xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

25411c3e6ffef0272e0c6df5d33d98f728773bcf

40

py

Python

models/__init__.py

GuohongLi/face-alignment-pytorch

6864da12ae855f680a01371937a95cd51d0ce95f

[ "BSD-3-Clause" ]

59

2018-12-13T10:14:58.000Z

2021-11-23T19:16:58.000Z

models/__init__.py

GuohongLi/face-alignment-pytorch

6864da12ae855f680a01371937a95cd51d0ce95f

[ "BSD-3-Clause" ]

4

2019-02-11T10:16:06.000Z

2021-03-12T08:09:28.000Z

models/__init__.py

GuohongLi/face-alignment-pytorch

6864da12ae855f680a01371937a95cd51d0ce95f

[ "BSD-3-Clause" ]

13

2019-02-17T09:09:11.000Z

2021-02-21T18:24:59.000Z

from .fan import * from .resnet import *

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py

Python

LogIn/views.py

code-xD/Django-Projects

41537bb21cc392c84e55bb029cfa09a3c7574fad

[ "MIT" ]

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LogIn/views.py

code-xD/Django-Projects

41537bb21cc392c84e55bb029cfa09a3c7574fad

[ "MIT" ]

null

LogIn/views.py

code-xD/Django-Projects

41537bb21cc392c84e55bb029cfa09a3c7574fad

[ "MIT" ]

null

from django.contrib.auth.decorators import login_required from django.shortcuts import render # # @login_required def home(request): return render(request, 'LogIn/home.html')

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Python

build/simtrack/simtrack_nodes/catkin_generated/pkg.develspace.context.pc.py

i10/3DTrackingViaKinect

04121488e87a40306e5f466f744bf5683e64bf63

[ "BSD-3-Clause" ]

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build/simtrack/simtrack_nodes/catkin_generated/pkg.develspace.context.pc.py

i10/3DTrackingViaKinect

04121488e87a40306e5f466f744bf5683e64bf63

[ "BSD-3-Clause" ]

null

build/simtrack/simtrack_nodes/catkin_generated/pkg.develspace.context.pc.py

i10/3DTrackingViaKinect

04121488e87a40306e5f466f744bf5683e64bf63

[ "BSD-3-Clause" ]

null

# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/home/shams3049/catkin_ws/devel/include;/home/shams3049/catkin_ws/src/simtrack/simtrack_nodes/include".split(';') if "/home/shams3049/catkin_ws/devel/include;/home/shams3049/catkin_ws/src/simtrack/simtrack_nodes/include" != "" else [] PROJECT_CATKIN_DEPENDS = "image_transport;tf;geometry_msgs;message_runtime;interface;cv_bridge".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lsimtrack_nodes".split(';') if "-lsimtrack_nodes" != "" else [] PROJECT_NAME = "simtrack_nodes" PROJECT_SPACE_DIR = "/home/shams3049/catkin_ws/devel" PROJECT_VERSION = "0.1.0"

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py

Python

Modules/alignment.py

Gyfis/sequence-comparison

6506f784af4902645bb4562d507d465d57ed4366

[ "MIT" ]

null

Modules/alignment.py

Gyfis/sequence-comparison

6506f784af4902645bb4562d507d465d57ed4366

[ "MIT" ]

null

Modules/alignment.py

Gyfis/sequence-comparison

6506f784af4902645bb4562d507d465d57ed4366

[ "MIT" ]

null

from enum import Enum import numpy as np import pickle __author__ = 'Gyfis' class ScoringMatrix(Enum): BLOSUM62 = 1 PAM40 = 5 PAM80 = 6 PAM120 = 7 PAM250 = 8 def gap_opening_penalty(self): return { ScoringMatrix.BLOSUM62: -11, ScoringMatrix.PAM40: -10, ScoringMatrix.PAM80: -10, ScoringMatrix.PAM120: -11, ScoringMatrix.PAM250: -12, }[self] def gap_extension_penalty(self): return { ScoringMatrix.BLOSUM62: -4, ScoringMatrix.PAM40: -2, ScoringMatrix.PAM80: -2, ScoringMatrix.PAM120: -8, ScoringMatrix.PAM250: -4 }[self] def get_matrix(self): return pickle.load(open({ ScoringMatrix.BLOSUM62: 'scoring_matrices/blosum62.p', ScoringMatrix.PAM40: 'scoring_matrices/pam40.p', ScoringMatrix.PAM80: 'scoring_matrices/pam80.p', ScoringMatrix.PAM120: 'scoring_matrices/pam120.p', ScoringMatrix.PAM250: 'scoring_matrices/pam250.p', }[self], 'rb')) __default_alignment_width = 100 def find_global_alignment_scoring_matrix(sequence_a, sequence_b, scoring_matrix='pam120', alignment_print_width=__default_alignment_width): scoring_matrix = ScoringMatrix[scoring_matrix.upper()] return _find_global_alignment(sequence_a, sequence_b, scoring_matrix.gap_opening_penalty(), scoring_matrix.gap_extension_penalty(), scoring_matrix.get_matrix(), None, None, alignment_print_width=alignment_print_width) def find_global_alignment_scoring_matrix_gaps(sequence_a, sequence_b, scoring_matrix='pam120', go=-11, ge=-8, alignment_print_width=__default_alignment_width): scoring_matrix = ScoringMatrix[scoring_matrix.upper()] return _find_global_alignment(sequence_a, sequence_b, go, ge, scoring_matrix.get_matrix(), None, None, alignment_print_width=alignment_print_width) def find_global_alignment_match_mismatch(sequence_a, sequence_b, match, mismatch, go, ge, alignment_print_width=__default_alignment_width): return _find_global_alignment(sequence_a, sequence_b, go, ge, None, match, mismatch, alignment_print_width=alignment_print_width) def _find_global_alignment(sequence_a, sequence_b, go, ge, scoring_matrix, match, mismatch, alignment_print_width): infinity = -100000 lenA = len(sequence_a) + 1 lenB = len(sequence_b) + 1 table = np.zeros((lenA, lenB, 2), dtype=np.int) backtrace = np.zeros((lenA, lenB, 2, 3), dtype=np.int) table[0, 0, 0] = 0 table[0, 0, 1] = infinity for i in xrange(1, lenA): table[i, 0, 0] = infinity table[i, 0, 1] = go + ge * (i - 1) for j in xrange(1, lenB): table[0, j, 0] = infinity table[0, j, 1] = go + ge * (j - 1) for i in xrange(1, lenA): for j in xrange(1, lenB): si = i - 1 sj = j - 1 score = scoring_matrix[sequence_a[si], sequence_b[sj]] if match is None else match if sequence_a[si] == sequence_b[sj] else mismatch table[i, j, 0] = max(table[i - 1, j - 1, 0] + score, table[i - 1, j - 1, 1] + score) table[i, j, 1] = max(table[i - 1, j, 0] + go, table[i - 1, j, 1] + ge, table[i, j - 1, 0] + go, table[i, j - 1, 1] + ge) max_score = table[i - 1, j - 1, 0] + score backtrace[i, j, 0] = [i - 1, j - 1, 0] if table[i - 1, j - 1, 1] + score > max_score: backtrace[i, j, 0] = [i - 1, j - 1, 1] max_score = table[i - 1, j, 0] + go backtrace[i, j, 1] = [i - 1, j, 0] if table[i - 1, j, 1] + ge > max_score: max_score = table[i - 1, j, 1] + ge backtrace[i, j, 1] = [i - 1, j, 1] if table[i, j - 1, 0] + go > max_score: max_score = table[i, j - 1, 0] + go backtrace[i, j, 1] = [i, j - 1, 0] if table[i, j - 1, 1] + ge > max_score: backtrace[i, j, 1] = [i, j - 1, 1] i = lenA - 1 j = lenB - 1 k = 0 if table[i, j, 0] > table[i, j, 1] else 1 score = table[i, j, k] alignment = [] while (i, j) != (0, 0): pi, pj, pk = tuple(backtrace[i, j, k]) if pi == i: alignment.append(('-', sequence_b[j - 1])) elif pj == j: alignment.append((sequence_a[i - 1], '-')) else: alignment.append((sequence_a[i - 1], sequence_b[j - 1])) if pi == 0 and pj != 0: j = pj - 1 while j != 0: alignment.append(('-', sequence_b[j - 1])) j -= 1 break if pj == 0 and pi != 0: i = pi - 1 while i != 0: alignment.append((sequence_a[i - 1], '-')) i -= 1 break if pi == 0 and pj == 0: break i, j, k = pi, pj, pk alignment = alignment[::-1] curent_print_width = 0 s_alignment = '' while curent_print_width < len(alignment): s_alignment += '<br><br></br></br>' + ''.join([a for a, b in alignment[curent_print_width:curent_print_width + alignment_print_width]]) s_alignment += '<br></br>' + ''.join([b for a, b in alignment[curent_print_width:curent_print_width + alignment_print_width]]) curent_print_width += alignment_print_width return alignment, s_alignment, score def find_local_alignment_scoring_matrix(sequence_a, sequence_b, scoring_matrix='pam120', alignment_print_width=__default_alignment_width): scoring_matrix = ScoringMatrix[scoring_matrix.upper()] return _find_local_alignment(sequence_a, sequence_b, scoring_matrix.gap_opening_penalty(), scoring_matrix.gap_extension_penalty(), scoring_matrix.get_matrix(), None, None, alignment_print_width=alignment_print_width) def find_local_alignment_scoring_matrix_gaps(sequence_a, sequence_b, scoring_matrix='pam120', go=-11, ge=-8, alignment_print_width=__default_alignment_width): scoring_matrix = ScoringMatrix[scoring_matrix.upper()] return _find_local_alignment(sequence_a, sequence_b, go, ge, scoring_matrix.get_matrix(), None, None, alignment_print_width=alignment_print_width) def find_local_alignment_match_mismatch(sequence_a, sequence_b, match, mismatch, go, ge, alignment_print_width=__default_alignment_width): return _find_local_alignment(sequence_a, sequence_b, go, ge, None, match, mismatch, alignment_print_width=alignment_print_width) def _find_local_alignment(sequence_a, sequence_b, go, ge, scoring_matrix, match, mismatch, alignment_print_width): infinity = -100000 lenA = len(sequence_a) + 1 lenB = len(sequence_b) + 1 table = np.zeros((lenA, lenB, 2), dtype=np.int) backtrace = np.zeros((lenA, lenB, 2, 3), dtype=np.int) table[0, 0, 0] = 0 table[0, 0, 1] = infinity for i in xrange(1, lenA): table[i, 0, 0] = infinity table[i, 0, 1] = go + ge * (i - 1) for j in xrange(1, lenB): table[0, j, 0] = infinity table[0, j, 1] = go + ge * (j - 1) for i in xrange(1, lenA): for j in xrange(1, lenB): si = i - 1 sj = j - 1 score = scoring_matrix[sequence_a[si], sequence_b[sj]] if match is None else match if sequence_a[si] == sequence_b[sj] else mismatch table[i, j, 0] = max(table[i - 1, j - 1, 0] + score, table[i - 1, j - 1, 1] + score, 0) table[i, j, 1] = max(table[i - 1, j, 0] + go, table[i - 1, j, 1] + ge, table[i, j - 1, 0] + go, table[i, j - 1, 1] + ge, 0) max_score = table[i - 1, j - 1, 0] + score backtrace[i, j, 0] = [i - 1, j - 1, 0] if table[i - 1, j - 1, 1] + score > max_score: backtrace[i, j, 0] = [i - 1, j - 1, 1] max_score = table[i - 1, j, 0] + go backtrace[i, j, 1] = [i - 1, j, 0] if table[i - 1, j, 1] + ge > max_score: max_score = table[i - 1, j, 1] + ge backtrace[i, j, 1] = [i - 1, j, 1] if table[i, j - 1, 0] + go > max_score: max_score = table[i, j - 1, 0] + go backtrace[i, j, 1] = [i, j - 1, 0] if table[i, j - 1, 1] + ge > max_score: backtrace[i, j, 1] = [i, j - 1, 1] max_score = -1 max_i = 0 max_j = 0 max_k = 0 for i in xrange(len(sequence_a)): for j in xrange(len(sequence_b)): if max_score < table[i, j, 0]: max_score = table[i, j, 0] max_i, max_j, max_k = i, j, 0 if max_score < table[i, j, 1]: max_score = table[i, j, 1] max_i, max_j, max_k = i, j, 1 i = max_i j = max_j k = max_k alignment = [] while table[i, j, k] > 0: pi, pj, pk = tuple(backtrace[i, j, k]) if pk == 0: alignment.append((sequence_a[i - 1], sequence_b[j - 1])) elif pi == i: alignment.append(('-', sequence_b[j - 1])) else: alignment.append((sequence_a[i - 1], '-')) if pi == 0 and pj != 0: while pj != 0: alignment.append(('-', sequence_b[pj - 1])) pj -= 1 break if pj == 0 and pi != 0: while pi != 0: alignment.append((sequence_a[pi - 1], '-')) pi -= 1 break if pi == 0 and pj == 0: break i, j, k = pi, pj, pk alignment = alignment[::-1] curent_print_width = 0 s_alignment = '' while curent_print_width < len(alignment): s_alignment += '<br><br></br></br>' + ''.join([a for a, b in alignment[curent_print_width:curent_print_width + alignment_print_width]]) s_alignment += '<br></br>' + ''.join([b for a, b in alignment[curent_print_width:curent_print_width + alignment_print_width]]) + '\n' curent_print_width += alignment_print_width return alignment, s_alignment, max_score

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Python

forms.py

drogomarks/heimdallv2

960260d0d238811bc7b4e0402aaa2498ee0bdb30

[ "MIT" ]

null

forms.py

drogomarks/heimdallv2

960260d0d238811bc7b4e0402aaa2498ee0bdb30

[ "MIT" ]

null

forms.py

drogomarks/heimdallv2

960260d0d238811bc7b4e0402aaa2498ee0bdb30

[ "MIT" ]

null

from flask_wtf import Form from wtforms import StringField, TextField, IntegerField, SubmitField, SelectField, validators class GetuserForm(Form): customer_id = IntegerField('Customer ID') domain = TextField('Domain') user = TextField('User') mbx_type = SelectField(u'Mailbox Type', choices=[('rs', 'rs'), ('ex', 'ex')]) class GetdomainForm(Form): customer_id = IntegerField('Customer ID') domain = TextField('Domain') mbx_type = SelectField(u'Mailbox Type', choices=[('rs', 'rs'), ('ex', 'ex')]) class GetallForm(Form): customer_id = IntegerField('Customer ID') domain = TextField('Domain') mbx_type = SelectField(u'Mailbox Type', choices=[('rs', 'rs'), ('ex', 'ex')]) class PutuservexForm(Form): customer_id = IntegerField('Customer ID') domain = TextField('Domain') user = TextField('User') mbx_type = SelectField(u'Mailbox Type', choices=[('rs', 'rs'), ('ex', 'ex')]) user_vex_val = SelectField(u'Value', choices=[('true', 'true'), ('false', 'false')]) class PutdomainvexForm(Form): customer_id = IntegerField('Customer ID') domain = TextField('Domain') mbx_type = SelectField(u'Mailbox Type', choices=[('rs', 'rs'), ('ex', 'ex')]) domain_vex_val = SelectField(u'Value', choices=[('true', 'true'), ('false', 'false')])

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231

py

Python

frontend/views.py

TheLastRKoch/Emmily.API

f7ac104dc7d78551d29ea201732d3f66c71fa02f

[ "TCP-wrappers" ]

null

frontend/views.py

TheLastRKoch/Emmily.API

f7ac104dc7d78551d29ea201732d3f66c71fa02f

[ "TCP-wrappers" ]

3

2021-01-01T17:56:29.000Z

2021-04-02T03:15:24.000Z

frontend/views.py

TheLastRKoch/Emmily.API

f7ac104dc7d78551d29ea201732d3f66c71fa02f

[ "TCP-wrappers" ]

null

from django.shortcuts import render, redirect, get_object_or_404 from django.db.models import Q from django.http import HttpResponse from django.http import JsonResponse def index(request): return render(request,'index.html')

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66e08155d6c17746aed86591bd2b19db0bd06982

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py

Python

commons/graph/graph_if.py

btc-ag/revengtools

d58680ef7d6bdc8ef518860d5d13a5acc0d01758

[ "Apache-2.0" ]

2

2019-07-15T14:59:59.000Z

2022-01-18T14:23:54.000Z

commons/graph/graph_if.py

btc-ag/revengtools

d58680ef7d6bdc8ef518860d5d13a5acc0d01758

[ "Apache-2.0" ]

10

2018-05-03T13:25:07.000Z

2021-06-25T15:14:55.000Z

commons/graph/graph_if.py

btc-ag/revengtools

d58680ef7d6bdc8ef518860d5d13a5acc0d01758

[ "Apache-2.0" ]

1

2018-05-02T13:59:27.000Z

# -*- coding: UTF-8 -*- ''' Contains basic interfaces for graph data structures. @todo: This is planned to be replaced by pygraph Created on 05.10.2010 @author: SIGIESEC ''' class Node(object): def get_id(self): raise NotImplementedError(self.__class__) class LabelledNode(Node): def get_label(self): raise NotImplementedError(self.__class__) def set_label(self, _label): raise NotImplementedError(self.__class__) class Edge(object): """ Implementations of Edge should implement __hash__, __eq__, __ne__ in addition, and __lt__, __gt__ if sorting should be supported. """ def get_from_node(self): raise NotImplementedError(self.__class__) def get_to_node(self): raise NotImplementedError(self.__class__) def node_set(self): raise NotImplementedError(self.__class__) def node_tuple(self): raise NotImplementedError(self.__class__) class BasicGraph(object): def edges(self): raise NotImplementedError(self.__class__) def nodeitems_iter(self): raise NotImplementedError(self.__class__) def nodes_raw(self): raise NotImplementedError(self.__class__) def node_names_iter(self): raise NotImplementedError(self.__class__) def node_names(self): raise NotImplementedError(self.__class__) def node_count(self): raise NotImplementedError(self.__class__) def edge_count(self): raise NotImplementedError(self.__class__) class MutableGraph(BasicGraph): """ A generic interface for manipulating graphs. """ # TODO A "Node" class should be used for __nodes # TODO An "Edge" class should be used for __edges, with a default "SimpleEdge" implementation def add_edge(self, _edge): """ Adds an edge to the graph. The required type of <edge> depends on the implementation. """ raise NotImplementedError(self.__class__) def add_node(self, _node, _allowChange = False): raise NotImplementedError(self.__class__) def change_node(self, node): raise NotImplementedError(self.__class__) def del_node(self, _node): raise NotImplementedError(self.__class__)

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py

Python

deeprobust/image/netmodels/__init__.py

smaranjitghose/DeepRobust

91f2b922e61a3fa0987e37b7ea6c1cd9150c1c81

[ "MIT" ]

647

2020-02-08T02:13:21.000Z

2022-03-31T07:44:00.000Z

deeprobust/image/netmodels/__init__.py

qilong-zhang/DeepRobust

276a7048aded2cf3a190d3851ffd4587b7d1dd49

[ "MIT" ]

77

2020-03-21T11:27:30.000Z

2022-03-23T10:55:53.000Z

deeprobust/image/netmodels/__init__.py

qilong-zhang/DeepRobust

276a7048aded2cf3a190d3851ffd4587b7d1dd49

[ "MIT" ]

139

2020-03-04T00:25:12.000Z

2022-03-21T15:45:29.000Z

#__init__.py from deeprobust.image.netmodels import CNN from deeprobust.image.netmodels import resnet from deeprobust.image.netmodels import YOPOCNN from deeprobust.image.netmodels import train_model __all__ = ['CNNmodel','resnet','YOPOCNN','train_model']

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py

Python

tests/test_verification.py

konradhalas/mimid

fca4b61c5ee9e5bbb2e60f9a3fcc3593a30333d2

[ "MIT" ]

11

2019-06-12T19:33:13.000Z

2021-07-12T01:20:55.000Z

tests/test_verification.py

konradhalas/mimid

fca4b61c5ee9e5bbb2e60f9a3fcc3593a30333d2

[ "MIT" ]

1

2021-08-29T15:27:17.000Z

tests/test_verification.py

konradhalas/mimid

fca4b61c5ee9e5bbb2e60f9a3fcc3593a30333d2

[ "MIT" ]

null

import pytest from mimid import mock, every, WrongNumberOfCallsException, verify, gt, NotMatchingSignatureException from tests.targets import A, function from tests.utils import not_raises def test_mock_method_verify_raises_exception_when_method_not_called(): obj = mock(A) every(obj.method).returns(2) with pytest.raises(WrongNumberOfCallsException): verify(obj.method).called() def test_mock_method_verify_does_not_raise_exception_when_method_called(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with not_raises(WrongNumberOfCallsException): verify(obj.method).called() def test_mock_method_verify_does_not_raise_exception_when_method_called_with_matching_arguments(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with not_raises(WrongNumberOfCallsException): verify(obj.method).with_args(1).called() def test_mock_method_verify_does_not_raise_exception_when_method_called_with_matching_kwargs_arguments(): obj = mock(A) every(obj.method).returns(2) obj.method(param=1) with not_raises(WrongNumberOfCallsException): verify(obj.method).with_args(param=1).called() def test_mock_method_verify_raises_exception_when_method_called_with_non_matching_arguments(): obj = mock(A) every(obj.method).returns(2) obj.method(2) with pytest.raises(WrongNumberOfCallsException): verify(obj.method).with_args(1).called() def test_mock_method_verify_raises_exception_when_method_called_different_number_of_times(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with pytest.raises(WrongNumberOfCallsException): verify(obj.method).called(times=2) def test_mock_method_verify_does_not_raise_exception_when_method_called_defined_number_of_times(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with not_raises(WrongNumberOfCallsException): verify(obj.method).called(times=1) def test_mock_method_verify_does_not_raise_exception_when_method_called_defined_number_of_times_with_matching_args(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with not_raises(WrongNumberOfCallsException): verify(obj.method).with_args(1).called(times=1) def test_mock_method_verify_raises_exception_when_method_called_different_number_of_times_with_matching_args(): obj = mock(A) every(obj.method).returns(2) obj.method(1) with pytest.raises(WrongNumberOfCallsException): verify(obj.method).with_args(1).called(times=2) def test_mock_function_verify_raises_exception_when_method_not_called(): func = mock(function) every(func).returns(2) with pytest.raises(WrongNumberOfCallsException): verify(func).called() def test_mock_function_verify_does_not_raise_exception_when_method_called(): func = mock(function) every(func).returns(2) func(1) with not_raises(WrongNumberOfCallsException): verify(func).called() def test_mock_function_verify_does_not_raise_exception_when_method_called_with_matching_arguments(): func = mock(function) every(func).returns(2) func(1) with not_raises(WrongNumberOfCallsException): verify(func).with_args(1).called() def test_mock_function_verify_raises_exception_when_method_called_with_non_matching_arguments(): func = mock(function) every(func).returns(2) func(2) with pytest.raises(WrongNumberOfCallsException): verify(func).with_args(1).called() def test_mock_method_verify_does_not_raise_exception_when_verify_with_matching_matcher(): obj = mock(A) every(obj.method).returns(2) obj.method(1) obj.method(1) with not_raises(WrongNumberOfCallsException): verify(obj.method).called(times=gt(1)) def test_verify_should_raises_exception_when_called_with_non_mock_object(): with pytest.raises(TypeError): verify(1) def test_mock_function_verify_raises_exception_when_args_does_not_match_signature(): func = mock(function) with pytest.raises(NotMatchingSignatureException): verify(func).with_args(other_param=1)

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null

0

6

b0faf897a273ce7be8f0caba7ee6d73bba9e68ed

179

py

Python

delivery/ext/migrate.py

JhonatasMenezes/flask_delivery

a95b1016f5be9ad5f4a24d7d4a34a9130f3e5f6e

[ "MIT" ]

2

2021-10-07T21:41:18.000Z

2021-11-23T23:17:40.000Z

delivery/ext/migrate.py

JhonatasMenezes/flask_delivery

a95b1016f5be9ad5f4a24d7d4a34a9130f3e5f6e

[ "MIT" ]

null

delivery/ext/migrate.py

JhonatasMenezes/flask_delivery

a95b1016f5be9ad5f4a24d7d4a34a9130f3e5f6e

[ "MIT" ]

null

from flask_migrate import Migrate from delivery.ext.db import db from delivery.ext.db import models # noqa migrate = Migrate() def init_app(app): migrate.init_app(app, db)

17.9

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null

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1

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6

b0231b2de3805aa67dc8dcc91fcb66fb803ae4a3

32

py

Python

petrophys/__init__.py

smiles21/petrophys

03ccf2c65cd472013596b293cb2daaa95a1a05a6

[ "MIT" ]

null

petrophys/__init__.py

smiles21/petrophys

03ccf2c65cd472013596b293cb2daaa95a1a05a6

[ "MIT" ]

null

petrophys/__init__.py

smiles21/petrophys

03ccf2c65cd472013596b293cb2daaa95a1a05a6

[ "MIT" ]

null

from .vshale import calc_vshale

16

31

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1

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6

b04de4dad239bea4d94577872ed653744b169c68

3,283

py

Python

migrations/versions/5137b683ddfb_.py

Spotimania/Spotimania

184b45b27ac717909b96ce6f958926a08cb0aa64

[ "MIT" ]

2

2021-01-26T06:03:00.000Z

2021-07-17T07:09:23.000Z

migrations/versions/5137b683ddfb_.py

Spotimania/Spotimania

184b45b27ac717909b96ce6f958926a08cb0aa64

[ "MIT" ]

8

2020-07-20T07:31:17.000Z

2020-07-28T06:49:34.000Z

migrations/versions/5137b683ddfb_.py

Spotimania/Spotimania

184b45b27ac717909b96ce6f958926a08cb0aa64

[ "MIT" ]

null

"""empty message Revision ID: 5137b683ddfb Revises: 4655bcb421e2 Create Date: 2020-05-07 17:56:20.916651 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '5137b683ddfb' down_revision = '4655bcb421e2' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('playlist_song', sa.Column('date_created', sa.DateTime(), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('id', sa.Integer(), nullable=False), sa.Column('playlistId', sa.Integer(), nullable=True), sa.Column('songId', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['playlistId'], ['playlist.id'], ), sa.ForeignKeyConstraint(['songId'], ['song.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('individualResults', sa.Column('date_created', sa.DateTime(), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('id', sa.Integer(), nullable=False), sa.Column('answerArtist', sa.String(length=100), nullable=True), sa.Column('answerSong', sa.String(length=100), nullable=True), sa.Column('isAnswerArtistCorrect', sa.Boolean(), nullable=True), sa.Column('isAnswerSongCorrect', sa.Boolean(), nullable=True), sa.Column('resultId', sa.Integer(), nullable=True), sa.Column('songId', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['resultId'], ['results.id'], ), sa.ForeignKeyConstraint(['songId'], ['song.id'], ), sa.PrimaryKeyConstraint('id') ) op.drop_table('playlist__song') op.drop_table('individual_results') # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('individual_results', sa.Column('date_created', sa.DATETIME(), nullable=True), sa.Column('date_modified', sa.DATETIME(), nullable=True), sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('answerArtist', sa.VARCHAR(length=100), nullable=True), sa.Column('answerSong', sa.VARCHAR(length=100), nullable=True), sa.Column('isAnswerArtistCorrect', sa.BOOLEAN(), nullable=True), sa.Column('isAnswerSongCorrect', sa.BOOLEAN(), nullable=True), sa.Column('resultId', sa.INTEGER(), nullable=True), sa.Column('songId', sa.INTEGER(), nullable=True), sa.CheckConstraint('"isAnswerArtistCorrect" IN (0, 1)'), sa.CheckConstraint('"isAnswerSongCorrect" IN (0, 1)'), sa.ForeignKeyConstraint(['resultId'], ['results.id'], ), sa.ForeignKeyConstraint(['songId'], ['song.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('playlist__song', sa.Column('date_created', sa.DATETIME(), nullable=True), sa.Column('date_modified', sa.DATETIME(), nullable=True), sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('playlistId', sa.INTEGER(), nullable=True), sa.Column('songId', sa.INTEGER(), nullable=True), sa.ForeignKeyConstraint(['playlistId'], ['playlist.id'], ), sa.ForeignKeyConstraint(['songId'], ['song.id'], ), sa.PrimaryKeyConstraint('id') ) op.drop_table('individualResults') op.drop_table('playlist_song') # ### end Alembic commands ###

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1

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6

b06c3bcd2063f2ea3feb47b5e679ae523215dcec

57,410

py

Python

imagersite/imager_images/tests.py

chaitanyanarukulla/django-imager

0c426729605936ecf4deea837295c0b0e22d7861

[ "MIT" ]

2

2018-05-29T23:39:40.000Z

2018-06-25T22:31:13.000Z

imagersite/imager_images/tests.py

chaitanyanarukulla/django-imager

0c426729605936ecf4deea837295c0b0e22d7861

[ "MIT" ]

15

2017-11-22T00:22:01.000Z

2017-12-14T21:11:13.000Z

imagersite/imager_images/tests.py

chaitanyanarukulla/django-imager

0c426729605936ecf4deea837295c0b0e22d7861

[ "MIT" ]

