.data.Dataset): CLASSES = ( "__background__ ", "car", ) def __init__(self, data_dir, split, use_difficult=False, transforms=None): self.root = data_dir self.image_set = split self.keep_difficult = use_difficult self.transforms = transforms self._annopath = os.path.join(self.root, "label_3d", "%s.xml") self._image_left_path = os.path.join(self.root, "image_2", "%s.png") self._image_right_path = os.path.join(self.root, "image_3", "%s.png") self._calib_path = os.path.join(self.root, "calib", "%s.txt") self._imgsetpath = os.path.join(self.root, "splits", "%s.txt") with open(self._imgsetpath % self.image_set) as f: self.ids = f.readlines() self.ids = [x.strip("\n") for x in self.ids] self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} cls = KittiDataset.CLASSES self.class_to_ind = dict(zip(cls, range(len(cls)))) self.categories = dict(zip(range(len(cls)), cls)) def __getitem__(self, index): img_id = self.ids[index] img_left = Image.open(self._image_left_path % img_id).convert("RGB") img_right = Image.open(self._image_right_path % img_id).convert("RGB") target = self.get_groundtruth(index) target_object = self.get_groundtruth(index) target_left = target_object.get_field("left_box") target_right = target_object.get_field("right_box") target_left = target_left.clip_to_image(remove_empty=True) target_right = target_right.clip_to_image(remove_empty=True) if self.transforms is not None: img_left, target_left = self.transforms(img_left, target_left) img_right, target_right = self.transforms(img_right, target_right) target_object.add_field("left_box", target_left) target_object.add_field("right_box", target_right) calib = self.preprocess_calib(index) return img_left, img_right, target, calib, index def __len__(self): return len(self.ids) def get_groundtruth(self, index): img_id = self.ids[index] anno = ET.parse(self._annopath % img_id).getroot() anno = self._preprocess_annotation(anno) height, width = anno["im_info"] left_target = BoxList(anno["left_boxes"], (width, height), mode="xyxy") left_target.add_field("labels", anno["labels"]) left_target.add_field("difficult", anno["difficult"]) right_target = BoxList(anno["right_boxes"], (width, height), mode="xyxy") right_target.add_field("labels", anno["labels"]) right_target.add_field("difficult", anno["difficult"]) object_target = ObjectList() object_target.add_field("left_box", left_target) object_target.add_field("right_box", right_target) object_target.add_field("labels", anno["labels"]) object_target.add_field("left_centers", anno["left_centers"]) object_target.add_field("right_centers", anno["right_centers"]) object_target.add_field("positions_xy", anno["positions_xy"]) object_target.add_field("positions_z", anno["positions_z"]) object_target.add_field("dimensions", anno["dimensions"]) object_target.add_field("alpha", anno["alpha"]) object_target.add_field("beta", anno["beta"]) object_target.add_field("corners", anno["corners"]) assert object_target.is_equal() return object_target def preprocess_calib(self, index): img_id = self.ids[index] calib_path = self._calib_path % img_id calib = read_calib(calib_path) P2 = np.reshape(calib['P2'], [3,4]) P3 = np.reshape(calib['P3'], [3,4]) c_u = P2[0,2] c_v = P2[1,2] f_u = P2[0,0] f_v = P2[1,1] b_x_2 = P2[0,3]/(f_u) # relative b_y_2 = P2[1,3]/(f_v) b_x_3 = P3[0,3]/(f_u) # relative b_y_3 = P3[1,3]/(f_v) b = abs(b_x_3 - b_x_2) return { "cu": c_u, "cv": c_v, "fu": f_u, "fv": f_v, "b": b, "bx2":b_x_2, } def _preprocess_annotation(self, target): left_boxes = [] right_boxes = [] gt_classes = [] difficult_boxes = [] TO_REMOVE = 0 #3d parameters left_centers = [] right_centers = [] dimensions = [] positions_xy = [] positions_z = [] rotations = [] alphas = [] pconers = [] #occluded = [] #truncted = [] for obj in target.iter("object"): difficult = int(obj.find("difficult").text) == 1 if not self.keep_difficult and difficult: continue name = obj.find("name").text.lower().strip() left_bb = obj.find("left_bndbox") left_box = [ left_bb.find("xmin").text, left_bb.find("ymin").text, left_bb.find("xmax").text, left_bb.find("ymax").text, ] left_bndbox = tuple( map(lambda x: x - TO_REMOVE, list(map(float, left_box))) ) left_boxes.append(left_bndbox) left_center = [ left_bb.find("center").find("x").text, left_bb.find("center").find("y").text, ] left_center = list(map(float, left_center)) left_centers.append(left_center) right_bb = obj.find("right_bndbox") right_box = [ right_bb.find("xmin").text, right_bb.find("ymin").text, right_bb.find("xmax").text, right_bb.find("ymax").text, ] right_bndbox = tuple( map(lambda x: x - TO_REMOVE, list(map(float, right_box))) ) right_boxes.append(right_bndbox) right_center = [ right_bb.find("center").find("x").text, right_bb.find("center").find("y").text, ] right_center = list(map(float, right_center)) right_centers.append(right_center) gt_classes.append(self.class_to_ind[name]) difficult_boxes.append(difficult) position_xy = [ obj.find("position").find("x").text, obj.find("position").find("y").text, ] position_xy = list(map(float, position_xy)) positions_xy.append(position_xy) position_z = [ obj.find("position").find("z").find("depth").text, obj.find("position").find("z").find("disp").text, ] position_z = list(map(float, position_z)) positions_z.append(position_z) dimension = [ obj.find("dimensions").find("h").text, obj.find("dimensions").find("w").text, obj.find("dimensions").find("l").text, ] dimension = list(map(float, dimension)) dimensions.append(dimension) alp = float(obj.find("alpha").text) alphas.append(alp) rot = float(obj.find("rotation").text) rotations.append(rot) pc = [] corners = obj.find("corners") for i in range(8): pc_str = corners.find("pc%d"%i).text pc_i = [float(pc_s) for pc_s in pc_str.split(',')] pc.append(pc_i) pconers.append(pc) size = target.find("size") im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) res = { "left_boxes": torch.tensor(left_boxes, dtype=torch.float32).view(-1,4), "right_boxes": torch.tensor(right_boxes, dtype=torch.float32).view(-1,4), "labels": torch.tensor(gt_classes), "difficult": torch.tensor(difficult_boxes), "left_centers": torch.tensor(left_centers, dtype=torch.float32).view(-1,2), "right_centers": torch.tensor(right_centers, dtype=torch.float32).view(-1,2), "positions_xy": torch.tensor(positions_xy, dtype=torch.float32).view(-1,2), "positions_z": torch.tensor(positions_z, dtype=torch.float32).view(-1,2), "dimensions": torch.tensor(dimensions, dtype=torch.float32).view(-1,3), "alpha": torch.tensor(alphas, dtype=torch.float32), "beta": torch.tensor(rotations, dtype=torch.float32), "corners": torch.tensor(pconers, dtype=torch.float32).view(-1,8,7), "im_info": im_info, } return res def get_img_info(self, index): img_id = self.ids[index] anno = ET.parse(self._annopath % img_id).getroot() size = anno.find("size") im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) return {"height": im_info[0], "width": im_info[1]} def map_class_id_to_class_name(self, class_id): return KittiDataset.CLASSES[class_id]

36.169742

89

0.576515

1,245

9,802

4.301205

0.160643

0.025397

0.039216

0.041083

0.302708

0.206162

0.133147

0.060878

0

0.012839

0.28484

9,802

270

90

36.303704

0.75107

0.005917

0

0.03653

0

0.079791

0

0.004566

1

0.041096

false

0

0.045662

0.009132

0.13242

0

null

0

null

0

1

0

15d22cd4619c04c4fa2d8d4c420166060bc9ee2a

25,744

py

Python

escriptcore/py_src/faultsystems.py

markendr/esys-escript.github.io

0023eab09cd71f830ab098cb3a468e6139191e8d

[ "Apache-2.0" ]

null

escriptcore/py_src/faultsystems.py

markendr/esys-escript.github.io

0023eab09cd71f830ab098cb3a468e6139191e8d

[ "Apache-2.0" ]

null

escriptcore/py_src/faultsystems.py

markendr/esys-escript.github.io

0023eab09cd71f830ab098cb3a468e6139191e8d

[ "Apache-2.0" ]

null

############################################################################## # # Copyright (c) 2003-2020 by The University of Queensland # http://www.uq.edu.au # # Primary Business: Queensland, Australia # Licensed under the Apache License, version 2.0 # http://www.apache.org/licenses/LICENSE-2.0 # # Development until 2012 by Earth Systems Science Computational Center (ESSCC) # Development 2012-2013 by School of Earth Sciences # Development from 2014 by Centre for Geoscience Computing (GeoComp) # Development from 2019 by School of Earth and Environmental Sciences # ############################################################################## from __future__ import print_function, division __copyright__="""Copyright (c) 2003-2020 by The University of Queensland http://www.uq.edu.au Primary Business: Queensland, Australia""" __license__="""Licensed under the Apache License, version 2.0 http://www.apache.org/licenses/LICENSE-2.0""" __url__="https://launchpad.net/escript-finley" #from esys.escript import sqrt, EPSILON, cos, sin, Lsup, atan, length, matrixmult, wherePositive, matrix_mult, inner, Scalar, whereNonNegative, whereNonPositive, maximum, minimum, sign, whereNegative, whereZero import esys.escriptcore.pdetools as pdt #from .util import * from . import util as es import numpy import math __all__= ['FaultSystem'] class FaultSystem(object): """ The FaultSystem class defines a system of faults in the Earth's crust. A fault system is defined by set of faults index by a tag. Each fault is defined by a starting point V0 and a list of strikes ``strikes`` and length ``l``. The strikes and the length is used to define a polyline with points ``V[i]`` such that - ``V[0]=V0`` - ``V[i]=V[i]+ls[i]*array(cos(strikes[i]),sin(strikes[i]),0)`` So ``strikes`` defines the angle between the direction of the fault segment and the x0 axis. ls[i]==0 is allowed. In case of a 3D model a fault plane is defined through a dip and depth. The class provides a mechanism to parametrise each fault with the domain [0,w0_max] x [0, w1_max] (to [0,w0_max] in the 2D case). """ NOTAG="__NOTAG__" MIN_DEPTH_ANGLE=0.1 def __init__(self,dim=3): """ Sets up the fault system :param dim: spatial dimension :type dim: ``int`` of value 2 or 3 """ if not (dim == 2 or dim == 3): raise ValueError("only dimension2 2 and 3 are supported.") self.__dim=dim self.__top={} self.__ls={} self.__strikes={} self.__strike_vectors={} self.__medDepth={} self.__total_length={} if dim ==2: self.__depths=None self.__depth_vectors=None self.__dips=None self.__bottom=None self.__normals=None else: self.__depths={} self.__depth_vectors={} self.__dips={} self.__bottom={} self.__normals={} self.__offsets={} self.__w1_max={} self.__w0_max={} self.__center=None self.__orientation = None def getStart(self,tag=None): """ returns the starting point of fault ``tag`` :rtype: ``numpy.array``. """ return self.getTopPolyline(tag)[0] def getTags(self): """ returns a list of the tags used by the fault system :rtype: ``list`` """ return list(self.__top.keys()) def getDim(self): """ returns the spatial dimension :rtype: ``int`` """ return self.__dim def getTopPolyline(self, tag=None): """ returns the polyline used to describe fault tagged by ``tag`` :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of vertices defining the top of the fault. The coordinates are ``numpy.array``. """ if tag is None: tag=self.NOTAG return self.__top[tag] def getStrikes(self, tag=None): """ :return: the strike of the segements in fault ``tag`` :rtype: ``list`` of ``float`` """ if tag is None: tag=self.NOTAG return self.__strikes[tag] def getStrikeVectors(self, tag=None): """ :return: the strike vectors of fault ``tag`` :rtype: ``list`` of ``numpy.array``. """ if tag is None: tag=self.NOTAG return self.__strike_vectors[tag] def getLengths(self, tag=None): """ :return: the lengths of segments in fault ``tag`` :rtype: ``list`` of ``float`` """ if tag is None: tag=self.NOTAG return self.__ls[tag] def getTotalLength(self, tag=None): """ :return: the total unrolled length of fault ``tag`` :rtype: ``float`` """ if tag is None: tag=self.NOTAG return self.__total_length[tag] def getMediumDepth(self,tag=None): """ returns the medium depth of fault ``tag`` :rtype: ``float`` """ if tag is None: tag=self.NOTAG return self.__medDepth[tag] def getDips(self, tag=None): """ returns the list of the dips of the segements in fault ``tag`` :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of segment dips. In the 2D case None is returned. """ if tag is None: tag=self.NOTAG if self.getDim()==3: return self.__dips[tag] else: return None def getBottomPolyline(self, tag=None): """ returns the list of the vertices defining the bottom of the fault ``tag`` :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of vertices. In the 2D case None is returned. """ if tag is None: tag=self.NOTAG if self.getDim()==3: return self.__bottom[tag] else: return None def getSegmentNormals(self, tag=None): """ returns the list of the normals of the segments in fault ``tag`` :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of vectors normal to the segments. In the 2D case None is returned. """ if tag is None: tag=self.NOTAG if self.getDim()==3: return self.__normals[tag] else: return None def getDepthVectors(self, tag=None): """ returns the list of the depth vector at top vertices in fault ``tag``. :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of segment depths. In the 2D case None is returned. """ if tag is None: tag=self.NOTAG if self.getDim()==3: return self.__depth_vectors[tag] else: return None def getDepths(self, tag=None): """ returns the list of the depths of the segements in fault ``tag``. :param tag: the tag of the fault :type tag: ``float`` or ``str`` :return: the list of segment depths. In the 2D case None is returned. """ if tag is None: tag=self.NOTAG if self.getDim()==3: return self.__depths[tag] else: return None def getW0Range(self,tag=None): """ returns the range of the parameterization in ``w0`` :rtype: two ``float`` """ return self.getW0Offsets(tag)[0], self.getW0Offsets(tag)[-1] def getW1Range(self,tag=None): """ returns the range of the parameterization in ``w1`` :rtype: two ``float`` """ if tag is None: tag=self.NOTAG return -self.__w1_max[tag],0 def getW0Offsets(self, tag=None): """ returns the offsets for the parametrization of fault ``tag``. :return: the offsets in the parametrization :rtype: ``list`` of ``float`` """ if tag is None: tag=self.NOTAG return self.__offsets[tag] def getCenterOnSurface(self): """ returns the center point of the fault system at the surface :rtype: ``numpy.array`` """ if self.__center is None: self.__center=numpy.zeros((3,),numpy.float) counter=0 for t in self.getTags(): for s in self.getTopPolyline(t): self.__center[:2]+=s[:2] counter+=1 self.__center/=counter return self.__center[:self.getDim()] def getOrientationOnSurface(self): """ returns the orientation of the fault system in RAD on the surface around the fault system center :rtype: ``float`` """ if self.__orientation is None: center=self.getCenterOnSurface() covariant=numpy.zeros((2,2)) for t in self.getTags(): for s in self.getTopPolyline(t): covariant[0,0]+=(center[0]-s[0])**2 covariant[0,1]+=(center[1]-s[1])*(center[0]-s[0]) covariant[1,1]+=(center[1]-s[1])**2 covariant[1,0]+=(center[1]-s[1])*(center[0]-s[0]) e, V=numpy.linalg.eigh(covariant) if e[0]>e[1]: d=V[:,0] else: d=V[:,1] if abs(d[0])>0.: self.__orientation=es.atan(d[1]/d[0]) else: self.__orientation=math.pi/2 return self.__orientation def transform(self, rot=0, shift=numpy.zeros((3,))): """ applies a shift and a consecutive rotation in the x0x1 plane. :param rot: rotation angle in RAD :type rot: ``float`` :param shift: shift vector to be applied before rotation :type shift: ``numpy.array`` of size 2 or 3 """ if self.getDim() == 2: mat=numpy.array([[es.cos(rot), -es.sin(rot)], [es.sin(rot), es.cos(rot)] ]) else: mat=numpy.array([[es.cos(rot), -es.sin(rot),0.], [es.sin(rot), es.cos(rot),0.], [0.,0.,1.] ]) for t in self.getTags(): strikes=[ s+ rot for s in self.getStrikes(t) ] V0=self.getStart(t) self.addFault(strikes = [ s+ rot for s in self.getStrikes(t) ], \ ls = self.getLengths(t), \ V0=numpy.dot(mat,self.getStart(t)+shift), \ tag =t, \ dips=self.getDips(t),\ depths=self.getDepths(t), \ w0_offsets=self.getW0Offsets(t), \ w1_max=-self.getW1Range(t)[0]) def addFault(self, strikes, ls, V0=[0.,0.,0.],tag=None, dips=None, depths= None, w0_offsets=None, w1_max=None): """ adds a new fault to the fault system. The fault is named by ``tag``. The fault is defined by a starting point V0 and a list of ``strikes`` and length ``ls``. The strikes and the length is used to define a polyline with points ``V[i]`` such that - ``V[0]=V0`` - ``V[i]=V[i]+ls[i]*array(cos(strikes[i]),sin(strikes[i]),0)`` So ``strikes`` defines the angle between the direction of the fault segment and the x0 axis. In 3D ``ls[i]`` ==0 is allowed. In case of a 3D model a fault plane is defined through a dip ``dips`` and depth ``depths``. From the dip and the depth the polyline ``bottom`` of the bottom of the fault is computed. Each segment in the fault is decribed by the for vertices ``v0=top[i]``, ``v1==top[i+1]``, ``v2=bottom[i]`` and ``v3=bottom[i+1]`` The segment is parametrized by ``w0`` and ``w1`` with ``w0_offsets[i]<=w0<=w0_offsets[i+1]`` and ``-w1_max<=w1<=0``. Moreover - ``(w0,w1)=(w0_offsets[i] , 0)->v0`` - ``(w0,w1)=(w0_offsets[i+1], 0)->v1`` - ``(w0,w1)=(w0_offsets[i] , -w1_max)->v2`` - ``(w0,w1)=(w0_offsets[i+1], -w1_max)->v3`` If no ``w0_offsets`` is given, - ``w0_offsets[0]=0`` - ``w0_offsets[i]=w0_offsets[i-1]+L[i]`` where ``L[i]`` is the length of the segments on the top in 2D and in the middle of the segment in 3D. If no ``w1_max`` is given, the average fault depth is used. :param strikes: list of strikes. This is the angle of the fault segment direction with x0 axis. Right hand rule applies. :type strikes: ``list`` of ``float`` :param ls: list of fault lengths. In the case of a 3D fault a segment may have length 0. :type ls: ``list`` of ``float`` :param V0: start point of the fault :type V0: ``list`` or ``numpy.array`` with 2 or 3 components. ``V0[2]`` must be zero. :param tag: the tag of the fault. If fault ``tag`` already exists it is overwritten. :type tag: ``float`` or ``str`` :param dips: list of dip angles. Right hand rule around strike direction applies. :type dips: ``list`` of ``float`` :param depths: list of segment depth. Value mut be positive in the 3D case. :type depths: ``list`` of ``float`` :param w0_offsets: ``w0_offsets[i]`` defines the offset of the segment ``i`` in the fault to be used in the parametrization of the fault. If not present the cumulative length of the fault segments is used. :type w0_offsets: ``list`` of ``float`` or ``None`` :param w1_max: the maximum value used for parametrization of the fault in the depth direction. If not present the mean depth of the fault segments is used. :type w1_max: ``float`` :note: In the three dimensional case the lists ``dip`` and ``top`` must have the same length. """ if tag is None: tag=self.NOTAG else: if self.NOTAG in self.getTags(): raise ValueError('Attempt to add a fault with no tag to a set of existing faults') if not isinstance(strikes, list): strikes=[strikes, ] n_segs=len(strikes) if not isinstance(ls, list): ls=[ ls for i in range(n_segs) ] if not n_segs==len(ls): raise ValueError("number of strike direction and length must match.") if len(V0)>2: if abs(V0[2])>0: raise Value("start point needs to be surface (3rd component ==0)") if self.getDim()==2 and not (dips is None and depths is None) : raise ValueError('Spatial dimension two does not support dip and depth for faults.') if not dips is None: if not isinstance(dips, list): dips=[dips for i in range(n_segs) ] if n_segs != len(dips): raise ValueError('length of dips must be one less than the length of top.') if not depths is None: if not isinstance(depths, list): depths=[depths for i in range(n_segs+1) ] if n_segs+1 != len(depths): raise ValueError('length of depths must be one less than the length of top.') if w0_offsets != None: if len(w0_offsets) != n_segs+1: raise ValueError('expected length of w0_offsets is %s'%(n_segs)) self.__center=None self.__orientation = None # # in the 2D case we don't allow zero length: # if self.getDim() == 2: for l in ls: if l<=0: raise ValueError("length must be positive") else: for l in ls: if l<0: raise ValueError("length must be non-negative") for i in range(n_segs+1): if depths[i]<0: raise ValueError("negative depth.") # # translate start point to numarray # V0= numpy.array(V0[:self.getDim()],numpy.double) # # set strike vectors: # strike_vectors=[] top_polyline=[V0] total_length=0 for i in range(n_segs): v=numpy.zeros((self.getDim(),)) v[0]=es.cos(strikes[i]) v[1]=es.sin(strikes[i]) strike_vectors.append(v) top_polyline.append(top_polyline[-1]+ls[i]*v) total_length+=ls[i] # # normal and depth direction # if self.getDim()==3: normals=[] for i in range(n_segs): normals.append(numpy.array([es.sin(dips[i])*strike_vectors[i][1],-es.sin(dips[i])*strike_vectors[i][0], es.cos(dips[i])]) ) d=numpy.cross(strike_vectors[0],normals[0]) if d[2]>0: f=-1 else: f=1 depth_vectors=[f*depths[0]*d/numpy.linalg.norm(d) ] for i in range(1,n_segs): d=-numpy.cross(normals[i-1],normals[i]) d_l=numpy.linalg.norm(d) if d_l<=0: d=numpy.cross(strike_vectors[i],normals[i]) d_l=numpy.linalg.norm(d) else: for L in [ strike_vectors[i], strike_vectors[i-1]]: if numpy.linalg.norm(numpy.cross(L,d)) <= self.MIN_DEPTH_ANGLE * numpy.linalg.norm(L) * d_l: raise ValueError("%s-th depth vector %s too flat."%(i, d)) if d[2]>0: f=-1 else: f=1 depth_vectors.append(f*d*depths[i]/d_l) d=numpy.cross(strike_vectors[n_segs-1],normals[n_segs-1]) if d[2]>0: f=-1 else: f=1 depth_vectors.append(f*depths[n_segs]*d/numpy.linalg.norm(d)) bottom_polyline=[ top_polyline[i]+depth_vectors[i] for i in range(n_segs+1) ] # # calculate offsets if required: # if w0_offsets is None: w0_offsets=[0.] for i in range(n_segs): if self.getDim()==3: w0_offsets.append(w0_offsets[-1]+(float(numpy.linalg.norm(bottom_polyline[i+1]-bottom_polyline[i]))+ls[i])/2.) else: w0_offsets.append(w0_offsets[-1]+ls[i]) w0_max=max(w0_offsets) if self.getDim()==3: self.__normals[tag]=normals self.__depth_vectors[tag]=depth_vectors self.__depths[tag]=depths self.__dips[tag]=dips self.__bottom[tag]=bottom_polyline self.__ls[tag]=ls self.__strikes[tag]=strikes self.__strike_vectors[tag]=strike_vectors self.__top[tag]=top_polyline self.__total_length[tag]=total_length self.__offsets[tag]=w0_offsets if self.getDim()==2: self.__medDepth[tag]=0. else: self.__medDepth[tag]=sum([ numpy.linalg.norm(v) for v in depth_vectors])/len(depth_vectors) if w1_max is None or self.getDim()==2: w1_max=self.__medDepth[tag] self.__w0_max[tag]=w0_max self.__w1_max[tag]=w1_max def getMaxValue(self,f, tol=es.sqrt(es.EPSILON)): """ returns the tag of the fault of where ``f`` takes the maximum value and a `Locator` object which can be used to collect values from `Data` class objects at the location where the minimum is taken. :param f: a distribution of values :type f: `escript.Data` :param tol: relative tolerance used to decide if point is on fault :type tol: ``tol`` :return: the fault tag the maximum is taken, and a `Locator` object to collect the value at location of maximum value. """ ref=-es.Lsup(f)*2 f_max=ref t_max=None loc_max=None x=f.getFunctionSpace().getX() for t in self.getTags(): p,m=self.getParametrization(x,tag=t, tol=tol) loc=((m*f)+(1.-m)*ref).internal_maxGlobalDataPoint() f_t=f.getTupleForGlobalDataPoint(*loc)[0] if f_t>f_max: f_max=f_t t_max=t loc_max=loc if loc_max is None: return None, None else: return t_max, pdt.Locator(x.getFunctionSpace(),x.getTupleForGlobalDataPoint(*loc_max)) def getMinValue(self,f, tol=es.sqrt(es.EPSILON)): """ returns the tag of the fault of where ``f`` takes the minimum value and a `Locator` object which can be used to collect values from `Data` class objects at the location where the minimum is taken. :param f: a distribution of values :type f: `escript.Data` :param tol: relative tolerance used to decide if point is on fault :type tol: ``tol`` :return: the fault tag the minimum is taken, and a `Locator` object to collect the value at location of minimum value. """ ref=es.Lsup(f)*2 f_min=ref t_min=None loc_min=None x=f.getFunctionSpace().getX() for t in self.getTags(): p,m=self.getParametrization(x,tag=t, tol=tol) loc=((m*f)+(1.-m)*ref).internal_minGlobalDataPoint() f_t=f.getTupleForGlobalDataPoint(*loc)[0] if f_t<f_min: f_min=f_t t_min=t loc_min=loc if loc_min is None: return None, None else: return t_min, pdt.Locator(x.getFunctionSpace(),x.getTupleForGlobalDataPoint(*loc_min)) def getParametrization(self,x,tag=None, tol=es.sqrt(es.EPSILON), outsider=None): """ returns the parametrization of the fault ``tag`` in the fault system. In fact the values of the parametrization for at given coordinates ``x`` is returned. In addition to the value of the parametrization a mask is returned indicating if the given location is on the fault with given tolerance ``tol``. Typical usage of the this method is dom=Domain(..) x=dom.getX() fs=FaultSystem() fs.addFault(tag=3,...) p, m=fs.getParametrization(x, outsider=0,tag=3) saveDataCSV('x.csv',p=p, x=x, mask=m) to create a file with the coordinates of the points in ``x`` which are on the fault (as ``mask=m``) together with their location ``p`` in the fault coordinate system. :param x: location(s) :type x: `escript.Data` object or ``numpy.array`` :param tag: the tag of the fault :param tol: relative tolerance to check if location is on fault. :type tol: ``float`` :param outsider: value used for parametrization values outside the fault. If not present an appropriate value is choosen. :type outsider: ``float`` :return: the coordinates ``x`` in the coordinate system of the fault and a mask indicating coordinates in the fault by 1 (0 elsewhere) :rtype: `escript.Data` object or ``numpy.array`` """ offsets=self.getW0Offsets(tag) w1_range=self.getW1Range(tag) w0_range=self.getW0Range(tag)[1]-self.getW0Range(tag)[0] if outsider is None: outsider=min(self.getW0Range(tag)[0],self.getW0Range(tag)[1])-abs(w0_range)/es.sqrt(es.EPSILON) if isinstance(x,list): x=numpy.array(x, numpy.double) updated=x[0]*0 if self.getDim()==2: # # p=x[0]*0 + outsider top=self.getTopPolyline(tag) for i in range(1,len(top)): d=top[i]-top[i-1] h=x-top[i-1] h_l=es.length(h) d_l=es.length(d) s=es.inner(h,d)/d_l**2 s=s*es.whereNonPositive(s-1.-tol)*es.whereNonNegative(s+tol) m=es.whereNonPositive(es.length(h-s*d)-tol*es.maximum(h_l,d_l))*(1.-updated) p=(1.-m)*p+m*(offsets[i-1]+(offsets[i]-offsets[i-1])*s) updated=es.wherePositive(updated+m) else: p=x[:2]*0 + outsider top=self.getTopPolyline(tag) bottom=self.getBottomPolyline(tag) n=self.getSegmentNormals(tag) for i in range(len(top)-1): h=x-top[i] R=top[i+1]-top[i] r=bottom[i+1]-bottom[i] D0=bottom[i]-top[i] D1=bottom[i+1]-top[i+1] s_upper=es.matrix_mult(numpy.linalg.pinv(numpy.vstack((R,D1)).T),h) s_lower=es.matrix_mult(numpy.linalg.pinv(numpy.vstack((r,D0)).T),h) m_ul=es.wherePositive(s_upper[0]-s_upper[1]) s=s_upper*m_ul+s_lower*(1-m_ul) s0=s[0] s1=s[1] m=es.whereNonNegative(s0+tol)*es.whereNonPositive(s0-1.-tol)*es.whereNonNegative(s1+tol)*es.whereNonPositive(s1-1.-tol) s0=s0*m s1=s1*m atol=tol*es.maximum(es.length(h),es.length(top[i]-bottom[i+1])) m=es.whereNonPositive(es.length(h-s0*R-s1*D1)*m_ul+(1-m_ul)*es.length(h-s0*r-s1*D0)-atol) p[0]=(1.-m)*p[0]+m*(offsets[i]+(offsets[i+1]-offsets[i])*s0) p[1]=(1.-m)*p[1]+m*(w1_range[1]+(w1_range[0]-w1_range[1])*s1) updated=es.wherePositive(updated+m) return p, updated def getSideAndDistance(self,x,tag=None): """ returns the side and the distance at ``x`` from the fault ``tag``. :param x: location(s) :type x: `escript.Data` object or ``numpy.array`` :param tag: the tag of the fault :return: the side of ``x`` (positive means to the right of the fault, negative to the left) and the distance to the fault. Note that a value zero for the side means that that the side is undefined. """ d=None side=None if self.getDim()==2: mat=numpy.array([[0., 1.], [-1., 0.] ]) s=self.getTopPolyline(tag) for i in range(1,len(s)): q=(s[i]-s[i-1]) h=x-s[i-1] q_l=es.length(q) qt=es.matrixmult(mat,q) # orthogonal direction t=es.inner(q,h)/q_l**2 t=es.maximum(es.minimum(t,1,),0.) p=h-t*q dist=es.length(p) lside=es.sign(es.inner(p,qt)) if d is None: d=dist side=lside else: m=es.whereNegative(d-dist) m2=es.wherePositive(es.whereZero(abs(lside))+m) d=dist*(1-m)+d*m side=lside*(1-m2)+side*m2 else: ns=self.getSegmentNormals(tag) top=self.getTopPolyline(tag) bottom=self.getBottomPolyline(tag) for i in range(len(top)-1): h=x-top[i] R=top[i+1]-top[i] r=bottom[i+1]-bottom[i] D0=bottom[i]-top[i] D1=bottom[i+1]-top[i+1] s_upper=es.matrix_mult(numpy.linalg.pinv(numpy.vstack((R,D1)).T),h) s_lower=es.matrix_mult(numpy.linalg.pinv(numpy.vstack((r,D0)).T),h) m_ul=es.wherePositive(s_upper[0]-s_upper[1]) s=s_upper*m_ul+s_lower*(1-m_ul) s=es.maximum(es.minimum(s,1.),0) p=h-(m_ul*R+(1-m_ul)*r)*s[0]-(m_ul*D1+(1-m_ul)*D0)*s[1] dist=es.length(p) lside=es.sign(es.inner(p,ns[i])) if d is None: d=dist side=lside else: m=es.whereNegative(d-dist) m2=es.wherePositive(es.whereZero(abs(lside))+m) d=dist*(1-m)+d*m side=lside*(1-m2)+side*m2 return side, d