3

2018-01-12T08:21:46.000Z

2018-05-20T05:27:33.000Z

"""Tests for the Photo and Album models.""" from django.conf import settings from django.contrib.auth.models import AnonymousUser from django.core.files.uploadedfile import SimpleUploadedFile from django.forms.models import modelform_factory from django.http import Http404 from django.test import override_settings, RequestFactory, TestCase from django.urls import reverse_lazy from imager_images.models import Photo, Album, AlbumForm from imager_profile.tests import UserFactory from datetime import datetime import factory import os class PhotoFactory(factory.django.DjangoModelFactory): """Factory for fake Photo.""" class Meta: """Meta.""" model = Photo image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) title = factory.Faker('word') description = factory.Faker('sentence') published = 'PUBLIC' class AlbumFactory(factory.django.DjangoModelFactory): """Factory for fake Album.""" class Meta: """Meta.""" model = Album title = factory.Faker('word') description = factory.Faker('sentence') published = 'PUBLIC' class PhotoAlbumTests(TestCase): """Tests for the imager_profile module.""" @classmethod @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photos")) def setUpClass(cls): """Add one minimal user to the database.""" super(PhotoAlbumTests, cls).setUpClass() os.system('mkdir {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photos') )) user = UserFactory() user.set_password(factory.Faker('password')) user.save() cls.user = user photo = PhotoFactory(title='wedding', description='lovely wedding', published='PRIVATE', user=user) photo.save() album1 = AlbumFactory(title='first', description='this is first', published='PRIVATE', user=user) album1.save() for _ in range(10): photo = PhotoFactory(user=user) photo.save() album1.photos.add(photo) album2 = AlbumFactory(title='second', user=user) album2.save() for _ in range(20): photo = PhotoFactory(user=user) photo.save() album2.photos.add(photo) for _ in range(5): photo = PhotoFactory(user=user) photo.save() album1.photos.add(photo) album2.photos.add(photo) user = UserFactory() user.set_password(factory.Faker('password')) user.save() photo = PhotoFactory(title='sport', user=user) photo.save() album1 = AlbumFactory(title='sports', user=user) album1.save() @classmethod def tearDownClass(cls): """Remove the test directory.""" super(PhotoAlbumTests, cls).tearDownClass() os.system('rm -rf {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photos') )) def test_photo_to_string_is_correct(self): """Test that the __str__ method returns the photo title.""" one_photo = Photo.objects.get(title='wedding') self.assertEqual(str(one_photo), 'wedding') def test_album_to_string_is_correct(self): """Test that the __str__ method returns the album title.""" one_album = Album.objects.get(title='first') self.assertEqual(str(one_album), 'first') def test_all_photos_are_added_to_the_database(self): """Test that all created photos are added to the database.""" self.assertEqual(Photo.objects.count(), 37) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photos")) def test_all_photos_are_added_to_the_media_directory(self): """Test that all created photos are added to the media directory.""" path = os.path.join(settings.MEDIA_ROOT, 'images') files = [name for name in os.listdir(path) if name.endswith('.jpg')] self.assertEqual(len(files), 37) def test_photos_are_added_to_an_album(self): """Test that all created photos are added to the database.""" photos = Photo.objects.filter(albums__title='first').count() self.assertEqual(photos, 15) def test_photo_has_user(self): """Test that the photo has a user.""" one_photo = Photo.objects.get(title='wedding') self.assertEqual(one_photo.user, self.user) def test_photo_has_description(self): """Test that the photo has a description.""" one_photo = Photo.objects.get(title='wedding') self.assertEqual(one_photo.description, 'lovely wedding') def test_photo_has_published(self): """Test that the photo has a published.""" one_photo = Photo.objects.get(title='wedding') self.assertEqual(one_photo.published, 'PRIVATE') def test_photo_has_date_uploaded(self): """Test that the photo has a date-uploaded that is now by default.""" one_photo = Photo.objects.get(title='wedding') now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_photo.date_uploaded.strftime('%x %X')[:2], now) def test_photo_has_date_modified(self): """Test that the photo has a date-modified that is now by default.""" one_photo = Photo.objects.get(title='wedding') now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_photo.date_modified.strftime('%x %X')[:2], now) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photos")) def test_photo_modified_changes_modified_date_not_uploaded_date(self): """Test the uploaded date does not change when a photo is modified.""" user = UserFactory() user.set_password(factory.Faker('password')) user.save() photo = PhotoFactory(user=user) photo.save() created_upload_date = photo.date_uploaded created_modify_date = photo.date_modified photo.title = 'fun' photo.save() self.assertEqual(photo.date_uploaded, created_upload_date) self.assertNotEqual(photo.date_modified, created_modify_date) def test_private_photo_has_no_date_published(self): """Test that private photo has date-published that is None.""" one_photo = Photo.objects.get(title='wedding') self.assertIsNone(one_photo.date_published) def test_public_photo_has_date_published(self): """Test that public photo has a date-published.""" one_photo = Photo.objects.filter(albums__title='second').first() now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_photo.date_published.strftime('%x %X')[:2], now) def test_all_albums_are_added_to_the_database(self): """Test that all created albums are added to the database.""" self.assertEqual(Album.objects.count(), 3) def test_album_has_user(self): """Test that the album has a user.""" one_album = Album.objects.get(title='first') self.assertEqual(one_album.user, self.user) def test_album_has_description(self): """Test that the album has a description.""" one_album = Album.objects.get(title='first') self.assertEqual(one_album.description, 'this is first') def test_album_has_published(self): """Test that the album has a published.""" one_album = Album.objects.get(title='first') self.assertEqual(one_album.published, 'PRIVATE') def test_album_has_date_uploaded(self): """Test that the album has a date-uploaded that is now by default.""" one_album = Album.objects.get(title='first') now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_album.date_uploaded.strftime('%x %X')[:2], now) def test_album_has_date_modified(self): """Test that the album has a date-modified that is now by default.""" one_album = Album.objects.get(title='first') now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_album.date_modified.strftime('%x %X')[:2], now) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photos")) def test_album_modified_changes_modified_date_not_uploaded_date(self): """Test the uploaded date does not change when a album is modified.""" user = UserFactory() user.set_password(factory.Faker('password')) user.save() album = AlbumFactory(user=user) album.save() created_upload_date = album.date_uploaded created_modify_date = album.date_modified album.title = 'fun' album.save() self.assertEqual(album.date_uploaded, created_upload_date) self.assertNotEqual(album.date_modified, created_modify_date) def test_private_album_has_no_date_published(self): """Test that private album has a date-published that is None.""" one_album = Album.objects.get(title='first') self.assertIsNone(one_album.date_published) def test_public_album_has_date_published(self): """Test that public album has a date-published.""" one_album = Album.objects.get(title='second') now = datetime.now().strftime('%x %X')[:2] self.assertEqual(one_album.date_published.strftime('%x %X')[:2], now) def test_album_form_photos_are_limited_to_current_users(self): """Test album form photos are limited to current users.""" form = AlbumForm(username=self.user.username) self.assertEqual(form.fields['photos'].queryset.count(), 36) """Unit tests for the Photo and Album view classes.""" class PhotoAlbumViewTests(TestCase): """Tests for the views of imager_images.""" @classmethod @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_view")) def setUpClass(cls): """Add one user to the database.""" super(PhotoAlbumViewTests, cls).setUpClass() os.system('mkdir {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photo_view') )) cls.request = RequestFactory() user = UserFactory(username='bob') user.set_password(factory.Faker('password')) user.save() cls.bob = user album = AlbumFactory(title='bob first', description='this is first', published='PRIVATE', user=user) album.save() for _ in range(5): photo = PhotoFactory(user=user, published='PRIVATE') photo.save() album.photos.add(photo) album = AlbumFactory(title='bob second', description='this is second', published='PUBLIC', user=user) album.save() for _ in range(10): photo = PhotoFactory(user=user) photo.save() album.photos.add(photo) user = UserFactory(username='rob') user.set_password(factory.Faker('password')) user.save() album = AlbumFactory(title='rob first', description='this is first', published='PRIVATE', user=user) album.save() for _ in range(3): photo = PhotoFactory(user=user, published='PRIVATE') photo.save() album.photos.add(photo) album = AlbumFactory(title='rob second', description='this is second', published='PUBLIC', user=user) album.save() for _ in range(13): photo = PhotoFactory(user=user) photo.save() album.photos.add(photo) @classmethod def tearDownClass(cls): """Remove the test directory.""" super(PhotoAlbumViewTests, cls).tearDownClass() os.system('rm -rf {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photo_view'))) def test_library_view_logged_in_displays_all_photos_and_albums(self): """Test that library_view displays all logged in user's things.""" from imager_images.views import LibraryView request = self.request.get('') request.user = self.bob view = LibraryView(request=request) response = view.get(request) response.render() image_count = response.content.count(b'<img') self.assertEqual(image_count, 6) def test_library_view_get_queryset_list_all_users_albums(self): """Test library view get queryset list all users albums.""" from imager_images.views import LibraryView view = LibraryView() albums = view.get_queryset(self.bob) self.assertEqual(albums.count(), 2) def test_library_view_logged_in_context_has_all_photos_and_albums(self): """Test libraryview returns context data with all logged in user's photos and albums.""" from imager_images.views import LibraryView request = self.request.get('') request.user = self.bob view = LibraryView(request=request, object_list='') data = view.get_context_data() self.assertEqual(len(data['albums']), 2) self.assertEqual(len(data['photos']), 4) self.assertIn('default_cover', data) def test_library_view_get_context_data_non_int_album_page_has_page1(self): """Test library view get_context_data non int album page has page1.""" from imager_images.views import LibraryView request = self.request.get('', {'album_page': 'bobspage'}) request.user = self.bob view = LibraryView(object_list='', request=request) data = view.get_context_data() self.assertEqual(data['albums'].number, 1) def test_library_view_get_context_data_invalid_album_page_has_last_page(self): """Test library view get_context_data invalid album page has last page.""" from imager_images.views import LibraryView request = self.request.get('', {'album_page': 1000000000}) request.user = self.bob view = LibraryView(object_list='', request=request) data = view.get_context_data() page = data['albums'] self.assertEqual(page.number, page.paginator.num_pages) def test_library_view_get_context_data_non_int_photo_page_has_page1(self): """Test library view get_context_data non int photo page has page1.""" from imager_images.views import LibraryView request = self.request.get('', {'photo_page': 'bobspage'}) request.user = self.bob view = LibraryView(object_list='', request=request) data = view.get_context_data() self.assertEqual(data['photos'].number, 1) def test_library_view_get_context_data_invalid_photo_page_has_last_page(self): """Test library view get_context_data invalid photo page has last page.""" from imager_images.views import LibraryView request = self.request.get('', {'photo_page': 1000000000}) request.user = self.bob view = LibraryView(object_list='', request=request) data = view.get_context_data() page = data['photos'] self.assertEqual(page.number, page.paginator.num_pages) def test_album_gallery_view_has_all_public_albums(self): """Test that the album_gallery_view has all public albums.""" from imager_images.views import AlbumGalleryView view = AlbumGalleryView(object_list='') data = view.get_context_data() self.assertIn('albums', data) self.assertIn('default_cover', data) def test_photo_detail_view_non_public_photo_raises_404(self): """Test that a non public photo for photo_detail_view raises a 404.""" from imager_images.views import PhotoDetailView photo = Photo.objects.filter(published='PRIVATE').first() request = self.request.get('') request.user = AnonymousUser() view = PhotoDetailView(request=request, kwargs={'id': photo.id}) with self.assertRaises(Http404): view.get_object() def test_photo_detail_view_valid_id_gets_correct_photo(self): """Test that photo_detail_view for valid id has correct photo.""" from imager_images.views import PhotoDetailView photo = Photo.objects.filter(published='PUBLIC').first() request = self.request.get('') request.user = AnonymousUser() view = PhotoDetailView(request=request, kwargs={'id': photo.id}) response = view.get_object() self.assertEqual(response.image, photo.image) def test_album_detail_view_non_public_album_raises_404(self): """Test that a non public album for album_detail_view raises a 404.""" from imager_images.views import AlbumDetailView album = Album.objects.filter(published='PRIVATE').first() request = self.request.get('') request.user = AnonymousUser() view = AlbumDetailView(request=request, kwargs={'id': album.id}) with self.assertRaises(Http404): view.get_object() def test_album_detail_view_valid_id_gets_correct_album(self): """Test that album_detail_view for valid id has correct album.""" from imager_images.views import AlbumDetailView album = Album.objects.filter(published='PUBLIC').first() request = self.request.get('') request.user = AnonymousUser() view = AlbumDetailView(request=request, kwargs={'id': album.id}) response = view.get_object() self.assertEqual(response.photos, album.photos) def test_album_detail_view_get_context_data_has_album(self): """Test that the album detail_view has album.""" from imager_images.views import AlbumDetailView request = self.request.get('') view = AlbumDetailView(object=self.bob.albums.first(), request=request) data = view.get_context_data() self.assertIn('view', data) self.assertIn('default_cover', data) self.assertIn('photos_page', data) def test_album_detail_view_get_context_data_non_int_page_has_page1(self): """Test album detail view get_context_data non int page has page1.""" from imager_images.views import AlbumDetailView request = self.request.get('', {'page': 'bobspage'}) view = AlbumDetailView(object=self.bob.albums.first(), request=request) data = view.get_context_data() self.assertEqual(data['photos_page'].number, 1) def test_album_detail_view_get_context_data_invalid_page_has_last_page(self): """Test album detail view get_context_data invalid page has last page.""" from imager_images.views import AlbumDetailView request = self.request.get('', {'page': 1000000000}) view = AlbumDetailView(object=self.bob.albums.first(), request=request) data = view.get_context_data() page = data['photos_page'] self.assertEqual(page.number, page.paginator.num_pages) def test_photo_create_view_logged_in_has_upload_form(self): """Test that photo create view has upload form.""" from imager_images.views import PhotoCreateView request = self.request.get('') request.user = self.bob view = PhotoCreateView(request=request) response = view.get(request) response.render() self.assertIn(b'Upload New Photo', response.content) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_view")) def test_photo_create_view_post_logged_in_create_new_photo(self): """Test that photo create view post login creates new photo.""" from imager_images.views import PhotoCreateView request = self.request.post('') request.user = self.bob request.POST = {'title': 'test', 'description': 'testing', 'published': 'PRIVATE'} image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) request._files = {'image': image} view = PhotoCreateView(request=request) view.post(request) photo = Photo.objects.get(title='test') self.assertIsNotNone(photo) self.assertEqual(photo.description, 'testing') @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_view")) def test_photo_create_view_form_valid_sets_current_user_as_user(self): """Test photo create view form valid sets current user as user.""" from imager_images.views import PhotoCreateView form_class = modelform_factory(Photo, fields=['title', 'description', 'image', 'published']) request = self.request.post('') request.user = self.bob view = PhotoCreateView(request=request) image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) files = {'image': image} data = {'title': 'test2', 'description': 'testing2', 'published': 'PRIVATE'} form = form_class(data=data, files=files) form.is_valid() view.form_valid(form) self.assertIs(self.bob, form.instance.user) def test_album_create_view_logged_in_has_upload_form(self): """Test that album create view has upload form.""" from imager_images.views import AlbumCreateView request = self.request.get('') request.user = self.bob view = AlbumCreateView(request=request) response = view.get(request) response.render() self.assertIn(b'Create New Album', response.content) def test_album_create_view_post_logged_in_create_new_album(self): """Test that album create view post login creates new album.""" from imager_images.views import AlbumCreateView request = self.request.post('') request.user = self.bob photo_id = self.bob.photos.first().id request.POST = {'title': 'test', 'description': 'testing', 'photos': [photo_id], 'published': 'PRIVATE', 'cover': ''} view = AlbumCreateView(request=request) view.post(request) album = Album.objects.get(title='test') self.assertIsNotNone(album) self.assertEqual(album.description, 'testing') def test_album_create_view_form_valid_sets_current_user_as_user(self): """Test album create view form valid sets current user as user.""" from imager_images.views import AlbumCreateView form_class = AlbumForm request = self.request.post('') request.user = self.bob photo_id = self.bob.photos.first().id view = AlbumCreateView(request=request) data = {'title': 'test', 'description': 'testing', 'photos': [photo_id], 'published': 'PRIVATE', 'cover': ''} form = form_class(data=data, username=request.user.username) form.is_valid() view.form_valid(form) self.assertIs(self.bob, form.instance.user) def test_album_create_view_get_form_kwargs_assigns_current_user(self): """Test album create view get form kwargs assigns current user.""" from imager_images.views import AlbumCreateView request = self.request.get('') request.user = self.bob view = AlbumCreateView(request=request) kwargs = view.get_form_kwargs() self.assertIn('username', kwargs) self.assertEqual(kwargs['username'], 'bob') def test_photo_edit_get_queryset_list_all_users_photos(self): """Test photo_edit view get queryset list all users photos.""" from imager_images.views import PhotoEditView request = self.request.get('') request.user = self.bob view = PhotoEditView(request=request) photos = view.get_queryset() self.assertEqual(photos.count(), 15) def test_album_edit_view_get_form_kwargs_assigns_current_user(self): """Test album edit view get form kwargs assigns current user.""" from imager_images.views import AlbumEditView request = self.request.get('') request.user = self.bob view = AlbumEditView(request=request) kwargs = view.get_form_kwargs() self.assertIn('username', kwargs) self.assertEqual(kwargs['username'], 'bob') def test_album_edit_get_queryset_list_all_users_albums(self): """Test album_edit view get queryset list all users albums.""" from imager_images.views import AlbumEditView request = self.request.get('') request.user = self.bob view = AlbumEditView(request=request) albums = view.get_queryset() self.assertEqual(albums.count(), 2) """Tests for the Photo and Album routes.""" class PhotoAlbumRouteTests(TestCase): """Tests for the routes of imager_images.""" @classmethod @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def setUpClass(cls): """Add one minimal user to the database.""" super(PhotoAlbumRouteTests, cls).setUpClass() os.system('mkdir {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photo_route') )) user = UserFactory(username='bob') user.set_password('password') user.save() cls.bob = user album = AlbumFactory(title='bob first', description='this is first', published='PRIVATE', user=user) album.save() for _ in range(5): photo = PhotoFactory(user=user, published='PRIVATE') photo.save() album.photos.add(photo) album = AlbumFactory(title='bob second', description='this is second', published='PUBLIC', user=user) album.save() for _ in range(10): photo = PhotoFactory(user=user) photo.save() album.photos.add(photo) user = UserFactory(username='rob') user.set_password(factory.Faker('password')) user.save() album = AlbumFactory(title='rob first', description='this is first', published='PRIVATE', user=user) album.save() for _ in range(3): photo = PhotoFactory(user=user, published='PRIVATE') photo.save() album.photos.add(photo) album = AlbumFactory(title='rob second', description='this is second', published='PUBLIC', user=user) album.save() for _ in range(13): photo = PhotoFactory(user=user) photo.save() album.photos.add(photo) @classmethod def tearDownClass(cls): """Remove the test directory.""" super(PhotoAlbumRouteTests, cls).tearDownClass() os.system('rm -rf {}'.format( os.path.join(settings.BASE_DIR, 'test_media_for_photo_route'))) def test_library_route_not_logged_in_gets_302(self): """Test that library route gets 302 status code if not logged in.""" response = self.client.get(reverse_lazy('library')) self.assertEqual(response.status_code, 302) def test_library_route_redirects_login_not_logged_in(self): """Test that library route redirects login if not logged in.""" response = self.client.get(reverse_lazy('library'), follow=True) self.assertIn(b'Login</h1>', response.content) def test_library_route_logged_in_displays_all_photos_and_albums(self): """Test that library route displays all logged in user's things.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('library')) image_count = response.content.count(b'<img') db_photo_count = Photo.objects.filter(user=self.bob).count() db_photo_count = min(db_photo_count, 4) db_album_count = Album.objects.filter(user=self.bob).count() db_album_count = min(db_album_count, 4) self.assertEqual(image_count, db_photo_count + db_album_count) def test_library_route_logged_in_non_int_album_page_num_is_page_1(self): """Test that the library route displays page 1 for invalid page num.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('library'), {'album_page': 'bobspage'}) self.assertIn(b'bob first', response.content) def test_library_route_logged_in_empty_album_page_is_last_page(self): """Test that the library route displays last page for empty page num.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('library'), {'album_page': 1000000000}) self.assertIn(b'bob second', response.content) def test_library_route_logged_in_non_int_photo_page_num_is_page_1(self): """Test that the library route displays page 1 for invalid page num.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('library'), {'photo_page': 'bobspage'}) first_photo = self.bob.photos.order_by('date_uploaded').first() self.assertIn(first_photo.title.encode('utf-8'), response.content) def test_library_route_logged_in_empty_photo_page_is_last_page(self): """Test that the library route displays last page for empty page num.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('library'), {'photo_page': 1000000000}) last_photo = self.bob.photos.order_by('date_uploaded').last() self.assertIn(last_photo.title.encode('utf-8'), response.content) def test_photo_gallery_route_has_all_public_photos(self): """Test that the photo gallery route has all public photos.""" response = self.client.get(reverse_lazy('photo_gallery')) image_count = response.content.count(b'<img') db_count = Photo.objects.filter(published='PUBLIC').count() self.assertEqual(image_count, db_count) def test_album_gallery_route_has_all_public_albums(self): """Test that the album gallery route has all public albums.""" response = self.client.get(reverse_lazy('album_gallery')) image_count = response.content.count(b'<img') db_count = Album.objects.filter(published='PUBLIC').count() self.assertEqual(image_count, db_count) def test_photo_detail_route_invalid_id_raises_404(self): """Test that an invalid id for photo detail route raises a 404.""" response = self.client.get(reverse_lazy('photo_detail', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_photo_detail_route_non_public_photo_raises_404(self): """Test that a non public photo for photo_detail_route raises a 404.""" photo = Photo.objects.filter(published='PRIVATE').first() response = self.client.get(reverse_lazy('photo_detail', kwargs={'id': photo.id})) self.assertEqual(response.status_code, 404) def test_photo_detail_route_valid_id_gets_correct_photo(self): """Test that photo_detail_route for valid id has correct photo.""" photo = Photo.objects.filter(published='PUBLIC').first() response = self.client.get(reverse_lazy('photo_detail', kwargs={'id': photo.id})) self.assertIn(photo.title.encode('utf8'), response.content) def test_album_detail_route_invalid_id_raises_404(self): """Test that an invalid id for album detail route raises a 404.""" response = self.client.get(reverse_lazy('album_detail', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_album_detail_route_non_public_album_raises_404(self): """Test that a non public album for album_detail_route raises a 404.""" album = Album.objects.filter(published='PRIVATE').first() response = self.client.get(reverse_lazy('album_detail', kwargs={'id': album.id})) self.assertEqual(response.status_code, 404) def test_album_detail_route_valid_id_gets_correct_album(self): """Test that album_detail_route for valid id has correct album.""" album = Album.objects.filter(published='PUBLIC').first() response = self.client.get(reverse_lazy('album_detail', kwargs={'id': album.id})) self.assertIn(album.title.encode('utf8'), response.content) def test_photo_create_route_get_no_login_has_302(self): """Test that photo create route get with no login has 302 code.""" response = self.client.get(reverse_lazy('photo_create')) self.assertEqual(response.status_code, 302) def test_photo_create_route_get_no_login_redirects_login(self): """Test that photo create route get with no login redirects login.""" response = self.client.get(reverse_lazy('photo_create'), follow=True) self.assertIn(b'Login</h1>', response.content) def test_photo_create_route_get_login_has_200(self): """Test that photo create route get with login has 200 code.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('photo_create')) self.assertEqual(response.status_code, 200) def test_photo_create_route_get_login_has_create_form(self): """Test that photo create route get with login has create form.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('photo_create')) self.assertIn(b'Upload New Photo', response.content) def test_photo_create_route_post_no_login_has_302(self): """Test that photo create route post with no login has 302 code.""" response = self.client.post(reverse_lazy('photo_create')) self.assertEqual(response.status_code, 302) def test_photo_create_route_post_no_login_redirects_login(self): """Test that photo create route post with no login redirects login.""" response = self.client.post(reverse_lazy('photo_create'), follow=True) self.assertIn(b'Login</h1>', response.content) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_create_route_post_login_has_302(self): """Test that photo create route post with login has 302 code.""" self.client.login(username='bob', password='password') image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test', 'description': 'testing', 'image': image, 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_create'), data) self.assertEqual(response.status_code, 302) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_create_route_post_login_redirects_library(self): """Test that photo create route post with login redirects library.""" self.client.login(username='bob', password='password') image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test2', 'description': 'testing2', 'image': image, 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_create'), data, follow=True) self.assertIn(b'<h1>Library</h1>', response.content) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_create_route_post_login_creates_new_photo(self): """Test that photo create route post with login creates a new photo.""" self.client.login(username='bob', password='password') image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test3', 'description': 'testing3', 'image': image, 'published': 'PRIVATE' } self.client.post(reverse_lazy('photo_create'), data) photo = Photo.objects.get(title='test3') self.assertIsNotNone(photo) self.assertEqual(photo.description, 'testing3') @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_create_route_post_login_bad_data_has_200(self): """Test that photo create route post with login has 200 code.""" self.client.login(username='bob', password='password') data = { 'title': 'test', 'description': 'testing', 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_create'), data) self.assertEqual(response.status_code, 200) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_create_route_post_login_bad_data_has_error(self): """Test that photo create route post with login has error.""" self.client.login(username='bob', password='password') data = { 'title': 'test2', 'description': 'testing2', 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_create'), data) self.assertIn(b'class="errorlist"', response.content) def test_album_create_route_get_no_login_has_302(self): """Test that album create route get with no login has 302 code.""" response = self.client.get(reverse_lazy('album_create')) self.assertEqual(response.status_code, 302) def test_album_create_route_get_no_login_redirects_login(self): """Test that album create route get with no login redirects login.""" response = self.client.get(reverse_lazy('album_create'), follow=True) self.assertIn(b'Login</h1>', response.content) def test_album_create_route_get_login_has_200(self): """Test that album create route get with login has 200 code.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('album_create')) self.assertEqual(response.status_code, 200) def test_album_create_route_get_login_has_create_form(self): """Test that album create route get with login has create form.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('album_create')) self.assertIn(b'Create New Album', response.content) def test_album_create_route_post_no_login_has_302(self): """Test that album create route post with no login has 302 code.""" response = self.client.post(reverse_lazy('album_create')) self.assertEqual(response.status_code, 302) def test_album_create_route_post_no_login_redirects_login(self): """Test that album create route post with no login redirects login.""" response = self.client.post(reverse_lazy('album_create'), follow=True) self.assertIn(b'Login</h1>', response.content) def test_album_create_route_post_login_has_302(self): """Test that album create route post with login has 302 code.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test', 'description': 'testing', 'cover': '', 'photos': [photo_id], 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('album_create'), data) self.assertEqual(response.status_code, 302) def test_album_create_route_post_login_redirects_library(self): """Test that album create route post with login redirects library.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test2', 'description': 'testing2', 'cover': '', 'photos': [photo_id], 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('album_create'), data, follow=True) self.assertIn(b'<h1>Library</h1>', response.content) def test_album_create_route_post_login_creates_new_album(self): """Test that album create route post with login creates a new album.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test3', 'description': 'testing3', 'cover': '', 'photos': [photo_id], 'published': 'PRIVATE' } self.client.post(reverse_lazy('album_create'), data) album = Album.objects.get(title='test3') self.assertIsNotNone(album) self.assertEqual(album.description, 'testing3') def test_album_create_route_post_login_bad_data_has_200(self): """Test that album create route post with login has 200 code.""" self.client.login(username='bob', password='password') data = { 'title': 'test', 'description': 'testing', 'cover': '', 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('album_create'), data) self.assertEqual(response.status_code, 200) def test_album_create_route_post_login_bad_data_has_error(self): """Test that album create route post with login has error.""" self.client.login(username='bob', password='password') data = { 'title': 'test', 'description': 'testing', 'cover': '', 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('album_create'), data) self.assertIn(b'class="errorlist"', response.content) def test_photo_edit_route_get_bad_id_gets_404(self): """Test get to photo edit route for bad id has 404 code.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('photo_edit', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_photo_edit_route_get_not_logged_in_has_302(self): """Test get to photo edit route when not logged in has 302 code.""" photo_id = self.bob.photos.first().id response = self.client.get(reverse_lazy('photo_edit', kwargs={'id': photo_id})) self.assertEqual(response.status_code, 302) def test_photo_edit_route_get_not_logged_in_redirects_to_login(self): """Test get to photo edit route when not logged in redirects to login.""" photo_id = self.bob.photos.first().id response = self.client.get(reverse_lazy('photo_edit', kwargs={'id': photo_id}), follow=True) self.assertIn(b'Login</h1>', response.content) def test_photo_edit_route_get_logged_in_has_200(self): """Test get to photo edit route when logged in has 200 code.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('photo_edit', kwargs={'id': photo_id})) self.assertEqual(response.status_code, 200) def test_photo_edit_route_get_logged_in_has_edit_form(self): """Test get to photo edit route when logged in has edit form.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('photo_edit', kwargs={'id': photo_id})) self.assertIn(b'Edit Photo</h1>', response.content) def test_photo_edit_route_post_bad_id_gets_404(self): """Test get to photo edit route for bad id has 404 code.""" self.client.login(username='bob', password='password') response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_photo_edit_route_post_not_logged_in_has_302(self): """Test post to photo edit route when not logged in has 302 code.""" photo_id = self.bob.photos.first().id response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id})) self.assertEqual(response.status_code, 302) def test_photo_edit_route_post_not_logged_in_redirects_to_login(self): """Test post to photo edit route when not logged in redirects to login.""" photo_id = self.bob.photos.first().id response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), follow=True) self.assertIn(b'Login</h1>', response.content) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_edit_route_post_logged_in_has_302(self): """Test post to photo edit route when logged in has 302 code.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test4', 'description': 'testing4', 'image': image, 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data) self.assertEqual(response.status_code, 302) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_edit_route_post_logged_in_updates_the_correct_photo(self): """Test photo edit route post logged in updates the users photo.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') original_photo = Photo.objects.get(id=photo_id) image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test5', 'description': 'testing5', 'image': image, 'published': 'PRIVATE' } self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data) photo = Photo.objects.get(id=photo_id) self.assertEqual(photo.title, 'test5') self.assertEqual(photo.description, 'testing5') self.assertIsNot(photo.image, original_photo.image) def test_photo_edit_route_post_logged_in_public_adds_published_date(self): """Test photo edit route post logged in set published to public sets published date.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'published': 'PUBLIC' } self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data) photo = Photo.objects.get(id=photo_id) now = datetime.now().strftime('%x %X')[:2] self.assertEqual(photo.published, 'PUBLIC') self.assertEqual(photo.date_published.strftime('%x %X')[:2], now) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_edit_route_post_logged_in_done_redirects_to_library(self): """Test post to photo edit route when logged in redirects to library when done.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') image = SimpleUploadedFile( name='sample_img.jpg', content=open( os.path.join(settings.BASE_DIR, 'static/test_image.jpg'), 'rb' ).read(), content_type="image/jpeg" ) data = { 'title': 'test6', 'description': 'testing6', 'image': image, 'published': 'PRIVATE' } response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data, follow=True) self.assertIn(b'<h1>Library</h1>', response.content) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_edit_route_post_logged_in_bad_data_has_200(self): """Test that photo create route post with login has 200 code.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = {} response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data) self.assertEqual(response.status_code, 200) @override_settings(MEDIA_ROOT=os.path.join(settings.BASE_DIR, "test_media_for_photo_route")) def test_photo_edit_route_post_login_bad_data_has_error(self): """Test that photo create route post with login has error.""" photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = {} response = self.client.post(reverse_lazy('photo_edit', kwargs={'id': photo_id}), data) self.assertIn(b'class="errorlist"', response.content) def test_album_edit_route_get_bad_id_gets_404(self): """Test get to album edit route for bad id has 404 code.""" self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('album_edit', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_album_edit_route_get_not_logged_in_has_302(self): """Test get to album edit route when not logged in has 302 code.""" album_id = self.bob.albums.first().id response = self.client.get(reverse_lazy('album_edit', kwargs={'id': album_id})) self.assertEqual(response.status_code, 302) def test_album_edit_route_get_not_logged_in_redirects_to_login(self): """Test get to album edit route when not logged in redirects to login.""" album_id = self.bob.albums.first().id response = self.client.get(reverse_lazy('album_edit', kwargs={'id': album_id}), follow=True) self.assertIn(b'Login</h1>', response.content) def test_album_edit_route_get_logged_in_has_200(self): """Test get to album edit route when logged in has 200 code.""" album_id = self.bob.albums.first().id self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('album_edit', kwargs={'id': album_id})) self.assertEqual(response.status_code, 200) def test_album_edit_route_get_logged_in_has_edit_form(self): """Test get to album edit route when logged in has edit form.""" album_id = self.bob.albums.first().id self.client.login(username='bob', password='password') response = self.client.get(reverse_lazy('album_edit', kwargs={'id': album_id})) self.assertIn(b'Edit Album</h1>', response.content) def test_album_edit_route_post_bad_id_gets_404(self): """Test get to album edit route for bad id has 404 code.""" self.client.login(username='bob', password='password') response = self.client.post(reverse_lazy('album_edit', kwargs={'id': 1000000000})) self.assertEqual(response.status_code, 404) def test_album_edit_route_post_not_logged_in_has_302(self): """Test post to album edit route when not logged in has 302 code.""" album_id = self.bob.albums.first().id response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id})) self.assertEqual(response.status_code, 302) def test_album_edit_route_post_not_logged_in_redirects_to_login(self): """Test post to album edit route when not logged in redirects to login.""" album_id = self.bob.albums.first().id response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), follow=True) self.assertIn(b'Login</h1>', response.content) def test_album_edit_route_post_logged_in_has_302(self): """Test post to album edit route when logged in has 302 code.""" album_id = self.bob.albums.first().id photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test4', 'description': 'testing4', 'published': 'PRIVATE', 'photos': [photo_id] } response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data) self.assertEqual(response.status_code, 302) def test_album_edit_route_post_logged_in_updates_the_correct_album(self): """Test album edit route post logged in updates the users album.""" album_id = self.bob.albums.first().id photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test5', 'description': 'testing5', 'published': 'PRIVATE', 'photos': [photo_id] } self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data) album = Album.objects.get(id=album_id) self.assertEqual(album.title, 'test5') self.assertEqual(album.description, 'testing5') def test_album_edit_route_post_logged_in_public_adds_published_date(self): """Test album edit route post logged in set published to public sets published date.""" album_id = self.bob.albums.first().id photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'published': 'PUBLIC', 'photos': [photo_id] } self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data) album = Album.objects.get(id=album_id) now = datetime.now().strftime('%x %X')[:2] self.assertEqual(album.published, 'PUBLIC') self.assertEqual(album.date_published.strftime('%x %X')[:2], now) def test_album_edit_route_post_logged_in_done_redirects_to_library(self): """Test post to album edit route when logged in redirects to library when done.""" album_id = self.bob.albums.first().id photo_id = self.bob.photos.first().id self.client.login(username='bob', password='password') data = { 'title': 'test6', 'description': 'testing6', 'published': 'PRIVATE', 'photos': [photo_id] } response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data, follow=True) self.assertIn(b'<h1>Library</h1>', response.content) def test_album_edit_route_post_logged_in_bad_data_has_200(self): """Test that album create route post with login has 200 code.""" album_id = self.bob.albums.first().id self.client.login(username='bob', password='password') data = {} response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data) self.assertEqual(response.status_code, 200) def test_album_edit_route_post_login_bad_data_has_error(self): """Test that album create route post with login has error.""" album_id = self.bob.albums.first().id self.client.login(username='bob', password='password') data = {} response = self.client.post(reverse_lazy('album_edit', kwargs={'id': album_id}), data) self.assertIn(b'class="errorlist"', response.content)

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0.645968

7,109

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0.022277

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0.025262

0.912048

0.879661

0.856511

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0.766222

0.730652

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108

45.31176

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0.175983

0

null

0

1

0

null

0

6

c67cf4fc4783688de8719013d91213bc05b542c3

2,619

py

Python

code/producer.py

Niroshamurugan/Revature--project-03

24095c5c43c6da3901e3312eae0a003eaf090b03

[ "MIT" ]

null

code/producer.py

Niroshamurugan/Revature--project-03

24095c5c43c6da3901e3312eae0a003eaf090b03

[ "MIT" ]

null

code/producer.py

Niroshamurugan/Revature--project-03

24095c5c43c6da3901e3312eae0a003eaf090b03

[ "MIT" ]

3

2021-09-22T05:50:52.000Z

2021-09-27T08:29:15.000Z

import requests import time import json from kafka import KafkaProducer import threading import credentials as cred key = cred.login['private_key'] def send_Mumbai(): url = 'https://api.weatherbit.io/v2.0/history/subhourly?&city=Mumbai&country=IN&start_date=2021-09-24&end_date=2021-09-26&key={}'.format(key) data = requests.get(url).json() city_name = data['city_name'] for dataval in data['data']: dataval["city"] = city_name del dataval["weather"] del dataval["timestamp_utc"] del dataval["timestamp_local"] output = json.dumps(dataval) producer.send('weather', output) producer.flush() time.sleep(4) def send_Chennai(): url = 'https://api.weatherbit.io/v2.0/history/subhourly?&city=Chennai&country=IN&start_date=2021-09-24&end_date=2021-09-26&key={}'.format(key) data = requests.get(url).json() city_name = data['city_name'] for dataval in data['data']: dataval["city"] = city_name del dataval["weather"] del dataval["timestamp_utc"] del dataval["timestamp_local"] output = json.dumps(dataval) producer.send('weather', output) producer.flush() time.sleep(4) def send_Banglore(): url = 'https://api.weatherbit.io/v2.0/history/subhourly?&city=Banglore&country=IN&start_date=2021-09-24&end_date=2021-09-26&key={}'.format(key) data = requests.get(url).json() city_name = data['city_name'] for dataval in data['data']: dataval["city"] = city_name del dataval["weather"] del dataval["timestamp_utc"] del dataval["timestamp_local"] output = json.dumps(dataval) producer.send('weather', output) producer.flush() time.sleep(4) def send_Hyderabad(): url = 'https://api.weatherbit.io/v2.0/history/subhourly?&city=Hyderabad&country=IN&start_date=2021-09-24&end_date=2021-09-26&key={}'.format(key) data = requests.get(url).json() city_name = data['city_name'] for dataval in data['data']: dataval["city"] = city_name del dataval["weather"] del dataval["timestamp_utc"] del dataval["timestamp_local"] output = json.dumps(dataval) producer.send('weather', output) producer.flush() time.sleep(4) producer = KafkaProducer(value_serializer = lambda x: str(x).encode("utf-8"), bootstrap_servers = ['localhost:9092']) threading.Thread(target=send_Mumbai).start() threading.Thread(target=send_Chennai).start() threading.Thread(target=send_Banglore).start() threading.Thread(target=send_Hyderabad).start()

34.460526

148

0.66323

348

2,619

4.87931

0.201149

0.056537

0.047114

0.04947

0.802709

0.749706

0

0.03801

0.186331

2,619

75

149

34.92

0.758799

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0.6875

0

0.0625

0.28866

0

1

0.0625

false

0

0.09375

0

0.15625

0

null

0

1

0

null

0

6

c68b2b14b1498e2949ccda702dd2c2ca77a60435

678

py

Python

tests/expectations/cat-hs-x-cat-date-smoothed-col-idx-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

3

2021-01-22T20:42:31.000Z

2021-06-02T17:53:19.000Z

tests/expectations/cat-hs-x-cat-date-smoothed-col-idx-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

331

2017-11-13T22:41:56.000Z

2021-12-02T21:59:43.000Z

tests/expectations/cat-hs-x-cat-date-smoothed-col-idx-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

1

2021-02-19T02:49:00.000Z

[ [float("NaN"), float("NaN"), float("NaN"), float("NaN")], [float("NaN"), float("NaN"), 98.94756383, 102.48945473], [float("NaN"), float("NaN"), 99.37642288, 86.00386085], [float("NaN"), float("NaN"), 101.42881595, 80.4871013], [float("NaN"), float("NaN"), float("NaN"), float("NaN")], [float("NaN"), float("NaN"), 114.65617463, 131.06169493], [float("NaN"), float("NaN"), 114.45648453, 77.61954936], [float("NaN"), float("NaN"), 117.08393631, 129.63525462], [float("NaN"), float("NaN"), 97.76074163, 132.11504182], [float("NaN"), float("NaN"), 76.37474542, 144.68075635], [float("NaN"), float("NaN"), float("NaN"), float("NaN")], ]

48.428571

61

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678

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0.382653

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0.132743

678

13

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0

1

0

1

0

null

0

1

0

6

c69b5828fe8be59e297ae10d33bcab46283c11e0

12,542

py

Python

scenarios/bad_node_variations_10.py

tari-labs/modelling

27dd69f5fc658a69a2e2db273231c0f017448586

[ "Apache-2.0" ]

null

scenarios/bad_node_variations_10.py

tari-labs/modelling

27dd69f5fc658a69a2e2db273231c0f017448586

[ "Apache-2.0" ]

1

2019-04-01T06:19:43.000Z

scenarios/bad_node_variations_10.py

tari-labs/modelling

27dd69f5fc658a69a2e2db273231c0f017448586

[ "Apache-2.0" ]

1

2021-07-28T04:07:41.000Z

import sys, os #Add ../utils to the Python system path try: sys.path.index(os.getcwd().split(os.getcwd().split(os.sep)[-1])[0] + 'utils'); except ValueError: sys.path.append(os.getcwd().split(os.getcwd().split(os.sep)[-1])[0] + 'utils'); import hyper_dist_prob as hdp import numpy as np #from IPython import get_ipython #get_ipython().magic('clear') # Set environment with m = 10% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start =100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_10 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.1 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_10 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_10.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_10, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_10, bft_threshold, j, P_tot_10[j])) # Set environment with m = 20% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start =100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_20 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.2 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_20 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_20.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_20, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_20, bft_threshold, j, P_tot_20[j])) # Set environment with m = 30% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start = 100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_30 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.3 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_30 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_30.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_30, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_30, bft_threshold, j, P_tot_30[j])) # Set environment with m = 40% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start = 100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_40 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.4 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_40 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_40.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_40, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_40, bft_threshold, j, P_tot_40[j])) # Set environment with m = 50% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start = 100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_50 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.5 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_50 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_50.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_50, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_50, bft_threshold, j, P_tot_50[j])) # Set environment with m = 60% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start = 100 no_of_nodes_max = 20000 ## Node increment incr = 2000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_60 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.6 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_60 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_60.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_60, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_60, bft_threshold, j, P_tot_60[j])) # Set environment with m = 70% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start =100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_70 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.7 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_70 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_70.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_70, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_70, bft_threshold, j, P_tot_70[j])) # Set environment with m = 80% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start =100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_80 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.8 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_80 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_80.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_80, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_80, bft_threshold, j, P_tot_80[j])) # Set environment with m = 90% # Declare and initialize variables no_of_nodes = [] no_of_nodes_start =100 no_of_nodes_max = 20000 ## Node increment incr = 1000 ## Create nodes array no_of_nodes = np.arange(no_of_nodes_start, no_of_nodes_max + 1, incr) # Quantify Committee size committee_size_90 = 100 # Create and Initialize scenario variables ## Percentage of Bad actors no_of_bad_actors = [] bad_actors_percentage = 0.9 ## Quantify BFT threshold bft_threshold = 67 # Populate input arrays ## Calculate the number of Bad actors (product of total nodes (N) and Bad actors percentage, round down) no_of_bad_actors = np.int_(np.floor(no_of_nodes * bad_actors_percentage)) # Calculate the total probability of bad actors controlling the committee, starting from the BFT threshold # up to the committee size P_tot_90 = [] #print('Tot_nodes Bad_nodes Committee Threshold index Probability to control') for j in range(0, len(no_of_nodes)): P_tot_90.append(hdp.probability(bft_threshold, no_of_bad_actors[j], committee_size_90, no_of_nodes[j])) print(' %3s %3s %3s %3s j=%3s P_tot= %-20s' % \ (no_of_nodes[j], no_of_bad_actors[j], committee_size_90, bft_threshold, j, P_tot_90[j])) #Plots import matplotlib.pyplot as plt ## Standard graph settings fig, ax1 = plt.subplots(figsize=(12,9)) ax1.grid(True, linestyle='-.') ax1.xaxis.grid(True, which='minor', linestyle='-.') ax1.set_xlabel('Total Nodes', fontsize='18') ax1.set_ylabel('Probability of bad actors controlling the network', fontsize='18') ax1.set_title('Bad actor variation when committee size = 100') plt.plot(no_of_nodes, P_tot_10, 'c-', label='m = 10%') plt.plot(no_of_nodes, P_tot_20, 'c-', label='m = 20%') plt.plot(no_of_nodes, P_tot_30, 'm-', label='m = 30%') plt.plot(no_of_nodes, P_tot_40, 'k-', label='m = 40%') plt.plot(no_of_nodes, P_tot_50, 'r-', label='m = 50%') plt.plot(no_of_nodes, P_tot_60, 'y-', label='m = 60%') plt.plot(no_of_nodes, P_tot_70, 'y-', label='m = 70%') plt.plot(no_of_nodes, P_tot_80, 'y-', label='m = 80%') plt.plot(no_of_nodes, P_tot_90, 'y-', label='m = 90%') plt.xlim(0,20000) plt.ylim(3e-53,1) plt.legend(loc=3) plt.show()