37.803231

305

0.595284

3,880

25,744

3.862113

0.106186

0.015682

0.018018

0.010277

0.445245

0.389456

0.363764

0.335269

0.305706

0.292759

0

0.022846

0.26379

25,744

680

306

37.858824

0.767794

0.361793

0

0.338462

0

0.050321

0

1

0.066667

false

0

0.012821

0

0.164103

0.002564

0

null

0

null

0

1

0

15d2546aeaf271239203d19ad3ebcd6e67edaf83

9,680

py

Python

acs/acs/Core/FileParsingManager.py

wangji1/test-framework-and-suites-for-android

59564f826f205fe7fab64f45b88b1a6dde6900af

[ "Apache-2.0" ]

null

acs/acs/Core/FileParsingManager.py

wangji1/test-framework-and-suites-for-android

59564f826f205fe7fab64f45b88b1a6dde6900af

[ "Apache-2.0" ]

null

acs/acs/Core/FileParsingManager.py

wangji1/test-framework-and-suites-for-android

59564f826f205fe7fab64f45b88b1a6dde6900af

[ "Apache-2.0" ]

null

""" :copyright: (c)Copyright 2013, Intel Corporation All Rights Reserved. The source code contained or described here in and all documents related to the source code ("Material") are owned by Intel Corporation or its suppliers or licensors. Title to the Material remains with Intel Corporation or its suppliers and licensors. The Material contains trade secrets and proprietary and confidential information of Intel or its suppliers and licensors. The Material is protected by worldwide copyright and trade secret laws and treaty provisions. No part of the Material may be used, copied, reproduced, modified, published, uploaded, posted, transmitted, distributed, or disclosed in any way without Intel's prior express written permission. No license under any patent, copyright, trade secret or other intellectual property right is granted to or conferred upon you by disclosure or delivery of the Materials, either expressly, by implication, inducement, estoppel or otherwise. Any license under such intellectual property rights must be express and approved by Intel in writing. :organization: INTEL MCG PSI :summary: Implements file parsing manager :since: 05/03/2013 :author: vdechefd """ import os import lxml.etree as et from acs.ErrorHandling.AcsConfigException import AcsConfigException from acs.Core.Report.ACSLogging import LOGGER_FWK from acs.Core.PathManager import Paths import acs.UtilitiesFWK.Utilities as Utils class FileParsingManager: """ FileParsingManager This class implements the File Parsing Manager. This manager takes XML files as inputs and parses them into dictionaries. It will parse: - use case catalog - bench config - equipment catalog - campaign """ def __init__(self, bench_config_name, equipment_catalog, global_config): self._file_extention = ".xml" self._execution_config_path = Paths.EXECUTION_CONFIG self._equipment_catalog_path = Paths.EQUIPMENT_CATALOG self._bench_config_name = (bench_config_name if os.path.isfile(bench_config_name) else os.path.join(self._execution_config_path, bench_config_name + self._file_extention)) self._equipment_catalog_name = equipment_catalog + self._file_extention self._global_config = global_config self._ucase_catalogs = None self._logger = LOGGER_FWK def parse_bench_config(self): """ This function parses the bench config XML file into a dictionary. """ def __parse_node(node): """ This private function parse a node from bench_config parsing. :rtype: dict :return: Data stocked into a dictionnary. """ dico = {} name = node.get('name', "") if name: # store all keys (except 'name')/value in a dict for key in [x for x in node.attrib if x != "name"]: dico[key] = node.attrib[key] node_list = node.xpath('./*') if node_list: for node_item in node_list: name = node_item.get('name', "") if name: dico[name] = __parse_node(node_item) return dico def __parse_bench_config(document): """ Last version of function parsing bench_config adapted for Multiphone. :type document: object :param document: xml document parsed by etree :rtype: dict :return: Data stocked into a dictionary. """ # parse bench_config (dom method) bench_config = {} node_list = document.xpath('/BenchConfig/*/*') for node in node_list: name = node.get('name', "") if name: bench_config[name] = __parse_node(node) return bench_config # body of the parse_bench_config() function. if not os.path.isfile(self._bench_config_name): error_msg = "Bench config file : %s does not exist" % self._bench_config_name raise AcsConfigException(AcsConfigException.FILE_NOT_FOUND, error_msg) try: document = et.parse(self._bench_config_name) except et.XMLSyntaxError: _, error_msg, _ = Utils.get_exception_info() error_msg = "{}; {}".format(self._bench_config_name, error_msg) raise AcsConfigException(AcsConfigException.XML_PARSING_ERROR, error_msg) result = __parse_bench_config(document) bench_config_parameters = Utils.BenchConfigParameters(dictionnary=result, bench_config_file=self._bench_config_name) return bench_config_parameters def parse_equipment_catalog(self): """ This function parses the equipment catalog XML file into a dictionary. """ # Instantiate empty dictionaries eqt_type_dic = {} # Get the xml doc equipment_catalog_path = os.path.join(self._equipment_catalog_path, self._equipment_catalog_name) if not os.path.isfile(equipment_catalog_path): error_msg = "Equipment catalog file : %s does not exist" % equipment_catalog_path raise AcsConfigException(AcsConfigException.FILE_NOT_FOUND, error_msg) try: equipment_catalog_doc = et.parse(equipment_catalog_path) except et.XMLSyntaxError: _, error_msg, _ = Utils.get_exception_info() error_msg = "{}; {}".format(equipment_catalog_path, error_msg) raise AcsConfigException(AcsConfigException.XML_PARSING_ERROR, error_msg) root_node = equipment_catalog_doc.xpath('/Equipment_Catalog') if not root_node: raise AcsConfigException(AcsConfigException.FILE_NOT_FOUND, "Wrong XML: could not find expected document root node: " "'Equipment_Catalog'") # Parse EquipmentTypes list_eq_types = root_node[0].xpath('./EquipmentType') for eq_type in list_eq_types: eqt_type_dic.update(self._load_equipment_type(eq_type)) self._global_config.equipmentCatalog = eqt_type_dic.copy() def _load_equipment_type(self, node): """ This function parses an "EquipmentType" XML Tag into a dictionary :type node: Etree node :param node: the "EquipmentType" node :rtype dic: dict :return: a dictionary of equipment """ dic = {} eqt_type_name = node.get("name", "") if eqt_type_name: dic[eqt_type_name] = self._load_equipments(node) return dic def _load_equipments(self, node): """ This function parses "Equipment" XML Tags into a dictionary :type node: Etree node :param node: the node containing "Equipment" nodes """ # Get common equipment type parameters dic = {} dic.update(self._get_parameters(node)) eqt_nodes = node.xpath('./Equipment') for sub_node in eqt_nodes: eqt_model = sub_node.get("name", "") if eqt_model: dic[eqt_model] = self._get_parameters(sub_node) dic[eqt_model].update(self._load_transport(sub_node)) dic[eqt_model].update(self._load_features(sub_node)) dic[eqt_model].update(self._load_controllers(sub_node)) return dic def _load_transport(self, node): """ This function parses a "Transport" XML Tags from a node into a dictionary :type node: DOM node :param node: the node from which to get all parameters value :rtype dic: dict :return: a dictionary of transports """ dic = {} transport_node = node.xpath('./Transports') if transport_node: dic["Transports"] = self._get_parameters(transport_node[0]) return dic def _load_controllers(self, node): """ This function parses a "Controllers" XML Tags from a node into a dictionary :type node: DOM node :param node: the node from which to get all parameters value :rtype dic: dict :return: the dictionary of controllers """ dic = {} transport_node = node.xpath('./Controllers') if transport_node: dic["Controllers"] = self._get_parameters(transport_node[0]) return dic def _load_features(self, node): """ This function parses a "Features" XML Tags from a node into a dictionary :type node: Element node :param node: the node from which to get all parameters value :rtype dic: dict :return: a dictionary of features """ dic = {} transport_node = node.xpath('./Features') if transport_node: dic["Features"] = self._get_parameters(transport_node[0]) return dic def _get_parameters(self, node): """ This function parses all "Parameter" XML Tags from a node into a dictionary :type node: Element node :param node: the node from which to get all parameters value :rtype dic: dict :return: a dictionary of parameters """ dic = {} parameters = node.xpath('./Parameter') for parameter in parameters: name = parameter.get("name", "") value = parameter.get("value", "") if name: dic[name] = value return dic

36.946565

119

0.630785

1,135

9,680

5.171806

0.21674

0.048722

0.028109

0.022658

0.36184

0.273765

0.235945

0.207496

0.191141

0.16201

0

0.002343

0.294421

9,680

261

120

37.088123

0.857101

0.335537

0

0.262712

0

0.05807

0

1

0.09322

false

0

0.050847

0

0.228814

0

null

0

null

0

1

0

15d53e64696bdcd31c356310ca351fd13adec82e

2,256

py

Python

Chapter 16/excel_to_csv.py

ostin-r/automate-boring-stuff-solutions

78f0a2981e6520ff2907285e666168a0f35eba02

[ "FTL" ]

4

2021-06-14T10:37:58.000Z

2021-12-30T17:49:17.000Z

Chapter 16/excel_to_csv.py

ostin-r/automate-boring-stuff-solutions

78f0a2981e6520ff2907285e666168a0f35eba02

[ "FTL" ]

null

Chapter 16/excel_to_csv.py

ostin-r/automate-boring-stuff-solutions

78f0a2981e6520ff2907285e666168a0f35eba02

[ "FTL" ]

1

2021-07-29T15:26:54.000Z

''' Austin Richards 4/14/21 excel_to_csv.py automates the conversion of many xlsx files into csv files. This program names the csv file in the format <filename>_<sheetname>.csv ''' import logging import os, csv, openpyxl from pathlib import Path from openpyxl.utils import get_column_letter logging.basicConfig(level=logging.DEBUG, format='%(asctime)s: %(message)s') def get_all_paths(directory): ''' returns all paths in a directory (and it's sub-directories) in a list type ''' file_paths = [] for path, dirs, files in os.walk(directory): for filename in files: filepath = os.path.join(path, filename) file_paths.append(filepath) return file_paths def excel_to_csv(directory): # get the absolute path, make a folder within it to save converted files, get files to convert directory = os.path.abspath(directory) new_dir = os.path.join(directory, 'converted_csv_files') os.makedirs(new_dir, exist_ok=True) all_paths = [path for path in get_all_paths(directory) if path.endswith('.xlsx')] for excel_file in all_paths: workbook = openpyxl.load_workbook(excel_file) excel_filename = Path(excel_file).stem print(f'copying {excel_filename}...') for sheet_name in workbook.sheetnames: print(f' copying {sheet_name}...') # create a csv filename with the excel filename and sheetname, put in new folder csv_filename = f'{excel_filename}_{sheet_name}.csv' csv_filepath = os.path.join(new_dir, csv_filename) new_file = open(csv_filepath, 'w', newline='') csv_writer = csv.writer(new_file) # get data from the xlsx file and write it to the new csv sheet = workbook[sheet_name] for row_num in range(1, sheet.max_row + 1): row_data = [] for col_num in range(1, sheet.max_column + 1): col_letter = get_column_letter(col_num) row_data.append(sheet[col_letter + str(row_num)].value) # write data to the new csv csv_writer.writerow(row_data) new_file.close() print('files copied.') excel_to_csv('Chapter 16')

35.25

98

0.649379

318

2,256

4.424528

0.339623

0.028429

0.021322

0.028429

0.027008

0

0.006579

0.258865

2,256

64

99

35.25

0.834928

0.223404

0

0.092281

0.019153

0

1

0.054054

false

0

0.108108

0

0.189189

0.081081

0

null

0

null

0

1

0

15d571532daed68e7ee3f7aca094467594fa8bdd

2,041

py

Python

kg/diff/templates/real_abs_rel_template.py

kevinsogo/compgen

c765fdb3008d41f409836a45ad5a506db6a99e74

[ "MIT" ]

6

2019-11-30T17:03:13.000Z

2021-09-30T05:08:31.000Z

kg/diff/templates/real_abs_rel_template.py

kevinsogo/compgen

c765fdb3008d41f409836a45ad5a506db6a99e74

[ "MIT" ]

1

2020-01-20T12:13:03.000Z

kg/diff/templates/real_abs_rel_template.py

kevinsogo/compgen

c765fdb3008d41f409836a45ad5a506db6a99e74

[ "MIT" ]

null

# Checks for an XXX error ### @replace "XXX", ('absolute/relative' if has_rel else 'absolute') # with an error of at most 1e-XXX ### @replace "XXX", prec # Don't edit this file. Edit real_abs_rel_template.py instead, and then run _real_check_gen.py # Oh, actually, you're editing the correct file. Go on. ### @if False raise Exception("You're not supposed to run this!!!") ### @if False from itertools import zip_longest from decimal import Decimal, InvalidOperation from kg.checkers import * ### @keep @import EPS = 0 ### @replace 0, f"Decimal('1e-{prec}')" EPS *= 1+Decimal('1e-5') # add some leniency @set_checker() @default_score def checker(input_file, output_file, judge_file, **kwargs): worst = 0 for line1, line2 in zip_longest(output_file, judge_file): if (line1 is None) != (line2 is None): raise WA("Unequal number of lines") p1 = line1.rstrip().split(" ") p2 = line2.rstrip().split(" ") if len(p1) != len(p2): raise WA("Incorrect number of values in line") for v1, v2 in zip(p1, p2): if v1 != v2: # they're different as tokens. try considering them as numbers try: err = error(Decimal(v1), Decimal(v2)) ### @replace "error", "abs_rel_error" if has_rel else "abs_error" except InvalidOperation: raise WA(f"Unequal tokens that are not numbers: {v1!r} != {v2!r}") worst = max(worst, err) if err > EPS: print('Found an error of', worst) ### @keep @if format not in ('hr', 'cms') raise WA("Bad precision.") print('Worst error:', worst) ### @keep @if format not in ('pg', 'hr', 'cms') help_ = ('Compare if two sequences of real numbers are "close enough" (by XXX). ' ### @replace 'XXX', '1e-' + str(prec) "Uses XXX error.") ### @replace 'XXX', 'absolute/relative' if has_rel else 'absolute' if __name__ == '__main__': chk(help=help_)

52.333333

123

0.588437

283

2,041

4.134276

0.45583

0.034188

0.020513

0.030769

0.129915

0.092308

0

0.019555

0.273395

2,041

38

124

53.710526

0.769386

0.353748

0

0.223612

0

1

0.034483

false

0

0.103448

0

0.137931

0.068966

0

null

0

null

0

1

0

15d5daf61123ba5cf4f2cb454d6eaf622ef68705

1,099

py

Python

osspeak/recognition/actions/library/process.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

1

2020-03-17T10:24:41.000Z

osspeak/recognition/actions/library/process.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

12

2016-09-28T05:16:00.000Z

2020-11-27T22:32:40.000Z

osspeak/recognition/actions/library/process.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

null

import time import os import subprocess import threading class ProcessHandler: def __init__(self, *args, on_output=None): self.process = subprocess.Popen(args, stdin=subprocess.PIPE, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True) self.on_output = on_output self.start_stdout_listening() def send_message(self, msg): if not isinstance(msg, bytes): msg = msg.encode('utf8') if not msg.endswith(b'\n'): msg += b'\n' self.process.stdin.write(msg) try: self.process.stdin.flush() except OSError: print(f'Process {self} already closed') def dispatch_process_output(self): for line in self.process.stdout: line = line.decode('utf8') self.on_output(line) def start_stdout_listening(self): t = threading.Thread(target=self.dispatch_process_output, daemon=True) t.start() def run(s): proc = ProcessHandler(s) return proc def run_sync(s): return subprocess.run(s, shell=True).stdout

28.179487

78

0.626934

138

1,099

4.862319

0.427536

0.04769

0.035768

0

0.002481

0.266606

1,099

39

79

28.179487

0.830025

0

0.037273

0

1

0.1875

false

0

0.125

0.03125

0.40625

0.03125

0

null

0

null

0

1

0

15d6695d5c93e5916f4ed45db19a2876ea82e9ee

977

py

Python

setup.py

haaspt/whatsnew

0524ad2b6132593282946073f2d647ea0ce960e8

[ "MIT" ]

2

2015-09-02T21:14:26.000Z

2015-09-02T22:23:04.000Z

setup.py

haaspt/whatsnew

0524ad2b6132593282946073f2d647ea0ce960e8

[ "MIT" ]

2

2018-01-02T00:54:49.000Z

2018-01-02T00:56:01.000Z

setup.py

haaspt/whatsnew

0524ad2b6132593282946073f2d647ea0ce960e8

[ "MIT" ]

null

import os from setuptools import setup readme = open('README.md').read() requirements = ['click', 'feedparser', 'beautifulsoup4'] setup( name = "whatsnew", version = "0.13", author = "Patrick Tyler Haas", author_email = "patrick.tyler.haas@gmail.com", description = ("A lightweight, convenient tool to get an overview of the day's headlines right from your command line."), license = "MIT", keywords = "", url = "https://github.com/haaspt/whatsnew", scripts=['main.py', 'newsfeeds.py', 'config.py'], install_requires=requirements, long_description=readme, entry_points = { 'console_scripts': [ 'whatsnew = main:main' ], }, classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Programming Language :: Python :: 3.6', ], )

29.606061

125

0.602866

102

977

5.72549

0.784314

0.041096

0.054795

0

0.009615

0.254862

977

32

126

30.53125

0.792582

0

0.068966

0

0.034483

0.47697

0.028659

0

1

0

false

0

0.068966

0

0.068966

0

null

0

null

0

1

0

15da0373b1191b0c411cc06fa6774cfb3700fb3a

2,880

py

Python

Flask-Server/timeswitch/server.py

weichweich/pi-timeswitch

c4428783fbf8b2294f7a6f55c312beeabae94d6f

[ "MIT" ]

4

2015-10-12T19:13:22.000Z

2018-07-18T17:55:48.000Z

Flask-Server/timeswitch/server.py

weichweich/pi-timeswitch

c4428783fbf8b2294f7a6f55c312beeabae94d6f

[ "MIT" ]

null

Flask-Server/timeswitch/server.py

weichweich/pi-timeswitch

c4428783fbf8b2294f7a6f55c312beeabae94d6f

[ "MIT" ]

2

2017-04-25T16:19:09.000Z

2022-01-24T08:15:12.000Z

import argparse import logging import sys from timeswitch.switch.manager import SwitchManager from timeswitch.app import setup_app from timeswitch.api import setup_api from timeswitch.model import setup_model # ###################################### # # parsing commandline args # ###################################### def parse_arguments(): PARSER = argparse.ArgumentParser(description='Timeswitch for the\ GPIOs of an Raspberry Pi with a webinterface.') PARSER.add_argument('-f', '--file', dest='schedule_file', metavar='file', type=str, required=True, help='A JSON-file containing the schedule.') PARSER.add_argument('--debug', action='store_true', help='A JSON-file containing the schedule.') PARSER.add_argument('--create', dest='create', action='store_true', help='Creates a new database. DELETES ALL DATA!!') PARSER.add_argument('--manager', dest='manager', action='store_true', help='Start the manager which switches the GPIOs at specified times.') PARSER.add_argument('--static', dest='static_dir', metavar='file', type=str, help='Folder with static files to serve') return PARSER.parse_args() # ###################################### # # Logging: # ###################################### def setup_logger(debug=True): # set up logging to file - see previous section for more details logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-20s \ %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M', filename='piSwitch.log', filemode='a') # define a Handler which writes INFO messages or higher to the sys.stderr console = logging.StreamHandler() if debug: console.setLevel(logging.DEBUG) else: console.setLevel(logging.INFO) # set a format which is simpler for console use formatter = logging.Formatter('%(levelname)-8s:%(name)-8s:%(message)s') # tell the handler to use this format console.setFormatter(formatter) # add the handler to the root logger logging.getLogger('').addHandler(console) def start(cmd_args, app, switch_model): switch_manager = None if cmd_args.manager: switch_manager = SwitchManager(switch_model) switch_manager.start() try: app.run(debug=cmd_args.debug) finally: if cmd_args.manager: switch_manager.stop() def main(): cmd_args = parse_arguments() setup_logger(cmd_args.debug) app = setup_app(static_folder=cmd_args.static_dir, static_url_path='') model = setup_model(app) _ = setup_api(app, model) start(cmd_args, app, model) if __name__ == '__main__': main()

32

94

0.601389

329

2,880

5.118541

0.386018

0.033254

0.050475

0.033848

0.099762

0.065321

0

0.002291

0.242361

2,880

89

95

32.359551

0.769478

0.100347

0

0.071429

0

0.162196

0.015683

0

1

0.071429

false

0

0.125

0

0.214286

0

null

0

null

0

1

0

15db2920ca4b3d1d42695dca0b6816fa94b0f30d

12,055

py

Python

utils/videoloader.py

mie-lab/traffic4cast

aea6f90e8884c01689c84255c99e96d2b58dc470

[ "Apache-2.0" ]

8

2020-07-26T20:54:58.000Z

2022-03-01T14:36:13.000Z

utils/videoloader.py

mie-lab/traffic4cast

aea6f90e8884c01689c84255c99e96d2b58dc470

[ "Apache-2.0" ]

null

utils/videoloader.py

mie-lab/traffic4cast

aea6f90e8884c01689c84255c99e96d2b58dc470

[ "Apache-2.0" ]

5

2019-11-05T09:46:01.000Z

2021-01-24T04:42:53.000Z

import sys, os, time from pathlib import Path import pickle import datetime as dt import glob import h5py import numpy as np from matplotlib import pyplot as plt from multiprocessing import Pool from functools import partial import torch from torchvision import datasets, transforms def subsample(x, n=0, m=200): return x[..., n:m, n:m] def _get_tstamp_string(tstamp_ix): """Calculates the timestamp in hh:mm based on the file index Args: tstamp_ix (int): Index of the single frame Returns: Str: hh:mm """ total_minutes = tstamp_ix*5 hours = total_minutes // 60 minutes = total_minutes % 60 return hours, minutes class trafic4cast_dataset(torch.utils.data.Dataset): """Dataloader for trafic4cast data Attributes: compression (TYPE): Description do_precomp_path (TYPE): Description num_frames (TYPE): Description reduce (TYPE): Description source_root (TYPE): Description split_type (TYPE): Description target_file_paths (TYPE): Description target_root (TYPE): Description transform (TYPE): Description valid_test_clips (TYPE): Description """ def __init__(self, source_root, target_root="precomuted_data", split_type='train', cities=['Berlin', 'Istanbul', 'Moscow'], transform=None, reduce=False, compression=None, num_frames=15, do_subsample=None, filter_test_times=False, return_features=False, return_city=False): """Dataloader for the trafic4cast competition Usage Dataloader: The dataloader is situated in "videoloader.py", to use it, you have to download the competition data and set two paths. "source_root" and "target_root". source_root: Is the directory with the raw competition data. The expected file structure is shown below. target_root: This directory will be used to store the preprocessed data (about 200 GB) Expected folder structure for raw data: -source_root - Berlin -Berlin_test -Berlin_training -Berlin_validation -Istanbul -Instanbul_test -… -Moscow -… Args: source_root (str): Is the directory with the raw competition data. target_root (str, optional): This directory will be used to store the preprocessed data split_type (str, optional): Can be ['training', 'validation', 'test'] cities (list, optional): This can be used to limit the data loader to a subset of cities. Has to be a list! Default is ['Berlin', 'Moscow', 'Istanbul'] transform (None, optional): Transform applied to x before returning it. reduce (bool, optional): This option collapses the time dimension into the (color) channel dimension. compression (str, optional): The h5py compression method to store the preprocessed data. 'compression=None' is the fastest. num_frames (int, optional): do_subsample (tuple, optional): Tuple of two integers. Returns only a part of the image. Slices the image in the 'pixel' dimensions with x = x[n:m, n:m]. with m>n filter_test_times (bool, optional): Filters output data, such that only valid (city-dependend) test-times are returned. """ self.reduce = reduce self.source_root = source_root self.target_root = target_root self.transform = transform self.split_type = split_type self.compression = compression self.cities = cities self.num_frames = num_frames self.subsample = False self.filter_test_times = filter_test_times self.return_features = return_features self.return_city = return_city if self.filter_test_times: tt_dict2 = {} tt_dict = pickle.load(open(os.path.join('.', 'utils', 'test_timestamps.dict'), "rb")) for city, values in tt_dict.items(): values.sort() tt_dict2[city] = values self.valid_test_times = tt_dict2 if do_subsample is not None: self.subsample = True self.n = do_subsample[0] self.m = do_subsample[1] source_file_paths = [] for city in cities: source_file_paths = source_file_paths + glob.glob( os.path.join(self.source_root, city, '*_' + self.split_type, '*.h5')) do_precomp_path = [] missing_target_files = [] for raw_file_path in source_file_paths: target_file = raw_file_path.replace( self.source_root, self.target_root) if not os.path.exists(target_file): do_precomp_path.append(raw_file_path) missing_target_files.append(target_file) self.do_precomp_path = do_precomp_path target_dirs = list(set([str(Path(x).parent) for x in missing_target_files])) for target_dir in target_dirs: if not os.path.exists(target_dir): os.makedirs(target_dir) with Pool() as pool: pool.map(self.precompute_clip, self.do_precomp_path) pool.close() pool.join() target_file_paths = [] for city in cities: target_file_paths = target_file_paths + glob.glob( os.path.join(self.target_root, city, '*_' + self.split_type, '*.h5')) self.target_file_paths = target_file_paths if self.split_type == 'test': precomp_readt_test = partial(self.precompute_clip, mode='reading_test') with Pool() as pool: valid_test_clips = pool.map(precomp_readt_test, self.target_file_paths) pool.close() pool.join() valid_test_clips = [valid_tuple for sublist in valid_test_clips for valid_tuple in sublist] valid_test_clips.sort() self.valid_test_clips = valid_test_clips def precompute_clip(self, source_path, mode='writing'): """Summary Args: source_path (TYPE): Description mode (str, optional): Description Returns: TYPE: Description """ target_path = source_path.replace(self.source_root, self.target_root) f_source = h5py.File(source_path, 'r') data1 = f_source['array'] data1 = data1[:] if mode == 'writing': data1 = np.moveaxis(data1, 3, 1) f_target = h5py.File(target_path, 'w') dset = f_target.create_dataset('array', (288, 3, 495, 436), chunks=(1, 3, 495, 436), dtype='uint8', data=data1, compression=self.compression) f_target.close() if mode == 'reading_test': valid_test_clips = list = [] for tstamp_ix in range(288-15): clip = data1[tstamp_ix:tstamp_ix+self.num_frames, :, :, :] sum_first_train_frame = np.sum(clip[0, :, :, :]) sum_last_train_frame = np.sum(clip[11, :, :, :]) if (sum_first_train_frame != 0) and (sum_last_train_frame != 0): valid_test_clips.append((source_path, tstamp_ix)) f_source.close() if mode == 'reading_test': return valid_test_clips def __len__(self): if self.split_type == 'test': pass return len(self.valid_test_clips) elif self.filter_test_times: return len(self.target_file_paths) * 5 else: return len(self.target_file_paths) * 272 def __getitem__(self, idx): """Summary Args: idx (TYPE): Description Returns: TYPE: Description """ return_dict = {} if torch.is_tensor(idx): idx = idx.tolist() if self.split_type == 'test': target_file_path, tstamp_ix = self.valid_test_clips[idx] elif self.filter_test_times: file_ix = idx // 5 valid_tstamp_ix = idx % 5 target_file_path = self.target_file_paths[file_ix] city_name_path = Path(target_file_path.replace(self.target_root,'')) city_name = city_name_path.parts[1] tstamp_ix = self.valid_test_times[city_name][valid_tstamp_ix] else: file_ix = idx // 272 tstamp_ix = idx % 272 target_file_path = self.target_file_paths[file_ix] if self.return_features: # create feature vector date_string = Path(target_file_path).name.split('_')[0] date_datetime = dt.datetime.strptime(date_string, '%Y%m%d') hour, minute = _get_tstamp_string(tstamp_ix) # feature_vector = [] sin_hours = np.sin(2*np.pi/24 * hour) cos_hours = np.cos(2*np.pi/24 * hour) sin_mins = np.sin(2*np.pi/60 * minute) cos_mins = np.cos(2*np.pi/60 * minute) sin_month = np.sin(2*np.pi/12 * date_datetime.month) cos_month = np.cos(2*np.pi/12 * date_datetime.month) weekday_ix = date_datetime.weekday() / 6 week_number = date_datetime.isocalendar()[1] / 52 feature_vector = np.asarray([sin_hours, cos_hours, sin_mins, cos_mins, sin_month, cos_month, weekday_ix, week_number]).ravel() feature_vector = torch.from_numpy(feature_vector) feature_vector = feature_vector.to(dtype=torch.float) return_dict['feature_vector'] = feature_vector if self.return_city: city_name_path = Path(target_file_path.replace(self.target_root,'')) city_name = city_name_path.parts[1] return_dict['city_names'] = city_name # we want to predict the image at idx+1 based on the image with idx f = h5py.File(target_file_path, 'r') sample = f.get('array') x = sample[tstamp_ix:tstamp_ix+12, :, :, :] y = sample[tstamp_ix+12:tstamp_ix+15, :, :, :] if self.reduce: # stack all time dimensions into the channels. # all channels of the same timestamp are left togehter x = np.moveaxis(x, (0, 1), (2, 3)) x = np.reshape(x, (495, 436, 36)) x = torch.from_numpy(x) x = x.permute(2, 0, 1) # Dimensions: time/channels, h, w y = np.moveaxis(y, (0, 1), (2, 3)) y = np.reshape(y, (495, 436, 9)) y = torch.from_numpy(y) y = y.permute(2, 0, 1) y = y.to(dtype=torch.float) # is ByteTensor? x = x.to(dtype=torch.float) # is ByteTensor? else: x = torch.from_numpy(x) y = torch.from_numpy(y) y = y.to(dtype=torch.float) # is ByteTensor? x = x.to(dtype=torch.float) # is ByteTensor? f.close() if self.subsample: x = subsample(x,self.n,self.m) y = subsample(y,self.n,self.m) if self.transform is not None: x = self.transform(x) return x, y, return_dict

34.942029

132

0.555454

1,436

12,055

4.443593

0.194986

0.03291

0.026328

0.017866

0.226297

0.156872

0.118163

0.105313

0.071149

0.058925

0

0.018006

0.355039

12,055

345

133

34.942029

0.801929

0.233513

0

0.181818

0

0.022516

0

1

0.032086

false

0.005348

0.064171

0.005348

0.139037

0

null

0

null

0

1

0

15de738d2ebcbbe25e92a2d8f2d43e4cb6af3d82

1,760

py

Python

pychunkedgraph/ingest/ingestion_utils.py

perlman/PyChunkedGraph

2c582f46a8292010e8f9f54c94c63af0b172bdad

[ "MIT" ]

null

pychunkedgraph/ingest/ingestion_utils.py

perlman/PyChunkedGraph

2c582f46a8292010e8f9f54c94c63af0b172bdad

[ "MIT" ]

null

pychunkedgraph/ingest/ingestion_utils.py

perlman/PyChunkedGraph

2c582f46a8292010e8f9f54c94c63af0b172bdad

[ "MIT" ]

null

import numpy as np from pychunkedgraph.backend import chunkedgraph, chunkedgraph_utils import cloudvolume def initialize_chunkedgraph(cg_table_id, ws_cv_path, chunk_size, cg_mesh_dir, fan_out=2, instance_id=None, project_id=None): """ Initalizes a chunkedgraph on BigTable :param cg_table_id: str name of chunkedgraph :param ws_cv_path: str path to watershed segmentation on Google Cloud :param chunk_size: np.ndarray array of three ints :param cg_mesh_dir: str mesh folder name :param fan_out: int fan out of chunked graph (2 == Octree) :param instance_id: str Google instance id :param project_id: str Google project id :return: ChunkedGraph """ ws_cv = cloudvolume.CloudVolume(ws_cv_path) bbox = np.array(ws_cv.bounds.to_list()).reshape(2, 3) # assert np.all(bbox[0] == 0) # assert np.all((bbox[1] % chunk_size) == 0) n_chunks = ((bbox[1] - bbox[0]) / chunk_size).astype(np.int) n_layers = int(np.ceil(chunkedgraph_utils.log_n(np.max(n_chunks), fan_out))) + 2 dataset_info = ws_cv.info dataset_info["mesh"] = cg_mesh_dir dataset_info["data_dir"] = ws_cv_path dataset_info["graph"] = {"chunk_size": [int(s) for s in chunk_size]} kwargs = {"table_id": cg_table_id, "chunk_size": chunk_size, "fan_out": np.uint64(fan_out), "n_layers": np.uint64(n_layers), "dataset_info": dataset_info, "is_new": True} if instance_id is not None: kwargs["instance_id"] = instance_id if project_id is not None: kwargs["project_id"] = project_id cg = chunkedgraph.ChunkedGraph(**kwargs) return cg

30.877193

84

0.64375

251

1,760

4.25498

0.318725

0.067416

0.029963

0.02809

0.031835

0

0.011416

0.253409

1,760

56

85

31.428571

0.80137

0.289205

0

0.084041

0

1

0.04

false

0

0.12

0

0.2

0

null

0

null

0

1

0

15dec2f55095e3b6ec802c41f0cedadb58146312

2,691

py

Python

brew_gui.py

mburgess00/brew_controller

913e3b37b9421759db5186e5f0e44cf8f4fd7f6a

[ "Apache-2.0" ]

null

brew_gui.py

mburgess00/brew_controller

913e3b37b9421759db5186e5f0e44cf8f4fd7f6a

[ "Apache-2.0" ]

null

brew_gui.py

mburgess00/brew_controller

913e3b37b9421759db5186e5f0e44cf8f4fd7f6a

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 from guizero import App, Text, Slider, Combo, PushButton, Box, Picture pause = True def readsensors(): return {"hlt" : 160, "rims" : 152, "bk" : 75} def handlepause(): global pause global pauseState print("Pause Button pressed") if pause: print("running") pause = not pause pauseState.value=("Running") hltFlame.visible=True rimsFlame.visible=True bkFlame.visible=True else: print("pausing") pause = not pause pauseState.value=("Paused") hltFlame.visible=False rimsFlame.visible=False bkFlame.visible=False return app = App(title="Brew GUI", width=1280, height=768, layout="grid") vertPad = Picture(app, image="blank_vert.gif", grid=[0,0]) hltBox = Box(app, layout="grid", grid=[1,0]) hltPad = Picture(hltBox, image="blank.gif", grid=[0,0]) hltTitle = Text(hltBox, text="HLT", grid=[0,1], align="top") hltText = Text(hltBox, text="180", grid=[0,2], align="top") hltSlider = Slider(hltBox, start=212, end=100, horizontal=False, grid=[0,3], align="top") hltSlider.tk.config(length=500, width=50) hltFlamePad = Picture(hltBox, image="blank_flame.gif", grid=[0,4]) hltFlame = Picture(hltBox, image="flame.gif", grid=[0,4]) rimsBox = Box(app, layout="grid", grid=[2,0]) rimsPad = Picture(rimsBox, image="blank.gif", grid=[0,0]) rimsTitle = Text(rimsBox, text="RIMS", grid=[0,1], align="top") rimsText = Text(rimsBox, text="180", grid=[0,2], align="top") rimsSlider = Slider(rimsBox, start=212, end=100, horizontal=False, grid=[0,3], align="top") rimsSlider.tk.config(length=500, width=50) rimsFlamePad = Picture(rimsBox, image="blank_flame.gif", grid=[0,4]) rimsFlame = Picture(rimsBox, image="flame.gif", grid=[0,4]) bkBox = Box(app, layout="grid", grid=[3,0]) bkPad = Picture(bkBox, image="blank.gif", grid=[0,0]) bkTitle = Text(bkBox, text="BK", grid=[0,1], align="top") bkText = Text(bkBox, text="75", grid=[0,2], align="top") bkSlider = Slider(bkBox, start=100, end=0, horizontal=False, grid=[0,3], align="top") bkSlider.tk.config(length=500, width=50) bkFlamePad = Picture(bkBox, image="blank_flame.gif", grid=[0,4]) bkFlame = Picture(bkBox, image="flame.gif", grid=[0,4]) modeBox = Box(app, layout="grid", grid=[4,0]) modePad = Picture(modeBox, image="blank.gif", grid=[0,0]) modeTitle = Text(modeBox, text="Mode", grid=[0,0], align="top") mode = Combo(modeBox, options=["HLT", "RIMS", "BK"], grid=[1,0]) pauseState = Text(modeBox, text="Paused", grid=[0,1]) pauseButton = PushButton(modeBox, icon="pause-play.gif", command=handlepause, grid=[1,1]) hltFlame.visible=False rimsFlame.visible=False bkFlame.visible=False app.display()

37.901408

91

0.670011

393

2,691

4.577608

0.264631

0.061145

0.048916

0.043357

0.414675

0.307949

0.193997

0.114508

0.047804

0

0.047783

0.136752

2,691

70

92

38.442857

0.726647

0.007804

0

0.135593

0

0.107156

0

1

0.033898

false

0

0.016949

0.084746

0.050847

0

null

0

null

0

1

0

15e018d003308b6e32514ff4f0cb219f8f099c3b

2,498

py

Python

tests/integration/test_integration.py

nuvolos-cloud/resolos

0918066cab7b11ef04ae005f3e052b14a65ded68

[ "MIT" ]

1

2021-11-30T06:47:24.000Z

tests/integration/test_integration.py

nuvolos-cloud/resolos

0918066cab7b11ef04ae005f3e052b14a65ded68

[ "MIT" ]