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null

0

1

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null

0

6

c6f655f992ed153b136d10a6015387ee48623ff1

14,483

py

Python

tests/features/test_pmi_controller.py

keaparrot/secbootctl

a624677a8987023a3a55d7e002c3c980bdd0e0bd

[ "MIT" ]

null

tests/features/test_pmi_controller.py

keaparrot/secbootctl

a624677a8987023a3a55d7e002c3c980bdd0e0bd

[ "MIT" ]

null

tests/features/test_pmi_controller.py

keaparrot/secbootctl

a624677a8987023a3a55d7e002c3c980bdd0e0bd

[ "MIT" ]

null

import unittest from io import StringIO from pathlib import Path from unittest.mock import MagicMock, Mock from unittest.mock import call from unittest.mock import patch from secbootctl.core import AppError from secbootctl.env import Env from secbootctl.helpers.cli import CliPrintHelper from tests import unittest_helper class TestPmiController(unittest_helper.ControllerTestCase): FEATURE_NAME: str = 'pmi' @patch('secbootctl.features.pmi.Path.is_dir') @patch('secbootctl.features.pmi.os') @patch('secbootctl.features.pmi.shutil') @patch('secbootctl.features.pmi.glob') def test_install_pacman_it_installs_hook_files(self, glob_patch_mock: MagicMock, shutil_patch_mock: MagicMock, os_patch_mock: MagicMock, path_is_dir_patch_mock: MagicMock): pm_name: str = 'pacman' hook_path: Path = Env.APP_HOOK_PATH / pm_name hook_target_path: Path = Path('/etc/pacman.d/hooks') self._config_mock.configure_mock(package_manager_name=pm_name) glob_patch_mock.glob.return_value = [ f'{hook_path}/update-hook.hook', f'{hook_path}/remove-hook.hook' ] path_is_dir_patch_mock.return_value = True self._controller.install() glob_patch_mock.glob.assert_called_once_with( str(hook_path / '*.*') ) shutil_patch_mock.copy.assert_has_calls([ call(hook_path / 'update-hook.hook', hook_target_path), call(hook_path / 'remove-hook.hook', hook_target_path) ]) shutil_patch_mock.chown.assert_has_calls([ call(hook_target_path / 'update-hook.hook', 'root', 'root'), call(hook_target_path / 'remove-hook.hook', 'root', 'root') ]) os_patch_mock.chmod.assert_has_calls([ call(hook_target_path / 'update-hook.hook', 0o700), call(hook_target_path / 'remove-hook.hook', 0o700) ]) self._cli_print_helper_mock.print_status.assert_has_calls([ call(f'installing hook files for package manager: {pm_name}', CliPrintHelper.Status.PENDING), call(f'installed hook files for package manager: {pm_name}', CliPrintHelper.Status.SUCCESS) ]) def test_install_if_pm_not_supported_it_raises_an_error(self): pm_name: str = 'unknown' self._config_mock.configure_mock(package_manager_name=pm_name) with self.assertRaises(AppError) as context_manager: self._controller.install() error: AppError = context_manager.exception self.assertEqual( error.message, f'configured package manager "{pm_name}" is not supported' ) self.assertEqual( error.code, 1 ) @patch('secbootctl.features.pmi.Path.unlink') @patch('secbootctl.features.pmi.glob') def test_remove_pacman_it_removes_hook_files(self, glob_patch_mock: MagicMock, path_unlink_patch_mock: MagicMock): pm_name: str = 'pacman' hook_path: Path = Env.APP_HOOK_PATH / pm_name self._config_mock.configure_mock(package_manager_name=pm_name) glob_patch_mock.glob.return_value = [ f'{hook_path}/update-hook.hook', f'{hook_path}/remove-hook.hook' ] self._controller.remove() glob_patch_mock.glob.assert_called_once_with( str(hook_path / '*.*') ) path_unlink_patch_mock.assert_called_with(missing_ok=True) self._cli_print_helper_mock.print_status.assert_has_calls([ call(f'removing hook files for package manager: {pm_name}', CliPrintHelper.Status.PENDING), call(f'removed hook files for package manager: {pm_name}', CliPrintHelper.Status.SUCCESS) ]) @patch('secbootctl.features.pmi.glob') def test_remove_if_pm_not_supported_it_raises_an_error(self, glob_patch_mock: MagicMock): pm_name: str = 'unknown' self._config_mock.configure_mock(package_manager_name=pm_name) with self.assertRaises(AppError) as context_manager: self._controller.remove() error: AppError = context_manager.exception self.assertEqual( error.message, f'configured package manager "{pm_name}" is not supported' ) self.assertEqual( error.code, 1 ) @patch('sys.stdin', StringIO('linux')) @patch('secbootctl.features.pmi.glob') def test_hook_callback_pacman_update_if_no_systemd_update_it_signs_all_kernels(self, glob_patch_mock: MagicMock): pm_name: str = 'pacman' boot_path: Path = Path('/boot') kernel_image_name_prefix: str = 'vmlinuz' self._config_mock.configure_mock( boot_path=boot_path, kernel_image_name_prefix=kernel_image_name_prefix, package_manager_name=pm_name ) glob_patch_mock.glob.return_value = [ f'{boot_path}/{kernel_image_name_prefix}-linux', f'{boot_path}/{kernel_image_name_prefix}-linux-lts', f'{boot_path}/{kernel_image_name_prefix}-5.10.0.14-generic', ] self._controller.hook_callback('update') glob_patch_mock.glob.assert_called_once_with( str(boot_path / (kernel_image_name_prefix + '-*')) ) self._dispatcher_mock.dispatch.assert_has_calls([ call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': 'linux'} }), call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': 'linux-lts'} }), call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': '5.10.0.14-generic'} }), ]) @patch('sys.stdin', StringIO('linux\nsystemd')) @patch('secbootctl.features.pmi.glob') def test_hook_callback_pacman_update_if_systemd_update_it_signs_all_kernels_and_updates_bootloader(self, glob_patch_mock: MagicMock): pm_name: str = 'pacman' boot_path: Path = Path('/boot') kernel_image_name_prefix: str = 'vmlinuz' self._config_mock.configure_mock( boot_path=boot_path, kernel_image_name_prefix=kernel_image_name_prefix, package_manager_name=pm_name ) glob_patch_mock.glob.return_value = [ f'{boot_path}/{kernel_image_name_prefix}-linux', f'{boot_path}/{kernel_image_name_prefix}-linux-lts', f'{boot_path}/{kernel_image_name_prefix}-5.10.0.14-generic', ] self._controller.hook_callback('update') glob_patch_mock.glob.assert_called_once_with( str(boot_path / (kernel_image_name_prefix + '-*')) ) self._dispatcher_mock.dispatch.assert_has_calls([ call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': 'linux'} }), call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': 'linux-lts'} }), call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': '5.10.0.14-generic'} }), call({ 'module_name': 'secbootctl.features.bootloader', 'controller_name': 'BootloaderController', 'action_name': 'update', 'params': {} }) ]) def test_hook_callback_pacman_update_if_not_supported_it_raises_an_error(self): pm_name: str = 'unknown' self._config_mock.configure_mock(package_manager_name=pm_name) with self.assertRaises(AppError) as context_manager: self._controller.hook_callback('update') error: AppError = context_manager.exception self.assertEqual( error.message, f'configured package manager "{pm_name}" is not supported' ) self.assertEqual( error.code, 1 ) @patch('sys.stdin', StringIO('/usr/lib/modules/5.15.8/vmlinuz\n/usr/lib/modules/5.10.85-lts/vmlinuz')) @patch('secbootctl.features.pmi.Path.read_text') def test_hook_callback_pacman_remove_it_removes_given_kernels(self, path_read_text_patch_mock: MagicMock): pm_name: str = 'pacman' self._config_mock.configure_mock(package_manager_name=pm_name) path_read_text_patch_mock.side_effect = ['linux', 'linux-lts'] self._controller.hook_callback('remove') self._dispatcher_mock.dispatch.assert_has_calls([ call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'remove', 'params': {'kernel_name': 'linux'} }), call({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'remove', 'params': {'kernel_name': 'linux-lts'} }) ]) def test_hook_callback_pacman_remove_if_not_supported_it_raises_an_error(self): pm_name: str = 'unknown' self._config_mock.configure_mock(package_manager_name=pm_name) with self.assertRaises(AppError) as context_manager: self._controller.hook_callback('remove') error: AppError = context_manager.exception self.assertEqual( error.message, f'configured package manager "{pm_name}" is not supported' ) self.assertEqual( error.code, 1 ) @patch('secbootctl.features.pmi.Path.is_dir') @patch('secbootctl.features.pmi.os') @patch('secbootctl.features.pmi.shutil') def test_install_apt_it_installs_hook_files(self, shutil_patch_mock: MagicMock, os_patch_mock: MagicMock, path_is_dir_patch_mock: MagicMock): pm_name: str = 'apt' hook_path: Path = Env.APP_HOOK_PATH / pm_name self._config_mock.configure_mock(package_manager_name=pm_name) path_is_dir_patch_mock.return_value = True self._controller.install() shutil_patch_mock.copy.assert_has_calls([ call(hook_path / 'initramfs' / 'yy-secbootctl-update', Path('/etc/initramfs/post-update.d')), call(hook_path / 'kernel' / 'yy-secbootctl-update', Path('/etc/kernel/postinst.d')), call(hook_path / 'kernel' / 'yy-secbootctl-remove', Path('/etc/kernel/postrm.d')) ]) shutil_patch_mock.chown.assert_has_calls([ call(Path('/etc/initramfs/post-update.d') / 'yy-secbootctl-update', 'root', 'root'), call(Path('/etc/kernel/postinst.d') / 'yy-secbootctl-update', 'root', 'root'), call(Path('/etc/kernel/postrm.d') / 'yy-secbootctl-remove', 'root', 'root'), ]) os_patch_mock.chmod.assert_has_calls([ call(Path('/etc/initramfs/post-update.d') / 'yy-secbootctl-update', 0o700), call(Path('/etc/kernel/postinst.d') / 'yy-secbootctl-update', 0o700), call(Path('/etc/kernel/postrm.d') / 'yy-secbootctl-remove', 0o700) ]) self._cli_print_helper_mock.print_status.assert_has_calls([ call(f'installing hook files for package manager: {pm_name}', CliPrintHelper.Status.PENDING), call(f'installed hook files for package manager: {pm_name}', CliPrintHelper.Status.SUCCESS) ]) @patch('secbootctl.features.pmi.Path') def test_remove_apt_it_removes_hook_files(self, path_patch_mock: MagicMock): pm_name: str = 'apt' self._config_mock.configure_mock(package_manager_name=pm_name) path_mock: Mock = Mock() path_patch_mock.side_effect = [path_mock, path_mock, path_mock] self._controller.remove() path_patch_mock.assert_has_calls([ call('/etc/initramfs/post-update.d/yy-secbootctl-update'), call('/etc/kernel/postinst.d/yy-secbootctl-update'), call('/etc/kernel/postrm.d/yy-secbootctl-remove') ]) path_mock.unlink.assert_called_with(missing_ok=True) self._cli_print_helper_mock.print_status.assert_has_calls([ call(f'removing hook files for package manager: {pm_name}', CliPrintHelper.Status.PENDING), call(f'removed hook files for package manager: {pm_name}', CliPrintHelper.Status.SUCCESS) ]) def test_hook_callback_apt_update_it_updates_given_kernel(self): pm_name: str= 'apt' self._config_mock.configure_mock(package_manager_name=pm_name) kernel_name: str = '5.10.0.14-generic' self._controller.hook_callback('update', kernel_name) self._dispatcher_mock.dispatch.assert_called_once_with({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'install', 'params': {'kernel_name': kernel_name} }) def test_hook_callback_apt_remove_it_removes_given_kernel(self): pm_name: str= 'apt' self._config_mock.configure_mock(package_manager_name=pm_name) kernel_name: str = '5.10.0.14-generic' self._controller.hook_callback('remove', kernel_name) self._dispatcher_mock.dispatch.assert_called_once_with({ 'module_name': 'secbootctl.features.kernel', 'controller_name': 'KernelController', 'action_name': 'remove', 'params': {'kernel_name': kernel_name} }) if __name__ == '__main__': unittest.main()

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0.090194

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0.841459

0.823988

0.77724

0.757998

0.738166

0

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0.262031

14,483

343

132

42.22449

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0

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0

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1

0.043624

false

0

0.033557

0

0.083893

0.013423

0

null

0

1

0

null

0

6

05cb4a47a6e0fa5447949be9dc6c8acde7d7e025

234,179

py

Python

dfs/nfl/data.py

sullivanja92/dfs-lineup-optimizer

fea6b9bd76c806f7888fae44d7d113d54e3b4f4d

[ "MIT" ]

null

dfs/nfl/data.py

sullivanja92/dfs-lineup-optimizer

fea6b9bd76c806f7888fae44d7d113d54e3b4f4d

[ "MIT" ]

null

dfs/nfl/data.py

sullivanja92/dfs-lineup-optimizer

fea6b9bd76c806f7888fae44d7d113d54e3b4f4d

[ "MIT" ]

null

from typing import List import pandas as pd from numpy import nan # TODO: should be able to provide week for single week df def load_2020_data(weeks: List[int] = range(1, 5)) -> pd.DataFrame: # TODO: correct dk and fd points """ This function is used to provide sample data from the 2019 season. The return value is a DataFrame containing the following columns: name, position, year, week, team, opponent, dk_points, fd_points, dk_salary and fd_salary. Weeks one through four of the 2019 season are available. :param weeks: The weeks to load fantasy data for. :return: A DataFrame containing fantasy data for the given weeks. :raises: A ValueError if weeks argument is invalid. """ if weeks is None or len(weeks) == 0: raise ValueError('Weeks argument must not be none or empty') if any(w <= 0 for w in weeks) or any(w > 4 for w in weeks): raise ValueError('Invalid weeks argument. Weeks 1-4 are available') df = pd.DataFrame.from_dict( data={'week': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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Allen', 'Lamar Jackson', 'Kyler Murray', 'Cam Newton', 'Matt Ryan', 'Mitchell Trubisky', 'Kirk Cousins', 'Tom Brady', 'Ben Roethlisberger', 'Deshaun Watson', 'Gardner Minshew', 'Patrick Mahomes II', 'Teddy Bridgewater', 'Ryan Tannehill', 'Daniel Jones', 'Jimmy Garoppolo', 'Dak Prescott', 'Joe Burrow', 'Matthew Stafford', 'Philip Rivers', 'Carson Wentz', 'Drew Brees', 'Derek Carr', 'Drew Lock', 'Dwayne Haskins', 'Sam Darnold', 'Jared Goff', 'Baker Mayfield', 'Tyrod Taylor', 'Ryan Fitzpatrick', 'Taysom Hill', 'Chris Streveler', 'Robert Griffin III', 'Brian Hoyer', 'Tua Tagovailoa', 'Jacoby Brissett', 'Tim Boyle', 'Josh Jacobs', 'Christian McCaffrey', 'Ezekiel Elliott', 'Malcolm Brown', 'Nyheim Hines', 'Raheem Mostert', 'Chris Carson', 'Dalvin Cook', 'Alvin Kamara', 'Clyde Edwards-Helaire', 'David Johnson', 'Aaron Jones', 'Peyton Barber', 'Derrick Henry', 'J.K. Dobbins', 'Melvin Gordon', 'Kenyan Drake', 'Adrian Peterson', 'Todd Gurley', 'Joshua Kelley', 'Chase Edmonds', 'Jerick McKinnon', 'Jonathan Taylor', 'Benny Snell', 'Zack Moss', 'Kareem Hunt', 'Alexander Mattison', "D'Andre Swift", 'Sony Michel', 'Saquon Barkley', 'James Robinson', 'Ronald Jones', 'Josh Adams', 'Austin Ekeler', 'Myles Gaskin', 'Carlos Hyde', 'David Montgomery', 'Devin Singletary', 'Marlon Mack', 'James White', 'Jordan Howard', 'Devontae Booker', 'Boston Scott', 'Jamaal Williams', 'Tarik Cohen', 'Joe Mixon', "Le'Veon Bell", 'Antonio Gibson', 'Kyle Juszczyk', 'Nick Chubb', 'Tyler Ervin', 'Giovani Bernard', 'Latavius Murray', 'Cam Akers', 'Tony Pollard', 'Phillip Lindsay', 'Darrel Williams', 'J.J. Taylor', 'Cordarrelle Patterson', 'Rex Burkhead', 'Corey Clement', 'Brian Hill', 'Tevin Coleman', 'Mark Ingram', 'James Conner', 'Leonard Fournette', 'Frank Gore', 'Matt Breida', 'Ito Smith', 'Jalen Richard', 'Patrick Laird', 'Wayne Gallman', 'Gus Edwards', 'Royce Freeman', 'Chris Thompson', 'AJ Dillon', 'Kerryon Johnson', 'Duke Johnson', 'Travis Homer', 'Khari Blasingame', 'Jaylen Samuels', 'Bruce Miller', 'LeSean McCoy', 'Darrell Henderson', "D'Ernest Johnson", 'Justin Jackson', 'J.D. McKissic', 'Anthony Sherman', 'Dion Lewis', 'Jason Huntley', 'Alex Armah', 'Darwin Thompson', 'Mike Boone', 'Jeff Wilson Jr.', 'Michael Burton', 'Jakob Johnson', 'Nick Bellore', 'C.J. Ham', 'Jordan Wilkins', 'Mike Davis', 'Ryan Nall', 'D.J. Foster', 'Chandler Cox', 'Elijhaa Penny', 'Ameer Abdullah', 'Senorise Perry', 'Ty Johnson', 'Buddy Howell', 'Derek Watt', 'Keith Smith', 'Taiwan Jones', 'Samaje Perine', 'Andy Janovich', 'Alec Ingold', 'Trenton Cannon', 'Ty Montgomery', 'Patrick Ricard', 'Davante Adams', 'Calvin Ridley', 'Adam Thielen', 'Darius Slayton', 'Robby Anderson', 'DeAndre Hopkins', 'JuJu Smith-Schuster', 'Jamison Crowder', 'Julio Jones', 'Sammy Watkins', 'Marquez Valdes-Scantling', 'D.K. Metcalf', 'Allen Lazard', 'John Brown', 'Russell Gage', 'Anthony Miller', 'Will Fuller', 'Robert Woods', 'Willie Snead', 'Corey Davis', 'Tyler Lockett', 'Keelan Cole', 'Amari Cooper', 'Tyreek Hill', 'Marquise Brown', 'Stefon Diggs', 'Laviska Shenault Jr.', 'Parris Campbell', 'Chris Godwin', 'Danny Amendola', 'Julian Edelman', 'Scott Miller', 'James Washington', 'D.J. Chark', 'Allen Robinson', 'Emmanuel Sanders', 'Mike Williams', 'Nelson Agholor', 'Jarvis Landry', 'Terry McLaurin', 'CeeDee Lamb', 'Henry Ruggs III', 'Cole Beasley', 'Sterling Shepard', 'Adam Humphries', 'A.J. Green', 'Jerry Jeudy', 'Marvin Jones', 'D.J. Moore', 'T.Y. Hilton', 'Olabisi Johnson', 'Curtis Samuel', 'Mike Evans', 'DeVante Parker', 'Diontae Johnson', 'Javon Wims', 'Steven Sims Jr.', 'Michael Gallup', 'A.J. Brown', 'Jalen Reagor', 'Cooper Kupp', 'Quintez Cephus', 'Keenan Allen', 'Chase Claypool', 'DeSean Jackson', 'Greg Ward', 'David Moore', 'Larry Fitzgerald', 'Tyler Boyd', 'Darnell Mooney', 'Miles Boykin', 'Preston Williams', 'Tim Patrick', 'Kendrick Bourne', "N'Keal Harry", 'Jakeem Grant', 'Odell Beckham Jr.', 'Justin Jefferson', 'Van Jefferson', 'Demarcus Robinson', 'Randall Cobb', 'Michael Thomas', 'Breshad Perriman', 'Isaiah McKenzie', 'Hunter Renfrow', 'Dontrelle Inman', 'Deonte Harris', 'Marvin Hall', 'Brandin Cooks', 'Zach Pascal', 'C.J. Board', 'John Ross', 'Mike Thomas', 'Gabriel Davis', 'DaeSean Hamilton', 'Isaiah Ford', 'Freddie Swain', 'Josh Reynolds', 'Jalen Guyton', 'Michael Pittman Jr.', 'Collin Johnson', 'Trent Taylor', 'KhaDarel Hodge', 'Devin Duvernay', 'Bryan Edwards', 'Rashard Higgins', 'DeAndre Carter', 'Tyrie Cleveland', 'Chris Conley', 'Justin Watson', 'Mecole Hardman', 'Seth Roberts', "Tre'Quan Smith", 'Chris Hogan', 'Christian Kirk', 'John Hightower', 'Marcus Kemp', 'Tajae Sharpe', 'Mack Hollins', 'Byron Pringle', 'Ted Ginn Jr.', 'Zay Jones', 'Brandon Zylstra', 'Bennie Fowler', 'Brandon Powell', 'Alex Erickson', 'Malik Turner', 'Damiere Byrd', 'Trent Sherfield', 'K.J. Osborn', 'James Proche', 'Josh Malone', 'Pharoh Cooper', 'Cedrick Wilson', 'Braxton Berrios', 'Tee Higgins', 'JoJo Natson', 'Diontae Spencer', 'Noah Brown', 'Jaydon Mickens', 'Joe Reed', 'Jamal Agnew', 'Auden Tate', 'Cody Hollister', 'Andre Roberts', 'Cam Sims', 'Christian Blake', 'Olamide Zaccheaus', 'Matthew Slater', 'Kalif Raymond', 'JJ Arcega-Whiteside', 'Richie James', 'Andy Isabella', 'Damion Ratley', 'Jakobi Meyers', 'Ray-Ray McCloud', 'Ashton Dulin', 'Nsimba Webster', 'Dante Pettis', 'Kenny Stills', 'Mark Andrews', 'Dallas Goedert', 'Noah Fant', 'T.J. Hockenson', 'Travis Kelce', 'David Njoku', 'Logan Thomas', 'Jonnu Smith', 'O.J. Howard', 'Jordan Akins', 'Jared Cook', 'Greg Olsen', 'Jimmy Graham', 'Hunter Henry', 'Zach Ertz', 'Darren Waller', 'George Kittle', 'MyCole Pruitt', 'C.J. Uzomah', 'Jack Doyle', 'Tyler Higbee', 'Hayden Hurst', 'Chris Herndon', 'Mike Gesicki', 'Kyle Rudolph', 'Dawson Knox', 'Dan Arnold', 'Ryan Izzo', 'Mo Alie-Cox', 'Darren Fells', 'Kaden Smith', 'Ian Thomas', 'Austin Hooper', 'Eric Ebron', 'Jordan Reed', 'Rob Gronkowski', 'Evan Engram', 'Will Dissly', 'Levine Toilolo', 'Blake Jarwin', 'Josiah Deguara', 'Dalton Schultz', 'Irv Smith Jr.', 'Anthony Firkser', 'Virgil Green', 'Jacob Hollister', 'Tyler Eifert', 'Nick Boyle', 'Gerald Everett', 'Drew Sample', 'Harrison Bryant', 'Chris Manhertz', 'Vance McDonald', 'Jason Witten', 'Jake Butt', "James O'Shaughnessy", 'Jeremy Sprinkle', 'Richard Rodgers', 'Jaeden Graham', 'Jesse James', 'Andrew Beck', 'Robert Tonyan', 'Antony Auclair', 'Marcedes Lewis', 'James Winchester', 'Cethan Carter', 'Adam Shaheen', 'Cole Kmet', 'Eric Tomlinson', 'Derek Carrier', 'Demetrius Harris', 'Johnny Mundt', 'Foster Moreau', 'J.P. Holtz', 'Luke Willson', 'Devin Asiasi', 'Ryan Griffin', 'Tyler Kroft', 'Blake Bell', 'Darrell Daniels', 'Stephen Anderson', 'Josh Hill', 'Luke Stocker', 'Daniel Brown', 'Stephen Carlson', 'Jace Sternberger', 'Maxx Williams', 'Durham Smythe', 'Tyler Conklin', 'Cameron Brate', 'Trevon Wesco', 'Ross Dwelley', 'Nick Vannett', 'New Orleans', 'Washington', 'Baltimore', 'New England', 'LA Chargers', 'Buffalo', 'Pittsburgh', 'New York J', 'Kansas City', 'Jacksonville', 'Seattle', 'Arizona', 'Green Bay', 'Denver', 'Dallas', 'San Francisco', 'Miami', 'LA Rams', 'New York G', 'Indianapolis', 'Philadelphia', 'Cincinnati', 'Chicago', 'Tennessee', 'Detroit', 'Las Vegas', 'Tampa Bay', 'Houston', 'Cleveland', 'Carolina', 'Atlanta', 'Minnesota', 'Jeremy McNichols', 'Cordarrelle Patterson', 'Josh Lambo', 'Joey Slye', 'Mason Crosby', 'Matt Prater', 'Daniel Carlson', 'Mike Badgley', 'Wil Lutz', 'Robbie Gould', 'Dustin Hopkins', 'Harrison Butker', 'Randy Bullock', 'Younghoe Koo', 'Jason Myers', 'Tyler Bass', 'Cairo Santos', 'Chris Boswell', 'Justin Tucker', 'Zane Gonzalez', 'Rodrigo Blankenship', 'Sam Sloman', 'Ryan Succop', 'Jake Elliott', 'Greg Zuerlein', 'Sam Ficken', 'Dan Bailey', 'Stephen Gostkowski', 'Jason Sanders', 'Graham Gano', 'Nick Folk', 'Brandon McManus', "Ka'imi Fairbairn", 'Dak Prescott', 'Cam Newton', 'Josh Allen', 'Russell Wilson', 'Kyler Murray', 'Matt Ryan', 'Patrick Mahomes II', 'Ryan Tannehill', 'Gardner Minshew', 'Joe Burrow', 'Ryan Fitzpatrick', 'Jared Goff', 'Justin Herbert', 'Derek Carr', 'Jeff Driskel', 'Ben Roethlisberger', 'Aaron Rodgers', 'Lamar Jackson', 'Matthew Stafford', 'Baker Mayfield', 'Deshaun Watson', 'Drew Brees', 'Mitchell Trubisky', 'Carson Wentz', 'Jimmy Garoppolo', 'Philip Rivers', 'Sam Darnold', 'Dwayne Haskins', 'Teddy Bridgewater', 'Tom Brady', 'Daniel Jones', 'Nick Mullens', 'Kirk Cousins', 'Taysom Hill', 'Chris Streveler', 'Andy Dalton', 'Tim Boyle', 'Drew Lock', 'Aaron Jones', 'Alvin Kamara', 'Nick Chubb', 'Leonard Fournette', 'Kareem Hunt', 'Christian McCaffrey', 'David Montgomery', 'James Robinson', 'Ezekiel Elliott', 'James Conner', 'Darrell Henderson', 'Miles Sanders', 'Chris Carson', 'Jonathan Taylor', 'Raheem Mostert', 'Austin Ekeler', 'Dalvin Cook', 'Melvin Gordon', 'Mark Ingram', 'Jerick McKinnon', 'Dion Lewis', 'Joshua Kelley', 'Josh Jacobs', 'Antonio Gibson', 'Mike Davis', 'Myles Gaskin', 'Joe Mixon', 'Kenyan Drake', 'Chris Thompson', 'Clyde Edwards-Helaire', 'Ronald Jones', "D'Andre Swift", 'Kerryon Johnson', 'Jalen Richard', 'Devin Singletary', 'Derrick Henry', 'Jamaal Williams', 'Alec Ingold', 'Gus Edwards', 'Rex Burkhead', 'Jakob Johnson', 'J.K. Dobbins', 'Patrick Ricard', 'Jordan Howard', 'Frank Gore', 'Todd Gurley', 'David Johnson', 'Boston Scott', 'J.D. McKissic', 'Tevin Coleman', 'Giovani Bernard', 'Malcolm Brown', 'Carlos Hyde', 'LeSean McCoy', 'Matt Breida', 'Latavius Murray', 'Cordarrelle Patterson', 'Adrian Peterson', 'Jordan Wilkins', 'Zack Moss', 'Ty Montgomery', 'Brian Hill', 'Tarik Cohen', 'Chase Edmonds', 'Saquon Barkley', 'Kalen Ballage', 'Darwin Thompson', 'Alexander Mattison', 'Travis Homer', 'Ito Smith', 'Royce Freeman', 'Sony Michel', 'Alex Armah', 'AJ Dillon', "La'Mical Perine", 'Darrel Williams', 'Kyle Juszczyk', 'Tony Pollard', 'Cam Akers', 'Devontae Booker', 'Nyheim Hines', 'Jaylen Samuels', 'Senorise Perry', 'Tyler Ervin', 'Keith Smith', 'Ryan Nall', 'Jeremy McNichols', 'Andy Janovich', 'C.J. Ham', 'Jeff Wilson Jr.', 'Peyton Barber', 'Mike Boone', 'Michael Burton', 'Nick Bellore', 'Bruce Miller', 'Khari Blasingame', 'Cullen Gillaspia', 'D.J. Foster', 'Chandler Cox', 'Elijhaa Penny', 'Ameer Abdullah', 'Ty Johnson', 'Anthony Sherman', 'Patrick Laird', "D'Ernest Johnson", 'Buddy Howell', 'Derek Watt', 'Dare Ogunbowale', "Ke'Shawn Vaughn", 'Josh Adams', 'Taiwan Jones', 'Samaje Perine', 'Corey Clement', 'Trenton Cannon', 'J.J. Taylor', 'Benny Snell', 'Calvin Ridley', 'Stefon Diggs', 'Terry McLaurin', 'Julian Edelman', 'Mike Evans', 'Tyreek Hill', 'Diontae Johnson', 'D.K. Metcalf', 'DeAndre Hopkins', 'Tyler Boyd', 'Chase Claypool', 'Tyler Lockett', 'John Brown', 'D.J. Moore', 'Odell Beckham Jr.', 'Braxton Berrios', 'Keelan Cole', 'CeeDee Lamb', 'DeVante Parker', 'Russell Gage', 'Robby Anderson', 'Adam Humphries', 'Keenan Allen', 'Amari Cooper', 'David Moore', 'Brandin Cooks', "N'Keal Harry", 'Mike Thomas', 'Corey Davis', "Tre'Quan Smith", 'Darnell Mooney', 'Isaiah Ford', 'Chris Hogan', 'Cooper Kupp', 'D.J. Chark', 'Marvin Jones', 'Robert Woods', 'Damiere Byrd', 'Cole Beasley', 'DeSean Jackson', 'Zach Pascal', 'Marvin Hall', 'Kendrick Bourne', 'Laviska Shenault Jr.', 'Freddie Swain', 'Larry Fitzgerald', 'Randall Cobb', 'JuJu Smith-Schuster', 'Jerry Jeudy', 'Courtland Sutton', 'Zay Jones', 'Jalen Guyton', 'Marquez Valdes-Scantling', 'Andy Isabella', 'KJ Hamler', 'Golden Tate', 'Gabriel Davis', 'Christian Kirk', 'Quintez Cephus', 'Chris Conley', 'Michael Gallup', 'Marquise Brown', 'Van Jefferson', 'Mecole Hardman', 'Jarvis Landry', 'Jalen Reagor', 'Allen Lazard', 'Justin Jefferson', 'Miles Boykin', 'Justin Watson', 'Michael Pittman Jr.', 'Isaiah McKenzie', 'Hunter Renfrow', 'Bryan Edwards', 'Davante Adams', 'Preston Williams', 'Tee Higgins', 'KhaDarel Hodge', 'Curtis Samuel', 'Deonte Harris', 'Josh Malone', 'Steven Sims Jr.', 'Darius Slayton', 'Allen Robinson', 'Cameron Batson', 'C.J. Board', 'Adam Thielen', 'Sterling Shepard', 'A.J. Green', 'Demarcus Robinson', 'T.Y. Hilton', 'Josh Reynolds', 'Willie Snead', 'Noah Brown', 'James Washington', 'Julio Jones', 'Tim Patrick', 'Brandon Aiyuk', 'Danny Amendola', 'Olabisi Johnson', 'Javon Wims', 'Dontrelle Inman', 'Kenny Stills', 'Nelson Agholor', 'Devin Duvernay', 'Mike Williams', 'Emmanuel Sanders', 'Scott Miller', 'Breshad Perriman', 'Trent Taylor', 'Sammy Watkins', 'Olamide Zaccheaus', 'Jakobi Meyers', 'Ashton Dulin', 'DaeSean Hamilton', 'Greg Ward', 'Henry Ruggs III', 'Antonio Gandy-Golden', 'Joe Reed', 'Parris Campbell', 'Jakeem Grant', 'Jamal Agnew', 'Lynn Bowden', 'Keke Coutee', 'Marcus Kemp', 'Tajae Sharpe', 'Mack Hollins', 'Byron Pringle', 'Seth Roberts', 'Brandon Zylstra', 'Bennie Fowler', 'Brandon Powell', 'Alex Erickson', 'John Ross', 'Malik Turner', 'John Hightower', 'Trent Sherfield', 'K.J. Osborn', 'James Proche', 'Pharoh Cooper', 'Cedrick Wilson', 'JoJo Natson', 'Will Fuller', 'Diontae Spencer', 'Anthony Miller', 'Jaydon Mickens', 'Rashard Higgins', 'Cyril Grayson', 'Andre Roberts', 'Cam Sims', 'Christian Blake', 'Matthew Slater', 'DeAndre Carter', 'Kalif Raymond', 'JJ Arcega-Whiteside', 'Damion Ratley', 'Ray-Ray McCloud', 'Nsimba Webster', 'Collin Johnson', 'Dante Pettis', 'Tyler Higbee', 'Mike Gesicki', 'Jonnu Smith', 'Darren Waller', 'Jordan Reed', 'Travis Kelce', 'Dalton Schultz', 'Noah Fant', 'Hayden Hurst', 'Mo Alie-Cox', 'C.J. Uzomah', 'Hunter Henry', 'Tyler Eifert', 'Robert Tonyan', 'Evan Engram', 'Darren Fells', 'Jordan Akins', 'Jared Cook', 'T.J. Hockenson', 'Drew Sample', 'Zach Ertz', 'Reggie Gilliam', 'Anthony Firkser', "James O'Shaughnessy", 'Eric Ebron', 'Dallas Goedert', 'Logan Thomas', 'Blake Bell', 'Foster Moreau', 'Dan Arnold', 'Mark Andrews', 'Austin Hooper', 'Ryan Izzo', 'Jimmy Graham', 'Dawson Knox', 'Adam Trautman', 'Josh Hill', 'Harrison Bryant', 'Cole Kmet', 'O.J. Howard', 'Nick Boyle', 'Will Dissly', 'Chris Manhertz', 'Chris Herndon', 'Ross Dwelley', 'Marcedes Lewis', 'Adam Shaheen', 'Kaden Smith', 'Irv Smith Jr.', 'Vance McDonald', 'Jason Witten', 'Marcus Baugh', 'Jeremy Sprinkle', 'Gerald Everett', 'Richard Rodgers', 'Jake Butt', 'Kyle Rudolph', 'Jaeden Graham', 'Jesse James', 'Andrew Beck', 'James Winchester', 'Cethan Carter', 'Greg Olsen', 'Ian Thomas', 'Derek Carrier', 'Demetrius Harris', 'Johnny Mundt', 'Virgil Green', 'J.P. Holtz', 'Luke Willson', 'Devin Asiasi', 'Ryan Griffin', 'MyCole Pruitt', 'Tyler Kroft', 'Darrell Daniels', 'Stephen Anderson', 'Levine Toilolo', 'Nick Vannett', 'Luke Stocker', 'Rob Gronkowski', 'Daniel Brown', 'Stephen Carlson', 'Jace Sternberger', 'Jacob Hollister', 'Durham Smythe', 'Tyler Conklin', 'Cameron Brate', 'Trevon Wesco', 'Baltimore', 'Indianapolis', 'Tampa Bay', 'Pittsburgh', 'Chicago', 'Green Bay', 'Arizona', 'New York G', 'LA Rams', 'New England', 'Denver', 'Kansas City', 'San Francisco', 'Tennessee', 'New Orleans', 'New York J', 'Houston', 'Cleveland', 'Washington', 'Minnesota', 'Carolina', 'Atlanta', 'Seattle', 'LA Chargers', 'Buffalo', 'Las Vegas', 'Miami', 'Philadelphia', 'Jacksonville', 'Cincinnati', 'Detroit', 'Dallas', 'C.J. Prosise', 'Cordarrelle Patterson', 'Mohamed Sanu', 'Tanner Hudson', 'Jordan Thomas', 'Younghoe Koo', 'Justin Tucker', 'Rodrigo Blankenship', 'Zane Gonzalez', 'Harrison Butker', 'Randy Bullock', 'Stephen Gostkowski', "Ka'imi Fairbairn", 'Daniel Carlson', 'Jason Sanders', 'Greg Zuerlein', 'Mason Crosby', 'Mike Badgley', 'Brandon McManus', 'Robbie Gould', 'Sam Ficken', 'Tyler Bass', 'Ryan Succop', 'Jake Elliott', 'Graham Gano', 'Sam Sloman', 'Josh Lambo', 'Wil Lutz', 'Nick Folk', 'Chris Boswell', 'Jason Myers', 'Cairo Santos', 'Cody Parkey', 'Joey Slye', 'Dan Bailey', 'Dustin Hopkins', 'Matt Prater', 'Patrick Mahomes II', 'Russell Wilson', 'Josh Allen', 'Dak Prescott', 'Jared Goff', 'Kyler Murray', 'Ryan Fitzpatrick', 'Aaron Rodgers', 'Tom Brady', 'Drew Brees', 'Carson Wentz', 'Kirk Cousins', 'Joe Burrow', 'Ben Roethlisberger', 'Matthew Stafford', 'Nick Foles', 'Deshaun Watson', 'Nick Mullens', 'Justin Herbert', 'Derek Carr', 'Teddy Bridgewater', 'Baker Mayfield', 'Lamar Jackson', 'Ryan Tannehill', 'Philip Rivers', 'Mitchell Trubisky', 'Matt Ryan', 'Cam Newton', 'Dwayne Haskins', 'Jeff Driskel', 'Gardner Minshew', 'Sam Darnold', 'Daniel Jones', 'Brett Rypien', 'Jalen Hurts', 'Jacoby Brissett', 'Chris Streveler', 'Ryan Finley', 'Taysom Hill', 'Alvin Kamara', 'Rex Burkhead', 'James Robinson', 'Derrick Henry', 'Austin Ekeler', 'Dalvin Cook', 'Nick Chubb', 'James Conner', 'Jeff Wilson Jr.', 'Mike Davis', 'Darrell Henderson', 'Clyde Edwards-Helaire', 'Aaron Jones', 'Jerick McKinnon', 'Sony Michel', 'Ezekiel Elliott', 'Todd Gurley', 'Brian Hill', 'Devin Singletary', 'Kareem Hunt', 'Miles Sanders', 'Jonathan Taylor', 'Myles Gaskin', 'David Johnson', 'Antonio Gibson', 'Chris Carson', 'Adrian Peterson', 'Kenyan Drake', 'Ronald Jones', 'Nyheim Hines', 'Josh Jacobs', 'Kalen Ballage', 'Joe Mixon', 'Giovani Bernard', 'Latavius Murray', 'Anthony Sherman', 'Frank Gore', 'J.D. McKissic', 'J.K. Dobbins', 'David Montgomery', 'Chris Thompson', 'Jordan Howard', 'Melvin Gordon', 'Tarik Cohen', 'Anthony McFarland', 'Jordan Wilkins', 'Royce Freeman', 'Chase Edmonds', 'J.J. Taylor', 'Joshua Kelley', 'Gus Edwards', 'Jalen Richard', 'Kerryon Johnson', 'LeSean McCoy', "La'Mical Perine", 'Leonard Fournette', 'Devontae Booker', 'Mark Ingram', 'Carlos Hyde', 'Alexander Mattison', "D'Andre Swift", 'Wayne Gallman', 'Tyler Ervin', 'JaMycal Hasty', 'Jamaal Williams', 'Malcolm Brown', 'Travis Homer', 'Andy Janovich', 'T.J. Yeldon', 'Darrel Williams', 'Reggie Bonnafon', 'C.J. Ham', 'Alec Ingold', 'Dion Lewis', 'Cordarrelle Patterson', 'Kyle Juszczyk', 'Jakob Johnson', 'Cullen Gillaspia', 'Benny Snell', 'Jeremy McNichols', 'Devonta Freeman', 'Darrynton Evans', 'Keith Smith', 'Corey Clement', 'Peyton Barber', 'Boston Scott', 'Matt Breida', 'Jaylen Samuels', 'C.J. Prosise', 'Mike Boone', 'Dwayne Washington', 'Michael Burton', 'Nick Bellore', 'Bruce Miller', 'Tony Pollard', 'Khari Blasingame', 'Ito Smith', 'Ryan Nall', 'AJ Dillon', 'Chandler Cox', 'Elijhaa Penny', 'Ameer Abdullah', 'Patrick Laird', "D'Ernest Johnson", 'Buddy Howell', 'Qadree Ollison', 'Derek Watt', 'Dare Ogunbowale', 'Alex Armah', 'Taiwan Jones', 'Samaje Perine', 'Patrick Ricard', 'Raymond Calais', 'Darwin Thompson', 'Tyler Lockett', 'Justin Jefferson', 'Cedrick Wilson', 'Keenan Allen', 'Allen Lazard', 'Allen Robinson', 'Michael Gallup', 'Cooper Kupp', 'Robert Woods', 'DeAndre Hopkins', 'Tyreek Hill', 'Brandon Aiyuk', 'Tee Higgins', 'Andy Isabella', 'Tyler Boyd', 'Randall Cobb', 'Hunter Renfrow', 'Dontrelle Inman', 'D.K. Metcalf', 'Greg Ward', 'Mecole Hardman', 'Chris Godwin', 'Kenny Golladay', 'Braxton Berrios', 'JuJu Smith-Schuster', 'Calvin Ridley', 'Emmanuel Sanders', 'Will Fuller', 'Mike Evans', 'Amari Cooper', 'Cole Beasley', 'Kalif Raymond', 'Stefon Diggs', 'Tim Patrick', 'Anthony Miller', 'Adam Thielen', 'Terry McLaurin', 'Gabriel Davis', 'Scott Miller', 'Sammy Watkins', 'Corey Davis', 'DeVante Parker', 'CeeDee Lamb', 'Kendrick Bourne', 'Robby Anderson', 'Jerry Jeudy', 'Odell Beckham Jr.', 'Devin Duvernay', 'Preston Williams', 'D.J. Moore', 'Curtis Samuel', 'T.Y. Hilton', 'Marvin Jones', 'Keelan Cole', "Tre'Quan Smith", 'Golden Tate', 'A.J. Green', 'Adam Humphries', 'James Washington', 'Olamide Zaccheaus', 'Josh Reynolds', 'Laviska Shenault Jr.', 'Bryan Edwards', 'Jarvis Landry', 'Jakeem Grant', 'Isaiah Wright', 'Kenny Stills', 'Chris Conley', 'Darius Slayton', 'Zay Jones', "N'Keal Harry", 'Lawrence Cager', 'Zach Pascal', 'KJ Hamler', 'Brandon Powell', 'Damiere Byrd', 'Nelson Agholor', 'Michael Pittman Jr.', 'Auden Tate', 'Brandin Cooks', 'Russell Gage', 'Ted Ginn Jr.', 'KeeSean Johnson', 'Deontay Burnett', 'Damion Ratley', 'Jalen Guyton', 'Daurice Fountain', 'Julian Edelman', 'Jamal Agnew', 'John Hightower', 'Chase Claypool', 'Darnell Mooney', 'Seth Roberts', 'Trent Taylor', 'Isaiah Ford', 'Marquise Brown', 'Danny Amendola', 'Antonio Gandy-Golden', 'Mike Williams', 'DeSean Jackson', 'K.J. Hill', 'Mike Thomas', 'Freddie Swain', 'Isaiah McKenzie', 'Deonte Harris', 'C.J. 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Hockenson', 'Noah Fant', 'Tyler Higbee', 'Nick Boyle', 'Ross Dwelley', 'Dalton Schultz', 'Harrison Bryant', 'Lee Smith', 'Hayden Hurst', 'O.J. Howard', 'Logan Thomas', 'Jace Sternberger', 'Vance McDonald', 'Gerald Everett', "James O'Shaughnessy", 'Austin Hooper', 'Jordan Akins', 'Evan Engram', 'Mark Andrews', 'Chris Herndon', 'Darrell Daniels', 'Jordan Reed', 'Jared Cook', 'Demetrius Harris', 'Adam Trautman', 'MyCole Pruitt', 'Richard Rodgers', 'Tyler Eifert', 'Darren Waller', 'Kaden Smith', 'Dan Arnold', 'Will Dissly', 'Ian Thomas', 'Dallas Goedert', 'Durham Smythe', 'Luke Stocker', 'Jake Butt', 'Cethan Carter', 'Drew Sample', 'Tanner Hudson', 'Jeremy Sprinkle', 'Anthony Firkser', 'Jack Doyle', 'Chris Manhertz', 'Jaeden Graham', 'Darren Fells', 'Ryan Izzo', 'Andrew Beck', 'Noah Togiai', 'James Winchester', 'Adam Shaheen', 'Cole Kmet', 'Pharaoh Brown', 'Derek Carrier', 'Johnny Mundt', 'Virgil Green', 'J.P. 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2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2700.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 3000.0, 2900.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 3200.0, 2600.0, 3900.0, 2400.0, 3300.0, 3900.0, 3700.0, 3400.0, 3700.0, 4000.0, 2900.0, 2300.0, 3400.0, 2900.0, 2800.0, 2600.0, 3500.0, 2100.0, 3200.0, 2900.0, 2700.0, 2000.0, 3000.0, 2500.0, 3100.0, 3600.0, 2700.0, 2200.0, 2400.0, 3300.0, 3700.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]}) df['id'] = range(len(df)) df['is_home'] = [True for i in range(len(df))] # mock this for now; not currently used in tests return df[(df['week'].isin(weeks)) & (df['dk_salary'] > 0) & (df['fd_salary'] > 0)]