1

2021-04-08T12:56:39.000Z

tests/integration/test_integration.py

nuvolos-cloud/resolos

0918066cab7b11ef04ae005f3e052b14a65ded68

[ "MIT" ]

null

from click.testing import CliRunner from resolos.interface import ( res_remote_add, res_remote_remove, res_init, res_sync, res ) from resolos.remote import read_remote_db, list_remote_ids, delete_remote from tests.common import verify_result import logging from pathlib import Path import os logger = logging.getLogger(__name__) USER = os.environ["TEST_USER"] PWD = os.environ["SSHPASS"] HOST = os.environ["TEST_HOST"] class TestIntegration: remote_id = "test_remote" def test_job(self, *args): runner = CliRunner() with runner.isolated_filesystem() as fs: # Initialize a new local project logger.info(f"Initializing new project in {fs}") verify_result(runner.invoke(res, ["-v", "DEBUG", "info"])) verify_result(runner.invoke(res_init, ["-y"])) # Add remote logger.info(f"### Adding remote in {fs}") verify_result( runner.invoke( res_remote_add, [self.remote_id, "-y", "-h", HOST, "-p", "3144", "-u", USER, "--remote-path", "/data/integration_test", "--conda-install-path", "/data", "--conda-load-command", "source /data/miniconda/bin/activate"] ) ) remotes_list = read_remote_db() assert self.remote_id in remotes_list remotes_settings = remotes_list[self.remote_id] assert remotes_settings["hostname"] == HOST assert remotes_settings["username"] == USER # Run job with (Path(fs) / "test_script.py").open("w") as py: py.write("""with open('test_output.txt', 'w') as txtf: txtf.write('Hello, world!')""") logger.info(f"### Syncing with remote {self.remote_id}") verify_result(runner.invoke(res_sync, ["-r", self.remote_id])) logger.info(f"### Running test job on {self.remote_id}") verify_result(runner.invoke(res, ["-v", "DEBUG", "job", "-r", self.remote_id, "run", "-p", "normal", "python test_script.py"])) # Sync back job results logger.info(f"### Syncing results from remote {self.remote_id}") verify_result(runner.invoke(res_sync, ["-r", self.remote_id])) assert (Path(fs) / "test_output.txt").exists() # Remove remote logger.info(f"### Removing remote {self.remote_id}") verify_result(runner.invoke(res_remote_remove, [self.remote_id]))

43.068966

219

0.601281

308

2,498

4.681818

0.331169

0.066574

0.09154

0.116505

0.222607

0.203884

0.195562

0.144244

0.117198

0.085992

0

0.002158

0.257806

2,498

58

220

43.068966

0.77562

0.034027

0

0.081633

0

0.234635

0.030316

0

0.081633

1

0.020408

false

0.020408

0.142857

0

0.204082

0

null

0

null

0

1

0

15e034a0325db7cea2ebf82178c6ea8ad80d5cda

946

py

Python

test/test_add_group.py

romanovaes/python_training

5df3a9b716e7659fb8f61e0b55e5217cc6a1a89e

[ "Apache-2.0" ]

null

test/test_add_group.py

romanovaes/python_training

5df3a9b716e7659fb8f61e0b55e5217cc6a1a89e

[ "Apache-2.0" ]

null

test/test_add_group.py

romanovaes/python_training

5df3a9b716e7659fb8f61e0b55e5217cc6a1a89e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from builtins import * from model.group import Group import pytest import allure #@pytest.mark.parametrize("group", testdata, ids=[repr(x) for x in testdata]) @allure.step('test_add_group') def test_add_group(app, db, json_groups, check_ui): group=json_groups #with pytest.allure.step('Given a group list'): old_group=db.get_group_list() #with pytest.allure.step('When I add a group % to the list' % group): app.group.create(group) #with pytest.allure.step('Then the group list is equal to the old list with the added group'): new_group=db.get_group_list() old_group.append(group) assert sorted(old_group, key=Group.id_or_max)==sorted(new_group, key=Group.id_or_max) if check_ui: assert sorted(map(app.group.clean_gap_from_group, new_group), key=Group.id_or_max) == sorted(app.group.get_group_list(), key=Group.id_or_max)

35.037037

153

0.689218

151

946

4.112583

0.364238

0.072464

0.064412

0.077295

0.21095

0.125604

0

0.001305

0.190275

946

26

154

36.384615

0.809399

0.32241

0

0.022187

0

0.142857

1

0.071429

false

0

0.285714

0

0.357143

0

null

0

null

0

1

0

15e3d38045eea34ae5ee1b0e2ce54a0a8b72223a

3,988

py

Python

test/test_docx_context.py

ieric2/docx2python

4a1266811f9bd71f5eea5e0d458a391fd9eb4f73

[ "MIT" ]

52

2019-07-08T19:37:45.000Z

2022-03-30T11:36:08.000Z

test/test_docx_context.py

ieric2/docx2python

4a1266811f9bd71f5eea5e0d458a391fd9eb4f73

[ "MIT" ]

29

2019-08-29T09:48:24.000Z

2022-03-13T13:58:58.000Z

test/test_docx_context.py

ieric2/docx2python

4a1266811f9bd71f5eea5e0d458a391fd9eb4f73

[ "MIT" ]

23

2019-08-29T11:33:13.000Z

2022-03-03T17:22:35.000Z

#!/usr/bin/env python3 # _*_ coding: utf-8 _*_ """Test docx2python.docx_context.py author: Shay Hill created: 6/26/2019 """ import os import shutil import zipfile from collections import defaultdict from typing import Any, Dict import pytest from docx2python.docx_context import ( collect_docProps, collect_numFmts, get_context, pull_image_files, ) class TestCollectNumFmts: """Test strip_text.collect_numFmts """ # noinspection PyPep8Naming def test_gets_formats(self) -> None: """Retrieves formats from example.docx This isn't a great test. There are numbered lists I've added then removed as I've edited my test docx. These still appear in the docx file. I could compare directly with the extracted numbering xml file, but even then I'd be comparing to something I don't know to be accurate. This just tests that all numbering formats are represented. """ zipf = zipfile.ZipFile("resources/example.docx") numId2numFmts = collect_numFmts(zipf.read("word/numbering.xml")) formats = {x for y in numId2numFmts.values() for x in y} assert formats == { "lowerLetter", "upperLetter", "lowerRoman", "upperRoman", "bullet", "decimal", } class TestCollectDocProps: """Test strip_text.collect_docProps """ def test_gets_properties(self) -> None: """Retrieves properties from docProps""" zipf = zipfile.ZipFile("resources/example.docx") props = collect_docProps(zipf.read("docProps/core.xml")) assert props["creator"] == "Shay Hill" assert props["lastModifiedBy"] == "Shay Hill" @pytest.fixture def docx_context() -> Dict[str, Any]: """result of running strip_text.get_context""" zipf = zipfile.ZipFile("resources/example.docx") return get_context(zipf) # noinspection PyPep8Naming class TestGetContext: """Text strip_text.get_context """ def test_docProp2text(self, docx_context) -> None: """All targets mapped""" zipf = zipfile.ZipFile("resources/example.docx") props = collect_docProps(zipf.read("docProps/core.xml")) assert docx_context["docProp2text"] == props def test_numId2numFmts(self, docx_context) -> None: """All targets mapped""" zipf = zipfile.ZipFile("resources/example.docx") numId2numFmts = collect_numFmts(zipf.read("word/numbering.xml")) assert docx_context["numId2numFmts"] == numId2numFmts def test_numId2count(self, docx_context) -> None: """All numIds mapped to a default dict defaulting to 0""" for numId in docx_context["numId2numFmts"]: assert isinstance(docx_context["numId2count"][numId], defaultdict) assert docx_context["numId2count"][numId][0] == 0 def test_lists(self) -> None: """Pass silently when no numbered or bulleted lists.""" zipf = zipfile.ZipFile("resources/basic.docx") context = get_context(zipf) assert "numId2numFmts" not in context assert "numId2count" not in context class TestPullImageFiles: """Test strip_text.pull_image_files """ def test_pull_image_files(self) -> None: """Copy image files to output path.""" zipf = zipfile.ZipFile("resources/example.docx") context = get_context(zipf) pull_image_files(zipf, context, "delete_this/path/to/images") assert os.listdir("delete_this/path/to/images") == ["image1.png", "image2.jpg"] # clean up shutil.rmtree("delete_this") def test_no_image_files(self) -> None: """Pass silently when no image files.""" zipf = zipfile.ZipFile("resources/basic.docx") context = get_context(zipf) pull_image_files(zipf, context, "delete_this/path/to/images") assert os.listdir("delete_this/path/to/images") == [] # clean up shutil.rmtree("delete_this")

33.79661

87

0.658977

476

3,988

5.388655

0.327731

0.060039

0.05614

0.084211

0.363743

0.355166

0.284211

0

0.010407

0.228937

3,988

117

88

34.08547

0.82374

0.243731

0

0.287879

0

0.195848

0.081661

0

0.166667

1

0.136364

false

0

0.106061

0

0.318182

0

null

0

null

0

1

0

15e42de122c93447226408dd404b3ccbe892d9e6

1,022

py

Python

src/transporters/approximator/transporter.py

mucharafal/optics_generator_python

c14d4e5f19f921f4dc0a98129bca9d31754b72ad

[ "MIT" ]

null

src/transporters/approximator/transporter.py

mucharafal/optics_generator_python

c14d4e5f19f921f4dc0a98129bca9d31754b72ad

[ "MIT" ]

null

src/transporters/approximator/transporter.py

mucharafal/optics_generator_python

c14d4e5f19f921f4dc0a98129bca9d31754b72ad

[ "MIT" ]

null

import transporters.approximator.runner as ra from data.parameters_names import ParametersNames as Parameters def transport(approximator, particles): """matrix in format returned by data.particles_generator functions""" segments = dict() segments["start"] = particles matrix_for_transporter = particles.get_default_coordinates_of(Parameters.X, Parameters.THETA_X, Parameters.Y, Parameters.THETA_Y, Parameters.PT) transported_particles = ra.transport(approximator, matrix_for_transporter) segments["end"] = particles.__class__(transported_particles, get_mapping()) return segments def get_mapping(): mapping = { Parameters.X: 0, Parameters.THETA_X: 1, Parameters.Y: 2, Parameters.THETA_Y: 3, Parameters.PT: 4 } return mapping def get_transporter(approximator): def transporter(particles): return transport(approximator, particles) return transporter

28.388889

113

0.683953

106

1,022

6.386792

0.415094

0.088626

0

0.006418

0.237769

1,022

35

114

29.2

0.862644

0.061644

0

0.008395

0

1

0.173913

false

0

0.086957

0.043478

0.434783

0

null

0

null

0

1

0

15f39d0f421f2c653443422d6ac14afb93981bf5

5,264

py

Python

app/projects/tests/test_portfolio_api.py

nestor-san/cooperation-fit

1a922233345698970c7e18e6213ad0320de70cce

[ "MIT" ]

null

app/projects/tests/test_portfolio_api.py

nestor-san/cooperation-fit

1a922233345698970c7e18e6213ad0320de70cce

[ "MIT" ]

null

app/projects/tests/test_portfolio_api.py

nestor-san/cooperation-fit

1a922233345698970c7e18e6213ad0320de70cce

[ "MIT" ]

null

from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from core.models import PortfolioItem from projects.serializers import PortfolioItemSerializer PORTFOLIO_URL = reverse('projects:portfolioitem-list') def detail_url(portfolio_id): """Return the detail URL of a portfolio item""" return reverse('projects:portfolioitem-detail', args=[portfolio_id]) class PublicPortfolioApiTests(TestCase): """Test the publicly available projects API""" def setUp(self): self.client = APIClient() def test_login_not_required(self): """Test that login is not required to access the endpoint""" res = self.client.get(PORTFOLIO_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) def test_retrieve_portfolio_list(self): """Test retrieving a list of portfolio items""" sample_user = get_user_model().objects.create_user( 'test@xemob.com', 'testpass' ) PortfolioItem.objects.create(user=sample_user, name='Portfolio Item 1') PortfolioItem.objects.create(user=sample_user, name='Portfolio Item 2') res = self.client.get(PORTFOLIO_URL) portfolio_items = PortfolioItem.objects.all().order_by('-name') serializer = PortfolioItemSerializer(portfolio_items, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) class PrivatePortfolioApiTests(TestCase): """Test the private portfolio API""" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'test@xemob.com', 'testpass' ) self.client.force_authenticate(self.user) def test_create_portfolio_item_successfully(self): """Test creating a new portfolio item""" payload = {'name': 'New portfolio item', 'user': self.user.id} self.client.post(PORTFOLIO_URL, payload) exists = PortfolioItem.objects.filter( user=self.user, name=payload['name'] ).exists() self.assertTrue(exists) def test_create_portfolio_item_invalid(self): """Test creating a portfolio item with invalid payload""" payload = {'name': '', 'user': self.user.id} res = self.client.post(PORTFOLIO_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_partial_portfolio_update_successfully(self): """Test partial updating a project by owner is successful""" portfolio_item = PortfolioItem.objects.create(user=self.user, name='Portfolio Item 1') payload = {'name': 'Alt portfolio item'} url = detail_url(portfolio_item.id) res = self.client.patch(url, payload) portfolio_item.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(portfolio_item.name, payload['name']) def test_partial_portfolio_update_invalid(self): """Test updating a portfolio item by not owner is invalid""" self.user2 = get_user_model().objects.create_user( 'other@xemob.com', 'testpass' ) portfolio_item = PortfolioItem.objects.create(user=self.user2, name='Portfolio Item 1') payload = {'name': 'Alt portfolio item'} url = detail_url(portfolio_item.id) res = self.client.patch(url, payload) portfolio_item.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_403_FORBIDDEN) self.assertNotEqual(portfolio_item.name, payload['name']) def test_full_portfolio_update_successful(self): """Test updating a portfolio item by owner is successful with PUT""" portfolio_item = PortfolioItem.objects.create(user=self.user, name='Portfolio Item 1') payload = {'user': self.user.id, 'name': 'Alt portfolio item'} url = detail_url(portfolio_item.id) res = self.client.put(url, payload) portfolio_item.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(portfolio_item.name, payload['name']) def test_full_portfolio_update_invalid(self): """Test updateing a portfolio item by not owner is invalid with PUT""" self.user2 = get_user_model().objects.create_user( 'other@xemob.com', 'testpass' ) portfolio_item = PortfolioItem.objects.create(user=self.user2, name='Portfolio Item 1') payload = {'user': self.user.id, 'name': 'Alt portfolio item'} url = detail_url(portfolio_item.id) res = self.client.put(url, payload) portfolio_item.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_403_FORBIDDEN) self.assertNotEqual(portfolio_item.name, payload['name'])

38.992593

78

0.644187

606

5,264

5.412541

0.181518

0.13872

0.051829

0.05122

0.629268

0.590854

0.553659

0.506707

0.486585

0.43872

0

0.007912

0.255699

5,264

134

79

39.283582

0.82925

0.101634

0

0.56383

0

0.084956

0.011984

0

0.138298

1

0.117021

false

0.042553

0.074468

0

0.223404

0

null

0

null

0

1

0

15f6ebf3d219ce6b1cd4eb680d9639aa61bcb259

369

py

Python

test.py

liyao001/EVBUS

8730ce6b062bc31df27506a06723dee3b5ab511a

[ "Apache-2.0" ]

null

test.py

liyao001/EVBUS

8730ce6b062bc31df27506a06723dee3b5ab511a

[ "Apache-2.0" ]

null

test.py

liyao001/EVBUS

8730ce6b062bc31df27506a06723dee3b5ab511a

[ "Apache-2.0" ]

null

from EVBUS import EVBUS from sklearn.datasets import load_boston import sklearn.model_selection as xval boston = load_boston() Y = boston.data[:, 12] X = boston.data[:, 0:12] bos_X_train, bos_X_test, bos_y_train, bos_y_test = xval.train_test_split(X, Y, test_size=0.3) evbus = EVBUS.varU(bos_X_train, bos_y_train, bos_X_test) v = evbus.calculate_variance() print(v)

26.357143

93

0.772358

68

369

3.882353

0.397059

0.060606

0.068182

0.090909

0

0.021472

0.116531

369

13

94

28.384615

0.788344

0

1

0

false

0

0.3

0

0.3

0.1

0

null

0

null

0

1

0

15f98430ae44305a9e31a13aeb782706e3a1fd29

2,141

py

Python

aws/olympic-api/olympic/app.py

kevinle-1/olympic-api

c341328eb9c4ce26fcd08199abf1fb996deebbbf

[ "MIT" ]

4

2021-07-29T06:29:33.000Z

2021-08-31T22:38:21.000Z

aws/olympic-api/olympic/app.py

kevinle-1/olympic-api

c341328eb9c4ce26fcd08199abf1fb996deebbbf

[ "MIT" ]

null

aws/olympic-api/olympic/app.py

kevinle-1/olympic-api

c341328eb9c4ce26fcd08199abf1fb996deebbbf

[ "MIT" ]

2

2021-07-25T08:51:52.000Z

2021-07-25T18:06:24.000Z

import json import requests from bs4 import BeautifulSoup PAGE_URL = 'https://olympics.com/tokyo-2020/olympic-games/en/results/all-sports/medal-standings.htm' def get_table(html=None): if not html: html = requests.get(PAGE_URL).content site = BeautifulSoup(html, 'html.parser') table = site.find('table', { 'id': 'medal-standing-table' }) return table.findAll('tr')[1:] # Remove header def get_num(value): try: return int(value.find('a').getText()) except: return 0 def get_counts(entry): values = entry.findAll('td', { 'class': 'text-center'}) return int(values[0].find('strong').getText()), { # 4 total, 3 bronze, 2 silver, 1 gold, 0 rank 'gold': get_num(values[1]), 'silver': get_num(values[2]), 'bronze': get_num(values[3]), 'total': get_num(values[4]), } def get_rankings(): rankings = [] for country in get_table(): rank, medals = get_counts(country) rankings.append({ 'country': country.find('a', { 'class': 'country'}).getText(), 'country_alpha3': country.find('div', { 'class': 'playerTag'})['country'], 'rank': rank, 'medals': medals }) return rankings def lambda_handler(event, context): try: country = event['queryStringParameters']['country'] except: country = None print(f'Request -> Country: {country}') rankings = get_rankings() if country: if len(country) == 3: for country_ranking in rankings: if country == country_ranking['country_alpha3']: rankings = country_ranking return { "statusCode": 200, "headers": { "Access-Control-Allow-Headers": "Content-Type,X-Amz-Date,X-Amz-Security-Token,Authorization,X-Api-Key,X-Requested-With,Accept,Access-Control-Allow-Methods,Access-Control-Allow-Origin,Access-Control-Allow-Headers", "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Methods": "GET" }, "body": json.dumps(rankings), "isBase64Encoded": False }

30.15493

225

0.601588

250

2,141

5.076

0.432

0.061466

0.085106

0.039401

0.066194

0

0.015442

0.243811

2,141

70

226

30.585714

0.768376

0.026623

0

0.072727

0

0.036364

0.294712

0.135577

0

1

0.090909

false

0

0.054545

0

0.254545

0.018182

0

null

0

null

0

1

0

15fb00a29a6af7f533e2efe7c7832130560820ef

5,348

py

Python

crawlers/core/thread_types.py

nonemaw/YeTi

92a3ba89f5b7fd8b2d5d3f5929ade0bf0b9e5cbe

[ "MIT" ]

1

2017-10-04T12:21:20.000Z

crawlers/core/thread_types.py

nonemaw/YeTi

92a3ba89f5b7fd8b2d5d3f5929ade0bf0b9e5cbe

[ "MIT" ]

null

crawlers/core/thread_types.py

nonemaw/YeTi

92a3ba89f5b7fd8b2d5d3f5929ade0bf0b9e5cbe

[ "MIT" ]

null

import queue import json import logging import threading from crawlers.core.flags import FLAGS class BaseThread(threading.Thread): def __init__(self, name: str, worker, pool): threading.Thread.__init__(self, name=name) self._worker = worker # can be a Fetcher/Parser/Saver instance self._thread_pool = pool # ThreadPool def running(self): return def run(self): logging.warning(f'{self.__class__.__name__}[{self.getName()}] started...') while True: try: # keep running self.working() and checking result # break (terminate) thread when self.working() failed # break (terminate) thread when queue is empty, and all jobs # are done if not self.running(): break except queue.Empty: if self._thread_pool.all_done(): break except Exception as e: import sys, os exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] logging.warning(f'{self.__class__.__name__} end: error={str(e)}, file={str(fname)}, line={str(exc_tb.tb_lineno)}') break logging.warning(f'{self.__class__.__name__}[{self.getName()}] ended...') class FetcherThread(BaseThread): def __init__(self, name: str, worker, pool, session=None): super().__init__(name, worker, pool) self.session = session def running(self): """ invoke Fetcher's working() content: (status_code, url, html_text) """ priority, url, data, deep, repeat = self._thread_pool.get_task(FLAGS.FETCH) try: data = json.loads(data) except: data = {} fetch_result, data, content = self._worker.working(url, data, repeat, self.session) # fetch success, update FETCH counter, add task to task_queue_p, for # parser's further process if isinstance(data, dict): data = json.dumps(data) if fetch_result == 1: self._thread_pool.update_flag(FLAGS.FETCH, 1) self._thread_pool.put_task(FLAGS.PARSE, (priority, url, data, deep, content)) # fetch failed, put back to task_queue_f and repeat later elif fetch_result == 0: self._thread_pool.put_task(FLAGS.FETCH, (priority + 1, url, data, deep, repeat + 1)) # current round of fetcher is done, notify task_queue_f with # task_done() to stop block self._thread_pool.finish_task(FLAGS.FETCH) return False if fetch_result == -1 else True class ParserThread(BaseThread): def __init__(self, name: str, worker, pool): super().__init__(name, worker, pool) def running(self): """ invoke Parser's working() get all required urls from target html text content: (status_code, url, html_text) """ priority, url, data, deep, content = self._thread_pool.get_task(FLAGS.PARSE) try: data = json.loads(data) except: data = {} parse_result = 1 urls = [] stamp = () # if data is negative or data has a negative 'save' value, parse the # html, otherwise skip if not data or not data.get('save'): parse_result, urls, stamp = self._worker.working(priority, url, data, deep, content) if parse_result > 0: self._thread_pool.update_flag(FLAGS.PARSE, 1) # add each url in urls list into task_queue_f, waiting for # fetcher's further process for _url, _data, _priority in urls: if isinstance(_data, dict): _data = json.dumps(_data) self._thread_pool.put_task(FLAGS.FETCH, (_priority, _url, _data, deep + 1, 0)) # add current url (already fetched/parsed) into task_queue_s, # waiting for saver's further process # # if data in task_queue_p has a positive 'save' value, or no data but with an url if (data and data.get('save')) or (not data and url): try: # when saving to task_queue_s, delete 'save' key del data['save'] del data['type'] data = json.dumps(data) except: pass self._thread_pool.put_task(FLAGS.SAVE, (url, data, stamp)) # current round of parser is done, notify task_queue_p with # task_done() to stop block self._thread_pool.finish_task(FLAGS.PARSE) return True class SaverThread(BaseThread): def __init__(self, name: str, worker, pool): super().__init__(name, worker, pool) def running(self): """ invoke Saver's working() """ url, data, stamp = self._thread_pool.get_task(FLAGS.SAVE) save_result = self._worker.working(url, data, stamp) if save_result: self._thread_pool.update_flag(FLAGS.SAVE, 1) # current round of saver is done, notify task_queue_s with # task_done() to stop block self._thread_pool.finish_task(FLAGS.SAVE) return True