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Python

models/__init__.py

RishiTejaMadduri/pyramid-fuse

a8bad9adc2734572c87c5ee4c2a956aa2d04fb97

[ "MIT" ]

null

models/__init__.py

RishiTejaMadduri/pyramid-fuse

a8bad9adc2734572c87c5ee4c2a956aa2d04fb97

[ "MIT" ]

null

models/__init__.py

RishiTejaMadduri/pyramid-fuse

a8bad9adc2734572c87c5ee4c2a956aa2d04fb97

[ "MIT" ]

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from . import pyramid_fusion2

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Python

openpnm/models/geometry/pore_surface_area/__init__.py

lixuekai2001/OpenPNM

9026f0fed427d37f4caf1a79e4a7684490d52cf6

[ "MIT" ]

null

openpnm/models/geometry/pore_surface_area/__init__.py

lixuekai2001/OpenPNM

9026f0fed427d37f4caf1a79e4a7684490d52cf6

[ "MIT" ]

null

openpnm/models/geometry/pore_surface_area/__init__.py

lixuekai2001/OpenPNM

9026f0fed427d37f4caf1a79e4a7684490d52cf6

[ "MIT" ]

null

r""" pore_surface_area Models ------------------------- This model contains a selection of functions for Calculating pore surface area. """ from ._funcs import *

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bb89be37c57b1dc63ec322c3bfbfc5183e296c3a

147

py

Python

inscribete/views.py

Kiri23/Django-custom-user-template

74ed531ade35c3018ca3068e8c68b966bcc4f316

[ "MIT" ]

null

inscribete/views.py

Kiri23/Django-custom-user-template

74ed531ade35c3018ca3068e8c68b966bcc4f316

[ "MIT" ]

3

2020-02-12T00:04:04.000Z

2021-06-10T21:33:23.000Z

inscribete/views.py

Kiri23/Django-custom-user-template

74ed531ade35c3018ca3068e8c68b966bcc4f316

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def inscribete(request): return render(request, "inscribete/inscribete.html")

18.375

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0.768707

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147

6.277778

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0.142857

147

7

57

21

0.896825

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1

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1

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6

a59ee766bfce49eeaf29b59f6c937d9ac6c34ea5

29

py

Python

evaluator/__init__.py

SoluMilken/evaluator

b192d81d5caf7e66f27f2da8484b92e5f14feadc

[ "MIT" ]

null

evaluator/__init__.py

SoluMilken/evaluator

b192d81d5caf7e66f27f2da8484b92e5f14feadc

[ "MIT" ]

null

evaluator/__init__.py

SoluMilken/evaluator

b192d81d5caf7e66f27f2da8484b92e5f14feadc

[ "MIT" ]

null

from .summary import summary

14.5

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0.827586

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0.96

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null

0

1

0

1

0

1

0

6

3c10409f819a1a9b3db0fd6425c6a7944a5e37dc

101

py

Python

dags/hechms_distributed_dag.py

hasithadkr7/HecHmsDistributed

c64708d0b19c457aff9ecd0b5f235afad9744e8a

[ "MIT" ]

1

2019-04-17T03:21:58.000Z

dags/hechms_distributed_dag.py

hasithadkr7/HecHmsDistributed

c64708d0b19c457aff9ecd0b5f235afad9744e8a

[ "MIT" ]

null

dags/hechms_distributed_dag.py

hasithadkr7/HecHmsDistributed

c64708d0b19c457aff9ecd0b5f235afad9744e8a

[ "MIT" ]

null

import airflow from airflow import DAG from airflow.operators.python_operator import PythonOperator

20.2

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101

6.692308

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1

0

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6

3c180f7d5cdfbc1aa9926b28e9bafe6c640ca097

173

py

Python

imports/twofiles/main.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

imports/twofiles/main.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

imports/twofiles/main.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

# import for_import # for_import.x("test") ########## # from for_import import x # x = x("test") ########## import for_import x = for_import.x("hello") x("hello")

8.65

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0.560694

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1

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null

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6

3c6329624aff6fbfcfa08a220c34166fa19673c5

96

py

Python

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_symbolic.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_symbolic.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/f2py/tests/test_symbolic.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/ef/1b/d7/4df76dada3c65c78d83b067cb389f2411b9353e036ca1793e79dd329c4

96

0.895833

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0

1

0

1

0

null

1

0

1

0

6

b1ded9c5716ea056fbed13630a3946228f967176

3,160

py

Python

pyramid_describe/test_constant.py

cadithealth/pyramid_describe

7b4abd15738dc3afbe67bd2f968bcf6bab66def2

[ "MIT" ]

3

2015-03-11T22:43:02.000Z

2016-06-09T09:58:41.000Z

pyramid_describe/test_constant.py

cadithealth/pyramid_describe

7b4abd15738dc3afbe67bd2f968bcf6bab66def2

[ "MIT" ]

1

2015-10-13T20:49:09.000Z

2015-10-16T03:09:20.000Z

pyramid_describe/test_constant.py

cadithealth/pyramid_describe

7b4abd15738dc3afbe67bd2f968bcf6bab66def2

[ "MIT" ]

null

# -*- coding: utf-8 -*- #------------------------------------------------------------------------------ # file: $Id$ # auth: Philip J Grabner <grabner@cadit.com> # date: 2016/02/19 # copy: (C) Copyright 2016-EOT Cadit Inc., All Rights Reserved. #------------------------------------------------------------------------------ import unittest from . import constant #------------------------------------------------------------------------------ class TestConstant(unittest.TestCase): #---------------------------------------------------------------------------- def test_parse_invalid(self): for val, exc in [ ('', "invalid constant: ''"), ('0x', "invalid constant (tried hex): '0x'"), ('-x', "No JSON object could be decoded"), ('- 1', "No JSON object could be decoded"), ('no', "No JSON object could be decoded"), ('"no', "invalid constant (tried yaml): '\"no'"), ]: with self.assertRaises(ValueError) as cm: constant.parse(val) self.assertEqual(str(cm.exception), exc) #---------------------------------------------------------------------------- def test_hex(self): self.assertEqual(constant.parse('0x61'), 'a') self.assertEqual(constant.parse('0x616263'), 'abc') #---------------------------------------------------------------------------- def test_bool(self): self.assertEqual(constant.parse('true'), True) self.assertEqual(constant.parse('false'), False) #---------------------------------------------------------------------------- def test_null(self): self.assertEqual(constant.parse('null'), None) self.assertEqual(constant.parse(' null '), None) #---------------------------------------------------------------------------- def test_num(self): self.assertEqual(constant.parse('61'), 61) self.assertEqual(constant.parse('87.8'), 87.8) self.assertEqual(constant.parse('-87.8'), -87.8) #---------------------------------------------------------------------------- def test_str(self): self.assertEqual(constant.parse('"foo"'), 'foo') self.assertEqual(constant.parse("'foo'"), 'foo') self.assertEqual(constant.parse('0x666f6f'), 'foo') #---------------------------------------------------------------------------- def test_list(self): self.assertEqual(constant.parse('["foo", \'bar\', 6]'), ['foo', 'bar', 6]) #---------------------------------------------------------------------------- def test_dict(self): self.assertEqual(constant.parse('{a: 10, "b": foo}'), dict(a=10, b='foo')) #---------------------------------------------------------------------------- def test_multi(self): self.assertEqual(constant.parseMulti('"foo"', '|'), ['foo']) self.assertEqual(constant.parseMulti('"foo" |6|0x61 ', '|'), ['foo', 6, 'a']) self.assertEqual(constant.parseMulti(' null | 6|0x61 ', '|'), [None, 6, 'a']) #------------------------------------------------------------------------------ # end of $Id$ # $ChangeLog$ #------------------------------------------------------------------------------

41.578947

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6

590007c579bc919e42cfa7910d740a75b17ed6e4

209

py

Python

python3/learn-python/slicing-list.py

Nahid-Hassan/code-snippets

24bd4b81564887822a0801a696001fcbeb6a7a75

[ "MIT" ]

2

2020-09-29T04:09:41.000Z

2020-10-18T13:33:36.000Z

python3/learn-python/slicing-list.py

Nahid-Hassan/code-snippets

24bd4b81564887822a0801a696001fcbeb6a7a75

[ "MIT" ]

null

python3/learn-python/slicing-list.py

Nahid-Hassan/code-snippets

24bd4b81564887822a0801a696001fcbeb6a7a75

[ "MIT" ]

1

2021-12-26T04:55:55.000Z

my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 # -10,-9,-8,-7,-6,-5,-4,-3,-2,-1 # list[start:end:step] print(my_list[2:-1:2]) print(my_list[:]) print(my_list[::-1])

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1

0

1

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null

0

1

0

6

5913f2bb776adf0ad1113e9a53a7b46b0d4a87c8

262

py

Python

flask_toolkit/shared/exceptions.py

Creditas/flask-toolkit

830354b4e2c0f9fdac1d000dcc760226b704628a

[ "MIT" ]

3

2018-07-31T16:11:17.000Z

2021-08-14T17:03:44.000Z

flask_toolkit/shared/exceptions.py

Creditas/flask-toolkit

830354b4e2c0f9fdac1d000dcc760226b704628a

[ "MIT" ]

6

2018-07-11T14:34:09.000Z

2019-11-29T13:53:13.000Z

flask_toolkit/shared/exceptions.py

Creditas/flask-toolkit

830354b4e2c0f9fdac1d000dcc760226b704628a

[ "MIT" ]

null

class ObjectDoesNotExistException(Exception): pass class ForbiddenException(Exception): pass class BadRequestException(Exception): pass class InvalidDomainConditions(Exception): pass class ObjectAlreadyExistException(Exception): pass

13.789474

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0

0.5

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1

null

1

0

null

0

1

0

6

3ce1a24465550bcd389a9720d94322b5b07f6b48

174

py

Python

backend/db/entities/__init__.py

R-N/sistem_gaji_vue_thrift

9ba800b4d8e7849e2c6c4016cb32633caab087be

[ "MIT" ]

null

backend/db/entities/__init__.py

R-N/sistem_gaji_vue_thrift

9ba800b4d8e7849e2c6c4016cb32633caab087be

[ "MIT" ]

null

backend/db/entities/__init__.py

R-N/sistem_gaji_vue_thrift

9ba800b4d8e7849e2c6c4016cb32633caab087be

[ "MIT" ]

null

from .general import * from .general import DbGeneralEntity from .staging import DbStagingEntity from .commited import DbCommitedEntity from .laporan import DbLaporanEntity

24.857143

38

0.844828

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174

7.736842

0.526316

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0.231293

0

0.12069

174

6

39

29

0.960784

0

1

0

true

0

1

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1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

a73908ff728f9980a2924161adcd75776b911a41

30

py

Python

trac/Lib/site-packages/tracaddheadersplugin-0.3-py2.7.egg/tracaddheaders/__init__.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

2

2015-08-06T04:19:21.000Z

2020-04-29T23:52:10.000Z

trac/Lib/site-packages/tracaddheadersplugin-0.3-py2.7.egg/tracaddheaders/__init__.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

null

trac/Lib/site-packages/tracaddheadersplugin-0.3-py2.7.egg/tracaddheaders/__init__.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

null

from tracaddheaders import *

15

29

0.8

3

30

8

1

0

0.166667

30

1

30

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

598c213f5a6d0f0905a56bf52bcd56989cf40c85

46

py

Python

ml_repo/1. Python Programming/m2.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

ml_repo/1. Python Programming/m2.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

ml_repo/1. Python Programming/m2.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

import m1 print("M2 Module %s",(__name__))

7.666667

32

0.652174

7

46

3.714286

1

0

0.052632

0.173913

46

5

33

9.2

0.631579

0

0.266667

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5991f26a406bb63bc45bda875058819a6cf841cd

80

py

Python

chzhshch/__init__.py

hpzhen/ChZhShCh

a5af067e342cf734fafb827eab17462c4bd6f74d

[ "Apache-2.0" ]

77

2017-12-17T12:19:49.000Z

2022-02-15T07:59:55.000Z

chzhshch/__init__.py

hpzhen/ChZhShCh

a5af067e342cf734fafb827eab17462c4bd6f74d

[ "Apache-2.0" ]

14

2017-12-18T15:43:52.000Z

2022-03-11T23:49:45.000Z

chzhshch/__init__.py

hpzhen/ChZhShCh

a5af067e342cf734fafb827eab17462c4bd6f74d

[ "Apache-2.0" ]

62

2018-01-06T16:31:45.000Z

2022-03-20T01:42:55.000Z

from . import feature from . import external_package from . import inner_package

26.666667

30

0.825

11

80

5.818182

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0.46875

0

0.1375

80

3

31

26.666667

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true

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null

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1

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1

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1

0

6

abe5b7fd6c3a1f1f362502cf073f8ad2cd084a72

177

py

Python

metashare/repository/templatetags/get_media_url.py

zeehio/META-SHARE

b796769629734353a63d98db72c84617f725e544

[ "BSD-3-Clause" ]

11

2015-07-13T13:36:44.000Z

2021-11-15T08:07:25.000Z

metashare/repository/templatetags/get_media_url.py

zeehio/META-SHARE

b796769629734353a63d98db72c84617f725e544

[ "BSD-3-Clause" ]

13

2015-03-21T14:08:31.000Z

2021-05-18T18:47:58.000Z

metashare/repository/templatetags/get_media_url.py

zeehio/META-SHARE

b796769629734353a63d98db72c84617f725e544

[ "BSD-3-Clause" ]

12

2015-01-07T02:16:50.000Z

2021-05-18T08:25:31.000Z

from django import template from metashare.settings import MEDIA_URL def get_media_url(): return MEDIA_URL register = template.Library() register.simple_tag(get_media_url)

22.125

40

0.819209

26

177

5.307692

0.576923

0.231884

0.15942

0

0.118644

177

8

41

22.125

0.884615

0

1

0.166667

false

0

0.333333

0.166667

0.666667

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null

1

0

1

0

null

0

1

0

6

f9ec9dfff3d9123a7bcb904074d5acdd734974f6

149

py

Python

python/base-pyside-utils.py

n-kats/template

ab2a02b35b7acd5bba846e926d53bf7e10dac4c1

[ "MIT" ]

1

2016-11-07T05:58:50.000Z

python/base-pyside-utils.py

n-kats/template

ab2a02b35b7acd5bba846e926d53bf7e10dac4c1

[ "MIT" ]

null

python/base-pyside-utils.py

n-kats/template

ab2a02b35b7acd5bba846e926d53bf7e10dac4c1

[ "MIT" ]

null

from PySide.QtCore import QObject def _enter(self): return self def _exit(self, *args): pass QObject.__enter__ = _enter QObject.__exit__ = _exit

14.9

33

0.765101

21

149

4.857143

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0

0.147651

149

9

34

16.555556

0.80315

0

1

0.285714

false

0.142857

0.571429

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

e6631a6814096c397c45bb15e33987e1d05e763f

86

py

Python

lab3/tcp_ip_protocol/__init__.py

UnstoppableGuy/Information-networks-security-basics

2135107054f69c6d137480be4436e68a61bf754d

[ "MIT" ]

null

lab3/tcp_ip_protocol/__init__.py

UnstoppableGuy/Information-networks-security-basics

2135107054f69c6d137480be4436e68a61bf754d

[ "MIT" ]

null

lab3/tcp_ip_protocol/__init__.py

UnstoppableGuy/Information-networks-security-basics

2135107054f69c6d137480be4436e68a61bf754d

[ "MIT" ]

null

from tcp_ip_protocol.client import Client from tcp_ip_protocol.router import register

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86

5.142857

0.571429

0.194444

0.25

0.472222

0

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86

2

44

43

0.923077

0

1

0

true

0

1

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1

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1

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null

0

1

0

1

0

null

0

1

0

1

0

6

557797ed623565c55bdbcbf9ba81acb1a5104b39

177

py

Python

Processing/GausianBlur.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

Processing/GausianBlur.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

Processing/GausianBlur.py

damirmardanov/figure-generator

1ed7ee6d423f2d7392e95529336b8f2d35ea65fd

[ "MIT" ]

null

import cv2 import numpy as np class GaussianBlur: @staticmethod def process(image_to_filter): return cv2.GaussianBlur(np.uint8(image_to_filter), (3, 3), 0, 0)

19.666667

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0.706215

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177

4.653846

0.653846

0.115702

0.214876

0

0.049296

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177

8

73

22.125

0.802817

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0.166667

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0.333333

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1

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null

0

1

0

1

0

null

0

1

0

6

55873ce25198a8d70e6600e0765a9745cb1fa5e2

152,521

py

Python

MolNotator/Mode_merger.py

ZzakB/MolNotator

9da9eae905d19b8272cac28c19c459f27159b77e

[ "MIT" ]

7

2021-12-15T16:02:41.000Z

2022-01-06T09:42:47.000Z

MolNotator/Mode_merger.py

ZzakB/MolNotator

9da9eae905d19b8272cac28c19c459f27159b77e

[ "MIT" ]

2

2022-01-12T19:22:32.000Z

2022-02-11T10:57:21.000Z

MolNotator/Mode_merger.py

ZzakB/MolNotator

9da9eae905d19b8272cac28c19c459f27159b77e

[ "MIT" ]