35.184211

130

0.583209

665

5,348

4.454135

0.237594

0.050641

0.070898

0.032073

0.411884

0.341999

0.259284

0.229575

0.139095

0

0.003572

0.319559

5,348

151

131

35.417219

0.810387

0.227188

0

0.318182

0

0.011364

0.053785

0.034612

0

1

0.102273

false

0.011364

0.068182

0.011364

0.261364

0

null

0

null

0

1

0

15fb6a076074eb470434200bde6610f2e5b3ecae

1,848

py

Python

develop/tools/import-prep.py

Gautamverma66/pycon

1ca95727619dbbe82483227e0964402b433131ee

[ "BSD-3-Clause" ]

null

develop/tools/import-prep.py

Gautamverma66/pycon

1ca95727619dbbe82483227e0964402b433131ee

[ "BSD-3-Clause" ]

null

develop/tools/import-prep.py

Gautamverma66/pycon

1ca95727619dbbe82483227e0964402b433131ee

[ "BSD-3-Clause" ]

1

2020-09-30T18:09:16.000Z

#!/usr/bin/env python2.7 # # Take various CSV inputs and produce a read-to-import conference schedule. import pandas as pd from datetime import date def main(): dfs = [] t = pd.read_csv('talks.csv') t['kind_slug'] = 'talk' t['proposal_id'] = t.pop('proposal') t['day'] = date(2016, 5, 30) + pd.to_timedelta(t['day'], 'd') t['room'] = 'Session ' + t['room'] t = t[['kind_slug', 'proposal_id', 'day', 'time', 'duration', 'room']] dfs.append(t) t = pd.read_csv('~/Downloads/PyCon 2016 Tutorial Counts - Sheet1.csv') rooms = {str(title).strip().lower(): room_name for title, room_name in t[['Title', 'Room Name']].values} t = pd.read_csv('tutorials.csv') t['kind_slug'] = 'tutorial' t['proposal_id'] = t.pop('ID') t['day'] = pd.to_datetime(t['Day Slot']) t['time'] = t['Time Slot'].str.extract('([^ ]*)') t['duration'] = 200 t['room'] = t['Title'].str.strip().str.lower().map(rooms) t = t[['kind_slug', 'proposal_id', 'day', 'time', 'duration', 'room']] dfs.append(t) t = pd.read_csv('sponsor-tutorials-edited.csv') t = t[t['ID'].notnull()].copy() t['kind_slug'] = 'sponsor-tutorial' #t['kind_slug'] = 'tutorial' t['proposal_id'] = t.pop('ID').astype(int) t['day'] = pd.to_datetime(t['Day Slot']) t['time'] = t['Time Slot'].str.extract('([^ ]*)') t['room'] = t['Room'] # t = t.sort_values(['Title']) # t['room'] = t.groupby(['day', 'time'])['room'].cumsum() # t['room'] = t['room'].apply(lambda n: 'Sponsor Room {}'.format(n)) t = t[['kind_slug', 'proposal_id', 'day', 'time', 'duration', 'room']] dfs.append(t) #t.to_csv('schedule.csv', index=False) c = pd.concat(dfs).rename(columns={'time': 'start'}) c.to_csv('schedule.csv', index=False) if __name__ == '__main__': main()

28.875

75

0.566017

272

1,848

3.724265

0.3125

0.017769

0.062192

0.039487

0.395854

0.381046

0.329714

0

0.011371

0.191017

1,848

63

76

29.333333

0.666221

0.169372

0

0.277778

0

0.309758

0.018337

0

1

0.027778

false

0

0.055556

0

0.083333

0

null

0

null

0

1

0

15fdf4924a3b098bc325a7b35edf84b4ae175290

997

py

Python

utilities/resize_images.py

bmhopkinson/Marsh_Ann

7d1baaa444392622967dd1ed12f9c7a23c5fb018

[ "MIT" ]

null

utilities/resize_images.py

bmhopkinson/Marsh_Ann

7d1baaa444392622967dd1ed12f9c7a23c5fb018

[ "MIT" ]

null

utilities/resize_images.py

bmhopkinson/Marsh_Ann

7d1baaa444392622967dd1ed12f9c7a23c5fb018

[ "MIT" ]

null

import os import cv2 import numpy as np import re path_regex = re.compile('^.+?/(.*)') def resize_image(im, factor): row, col, chan = im.shape col_re = np.rint(col*factor).astype(int) row_re = np.rint(row*factor).astype(int) im = cv2.resize(im, (col_re, row_re)) #resize patch return im imdir = '../Marsh_Images_BH/Row1_1_2748to2797' outdir = './image_resize_BH' for (dirpath, dirname, files) in os.walk(imdir, topdown='True'): for name in files: fullpath = os.path.join(dirpath,name) print(name) m = path_regex.findall(dirpath) dirpath_sub = m[0] new_dirpath = os.path.join(outdir,dirpath_sub) if not os.path.isdir(new_dirpath): os.makedirs(new_dirpath) file_base = os.path.splitext(name)[0] im = cv2.imread(fullpath) im_alt = resize_image(im, 0.2) outfile = file_base + '_small.jpg' outpath = os.path.join(new_dirpath, outfile) cv2.imwrite(outpath,im_alt)

27.694444

64

0.636911

148

997

4.121622

0.432432

0.04918

0

0.023438

0.229689

997

35

65

28.485714

0.770833

0.012036

0

0.077236

0.036585

0

1

0.035714

false

0

0.142857

0

0.214286

0.035714

0

null

0

null

0

1

0

15fe4d22550f5d656a8766cbb160d9cad971c027

5,133

py

Python

load.py

OdysseyScorpio/FactionGist

a3af4b52557890cb9c2cad20a740545917db7ec4

[ "MIT" ]

3

2019-10-17T08:28:55.000Z

2020-06-02T15:43:32.000Z

load.py

OdysseyScorpio/FactionGist

a3af4b52557890cb9c2cad20a740545917db7ec4

[ "MIT" ]

11

2019-10-17T08:32:09.000Z

2019-10-21T07:14:13.000Z

load.py

OdysseyScorpio/FactionGist

a3af4b52557890cb9c2cad20a740545917db7ec4

[ "MIT" ]

3

2019-10-17T08:33:51.000Z

2021-07-05T18:05:38.000Z

import sys import os import ttk import Tkinter as tk import tkMessageBox from ttkHyperlinkLabel import HyperlinkLabel from config import applongname, appversion import myNotebook as nb import json import requests import zlib import re import webbrowser this = sys.modules[__name__] this.apiURL = "http://factiongist.herokuapp.com" FG_VERSION = "0.0.3" availableFactions = tk.StringVar() try: this_fullpath = os.path.realpath(__file__) this_filepath, this_extension = os.path.splitext(this_fullpath) config_file = this_filepath + "config.json" with open(config_file) as f: data = json.load(f) availableFactions.set(data) except: availableFactions.set("everyone") if(availableFactions.get() == "everyone"): msginfo = ['Please update your Reporting Faction.', '\nYou can report to one or many factions,' 'simply separate each faction with a comma.\n' '\nFile > Settings > FactionGist'] tkMessageBox.showinfo("Reporting Factions", "\n".join(msginfo)) def plugin_app(parent): this.parent = parent this.frame = tk.Frame(parent) filter_update() return this.frame def filter_update(): this.parent.after(300000, filter_update) response = requests.get(this.apiURL + "/listeningFor") if(response.status_code == 200): this.listening = response.content def plugin_start(plugin_dir): awake = requests.get(this.apiURL) check_version() return 'FactionGist' def plugin_prefs(parent): PADX = 10 # formatting frame = nb.Frame(parent) frame.columnconfigure(5, weight=1) HyperlinkLabel(frame, text='FactionGist GitHub', background=nb.Label().cget('background'), url='https://github.com/OdysseyScorpio/FactionGist', underline=True).grid(columnspan=2, padx=PADX, sticky=tk.W) nb.Label(frame, text="FactionGist - crazy-things-might-happen-pre-pre-alpha release Version {VER}".format( VER=FG_VERSION)).grid(columnspan=2, padx=PADX, sticky=tk.W) nb.Label(frame).grid() # spacer nb.Button(frame, text="UPGRADE", command=upgrade_callback).grid(row=10, column=0, columnspan=2, padx=PADX, sticky=tk.W) nb.lblReportingFactions = tk.Label(frame) nb.lblReportingFactions.grid( row=3, column=0, columnspan=2, padx=PADX, sticky=tk.W) nb.lblReportingFactions.config(text='Factions I am supporting') nb.Entry1 = tk.Entry(frame, textvariable=availableFactions) nb.Entry1.grid(row=4, column=0, columnspan=2, padx=PADX, sticky=tk.W+tk.E) return frame def check_version(): response = requests.get(this.apiURL + "/version") version = response.content if version != FG_VERSION: upgrade_callback() def upgrade_callback(): this_fullpath = os.path.realpath(__file__) this_filepath, this_extension = os.path.splitext(this_fullpath) corrected_fullpath = this_filepath + ".py" try: response = requests.get(this.apiURL + "/download") if (response.status_code == 200): with open(corrected_fullpath, "wb") as f: f.seek(0) f.write(response.content) f.truncate() f.flush() os.fsync(f.fileno()) this.upgrade_applied = True # Latch on upgrade successful msginfo = ['Upgrade has completed sucessfully.', 'Please close and restart EDMC'] tkMessageBox.showinfo("Upgrade status", "\n".join(msginfo)) sys.stderr.write("Finished plugin upgrade!\n") else: msginfo = ['Upgrade failed. Bad server response', 'Please try again'] tkMessageBox.showinfo("Upgrade status", "\n".join(msginfo)) except: sys.stderr.writelines( "Upgrade problem when fetching the remote data: {E}\n".format(E=sys.exc_info()[0])) msginfo = ['Upgrade encountered a problem.', 'Please try again, and restart if problems persist'] tkMessageBox.showinfo("Upgrade status", "\n".join(msginfo)) def dashboard_entry(cmdr, is_beta, entry): this.cmdr = cmdr def journal_entry(cmdr, is_beta, system, station, entry, state): if entry['event'] in this.listening: entry['commanderName'] = cmdr entry['pluginVersion'] = FG_VERSION entry['currentSystem'] = system entry['currentStation'] = station entry['reportingFactions'] = [availableFactions.get()] transmit_json = json.dumps(entry) url_jump = this.apiURL + '/events' headers = {'content-type': 'application/json'} response = requests.post( url_jump, data=transmit_json, headers=headers, timeout=7) def plugin_stop(): sys.stderr.writelines("\nGood bye commander\n") config = availableFactions.get() this_fullpath = os.path.realpath(__file__) this_filepath, this_extension = os.path.splitext(this_fullpath) config_file = this_filepath + "config.json" with open(config_file, 'w') as f: json.dump(config, f)

36.147887

130

0.653029

607

5,133

5.413509

0.352554

0.018259

0.024346

0.028911

0.258065

0.21759

0.176506

0.165855

0

0.0091

0.229301

5,133

141

131

36.404255

0.821537

0.008767

0

0.142857

0

0.181943

0.007671

0

1

0.07563

false

0

0.109244

0

0.210084

0

null

0

null

0

1

0

15ff229d57bd444a73d08386dd948e890ca375a0

12,542

py

Python

hexa/plugins/connector_s3/models.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

4

2021-07-19T12:53:21.000Z

2022-01-26T17:45:02.000Z

hexa/plugins/connector_s3/models.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

20

2021-05-17T12:27:06.000Z

2022-03-30T11:35:26.000Z

hexa/plugins/connector_s3/models.py

qgerome/openhexa-app

8c9377b2ad972121d8e9575f5d52420212b52ed4

[ "MIT" ]

2

2021-09-07T04:19:59.000Z

2022-02-08T15:33:29.000Z

import os from logging import getLogger from django.core.exceptions import ValidationError from django.db import models, transaction from django.template.defaultfilters import filesizeformat, pluralize from django.urls import reverse from django.utils import timezone from django.utils.translation import gettext_lazy as _ from hexa.catalog.models import CatalogQuerySet, Datasource, Entry from hexa.catalog.sync import DatasourceSyncResult from hexa.core.models import Base, Permission from hexa.core.models.cryptography import EncryptedTextField from hexa.plugins.connector_s3.api import ( S3ApiError, get_object_metadata, head_bucket, list_objects_metadata, ) from hexa.plugins.connector_s3.region import AWSRegion logger = getLogger(__name__) class Credentials(Base): """We actually only need one set of credentials. These "principal" credentials will be then used to generate short-lived credentials with a tailored policy giving access only to the buckets that the user team can access""" class Meta: verbose_name = "S3 Credentials" verbose_name_plural = "S3 Credentials" ordering = ("username",) username = models.CharField(max_length=200) access_key_id = EncryptedTextField() secret_access_key = EncryptedTextField() default_region = models.CharField( max_length=50, default=AWSRegion.EU_CENTRAL_1, choices=AWSRegion.choices ) user_arn = models.CharField(max_length=200) app_role_arn = models.CharField(max_length=200) @property def display_name(self): return self.username class BucketPermissionMode(models.IntegerChoices): READ_ONLY = 1, "Read Only" READ_WRITE = 2, "Read Write" class BucketQuerySet(CatalogQuerySet): def filter_by_mode(self, user, mode: BucketPermissionMode = None): if user.is_active and user.is_superuser: # if SU -> all buckets are RW; so if mode is provided and mode == RO -> no buckets available if mode == BucketPermissionMode.READ_ONLY: return self.none() else: return self if mode is None: # return all buckets modes = [BucketPermissionMode.READ_ONLY, BucketPermissionMode.READ_WRITE] else: modes = [mode] return self.filter( bucketpermission__team__in=[t.pk for t in user.team_set.all()], bucketpermission__mode__in=modes, ).distinct() def filter_for_user(self, user): if user.is_active and user.is_superuser: return self return self.filter( bucketpermission__team__in=[t.pk for t in user.team_set.all()], ).distinct() class Bucket(Datasource): def get_permission_set(self): return self.bucketpermission_set.all() class Meta: verbose_name = "S3 Bucket" ordering = ("name",) name = models.CharField(max_length=200) region = models.CharField( max_length=50, default=AWSRegion.EU_CENTRAL_1, choices=AWSRegion.choices ) objects = BucketQuerySet.as_manager() searchable = True # TODO: remove (see comment in datasource_index command) @property def principal_credentials(self): try: return Credentials.objects.get() except (Credentials.DoesNotExist, Credentials.MultipleObjectsReturned): raise ValidationError( "The S3 connector plugin should be configured with a single Credentials entry" ) def refresh(self, path): metadata = get_object_metadata( principal_credentials=self.principal_credentials, bucket=self, object_key=path, ) try: s3_object = Object.objects.get(bucket=self, key=path) except Object.DoesNotExist: Object.create_from_metadata(self, metadata) except Object.MultipleObjectsReturned: logger.warning( "Bucket.refresh(): incoherent object list for bucket %s", self.id ) else: s3_object.update_from_metadata(metadata) s3_object.save() def clean(self): try: head_bucket(principal_credentials=self.principal_credentials, bucket=self) except S3ApiError as e: raise ValidationError(e) def sync(self): """Sync the bucket by querying the S3 API""" s3_objects = list_objects_metadata( principal_credentials=self.principal_credentials, bucket=self, ) # Lock the bucket with transaction.atomic(): Bucket.objects.select_for_update().get(pk=self.pk) # Sync data elements with transaction.atomic(): created_count = 0 updated_count = 0 identical_count = 0 deleted_count = 0 remote = set() local = {str(x.key): x for x in self.object_set.all()} for s3_object in s3_objects: key = s3_object["Key"] remote.add(key) if key in local: if ( s3_object.get("ETag") == local[key].etag and s3_object["Type"] == local[key].type ): # If it has the same key bot not the same ETag: the file was updated on S3 # (Sometime, the ETag contains double quotes -> strip them) identical_count += 1 else: updated_count += 1 local[key].update_from_metadata(s3_object) local[key].save() else: Object.create_from_metadata(self, s3_object) created_count += 1 # cleanup unmatched objects for key, obj in local.items(): if key not in remote: deleted_count += 1 obj.delete() # Flag the datasource as synced self.last_synced_at = timezone.now() self.save() return DatasourceSyncResult( datasource=self, created=created_count, updated=updated_count, identical=identical_count, deleted=deleted_count, ) @property def content_summary(self): count = self.object_set.count() return ( "" if count == 0 else _("%(count)d object%(suffix)s") % {"count": count, "suffix": pluralize(count)} ) def populate_index(self, index): index.last_synced_at = self.last_synced_at index.content = self.content_summary index.path = [self.pk.hex] index.external_id = self.name index.external_name = self.name index.external_type = "bucket" index.search = f"{self.name}" index.datasource_name = self.name index.datasource_id = self.id @property def display_name(self): return self.name def __str__(self): return self.display_name def writable_by(self, user): if not user.is_active: return False elif user.is_superuser: return True elif ( BucketPermission.objects.filter( bucket=self, team_id__in=user.team_set.all().values("id"), mode=BucketPermissionMode.READ_WRITE, ).count() > 0 ): return True else: return False def get_absolute_url(self): return reverse( "connector_s3:datasource_detail", kwargs={"datasource_id": self.id} ) class BucketPermission(Permission): bucket = models.ForeignKey("Bucket", on_delete=models.CASCADE) mode = models.IntegerField( choices=BucketPermissionMode.choices, default=BucketPermissionMode.READ_WRITE ) class Meta: unique_together = [("bucket", "team")] def index_object(self): self.bucket.build_index() def __str__(self): return f"Permission for team '{self.team}' on bucket '{self.bucket}'" class ObjectQuerySet(CatalogQuerySet): def filter_for_user(self, user): if user.is_active and user.is_superuser: return self return self.filter(bucket__in=Bucket.objects.filter_for_user(user)) class Object(Entry): def get_permission_set(self): return self.bucket.bucketpermission_set.all() class Meta: verbose_name = "S3 Object" ordering = ("key",) unique_together = [("bucket", "key")] bucket = models.ForeignKey("Bucket", on_delete=models.CASCADE) key = models.TextField() parent_key = models.TextField() size = models.PositiveBigIntegerField() storage_class = models.CharField(max_length=200) # TODO: choices type = models.CharField(max_length=200) # TODO: choices last_modified = models.DateTimeField(null=True, blank=True) etag = models.CharField(max_length=200, null=True, blank=True) objects = ObjectQuerySet.as_manager() searchable = True # TODO: remove (see comment in datasource_index command) def save(self, *args, **kwargs): if self.parent_key is None: self.parent_key = self.compute_parent_key(self.key) super().save(*args, **kwargs) def populate_index(self, index): index.last_synced_at = self.bucket.last_synced_at index.external_name = self.filename index.path = [self.bucket.pk.hex, self.pk.hex] index.context = self.parent_key index.external_id = self.key index.external_type = self.type index.external_subtype = self.extension index.search = f"{self.filename} {self.key}" index.datasource_name = self.bucket.name index.datasource_id = self.bucket.id def __repr__(self): return f"<Object s3://{self.bucket.name}/{self.key}>" @property def display_name(self): return self.filename @property def filename(self): if self.key.endswith("/"): return os.path.basename(self.key[:-1]) return os.path.basename(self.key) @property def extension(self): return os.path.splitext(self.key)[1].lstrip(".") def full_path(self): return f"s3://{self.bucket.name}/{self.key}" @classmethod def compute_parent_key(cls, key): if key.endswith("/"): # This is a directory return os.path.dirname(os.path.dirname(key)) + "/" else: # This is a file return os.path.dirname(key) + "/" @property def file_size_display(self): return filesizeformat(self.size) if self.size > 0 else "-" @property def type_display(self): if self.type == "directory": return _("Directory") else: if verbose_file_type := self.verbose_file_type: return verbose_file_type else: return _("File") @property def verbose_file_type(self): file_type = { "xlsx": "Excel file", "md": "Markdown document", "ipynb": "Jupyter Notebook", "csv": "CSV file", }.get(self.extension) if file_type: return _(file_type) else: return None def update_from_metadata(self, metadata): self.key = metadata["Key"] self.parent_key = self.compute_parent_key(metadata["Key"]) self.size = metadata["Size"] self.storage_class = metadata["StorageClass"] self.type = metadata["Type"] self.last_modified = metadata["LastModified"] self.etag = metadata["ETag"] @classmethod def create_from_metadata(cls, bucket, metadata): return cls.objects.create( bucket=bucket, key=metadata["Key"], parent_key=cls.compute_parent_key(metadata["Key"]), storage_class=metadata["StorageClass"], last_modified=metadata["LastModified"], etag=metadata["ETag"], type=metadata["Type"], size=metadata["Size"], ) def get_absolute_url(self): return reverse( "connector_s3:object_detail", kwargs={"bucket_id": self.bucket.id, "path": self.key}, )

32.492228

112

0.603253

1,391

12,542

5.265996

0.196262

0.020478

0.022116

0.029488

0.264164

0.22116

0.199317

0.148805

0.102116

0.089829

0

0.007787

0.303779

12,542

385

113

32.576623

0.831081

0.060676

0

0.2443

0

0.062048

0.010639

0

0.002597

0

1

0.104235

false

0

0.045603

0.384365

0

null

0

null

0

1

0

c60143a70f259a4e959098d87fdd7674fa7e33dc

1,618

py

Python

vi/vi/csp/backtrack_search.py

pveierland/permve-ntnu-it3105

6a7e4751de47b091c1c9c59560c19a8452698d81

[ "CC0-1.0" ]

null

vi/vi/csp/backtrack_search.py

pveierland/permve-ntnu-it3105

6a7e4751de47b091c1c9c59560c19a8452698d81

[ "CC0-1.0" ]

null

vi/vi/csp/backtrack_search.py

pveierland/permve-ntnu-it3105

6a7e4751de47b091c1c9c59560c19a8452698d81

[ "CC0-1.0" ]

null

import vi.csp import collections import operator BacktrackStatistics = collections.namedtuple( 'BacktrackStatistics', ['calls', 'failures']) def backtrack_search(network): statistics = BacktrackStatistics(calls=0, failures=0) # Ensure arc consistency before making any assumptions: return backtrack(vi.csp.general_arc_consistency(network), statistics) def backtrack(network, statistics): def select_unassigned_variable(): # Use Minimum-Remaining-Values heuristic: return min(((variable, domain) for variable, domain in network.domains.items() if len(domain) > 1), key=operator.itemgetter(1))[0] def order_domain_variables(): return network.domains[variable] statistics = BacktrackStatistics(statistics.calls + 1, statistics.failures) if all(len(domain) == 1 for domain in network.domains.values()): return network, statistics variable = select_unassigned_variable() for value in order_domain_variables(): successor = network.copy() successor.domains[variable] = [value] successor = vi.csp.general_arc_consistency_rerun(successor, variable) if all(len(domain) >= 1 for domain in successor.domains.values()): result, statistics = backtrack(successor, statistics) if result: return result, statistics statistics = BacktrackStatistics(statistics.calls, statistics.failures + 1) return None, statistics