null

def Moder_merger(params : dict): def Solo_M1mHpC4H11N(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + mz - 72.081324 mz_Cl = 34.968853 + mz - 72.081324 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl]) def Solo_M1mHpHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + mz - 44.997654 mz_Cl = 34.968853 + mz - 44.997654 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl]) def Solo_M1m2HpNapHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + mz - 66.979600 mz_Cl = 34.968853 + mz - 66.979600 mz_m2HpNa = 20.97412 + mz - 66.979600 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa]) def Solo_M1m2HpNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + mz - 66.979600 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H]) def Solo_M1m2HpK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + mz - 36.948058 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H]) def Solo_M2mHpC4H11N(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 72.081324)/2 mz_Cl = 34.968853 + (mz - 72.081324)/2 mz_m2HpNa = 20.97412 + (mz - 72.081324)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa]) def Solo_M2mHpHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 44.997654)/2 mz_Cl = 34.968853 + (mz - 44.997654)/2 mz_m2HpNa = 20.97412 + (mz - 44.997654)/2 mz_mHpHCOOH = 44.997654 + (mz - 44.997654)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_mHpHCOOH = peaks.between(mz_mHpHCOOH - prec_mass_error, mz_mHpHCOOH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_mHpHCOOH]) def Solo_M2mH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz + 1.007825)/2 mz_Cl = 34.968853 + (mz + 1.007825)/2 mz_m2HpNa = 20.97412 + (mz + 1.007825)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa]) def Solo_M2pCl(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 34.968853)/2 mz_Cl = 34.968853 + (mz - 34.968853)/2 mz_m2HpNa = 20.97412 + (mz - 34.968853)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa]) def Solo_M2m2HpNapHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 66.979600)/2 mz_Cl = 34.968853 + (mz - 66.979600)/2 mz_m2HpNa = 20.97412 + (mz - 66.979600)/2 mz_m2HpNapHCOOH = 66.9796 + (mz - 66.979600)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH]) def Solo_M2m2HpNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 20.97412)/2 mz_Cl = 34.968853 + (mz - 20.97412)/2 mz_m2HpNa = 20.97412 + (mz - 20.97412)/2 mz_m2HpNapHCOOH = 66.9796 + (mz - 20.97412)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH]) def Solo_M2m2HpK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 36.948058)/2 mz_Cl = 34.968853 + (mz - 36.948058)/2 mz_m2HpNa = 20.97412 + (mz - 36.948058)/2 mz_m2HpNapHCOOH = 66.9796 + (mz - 36.948058)/2 mz_m2HpK = 36.948058 + (mz - 36.948058)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK]) def Solo_M3mH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz + 1.007825)/3 mz_Cl = 34.968853 + (mz + 1.007825)/3 mz_m2HpNa = 20.97412 + (mz + 1.007825)/3 mz_m2HpNapHCOOH = 66.9796 + (mz + 1.007825)/3 mz_m2HpK = 36.948058 + (mz + 1.007825)/3 mz_M2mH = -1.007825 + (mz + 1.007825)*(2/3) mz_M2pCl = 34.968853 + (mz + 1.007825)*(2/3) mz_M2m2HpNa = 20.97412 + (mz + 1.007825)*(2/3) mz_M2m2HpNapHCOOH = 66.9796 + (mz + 1.007825)*(2/3) mz_M2m2HpK = 36.948058 + (mz + 1.007825)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK]) def Solo_M3pCl(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 34.968853)/3 mz_Cl = 34.968853 + (mz - 34.968853)/3 mz_m2HpNa = 20.97412 + (mz - 34.968853)/3 mz_m2HpNapHCOOH = 66.9796 + (mz - 34.968853)/3 mz_m2HpK = 36.948058 + (mz - 34.968853)/3 mz_M2mH = -1.007825 + (mz - 34.968853)*(2/3) mz_M2pCl = 34.968853 + (mz - 34.968853)*(2/3) mz_M2m2HpNa = 20.97412 + (mz - 34.968853)*(2/3) mz_M2m2HpNapHCOOH = 66.9796 + (mz - 34.968853)*(2/3) mz_M2m2HpK = 36.948058 + (mz - 34.968853)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK]) def Solo_M3m2HpNapHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 66.979600)/3 mz_Cl = 34.968853 + (mz - 66.979600)/3 mz_m2HpNa = 20.97412 + (mz - 66.979600)/3 mz_m2HpNapHCOOH = 66.9796 + (mz - 66.979600)/3 mz_m2HpK = 36.948058 + (mz - 66.979600)/3 mz_M2mH = -1.007825 + (mz - 66.979600)*(2/3) mz_M2pCl = 34.968853 + (mz - 66.979600)*(2/3) mz_M2m2HpNa = 20.97412 + (mz - 66.979600)*(2/3) mz_M2m2HpNapHCOOH = 66.9796 + (mz - 66.979600)*(2/3) mz_M2m2HpK = 36.948058 + (mz - 66.979600)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK]) def Solo_M3m2HpNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 20.97412)/3 mz_Cl = 34.968853 + (mz - 20.97412)/3 mz_m2HpNa = 20.97412 + (mz - 20.97412)/3 mz_m2HpNapHCOOH = 66.9796 + (mz - 20.97412)/3 mz_m2HpK = 36.948058 + (mz - 20.97412)/3 mz_M2mH = -1.007825 + (mz - 20.97412)*(2/3) mz_M2pCl = 34.968853 + (mz - 20.97412)*(2/3) mz_M2m2HpNa = 20.97412 + (mz - 20.97412)*(2/3) mz_M2m2HpNapHCOOH = 66.9796 + (mz - 20.97412)*(2/3) mz_M2m2HpK = 36.948058 + (mz - 20.97412)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK]) def Solo_M4mH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz + 1.007825)/4 mz_Cl = 34.968853 + (mz + 1.007825)/4 mz_m2HpNa = 20.97412 + (mz + 1.007825)/4 mz_m2HpNapHCOOH = 66.9796 + (mz + 1.007825)/4 mz_m2HpK = 36.948058 + (mz + 1.007825)/4 mz_M2mH = -1.007825 + (mz + 1.007825)/2 mz_M2pCl = 34.968853 + (mz + 1.007825)/2 mz_M2m2HpNa = 20.97412 + (mz + 1.007825)/2 mz_M2m2HpNapHCOOH = 66.9796 + (mz + 1.007825)/2 mz_M2m2HpK = 36.948058 + (mz + 1.007825)/2 mz_M3mH = -1.007825 + (mz + 1.007825)*(3/4) mz_M3pCl = 34.968853 + (mz + 1.007825)*(3/4) mz_M3m2HpNa = 20.97412 + (mz + 1.007825)*(3/4) mz_M3m2HpNapHCOOH = 66.9796 + (mz + 1.007825)*(3/4) mz_M3m2HpK = 36.948058 + (mz + 1.007825)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3mH = peaks.between(mz_M3mH - prec_mass_error, mz_M3mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pCl = peaks.between(mz_M3pCl - prec_mass_error, mz_M3pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNa = peaks.between(mz_M3m2HpNa - prec_mass_error, mz_M3m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNapHCOOH = peaks.between(mz_M3m2HpNapHCOOH - prec_mass_error, mz_M3m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpK = peaks.between(mz_M3m2HpK - prec_mass_error, mz_M3m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK, valid_M3mH, valid_M3pCl, valid_M3m2HpNa, valid_M3m2HpNapHCOOH, valid_M3m2HpK]) def Solo_M4pCl(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 34.968853)/4 mz_Cl = 34.968853 + (mz - 34.968853)/4 mz_m2HpNa = 20.97412 + (mz - 34.968853)/4 mz_m2HpNapHCOOH = 66.9796 + (mz - 34.968853)/4 mz_m2HpK = 36.948058 + (mz - 34.968853)/4 mz_M2mH = -1.007825 + (mz - 34.968853)/2 mz_M2pCl = 34.968853 + (mz - 34.968853)/2 mz_M2m2HpNa = 20.97412 + (mz - 34.968853)/2 mz_M2m2HpNapHCOOH = 66.9796 + (mz - 34.968853)/2 mz_M2m2HpK = 36.948058 + (mz - 34.968853)/2 mz_M3mH = -1.007825 + (mz - 34.968853)*(3/4) mz_M3pCl = 34.968853 + (mz - 34.968853)*(3/4) mz_M3m2HpNa = 20.97412 + (mz - 34.968853)*(3/4) mz_M3m2HpNapHCOOH = 66.9796 + (mz - 34.968853)*(3/4) mz_M3m2HpK = 36.948058 + (mz - 34.968853)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3mH = peaks.between(mz_M3mH - prec_mass_error, mz_M3mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pCl = peaks.between(mz_M3pCl - prec_mass_error, mz_M3pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNa = peaks.between(mz_M3m2HpNa - prec_mass_error, mz_M3m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNapHCOOH = peaks.between(mz_M3m2HpNapHCOOH - prec_mass_error, mz_M3m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpK = peaks.between(mz_M3m2HpK - prec_mass_error, mz_M3m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK, valid_M3mH, valid_M3pCl, valid_M3m2HpNa, valid_M3m2HpNapHCOOH, valid_M3m2HpK]) def Solo_M4m2HpNapHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 66.979600)/4 mz_Cl = 34.968853 + (mz - 66.979600)/4 mz_m2HpNa = 20.97412 + (mz - 66.979600)/4 mz_m2HpNapHCOOH = 66.9796 + (mz - 66.979600)/4 mz_m2HpK = 36.948058 + (mz - 66.979600)/4 mz_M2mH = -1.007825 + (mz - 66.979600)/2 mz_M2pCl = 34.968853 + (mz - 66.979600)/2 mz_M2m2HpNa = 20.97412 + (mz - 66.979600)/2 mz_M2m2HpNapHCOOH = 66.9796 + (mz - 66.979600)/2 mz_M2m2HpK = 36.948058 + (mz - 66.979600)/2 mz_M3mH = -1.007825 + (mz - 66.979600)*(3/4) mz_M3pCl = 34.968853 + (mz - 66.979600)*(3/4) mz_M3m2HpNa = 20.97412 + (mz - 66.979600)*(3/4) mz_M3m2HpNapHCOOH = 66.9796 + (mz - 66.979600)*(3/4) mz_M3m2HpK = 36.948058 + (mz - 66.979600)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3mH = peaks.between(mz_M3mH - prec_mass_error, mz_M3mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pCl = peaks.between(mz_M3pCl - prec_mass_error, mz_M3pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNa = peaks.between(mz_M3m2HpNa - prec_mass_error, mz_M3m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNapHCOOH = peaks.between(mz_M3m2HpNapHCOOH - prec_mass_error, mz_M3m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpK = peaks.between(mz_M3m2HpK - prec_mass_error, mz_M3m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK, valid_M3mH, valid_M3pCl, valid_M3m2HpNa, valid_M3m2HpNapHCOOH, valid_M3m2HpK]) def Solo_M4m2HpNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = -1.007825 + (mz - 20.97412)/4 mz_Cl = 34.968853 + (mz - 20.97412)/4 mz_m2HpNa = 20.97412 + (mz - 20.97412)/4 mz_m2HpNapHCOOH = 66.9796 + (mz - 20.97412)/4 mz_m2HpK = 36.948058 + (mz - 20.97412)/4 mz_M2mH = -1.007825 + (mz - 20.97412)/2 mz_M2pCl = 34.968853 + (mz - 20.97412)/2 mz_M2m2HpNa = 20.97412 + (mz - 20.97412)/2 mz_M2m2HpNapHCOOH = 66.9796 + (mz - 20.97412)/2 mz_M2m2HpK = 36.948058 + (mz - 20.97412)/2 mz_M3mH = -1.007825 + (mz - 20.97412)*(3/4) mz_M3pCl = 34.968853 + (mz - 20.97412)*(3/4) mz_M3m2HpNa = 20.97412 + (mz - 20.97412)*(3/4) mz_M3m2HpNapHCOOH = 66.9796 + (mz - 20.97412)*(3/4) mz_M3m2HpK = 36.948058 + (mz - 20.97412)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Cl = peaks.between(mz_Cl - prec_mass_error, mz_Cl + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNa = peaks.between(mz_m2HpNa - prec_mass_error, mz_m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpNapHCOOH = peaks.between(mz_m2HpNapHCOOH - prec_mass_error, mz_m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_m2HpK = peaks.between(mz_m2HpK - prec_mass_error, mz_m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2mH = peaks.between(mz_M2mH - prec_mass_error, mz_M2mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pCl = peaks.between(mz_M2pCl - prec_mass_error, mz_M2pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNa = peaks.between(mz_M2m2HpNa - prec_mass_error, mz_M2m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpNapHCOOH = peaks.between(mz_M2m2HpNapHCOOH - prec_mass_error, mz_M2m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2m2HpK = peaks.between(mz_M2m2HpK - prec_mass_error, mz_M2m2HpK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3mH = peaks.between(mz_M3mH - prec_mass_error, mz_M3mH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pCl = peaks.between(mz_M3pCl - prec_mass_error, mz_M3pCl + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNa = peaks.between(mz_M3m2HpNa - prec_mass_error, mz_M3m2HpNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpNapHCOOH = peaks.between(mz_M3m2HpNapHCOOH - prec_mass_error, mz_M3m2HpNapHCOOH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3m2HpK = peaks.between(mz_M3m2HpK - prec_mass_error, mz_M3m2HpK + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Cl, valid_m2HpNa, valid_m2HpNapHCOOH, valid_m2HpK, valid_M2mH, valid_M2pCl, valid_M2m2HpNa, valid_M2m2HpNapHCOOH, valid_M2m2HpK, valid_M3mH, valid_M3pCl, valid_M3m2HpNa, valid_M3m2HpNapHCOOH, valid_M3m2HpK]) def Solo_M2pH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 1.007825)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H]) def Solo_M2pHpCH3CN(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 42.034374)/2 mz_Na = 22.98977 + (mz - 42.034374)/2 mz_K = 38.963708 + (mz - 42.034374)/2 mz_HpCH3CN = 42.034374 + (mz - 42.034374)/2 mz_HpCH3OH = 33.034040 + (mz - 42.034374)/2 mz_NapCH3CN = 64.016319 + (mz - 42.034374)/2 mz_NapCH3OH = 55.015985 + (mz - 42.034374)/2 mz_KpCH3CN = 79.990257 + (mz - 42.034374)/2 mz_KpCH3OH = 70.989923 + (mz - 42.034374)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH]) def Solo_M2pHpCH3OH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 33.034040)/2 mz_Na = 22.98977 + (mz - 33.034040)/2 mz_K = 38.963708 + (mz - 33.034040)/2 mz_HpCH3CN = 42.034374 + (mz - 33.034040)/2 mz_HpCH3OH = 33.034040 + (mz - 33.034040)/2 mz_NapCH3CN = 64.016319 + (mz - 33.034040)/2 mz_NapCH3OH = 55.015985 + (mz - 33.034040)/2 mz_KpCH3CN = 79.990257 + (mz - 33.034040)/2 mz_KpCH3OH = 70.989923 + (mz - 33.034040)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH]) def Solo_M2pHpHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 47.013304)/2 mz_Na = 22.98977 + (mz - 47.013304)/2 mz_K = 38.963708 + (mz - 47.013304)/2 mz_HpCH3CN = 42.034374 + (mz - 47.0133042)/2 mz_HpCH3OH = 33.034040 + (mz - 47.013304)/2 mz_NapCH3CN = 64.016319 + (mz - 47.013304)/2 mz_NapCH3OH = 55.015985 + (mz - 47.013304)/2 mz_KpCH3CN = 79.990257 + (mz - 47.013304)/2 mz_KpCH3OH = 70.989923 + (mz - 47.013304)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH]) def Solo_M2pNH4(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 18.034374)/2 mz_NH4 = 18.034374 + (mz - 18.034374)/2 mz_Na = 22.98977 + (mz - 18.034374)/2 mz_K = 38.963708 + (mz - 18.034374)/2 mz_HpCH3CN = 42.034374 + (mz - 18.034374)/2 mz_HpCH3OH = 33.034040 + (mz - 18.034374)/2 mz_NapCH3CN = 64.016319 + (mz - 18.034374)/2 mz_NapCH3OH = 55.015985 + (mz - 18.034374)/2 mz_KpCH3CN = 79.990257 + (mz - 18.034374)/2 mz_KpCH3OH = 70.989923 + (mz - 18.034374)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH]) def Solo_M2pNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 22.98977)/2 mz_Na = 22.98977 + (mz - 22.98977)/2 mz_NapCH3CN = 64.016319 + (mz - 22.98977)/2 mz_NapCH3OH = 55.015985 + (mz - 22.98977)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_NapCH3CN, valid_NapCH3OH]) def Solo_M2pNapCH3OH(ion_idx, mgf_file) : mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 55.015985)/2 mz_Na = 22.98977 + (mz - 55.015985)/2 mz_NapCH3CN = 64.016319 + (mz - 55.015985)/2 mz_NapCH3OH = 55.015985 + (mz - 55.015985)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_NapCH3CN, valid_NapCH3OH]) def Solo_M2pNapCH3CN(ion_idx, mgf_file) : mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 64.016319)/2 mz_Na = 22.98977 + (mz - 64.016319)/2 mz_NapCH3CN = 64.016319 + (mz - 64.016319)/2 mz_NapCH3OH = 55.015985 + (mz - 64.016319)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_NapCH3CN, valid_NapCH3OH]) def Solo_M2pK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 38.963708)/2 mz_Na = 22.98977 + (mz - 38.963708)/2 mz_K = 38.963708 + (mz - 38.963708)/2 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_K]) def Solo_M1pHpCH3CN(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 42.034374 mz_Na = 22.98977 + mz - 42.034374 mz_HpCH3OH = 33.034040 + mz - 42.034374 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_HpCH3OH]) def Solo_M1pHpCH3OH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 33.034040 mz_Na = 22.98977 + mz - 33.034040 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na]) def Solo_M1pHpHCOOH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 47.013304 mz_Na = 22.98977 + mz - 47.013304 mz_HpCH3OH = 33.034040 + mz - 47.013304 mz_HpCH3CN = 42.034374 + mz - 47.013304 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_HpCH3OH, valid_HpCH3CN]) def Solo_M1pNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 22.989770 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() return valid_H def Solo_M1pNapCH3CN(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = mz - 64.016319 + 1.007825 mz_Na = mz - 64.016319 + 22.98977 mz_NapCH3OH = mz - 64.016319 + 55.015985 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na, valid_NapCH3OH]) def Solo_M1pNapCH3OH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 55.015985 mz_Na = 22.98977 + mz - 55.015985 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_Na]) def Solo_M1pNH4(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 18.034374 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() return valid_H def Solo_M1pNH4pCH3CN(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 59.060923 mz_NH4 = 18.034374 + mz - 59.060923 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4]) def Solo_M1pK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 38.963708 mz_NH4 = 18.034374 + mz - 38.963708 mz_Na = 22.98977 + mz - 38.963708 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() return sum([valid_H, valid_NH4, valid_Na]) def Solo_M1pKpCH3OH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + mz - 70.989923 mz_NH4 = 18.034374 + mz - 70.989923 mz_Na = 22.98977 + mz - 70.989923 mz_K = 38.963708 + mz - 70.989923 valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() return sum([valid_H, valid_NH4, valid_Na, valid_K]) def Solo_M3pH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 1.007825)/3 mz_M2pH = 1.007825 + (mz - 1.007825)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_M2pH]) def Solo_M3pNH4(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 18.034374)/3 mz_NH4 = 18.034374 + (mz - 18.034374)/3 mz_M2pH = 1.007825 + (mz - 18.034374)*(2/3) mz_M2pNH4 = 18.034374 + (mz - 18.034374)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_M2pH, valid_M2pNH4]) def Solo_M3pNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 22.989770)/3 mz_NH4 = 18.034374 + (mz - 22.989770)/3 mz_Na = 22.98977 + (mz - 22.989770)/3 mz_K = 38.963708 + (mz - 22.989770)/3 mz_HpCH3CN = 42.034374 + (mz - 22.989770)/3 mz_HpCH3OH = 33.034040 + (mz - 22.989770)/3 mz_NapCH3CN = 64.016319 + (mz - 22.989770)/3 mz_NapCH3OH = 55.015985 + (mz - 22.989770)/3 mz_KpCH3CN = 79.990257 + (mz - 22.989770)/3 mz_KpCH3OH = 70.989923 + (mz - 22.989770)/3 mz_M2pH = 1.007825 + (mz - 22.989770)*(2/3) mz_M2pNH4 = 18.034374 + (mz - 22.989770)*(2/3) mz_M2pNa = 22.98977 + (mz - 22.989770)*(2/3) mz_M2pK = 38.963708 + (mz - 22.989770)*(2/3) mz_M2pHpCH3CN = 42.034374 + (mz - 22.989770)*(2/3) mz_M2pHpCH3OH = 33.034040 + (mz - 22.989770)*(2/3) mz_M2pNapCH3CN = 64.016319 + (mz - 22.989770)*(2/3) mz_M2pNapCH3OH = 55.015985 + (mz - 22.989770)*(2/3) mz_M2pKpCH3CN = 79.990257 + (mz - 22.989770)*(2/3) mz_M2pKpCH3OH = 70.989923 + (mz - 22.989770)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH]) def Solo_M3pK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 38.963708)/3 mz_NH4 = 18.034374 + (mz - 38.963708)/3 mz_Na = 22.98977 + (mz - 38.963708)/3 mz_K = 38.963708 + (mz - 38.963708)/3 mz_HpCH3CN = 42.034374 + (mz - 38.963708)/3 mz_HpCH3OH = 33.034040 + (mz - 38.9637080)/3 mz_NapCH3CN = 64.016319 + (mz - 38.9637080)/3 mz_NapCH3OH = 55.015985 + (mz - 38.963708)/3 mz_KpCH3CN = 79.990257 + (mz - 38.963708)/3 mz_KpCH3OH = 70.989923 + (mz - 38.963708)/3 mz_M2pH = 1.007825 + (mz - 38.963708)*(2/3) mz_M2pNH4 = 18.034374 + (mz - 38.963708)*(2/3) mz_M2pNa = 22.98977 + (mz - 38.963708)*(2/3) mz_M2pK = 38.963708 + (mz - 38.963708)*(2/3) mz_M2pHpCH3CN = 42.034374 + (mz - 38.963708)*(2/3) mz_M2pHpCH3OH = 33.034040 + (mz - 38.963708)*(2/3) mz_M2pNapCH3CN = 64.016319 + (mz - 38.963708)*(2/3) mz_M2pNapCH3OH = 55.015985 + (mz - 38.963708)*(2/3) mz_M2pKpCH3CN = 79.990257 + (mz - 38.963708)*(2/3) mz_M2pKpCH3OH = 70.989923 + (mz - 38.963708)*(2/3) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH]) def Solo_M4pK(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 38.963708)/4 mz_NH4 = 18.034374 + (mz - 38.963708)/4 mz_Na = 22.98977 + (mz - 38.963708)/4 mz_K = 38.963708 + (mz - 38.963708)/4 mz_HpCH3CN = 42.034374 + (mz - 38.963708)/4 mz_HpCH3OH = 33.034040 + (mz - 38.963708)/4 mz_NapCH3CN = 64.016319 + (mz - 38.963708)/4 mz_NapCH3OH = 55.015985 + (mz - 38.963708)/4 mz_KpCH3CN = 79.990257 + (mz - 38.963708)/4 mz_KpCH3OH = 70.989923 + (mz - 38.963708)/4 mz_M2pH = 1.007825 + (mz - 38.963708)/2 mz_M2pNH4 = 18.034374 + (mz - 38.963708)/2 mz_M2pNa = 22.98977 + (mz - 38.963708)/2 mz_M2pK = 38.963708 + (mz - 38.963708)/2 mz_M2pHpCH3CN = 42.034374 + (mz - 38.963708)/2 mz_M2pHpCH3OH = 33.034040 + (mz - 38.963708)/2 mz_M2pNapCH3CN = 64.016319 + (mz - 38.963708)/2 mz_M2pNapCH3OH = 55.015985 + (mz - 38.963708)/2 mz_M2pKpCH3CN = 79.990257 + (mz - 38.963708)/2 mz_M2pKpCH3OH = 70.989923 + (mz - 38.963708)/2 mz_M3pH = 1.007825 + (mz - 38.963708)*(3/4) mz_M3pNH4 = 18.034374 + (mz - 38.963708)*(3/4) mz_M3pNa = 22.98977 + (mz - 38.963708)*(3/4) mz_M3pK = 38.963708 + (mz - 38.963708)*(3/4) mz_M3pHpCH3CN = 42.034374 + (mz - 38.963708)*(3/4) mz_M3pHpCH3OH = 33.034040 + (mz - 38.963708)*(3/4) mz_M3pNapCH3CN = 64.016319 + (mz - 38.963708)*(3/4) mz_M3pNapCH3OH = 55.015985 + (mz - 38.963708)*(3/4) mz_M3pKpCH3CN = 79.990257 + (mz - 38.963708)*(3/4) mz_M3pKpCH3OH = 70.989923 + (mz - 38.963708)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pH = peaks.between(mz_M3pH - prec_mass_error, mz_M3pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNH4 = peaks.between(mz_M3pNH4 - prec_mass_error, mz_M3pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNa = peaks.between(mz_M3pNa - prec_mass_error, mz_M3pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pK = peaks.between(mz_M3pK - prec_mass_error, mz_M3pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3CN = peaks.between(mz_M3pHpCH3CN - prec_mass_error, mz_M3pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3OH = peaks.between(mz_M3pHpCH3OH - prec_mass_error, mz_M3pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3CN = peaks.between(mz_M3pNapCH3CN - prec_mass_error, mz_M3pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3OH = peaks.between(mz_M3pNapCH3OH - prec_mass_error, mz_M3pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3CN = peaks.between(mz_M3pKpCH3CN - prec_mass_error, mz_M3pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3OH = peaks.between(mz_M3pKpCH3OH - prec_mass_error, mz_M3pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH, valid_M3pH, valid_M3pNH4, valid_M3pNa, valid_M3pK, valid_M3pHpCH3CN, valid_M3pHpCH3OH, valid_M3pNapCH3CN, valid_M3pNapCH3OH, valid_M3pKpCH3CN, valid_M3pKpCH3OH]) def Solo_M4pH(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 1.007825)/4 mz_NH4 = 18.034374 + (mz - 1.007825)/4 mz_Na = 22.98977 + (mz - 1.007825)/4 mz_K = 38.963708 + (mz - 1.007825)/4 mz_HpCH3CN = 42.034374 + (mz - 1.007825)/4 mz_HpCH3OH = 33.034040 + (mz - 1.007825)/4 mz_NapCH3CN = 64.016319 + (mz - 1.007825)/4 mz_NapCH3OH = 55.015985 + (mz - 1.007825)/4 mz_KpCH3CN = 79.990257 + (mz - 1.007825)/4 mz_KpCH3OH = 70.989923 + (mz - 1.007825)/4 mz_M2pH = 1.007825 + (mz - 1.007825)/2 mz_M2pNH4 = 18.034374 + (mz - 1.007825)/2 mz_M2pNa = 22.98977 + (mz - 1.007825)/2 mz_M2pK = 38.963708 + (mz - 1.007825)/2 mz_M2pHpCH3CN = 42.034374 + (mz - 1.007825)/2 mz_M2pHpCH3OH = 33.034040 + (mz - 1.007825)/2 mz_M2pNapCH3CN = 64.016319 + (mz - 1.007825)/2 mz_M2pNapCH3OH = 55.015985 + (mz - 1.007825)/2 mz_M2pKpCH3CN = 79.990257 + (mz - 1.007825)/2 mz_M2pKpCH3OH = 70.989923 + (mz - 1.007825)/2 mz_M3pH = 1.007825 + (mz - 1.007825)*(3/4) mz_M3pNH4 = 18.034374 + (mz - 1.007825)*(3/4) mz_M3pNa = 22.98977 + (mz - 1.007825)*(3/4) mz_M3pK = 38.963708 + (mz - 1.007825)*(3/4) mz_M3pHpCH3CN = 42.034374 + (mz - 1.007825)*(3/4) mz_M3pHpCH3OH = 33.034040 + (mz - 1.007825)*(3/4) mz_M3pNapCH3CN = 64.016319 + (mz - 1.007825)*(3/4) mz_M3pNapCH3OH = 55.015985 + (mz - 1.007825)*(3/4) mz_M3pKpCH3CN = 79.990257 + (mz - 1.007825)*(3/4) mz_M3pKpCH3OH = 70.989923 + (mz - 1.007825)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pH = peaks.between(mz_M3pH - prec_mass_error, mz_M3pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNH4 = peaks.between(mz_M3pNH4 - prec_mass_error, mz_M3pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNa = peaks.between(mz_M3pNa - prec_mass_error, mz_M3pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pK = peaks.between(mz_M3pK - prec_mass_error, mz_M3pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3CN = peaks.between(mz_M3pHpCH3CN - prec_mass_error, mz_M3pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3OH = peaks.between(mz_M3pHpCH3OH - prec_mass_error, mz_M3pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3CN = peaks.between(mz_M3pNapCH3CN - prec_mass_error, mz_M3pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3OH = peaks.between(mz_M3pNapCH3OH - prec_mass_error, mz_M3pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3CN = peaks.between(mz_M3pKpCH3CN - prec_mass_error, mz_M3pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3OH = peaks.between(mz_M3pKpCH3OH - prec_mass_error, mz_M3pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH, valid_M3pH, valid_M3pNH4, valid_M3pNa, valid_M3pK, valid_M3pHpCH3CN, valid_M3pHpCH3OH, valid_M3pNapCH3CN, valid_M3pNapCH3OH, valid_M3pKpCH3CN, valid_M3pKpCH3OH]) def Solo_M4pNH4(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 18.034374)/4 mz_NH4 = 18.034374 + (mz - 18.034374)/4 mz_Na = 22.98977 + (mz - 18.034374)/4 mz_K = 38.963708 + (mz - 18.034374)/4 mz_HpCH3CN = 42.034374 + (mz - 18.034374)/4 mz_HpCH3OH = 33.034040 + (mz - 18.034374)/4 mz_NapCH3CN = 64.016319 + (mz - 18.034374)/4 mz_NapCH3OH = 55.015985 + (mz - 18.034374)/4 mz_KpCH3CN = 79.990257 + (mz - 18.034374)/4 mz_KpCH3OH = 70.989923 + (mz - 18.034374)/4 mz_M2pH = 1.007825 + (mz - 18.034374)/2 mz_M2pNH4 = 18.034374 + (mz - 18.034374)/2 mz_M2pNa = 22.98977 + (mz - 18.034374)/2 mz_M2pK = 38.963708 + (mz - 18.034374)/2 mz_M2pHpCH3CN = 42.034374 + (mz - 18.034374)/2 mz_M2pHpCH3OH = 33.034040 + (mz - 18.034374)/2 mz_M2pNapCH3CN = 64.016319 + (mz - 18.034374)/2 mz_M2pNapCH3OH = 55.015985 + (mz - 18.034374)/2 mz_M2pKpCH3CN = 79.990257 + (mz - 18.034374)/2 mz_M2pKpCH3OH = 70.989923 + (mz - 18.034374)/2 mz_M3pH = 1.007825 + (mz - 18.034374)*(3/4) mz_M3pNH4 = 18.034374 + (mz - 18.034374)*(3/4) mz_M3pNa = 22.98977 + (mz - 18.034374)*(3/4) mz_M3pK = 38.963708 + (mz - 18.034374)*(3/4) mz_M3pHpCH3CN = 42.034374 + (mz - 18.034374)*(3/4) mz_M3pHpCH3OH = 33.034040 + (mz - 18.034374)*(3/4) mz_M3pNapCH3CN = 64.016319 + (mz - 18.034374)*(3/4) mz_M3pNapCH3OH = 55.015985 + (mz - 18.034374)*(3/4) mz_M3pKpCH3CN = 79.990257 + (mz - 18.034374)*(3/4) mz_M3pKpCH3OH = 70.989923 + (mz - 18.034374)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pH = peaks.between(mz_M3pH - prec_mass_error, mz_M3pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNH4 = peaks.between(mz_M3pNH4 - prec_mass_error, mz_M3pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNa = peaks.between(mz_M3pNa - prec_mass_error, mz_M3pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pK = peaks.between(mz_M3pK - prec_mass_error, mz_M3pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3CN = peaks.between(mz_M3pHpCH3CN - prec_mass_error, mz_M3pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3OH = peaks.between(mz_M3pHpCH3OH - prec_mass_error, mz_M3pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3CN = peaks.between(mz_M3pNapCH3CN - prec_mass_error, mz_M3pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3OH = peaks.between(mz_M3pNapCH3OH - prec_mass_error, mz_M3pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3CN = peaks.between(mz_M3pKpCH3CN - prec_mass_error, mz_M3pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3OH = peaks.between(mz_M3pKpCH3OH - prec_mass_error, mz_M3pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH, valid_M3pH, valid_M3pNH4, valid_M3pNa, valid_M3pK, valid_M3pHpCH3CN, valid_M3pHpCH3OH, valid_M3pNapCH3CN, valid_M3pNapCH3OH, valid_M3pKpCH3CN, valid_M3pKpCH3OH]) def Solo_M4pNa(ion_idx, mgf_file): mz = mgf_file[ion_idx].get('pepmass')[0] peaks = pd.Series(mgf_file[ion_idx].peaks.mz) mz_H = 1.007825 + (mz - 22.98977)/4 mz_NH4 = 18.034374 + (mz - 22.98977)/4 mz_Na = 22.98977 + (mz - 22.98977)/4 mz_K = 38.963708 + (mz - 22.98977)/4 mz_HpCH3CN = 42.034374 + (mz - 22.98977)/4 mz_HpCH3OH = 33.034040 + (mz - 22.98977)/4 mz_NapCH3CN = 64.016319 + (mz - 22.98977)/4 mz_NapCH3OH = 55.015985 + (mz - 22.98977)/4 mz_KpCH3CN = 79.990257 + (mz - 22.98977)/4 mz_KpCH3OH = 70.989923 + (mz - 22.98977)/4 mz_M2pH = 1.007825 + (mz - 22.98977)/2 mz_M2pNH4 = 18.034374 + (mz - 22.98977)/2 mz_M2pNa = 22.98977 + (mz - 22.98977)/2 mz_M2pK = 38.963708 + (mz - 22.98977)/2 mz_M2pHpCH3CN = 42.034374 + (mz - 22.98977)/2 mz_M2pHpCH3OH = 33.034040 + (mz - 22.98977)/2 mz_M2pNapCH3CN = 64.016319 + (mz - 22.98977)/2 mz_M2pNapCH3OH = 55.015985 + (mz - 22.98977)/2 mz_M2pKpCH3CN = 79.990257 + (mz - 22.98977)/2 mz_M2pKpCH3OH = 70.989923 + (mz - 22.98977)/2 mz_M3pH = 1.007825 + (mz - 22.98977)*(3/4) mz_M3pNH4 = 18.034374 + (mz - 22.98977)*(3/4) mz_M3pNa = 22.98977 + (mz - 22.98977)*(3/4) mz_M3pK = 38.963708 + (mz - 22.98977)*(3/4) mz_M3pHpCH3CN = 42.034374 + (mz - 22.98977)*(3/4) mz_M3pHpCH3OH = 33.034040 + (mz - 22.98977)*(3/4) mz_M3pNapCH3CN = 64.016319 + (mz - 22.98977)*(3/4) mz_M3pNapCH3OH = 55.015985 + (mz - 22.98977)*(3/4) mz_M3pKpCH3CN = 79.990257 + (mz - 22.98977)*(3/4) mz_M3pKpCH3OH = 70.989923 + (mz - 22.98977)*(3/4) valid_H = peaks.between(mz_H - prec_mass_error, mz_H + prec_mass_error, inclusive = "both").sum() > 0 valid_NH4 = peaks.between(mz_NH4 - prec_mass_error, mz_NH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_Na = peaks.between(mz_Na - prec_mass_error, mz_Na + prec_mass_error, inclusive = "both").sum() > 0 valid_K = peaks.between(mz_K - prec_mass_error, mz_K + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3CN = peaks.between(mz_HpCH3CN - prec_mass_error, mz_HpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_HpCH3OH = peaks.between(mz_HpCH3OH - prec_mass_error, mz_HpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3CN = peaks.between(mz_NapCH3CN - prec_mass_error, mz_NapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_NapCH3OH = peaks.between(mz_NapCH3OH - prec_mass_error, mz_NapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3CN = peaks.between(mz_KpCH3CN - prec_mass_error, mz_KpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_KpCH3OH = peaks.between(mz_KpCH3OH - prec_mass_error, mz_KpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pH = peaks.between(mz_M2pH - prec_mass_error, mz_M2pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNH4 = peaks.between(mz_M2pNH4 - prec_mass_error, mz_M2pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNa = peaks.between(mz_M2pNa - prec_mass_error, mz_M2pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pK = peaks.between(mz_M2pK - prec_mass_error, mz_M2pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3CN = peaks.between(mz_M2pHpCH3CN - prec_mass_error, mz_M2pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pHpCH3OH = peaks.between(mz_M2pHpCH3OH - prec_mass_error, mz_M2pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3CN = peaks.between(mz_M2pNapCH3CN - prec_mass_error, mz_M2pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pNapCH3OH = peaks.between(mz_M2pNapCH3OH - prec_mass_error, mz_M2pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3CN = peaks.between(mz_M2pKpCH3CN - prec_mass_error, mz_M2pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M2pKpCH3OH = peaks.between(mz_M2pKpCH3OH - prec_mass_error, mz_M2pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pH = peaks.between(mz_M3pH - prec_mass_error, mz_M3pH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNH4 = peaks.between(mz_M3pNH4 - prec_mass_error, mz_M3pNH4 + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNa = peaks.between(mz_M3pNa - prec_mass_error, mz_M3pNa + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pK = peaks.between(mz_M3pK - prec_mass_error, mz_M3pK + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3CN = peaks.between(mz_M3pHpCH3CN - prec_mass_error, mz_M3pHpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pHpCH3OH = peaks.between(mz_M3pHpCH3OH - prec_mass_error, mz_M3pHpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3CN = peaks.between(mz_M3pNapCH3CN - prec_mass_error, mz_M3pNapCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pNapCH3OH = peaks.between(mz_M3pNapCH3OH - prec_mass_error, mz_M3pNapCH3OH + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3CN = peaks.between(mz_M3pKpCH3CN - prec_mass_error, mz_M3pKpCH3CN + prec_mass_error, inclusive = "both").sum() > 0 valid_M3pKpCH3OH = peaks.between(mz_M3pKpCH3OH - prec_mass_error, mz_M3pKpCH3OH + prec_mass_error, inclusive = "both").sum() > 0 return sum([valid_H, valid_NH4, valid_Na, valid_K, valid_HpCH3CN, valid_HpCH3OH, valid_NapCH3CN, valid_NapCH3OH, valid_KpCH3CN, valid_KpCH3OH, valid_M2pH, valid_M2pNH4, valid_M2pNa, valid_M2pK, valid_M2pHpCH3CN, valid_M2pHpCH3OH, valid_M2pNapCH3CN, valid_M2pNapCH3OH, valid_M2pKpCH3CN, valid_M2pKpCH3OH, valid_M3pH, valid_M3pNH4, valid_M3pNa, valid_M3pK, valid_M3pHpCH3CN, valid_M3pHpCH3OH, valid_M3pNapCH3CN, valid_M3pNapCH3OH, valid_M3pKpCH3CN, valid_M3pKpCH3OH]) def Not_validable(ion_idx, mgf_file): return 0 def Validator_choice(adduct, ion_mode): """Selects the Species_rule function appropriate to the provided adduct. """ if ion_mode == "POS": if adduct == 'M1|p1H|pCH3OH' : return Solo_M1pHpCH3OH elif adduct == 'M1|p1H|pHCOOH' : return Solo_M1pHpHCOOH elif adduct == 'M2|p1Na|' : return Solo_M2pNa elif adduct == 'M1|p1NH4|' : return Solo_M1pNH4 elif adduct == 'M1|p1NH4|pCH3CN' : return Solo_M1pNH4pCH3CN elif adduct == 'M1|p1Na|' : return Solo_M1pNa elif adduct == 'M1|p1H|' : return Not_validable elif adduct == 'M1|p1Na|pCH3CN' : return Solo_M1pNapCH3CN elif adduct == 'M1|p1H|pCH3CN' : return Solo_M1pHpCH3CN elif adduct == 'M2|p1H|' : return Solo_M2pH elif adduct == 'M2|p1H|pCH3CN' : return Solo_M2pHpCH3CN elif adduct == 'M2|p1NH4|' : return Solo_M2pNH4 elif adduct == 'M2|p1K|' : return Solo_M2pK elif adduct == 'M1|p1K|' : return Solo_M1pK elif adduct == 'M1|p1K|pCH3OH' : return Solo_M1pKpCH3OH elif adduct == 'M2|p1Na|pCH3OH' : return Solo_M2pNapCH3OH elif adduct == 'M2|p1H|pHCOOH' : return Solo_M2pHpHCOOH elif adduct == 'M2|p1Na|pCH3CN' : return Solo_M2pNapCH3CN elif adduct == 'M2|p1H|pCH3OH' : return Solo_M2pHpCH3OH elif adduct == 'M3|p1H|' : return Solo_M3pH elif adduct == 'M3|p1Na|' : return Solo_M3pNa elif adduct == 'M3|p1NH4|' : return Solo_M3pNH4 elif adduct == 'M4|p1K|' : return Solo_M4pK elif adduct == 'M4|p1H|' : return Solo_M4pH elif adduct == 'M4|p1NH4|' : return Solo_M4pNH4 elif adduct == 'M4|p1Na|' : return Solo_M4pNa elif adduct == 'M3|p1K|' : return Solo_M3pK elif ion_mode == "NEG": if adduct == 'M1|m1H|' : return Not_validable elif adduct == 'M1|p1Cl|' : return Not_validable elif adduct == 'M1|m1H|pC4H11N' : return Solo_M1mHpC4H11N elif adduct == 'M1|m1H|pHCOOH' : return Solo_M1mHpHCOOH elif adduct == 'M1|m2Hp1Na|pHCOOH' : return Solo_M1m2HpNapHCOOH elif adduct == 'M1|m2Hp1Na|' : return Solo_M1m2HpNa elif adduct == 'M1|m2Hp1K|' : return Solo_M1m2HpK elif adduct == 'M2|m1H|pC4H11N' : return Solo_M2mHpC4H11N elif adduct == 'M2|m1H|pHCOOH' : return Solo_M2mHpHCOOH elif adduct == 'M2|m1H|' : return Solo_M2mH elif adduct == 'M2|p1Cl|' : return Solo_M2pCl elif adduct == 'M2|m2Hp1Na|pHCOOH' : return Solo_M2m2HpNapHCOOH elif adduct == 'M2|m2Hp1Na|' : return Solo_M2m2HpNa elif adduct == 'M2|m2Hp1K|' : return Solo_M2m2HpK elif adduct == 'M3|m1H|' : return Solo_M3mH elif adduct == 'M3|p1Cl|' : return Solo_M3pCl elif adduct == 'M3|m2Hp1Na|pHCOOH' : return Solo_M3m2HpNapHCOOH elif adduct == 'M3|m2Hp1Na|' : return Solo_M3m2HpNa elif adduct == 'M4|m1H|' : return Solo_M4mH elif adduct == 'M4|p1Cl|' : return Solo_M4pCl elif adduct == 'M4|m2Hp1Na|pHCOOH' : return Solo_M4m2HpNapHCOOH elif adduct == 'M4|m2Hp1Na|' : return Solo_M4m2HpNa def Column_correction(table): drop_col = [i for i in table.columns if "Unnamed" in i] table.drop(drop_col, axis = 1, inplace = True) return table def Neutral_table(ion1_mz, adduct_table): """Computes all possible molecular masses (hypothetical neutrals) for an ion (ion 1) given the different ion species available in the adducts table. """ neutral_table = adduct_table.copy() neutral_table['neutral_mass'] = [0.0]*len(neutral_table) for i in neutral_table.index : neutral_table.loc[i, 'neutral_mass'] = Neutral_mass_calculator(ion1_mz, neutral_table['Adduct_mass'][i], neutral_table['Mol_multiplier'][i], neutral_table['Charge'][i]) return neutral_table def Ion_table(neutral_mass, adduct_table): """Computes all possible ion m/z values for an given molecular mass given the different ion species available in the adducts table. """ ion_table = adduct_table.copy() mz_list = [] for i in ion_table.index: mz_list.append(Neutral_to_adduct(neutral_mass, ion_table.loc[i, "Adduct_mass"], ion_table.loc[i, "Mol_multiplier"], ion_table.loc[i, "Charge"])) ion_table['ion_mz'] = mz_list return ion_table def Neutral_to_adduct(mol_mass, adduct_mass, mol_count, ion_charge) : """Calculates the m/z value of an ion species, given its supposed molecular mass (neutral) and other parameters from the adducts table. """ return round((mol_count*mol_mass + adduct_mass)/abs(ion_charge), 4) def Neutral_mass_calculator(input_mz, adduct_mass, mol_count, ion_charge) : """Calculates the molecular mass of a molecule given an ion species, its m/z value and other parameters from the adducts table. """ return round(((input_mz*abs(ion_charge)) - adduct_mass)/mol_count, 4) def Samplewise_export(neg_csv_file, pos_csv_file, out_path, merged_edge_table, merged_node_table) : print("Exporting sample-wise tables...") neg_csv = pd.read_csv(neg_csv_file, index_col ="row ID") pos_csv = pd.read_csv(pos_csv_file, index_col ="row ID") neg_csv = Column_correction(neg_csv) pos_csv = Column_correction(pos_csv) neg_csv.columns = neg_csv.columns.str.replace(".mzXML Peak area", "").str.replace('NEG_', '', regex = False) pos_csv.columns = pos_csv.columns.str.replace(".mzXML Peak area", "").str.replace('POS_', '', regex = False) neg_csv.drop(["row m/z", "row retention time"], axis = 1, inplace = True) pos_csv.drop(["row m/z", "row retention time"], axis = 1, inplace = True) samples = list(set(list(neg_csv.columns) + list(pos_csv.columns))) samples.sort() for sample in tqdm(samples): #sample = samples[0] ion_ids_neg = neg_csv.index[neg_csv[sample] > 0.0] ion_ids_pos = pos_csv.index[pos_csv[sample] > 0.0] #convert feature_ids to the new indexes tmp_table = merged_node_table[merged_node_table['status'] != "neg_neutral"] tmp_table = tmp_table[tmp_table['status'] != "pos_neutral"] tmp_table = tmp_table[tmp_table['status'] != "mix_neutral"] tmp_table_pos = tmp_table[tmp_table['ion_mode'] == "POS"] tmp_table_neg = tmp_table[tmp_table['ion_mode'] == "NEG"] ion_idx_neg = pd.Series(tmp_table_neg.index, index = tmp_table_neg['feature_id']) ion_idx_neg = list(ion_idx_neg[ion_ids_neg]) ion_idx_pos = pd.Series(tmp_table_pos.index, index = tmp_table_pos['feature_id']) ion_idx_pos = list(ion_idx_pos[ion_ids_pos]) ion_idx_mix = ion_idx_neg + ion_idx_pos # Get sample neutrals neutral_edges = merged_edge_table.loc[merged_edge_table["Adnotation"].dropna().index] kept_edges = [i for i in neutral_edges.index if neutral_edges.loc[i, "node_2"] in ion_idx_mix] # Get ion edges ion_edges = merged_edge_table[merged_edge_table['status'] != "neg_add_edge"] ion_edges = ion_edges[ion_edges['status'] != "pos_add_edge"] for i in ion_edges.index: if ion_edges.loc[i, "node_1"] in ion_idx_mix: if ion_edges.loc[i, "node_2"] in ion_idx_mix: kept_edges.append(i) kept_edges.sort() sample_edges = merged_edge_table.loc[kept_edges] sample_edges.sort_values('node_1', inplace = True) sample_edges.reset_index(inplace = True, drop = True) kept_nodes = list(set(list(sample_edges['node_1']) + list(sample_edges['node_2']))) kept_nodes.sort() sample_nodes = merged_node_table.loc[kept_nodes].copy() sample_nodes.drop(pd.Series(samples) + ".mzXML Peak area", axis = 1, inplace = True) sample_nodes[sample] = merged_node_table[sample + ".mzXML Peak area"] sample_nodes.to_csv(out_path + "MIX_" + sample + "_nodes.csv", index_label = "Index") sample_edges.to_csv(out_path + "MIX_" + sample + "_edges.csv", index_label = "Index") return import os import pandas as pd from pandas.core.common import flatten import sys from tqdm import tqdm from matchms.importing import load_from_mgf from matchms.filtering import default_filters def Spectrum_processing(s): s = default_filters(s) return s # Load parameters: in_path_full_neg= params['neg_out_0'] in_path_full_pos= params['pos_out_0'] neg_mgf= params['neg_mgf'] pos_mgf= params['pos_mgf'] pos_csv= params['pos_csv'] neg_csv= params['neg_csv'] adnotation_pos_full= params['pos_out_3_1'] adnotation_neg_full= params['neg_out_3_1'] out_full= params['mix_out_4_1'] out_samples= params['mix_out_4_2'] neg_nodes_full = "NEG_full_nodes.csv" neg_edges_full = "NEG_full_edges.csv" pos_nodes_full = "POS_full_nodes.csv" pos_edges_full = "POS_full_edges.csv" adduct_table_primary_neg= params['mm_addtable_primary_neg'] adduct_table_secondary_neg= params['mm_addtable_secondary_neg'] adduct_table_primary_pos= params['mm_addtable_primary_pos'] adduct_table_secondary_pos= params['mm_addtable_secondary_pos'] mass_error= params['mm_mass_error'] prec_mass_error = params['mm_prec_mass_error'] rt_error= params['mm_rt_error'] bnr_list_neg = params['mm_bnr_neg'] bnr_list_pos = params['mm_bnr_pos'] #Load and filter MGF files print("Loading and filtering NEG MGF file...") mgf_file_neg = list(load_from_mgf(in_path_full_neg + neg_mgf)) mgf_file_neg = [Spectrum_processing(s) for s in mgf_file_neg] print("Loading and filtering POS MGF file...") mgf_file_pos = list(load_from_mgf(in_path_full_pos + pos_mgf)) mgf_file_pos = [Spectrum_processing(s) for s in mgf_file_pos] #Load adduct tables adduct_table_primary_neg = pd.read_csv("./params/" + adduct_table_primary_neg, sep = "\t") adduct_table_secondary_neg = pd.read_csv("./params/" + adduct_table_secondary_neg, sep = "\t") adduct_table_merged_neg = adduct_table_primary_neg.append(adduct_table_secondary_neg, ignore_index = True) adduct_table_primary_pos = pd.read_csv("./params/" + adduct_table_primary_pos, sep = "\t") adduct_table_secondary_pos = pd.read_csv("./params/" + adduct_table_secondary_pos, sep = "\t") adduct_table_merged_pos = adduct_table_primary_pos.append(adduct_table_secondary_pos, ignore_index = True) # Produce base neutral adduct tables adduct_table_base_neg = [adduct_table_merged_neg.index[adduct_table_merged_neg['Adduct'] == adduct][0] for adduct in bnr_list_neg] adduct_table_base_neg = adduct_table_merged_neg.loc[adduct_table_base_neg] adduct_table_base_pos = [adduct_table_merged_pos.index[adduct_table_merged_pos['Adduct'] == adduct][0] for adduct in bnr_list_pos] adduct_table_base_pos = adduct_table_merged_pos.loc[adduct_table_base_pos] # Loading files: POS and NEG node tables, edge tables and MZmine CSV output neg_mzmine_csv = pd.read_csv(in_path_full_neg + neg_csv, index_col = "row ID") neg_mzmine_csv = Column_correction(neg_mzmine_csv) pos_mzmine_csv = pd.read_csv(in_path_full_pos + pos_csv, index_col = "row ID") pos_mzmine_csv = Column_correction(pos_mzmine_csv) edge_table_all_neg = pd.read_csv(adnotation_neg_full + neg_edges_full, index_col = "Index") node_table_all_neg = pd.read_csv(adnotation_neg_full + neg_nodes_full, index_col = "feature_id") edge_table_all_pos = pd.read_csv(adnotation_pos_full + pos_edges_full, index_col = "Index") node_table_all_pos = pd.read_csv(adnotation_pos_full + pos_nodes_full, index_col = "feature_id") # Setting ion modes in tables node_table_all_neg['ion_mode'] = ['NEG']*len(node_table_all_neg) edge_table_all_neg['ion_mode'] = ['NEG']*len(edge_table_all_neg) node_table_all_pos['ion_mode'] = ['POS']*len(node_table_all_pos) edge_table_all_pos['ion_mode'] = ['POS']*len(edge_table_all_pos) # New indexes will be used when merging POS and NEG tables, feature_IDs are saved in a dedicated column node_table_all_neg['feature_id'] = node_table_all_neg.index node_table_all_pos['feature_id'] = node_table_all_pos.index # Set samples in node tables : neg_mzmine_csv.drop(['row m/z', 'row retention time'], axis = 1, inplace = True) pos_mzmine_csv.drop(['row m/z', 'row retention time'], axis = 1, inplace = True) neg_mzmine_csv.columns = pd.Series(neg_mzmine_csv.columns).str.replace('NEG_', '', regex = False) pos_mzmine_csv.columns = pd.Series(pos_mzmine_csv.columns).str.replace('POS_', '', regex = False) node_table_all_neg["samples"] = ['']*len(node_table_all_neg) node_table_all_pos["samples"] = ['']*len(node_table_all_pos) neg_ions_idx = node_table_all_neg[node_table_all_neg['status'] != "neutral"].index pos_ions_idx = node_table_all_pos[node_table_all_pos['status'] != "neutral"].index print("Adding samples to neg ions...") for ion in tqdm(neg_ions_idx): samples = list(neg_mzmine_csv.columns[neg_mzmine_csv.loc[ion] > 0]) node_table_all_neg.loc[ion, "samples"] = '|'.join(samples) print("Adding samples to pos ions...") for ion in tqdm(pos_ions_idx): samples = list(pos_mzmine_csv.columns[pos_mzmine_csv.loc[ion] > 0]) node_table_all_pos.loc[ion, "samples"] = '|'.join(samples) # Add samples to neutrals neg_neutrals_idx = node_table_all_neg[node_table_all_neg['status'] == "neutral"].index pos_neutrals_idx = node_table_all_pos[node_table_all_pos['status'] == "neutral"].index print("Adding samples to neg neutrals...") for neutral in tqdm(neg_neutrals_idx): tmp_ions = list(edge_table_all_neg['node_2'][edge_table_all_neg['node_1'] == neutral]) tmp_samples = [] for ion in tmp_ions: tmp_samples += node_table_all_neg.loc[ion, "samples"].split('|') tmp_samples = list(set(tmp_samples)) tmp_samples.sort() tmp_samples = '|'.join(tmp_samples) node_table_all_neg.loc[neutral, "samples"] = tmp_samples print("Adding samples to pos neutrals...") for neutral in tqdm(pos_neutrals_idx): tmp_ions = list(edge_table_all_pos['node_2'][edge_table_all_pos['node_1'] == neutral]) tmp_samples = [] for ion in tmp_ions: tmp_samples += node_table_all_pos.loc[ion, "samples"].split('|') tmp_samples = list(set(tmp_samples)) tmp_samples.sort() tmp_samples = '|'.join(tmp_samples) node_table_all_pos.loc[neutral, "samples"] = tmp_samples # Produce the merged mode edge and node tables node_table_ions_neg= node_table_all_neg[node_table_all_neg['status'] != "neutral"] node_table_ions_pos= node_table_all_pos[node_table_all_pos['status'] != "neutral"] merged_node_table = pd.DataFrame() merged_node_table = merged_node_table.append(node_table_ions_neg, ignore_index = True) merged_node_table = merged_node_table.append(node_table_ions_pos, ignore_index = True) merged_edge_table = pd.DataFrame() merged_edge_table = merged_edge_table.append(edge_table_all_neg, ignore_index = True) merged_edge_table = merged_edge_table.append(edge_table_all_pos, ignore_index = True) #Chekc from NEG to POS if there are neutral nodes that might match neg_neutrals = node_table_all_neg[node_table_all_neg['status'] == "neutral"].copy() neg_neutrals['feature_id'] = [None]*len(neg_neutrals) pos_neutrals = node_table_all_pos[node_table_all_pos['status'] == "neutral"].copy() pos_neutrals['feature_id'] = [None]*len(pos_neutrals) merged_neutrals = [] for neutral in tqdm(neg_neutrals_idx): # NEG -> POS #neutral = neg_neutrals_idx[0] mz_neg = neg_neutrals.loc[neutral, "mz"] rt_neg = neg_neutrals.loc[neutral, "rt"] samples_neg = set(neg_neutrals.loc[neutral, "samples"].split('|')) counter_pos = pos_neutrals[pos_neutrals["mz"].between(mz_neg - mass_error, mz_neg + mass_error, inclusive = "both")] counter_pos = counter_pos[counter_pos["rt"].between(rt_neg - rt_error, rt_neg + rt_error, inclusive = "both")] if len(counter_pos) == 0 : continue counter_pos['d_rt'] = abs(counter_pos['rt']-rt_neg) shared_samples = [] for i in counter_pos.index: samples_pos = counter_pos.loc[i, "samples"].split('|') shared_samples.append(len(samples_neg.intersection(samples_pos))) counter_pos['shared_samples'] = shared_samples counter_pos = counter_pos[counter_pos['shared_samples'] > 0] counter_pos = counter_pos[counter_pos['shared_samples'] == counter_pos['shared_samples'].max()] counter_pos = counter_pos[counter_pos['d_rt'] == counter_pos['d_rt'].min()] if len(counter_pos) > 1 : sys.exit("MORE THAN 1 COUTNER NEUTRAL AT SAME RT") elif len(counter_pos) == 0 : continue else: mz_pos = counter_pos["mz"].iloc[0] rt_pos = counter_pos["rt"].iloc[0] merged_neutrals.append((neutral, mz_neg, rt_neg, counter_pos.index[0], mz_pos, rt_pos)) merged_neutrals = pd.DataFrame(merged_neutrals, columns = ['neg_neutral', 'mass_neg', 'rt_neg', 'pos_neutral', 'mass_pos', 'rt_pos']) merged_neutrals['d_rt'] = abs(merged_neutrals['rt_pos'] - merged_neutrals['rt_neg']) # Sometimes, several NEG neutrals might match the same POS neutral. # The NEG neutral with the closest RT to the POS neutral is chosen for neutral in merged_neutrals['pos_neutral'].unique(): tmp_table = merged_neutrals[merged_neutrals['pos_neutral'] == neutral].copy() if len(tmp_table) > 1 : tmp_table.sort_values('d_rt', inplace = True) for i in tmp_table.index[1:]: merged_neutrals.drop(i, inplace = True) # A transitions table is produced containing the neutrals to be merged and their # new ID. Later will also contain non-merged neutrals and their new IDs transitions_table = list() print("Adding merged neutrals to the merged node table...") for i in tqdm(merged_neutrals.index): new_idx = merged_node_table.index.max() + 1 neutral_neg = merged_neutrals.loc[i, "neg_neutral"] neutral_pos = merged_neutrals.loc[i, "pos_neutral"] neg_ions = list(edge_table_all_neg['node_2'][edge_table_all_neg['node_1'] == neutral_neg]) neg_count = len(neg_ions) neg_mz = node_table_all_neg.loc[neutral_neg, "mz"] neg_rt = node_table_all_neg.loc[neutral_neg, "rt"] neg_tic = node_table_all_neg.loc[neutral_neg, "TIC"] neg_samples = node_table_all_neg.loc[neutral_neg, "samples"].split('|') pos_ions = list(edge_table_all_pos['node_2'][edge_table_all_pos['node_1'] == neutral_pos]) pos_count = len(pos_ions) pos_mz = node_table_all_pos.loc[neutral_pos, "mz"] pos_rt = node_table_all_pos.loc[neutral_pos, "rt"] pos_tic = node_table_all_pos.loc[neutral_pos, "TIC"] pos_samples = node_table_all_pos.loc[neutral_pos, "samples"].split('|') mix_mz = round(((neg_mz*neg_count) + (pos_mz*pos_count))/(neg_count + pos_count), 4) mix_rt = round(((neg_rt*neg_count) + (pos_rt*pos_count))/(neg_count + pos_count), 3) mix_tic = neg_tic + pos_tic mix_samples = list(set(neg_samples + pos_samples)) mix_samples.sort() mix_samples = '|'.join(mix_samples) merged_node_table.loc[new_idx] = [mix_mz, mix_rt, mix_tic, 0, None, 0, "neutral", len(neg_ions) + len(pos_ions), None, "MIX", None, mix_samples] transitions_table.append((new_idx, neutral_neg, neutral_pos)) transitions_table = pd.DataFrame(transitions_table, columns = ['MIX', 'NEG', 'POS']) # Drop neutrals that were merged and redo the process for the remaining neutrals # that might have escaped, this time in POS to NEG for a change. neg_neutrals.drop(transitions_table['NEG'], inplace = True) pos_neutrals.drop(transitions_table['POS'], inplace = True) merged_neutrals = [] for neutral in tqdm(pos_neutrals.index): # POS -> NEG #neutral = pos_neutrals.index[0] mz_pos = pos_neutrals.loc[neutral, "mz"] rt_pos = pos_neutrals.loc[neutral, "rt"] samples_pos = set(pos_neutrals.loc[neutral, "samples"].split('|')) counter_neg = neg_neutrals[neg_neutrals["mz"].between(mz_pos - mass_error, mz_pos + mass_error, inclusive = "both")] counter_neg = counter_neg[counter_neg["rt"].between(rt_pos - rt_error, rt_pos + rt_error, inclusive = "both")] if len(counter_neg) == 0 : continue counter_neg['d_rt'] = abs(counter_neg['rt']-rt_pos) shared_samples = [] for i in counter_neg.index: samples_neg = counter_neg.loc[i, "samples"].split('|') shared_samples.append(len(samples_pos.intersection(samples_neg))) counter_neg['shared_samples'] = shared_samples counter_neg = counter_neg[counter_neg['shared_samples'] > 0] counter_neg = counter_neg[counter_neg['shared_samples'] == counter_neg['shared_samples'].max()] counter_neg = counter_neg[counter_neg['d_rt'] == counter_neg['d_rt'].min()] if len(counter_neg) > 1 : sys.exit("MORE THAN 1 COUTNER NEUTRAL AT SAME RT") elif len(counter_neg) == 0 : continue else: mz_neg = counter_neg["mz"].iloc[0] rt_neg = counter_neg["rt"].iloc[0] merged_neutrals.append((counter_neg.index[0], mz_neg, rt_neg, neutral, mz_pos, rt_pos)) merged_neutrals = pd.DataFrame(merged_neutrals, columns = ['neg_neutral', 'mass_neg', 'rt_neg', 'pos_neutral', 'mass_pos', 'rt_pos']) merged_neutrals['d_rt'] = abs(merged_neutrals['rt_pos'] - merged_neutrals['rt_neg']) # Same as before, if there are several POS neutrals per NEG neutral, choose the # closest one by RT for neutral in merged_neutrals['pos_neutral'].unique(): tmp_table = merged_neutrals[merged_neutrals['pos_neutral'] == neutral].copy() if len(tmp_table) > 1 : tmp_table.sort_values('d_rt', inplace = True) for i in tmp_table.index[1:]: merged_neutrals.drop(i, inplace = True) # Add the new merged neutrals to the transitions table print("Adding merged neutrals to the merged node table...") for i in tqdm(merged_neutrals.index): new_idx = merged_node_table.index.max() + 1 neutral_neg = merged_neutrals.loc[i, "neg_neutral"] neutral_pos = merged_neutrals.loc[i, "pos_neutral"] neg_ions = list(edge_table_all_neg['node_2'][edge_table_all_neg['node_1'] == neutral_neg]) neg_count = len(neg_ions) neg_mz = node_table_all_neg.loc[neutral_neg, "mz"] neg_rt = node_table_all_neg.loc[neutral_neg, "rt"] neg_tic = node_table_all_neg.loc[neutral_neg, "TIC"] neg_samples = node_table_all_neg.loc[neutral_neg, "samples"].split('|') pos_ions = list(edge_table_all_pos['node_2'][edge_table_all_pos['node_1'] == neutral_pos]) pos_count = len(pos_ions) pos_mz = node_table_all_pos.loc[neutral_pos, "mz"] pos_rt = node_table_all_pos.loc[neutral_pos, "rt"] pos_tic = node_table_all_pos.loc[neutral_pos, "TIC"] pos_samples = node_table_all_pos.loc[neutral_pos, "samples"].split('|') mix_mz = round(((neg_mz*neg_count) + (pos_mz*pos_count))/(neg_count + pos_count), 4) mix_rt = round(((neg_rt*neg_count) + (pos_rt*pos_count))/(neg_count + pos_count), 3) mix_tic = neg_tic + pos_tic mix_samples = list(set(neg_samples + pos_samples)) mix_samples.sort() mix_samples = '|'.join(mix_samples) merged_node_table.loc[new_idx] = [mix_mz, mix_rt, mix_tic, 0, None, 0, "neutral", len(neg_ions) + len(pos_ions), None, "MIX", None, mix_samples] transitions_table.loc[transitions_table.index.max() + 1] = [new_idx, neutral_neg, neutral_pos] # Add non merged neutrals to the merged_node_table (using their new IDs) and # to the transitions table # First, eliminate merged_neutrals intersect_neutrals = set(neg_neutrals.index) intersect_neutrals = intersect_neutrals.intersection(transitions_table['NEG']) neg_neutrals.drop(intersect_neutrals, inplace = True) intersect_neutrals = set(pos_neutrals.index) intersect_neutrals = intersect_neutrals.intersection(transitions_table['POS']) pos_neutrals.drop(intersect_neutrals, inplace = True) print('Adding non-merged neg neutrals to the merged node table...') for neutral in tqdm(neg_neutrals.index): new_idx = merged_node_table.index.max() + 1 merged_node_table.loc[new_idx] = neg_neutrals.loc[neutral].copy() new_row = pd.Series([new_idx, neutral, None], index = ["MIX", "NEG", "POS"]) transitions_table = transitions_table.append(new_row, ignore_index = True) print('Adding non-merged pos neutrals to the merged node table...') for neutral in tqdm(pos_neutrals.index): new_idx = merged_node_table.index.max() + 1 merged_node_table.loc[new_idx] = pos_neutrals.loc[neutral].copy() new_row = pd.Series([new_idx, None, neutral], index = ["MIX", "NEG", "POS"]) transitions_table = transitions_table.append(new_row, ignore_index = True) # Replace old IDs by new ones in the merged edge table. print('Resetting edge table IDs...') for i in tqdm(merged_edge_table.index): ion_mode = merged_edge_table.loc[i, "ion_mode"] if ion_mode == "NEG" : opposed_mode = "POS" else : opposed_mode = "NEG" merged_node_table_sub = merged_node_table[merged_node_table['ion_mode'] != opposed_mode] if merged_edge_table.loc[i, "status"] == "add_edge": node_1 = merged_edge_table.loc[i, "node_1"] node_2 = merged_edge_table.loc[i, "node_2"] new_node_1 = int(transitions_table["MIX"][transitions_table[ion_mode] == node_1].iloc[0]) new_node_2 = merged_node_table_sub.index[merged_node_table_sub["feature_id"] == node_2][0] merged_edge_table.loc[i, "node_1"] = new_node_1 merged_edge_table.loc[i, "node_2"] = new_node_2 else: node_1 = merged_edge_table.loc[i, "node_1"] node_2 = merged_edge_table.loc[i, "node_2"] new_node_1 = merged_node_table_sub.index[merged_node_table_sub["feature_id"] == node_1][0] new_node_2 = merged_node_table_sub.index[merged_node_table_sub["feature_id"] == node_2][0] merged_edge_table.loc[i, "node_1"] = new_node_1 merged_edge_table.loc[i, "node_2"] = new_node_2 # Search for remaining ion annotations remains_table_pos = merged_node_table[merged_node_table['status'] != "neutral"] remains_table_pos = remains_table_pos[remains_table_pos['status'] != "adduct"] remains_table_pos = remains_table_pos[remains_table_pos['ion_mode'] == "POS"] remains_table_neg = merged_node_table[merged_node_table['status'] != "neutral"] remains_table_neg = remains_table_neg[remains_table_neg['status'] != "adduct"] remains_table_neg = remains_table_neg[remains_table_neg['ion_mode'] == "NEG"] node_table_neutrals_pos = merged_node_table[merged_node_table["status"] == "neutral"] node_table_neutrals_pos = node_table_neutrals_pos[node_table_neutrals_pos["ion_mode"] == "POS"] node_table_neutrals_neg = merged_node_table[merged_node_table["status"] == "neutral"] node_table_neutrals_neg = node_table_neutrals_neg[node_table_neutrals_neg["ion_mode"] == "NEG"] print('Linking NEG neutrals to single POS ions...') candidates_table = list() for i in tqdm(node_table_neutrals_neg.index): mol_mass = node_table_neutrals_neg.loc[i, "mz"] mol_rt = node_table_neutrals_neg.loc[i, "rt"] mol_samples = set(node_table_neutrals_neg.loc[i, "samples"].split('|')) ion_table = Ion_table(mol_mass, adduct_table_merged_pos) ion_hits = list() hit_table_rt = remains_table_pos[remains_table_pos['rt'].between(mol_rt - rt_error, mol_rt + rt_error, inclusive = "both")].copy() shares_samples_list = list() for j in hit_table_rt.index: ion_samples = hit_table_rt.loc[j, "samples"].split('|') shared_samples = len(list(mol_samples.intersection(ion_samples))) shares_samples_list.append(shared_samples) hit_table_rt['shared_samples'] = shares_samples_list hit_table_rt = hit_table_rt[hit_table_rt['shared_samples'] > 0] for j in ion_table.index: ion_mz = ion_table.loc[j, "ion_mz"] hit_table = hit_table_rt[hit_table_rt['mz'].between(ion_mz - mass_error, ion_mz + mass_error, inclusive = "both")].copy() if len(hit_table) >0 : ion_hits.append('|'.join(hit_table.index.astype(str))) else: ion_hits.append(None) ion_table['ion_hits'] = ion_hits ion_table = ion_table[~ion_table['ion_hits'].isnull()] for j in ion_table.index: adduct = ion_table.loc[j, "Adduct"] for k in ion_table.loc[j, "ion_hits"].split('|'): k = int(k) d_rt = abs(mol_rt - remains_table_pos.loc[k, 'rt']) d_mz = abs(mol_mass - remains_table_pos.loc[k, 'mz']) candidates_table.append((k, i, adduct, d_rt, d_mz)) candidates_table = pd.DataFrame(candidates_table, columns = ['ion_idx', 'neutral', 'adduct', 'd_rt', 'd_mz']) # Migration of ions: unique_hits = candidates_table['ion_idx'].unique().tolist() selected_neutrals = list() selected_adducts = list() delta_rts = list() delta_mzs = list() for i in unique_hits: tmp_table = candidates_table[candidates_table['ion_idx'] == i] selected_neutral = tmp_table['d_rt'].idxmin() selected_neutrals.append(tmp_table.loc[selected_neutral, "neutral"]) selected_adducts.append(tmp_table.loc[selected_neutral, "adduct"]) delta_rts.append(tmp_table.loc[selected_neutral, "d_rt"]) delta_mzs.append(tmp_table.loc[selected_neutral, "d_mz"]) candidates_table= list(zip(unique_hits, selected_neutrals, selected_adducts, delta_rts, delta_mzs)) candidates_table = pd.DataFrame(candidates_table, columns = ['ion_idx', 'neutral', 'adduct', 'd_rt', 'd_mz']) # Report results and node and edge tables: print('Updating node and edge tables...') for i in tqdm(candidates_table.index): # Retrieve data for ion and neutral ion_idx = candidates_table.loc[i, "ion_idx"] neutral_idx = candidates_table.loc[i, "neutral"] adduct = candidates_table.loc[i, "adduct"] d_rt = candidates_table.loc[i, "d_rt"] d_mz = candidates_table.loc[i, "d_mz"] mgf_idx = int(merged_node_table.loc[ion_idx, "mgf_index"]) adduct_code = adduct_table_merged_pos['Adduct_code'][adduct_table_merged_pos['Adduct'] == adduct].iloc[0] Species_rule = Validator_choice(adduct_code, "POS") rule_points = Species_rule(mgf_idx, mgf_file_pos) ion_tic = merged_node_table.loc[ion_idx, 'TIC'] # Update node table with ion data merged_node_table.loc[ion_idx, 'status'] = "adduct" merged_node_table.loc[ion_idx, 'Adnotation'] = adduct merged_node_table.loc[ion_idx, 'rule_points'] = rule_points # Update node table with neutral data merged_node_table.loc[neutral_idx, "TIC"] += ion_tic merged_node_table.loc[neutral_idx, "adduct_count"] += 1 merged_node_table.loc[neutral_idx, "ion_mode"] = "MIX" tmp_samples = merged_node_table.loc[neutral_idx, "samples"].split('|') tmp_samples += merged_node_table.loc[ion_idx, "samples"].split('|') tmp_samples = list(set(tmp_samples)) tmp_samples.sort() tmp_samples = '|'.join(tmp_samples) merged_node_table.loc[neutral_idx, "samples"] = tmp_samples # Update edge table : del_edge = merged_edge_table[merged_edge_table['node_1'] == ion_idx] del_edge = del_edge.index[del_edge['node_2'] == ion_idx] if len(del_edge) > 0 : merged_edge_table.drop(del_edge[0], inplace = True) new_idx = merged_edge_table.index.max() + 1 merged_edge_table.loc[new_idx] = [neutral_idx, ion_idx, 0, 0, 0, d_rt, d_mz, "add_edge", None, adduct, adduct, "POS"] print('Linking POS neutrals to single NEG ions...') candidates_table = list() for i in tqdm(node_table_neutrals_pos.index): mol_mass = node_table_neutrals_pos.loc[i, "mz"] mol_rt = node_table_neutrals_pos.loc[i, "rt"] mol_samples = set(node_table_neutrals_pos.loc[i, "samples"].split('|')) ion_table = Ion_table(mol_mass, adduct_table_merged_neg) ion_hits = list() hit_table_rt = remains_table_neg[remains_table_neg['rt'].between(mol_rt - rt_error, mol_rt + rt_error, inclusive = "both")].copy() shares_samples_list = list() for j in hit_table_rt.index: ion_samples = hit_table_rt.loc[j, "samples"].split('|') shared_samples = len(list(mol_samples.intersection(ion_samples))) shares_samples_list.append(shared_samples) hit_table_rt['shared_samples'] = shares_samples_list hit_table_rt = hit_table_rt[hit_table_rt['shared_samples'] > 0] for j in ion_table.index: ion_mz = ion_table.loc[j, "ion_mz"] hit_table = hit_table_rt[hit_table_rt['mz'].between(ion_mz - mass_error, ion_mz + mass_error, inclusive = "both")].copy() if len(hit_table) >0 : ion_hits.append('|'.join(hit_table.index.astype(str))) else: ion_hits.append(None) ion_table['ion_hits'] = ion_hits ion_table = ion_table[~ion_table['ion_hits'].isnull()] for j in ion_table.index: adduct = ion_table.loc[j, "Adduct"] for k in ion_table.loc[j, "ion_hits"].split('|'): k = int(k) d_rt = abs(mol_rt - remains_table_neg.loc[k, 'rt']) d_mz = abs(mol_mass - remains_table_neg.loc[k, 'mz']) candidates_table.append((k, i, adduct, d_rt, d_mz)) candidates_table = pd.DataFrame(candidates_table, columns = ['ion_idx', 'neutral', 'adduct', 'd_rt', 'd_mz']) # Migration of ions: unique_hits = candidates_table['ion_idx'].unique().tolist() selected_neutrals = list() selected_adducts = list() delta_rts = list() delta_mzs = list() for i in unique_hits: tmp_table = candidates_table[candidates_table['ion_idx'] == i] selected_neutral = tmp_table['d_rt'].idxmin() selected_neutrals.append(tmp_table.loc[selected_neutral, "neutral"]) selected_adducts.append(tmp_table.loc[selected_neutral, "adduct"]) delta_rts.append(tmp_table.loc[selected_neutral, "d_rt"]) delta_mzs.append(tmp_table.loc[selected_neutral, "d_mz"]) candidates_table= list(zip(unique_hits, selected_neutrals, selected_adducts, delta_rts, delta_mzs)) candidates_table = pd.DataFrame(candidates_table, columns = ['ion_idx', 'neutral', 'adduct', 'd_rt', 'd_mz']) # Report results and node and edge tables: print('Updating node and edge tables...') for i in tqdm(candidates_table.index): # Retrieve data for ion and neutral ion_idx = candidates_table.loc[i, "ion_idx"] neutral_idx = candidates_table.loc[i, "neutral"] adduct = candidates_table.loc[i, "adduct"] d_rt = candidates_table.loc[i, "d_rt"] d_mz = candidates_table.loc[i, "d_mz"] mgf_idx = int(merged_node_table.loc[ion_idx, "mgf_index"]) adduct_code = adduct_table_merged_neg['Adduct_code'][adduct_table_merged_neg['Adduct'] == adduct].iloc[0] Species_rule = Validator_choice(adduct_code, "NEG") rule_points = Species_rule(mgf_idx, mgf_file_neg) ion_tic = merged_node_table.loc[ion_idx, 'TIC'] # Update node table with ion data merged_node_table.loc[ion_idx, 'status'] = "adduct" merged_node_table.loc[ion_idx, 'Adnotation'] = adduct merged_node_table.loc[ion_idx, 'rule_points'] = rule_points # Update node table with neutral data merged_node_table.loc[neutral_idx, "TIC"] += ion_tic merged_node_table.loc[neutral_idx, "adduct_count"] += 1 merged_node_table.loc[neutral_idx, "ion_mode"] = "MIX" tmp_samples = merged_node_table.loc[neutral_idx, "samples"].split('|') tmp_samples += merged_node_table.loc[ion_idx, "samples"].split('|') tmp_samples = list(set(tmp_samples)) tmp_samples.sort() tmp_samples = '|'.join(tmp_samples) merged_node_table.loc[neutral_idx, "samples"] = tmp_samples # Update edge table : del_edge = merged_edge_table[merged_edge_table['node_1'] == ion_idx] del_edge = del_edge.index[del_edge['node_2'] == ion_idx] if len(del_edge) > 0 : merged_edge_table.drop(del_edge[0], inplace = True) new_idx = merged_edge_table.index.max() + 1 merged_edge_table.loc[new_idx] = [neutral_idx, ion_idx, 0, 0, 0, d_rt, d_mz, "add_edge", None, adduct, adduct, "NEG"] # Produce cluster IDs. Making these is essential before opposed mode singletons # search, to filter out singletons already present in molecular clusters. node_pool = list(merged_node_table.index) singletons = list(merged_edge_table["node_1"][merged_edge_table['status'] == "self_edge"]) node_pool = list(set(node_pool) - set(singletons)) cluster_list = [] cluster_size_list = [] total_nodes = len(node_pool) while len(node_pool) > 0: new_cluster = [node_pool[0]] cluster_size = 0 perc = round((1-(len(node_pool)/total_nodes))*100,1) sys.stdout.write("\rDefining new clusters : {0}%".format(perc)) sys.stdout.flush() while cluster_size != len(new_cluster): cluster_size = len(new_cluster) tmp_idx = [] for i in new_cluster: tmp_idx += list(merged_edge_table.index[merged_edge_table['node_1'] == i]) tmp_idx += list(merged_edge_table.index[merged_edge_table['node_2'] == i]) new_cluster += list(merged_edge_table.loc[tmp_idx, 'node_1']) new_cluster += list(merged_edge_table.loc[tmp_idx, 'node_2']) new_cluster = list(set(new_cluster)) new_cluster.sort() node_pool = list(set(node_pool) - set(new_cluster)) cluster_size_list.append(len(new_cluster)) cluster_list.append('|'.join(list(map(str, new_cluster)))) cluster_table= pd.DataFrame() cluster_table['cluster'] = cluster_list cluster_table['cluster_size'] = cluster_size_list cluster_table.sort_values('cluster_size', ascending = False, inplace = True) cluster_table.reset_index(drop = True, inplace = True) # Identify molecular clusters cluster_molecular = list() for i in cluster_table.index: node_list = cluster_table.loc[i, "cluster"].split('|') node_list = list(map(int, node_list)) tmp_table_1 = merged_node_table.loc[node_list] if sum(tmp_table_1['status'] == "neutral") > 0 : cluster_molecular.append(True) else: cluster_molecular.append(False) cluster_table["molecular_cluster"] = cluster_molecular merged_node_table['cluster_id'] = [-1]*len(merged_node_table) print('Assigning new cluster indexes...') for i in tqdm(cluster_table.index): node_list = list(map(int, cluster_table.loc[i, 'cluster'].split('|'))) for j in node_list : merged_node_table.loc[j, 'cluster_id'] = i # Connect singletons to singletons (only precursors and nodes from non molecular clusters) remains_table = cluster_table.index[~cluster_table["molecular_cluster"]].tolist() remains_table = [merged_node_table.index[merged_node_table['cluster_id'] == i].tolist() for i in remains_table] remains_table = list(flatten(remains_table)) # Added precursors and fragments from non-molecular clusters remains_table += merged_node_table.index[merged_node_table['cluster_id'] == -1].tolist() # Added singleton nodes precursor_ions = list(set(merged_node_table.index) - set(remains_table)) precursor_ions = merged_node_table.loc[precursor_ions] precursor_ions = precursor_ions.index[precursor_ions['status'] == "precursor"].tolist() remains_table += precursor_ions # Added precursors from molecular clusters remains_table.sort() remains_table = merged_node_table.loc[remains_table] remains_table_pos = remains_table[remains_table['ion_mode'] == "POS"] remains_table_neg = remains_table[remains_table['ion_mode'] == "NEG"] print('Linking NEG singletons to POS singletons...') candidates_table = list() for i in tqdm(remains_table_neg.index): ion_1_mz = remains_table_neg.loc[i, "mz"] ion_1_rt = remains_table_neg.loc[i, "rt"] ion_1_samples = set(remains_table_neg.loc[i, "samples"].split('|')) neutral_table = Neutral_table(ion_1_mz, adduct_table_base_neg) hit_table_rt = remains_table_pos[remains_table_pos['rt'].between(ion_1_rt - rt_error, ion_1_rt + rt_error, inclusive = "both")].copy() shares_samples_list = list() for j in hit_table_rt.index: ion_samples = hit_table_rt.loc[j, "samples"].split('|') shared_samples = len(list(ion_1_samples.intersection(ion_samples))) shares_samples_list.append(shared_samples) hit_table_rt['shared_samples'] = shares_samples_list hit_table_rt = hit_table_rt[hit_table_rt['shared_samples'] > 0] if len(hit_table_rt) == 0 : continue for j in neutral_table.index: ion_table = Ion_table(neutral_table.loc[j, "neutral_mass"], adduct_table_base_pos) for k in ion_table.index: hit_table = hit_table_rt[hit_table_rt['mz'].between(ion_table.loc[k, "ion_mz"] - mass_error, ion_table.loc[k, "ion_mz"] + mass_error, inclusive = "both")] for l in hit_table.index: candidates_table.append((i, neutral_table.loc[j, "Adduct"], neutral_table.loc[j, "Complexity"], l, ion_table.loc[k, "Adduct"], ion_table.loc[k, "Complexity"])) candidates_table = pd.DataFrame(candidates_table, columns = ['neg_ion', 'neg_adduct', 'neg_complexity', 'pos_ion', 'pos_adduct', 'pos_complexity']) # Get the best hypotheses unique_negs = candidates_table['neg_ion'].unique().tolist() neg_ion_list = list() neg_adduct_list = list() neg_complexity_list = list() pos_ion_list = list() pos_adduct_list = list() pos_complexity_list = list() print('Selecting best NEG annotations...') for i in tqdm(unique_negs) : tmp_table_1 = candidates_table[candidates_table['neg_ion'] == i] unique_adducts = tmp_table_1['neg_adduct'].unique().tolist() point_list = list() for adduct in unique_adducts: tmp_table_2 = tmp_table_1[tmp_table_1['neg_adduct'] == adduct] points = 2*len(tmp_table_2) / (tmp_table_2['neg_complexity'].sum() + tmp_table_2['pos_complexity'].sum()) point_list.append(points) selected_adduct = unique_adducts[point_list.index(max(point_list))] tmp_table_1 = tmp_table_1[tmp_table_1['neg_adduct'] == selected_adduct] for j in tmp_table_1.index: neg_ion_list.append(tmp_table_1.loc[j, "neg_ion"]) neg_adduct_list.append(tmp_table_1.loc[j, "neg_adduct"]) neg_complexity_list.append(tmp_table_1.loc[j, "neg_complexity"]) pos_ion_list.append(tmp_table_1.loc[j, "pos_ion"]) pos_adduct_list.append(tmp_table_1.loc[j, "pos_adduct"]) pos_complexity_list.append(tmp_table_1.loc[j, "pos_complexity"]) candidates_table = list(zip(neg_ion_list, neg_adduct_list, neg_complexity_list, pos_ion_list, pos_adduct_list, pos_complexity_list)) candidates_table = pd.DataFrame(candidates_table, columns = ['neg_ion', 'neg_adduct', 'neg_complexity', 'pos_ion', 'pos_adduct', 'pos_complexity']) # Resolve POS ions with multiple annotations unique_pos = candidates_table['pos_ion'].unique().tolist() neg_ion_list = list() neg_adduct_list = list() neg_complexity_list = list() pos_ion_list = list() pos_adduct_list = list() pos_complexity_list = list() print('Selecting best POS annotations...') for i in tqdm(unique_pos): tmp_table_1 = candidates_table[candidates_table['pos_ion'] == i] unique_adducts = tmp_table_1['pos_adduct'].unique().tolist() point_list = list() for adduct in unique_adducts: tmp_table_2 = tmp_table_1[tmp_table_1['pos_adduct'] == adduct] points = 2*len(tmp_table_2) / (tmp_table_2['neg_complexity'].sum() + tmp_table_2['pos_complexity'].sum()) point_list.append(points) selected_adduct = unique_adducts[point_list.index(max(point_list))] tmp_table_1 = tmp_table_1[tmp_table_1['pos_adduct'] == selected_adduct] for j in tmp_table_1.index: neg_ion_list.append(tmp_table_1.loc[j, "neg_ion"]) neg_adduct_list.append(tmp_table_1.loc[j, "neg_adduct"]) neg_complexity_list.append(tmp_table_1.loc[j, "neg_complexity"]) pos_ion_list.append(tmp_table_1.loc[j, "pos_ion"]) pos_adduct_list.append(tmp_table_1.loc[j, "pos_adduct"]) pos_complexity_list.append(tmp_table_1.loc[j, "pos_complexity"]) candidates_table = list(zip(neg_ion_list, neg_adduct_list, neg_complexity_list, pos_ion_list, pos_adduct_list, pos_complexity_list)) candidates_table = pd.DataFrame(candidates_table, columns = ['neg_ion', 'neg_adduct', 'neg_complexity', 'pos_ion', 'pos_adduct', 'pos_complexity']) # Make a neutral table: neutral_idx = 0 neutral_table = list() while len(candidates_table) > 0 : idx = candidates_table.index[0] neg_pool = list() pos_pool = list() idx_pool = list() neg_pool.append(candidates_table.loc[idx, "neg_ion"]) pos_pool.append(candidates_table.loc[idx, "pos_ion"]) new_neg_pool = neg_pool new_pos_pool = pos_pool idx_pool.append(idx) len_0 = 0 while len_0 != len(idx_pool): len_0 = len(idx_pool) for i in new_neg_pool: idx_pool += candidates_table.index[candidates_table['neg_ion'] == i].tolist() for i in new_pos_pool: idx_pool += candidates_table.index[candidates_table['pos_ion'] == i].tolist() idx_pool = list(set(idx_pool)) tmp_table_1 = candidates_table.loc[idx_pool] for i in tmp_table_1['neg_ion'].unique(): idx = tmp_table_1.index[tmp_table_1['neg_ion'] == i][0] mgf_idx = int(merged_node_table.loc[i, "mgf_index"]) ion_mz = merged_node_table.loc[i, "mz"] ion_rt = merged_node_table.loc[i, "rt"] ion_tic = merged_node_table.loc[i, "TIC"] ion_adduct = tmp_table_1.loc[idx, "neg_adduct"] ion_samples = merged_node_table.loc[i, "samples"] adduct_idx = adduct_table_base_neg.index[adduct_table_base_neg["Adduct"] == ion_adduct][0] ion_adduct_code = adduct_table_base_neg.loc[adduct_idx, "Adduct_code"] mol_mass = Neutral_mass_calculator(ion_mz, adduct_table_base_neg.loc[adduct_idx, "Adduct_mass"], adduct_table_base_neg.loc[adduct_idx, "Mol_multiplier"], adduct_table_base_neg.loc[adduct_idx, "Charge"]) Species_rule = Validator_choice(ion_adduct_code, "NEG") ion_rule_points = Species_rule(mgf_idx, mgf_file_neg) neutral_table.append((neutral_idx, mol_mass, ion_mz, ion_rt, i, ion_adduct, ion_tic, ion_rule_points, ion_samples, "NEG")) for i in tmp_table_1['pos_ion'].unique(): idx = tmp_table_1.index[tmp_table_1['pos_ion'] == i][0] mgf_idx = int(merged_node_table.loc[i, "mgf_index"]) ion_mz = merged_node_table.loc[i, "mz"] ion_rt = merged_node_table.loc[i, "rt"] ion_tic = merged_node_table.loc[i, "TIC"] ion_adduct = tmp_table_1.loc[idx, "pos_adduct"] ion_samples = merged_node_table.loc[i, "samples"] adduct_idx = adduct_table_base_pos.index[adduct_table_base_pos["Adduct"] == ion_adduct][0] ion_adduct_code = adduct_table_base_pos.loc[adduct_idx, "Adduct_code"] mol_mass = Neutral_mass_calculator(ion_mz, adduct_table_base_pos.loc[adduct_idx, "Adduct_mass"], adduct_table_base_pos.loc[adduct_idx, "Mol_multiplier"], adduct_table_base_pos.loc[adduct_idx, "Charge"]) Species_rule = Validator_choice(ion_adduct_code, "POS") ion_rule_points = Species_rule(mgf_idx, mgf_file_pos) neutral_table.append((neutral_idx, mol_mass, ion_mz, ion_rt, i, ion_adduct, ion_tic, ion_rule_points, ion_samples, "POS")) candidates_table.drop(idx_pool, inplace = True) neutral_idx += 1 neutral_table = pd.DataFrame(neutral_table, columns = ['neutral_idx', 'neutral_mass', 'ion_mz', 'ion_rt', 'ion_idx', 'adduct', 'TIC', 'rule_points', 'samples', 'ion_mode']) # Report the results: print('Reporting results for NEG singletons paired to POS singletons...') for i in tqdm(neutral_table['neutral_idx'].unique()): tmp_table_1 = neutral_table[neutral_table['neutral_idx'] == i].copy() mol_mass = tmp_table_1['neutral_mass'].mean() mol_rt = tmp_table_1['ion_rt'].mean() mol_tic = tmp_table_1['TIC'].sum() mol_cluster = merged_node_table['cluster_id'].max() + 1 adduct_count = len(tmp_table_1) tmp_table_1['mz_gap'] = abs(mol_mass - tmp_table_1['ion_mz']) tmp_table_1['rt_gap'] = abs(mol_rt - tmp_table_1['ion_rt']) mol_samples = '|'.join(tmp_table_1['samples']) mol_samples = list(set(mol_samples.split('|'))) mol_samples.sort() mol_samples = '|'.join(mol_samples) new_idx = merged_node_table.index.max() + 1 merged_node_table.loc[new_idx] = [mol_mass, mol_rt, mol_tic, 0, None, 0, "neutral", adduct_count, None, "MIX", None, mol_samples, mol_cluster] for j in tmp_table_1.index: ion_idx = tmp_table_1.loc[j, 'ion_idx'] adduct = tmp_table_1.loc[j, 'adduct'] rule_points = tmp_table_1.loc[j, 'rule_points'] mz_gap = tmp_table_1.loc[j, 'mz_gap'] rt_gap = tmp_table_1.loc[j, 'rt_gap'] tmp_mode = tmp_table_1.loc[j, "ion_mode"] merged_node_table.loc[ion_idx, 'rule_points'] = rule_points merged_node_table.loc[ion_idx, 'status'] = "adduct" merged_node_table.loc[ion_idx, 'Adnotation'] = adduct merged_node_table.loc[ion_idx, 'cluster_id'] = mol_cluster new_edge = merged_edge_table.index.max() + 1 merged_edge_table.loc[new_edge] = [new_idx, ion_idx, 0 ,0 ,0 , rt_gap, mz_gap, "add_edge", None, adduct, adduct, tmp_mode] del_edge = merged_edge_table[merged_edge_table['node_1'] == ion_idx] del_edge = del_edge[del_edge['node_2'] == ion_idx].index if len(del_edge) > 0 : merged_edge_table.drop(del_edge[0], inplace = True) # Connect singletons to singletons (only precursors and nodes from non molecular clusters) remains_table = cluster_table.index[~cluster_table["molecular_cluster"]].tolist() remains_table = [merged_node_table.index[merged_node_table['cluster_id'] == i].tolist() for i in remains_table] remains_table = list(flatten(remains_table)) # Added precursors and fragments from non-molecular clusters remains_table += merged_node_table.index[merged_node_table['cluster_id'] == -1].tolist() # Added singleton nodes precursor_ions = list(set(merged_node_table.index) - set(remains_table)) precursor_ions = merged_node_table.loc[precursor_ions] precursor_ions = precursor_ions.index[precursor_ions['status'] == "precursor"].tolist() remains_table += precursor_ions # Added precursors from molecular clusters remains_table.sort() remains_table = merged_node_table.loc[remains_table] remains_table_pos = remains_table[remains_table['ion_mode'] == "POS"] remains_table_neg = remains_table[remains_table['ion_mode'] == "NEG"] print('Linking POS singletons to NEG singletons...') candidates_table = list() for i in tqdm(remains_table_pos.index): ion_1_mz = remains_table_pos.loc[i, "mz"] ion_1_rt = remains_table_pos.loc[i, "rt"] ion_1_samples = set(remains_table_pos.loc[i, "samples"].split('|')) neutral_table = Neutral_table(ion_1_mz, adduct_table_base_pos) hit_table_rt = remains_table_neg[remains_table_neg['rt'].between(ion_1_rt - rt_error, ion_1_rt + rt_error, inclusive = "both")].copy() shares_samples_list = list() for j in hit_table_rt.index: ion_samples = hit_table_rt.loc[j, "samples"].split('|') shared_samples = len(list(ion_1_samples.intersection(ion_samples))) shares_samples_list.append(shared_samples) hit_table_rt['shared_samples'] = shares_samples_list hit_table_rt = hit_table_rt[hit_table_rt['shared_samples'] > 0] if len(hit_table_rt) == 0 : continue for j in neutral_table.index: ion_table = Ion_table(neutral_table.loc[j, "neutral_mass"], adduct_table_base_neg) for k in ion_table.index: hit_table = hit_table_rt[hit_table_rt['mz'].between(ion_table.loc[k, "ion_mz"] - mass_error, ion_table.loc[k, "ion_mz"] + mass_error, inclusive = "both")] for l in hit_table.index: candidates_table.append((i, neutral_table.loc[j, "Adduct"], neutral_table.loc[j, "Complexity"], l, ion_table.loc[k, "Adduct"], ion_table.loc[k, "Complexity"])) candidates_table = pd.DataFrame(candidates_table, columns = ['pos_ion', 'pos_adduct', 'pos_complexity', 'neg_ion', 'neg_adduct', 'neg_complexity']) # Get the best hypotheses unique_pos = candidates_table['pos_ion'].unique().tolist() pos_ion_list = list() pos_adduct_list = list() pos_complexity_list = list() neg_ion_list = list() neg_adduct_list = list() neg_complexity_list = list() print('Selecting best POS annotations...') for i in tqdm(unique_pos) : tmp_table_1 = candidates_table[candidates_table['pos_ion'] == i] unique_adducts = tmp_table_1['pos_adduct'].unique().tolist() point_list = list() for adduct in unique_adducts: tmp_table_2 = tmp_table_1[tmp_table_1['pos_adduct'] == adduct] points = 2*len(tmp_table_2) / (tmp_table_2['pos_complexity'].sum() + tmp_table_2['neg_complexity'].sum()) point_list.append(points) selected_adduct = unique_adducts[point_list.index(max(point_list))] tmp_table_1 = tmp_table_1[tmp_table_1['pos_adduct'] == selected_adduct] for j in tmp_table_1.index: pos_ion_list.append(tmp_table_1.loc[j, "pos_ion"]) pos_adduct_list.append(tmp_table_1.loc[j, "pos_adduct"]) pos_complexity_list.append(tmp_table_1.loc[j, "pos_complexity"]) neg_ion_list.append(tmp_table_1.loc[j, "neg_ion"]) neg_adduct_list.append(tmp_table_1.loc[j, "neg_adduct"]) neg_complexity_list.append(tmp_table_1.loc[j, "neg_complexity"]) candidates_table = list(zip(pos_ion_list, pos_adduct_list, pos_complexity_list, neg_ion_list, neg_adduct_list, neg_complexity_list)) candidates_table = pd.DataFrame(candidates_table, columns = ['pos_ion', 'pos_adduct', 'pos_complexity', 'neg_ion', 'neg_adduct', 'neg_complexity']) # Resolve NEG ions with multiple annotations unique_neg = candidates_table['neg_ion'].unique().tolist() pos_ion_list = list() pos_adduct_list = list() pos_complexity_list = list() neg_ion_list = list() neg_adduct_list = list() neg_complexity_list = list() print('Selecting best NEG annotations...') for i in tqdm(unique_neg): tmp_table_1 = candidates_table[candidates_table['neg_ion'] == i] unique_adducts = tmp_table_1['neg_adduct'].unique().tolist() point_list = list() for adduct in unique_adducts: tmp_table_2 = tmp_table_1[tmp_table_1['neg_adduct'] == adduct] points = 2*len(tmp_table_2) / (tmp_table_2['pos_complexity'].sum() + tmp_table_2['neg_complexity'].sum()) point_list.append(points) selected_adduct = unique_adducts[point_list.index(max(point_list))] tmp_table_1 = tmp_table_1[tmp_table_1['neg_adduct'] == selected_adduct] for j in tmp_table_1.index: pos_ion_list.append(tmp_table_1.loc[j, "pos_ion"]) pos_adduct_list.append(tmp_table_1.loc[j, "pos_adduct"]) pos_complexity_list.append(tmp_table_1.loc[j, "pos_complexity"]) neg_ion_list.append(tmp_table_1.loc[j, "neg_ion"]) neg_adduct_list.append(tmp_table_1.loc[j, "neg_adduct"]) neg_complexity_list.append(tmp_table_1.loc[j, "neg_complexity"]) candidates_table = list(zip(pos_ion_list, pos_adduct_list, pos_complexity_list, neg_ion_list, neg_adduct_list, neg_complexity_list)) candidates_table = pd.DataFrame(candidates_table, columns = ['pos_ion', 'pos_adduct', 'pos_complexity', 'neg_ion', 'neg_adduct', 'neg_complexity']) # Make a neutral table: neutral_idx = 0 neutral_table = list() while len(candidates_table) > 0 : idx = candidates_table.index[0] pos_pool = list() neg_pool = list() idx_pool = list() pos_pool.append(candidates_table.loc[idx, "pos_ion"]) neg_pool.append(candidates_table.loc[idx, "neg_ion"]) new_pos_pool = pos_pool new_neg_pool = neg_pool idx_pool.append(idx) len_0 = 0 while len_0 != len(idx_pool): len_0 = len(idx_pool) for i in new_pos_pool: idx_pool += candidates_table.index[candidates_table['pos_ion'] == i].tolist() for i in new_neg_pool: idx_pool += candidates_table.index[candidates_table['neg_ion'] == i].tolist() idx_pool = list(set(idx_pool)) tmp_table_1 = candidates_table.loc[idx_pool] for i in tmp_table_1['pos_ion'].unique(): idx = tmp_table_1.index[tmp_table_1['pos_ion'] == i][0] mgf_idx = int(merged_node_table.loc[i, "mgf_index"]) ion_mz = merged_node_table.loc[i, "mz"] ion_rt = merged_node_table.loc[i, "rt"] ion_tic = merged_node_table.loc[i, "TIC"] ion_adduct = tmp_table_1.loc[idx, "pos_adduct"] ion_samples = merged_node_table.loc[i, "samples"] adduct_idx = adduct_table_base_pos.index[adduct_table_base_pos["Adduct"] == ion_adduct][0] ion_adduct_code = adduct_table_base_pos.loc[adduct_idx, "Adduct_code"] mol_mass = Neutral_mass_calculator(ion_mz, adduct_table_base_pos.loc[adduct_idx, "Adduct_mass"], adduct_table_base_pos.loc[adduct_idx, "Mol_multiplier"], adduct_table_base_pos.loc[adduct_idx, "Charge"]) Species_rule = Validator_choice(ion_adduct_code, "POS") ion_rule_points = Species_rule(mgf_idx, mgf_file_pos) neutral_table.append((neutral_idx, mol_mass, ion_mz, ion_rt, i, ion_adduct, ion_tic, ion_rule_points, ion_samples, "POS")) for i in tmp_table_1['neg_ion'].unique(): idx = tmp_table_1.index[tmp_table_1['neg_ion'] == i][0] mgf_idx = int(merged_node_table.loc[i, "mgf_index"]) ion_mz = merged_node_table.loc[i, "mz"] ion_rt = merged_node_table.loc[i, "rt"] ion_tic = merged_node_table.loc[i, "TIC"] ion_adduct = tmp_table_1.loc[idx, "neg_adduct"] ion_samples = merged_node_table.loc[i, "samples"] adduct_idx = adduct_table_base_neg.index[adduct_table_base_neg["Adduct"] == ion_adduct][0] ion_adduct_code = adduct_table_base_neg.loc[adduct_idx, "Adduct_code"] mol_mass = Neutral_mass_calculator(ion_mz, adduct_table_base_neg.loc[adduct_idx, "Adduct_mass"], adduct_table_base_neg.loc[adduct_idx, "Mol_multiplier"], adduct_table_base_neg.loc[adduct_idx, "Charge"]) Species_rule = Validator_choice(ion_adduct_code, "NEG") ion_rule_points = Species_rule(mgf_idx, mgf_file_neg) neutral_table.append((neutral_idx, mol_mass, ion_mz, ion_rt, i, ion_adduct, ion_tic, ion_rule_points, ion_samples, "NEG")) candidates_table.drop(idx_pool, inplace = True) neutral_idx += 1 neutral_table = pd.DataFrame(neutral_table, columns = ['neutral_idx', 'neutral_mass', 'ion_mz', 'ion_rt', 'ion_idx', 'adduct', 'TIC', 'rule_points', 'samples', 'ion_mode']) # Report the results: print('Reporting results for POS singletons paired to NEG singletons...') for i in tqdm(neutral_table['neutral_idx'].unique()): tmp_table_1 = neutral_table[neutral_table['neutral_idx'] == i].copy() mol_mass = tmp_table_1['neutral_mass'].mean() mol_rt = tmp_table_1['ion_rt'].mean() mol_tic = tmp_table_1['TIC'].sum() mol_cluster = merged_node_table['cluster_id'].max() + 1 adduct_count = len(tmp_table_1) tmp_table_1['mz_gap'] = abs(mol_mass - tmp_table_1['ion_mz']) tmp_table_1['rt_gap'] = abs(mol_rt - tmp_table_1['ion_rt']) mol_samples = '|'.join(tmp_table_1['samples']) mol_samples = list(set(mol_samples.split('|'))) mol_samples.sort() mol_samples = '|'.join(mol_samples) new_idx = merged_node_table.index.max() + 1 merged_node_table.loc[new_idx] = [mol_mass, mol_rt, mol_tic, 0, None, 0, "neutral", adduct_count, None, "MIX", None, mol_samples, mol_cluster] for j in tmp_table_1.index: ion_idx = tmp_table_1.loc[j, 'ion_idx'] adduct = tmp_table_1.loc[j, 'adduct'] rule_points = tmp_table_1.loc[j, 'rule_points'] mz_gap = tmp_table_1.loc[j, 'mz_gap'] rt_gap = tmp_table_1.loc[j, 'rt_gap'] tmp_mode = tmp_table_1.loc[j, "ion_mode"] merged_node_table.loc[ion_idx, 'rule_points'] = rule_points merged_node_table.loc[ion_idx, 'status'] = "adduct" merged_node_table.loc[ion_idx, 'Adnotation'] = adduct merged_node_table.loc[ion_idx, 'cluster_id'] = mol_cluster new_edge = merged_edge_table.index.max() + 1 merged_edge_table.loc[new_edge] = [new_idx, ion_idx, 0 ,0 ,0 , rt_gap, mz_gap, "add_edge", None, adduct, adduct, tmp_mode] del_edge = merged_edge_table[merged_edge_table['node_1'] == ion_idx] del_edge = del_edge[del_edge['node_2'] == ion_idx].index if len(del_edge) > 0 : merged_edge_table.drop(del_edge[0], inplace = True) # Update status node nodes (pos neutrals, neg neutrals, pos adducts_ neg_adducts) merged_node_table.insert(merged_node_table.columns.get_loc('status') + 1, "status_universal", merged_node_table['status'].copy()) merged_node_table['status'] = merged_node_table['ion_mode'].str.lower() + "_" + merged_node_table['status'] # Update status for edges (pos add, neg add, pos frag, neg frag, pos single, neg single) merged_edge_table.insert(merged_edge_table.columns.get_loc('status') + 1, "status_universal", merged_edge_table['status'].copy()) merged_edge_table['status'] = merged_edge_table['ion_mode'].str.lower() + "_" + merged_edge_table['status'] # Round values: merged_node_table['mz'] = merged_node_table['mz'].round(4) merged_node_table['rt'] = merged_node_table['rt'].round(3) merged_edge_table['mz_gap'] = merged_edge_table['mz_gap'].round(4) merged_node_table.drop("samples", axis = 1, inplace = True) # Adding MZmine csv columns to the node table (sample intensities) neutrals_idx = list(merged_node_table.index[merged_node_table['status'] == "neg_neutral"]) neutrals_idx += list(merged_node_table.index[merged_node_table['status'] == "pos_neutral"]) neutrals_idx += list(merged_node_table.index[merged_node_table['status'] == "mix_neutral"]) pos_ions_idx = list(set(merged_node_table.index[merged_node_table['ion_mode'] == "POS"]) - set(neutrals_idx)) neg_ions_idx = list(set(merged_node_table.index[merged_node_table['ion_mode'] == "NEG"]) - set(neutrals_idx)) samples = list(neg_mzmine_csv.columns) + list(pos_mzmine_csv.columns) samples = list(set(samples)) samples.sort() for sample in samples: merged_node_table[sample] = [0.0]*len(merged_node_table) print("Adding POS sample intensities to the merged node table...") for i in tqdm(pos_ions_idx): ion_id = merged_node_table.loc[i, "feature_id"] for sample in samples: merged_node_table.loc[i, sample] = pos_mzmine_csv.loc[ion_id, sample] print("Adding NEG sample intensities to the merged node table...") for i in tqdm(neg_ions_idx): ion_id = merged_node_table.loc[i, "feature_id"] for sample in samples: merged_node_table.loc[i, sample] = neg_mzmine_csv.loc[ion_id, sample] print("Adding neutral sample intensities to the merged node table...") for i in tqdm(neutrals_idx): ion_ids = list(merged_edge_table['node_2'][merged_edge_table['node_1'] == i]) #ion_ids = merged_node_table.loc[ion_ids, "feature_id"] for sample in samples: merged_node_table.loc[i, sample] = merged_node_table.loc[ion_ids, sample].sum() #export the data if not os.path.isdir(out_full) : os.mkdir(out_full) if not os.path.isdir(out_samples) : os.mkdir(out_samples) merged_edge_table.to_csv(out_full + 'MIX_edges.csv', index_label = "Index") merged_node_table.to_csv(out_full + 'MIX_nodes.csv', index_label = "Index") if params['mm_export_samples'] : Samplewise_export(neg_csv_file = in_path_full_neg + neg_csv, pos_csv_file = in_path_full_pos + pos_csv, out_path = out_samples, merged_edge_table = merged_edge_table, merged_node_table = merged_node_table) return