32.36

77

0.644623

158

1,618

6.512658

0.329114

0.066084

0.029155

0.101069

0.050535

0

0.007614

0.269468

1,618

49

78

33.020408

0.862944

0.057478

0

0.021025

0

1

0.121212

false

0

0.090909

0.060606

0.393939

0

null

0

null

0

1

0

c601aedeadeb98d2c741f809b0374722d418f823

1,377

py

Python

k_means.py

lgq9220/easy12306

e31abd1c7675e2acb37f4653ab88cae49d2317cc

[ "Artistic-2.0" ]

null

k_means.py

lgq9220/easy12306

e31abd1c7675e2acb37f4653ab88cae49d2317cc

[ "Artistic-2.0" ]

null

k_means.py

lgq9220/easy12306

e31abd1c7675e2acb37f4653ab88cae49d2317cc

[ "Artistic-2.0" ]

null

#! env python # coding: utf-8 # 功能：对文字部分使用k-means算法进行聚类 import os import time import sys import cv2 from sklearn.cluster import KMeans from sklearn.decomposition import PCA from sklearn.externals import joblib def get_img_as_vector(fn): im = cv2.imread(fn) im = im[:, :, 0] retval, dst = cv2.threshold(im, 128, 1, cv2.THRESH_BINARY_INV) return dst.reshape(dst.size) def main(): # 读取训练用数据 print('Start: read data', time.process_time()) fns = os.listdir('ocr') X = [get_img_as_vector(os.path.join('ocr', fn)) for fn in fns] print('Samples', len(X), 'Feature', len(X[0])) # PCA print('Start: PCA', time.process_time()) pca = PCA(n_components=0.99) pca.fit(X) X = pca.transform(X) print('Samples', len(X), 'Feature', len(X[0])) sys.stdout.flush() # 训练 print('Start: train', time.process_time()) n_clusters = 2000 # 聚类中心个数 estimator = KMeans(n_clusters, n_init=1, max_iter=20, verbose=True) estimator.fit(X) print('Clusters', estimator.n_clusters, 'Iter', estimator.n_iter_) print('Start: classify', time.process_time()) fp = open('result11.txt', 'w') for fn, c in zip(fns, estimator.labels_): print(fn, c, file=fp) fp.close() print('Start: save model', time.process_time()) joblib.dump(estimator, 'k-means11.pkl') if __name__ == '__main__': main()

27.54

71

0.647785

205

1,377

4.204878

0.473171

0.058005

0.087007

0.032483

0.064965

0

0.023466

0.195352

1,377

49

72

28.102041

0.754513

0.052288

0

0.054054

0

0.115562

0

1

0.054054

false

0

0.189189

0

0.27027

0.243243

0

null

0

null

0

1

0

c60244641d848895dbb47d903f04c522a8e5355d

14,735

py

Python

datasets/blender_efficient_sm.py

ktiwary2/nerf_pl

99d40cba3a2d9a11d6988cb1a74cf29035a1ab5e

[ "MIT" ]

null

datasets/blender_efficient_sm.py

ktiwary2/nerf_pl

99d40cba3a2d9a11d6988cb1a74cf29035a1ab5e

[ "MIT" ]

null

datasets/blender_efficient_sm.py

ktiwary2/nerf_pl

99d40cba3a2d9a11d6988cb1a74cf29035a1ab5e

[ "MIT" ]

null

import torch from torch.utils.data import Dataset import json import numpy as np import os from PIL import Image, ImageFilter from torchvision import transforms as T from models.camera import Camera from tqdm import tqdm from .ray_utils import * class BlenderEfficientShadows(Dataset): def __init__(self, root_dir, split='train', img_wh=(800, 800), hparams=None): self.root_dir = root_dir self.split = split assert img_wh[0] == img_wh[1], 'image width must equal image height!' self.img_wh = img_wh print("Training Image size:", img_wh) self.define_transforms() self.white_back = True # self.white_back = False # Setting it to False (!) self.hparams = hparams self.black_and_white = False if self.hparams is not None and self.hparams.black_and_white_test: self.black_and_white = True self.read_meta() self.hparams.coords_trans = False print("------------") print("NOTE: self.hparams.coords_trans is set to {} ".format(self.hparams.coords_trans)) print("------------") def read_meta(self): # self.split = 'train' with open(os.path.join(self.root_dir, # f"transforms_train.json"), 'r') as f: f"transforms_{self.split}.json"), 'r') as f: self.meta = json.load(f) w, h = self.img_wh print("Root Directory: ".format(self.root_dir)) # if 'bunny' or 'box' or 'vase' in self.root_dir: # res = 200 # these imgs have original size of 200 # else: # res = 800 res = 800 if 'resolution' in self.meta.keys(): res = self.meta['resolution'] print("-------------------------------") print("RESOLUTION OF THE ORIGINAL IMAGE IS SET TO {}".format(res)) print("-------------------------------") self.focal = 0.5*res/np.tan(0.5*self.meta['camera_angle_x']) # original focal length # when W=res self.focal *= self.img_wh[0]/res # modify focal length to match size self.img_wh ################ self.light_camera_focal = 0.5*res/np.tan(0.5*self.meta['light_camera_angle_x']) # original focal length ################ # if 'bunny' or 'box' or 'vase' in self.root_dir: # self.light_camera_focal = 0.5*res/np.tan(0.5*self.meta['light_angle_x']) # original focal length # else: # self.light_camera_focal = 0.5*res/np.tan(0.5*self.meta['light_camera_angle_x']) # original focal length # when W=res self.light_camera_focal *= self.img_wh[0]/res # modify focal length to match size self.img_wh # bounds, common for all scenes self.near = 1.0 self.far = 200.0 # probably need to change this self.light_near = 1.0 self.light_far = 200.0 self.bounds = np.array([self.near, self.far]) # ray directions for all pixels, same for all images (same H, W, focal) self.directions = \ get_ray_directions(h, w, self.focal) # (h, w, 3) ### Light Camera Matrix ################ pose = np.array(self.meta['light_camera_transform_matrix'])[:3, :4] ################ # if 'bunny' or 'box' or 'vase' in self.root_dir: # self.meta['light_angle_x'] = 0.5 * self.meta['light_angle_x'] # print("Changing the HFOV of Light") # pose = np.array(self.meta['frames'][0]['light_transform'])[:3, :4] # else: # pose = np.array(self.meta['light_camera_transform_matrix'])[:3, :4] self.l2w = torch.FloatTensor(pose) pixels_u = torch.arange(0, w, 1) pixels_v = torch.arange(0, h, 1) i, j = np.meshgrid(pixels_v.numpy(), pixels_u.numpy(), indexing='xy') i = torch.tensor(i) + 0.5 #.unsqueeze(2) j = torch.tensor(j)+ 0.5 #.unsqueeze(2) self.light_pixels = torch.stack([i,j, torch.ones_like(i)], axis=-1).view(-1, 3) # (H*W,3) light_directions = get_ray_directions(h, w, self.light_camera_focal) # (h, w, 3) rays_o, rays_d = get_rays(light_directions, self.l2w) # both (h*w, 3) self.light_rays = torch.cat([rays_o, rays_d, self.light_near*torch.ones_like(rays_o[:, :1]), self.light_far*torch.ones_like(rays_o[:, :1])], 1) # (h*w, 8) ################ hfov = self.meta['light_camera_angle_x'] * 180./np.pi ################ # if 'bunny' or 'box' or 'vase' in self.root_dir: # hfov = self.meta['light_angle_x'] * 180./np.pi # else: # hfov = self.meta['light_camera_angle_x'] * 180./np.pi self.light_ppc = Camera(hfov, (h, w)) self.light_ppc.set_pose_using_blender_matrix(self.l2w, self.hparams.coords_trans) print("LIGHT: c2w: {}\n, camera:{}\n, eye:{}\n".format(self.l2w, self.light_ppc.camera, self.light_ppc.eye_pos)) ### Light Camera Matrix # new_frames = [] # # only do on a single image # for frame in self.meta['frames']: # if 'r_137' in frame['file_path']: # a = [frame] # new_frames.extend(a * 10) # break # self.meta['frames'] = new_frames if self.split == 'val': new_frames = [] for frame in self.meta['frames']: ###### load the RGB+SM Image file_path = frame['file_path'].split('/') sm_file_path = 'sm_'+ file_path[-1] sm_path = os.path.join(self.root_dir, f"{sm_file_path}.png") ## Continue if not os.path.exists(shadows) if not os.path.exists(sm_path): continue else: new_frames.append(frame) self.meta['frames'] = new_frames if self.split == 'train': # create buffer of all rays and rgb data self.image_paths = [] self.poses = [] self.all_rays = [] self.all_rgbs = [] self.all_ppc = [] self.all_pixels = [] for frame in tqdm(self.meta['frames']): #### change it to load the shadow map file_path = frame['file_path'].split('/') file_path = 'sm_'+ file_path[-1] ################ image_path = os.path.join(self.root_dir, f"{file_path}.png") self.image_paths += [image_path] ## Continue if not os.path.exists(shadows) if not os.path.exists(image_path): continue print("Processing Frame {}".format(image_path)) ##### # real processing begins pose = np.array(frame['transform_matrix'])[:3, :4] self.poses += [pose] c2w = torch.FloatTensor(pose) hfov = self.meta['camera_angle_x'] * 180./np.pi ppc = Camera(hfov, (h, w)) ppc.set_pose_using_blender_matrix(c2w, self.hparams.coords_trans) self.all_ppc.extend([ppc]*h*w) img = Image.open(image_path) img = img.resize(self.img_wh, Image.LANCZOS) if not self.hparams.blur == -1: img = img.filter(ImageFilter.GaussianBlur(self.hparams.blur)) img = self.transform(img) # (4, h, w) img = img.view(3, -1).permute(1, 0) # (h*w, 4) RGBA # Figure out where the rays originated from pixels_u = torch.arange(0, w, 1) pixels_v = torch.arange(0, h, 1) i, j = np.meshgrid(pixels_v.numpy(), pixels_u.numpy(), indexing='xy') i = torch.tensor(i) + 0.5 #.unsqueeze(2) j = torch.tensor(j)+ 0.5 #.unsqueeze(2) pixels = torch.stack([i,j, torch.ones_like(i)], axis=-1).view(-1, 3) # (H*W,3) rays_o, rays_d = get_rays(self.directions, c2w) rays = torch.cat([rays_o, rays_d, self.near*torch.ones_like(rays_o[:, :1]), self.far*torch.ones_like(rays_o[:, :1])], 1) # (H*W, 8) print("-------------------------------") print("frame: {}\n, c2w: {}\n, camera:{}\n, eye:{}\n".format(file_path, c2w, ppc.camera, ppc.eye_pos)) print("-------------------------------") self.all_rgbs += [img] self.all_rays += [rays] self.all_pixels += [pixels] self.all_rays = torch.cat(self.all_rays, 0) # (len(self.meta['frames])*h*w, 3) self.all_pixels = torch.cat(self.all_pixels, 0) # (len(self.meta['frames])*h*w, 3) self.all_rgbs = torch.cat(self.all_rgbs, 0) # (len(self.meta['frames])*h*w, 3) print("self.all_rgbs.shape, self.all_rays.shape, self.all_pixels.shape, all_ppc.shape", self.all_rgbs.shape, self.all_rays.shape, self.all_pixels.shape, len(self.all_ppc)) if not (float(self.hparams.white_pix) == -1): print("-------------------------- rgb max {}, min {}".format(self.all_rgbs.max(), self.all_rgbs.min())) print("only Training on pixels with shadow map values > 0.") all_bw = (self.all_rgbs[:,0] + self.all_rgbs[:,1] + self.all_rgbs[:,2])/3. idx = torch.where(all_bw > float(self.hparams.white_pix)) self.all_rgbs = self.all_rgbs[idx] self.all_pixels = self.all_pixels[idx] self.all_rays = self.all_rays[idx] new_ppc = [] for i in idx[0]: new_ppc.append(self.all_ppc[i]) self.all_ppc = new_ppc print("self.all_rgbs.shape, self.all_rays.shape, self.all_pixels.shape, all_ppc.shape", self.all_rgbs.shape, self.all_rays.shape, self.all_pixels.shape, len(self.all_ppc)) def define_transforms(self): self.transform = T.ToTensor() def __len__(self): if self.split == 'train': return len(self.all_rays) elif self.split == 'val': return 8 # only validate 8 images (to support <=8 gpus) else: return len(self.meta['frames']) def __getitem__(self, idx): """ Processes and return rays, rgbs PER image instead of on a ray by ray basis. Albeit slower, Implementation of shadow mapping is easier this way. """ if self.split == 'train': # use data in the buffers # pose = self.poses[idx] # c2w = torch.FloatTensor(pose) sample = {'rays': self.all_rays[idx], # (8) Ray originating from pixel (i,j) 'pixels': self.all_pixels[idx], # pixel where the ray originated from 'rgbs': self.all_rgbs[idx], # (h*w,3) # 'ppc': [self.all_ppc[idx].eye_pos, self.all_ppc[idx].camera], # 'light_ppc': [self.light_ppc.eye_pos, self.light_ppc.camera], 'ppc': { 'eye_pos': self.all_ppc[idx].eye_pos, 'camera': self.all_ppc[idx].camera, }, 'light_ppc': { 'eye_pos': self.light_ppc.eye_pos, 'camera': self.light_ppc.camera, }, # 'c2w': pose, # (3,4) # pixel where the light ray originated from 'light_pixels': self.light_pixels, #(h*w, 3) # light rays 'light_rays': self.light_rays, #(h*w,8) } else: # create data for each image separately frame = self.meta['frames'][idx] file_path = frame['file_path'].split('/') file_path = 'sm_'+ file_path[-1] c2w = torch.FloatTensor(frame['transform_matrix'])[:3, :4] ########### w, h = self.img_wh hfov = self.meta['camera_angle_x'] * 180./np.pi ppc = Camera(hfov, (h, w)) ppc.set_pose_using_blender_matrix(c2w, self.hparams.coords_trans) eye_poses = [ppc.eye_pos]*h*w cameras = [ppc.camera]*h*w ########### img = Image.open(os.path.join(self.root_dir, f"{file_path}.png")) img = img.resize(self.img_wh, Image.LANCZOS) if not self.hparams.blur == -1: img = img.filter(ImageFilter.GaussianBlur(self.hparams.blur)) img = self.transform(img) # (3, H, W) img = img.view(3, -1).permute(1, 0) # (H*W, 3) RGBA # img = img[:, :3]*img[:, -1:] + (1-img[:, -1:]) # blend A to RGB pixels_u = torch.arange(0, w, 1) pixels_v = torch.arange(0, h, 1) i, j = np.meshgrid(pixels_v.numpy(), pixels_u.numpy(), indexing='xy') i = torch.tensor(i) + 0.5 #.unsqueeze(2) j = torch.tensor(j)+ 0.5 #.unsqueeze(2) pixels = torch.stack([i,j, torch.ones_like(i)], axis=-1).view(-1, 3) # (H*W,3) rays_o, rays_d = get_rays(self.directions, c2w) rays = torch.cat([rays_o, rays_d, self.near*torch.ones_like(rays_o[:, :1]), self.far*torch.ones_like(rays_o[:, :1])], 1) # (H*W, 8) # print("rays.shape", rays.shape) # valid_mask = (img[-1]>0).flatten() # (H*W) valid color area sample = {'rays': rays, 'pixels': pixels, # pixel where rays originated from 'rgbs': img, 'ppc': { 'eye_pos': eye_poses, 'camera': cameras, }, 'light_ppc': { 'eye_pos': self.light_ppc.eye_pos, 'camera': self.light_ppc.camera, }, # pixel where the light ray originated from 'light_pixels': self.light_pixels, #(h*w, 3) # light rays 'light_rays': self.light_rays, #(h*w,8) } return sample

44.651515

120

0.498812

1,852

14,735

3.805076

0.13067

0.048673

0.024975

0.018731

0.561799

0.490563

0.467149

0.438059

0.409394

0.406556

0

0.021092

0.350051

14,735

330

121

44.651515

0.714733

0.19905

0

0.382075

0

0.009434

0.100252

0.023977

0

0.004717

1

0.023585

false

0

0.04717

0

0.09434

0.080189

0

null

0

null

0

1

0

c60337a0cc834ad033d09738b410868a1fcb6ef6

625

py

Python

python_submission/5.longest-palindromic-substring.197920566.notac.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

python_submission/5.longest-palindromic-substring.197920566.notac.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

python_submission/5.longest-palindromic-substring.197920566.notac.py

stavanmehta/leetcode

1224e43ce29430c840e65daae3b343182e24709c

[ "Apache-2.0" ]

null

class Solution(object): def longestPalindrome(self, s): """ :type s: str :rtype: str """ if len(s) == 1: return s start = 0 end = len(s) maxlength = 0 longest = "" while(start < len(s)): substring = s[start:end] if substring == substring[::-1] and len(substring) > maxlength: maxlength = len(substring) longest = substring else: end -=1 if start == end: start += 1 end = len(s) return longest

26.041667

75

0.4192

60

625

4.366667

0.383333

0.061069

0.053435

0

0.018293

0.4752

625

23

76

27.173913

0.780488

0.0384

0

0.105263

0

1

0.052632

false

0

0.210526

0

null

0

null

0

1

0

c6074a650ad47398b9b59002465392e2c249c2e3

14,325

py

Python

data-preprocess/ice-vision-data-merger-pipeline.py

jingwoo4710/mmdetection-icevision

da82741b29fdd1eb77b4e7483ff2a515d43d1760

[ "Apache-2.0" ]

4

2020-03-13T00:12:44.000Z

2021-06-25T07:54:17.000Z

data-preprocess/ice-vision-data-merger-pipeline.py

jingwoo4710/mmdetection-icevision

da82741b29fdd1eb77b4e7483ff2a515d43d1760

[ "Apache-2.0" ]

4

2020-03-13T00:24:15.000Z

2022-03-12T00:19:03.000Z

data-preprocess/ice-vision-data-merger-pipeline.py

jingwoo4710/mmdetection-icevision

da82741b29fdd1eb77b4e7483ff2a515d43d1760

[ "Apache-2.0" ]

1

2021-03-07T06:24:08.000Z

#!/usr/bin/env python # coding: utf-8 # In[93]: import os from shutil import copyfile import json print("cwd = ", os.getcwd()) current_folder = os.getcwd() #extracted_train_data = os.path.join(current_folder, "extracted_train_data") extracted_train_data = "/dataset/training/" #annotations_dir = '/data/annotations' copied_train_data = "/data/dataset/training/" # In[100]: data_location = "/dataset/training/" files_list = [] neural_net_list = [] linear_mappings = [] for subdir, dirs, files in os.walk(data_location): for file in set(files): if file.endswith('.pnm'): current_file = os.path.join(subdir, file) files_list.append(current_file) print(len(files_list)) prev_file_number = 0 prev_file_dir_name = "" prev_neural_net = "" counter = 0 linear_list = [] ########################################## EDIT ##################################################################### for file in sorted(files_list): file_name_split = file.split('/') file_number = int(file_name_split[-1].split(".pnm")[0]) dir_name = file_name_split[-3] + file_name_split[-2] counter += file_number - prev_file_number if(prev_file_dir_name != dir_name): counter = 0 neural_net_list.append(file) prev_neural_net = file linear_list = [] else: if(counter >= 5): neural_net_list.append(file) linear_mappings.append({ "linear_list": linear_list, "predecessor": prev_neural_net, "successor": file }) counter = 0 prev_neural_net = file linear_list = [] else: #linear_mappings[file] = "linear" linear_list.append(file) # print("making linear", file) prev_file_number = file_number prev_file_dir_name = dir_name with open('linear_mappings.json', 'w') as outfile: json.dump(linear_mappings, outfile) # for file in file_body: # if (file_body[file] == "neuralnet"): # print(file) # for file in file_body: # if (file_body[file] == "linear"): # print(file) # In[97]: #neural_net_list[] - list of images to be sent to neural network import os import glob from mmdet.apis import init_detector, inference_detector, show_result, write_result import time import datetime config_file = '/root/ws/mmdetection-icevision/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x_all_classes.py' #model = init_detector(config_file, checkpoint_file, device='cuda:0') #epch_count = 1 #for epochs in glob.glob(os.path.join('/data_tmp/icevisionmodels/cascade_rcnn_dconv_c3-c5_r50_fpn_1x_all_classes/', '*.pth')): checkpoint_file = '/data/trained_models/cascade_rcnn_dconv_c3-c5_r50_fpn_1x_135_classes/epoch_15.pth' #checkpoint_file = epochs # build the model from a config file and a checkpoint file model = init_detector(config_file, checkpoint_file, device='cuda:0') TEST_RESULT_PATH = "/data/test_results/" img_count = 0 #print(img_count) FINAL_ONLINE_TEST_PATH = "/data/train_subset/" #FINAL_ONLINE_TEST_PATH = '/data/test_results/2018-02-13_1418/left/' #for TEST_SET_PATH in (FINAL_ONLINE_TEST_PATH + "2018-02-16_1515_left/", FINAL_ONLINE_TEST_PATH + "2018-03-16_1424_left/", FINAL_ONLINE_TEST_PATH + "2018-03-23_1352_right/"): #print(TEST_SET_PATH) #imgs = glob.glob('/dataset/training/**/*.pnm', recursive=True) for img in neural_net_list: ts = time.time() st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S') print ("time =", st) #imgs = ['test.jpg', '000000.jpg'] #print(img) # /dataset/training/2018-02-13_1418/left/020963.pnm --> required format 2018-02-13_1418_left/000033 name = img.split("/") # ['', 'home', 'luminosity', 'ws', 'icevision', 'data', 'final', '2018-02-16_1515_left', '001887.jpg'] #print(name) base = name[-1].split(".")[0] # ['001887', 'jpg'] #print(base) name = name[-3] + "_" + name[-2] tmp = name name = name + "/" + base #print(name) ######## Remove #name_tmp = base.split("_") #name = name_tmp[0] + "_" + name_tmp[1] + "_" + name_tmp[2] + "/" + name_tmp[-1] #name = "annotation_train_subset/" + base #base_list = base.split("_") #name = base_list[0] + "_" + base_list[1] + "_" + base_list[2] + "/" + base_list[3] ##########Remove result = inference_detector(model, img) #write_result(name, result, model.CLASSES, out_file=os.path.join(TEST_RESULT_PATH, 'my_test_multi_scale_epch_{}.tsv'.format(epch_count))) # use name instead name1 for hackthon submission #show_result(img, result, model.CLASSES, out_file= TEST_RESULT_PATH + 'bboxs/' + tmp + ".pnm") write_result(name, result, model.CLASSES, out_file=os.path.join(TEST_RESULT_PATH, 'my_test_epch_15_interpolation.tsv')) # use name instead name1 for hackthon submission img_count+=1 #print(img_count) print("num = %d name = %s" %(img_count,name)) # In[103]: import os import glob import csv from shutil import copyfile def linear_interpolation(pred, succ, lin_images, input_tsv, step, out_tsv): lin_images.sort() succ_base_name = os.path.basename(succ).split(".")[0] pred_base_name = os.path.basename(pred).split(".")[0] #copyfile(input_tsv, out_tsv) tsv_file = csv.reader(open(input_tsv, "r"), delimiter="\t") prd_classes = [] suc_classes = [] prd_keys = set() suc_keys = set() for row in tsv_file: # print("row = ", row) # print('ped_keys = ', prd_keys) # print('suc_keys = ', suc_keys) # frame xtl ytl xbr ybr class temporary data # 2018-02-13_1418_left/020963 679 866 754 941 3.27 prd_record = {} #defaultdict(list) suc_record = {} #defaultdict(list) #print("row[0] = ", row[0]) x = os.path.join(os.path.basename(os.path.dirname(pred)),os.path.basename(pred)) y = os.path.basename(os.path.dirname(os.path.dirname(pred))) dict_key = y + "_" + x x2 = os.path.join(os.path.basename(os.path.dirname(succ)),os.path.basename(succ)) y2 = os.path.basename(os.path.dirname(os.path.dirname(succ))) dict_key2 = y2 + "_" + x2 # print('y = ', y) # print("x = ", x) # print("dict_key = ", dict_key.split('.')[0]) if row[0] == dict_key.split('.')[0]: if row[5] not in prd_keys: print("pred check cleared") prd_record["class"] = row[5] prd_record["xtl"] = row[1] prd_record["ytl"] = row[2] prd_record["xbr"] = row[3] prd_record["ybr"] = row[4] print("prd_record['ybr'] = ", prd_record["ybr"]) prd_keys.add(row[5]) # #prd_record[row[5]].append(row[1]) #xtl # prd_record[row[5]].append(row[2]) #ytl # prd_record[row[5]].append(row[3]) #xbr # prd_record[row[5]].append(row[4]) #ybr prd_classes.append(prd_record) else: for prd_class in prd_classes: if prd_class["class"] == row[5]: del prd_class print("del prd_class") elif row[0] == dict_key2.split('.')[0]: print("Succ check cleared") if row[5] not in suc_keys: suc_record["class"] = row[5] suc_record["xtl"] = row[1] suc_record["ytl"] = row[2] suc_record["xbr"] = row[3] suc_record["ybr"] = row[4] suc_keys.add(row[5]) # suc_record[row[5]].append(row[1]) # suc_record[row[5]].append(row[2]) # suc_record[row[5]].append(row[3]) # suc_record[row[5]].append(row[4]) suc_classes.append(suc_record) else: for suc_class in suc_classes: if suc_class["class"] == row[5]: del suc_class print("del prd_class") #print("prd_keys = ", prd_keys) common_classes = prd_keys.intersection(suc_keys) print(common_classes) for common_class in common_classes: for prd_class in prd_classes: if prd_class["class"] == common_class: for suc_class in suc_classes: if suc_class["class"] == common_class: xtl_gr = (int(prd_class["xtl"]) - int(suc_class["xtl"])) / step ytl_gr = (int(prd_class["ytl"]) - int(suc_class["ytl"])) / step xbr_gr = (int(prd_class["xbr"]) - int(suc_class["xbr"])) / step ybr_gr = (int(prd_class["ybr"]) - int(suc_class["ybr"])) / step print(xtl_gr, ytl_gr, xbr_gr, ybr_gr) for f in lin_images: curr_base = os.path.basename(f).split(".")[0] # print("curr_base = ", curr_base) # print("pred_base_name = ", pred_base_name) # print("f = ", f) factor = int(curr_base) - int(pred_base_name) curr_xtl = int(prd_class["xtl"]) + (factor * xtl_gr) curr_ytl = int(prd_class["ytl"]) + (factor * ytl_gr) curr_xbr = int(prd_class["xbr"]) + (factor * xbr_gr) curr_ybr = int(prd_class["ybr"]) + (factor * ybr_gr) temp = '' with open(out_tsv, mode = 'a') as result_file: result_file_writer = csv.writer(result_file, delimiter = '\t') result_file_writer.writerow([f, str(curr_xtl), str(curr_ytl), str(curr_xbr), str(curr_ybr), prd_class["class"], temp, temp]) # In[105]: #load the linear mappings.json import csv linear_mappings = "/root/ws/mmdetection-icevision/data-preprocess/linear_mappings.json" input_tsv = os.path.join(TEST_RESULT_PATH, 'my_test_epch_15_interpolation_copy.tsv') out_tsv = os.path.join(TEST_RESULT_PATH, 'my_test_epch_15_interpolation_copy.tsv') interpolation_mappings = [] with open(linear_mappings, 'r') as f: interpolation_mappings = json.load(f) for i in interpolation_mappings: pred = i["predecessor"] succ = i['successor'] interpol_list = i['linear_list'] step = 5 linear_interpolation(pred, succ, interpol_list, input_tsv, step, out_tsv) # if i["predecessor"] == neural_net_list[100]: # break # In[70]: # trial code # extracted_train_data = "/home/sgj/temp/test_data/2018-03-16_1324" # for subdir, dirs, files in os.walk(extracted_train_data): # print("subdir = ", subdir) # for file in files: # if file.endswith('.jpg'): # current_file = os.path.join(subdir, file) # #folder_name = os.path.basename(os.path.dirname(current_file)) # #expected_name = folder_name + '_' + os.path.basename(current_file) # y = file.split("_") # expected_name = y[0] + "_" + y[1] + "_left_jpgs_" + y[2] # absolute_expected_name = os.path.join(os.path.dirname(current_file),expected_name) # os.rename(current_file, absolute_expected_name) # In[37]: extracted_train_data = "/home/sgj/temp/train_data/2018-02-13_1418_left_jpgs" for subdir, dirs, files in os.walk(extracted_train_data): print("subdir = ", subdir) for file in files: if file.endswith('.jpg'): current_file = os.path.join(subdir, file) folder_name = os.path.basename(os.path.dirname(current_file)) expected_name = folder_name + '_' + os.path.basename(current_file) absolute_expected_name = os.path.join(os.path.dirname(current_file),expected_name) os.rename(current_file, absolute_expected_name) # In[25]: # move out un-annotated images - # ARGS - # Annotations data tsv # Extracted images folder # Destination folder for annotated_data import os annotation_data_tsv_folder = "/home/sgj/nvme/ice-vision/annotations/test/all_validation_annotations" extracted_images_folder = "/home/sgj/temp/test_data/all_validation_images" #dest_annotated_imgs = "/home/sgj/nvme/ice-vision/annotated_data/val" dest_annotated_imgs = "/home/sgj/temp/ice-vision/annotated_data/val" os.makedirs(dest_annotated_imgs) img_count = 0 for root, dirs, files in os.walk(annotation_data_tsv_folder): for name in files: if name.endswith('.tsv'): prefix = name.split(".")[0] image_name = prefix + ".jpg" expected_img_path = os.path.join(extracted_images_folder, image_name) new_image_path = os.path.join(dest_annotated_imgs, image_name) if os.path.exists(expected_img_path): img_count = img_count + 1 os.rename(expected_img_path, new_image_path) else: print("image missing-----------------------") print("total images = ", img_count) # In[18]: temp = "2018-02-13_1418_left_jpgs_014810.tsv" temp.split(".")[0] # In[3]: for subdir, dirs, files in os.walk(copied_train_data): print("subdir = ", subdir) for file in files: if file.endswith('.pnm'): current_file = os.path.join(subdir, file) print('current file = ', current_file) cam_dir = current_file.split('/')[-2] #print("cam dir = ", cam_dir) date_dir = current_file.split('/')[-3] #print("date_dir = ", date_dir) expected_folder = '/data/train_subset/' expected_file_name = date_dir + "_" + cam_dir + "_" + os.path.basename(current_file) expected_file_path = os.path.join(expected_folder, expected_file_name) #copyfile(current_file, dst_file_path) os.rename(current_file, expected_file_path) print("expected_file_path = ", expected_file_path) # In[4]: # In[ ]:

35.02445

194

0.585829

1,859

14,325

4.243141

0.152232

0.031947

0.021552

0.016227

0.36676

0.29285

0.240365

0.208418

0.204868

0.177485

0

0.031364

0.269948

14,325

408

195

35.110294

0.722892

0.281396

0

0.207921

0

0.117788

0.064564

0

1

0.004951

false

0

0.069307

0

0.074257

0.084158

0

null

0

null

0

1

0

c609dcc818d4247b3e931541707e6e65fd9fc433

1,683

py

Python

osu/graph_metadata.py

brandondong/osu-beatmap-generator

7ca14793ef6a48a65cfd1a564f3b24d940a6051a

[ "MIT" ]

null

osu/graph_metadata.py

brandondong/osu-beatmap-generator

7ca14793ef6a48a65cfd1a564f3b24d940a6051a

[ "MIT" ]

1

2021-06-01T23:50:59.000Z

osu/graph_metadata.py

brandondong/osu-beatmap-generator

7ca14793ef6a48a65cfd1a564f3b24d940a6051a

[ "MIT" ]

null

import os import matplotlib.pyplot as plt import numpy as np from models import models_util DIFFICULTY_LABEL = "Star Difficulty" BPM_LABEL = "BPM" LENGTH_LABEL = "Length" CS_LABEL = "Circle Size" DRAIN_LABEL = "HP Drain" ACCURACY_LABEL = "Accuracy" AR_LABEL = "Approach Rate" SAVE_FOLDER = "visualization/" def print_property_values(labels, values): for idx, value in enumerate(values): print(f"{labels[idx]}: {value}") print() # Data rows are in the format of [difficulty_rating],[bpm],[total_length],[cs],[drain],[accuracy],[ar]. labels = [DIFFICULTY_LABEL, BPM_LABEL, LENGTH_LABEL, CS_LABEL, DRAIN_LABEL, ACCURACY_LABEL, AR_LABEL] filename_labels = [] for label in labels: filename_labels.append(label.lower().replace(" ", "_")) # Keep track of each property in separate rows. points = np.transpose(models_util.load_metadata_dataset()) mins = points.min(axis=-1) maxes = points.max(axis=-1) means = np.mean(points, axis=-1) print("Minimum values:") print_property_values(labels, mins) print("Maximum values:") print_property_values(labels, maxes) print("Mean values:") print_property_values(labels, means) # Plot graphs for each input output feature pair. for i in range(3): for j in range(3, 7): plt.hexbin(points[i], points[j], gridsize=50, cmap="inferno") plt.axis([mins[i], maxes[i], mins[j], maxes[j]]) x_label = labels[i] y_label = labels[j] plt.title(f"{y_label} vs {x_label}") plt.xlabel(x_label) plt.ylabel(y_label) x_file_label = filename_labels[i] y_file_label = filename_labels[j] image_name = os.path.join(SAVE_FOLDER, f"{y_file_label}_vs_{x_file_label}.png") print(f"Saving graph to {image_name}.") plt.savefig(image_name)

28.525424

103

0.734403

263

1,683

4.494297

0.387833

0.043993

0.064298

0.084602

0.07868

0

0.005431

0.124777

1,683

59

104

28.525424

0.797013

0.115865

0

0.160269

0.024242

0

1

0.022727

false

0

0.090909

0

0.113636

0.227273

0

null

0

null

0

1

0

c60bdc534000a18e4fc655a5cff50a94af3d4302

2,077

py

Python

ADE/train.py

BinahHu/ADE-Longtail

4aabf1cbf50746e610b91362c40cbcb7884dd170

[ "Apache-2.0" ]

null

ADE/train.py

BinahHu/ADE-Longtail

4aabf1cbf50746e610b91362c40cbcb7884dd170

[ "Apache-2.0" ]

null

ADE/train.py

BinahHu/ADE-Longtail

4aabf1cbf50746e610b91362c40cbcb7884dd170

[ "Apache-2.0" ]

null

# import some common libraries # import some common detectron2 utilities from detectron2.config import get_cfg from detectron2.data import ( build_detection_test_loader, build_detection_train_loader, ) from detectron2.engine import default_argument_parser, default_setup, launch, DefaultTrainer # import ADE related package from dataset.ade import register_all_ade from dataset.my_mapper import MyDatasetMapper from transforms.my_resize import MyResize from modeling.backbone.my_build import register_my_backbone from modeling.roi_heads.roi_cls import register_roi_cls from additional_cfg import set_additional_cfg class Trainer(DefaultTrainer): @classmethod def build_train_loader(cls, cfg): """ Returns: iterable It now calls :func:`detectron2.data.build_detection_train_loader`. Overwrite it if you'd like a different data loader. """ return build_detection_train_loader(cfg, mapper=MyDatasetMapper(cfg, is_train=True, augmentations=[ MyResize(cfg.INPUT.RESIZE_SHORT, cfg.INPUT.RESIZE_LONG)])) def setup(args): """ Create configs and perform basic setups. """ cfg = get_cfg() cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg = set_additional_cfg(cfg) cfg.freeze() default_setup( cfg, args ) # if you don't like any of the default setup, write your own setup code return cfg def register_all(cfg): register_all_ade(cfg.DATASETS.ADE_ROOT) register_my_backbone() register_roi_cls() def main(args): cfg = setup(args) register_all(cfg) trainer = Trainer(cfg) trainer.resume_or_load(resume=args.resume) return trainer.train() if __name__ == "__main__": args = default_argument_parser().parse_args() print("Command Line Args:", args) launch( main, args.num_gpus, num_machines=args.num_machines, machine_rank=args.machine_rank, dist_url=args.dist_url, args=(args,), )

26.628205

107

0.703418

269

2,077

5.159851

0.390335

0.040346

0.041066

0.054035

0

0.003081

0.218585

2,077

77

108

26.974026

0.852126

0.168031

0

0.01603

0

1

0.083333

false

0

0.1875

0

0.354167

0.020833

0

null

0

null

0

1

0

c60dde17e3e2f4dd2320cb3a7f244103bed46b66

2,411

py

Python

src/messages.py

Colk-tech/gcpdiscord

b2a43dca8db5a17e6e72f36c7e895db19b836067

[ "MIT" ]

4

2020-12-31T09:41:09.000Z

2022-02-20T14:13:41.000Z

src/messages.py

Colk-tech/gcpdiscord

b2a43dca8db5a17e6e72f36c7e895db19b836067

[ "MIT" ]

3

2020-12-28T18:19:44.000Z

2021-01-03T14:51:59.000Z

src/messages.py

Colk-tech/gcpdiscord

b2a43dca8db5a17e6e72f36c7e895db19b836067

[ "MIT" ]

2

2020-12-31T05:42:57.000Z

2022-03-24T07:54:25.000Z

PERMISSION_DENIED_MESSAGE: str = "***PERMISSION DENIED!*** \n" \ "You are not permitted to use this command. \n" \ "Please contact to your server master. \n." ERROR_OCCURRED_MESSAGE: str = "***ERROR OCCURRED!*** \n" \ "Error has occurred while executing gcp request command. \n" \ "Please contact to your server master or the software developer. \n" \ "Error: {} \n" OPERATION_COMPLETED_MESSAGE: str = "***Operation Completed! ***\n" \ "Operation: {} has successfully completed. \n" \ "This may take more 2~3 minutes that the Minecraft Server starts (stops)." INSTANCE_IS_ALREADY_IN_REQUESTED_STATUS: str = "***Already in status of {}.*** \n" \ "The instance is already in the status. \n" \ "No operation has done." PRE_STOP_OPERATION_PROCESSING: str = "Processing pre-stop operation... \n" \ "Trying to shutdown Minecraft server from the console channel. \n" \ "Whichever the operation is completed or not, " \ "the server will shutdown in 5 minutes forcibly." REQUEST_RECEIVED: str = "Operation: {} has requested. \n" \ "Please wait until the operation is done. \n" START_REQUEST_RECEIVED_MESSAGE = "Trying to start the gcp server. \n" \ "It takes 3 sec at least to complete the operation. \n" \ "The minecraft server will start as soon as gcp server started. \n" \ "PLEASE WAIT UNTIL YOU RECEIVE MESSAGE 'SERVER HAS STARTED!' " \ "BEFORE YOU JOIN THE MINECRAFT SERVER." STOP_REQUEST_RECEIVED_MESSAGE = "Trying to stop the gcp server. \n" \ "It takes 5 minutes at least to complete the operation. \n" \ "We will issue `stop` command in console channel. \n" \ "And then, we will wait for 5 minutes for the Minecraft server stops." \ "After all the process is done, we will shutdown GCP instance finally."

70.911765

109

0.517628

256

2,411

4.792969

0.34375

0.061125

0.05868

0.03423

0.193969

0.145069

0.112469

0.06357

0

0.004172

0.403567

2,411

33

110

73.060606

0.849096

0

0.542099

0

1

0

false

0

null

0

null

0

1

0

c60e59d24506527b02637849c4f5442ee5efa5c7

4,035

py

Python

util/SR2SC.py

lorteddie/dcmqi

4f668745f4f8b2a67e6dcfdee187ac7793e07116

[ "BSD-3-Clause" ]

null

util/SR2SC.py

lorteddie/dcmqi

4f668745f4f8b2a67e6dcfdee187ac7793e07116

[ "BSD-3-Clause" ]

null

util/SR2SC.py

lorteddie/dcmqi

4f668745f4f8b2a67e6dcfdee187ac7793e07116

[ "BSD-3-Clause" ]

null

from pydicom.sr import _snomed_dict import os import re import pydicom.sr._snomed_dict folder = "E:\\work\\QIICR\\dcmqi" Out_Folder = "E:\\work\\QIICR\\renamed_dcmqi" def recursive_file_find(address, regexp): filelist = os.listdir(address) approvedlist=[] for filename in filelist: fullpath = os.path.join(address, filename) if os.path.isdir(fullpath): approvedlist.extend(recursive_file_find(fullpath, regexp)) elif re.match(regexp, fullpath) is not None: approvedlist.append(fullpath) return approvedlist def GetFileString(filename): File_object = open(filename, "r") try: Content = File_object.read() except: print("Couldn't read the file") Content = "" File_object.close() return Content def WriteTextInFile(filename, txt): folder = os.path.dirname(filename) if not os.path.exists(folder): os.makedirs(folder) File_object = open(filename, "w") File_object.write(txt) File_object.close() def FindRegex(regexp, text, extend=[0, 0], printout=False): found_iters = re.finditer(regexp, text) founds = list(found_iters) ii = [] for mmatch in founds: yy = text[mmatch.start() - extend[0]:mmatch.end() + extend[1]] counter = "[%04d ]" % len(ii) if (printout): print(counter + yy) ii.append(yy) return ii def ReplaceQuotedText(find_text, rep_text, text): pattern = "(\"\s*" + find_text + "\s*\")|('\s*" + find_text + "\s*')" replacement = "\"" + rep_text + "\""; new_text = re.sub(pattern, replacement, text) View = ShowReplaceMent(pattern, replacement, text) return [new_text, View] def FindAndReplace(find_text, rep_text, text): newtext = re.sub(find_text, rep_text, text) x = ShowReplaceMent(find_text, rep_text, text) return [newtext, x] def ShowReplaceMent(find_text, rep_text, text): output = [] text_seq = FindRegex("\\n.*(" + find_text + ").*\\n", text, [-1, -1]) for line_txt in text_seq: found_iters = re.finditer(find_text, line_txt) founds = list(found_iters) if len(founds) > 0: mmatch = founds[0] yy = line_txt[:mmatch.start()] + \ "{ [" + line_txt[mmatch.start():mmatch.end()] + "]-->[" + rep_text + "] }" + \ line_txt[mmatch.end():] output.append(yy) return output dict = _snomed_dict.mapping["SCT"] details = [] # recursive_file_find(folder, all_files, "(.*\\.cpp$)|(.*\\.h$)|(.*\\.json$)") all_files = recursive_file_find(folder, "(?!.*\.git.*)") for f, jj in zip(all_files, range(1, len(all_files))): f_content = (GetFileString(f)) if len(f_content) == 0: continue [f_content, x] = ReplaceQuotedText("SCT", "SCT", f_content) details = x [f_content, x] = FindAndReplace(",\s*SRT\s*,", ",SCT,", f_content) details.extend(x) [f_content, x] = FindAndReplace("SCT", " SCT ", f_content) details.extend(x) [f_content, x] = FindAndReplace("_SCT_", "_SCT_", f_content) details.extend(x) [f_content, x] = FindAndReplace("sct.h", "sct.h", f_content) details.extend(x) for srt_code, sct_code in dict.items(): # f_content = ReplaceQuotedText(srt_code, sct_code, f_content) [f_content, x] = FindAndReplace(srt_code, sct_code, f_content) details.extend(x) if len(details) == 0: continue edited_file_name = f.replace(folder, Out_Folder) edited_file_log = f.replace(folder, os.path.join(Out_Folder, '..\\log')) + ".txt" WriteTextInFile(edited_file_name, f_content) print("------------------------------------------------------------------------") f_number = "(file %03d ) " % jj print(f_number + f) logg = "" for m, c in zip(details, range(0, len(details))): indent = "\t\t\t%04d" % c logg += (indent + m + "\n") if len(logg) != 0: WriteTextInFile(edited_file_log, logg) print("the find/replace process finished ...")