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6

e9d7e4d2f8de4076d09872c47c9b0fa0b4106fa5

2,616

py

Python

2016/day18/code.py

Fadi88/AoC

8b24f2f2cc7b4e1c63758e81e63d8670a261cc7c

[ "Unlicense" ]

12

2019-12-15T21:53:19.000Z

2021-12-24T17:03:41.000Z

2016/day18/code.py

Fadi88/adventofcode19

dd2456bdd163beb02dbfe9dcea2b021061c7671e

[ "Unlicense" ]

1

2021-12-15T20:40:51.000Z

2021-12-15T22:19:48.000Z

2016/day18/code.py

Fadi88/adventofcode19

dd2456bdd163beb02dbfe9dcea2b021061c7671e

[ "Unlicense" ]

5

2020-12-11T06:00:24.000Z

2021-12-20T21:37:46.000Z

import time import re import hashlib def profiler(method): def wrapper_method(*arg, **kw): t = time.time() ret = method(*arg, **kw) print('Method ' + method.__name__ + ' took : ' + "{:2.5f}".format(time.time()-t) + ' sec') return ret return wrapper_method @profiler def part1(): Traps = [] Traps.append([c == '^' for c in open('input.txt').read().strip()]) #Traps.append([c == '^' for c in '.^^.^.^^^^']) # Traps.append([False,False,True,True,False]) for l in range(1, 40): Traps.append([]) for x in range(len(Traps[0])): center = Traps[l-1][x] if 0 < x < len(Traps[0]) - 1: left = Traps[l-1][x-1] right = Traps[l-1][x+1] elif x == 0: left = False right = Traps[l-1][x+1] elif x == len(Traps[0]) - 1: left = Traps[l-1][x-1] right = False if left == center == True and right == False: Traps[l].append(True) elif center == right == True and left == False: Traps[l].append(True) elif left == True and center == right == False: Traps[l].append(True) elif right == True and center == left == False: Traps[l].append(True) else: Traps[l].append(False) print(len(Traps)*len(Traps[0]) - sum(map(sum, Traps))) @profiler def part2(): Traps = [] Traps.append([c == '^' for c in open('input.txt').read().strip()]) for l in range(1, 400000): Traps.append([]) for x in range(len(Traps[0])): center = Traps[l-1][x] if 0 < x < len(Traps[0]) - 1: left = Traps[l-1][x-1] right = Traps[l-1][x+1] elif x == 0: left = False right = Traps[l-1][x+1] elif x == len(Traps[0]) - 1: left = Traps[l-1][x-1] right = False if left == center == True and right == False: Traps[l].append(True) elif center == right == True and left == False: Traps[l].append(True) elif left == True and center == right == False: Traps[l].append(True) elif right == True and center == left == False: Traps[l].append(True) else: Traps[l].append(False) print(len(Traps)*len(Traps[0]) - sum(map(sum, Traps))) if __name__ == "__main__": part1() part2()

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null

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1

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null

0

6

e9d952e31300f5b8bfcf6237cdca79070255023d

5,307

py

Python

tests/test_clingy.py

mtkennerly/clingy

39454bcf535127ee80ca3e9fb1580dfefcb8aad9

[ "MIT" ]

1

2017-03-24T09:19:18.000Z

tests/test_clingy.py

mtkennerly/clingy

39454bcf535127ee80ca3e9fb1580dfefcb8aad9

[ "MIT" ]

null

tests/test_clingy.py

mtkennerly/clingy

39454bcf535127ee80ca3e9fb1580dfefcb8aad9

[ "MIT" ]

null

import six try: from unittest import mock except ImportError: import mock import pytest import clingy builtins_name = six.moves.builtins.__name__ def test__parse_args_need_at_least_one_filename(): with pytest.raises(SystemExit): clingy._parse_args([]) def test__parse_args_one_email(): argv = ["email.txt"] args = clingy._parse_args(argv) assert args.filenames == argv def test__parse_args_multiple_emails(): argv = ["email1.txt", "email2.txt"] args = clingy._parse_args(argv) assert args.filenames == argv def test__parse_args_directory_short(): argv = ["email.txt", "-d", "out"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.directory == "out" def test__parse_args_directory_long(): argv = ["email.txt", "--directory", "out"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.directory == "out" def test__parse_args_glob_short(): argv = ["email.txt", "-g", "*.txt"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.glob == "*.txt" def test__parse_args_glob_long(): argv = ["email.txt", "--glob", "*.txt"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.glob == "*.txt" def test__parse_args_regex_short(): argv = ["email.txt", "-r", r"\.txt$"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.regex == "\.txt$" def test__parse_args_regex_long(): argv = ["email.txt", "--regex", r"\.txt$"] args = clingy._parse_args(argv) assert args.filenames == ["email.txt"] assert args.regex == "\.txt$" def test_find_with_filename(): attachments = clingy.find("email.txt") assert len(attachments) == 2 assert attachments[0].get_filename() == "foo.txt" assert attachments[1].get_filename() == "bar.txt" def test_find_with_glob(): attachments = clingy.find("email.txt", glob="foo*") assert len(attachments) == 1 assert attachments[0].get_filename() == "foo.txt" def test_find_with_regex(): attachments = clingy.find("email.txt", regex=r"^foo") assert len(attachments) == 1 assert attachments[0].get_filename() == "foo.txt" def test_find_with_string(): with open("email.txt") as file: attachments = clingy.find(file.read()) assert len(attachments) == 2 assert attachments[0].get_filename() == "foo.txt" assert attachments[1].get_filename() == "bar.txt" def test_match_none(): assert clingy.match("") is True assert clingy.match("foo") is True def test_match_glob(): assert clingy.match("foo.txt", glob="*.txt") is True assert clingy.match("foo.txt", glob="*.pdf") is False def test_match_regex(): assert clingy.match("foo.txt", regex=r"^.+\.txt$") is True assert clingy.match("foo.txt", regex=r"^.+\.pdf$") is False def test_match_both(): assert clingy.match("foo.txt", glob="*.txt", regex=r"\.txt") is True assert clingy.match("foo.txt", glob="*.txt", regex=r"\.pdf") is False def test_save_with_filename(): with open("email.txt") as file: email = file.read() mo = mock.mock_open(read_data=email) with mock.patch("{}.open".format(builtins_name), mo): clingy.save(email) # Output filenames assert mock.call("foo.txt", "wb") in mo.mock_calls assert mock.call("bar.txt", "wb") in mo.mock_calls # Output file content handle = mo() assert mock.call(b"foo") in handle.write.mock_calls assert mock.call(b"bar") in handle.write.mock_calls def test_save_with_glob(): with open("email.txt") as file: email = file.read() mo = mock.mock_open() with mock.patch("{}.open".format(builtins_name), mo): clingy.save(email, glob="bar*") # Output filenames assert mock.call("bar.txt", "wb") in mo.mock_calls # Output file content handle = mo() assert mock.call(b"bar") in handle.write.mock_calls def test_save_with_message_object(): attachments = clingy.find("email.txt") mo = mock.mock_open() with mock.patch("{}.open".format(builtins_name), mo): clingy.save(attachments[0]) # Output filenames assert mock.call("foo.txt", "wb") in mo.mock_calls # Output file content handle = mo() assert mock.call(b"foo") in handle.write.mock_calls def test_save_with_regex(): with open("email.txt") as file: email = file.read() mo = mock.mock_open() with mock.patch("{}.open".format(builtins_name), mo): clingy.save(email, glob="bar*") # Output filenames assert mock.call("bar.txt", "wb") in mo.mock_calls # Output file content handle = mo() assert mock.call(b"bar") in handle.write.mock_calls def test_save_with_string(): with open("email.txt") as file: email = file.read() mo = mock.mock_open() with mock.patch("{}.open".format(builtins_name), mo): clingy.save(email) # Output filenames assert mock.call("foo.txt", "wb") in mo.mock_calls assert mock.call("bar.txt", "wb") in mo.mock_calls # Output file content handle = mo() assert mock.call(b"foo") in handle.write.mock_calls assert mock.call(b"bar") in handle.write.mock_calls

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0.05909

0.043413

0.831776

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0.762436

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0.725957

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0

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205

74

25.887805

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0.21875

0

null

0

1

0

null

0

6

756e0375de5bec14cdbdf2b29b644de8daa8e805

13,084

py

Python

tests/signatures/gc3_versioned_type/test_gc3_versioned_type.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

tests/signatures/gc3_versioned_type/test_gc3_versioned_type.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

tests/signatures/gc3_versioned_type/test_gc3_versioned_type.py

ericmharris/gc3-query

0bf5226130aafbb1974aeb96d93ee1996833e87d

[ "MIT" ]

null

from pathlib import Path import pytest from gc3_query.lib import gc3_cfg from gc3_query.lib.gc3_config import GC3Config from gc3_query.lib.iaas_classic.instances import Instances from gc3_query.lib.iaas_classic.sec_rules import SecRules # fixme? from gc3_query.lib.open_api import API_SPECS_DIR from gc3_query.lib.open_api.open_api_spec import OpenApiSpec from gc3_query.lib.signatures import GC3Type, GC3VersionedType # from pprint import pprint, pformat TEST_BASE_DIR: Path = Path(__file__).parent # config_dir = TEST_BASE_DIR.joinpath("config") config_dir = gc3_cfg.BASE_DIR.joinpath("etc/config") output_dir = TEST_BASE_DIR.joinpath('output') def test_setup(): assert TEST_BASE_DIR.exists() # assert API_SPECS_DIR.exists() if not config_dir.exists(): config_dir.mkdir() if not output_dir.exists(): output_dir.mkdir() def test_init(): service = 'Instances' gc3_config = GC3Config(atoml_config_dir=config_dir) service_cfg = gc3_config.iaas_classic.services.compute[service] api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog oapi_spec = OpenApiSpec(service_cfg=service_cfg, open_api_specs_cfg=api_catalog_config) assert oapi_spec.name == service gc3type = GC3Type(name='OpenApiSpec', descr="Some text", class_type=oapi_spec.__class__) gc3_ver_type = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=oapi_spec.__class__, version="18.1.2-20180126.052521") assert gc3type assert gc3_ver_type @pytest.fixture() def test_equality_setup(): idm_domain = 'gc30003' gc3_config = GC3Config(atoml_config_dir=config_dir) idm_cfg = gc3_config.idm.domains[idm_domain] api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog instances_service: str = 'Instances' instances_service_cfg = gc3_config.iaas_classic.services[instances_service] instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert instances_oapi_spec.name == instances_service secrules_service: str = 'SecRules' secrules_service_cfg = gc3_config.iaas_classic.services[secrules_service] sec_rules = SecRules(service_cfg=secrules_service_cfg, idm_cfg=idm_cfg, from_url=True) instances = Instances(service_cfg=instances_service_cfg, idm_cfg=idm_cfg, from_url=True) yield sec_rules, instances, idm_cfg, idm_domain, gc3_config def test_eqality(test_equality_setup): sec_rules, instances, idm_cfg, idm_domain, gc3_config = test_equality_setup api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog version = "18.1.2-20180126.052521" instances_service: str = 'Instances' instances_service_cfg = gc3_config.iaas_classic.services[instances_service] instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert instances_oapi_spec.name == instances_service instances_oapi_spec_type = GC3Type(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__) instances_oapi_spec_ver_type = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=version) instances_oapi_spec_type_2 = GC3Type(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__) instances_oapi_spec_ver_type_2 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=version) assert instances_oapi_spec_type==instances_oapi_spec_type assert instances_oapi_spec_type==instances_oapi_spec_type_2 assert instances_oapi_spec_ver_type==instances_oapi_spec_ver_type assert instances_oapi_spec_ver_type==instances_oapi_spec_ver_type_2 secrules_service: str = 'SecRules' secrules_service_cfg = gc3_config.iaas_classic.services[secrules_service] secrules_oapi_spec = OpenApiSpec(service_cfg=secrules_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) secrules_oapi_spec_2 = OpenApiSpec(service_cfg=secrules_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert secrules_oapi_spec.name == secrules_service secrules_oapi_spec_type = GC3Type(name='OpenApiSpec', descr="Some text", class_type=secrules_oapi_spec.__class__) secrules_oapi_spec_ver_type = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=secrules_oapi_spec.__class__, version=version) secrules_oapi_spec_type_2 = GC3Type(name='OpenApiSpec', descr="Some text", class_type=secrules_oapi_spec_2.__class__) secrules_oapi_spec_ver_type_2 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=secrules_oapi_spec.__class__, version=version) assert secrules_oapi_spec_type assert secrules_oapi_spec_type==secrules_oapi_spec_type assert secrules_oapi_spec_type==secrules_oapi_spec_type_2 assert secrules_oapi_spec_type==instances_oapi_spec_type assert secrules_oapi_spec_ver_type==secrules_oapi_spec_ver_type assert secrules_oapi_spec_ver_type==secrules_oapi_spec_ver_type_2 assert secrules_oapi_spec_ver_type==instances_oapi_spec_ver_type sec_rules_type = GC3Type(name='SecRules', descr="SecRules text", class_type=sec_rules.__class__) sec_rules_ver_type = GC3VersionedType(name='SecRules', descr="SecRules text", class_type=sec_rules.__class__, version=version) instances_type = GC3Type(name='Instances', descr="Instances text", class_type=instances.__class__) instances_ver_type = GC3VersionedType(name='Instances', descr="Instances text", class_type=instances.__class__, version=version) assert sec_rules_type!=secrules_oapi_spec_type assert sec_rules_type!=instances_type assert sec_rules_ver_type!=secrules_oapi_spec_ver_type assert sec_rules_type!=instances_ver_type assert sec_rules_ver_type!=instances_ver_type def test_gt_lt(test_equality_setup): sec_rules, instances, idm_cfg, idm_domain, gc3_config = test_equality_setup api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog version = "18.1.2-20180126.052521" i_version = "18.1.2-20180126.052521" i_version_eq = "18.1.2-20180126.052521" i_version_gt_00 = "18.1.2-20180126.052521" i_version_gt_01 = "19.1.2-20180126.052521" i_version_gt_02 = "18.2.2-20180126.052521" i_version_gt_03 = "18.1.3-20180126.052521" i_version_gt_04 = "18.1.2-22180126.052521" i_version_gt_05 = "18.1.2-20180126.052529" instances_service: str = 'Instances' instances_service_cfg = gc3_config.iaas_classic.services[instances_service] instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert instances_oapi_spec.name == instances_service i_ver_type = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=version) i_ver_type_eq = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_eq) i_ver_type_gt_00 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_00) i_ver_type_gt_01 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_01) i_ver_type_gt_02 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_02) i_ver_type_gt_03 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_03) i_ver_type_gt_04 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_04) i_ver_type_gt_05 = GC3VersionedType(name='OpenApiSpec', descr="Some text", class_type=instances_oapi_spec.__class__, version=i_version_gt_05) assert i_ver_type == i_ver_type_eq assert i_ver_type == i_ver_type_gt_00 assert i_ver_type < i_ver_type_gt_01 assert i_ver_type < i_ver_type_gt_02 assert i_ver_type < i_ver_type_gt_03 assert i_ver_type < i_ver_type_gt_04 assert i_ver_type < i_ver_type_gt_05 @pytest.fixture() def test_equality_with_mixin_setup(): idm_domain = 'gc30003' gc3_config = GC3Config(atoml_config_dir=config_dir) idm_cfg = gc3_config.idm.domains[idm_domain] api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog instances_service: str = 'Instances' instances_service_cfg = gc3_config.iaas_classic.services[instances_service] instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert instances_oapi_spec.name == instances_service secrules_service: str = 'SecRules' secrules_service_cfg = gc3_config.iaas_classic.services[secrules_service] sec_rules = SecRules(service_cfg=secrules_service_cfg, idm_cfg=idm_cfg, from_url=True) instances = Instances(service_cfg=instances_service_cfg, idm_cfg=idm_cfg, from_url=True) yield sec_rules, instances, idm_cfg, idm_domain def test_equality_with_mixin(test_equality_with_mixin_setup): sec_rules, instances, idm_cfg, idm_domain = test_equality_with_mixin_setup idm_domain = 'gc30003' instances_service: str = 'Instances' gc3_config = GC3Config(atoml_config_dir=config_dir) idm_cfg = gc3_config.idm.domains[idm_domain] instances_service_cfg = gc3_config.iaas_classic.services[instances_service] api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) instances_oapi_spec_2 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert instances_oapi_spec.name == instances_service assert instances_oapi_spec._spec_data['schemes'] == ['https'] assert instances_oapi_spec.title==instances_service secrules_service: str = 'SecRules' gc3_config = GC3Config(atoml_config_dir=config_dir) idm_cfg = gc3_config.idm.domains[idm_domain] sec_rules_service_cfg = gc3_config.iaas_classic.services[secrules_service] api_catalog_config = gc3_config.iaas_classic.open_api_spec_catalog sec_rules_oapi_spec = OpenApiSpec(service_cfg=sec_rules_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) assert sec_rules_oapi_spec.name == secrules_service assert sec_rules_oapi_spec._spec_data['schemes'] == ['https'] assert sec_rules_oapi_spec.title==secrules_service assert instances_oapi_spec==instances_oapi_spec_2 assert sec_rules_oapi_spec!=instances_oapi_spec assert sec_rules_oapi_spec!=instances_oapi_spec_2 version = "18.1.2-20180126.052521" i_version = "18.1.2-20180126.052521" i_version_eq = "18.1.2-20180126.052521" i_version_gt_00 = "18.1.2-20180126.052521" i_version_gt_01 = "19.1.2-20180126.052521" i_version_gt_02 = "18.2.2-20180126.052521" i_version_gt_03 = "18.1.3-20180126.052521" i_version_gt_04 = "18.1.2-22180126.052521" i_version_gt_05 = "18.1.2-20180126.052529" instances_oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg) oapi_spec = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=version) oapi_spec_eq = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_eq) oapi_spec_gt_00 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_00) oapi_spec_gt_01 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_01) oapi_spec_gt_02 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_02) oapi_spec_gt_03 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_03) oapi_spec_gt_04 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_04) oapi_spec_gt_05 = OpenApiSpec(service_cfg=instances_service_cfg, open_api_specs_cfg=api_catalog_config, idm_cfg=idm_cfg, mock_version=i_version_gt_05) assert oapi_spec == oapi_spec_eq assert oapi_spec == oapi_spec_gt_00 assert oapi_spec < oapi_spec_gt_01 assert oapi_spec < oapi_spec_gt_02 assert oapi_spec < oapi_spec_gt_03 assert oapi_spec < oapi_spec_gt_04 assert oapi_spec < oapi_spec_gt_05

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venv/lib/python3.6/site-packages/ppb/vector.py

Karagusto/Game_ppg

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Karagusto/Game_ppg

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Karagusto/Game_ppg

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pyzipper/__init__.py

MatthewWalker/pyzipper

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[ "MIT" ]

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2018-11-09T03:51:42.000Z

2022-03-30T06:06:28.000Z

pyzipper/__init__.py

MatthewWalker/pyzipper

ce50a5f48c78da4a67da3b8408af87b00b58efab

[ "MIT" ]

20

2019-02-05T12:56:47.000Z

2021-09-29T04:01:34.000Z

pyzipper/__init__.py

MatthewWalker/pyzipper

ce50a5f48c78da4a67da3b8408af87b00b58efab

[ "MIT" ]

15

2018-11-14T05:32:41.000Z

2022-01-31T11:32:21.000Z

from .zipfile import * # noqa: F401, F403 from .zipfile_aes import AESZipFile, WZ_AES # noqa: F401 from .__version__ import __version__ # noqa: F401

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Python

tests/unit/pack_encrypted/test_pack_encrypted_negative.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

8

2021-09-04T19:28:18.000Z

2021-12-22T16:00:18.000Z

tests/unit/pack_encrypted/test_pack_encrypted_negative.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

4

2021-07-27T23:44:33.000Z

2021-10-13T13:29:39.000Z

tests/unit/pack_encrypted/test_pack_encrypted_negative.py

alex-polosky/didcomm-python

955866024c9f6191df9c5a898cc77e1979781eae

[ "Apache-2.0" ]

7

2021-07-22T08:19:13.000Z

2022-01-04T14:46:38.000Z

import copy import pytest from didcomm.errors import ( DIDCommValueError, DIDDocNotResolvedError, SecretNotFoundError, DIDUrlNotFoundError, IncompatibleCryptoError, ) from didcomm.pack_encrypted import pack_encrypted from tests.test_vectors.common import BOB_DID, CHARLIE_DID, ALICE_DID from tests.test_vectors.didcomm_messages.messages import TEST_MESSAGE from tests.test_vectors.utils import ( get_key_agreement_methods_in_secrets, Person, KeyAgreementCurveType, get_key_agreement_methods_not_in_secrets, ) @pytest.mark.asyncio async def test_from_is_not_a_did_or_did_url(resolvers_config_alice): with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm="not-a-did", to=BOB_DID, ) @pytest.mark.asyncio async def test_to_is_not_a_did_or_did_url(resolvers_config_alice): with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, to="not-a-did", ) @pytest.mark.asyncio async def test_sign_frm_is_not_a_did_or_did_url(resolvers_config_alice): with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID, sign_frm="not-a-did", ) @pytest.mark.asyncio async def test_from_differs_from_msg_from(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.frm = CHARLIE_DID with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=ALICE_DID, to=BOB_DID, ) @pytest.mark.asyncio async def test_to_differs_from_msg_to(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = [CHARLIE_DID] with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=ALICE_DID, to=BOB_DID, ) @pytest.mark.asyncio async def test_to_present_in_msg_to(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = [BOB_DID, CHARLIE_DID] await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=ALICE_DID, to=BOB_DID ) @pytest.mark.asyncio async def test_from_is_not_a_did_or_did_url_in_msg(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.frm = "not-a-did" with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm="not-a-did", to=BOB_DID, ) @pytest.mark.asyncio async def test_to_is_not_a_did_or_did_url_in_msg(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = ["not-a-did"] with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, to="not-a-did", ) @pytest.mark.asyncio async def test_from_param_is_url_from_msg_is_did_positive(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.frm = ALICE_DID await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=get_key_agreement_methods_in_secrets(Person.ALICE)[0].id, to=BOB_DID, ) @pytest.mark.asyncio async def test_to_param_is_url_to_msg_is_did_positive(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = [ALICE_DID, BOB_DID] await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, to=get_key_agreement_methods_in_secrets(Person.BOB)[0].id, ) @pytest.mark.asyncio async def test_sign_from_differs_from_msg_from_positive( resolvers_config_alice_with_new_did, ): await pack_encrypted( resolvers_config=resolvers_config_alice_with_new_did, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID, sign_frm=CHARLIE_DID, ) @pytest.mark.asyncio async def test_from_param_is_did_from_msg_is_did_url(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.frm = get_key_agreement_methods_in_secrets(Person.ALICE)[0].id with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=ALICE_DID, to=BOB_DID, ) @pytest.mark.asyncio async def test_to_param_is_did_to_msg_is_did_url(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = [get_key_agreement_methods_in_secrets(Person.BOB)[0].id] with pytest.raises(DIDCommValueError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, to=BOB_DID ) @pytest.mark.asyncio async def test_from_unknown_did(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.frm = "did:example:unknown" with pytest.raises(DIDDocNotResolvedError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm="did:example:unknown", to=BOB_DID, ) @pytest.mark.asyncio async def test_from_unknown_did_url(resolvers_config_alice): with pytest.raises(SecretNotFoundError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID + "#unknown-key", to=BOB_DID, ) @pytest.mark.asyncio async def test_to_unknown_did(resolvers_config_alice): msg = copy.deepcopy(TEST_MESSAGE) msg.to = ["did:example:unknown"] with pytest.raises(DIDDocNotResolvedError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=msg, frm=ALICE_DID, to="did:example:unknown", ) @pytest.mark.asyncio async def test_to_unknown_did_url(resolvers_config_alice): with pytest.raises(DIDUrlNotFoundError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID + "#unknown-key", ) @pytest.mark.asyncio async def test_sign_from_unknown_did(resolvers_config_alice): with pytest.raises(DIDDocNotResolvedError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID, sign_frm="did:example:unknown", ) @pytest.mark.asyncio async def test_sign_from_unknown_did_url(resolvers_config_alice): with pytest.raises(SecretNotFoundError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID, sign_frm=ALICE_DID + "#unknown-key", ) @pytest.mark.asyncio async def test_from_not_in_secrets(resolvers_config_alice): frm = get_key_agreement_methods_not_in_secrets(Person.ALICE)[0].id with pytest.raises(SecretNotFoundError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=frm, to=BOB_DID, ) @pytest.mark.asyncio async def test_sign_from_not_in_secrets(resolvers_config_alice): frm = get_key_agreement_methods_not_in_secrets(Person.ALICE)[0].id with pytest.raises(SecretNotFoundError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=BOB_DID, sign_frm=frm, ) @pytest.mark.asyncio async def test_to_not_in_secrets_positive(resolvers_config_alice): to = get_key_agreement_methods_not_in_secrets(Person.BOB)[0].id await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=ALICE_DID, to=to, ) @pytest.mark.asyncio @pytest.mark.parametrize( "curve_type_sender", [ KeyAgreementCurveType.X25519, KeyAgreementCurveType.P256, KeyAgreementCurveType.P521, ], ) @pytest.mark.parametrize( "curve_type_recipient", [ KeyAgreementCurveType.X25519, KeyAgreementCurveType.P256, KeyAgreementCurveType.P521, ], ) async def test_frm_to_different_curves( curve_type_sender, curve_type_recipient, resolvers_config_alice ): if curve_type_sender == curve_type_recipient: return frm_kid = get_key_agreement_methods_in_secrets(Person.ALICE, curve_type_sender)[ 0 ].id to_kid = get_key_agreement_methods_in_secrets(Person.BOB, curve_type_recipient)[ 0 ].id with pytest.raises(IncompatibleCryptoError): await pack_encrypted( resolvers_config=resolvers_config_alice, message=TEST_MESSAGE, frm=frm_kid, to=to_kid, )