32.28

95

0.603965

512

4,035

4.576172

0.242188

0.058045

0.023047

0.03201

0.172002

0.113103

0.065301

0

0.006061

0.223048

4,035

124

96

32.540323

0.741308

0.033953

0

0.108911

0

0.092683

0.031836

0

1

0.069307

false

0

0.039604

0

0.168317

0.069307

0

null

0

null

0

1

0

c60ef77ba35cbe6b2d5368873bee63645c0514b7

9,417

py

Python

disentanglement_lib/visualize/visualize_util.py

erow/disentanglement_lib

c875207fdeadc44880277542447544941bc0bd0a

[ "Apache-2.0" ]

null

disentanglement_lib/visualize/visualize_util.py

erow/disentanglement_lib

c875207fdeadc44880277542447544941bc0bd0a

[ "Apache-2.0" ]

null

disentanglement_lib/visualize/visualize_util.py

erow/disentanglement_lib

c875207fdeadc44880277542447544941bc0bd0a

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2018 The DisentanglementLib Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utility functions for the visualization code.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math from disentanglement_lib.utils import resources import numpy as np from PIL import Image import scipy from six.moves import range import torch import imageio import matplotlib.pyplot as plt import matplotlib.animation as animation def array_animation(data, fps=20): fig, ax = plt.subplots(figsize=(3, 3)) plt.tight_layout() ax.set_axis_off() if len(data.shape) == 4: data = data.transpose([0, 2, 3, 1]) im = ax.imshow(data[0], vmin=0, vmax=1) def init(): im.set_data(data[0]) return (im,) # animation function. This is called sequentially def animate(i): data_slice = data[i] im.set_data(data_slice) return (im,) # call the animator. blit=True means only re-draw the parts that have changed. anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(data), interval=1000 / fps, blit=True) return anim def traversal_latents(base_latent, traversal_vector, dim): l = len(traversal_vector) traversals = base_latent.repeat(l, 1) traversals[:, dim] = traversal_vector return traversals def plot_bar(axes, images, label=None): for ax, img in zip(axes, images): if img.shape[2] == 3: ax.imshow(img) elif img.shape[2] == 1: ax.imshow(img.squeeze(2), cmap='gray') ax.axis('off') if label: axes[-1].get_yaxis().set_label_position("right") axes[-1].set_ylabel(label) def sigmoid(x): return 1 / (1 + np.exp(-np.clip(x, -20, 20))) def plt_sample_traversal(mu, decode, traversal_len=5, dim_list=range(4), r=3): """ :param mu: Tensor: [1,dim] :param decode: :param traversal_len: :param dim_list: :param r: :return: """ dim_len = len(dim_list) if len(mu.shape) == 1: mu = mu.unsqueeze(0) with torch.no_grad(): fig, axes = plt.subplots(dim_len, traversal_len, squeeze=False, figsize=(traversal_len, dim_len,)) plt.tight_layout(pad=0.1) plt.subplots_adjust(wspace=0.01, hspace=0.05) for i, dim in enumerate(dim_list): base_latents = mu.clone() linear_traversal = torch.linspace(-r, r, traversal_len) traversals = traversal_latents(base_latents, linear_traversal, dim) recon_batch = decode(traversals) plot_bar(axes[i, :], recon_batch) return fig def save_image(image, image_path): """Saves an image in the [0,1]-valued Numpy array to image_path. Args: image: Numpy array of shape (height, width, {1,3}) with values in [0, 1]. image_path: String with path to output image. """ # Copy the single channel if we are provided a grayscale image. if image.shape[2] == 1: image = np.repeat(image, 3, axis=2) image = np.ascontiguousarray(image) image *= 255. image = image.astype(np.uint8) # disable the converting warning with open(image_path, "wb") as path: img = Image.fromarray(image, mode="RGB") img.save(path) def grid_save_images(images, image_path): """Saves images in list of [0,1]-valued np.arrays on a grid. Args: images: List of Numpy arrays of shape (height, width, {1,3}) with values in [0, 1]. image_path: String with path to output image. """ side_length = int(math.floor(math.sqrt(len(images)))) image_rows = [ np.concatenate( images[side_length * i:side_length * i + side_length], axis=0) for i in range(side_length) ] tiled_image = np.concatenate(image_rows, axis=1) print(image_path) save_image(tiled_image, image_path) def padded_grid(images, num_rows=None, padding_px=10, value=None): """Creates a grid with padding in between images.""" num_images = len(images) if num_rows is None: num_rows = best_num_rows(num_images) # Computes how many empty images we need to add. num_cols = int(np.ceil(float(num_images) / num_rows)) num_missing = num_rows * num_cols - num_images # Add the empty images at the end. all_images = images + [np.ones_like(images[0])] * num_missing # Create the final grid. rows = [padded_stack(all_images[i * num_cols:(i + 1) * num_cols], padding_px, 1, value=value) for i in range(num_rows)] return padded_stack(rows, padding_px, axis=0, value=value) def padded_stack(images, padding_px=10, axis=0, value=None): """Stacks images along axis with padding in between images.""" padding_arr = padding_array(images[0], padding_px, axis, value=value) new_images = [images[0]] for image in images[1:]: new_images.append(padding_arr) new_images.append(image) return np.concatenate(new_images, axis=axis) def padding_array(image, padding_px, axis, value=None): """Creates padding image of proper shape to pad image along the axis.""" shape = list(image.shape) shape[axis] = padding_px if value is None: return np.ones(shape, dtype=image.dtype) else: assert len(value) == shape[-1] shape[-1] = 1 return np.tile(value, shape) def best_num_rows(num_elements, max_ratio=4): """Automatically selects a smart number of rows.""" best_remainder = num_elements best_i = None i = int(np.sqrt(num_elements)) while True: if num_elements > max_ratio * i * i: return best_i remainder = (i - num_elements % i) % i if remainder == 0: return i if remainder < best_remainder: best_remainder = remainder best_i = i i -= 1 def pad_around(image, padding_px=10, axis=None, value=None): """Adds a padding around each image.""" # If axis is None, pad both the first and the second axis. if axis is None: image = pad_around(image, padding_px, axis=0, value=value) axis = 1 padding_arr = padding_array(image, padding_px, axis, value=value) return np.concatenate([padding_arr, image, padding_arr], axis=axis) def add_below(image, padding_px=10, value=None): """Adds a footer below.""" if len(image.shape) == 2: image = np.expand_dims(image, -1) if image.shape[2] == 1: image = np.repeat(image, 3, 2) if image.shape[2] != 3: raise ValueError("Could not convert image to have three channels.") with open(resources.get_file("disentanglement_lib.png"), "rb") as f: footer = np.array(Image.open(f).convert("RGB")) * 1.0 / 255. missing_px = image.shape[1] - footer.shape[1] if missing_px < 0: return image if missing_px > 0: padding_arr = padding_array(footer, missing_px, axis=1, value=value) footer = np.concatenate([padding_arr, footer], axis=1) return padded_stack([image, footer], padding_px, axis=0, value=value) def save_animation(list_of_animated_images, image_path, fps): full_size_images = [] for single_images in zip(*list_of_animated_images): full_size_images.append( pad_around(add_below(padded_grid(list(single_images))))) imageio.mimwrite(image_path, full_size_images, fps=fps) def cycle_factor(starting_index, num_indices, num_frames): """Cycles through the state space in a single cycle.""" grid = np.linspace(starting_index, starting_index + 2 * num_indices, num=num_frames, endpoint=False) grid = np.array(np.ceil(grid), dtype=np.int64) grid -= np.maximum(0, 2 * grid - 2 * num_indices + 1) grid += np.maximum(0, -2 * grid - 1) return grid def cycle_gaussian(starting_value, num_frames, loc=0., scale=1.): """Cycles through the quantiles of a Gaussian in a single cycle.""" starting_prob = scipy.stats.norm.cdf(starting_value, loc=loc, scale=scale) grid = np.linspace(starting_prob, starting_prob + 2., num=num_frames, endpoint=False) grid -= np.maximum(0, 2 * grid - 2) grid += np.maximum(0, -2 * grid) grid = np.minimum(grid, 0.999) grid = np.maximum(grid, 0.001) return np.array([scipy.stats.norm.ppf(i, loc=loc, scale=scale) for i in grid]) def cycle_interval(starting_value, num_frames, min_val, max_val): """Cycles through the state space in a single cycle.""" starting_in_01 = (starting_value - min_val) / (max_val - min_val) grid = np.linspace(starting_in_01, starting_in_01 + 2., num=num_frames, endpoint=False) grid -= np.maximum(0, 2 * grid - 2) grid += np.maximum(0, -2 * grid) return grid * (max_val - min_val) + min_val

34.119565

84

0.653818

1,387

9,417

4.284066

0.240808

0.018176

0.015315

0.014137

0.143554

0.106698

0.099461

0.07001

0.056547

0

0.021859

0.232452

9,417

275

85

34.243636

0.800221

0.207922

0

0.063584

0

0.012598

0.003149

0

0.00578

1

0.109827

false

0

0.075145

0.00578

0.289017

0.011561

0

null

0

null

0

1

0

c60efc48bff6adf1e39c4b9e35874cde0aa11abd

6,515

py

Python

Lab/Lab5/lab5_group12_server.py

Zhuoyue-Xing/IOT---INTELLIG-CONNECTED-SYS

09111380e5ad36663e90de5bcd22691619c9a2f1

[ "Apache-2.0" ]

1

2020-03-04T21:51:42.000Z

Lab/Lab5/lab5_group12_server.py

Zhuoyue-Xing/IOT---INTELLIG-CONNECTED-SYS

09111380e5ad36663e90de5bcd22691619c9a2f1

[ "Apache-2.0" ]

null

Lab/Lab5/lab5_group12_server.py

Zhuoyue-Xing/IOT---INTELLIG-CONNECTED-SYS

09111380e5ad36663e90de5bcd22691619c9a2f1

[ "Apache-2.0" ]

null

# Created by Chenye Yang, Haokai Zhao, Zhuoyue Xing on 2019/10/13. # Copyright © 2019 Chenye Yang, Haokai Zhao, Zhuoyue Xing . All rights reserved. from machine import Pin, I2C, RTC, Timer import socket import ssd1306 import time import network import urequests import json # ESP8266 connects to a router def ConnectWIFI(essid, key): import network sta_if = network.WLAN(network.STA_IF) # config a station object if not sta_if.isconnected(): # if the connection is not established print('connecting to network...') sta_if.active(True) # activate the station interface sta_if.connect(essid, key) # connect to WiFi network while not sta_if.isconnected(): print('connecting') pass print('network config:', sta_if.ifconfig()) # check the IP address # ap_if.active(False) # disable the access-point interface else: print('network config:', sta_if.ifconfig()) # IP address, subnet mask, gateway and DNS server # Get Current Time and Set ESP8266 Time to current time def SetCurtTime(): # World Clock API url = "http://worldclockapi.com/api/json/est/now" webTime = json.loads(urequests.get(url).text) # returns a json string, convert it to json webTime = webTime['currentDateTime'].split('T') # currentDataTime string as 2019-10-13T15:05-04:00 date = list(map(int, webTime[0].split('-'))) # extract time numbers time = list(map(int, webTime[1].split('-')[0].split(':'))) timeTuple = (date[0], date[1], date[2], 0, time[0], time[1], 0, 0) # (year, month, day, weekday, hours, minutes, seconds, mseconds) rtc.datetime(timeTuple) # set a specific date and time # print(rtc.datetime()) # Show current time on OLED def OLEDShowTime(): weekday = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'} # Storeage the current date and time to <int> list timeList = list(map(int, rtc.datetime())) # Covert <int> list to <str> dateStr = "{:0>4d}".format(timeList[0])+'-'+"{:0>2d}".format(timeList[1])+'-'+"{:0>2d}".format(timeList[2]) # weekStr = weekday[timeList[3]] timeStr = "{:0>2d}".format(timeList[4])+':'+"{:0>2d}".format(timeList[5])+':'+"{:0>2d}".format(timeList[6]) # Put string to OLED oled.text(dateStr, 0, 0) # (message, x, y, color) # oled.text(weekStr, 0, 11) oled.text(timeStr, 0, 22) # OLED show whether ESP8266 received commands def OLEDRecvComd(received): if received: oled.text('RCVD', 80, 22) # Received the correct command else: oled.text('MISS', 80, 22) # Received a command ,but NOT a correct one # Judge the Command received def WhatCommand(cmd): # cmd is Command in string, like: "turn on display" global FLAG_True_Comd # Flag about whether it is a right command global FLAG_Display_On # Flag about whetehr OLED can display things global FLAG_Show_Time # Flag about whether the current time is shown on OLED if cmd == 'turn on display': FLAG_True_Comd = 1 FLAG_Display_On = 1 elif cmd == 'turn off display': FLAG_True_Comd = 1 FLAG_Display_On = 0 elif cmd == 'show current time': FLAG_True_Comd = 1 FLAG_Show_Time = 1 elif cmd == 'close current time': FLAG_True_Comd = 1 FLAG_Show_Time = 0 else: FLAG_True_Comd = 0 # not a right command # Judge what to display on OLED, and display OLED with Timer def WhatShowOLED(p): global FLAG_True_Comd # Flag about whether it is a right command global FLAG_Display_On # Flag about whetehr OLED can display things global FLAG_Show_Time # Flag about whether the current time is shown on OLED global showComd if FLAG_Display_On: # able to display OLED oled.text(showComd,0,11) OLEDRecvComd(FLAG_True_Comd) # whether it's a right command, show on OLED if FLAG_Show_Time: # show time on OLED if be able to OLEDShowTime() oled.show() # display text else: oled.fill(0) # fill OLED with black oled.show() # display all black oled.fill(0) # refresh, remove residue # ESP8266 as a server to listen and response def ListenResponse(): # a response ahout receiving a right JSON POST request goodHTML = """<!DOCTYPE html> <html> <head> <title>Good Command</title> </head> <body> <h1>The command from you is received by ESP8266</h1></body> </html> """ # a response ahout NOT receiving a JSON POST request badHTML = """<!DOCTYPE html> <html> <head> <title>Bad Command</title> </head> <body> <h1>The command from you is NOT a JSON format</h1></body> </html> """ addr = socket.getaddrinfo('0.0.0.0', 80)[0][-1] # Set web server port number to 80 s = socket.socket() s.bind(addr) # Bind the socket to address s.listen(1) # Enable a server to accept connections print('listening on', addr) global FLAG_True_Comd # Flag about whether it is a right command global FLAG_Display_On # Flag about whetehr OLED can display things global FLAG_Show_Time # Flag about whether the current time is shown on OLED FLAG_True_Comd = 0 FLAG_Display_On = 0 FLAG_Show_Time = 0 while True: print("FLAG_True_Comd", FLAG_True_Comd) print("FLAG_Display_On", FLAG_Display_On) print("FLAG_Show_Time", FLAG_Show_Time) # accept the connect to 80 port cl, addr = s.accept() print('client connected from', addr) # ESP8266 listen from the port # The client terminal instruction should be like: # curl -H "Content-Type:application/json" -X POST -d '{"Command":"turn on display"}' http://192.168.50.100:80 cl_receive = cl.recv(500).decode("utf-8").split("\r\n")[-1] # get the whole request and try to split it try: # if the request is in a JSON POST format cl_receive = json.loads(cl_receive) # convert the json string to json print(cl_receive['Command']) global showComd showComd = cl_receive['Command'] except ValueError: # if not, give the response ahout not receiving a JSON POST response = "HTTP/1.1 501 Implemented\r\n\r\nBad" else: # if can be trasformed to JSON, give good response response = "HTTP/1.1 200 OK\r\n\r\nGood" WhatCommand(cl_receive['Command']) # judge what's the command received # write to the port, i.e., give response cl.send(response) cl.close() if __name__ == '__main__': i2c = I2C(-1, scl=Pin(5), sda=Pin(4)) # initialize access to the I2C bus i2c.scan() oled = ssd1306.SSD1306_I2C(128, 32, i2c) # the width=128 and height=32 rtc = RTC() tim = Timer(-1) tim.init(period=100, mode=Timer.PERIODIC, callback=WhatShowOLED) ConnectWIFI('Columbia University','') # connect esp8266 to a router SetCurtTime() ListenResponse() # Show ESP8266 Pins to test server

35.601093

132

0.701765

1,024

6,515

4.383789

0.282227

0.021386

0.032078

0.018935

0.214079

0.203386

0.175763

0.160615

0.145912

0.129873

0

0.039351

0.176976

6,515

183

133

35.601093

0.79765

0.407982

0

0.31746

0

0.208344

0.005809

0.015873

0

1

0.055556

false

0.007937

0.063492

0

0.119048

0.079365

0

null

0

null

0

1

0

c60fff305331c5af7ece82691a51268226e5c661

453

py

Python

cord19/scripts/metadata/prefixes.py

udel-cbcb/covid19kg_rdf

3cd8dd6c4654333777db6127f2a3f2e01b92b0ac

[ "CC-BY-4.0" ]

null

cord19/scripts/metadata/prefixes.py

udel-cbcb/covid19kg_rdf

3cd8dd6c4654333777db6127f2a3f2e01b92b0ac

[ "CC-BY-4.0" ]

null

cord19/scripts/metadata/prefixes.py

udel-cbcb/covid19kg_rdf

3cd8dd6c4654333777db6127f2a3f2e01b92b0ac

[ "CC-BY-4.0" ]

null

from rdflib import Namespace, XSD from rdflib.namespace import DC, DCTERMS FHIRCAT_CORD = Namespace("http://fhircat.org/cord-19/") SSO = Namespace("http://semanticscholar.org/cv-research/") DOI = Namespace("https://doi.org/") PUBMED = Namespace("https://www.ncbi.nlm.nih.gov/pubmed/") PMC = Namespace("https://www.ncbi.nlm.nih.gov/pmc/articles/") MS_ACADEMIC = Namespace("https://academic.microsoft.com/paper/") FHIR = Namespace("http://hl7.org/fhir/")

45.3

64

0.735099

64

453

5.171875

0.515625

0.169184

0.102719

0.126888

0.181269

0

0.007109

0.068433

453

10

65

45.3

0.777251

0

0.477974

0

1

0

false

0

0.222222

0

0.222222

0

null

0

null

0

1

0

c6111b7e68c98d9dc89d8452745da9d3fb412b2a

6,568

py

Python

pbutils/argparsers.py

phonybone/phonybone_utils

d95f226ddfc62a1d69b5ff6f53de86188fe0c8f9

[ "MIT" ]

null

pbutils/argparsers.py

phonybone/phonybone_utils

d95f226ddfc62a1d69b5ff6f53de86188fe0c8f9

[ "MIT" ]

null

pbutils/argparsers.py

phonybone/phonybone_utils

d95f226ddfc62a1d69b5ff6f53de86188fe0c8f9

[ "MIT" ]

null

import sys import os import argparse from types import MethodType from importlib import import_module from argparse import RawTextHelpFormatter # Note: this applies to all options, might not always be what we want... from pbutils.configs import get_config, get_config_from_data, to_dict, inject_opts, CP from .strings import qw, ppjson from .streams import warn def parser_stub(docstr): parser = argparse.ArgumentParser(description=docstr, formatter_class=RawTextHelpFormatter) parser.add_argument('--config', default=_get_default_config_fn()) parser.add_argument('-v', '--verbose', action='store_true', help='verbose') parser.add_argument('-q', '--silent', action='store_true', help='silent mode') parser.add_argument('-d', '--debug', action='store_true', help='debugging flag') # leave comments here as templates # parser.add_argument('required_arg') # parser.add_argument('--something', default='', help='') # parser.add_argument('args', nargs=argparse.REMAINDER) return parser def _get_default_config_fn(): # warning: pyenv breaks this with its shims fn = sys.argv[0].replace('.py', '.ini') if not fn.endswith('.ini'): fn += '.ini' return fn def _assemble_config(opts, default_section_name='default'): ''' This builds a config by the following steps: 1. read config file (as specified in opts, otherwise empty) 2. inject environment vars as specified by config 3. inject opts returns a ConfigParserRaw object. ''' if opts.config: try: config = get_config(opts.config, config_type='Raw') except OSError as e: if e.errno == 2 and e.filename != _get_default_config_fn(): raise else: config = get_config_from_data(f'[{default_section_name}]') if opts.debug: warn(f'skipping non-existent config file {opts.config}') inject_opts(config, opts) # add a convenience method to get opts, which are stored in the default section: def opt(self, opt, default=None): try: return self.get(default_section_name, opt) except CP.NoOptionError: if default is not None: return default else: raise config.opt = MethodType(opt, config) return config def wrap_main(main, parser, args=sys.argv[1:]): ''' create config from config file and cmd-line args; set os.environ['DEBUG'] if -d; Call main(config); trap exceptions; if they occur, print an error message (with optional stack trace) and set exit value appropriately. ''' opts = parser.parse_args(args) config = _assemble_config(opts) if opts.debug: os.environ['DEBUG'] = 'True' warn(opts) if opts.silent and opts.verbose: warn('WARNING: both --silent and --verbose are set. Your output may be weird') try: rc = main(config) or 0 sys.exit(rc) except Exception as e: if 'DEBUG' in os.environ: import traceback traceback.print_exc() else: print('error: {} {}'.format(type(e), e)) sys.exit(1) def parser_config(parser, config): ''' Use sections/values from the config file to initialize an argparser. One cmd-line arg per config section; that is, each section contains all the args needed for a call to parser.add_argument() Example: names = ['-x', '--some-option'] section = {'type': int, 'action': 'store_true', etc} (Note: conflict) ''' for section in config.sections(): section_dict = to_dict(config, section) names = ['--'+section] if 'short_name' in section_dict: names.append('-'+section_dict.pop('short_name')) if 'type' in section_dict: actual_type = eval(section_dict['type']) section_dict['type'] = actual_type if 'default' in section_dict: section_dict['default'] = actual_type(section_dict['default']) if 'action' in section_dict: action = section_dict['action'] if action not in qw('store store_const store_true store_false append append_const count help version'): # action must be fully qualified name (module and class) of a class derived from argparse.Action modname, clsname = action.rsplit('.', 1) mod = import_module(modname) cls = getattr(mod, clsname) section_dict['action'] = cls parser.add_argument(*names, **section_dict) class FloatIntStrParserAction(argparse.Action): ''' Convert a string value to float, int, or str as possible. To be used as value to 'action' kwarg of argparse.parser.add_argument, eg: parser.add_argument('--some-value', action=FloatIntStrParserAction, ...) This is called one time for each value on command line. NOTE: use of this class as an Action precludes the use of the 'type' kwarg in add_argument! ''' def __init__(self, **kwargs): super(FloatIntStrParserAction, self).__init__(**kwargs) def __call__(self, parser, namespace, values, option_string): if self.type is not None: # coerce to that type setattr(namespace, self.dest, self.type(values)) return for t in [int, float, str]: # order important try: setattr(namespace, self.dest, t(values)) break except ValueError as e: pass else: parser.error("Error processing negc_var '{}'".format(values)) # should never get here if __name__ == '__main__': def getopts(opts_ini=None): import argparse parser = argparse.ArgumentParser() if opts_ini: opts_config = get_config(opts_ini) parser.add_argument('-v', action='store_true', help='verbose mode') parser.add_argument('-q', action='store_true', help='silent mode') parser.add_argument('-d', action='store_true', help='debugging flag') opts = parser.parse_args() if opts.debug: os.environ['DEBUG'] = 'True' print(ppjson(vars(opts))) return opts # ----------------------------------------------------------------------- opts_ini = os.path.abspath(os.path.join(os.path.dirname(__file__), 'opts.ini')) if not os.path.exists(opts_ini): warn('{}: no such file') opts_ini = None opts = getopts(opts_ini)

34.568421

115

0.622259

828

6,568

4.786232

0.300725

0.041635

0.060056

0.028766

0.079233

0.054504

0.038355

0.023719

0

0.00185

0.25944

6,568

189

116

34.751323

0.812911

0.25609

0

0.149123

0

0.124526

0.005057

0

1

0.078947

false

0.008772

0.105263

0

0.254386

0.026316

0

null

0

null

0

1

0

c612683f9e5f1b8a554762571a5c3752edd3f4c6

513

py

Python

api/management/commands/user_stats.py

kopf/zzzz

eeaebc24c7c2c290e167dcf1a74c18586a3a75a7

[ "BSD-3-Clause" ]

10

2019-04-16T18:08:55.000Z

2022-03-17T21:30:47.000Z

api/management/commands/user_stats.py

kopf/zzzz

eeaebc24c7c2c290e167dcf1a74c18586a3a75a7

[ "BSD-3-Clause" ]

3

2019-04-16T18:26:41.000Z

2021-06-10T21:22:13.000Z

api/management/commands/user_stats.py

kopf/zzzz

eeaebc24c7c2c290e167dcf1a74c18586a3a75a7

[ "BSD-3-Clause" ]

1

2021-05-23T07:10:04.000Z

#!/usr/bin/env python3 import json from django.core.management.base import BaseCommand from api.models import User class Command(BaseCommand): help = 'Print a dict of user status (number of users signed up per day) to stdout' def handle(self, *args, **options): stats = {} for user in User.objects.all(): date_str = user.date_joined.strftime('%Y-%m-%d') stats.setdefault(date_str, 0) stats[date_str] += 1 print(json.dumps(stats, indent=4))

27

86

0.639376

73

513

4.438356

0.739726

0.064815

0

0.010309

0.243665

513

18

87

28.5

0.824742

0.040936

0

0.164969

0

1

0.083333

false

0

0.25

0

0.5

0.083333

0

null

0

null

0

1

0