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f947801ad12f27d7b8bbcaafd1f69f89ca3c3cd2

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py

Python

improvisers/__init__.py

mvcisback/improvisers

d7ccc9c560fbe939a24a7ad61f1d12a7c31157ae

[ "MIT" ]

null

improvisers/__init__.py

mvcisback/improvisers

d7ccc9c560fbe939a24a7ad61f1d12a7c31157ae

[ "MIT" ]

null

improvisers/__init__.py

mvcisback/improvisers

d7ccc9c560fbe939a24a7ad61f1d12a7c31157ae

[ "MIT" ]

null

"""Library for synthesizing Entropic Reactive Control Improvisers for stochastic games.""" # flake8: noqa from improvisers.game_graph import * from improvisers.implicit import * from improvisers.explicit import * from improvisers.tabular import * from improvisers.policy import *

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f95504d8880230e5b6bce2d1fb610eca7848515f

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py

Python

PartDesignTests/TestPolarPattern.py

haisenzhao/CarpentryCompiler

c9714310b7ce7523a25becd397265bfaa3ab7ea3

[ "FSFAP" ]

21

2019-12-06T09:57:10.000Z

2021-09-22T12:58:09.000Z

PartDesignTests/TestPolarPattern.py

haisenzhao/CarpentryCompiler

c9714310b7ce7523a25becd397265bfaa3ab7ea3

[ "FSFAP" ]

null

PartDesignTests/TestPolarPattern.py

haisenzhao/CarpentryCompiler

c9714310b7ce7523a25becd397265bfaa3ab7ea3

[ "FSFAP" ]

5

2020-11-18T00:09:30.000Z

2021-01-13T04:40:47.000Z

# (c) Juergen Riegel (FreeCAD@juergen-riegel.net) 2011 LGPL * # * # This file is part of the FreeCAD CAx development system. * # * # This program is free software; you can redistribute it and/or modify * # it under the terms of the GNU Lesser General Public License (LGPL) * # as published by the Free Software Foundation; either version 2 of * # the License, or (at your option) any later version. * # for detail see the LICENCE text file. * # * # FreeCAD is distributed in the hope that it will be useful, * # but WITHOUT ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * # GNU Library General Public License for more details. * # * # You should have received a copy of the GNU Library General Public * # License along with FreeCAD; if not, write to the Free Software * # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 * # USA * #************************************************************************** import unittest import FreeCAD import TestSketcherApp class TestPolarPattern(unittest.TestCase): def setUp(self): self.Doc = FreeCAD.newDocument("PartDesignTestPolarPattern") def testXAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.Box = self.Doc.addObject('PartDesign::AdditiveBox','Box') self.Body.addObject(self.Box) self.Box.Length=10.00 self.Box.Width=10.00 self.Box.Height=10.00 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Box] self.PolarPattern.Axis = (self.Doc.X_Axis,[""]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def testYAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.Box = self.Doc.addObject('PartDesign::AdditiveBox','Box') self.Body.addObject(self.Box) self.Box.Length=10.00 self.Box.Width=10.00 self.Box.Height=10.00 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Box] self.PolarPattern.Axis = (self.Doc.Y_Axis,[""]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def testZAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.Box = self.Doc.addObject('PartDesign::AdditiveBox','Box') self.Body.addObject(self.Box) self.Box.Length=10.00 self.Box.Width=10.00 self.Box.Height=10.00 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Box] self.PolarPattern.Axis = (self.Doc.Z_Axis,[""]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def testNormalSketchAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.PadSketch = self.Doc.addObject('Sketcher::SketchObject', 'SketchPad') self.Body.addObject(self.PadSketch) TestSketcherApp.CreateRectangleSketch(self.PadSketch, (0, 0), (10, 10)) self.Doc.recompute() self.Pad = self.Doc.addObject("PartDesign::Pad", "Pad") self.Body.addObject(self.Pad) self.Pad.Profile = self.PadSketch self.Pad.Length = 10 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Pad] self.PolarPattern.Axis = (self.PadSketch,["N_Axis"]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def testVerticalSketchAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.PadSketch = self.Doc.addObject('Sketcher::SketchObject', 'SketchPad') self.Body.addObject(self.PadSketch) TestSketcherApp.CreateRectangleSketch(self.PadSketch, (0, 0), (10, 10)) self.Doc.recompute() self.Pad = self.Doc.addObject("PartDesign::Pad", "Pad") self.Body.addObject(self.Pad) self.Pad.Profile = self.PadSketch self.Pad.Length = 10 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Pad] self.PolarPattern.Axis = (self.PadSketch,["V_Axis"]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def testHorizontalSketchAxisPolarPattern(self): self.Body = self.Doc.addObject('PartDesign::Body','Body') self.PadSketch = self.Doc.addObject('Sketcher::SketchObject', 'SketchPad') self.Body.addObject(self.PadSketch) TestSketcherApp.CreateRectangleSketch(self.PadSketch, (0, 0), (10, 10)) self.Doc.recompute() self.Pad = self.Doc.addObject("PartDesign::Pad", "Pad") self.Body.addObject(self.Pad) self.Pad.Profile = self.PadSketch self.Pad.Length = 10 self.Doc.recompute() self.PolarPattern = self.Doc.addObject("PartDesign::PolarPattern","PolarPattern") self.PolarPattern.Originals = [self.Pad] self.PolarPattern.Axis = (self.PadSketch,["H_Axis"]) self.PolarPattern.Angle = 360 self.PolarPattern.Occurrences = 4 self.Body.addObject(self.PolarPattern) self.Doc.recompute() self.assertAlmostEqual(self.PolarPattern.Shape.Volume, 4000) def tearDown(self): #closing doc FreeCAD.closeDocument("PartDesignTestPolarPattern") # print ("omit closing document for debugging")

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1

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1

0

null

0

6

f960e17e7a4804bd248a9e4866f9fe09aabb64d9

337

py

Python

tests/utils.py

shrenik-jain/ComVEX

93622de3a4771cda13b14f8bba52990eb47c2409

[ "Apache-2.0" ]

29

2021-06-14T08:27:43.000Z

2022-02-07T13:40:27.000Z

tests/utils.py

shrenik-jain/ComVEX

93622de3a4771cda13b14f8bba52990eb47c2409

[ "Apache-2.0" ]

3

2021-11-23T16:11:51.000Z

2021-12-21T17:24:36.000Z

tests/utils.py

shrenik-jain/ComVEX

93622de3a4771cda13b14f8bba52990eb47c2409

[ "Apache-2.0" ]

3

2021-06-27T08:18:57.000Z

2021-12-17T07:29:59.000Z

import torch def assert_output_has_nan(x: torch.Tensor) -> None: assert torch.isnan(x).any() == False, "Output contains NaN." def assert_output_shape_wrong(x: torch.Tensor, expected_shape: tuple) -> None: assert ( x.shape == expected_shape ), f"Output's shape: {tuple(x.shape)} != Expected shape: {expected_shape}."

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null

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1

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f963a319b895bf4bc57d1d29c511bfb64ba84e76

177

py

Python

src/hrflow_connectors/connectors/destinations/flatchr/spec.py

Riminder/hrflow-connectors

6be229520657031a9dda5e6fc9680dc527d730ad

[ "Apache-2.0" ]

32

2021-02-15T15:17:28.000Z

2022-01-25T10:13:48.000Z

src/hrflow_connectors/connectors/destinations/flatchr/spec.py

Riminder/hrflow-connectors

6be229520657031a9dda5e6fc9680dc527d730ad

[ "Apache-2.0" ]

3

2021-12-29T19:26:58.000Z

2022-01-28T09:07:40.000Z

src/hrflow_connectors/connectors/destinations/flatchr/spec.py

Riminder/hrflow-connectors

6be229520657031a9dda5e6fc9680dc527d730ad

[ "Apache-2.0" ]

3

2021-12-17T08:49:58.000Z

2022-03-15T07:15:40.000Z

from pydantic import BaseModel from typing import Tuple from .actions import PushProfile, EnrichProfile class Spec(BaseModel): actions: Tuple[PushProfile, EnrichProfile]

19.666667

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f9712042add1347337523977d3e1a54bdf870384

65

py

Python

Machine Learning/Projects/Pokemon_Classifier/helper.py

HackerLion123/Machine-Learning

71224ea97ba4aaded13a700e07b498469299964b

[ "MIT" ]

1

2018-07-21T15:41:40.000Z

Machine Learning/Projects/Pokemon_Classifier/helper.py

HackerLion123/Machine-Learning

71224ea97ba4aaded13a700e07b498469299964b

[ "MIT" ]

null

Machine Learning/Projects/Pokemon_Classifier/helper.py

HackerLion123/Machine-Learning

71224ea97ba4aaded13a700e07b498469299964b

[ "MIT" ]

null

import numpy as np def resize(): pass def load_data(): pass

7.222222

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3.909091

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null

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1

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1

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6

ddd6e9ab89f145d7f012c7207822fb73a58ec2f3

114

py

Python

Easy/69 - Sqrt(x).py

gizemkurtoglu/LeetCode

5e4461e26b5d430c4ef50e7f820d22beea50a896

[ "MIT" ]

5

2022-01-03T10:22:26.000Z

2022-02-20T05:05:17.000Z

Easy/69 - Sqrt(x).py

gizemkurtoglu/LeetCode

5e4461e26b5d430c4ef50e7f820d22beea50a896

[ "MIT" ]

null

Easy/69 - Sqrt(x).py

gizemkurtoglu/LeetCode

5e4461e26b5d430c4ef50e7f820d22beea50a896

[ "MIT" ]

null

from math import floor, sqrt class Solution: def mySqrt(self, x: int) -> int: return floor(sqrt(x))

16.285714

36

0.631579

17

114

4.235294

0.764706

0.25

0

0.254386

114

6

37

19

0.847059

0

1

0.25

false

0

0.25

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null

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1

0

null

0

1

0

1

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6

ddf2ef50766d7471233815664e1898ec383688a9

296

py

Python

odin/fuel/bio_data/__init__.py

trungnt13/odin-ai

9c6986a854e62da39637ea463667841378b7dd84

[ "MIT" ]

7

2020-12-29T19:35:58.000Z

2022-01-31T21:01:30.000Z

odin/fuel/bio_data/__init__.py

imito/odin-ai

9c6986a854e62da39637ea463667841378b7dd84

[ "MIT" ]

3

2020-02-06T16:44:17.000Z

2020-09-26T05:26:14.000Z

odin/fuel/bio_data/__init__.py

trungnt13/odin-ai

9c6986a854e62da39637ea463667841378b7dd84

[ "MIT" ]

6

2019-02-14T01:36:28.000Z

2020-10-30T13:16:32.000Z

from odin.fuel.bio_data._base import GeneDataset from odin.fuel.bio_data.atac_datasets import * from odin.fuel.bio_data.cortex import Cortex from odin.fuel.bio_data.human_embryos import HumanEmbryos from odin.fuel.bio_data.human_genome import HumanGenome from odin.fuel.bio_data.pbmc import PBMC

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6

fb1247ee78c07eaeaeb04435e0d979f7fa61efc0

115

py

Python

python/src/algorithms/__init__.py

onyonkaclifford/jwt

e5bc6eab6724b9e76a52d8789f672889a5866fe9

[ "MIT" ]

null

python/src/algorithms/__init__.py

onyonkaclifford/jwt

e5bc6eab6724b9e76a52d8789f672889a5866fe9

[ "MIT" ]

null

python/src/algorithms/__init__.py

onyonkaclifford/jwt

e5bc6eab6724b9e76a52d8789f672889a5866fe9

[ "MIT" ]

null

from .algorithm import Algorithm from .hmac_algorithm import HMACAlgorithm from .rsa_algorithm import RSAAlgorithm

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1

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6

34b587fd620e4f1c6fe733582b67551722b31664

94

py

Python

orderbook_veinte/orderbook/views/__init__.py

morwen1/hyperion

f0d77a6cce6a366555e9f0ca0080f3da134862bf

[ "MIT" ]

null

orderbook_veinte/orderbook/views/__init__.py

morwen1/hyperion

f0d77a6cce6a366555e9f0ca0080f3da134862bf

[ "MIT" ]

null

orderbook_veinte/orderbook/views/__init__.py

morwen1/hyperion

f0d77a6cce6a366555e9f0ca0080f3da134862bf

[ "MIT" ]

null

from .bids import * from .asks import * from .transaction import * from .orders_user import *

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6

34b97a06077bc5a6ce5098d6132893c162e3d6a3

87

py

Python

Day9/String repeat.py

ColdTears619/My100DaysOfCode

aa33d721a0fa71b2e2c5ebf5b22594f40e443f68

[ "MIT" ]

1

2021-12-25T16:18:42.000Z

Day9/String repeat.py

ColdTears619/My100DaysOfCode

aa33d721a0fa71b2e2c5ebf5b22594f40e443f68

[ "MIT" ]

null

Day9/String repeat.py

ColdTears619/My100DaysOfCode

aa33d721a0fa71b2e2c5ebf5b22594f40e443f68

[ "MIT" ]

null

def repeat_str(repeat, string): return repeat * string print(repeat_str(6,'I'))

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6

551d8313af3bc436ad1047eaec2278a45e83086c

147

py

Python

modules/python-codes/modules/functions/src/02-say-hello.py

drigols/Studies

9c293156935b491ded24be6b511daac67fd43538

[ "MIT" ]

1

2020-09-06T22:17:19.000Z

modules/python-codes/modules/functions/src/02-say-hello.py

drigols/Studies

9c293156935b491ded24be6b511daac67fd43538

[ "MIT" ]

null

modules/python-codes/modules/functions/src/02-say-hello.py

drigols/Studies

9c293156935b491ded24be6b511daac67fd43538

[ "MIT" ]

null

# Cria a função que diz "Hello". def say_hello(): print("Hello!!") if __name__ == "__main__": # Chama a função que diz "Hello". say_hello()

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null

0

1

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6

9b366573ecba2f67af8ac2068bf9aa62e5e864bd

111

py

Python

pinballmap/exceptions.py

eyesee1/python-pinballmap

5605e6905e7feabf8fecda6fe0f5884e89d6412d

[ "MIT" ]

2

2017-02-17T18:39:42.000Z

2017-02-17T18:39:50.000Z

pinballmap/exceptions.py

eyesee1/python-pinballmap

5605e6905e7feabf8fecda6fe0f5884e89d6412d

[ "MIT" ]

3

2017-05-04T23:44:08.000Z

2022-01-23T02:23:23.000Z

pinballmap/exceptions.py

eyesee1/python-pinballmap

5605e6905e7feabf8fecda6fe0f5884e89d6412d

[ "MIT" ]

null

class TokenRequiredException(Exception): pass class PinballMapAuthenticationFailure(Exception): pass

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6

fd0a3585254c85443f75e40c004dafd9e2766585

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py

Python

catkin_ws/build/mrta/cmake/mrta-genmsg-context.py

people-robots/MRTA

3e10eda5157c6cde8668128b640c1a8737afb9b4

[ "MIT" ]

2

2021-06-30T09:00:52.000Z

2021-12-17T09:45:33.000Z

catkin_ws/build/mrta/cmake/mrta-genmsg-context.py

people-robots/MRTA

3e10eda5157c6cde8668128b640c1a8737afb9b4

[ "MIT" ]

1

2020-02-25T03:51:54.000Z

catkin_ws/build/mrta/cmake/mrta-genmsg-context.py

people-robots/MRTA

3e10eda5157c6cde8668128b640c1a8737afb9b4

[ "MIT" ]

null

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/Task.msg;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/AuctionRequest.msg;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/AuctionAck.msg;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/Bid.msg;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/Winner.msg;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg/ScheduledTasks.msg" services_str = "/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/srv/TerminateRobot.srv" pkg_name = "mrta" dependencies_str = "std_msgs" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "mrta;/home/kimwang/Desktop/MRTA-devel/catkin_ws/src/mrta/msg;std_msgs;/opt/ros/kinetic/share/std_msgs/cmake/../msg" PYTHON_EXECUTABLE = "/usr/bin/python" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/kinetic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

83.5

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null

0

1

0

1

null

0

6

fd10471a0e39d6df4986a7cb4b86cddc3f7b4e97

117

py

Python

src/entities/__init__.py

Asa-Nisi-Masa/movie-recommender

92752aa302e166885ab3e39b3e9d2ac9185a013e

[ "MIT" ]

null

src/entities/__init__.py

Asa-Nisi-Masa/movie-recommender

92752aa302e166885ab3e39b3e9d2ac9185a013e

[ "MIT" ]

null

src/entities/__init__.py

Asa-Nisi-Masa/movie-recommender

92752aa302e166885ab3e39b3e9d2ac9185a013e

[ "MIT" ]

null

from .movie import Movie from .searched_movie import SearchedMovie from .recommended_movies import RecommendedMovies

29.25

49

0.871795

14

117

7.142857

0.571429

0.22

0

0.102564

117

3

50

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null

0

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0

1

0

1

0

6

fd10f301aea3af92a6556abbfd73b8d429556cc4

146

py

Python

_benchmarks/hug_server.py

axegon/Milvago

4e05e54fa34190c8a5eb4d4913b5231e660e6711

[ "MIT" ]

null

_benchmarks/hug_server.py

axegon/Milvago

4e05e54fa34190c8a5eb4d4913b5231e660e6711

[ "MIT" ]

1

2020-08-11T12:50:50.000Z

_benchmarks/hug_server.py

axegon/Milvago

4e05e54fa34190c8a5eb4d4913b5231e660e6711

[ "MIT" ]

null

import hug from common_data import CONTENTS @hug.get('/') @hug.format.content_type('application/json') def happy_birthday(): return CONTENTS

18.25

44

0.760274

20

146

5.4

0.8

0

0.116438

146

8

45

18.25

0.837209

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true

0

0.333333

0.166667

0.666667

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0

null

0

1

0

null

0

1

0

1

0

6

fd46bb36c95d49583e23675149a4058ae6721e5d

152

py

Python

tests/asp/weakConstraints/weak_constraints_example3.gringo.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

19

2015-12-03T08:53:45.000Z

2022-03-31T02:09:43.000Z

tests/asp/weakConstraints/weak_constraints_example3.gringo.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

80

2017-11-25T07:57:32.000Z

2018-06-10T19:03:30.000Z

tests/asp/weakConstraints/weak_constraints_example3.gringo.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

6

2015-01-15T07:51:48.000Z

2020-06-18T14:47:48.000Z

input = """ 8 2 2 3 0 0 8 2 4 5 0 0 1 6 1 0 3 1 6 1 0 2 1 6 1 0 5 1 6 1 0 4 6 0 1 0 6 1 0 2 b 3 a 4 d 5 c 0 B+ 0 B- 1 0 1 """ output = """ COST 1@1 """

6.08

12

0.467105

59

152

1.20339

0.254237

0.197183

0.211268

0.225352

0

0.568182

0.421053

152

24

13

6.333333

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false

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0

1

0

1

0

1

0

null

0

6

b5d040a2ee46a1d6a8dfdd8f333019e77ad5339d

6,411

py

Python

models/VGG_loss.py

qcw171717/cs236_pix2pix_cyclegan

364a13b57748ba04367c7329fe51640ed9f7712e

[ "BSD-3-Clause" ]

null

models/VGG_loss.py

qcw171717/cs236_pix2pix_cyclegan

364a13b57748ba04367c7329fe51640ed9f7712e

[ "BSD-3-Clause" ]

null

models/VGG_loss.py

qcw171717/cs236_pix2pix_cyclegan

364a13b57748ba04367c7329fe51640ed9f7712e

[ "BSD-3-Clause" ]

null

# class VGGPerceptualLoss(torch.nn.Module): # ## input (N, C, H, W) # ## output (N, C, H, W) # def __init__(self, mask=None, resolution=(32, 32) ,resize=True): # super(VGGPerceptualLoss, self).__init__() # blocks = [] # blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval()) # for bl in blocks: # for p in bl.parameters(): # p.requires_grad = False # self.blocks = torch.nn.ModuleList(blocks) # self.transform = torch.nn.functional.interpolate # self.register_buffer('mean', torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1)) # self.register_buffer('std', torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1)) # # self.mean = torch.nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1)) # # self.std = torch.nn.Parameter(torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1)) # self.resize = resize # self.image_resolution = resolution # self.mask = mask # def forward(self, input, target, feature_layers=[0, 1, 2, 3], style_layers=[]): # def forward(self, model_output, gt, feature_layers=(0, 1, 2, 3), style_layers=()): # pred_img = dataio.lin2img(model_output['model_out'], self.image_resolution) # gt_img = dataio.lin2img(gt['img'], self.image_resolution) # if self.mask is not None: # pred_img = pred_img * self.mask # gt_img = gt_img * self.mask # if pred_img.shape[1] != 3: # pred_img = pred_img.repeat(1, 3, 1, 1) # gt_img = gt_img.repeat(1, 3, 1, 1) # pred_img = (pred_img-self.mean) / self.std # gt_img = (gt_img-self.mean) / self.std # if self.resize: # pred_img = self.transform(pred_img, mode='bilinear', size=(224, 224), align_corners=False) # gt_img = self.transform(gt_img, mode='bilinear', size=(224, 224), align_corners=False) # loss = 0.0 # x = pred_img # y = gt_img # for i, block in enumerate(self.blocks): # x = block(x) # y = block(y) # if i in feature_layers: # loss += torch.nn.functional.l1_loss(x, y) # if i in style_layers: # act_x = x.reshape(x.shape[0], x.shape[1], -1) # act_y = y.reshape(y.shape[0], y.shape[1], -1) # gram_x = act_x @ act_x.permute(0, 2, 1) # gram_y = act_y @ act_y.permute(0, 2, 1) # loss += torch.nn.functional.l1_loss(gram_x, gram_y) # return {'img_loss': loss} # def forward(self, fake, real, feature_layers=(0, 1, 2, 3), style_layers=()): # # pred_img = dataio.lin2img(model_output['model_out'], self.image_resolution) # # gt_img = dataio.lin2img(gt['img'], self.image_resolution) # if self.mask is not None: # fake= fake * self.mask # real = real * self.mask # if fake.shape[1] != 3: # fake = fake.repeat(1, 3, 1, 1) # real = real.repeat(1, 3, 1, 1) # fake = (pred_img-self.mean) / self.std # gt_img = (gt_img-self.mean) / self.std # if self.resize: # pred_img = self.transform(pred_img, mode='bilinear', size=(224, 224), align_corners=False) # gt_img = self.transform(gt_img, mode='bilinear', size=(224, 224), align_corners=False) # loss = 0.0 # x = pred_img # y = gt_img # for i, block in enumerate(self.blocks): # x = block(x) # y = block(y) # if i in feature_layers: # loss += torch.nn.functional.l1_loss(x, y) # if i in style_layers: # act_x = x.reshape(x.shape[0], x.shape[1], -1) # act_y = y.reshape(y.shape[0], y.shape[1], -1) # gram_x = act_x @ act_x.permute(0, 2, 1) # gram_y = act_y @ act_y.permute(0, 2, 1) # loss += torch.nn.functional.l1_loss(gram_x, gram_y) # return {'img_loss': loss} import torch import torchvision class VGGPerceptualLoss(torch.nn.Module): def __init__(self, resize=True): super(VGGPerceptualLoss, self).__init__() blocks = [] blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) # blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval()) for bl in blocks: for p in bl.parameters(): p.requires_grad = False self.blocks = torch.nn.ModuleList(blocks) self.transform = torch.nn.functional.interpolate self.resize = resize self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) def forward(self, input, target, feature_layers=[0, 1, 2, 3], style_layers=[]): if input.shape[1] != 3: input = input.repeat(1, 3, 1, 1) target = target.repeat(1, 3, 1, 1) input = (input-self.mean) / self.std target = (target-self.mean) / self.std if self.resize: input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False) target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False) loss = 0.0 x = input y = target for i, block in enumerate(self.blocks): x = block(x) y = block(y) if i in feature_layers: loss += torch.nn.functional.l1_loss(x, y) if i in style_layers: act_x = x.reshape(x.shape[0], x.shape[1], -1) act_y = y.reshape(y.shape[0], y.shape[1], -1) gram_x = act_x @ act_x.permute(0, 2, 1) gram_y = act_y @ act_y.permute(0, 2, 1) loss += torch.nn.functional.l1_loss(gram_x, gram_y) return loss

50.085938

104

0.562939

905

6,411

3.853039

0.110497

0.010324

0.013765

0.869229

0.826498

0.809865

0.802122

0.790938

0

0.058708

0.282639

6,411

128

105

50.085938

0.6995

0.648417

0

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0

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false

0

0.051282

0

0.153846

0

null

0

1

0

null

0

6

b5ddc010104e366893c1e863dc28aeee0aacc028

277

py

Python

ecommerce/webstore/views.py

SOORAJTS2001/HACKERCBS

3d3b0e5f58f5db0c50e8d9f581673c47d2add0ac

[ "MIT" ]

null

ecommerce/webstore/views.py

SOORAJTS2001/HACKERCBS

3d3b0e5f58f5db0c50e8d9f581673c47d2add0ac

[ "MIT" ]

null

ecommerce/webstore/views.py

SOORAJTS2001/HACKERCBS

3d3b0e5f58f5db0c50e8d9f581673c47d2add0ac

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. def index(request): return render(request,'webstore/index.html') def details(request): return render(request,'webstore/details.html') def contact(request): return render(request,'webstore/contact.html')

30.777778

50

0.761733

36

277

5.861111

0.472222

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0.270142

0.369668

0.483412

0

0.119134

277

9

51

30.777778

0.864754

0.083032

0

0.241107

0.166008

0

1

0.428571

false

0

0.142857

0.428571

1

0

null

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1

0

1

0

null

0

1

0

1

0

6

b5e88770bea50726818aa71a4901a63db3db8c96

135

py

Python

vicarui/src/vicarui/support/pipeline/__init__.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

1

2021-07-16T06:30:37.000Z

vicarui/src/vicarui/support/pipeline/__init__.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

null

vicarui/src/vicarui/support/pipeline/__init__.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

1

2021-05-26T03:53:41.000Z

from .statsmodels_adapter import * from .adapter_interface import * from .partial import * from .pipe import * from .powerlaw import *

22.5

34

0.777778

17

135

6.058824

0.470588

0.38835

0

0.148148

135

5

35

27

0.895652

0

1

0

true

0

1

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1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

6

b5fabf2bde7a9a60aa5580128b8ea91b15f6f07e

79

py

Python

vnpy/app/chart_wizard/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

19,529

2015-03-02T12:17:35.000Z

2022-03-31T17:18:27.000Z

vnpy/app/chart_wizard/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

2,186

2015-03-04T23:16:33.000Z

2022-03-31T03:44:01.000Z

vnpy/app/chart_wizard/__init__.py

ChaunceyDong/vnpy

1c1b683ffc1c842bb7661e8194eca61af30cf586

[ "MIT" ]

8,276

2015-03-02T05:21:04.000Z

2022-03-31T13:13:13.000Z

import sys import vnpy_chartwizard sys.modules[__name__] = vnpy_chartwizard

11.285714

40

0.822785

10

79

5.9

0.6

0.508475

0

0.126582

79

6

41

13.166667

0.855072

0

1

0

true

0

0.666667

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0.666667

0

1

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1

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0

null

0

1

0

1

0

1

0

6

1fc205f0a7ddf659616a67a25ceb06c5671a65a4

140

py

Python

hello.py

trintambogo/firstprogram

92fc1d0a93128afe466608747512ac18f1c16eaa

[ "MIT" ]

null

hello.py

trintambogo/firstprogram

92fc1d0a93128afe466608747512ac18f1c16eaa

[ "MIT" ]

null

hello.py

trintambogo/firstprogram

92fc1d0a93128afe466608747512ac18f1c16eaa

[ "MIT" ]

null

print("helloworld") print('my name is trinta') print('---------') print('________') print('| |') print('---------')

15.555556

27

0.442857

11

140

4.909091

0.545455

0.555556

0

0.242857

140

8

28

17.5

0.509434

0

0.333333

0

0.469697

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

1fff8e6aba6effabba855f53bb10124200b97f2c

29

py

Python

lego/apps/feed/models.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

lego/apps/feed/models.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

lego/apps/feed/models.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

from .verbs import * # noqa

14.5

28

0.655172

4

29

4.75

1

0

0.241379

29

1

29

0.863636

0.137931

0

1

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true

0

1

0

1

0

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null

0

1

0

null

0

1

0

1

0

1

0

6

1f39451f076aaf743137803618eb053a145f62ae

26,897

py

Python

mobotpy/models.py

botprof/agv-examples

a21b0f65fa50ad023864e18c40a37353f2a37f84

[ "MIT" ]

6

2021-11-06T11:14:39.000Z

2022-03-29T10:54:57.000Z

mobotpy/models.py

botprof/agv-examples

a21b0f65fa50ad023864e18c40a37353f2a37f84

[ "MIT" ]

3

2022-02-13T17:32:24.000Z

2022-02-13T22:50:30.000Z

mobotpy/models.py

botprof/agv-examples

a21b0f65fa50ad023864e18c40a37353f2a37f84

[ "MIT" ]

1

2022-01-21T10:48:22.000Z

""" Python module models.py for various vehicle models. Author: Joshua A. Marshall <joshua.marshall@queensu.ca> GitHub: https://github.com/botprof/agv-examples """ import numpy as np from mobotpy import graphics import matplotlib.pyplot as plt import matplotlib.animation as animation from scipy.stats import chi2 from matplotlib import patches class Cart: """1D vehicle class (i.e., a simple cart). Parameters ---------- length : float Length of the cart [m]. """ def __init__(self, length): """Constructor method.""" self.d = length def draw(self, x): """Finds the points to draw simple rectangular cart. The cart has position x and length d. The resulting cart has a height that is half the length. """ X = np.array(5) Y = np.array(5) X = [ x - self.d / 2, x - self.d / 2, x + self.d / 2, x + self.d / 2, x - self.d / 2, ] Y = [-self.d / 4, self.d / 4, self.d / 4, -self.d / 4, -self.d / 4] return X, Y def animate(self, x, T, save_ani=False, filename="animate_cart.gif"): """Create an animation of a simple 1D cart. Returns animation object for array of 1D cart positions x with time increments T [s], cart width d [m]. To save the animation to a GIF file, set save_ani to True and give a filename (default filename is 'animate_cart.gif'). """ fig, ax = plt.subplots() plt.plot([np.min(x) - self.d, np.max(x) + self.d], [0, 0], "k--") plt.xlabel(r"$x$ [m]") ax.set_xlim([np.min(x) - self.d, np.max(x) + self.d]) plt.yticks([]) plt.axis("equal") (polygon,) = ax.fill([], [], "C0", alpha=0.5) (line,) = plt.plot([], [], "ko") time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) # Initialization funcion def init(): polygon.set_xy(np.empty([5, 2])) line.set_data([], []) time_text.set_text("") return polygon, line, time_text # Function to draw cart def movie(k): X, Y = self.draw(x[k]) a = [X, Y] polygon.set_xy(np.transpose(a)) line.set_data(x[k], 0) time_text.set_text(r"$t$ = %.1f s" % (k * T)) return polygon, line, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) # Save to a file if requested if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani class DiffDrive: """Differential-drive vehicle class. Parameters ---------- ell : float The track length of the vehicle [m]. """ def __init__(self, ell): """Constructor method.""" self.ell = ell def f(self, x, u): """Differential drive kinematic vehicle kinematic model. Parameters ---------- x : ndarray of length 3 The vehicle's state (x, y, theta). u : ndarray of length 2 The left and right wheel speeds (v_L, v_R). Returns ------- f : ndarray of length 3 The rate of change of the vehicle states. """ f = np.zeros(3) f[0] = 0.5 * (u[0] + u[1]) * np.cos(x[2]) f[1] = 0.5 * (u[0] + u[1]) * np.sin(x[2]) f[2] = 1.0 / self.ell * (u[1] - u[0]) return f def uni2diff(self, u_in): """ Convert speed and anular rate inputs to differential drive wheel speeds. Parameters ---------- u_in : ndarray of length 2 The speed and turning rate of the vehicle (v, omega). Returns ------- u_out : ndarray of length 2 The left and right wheel speeds (v_L, v_R). """ v = u_in[0] omega = u_in[1] v_L = v - self.ell / 2 * omega v_R = v + self.ell / 2 * omega u_out = np.array([v_L, v_R]) return u_out def draw(self, x, y, theta): """ Finds points that draw a differential drive vehicle. The centre of the wheel axle is (x, y), the vehicle has orientation theta, and the vehicle's track length is ell. Returns X_L, Y_L, X_R, Y_R, X_BD, Y_BD, X_C, Y_C, where L is for the left wheel, R for the right wheel, B for the body, and C for the caster. """ # Left and right wheels X_L, Y_L = graphics.draw_rectangle( x - 0.5 * self.ell * np.sin(theta), y + 0.5 * self.ell * np.cos(theta), 0.5 * self.ell, 0.25 * self.ell, theta, ) X_R, Y_R = graphics.draw_rectangle( x + 0.5 * self.ell * np.sin(theta), y - 0.5 * self.ell * np.cos(theta), 0.5 * self.ell, 0.25 * self.ell, theta, ) # Body X_BD, Y_BD = graphics.draw_circle(x, y, self.ell) # Caster X_C, Y_C = graphics.draw_circle( x + 0.5 * self.ell * np.cos(theta), y + 0.5 * self.ell * np.sin(theta), 0.125 * self.ell, ) # Return the arrays of points return X_L, Y_L, X_R, Y_R, X_BD, Y_BD, X_C, Y_C def animate(self, x, T, save_ani=False, filename="animate_diffdrive.gif"): """Create an animation of a differential drive vehicle. Returns animation object for array of vehicle positions x with time increments T [s], track ell [m]. To save the animation to a GIF file, set save_ani to True and provide a filename (default 'animate_diffdrive.gif'). """ fig, ax = plt.subplots() plt.xlabel(r"$x$ [m]") plt.ylabel(r"$y$ [m]") plt.axis("equal") (line,) = ax.plot([], [], "C0") (leftwheel,) = ax.fill([], [], color="k") (rightwheel,) = ax.fill([], [], color="k") (body,) = ax.fill([], [], color="C0", alpha=0.5) (castor,) = ax.fill([], [], color="k") time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) def init(): """Function that initializes the animation.""" line.set_data([], []) leftwheel.set_xy(np.empty([5, 2])) rightwheel.set_xy(np.empty([5, 2])) body.set_xy(np.empty([36, 2])) castor.set_xy(np.empty([36, 2])) time_text.set_text("") return line, leftwheel, rightwheel, body, castor, time_text def movie(k): """Function called at each step of the animation.""" # Draw the path followed by the vehicle line.set_data(x[0, 0 : k + 1], x[1, 0 : k + 1]) # Draw the differential drive vehicle X_L, Y_L, X_R, Y_R, X_B, Y_B, X_C, Y_C = self.draw( x[0, k], x[1, k], x[2, k] ) leftwheel.set_xy(np.transpose([X_L, Y_L])) rightwheel.set_xy(np.transpose([X_R, Y_R])) body.set_xy(np.transpose([X_B, Y_B])) castor.set_xy(np.transpose([X_C, Y_C])) # Add the simulation time time_text.set_text(r"$t$ = %.1f s" % (k * T)) # Dynamically set the axis limits ax.set_xlim(x[0, k] - 10 * self.ell, x[0, k] + 10 * self.ell) ax.set_ylim(x[1, k] - 10 * self.ell, x[1, k] + 10 * self.ell) ax.figure.canvas.draw() # Return the objects to animate return line, leftwheel, rightwheel, body, castor, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x[0, :]), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani def animate_trajectory( self, x, xd, T, save_ani=False, filename="animate_diffdrive.gif" ): """Create an animation of a differential drive vehicle with plots of actual and desired trajectories. Returns animation object for array of vehicle positions and desired positions x with time increments T [s], track ell [m]. To save the animation to a GIF file, set save_ani to True and provide a filename (default 'animate_diffdrive.gif'). """ fig, ax = plt.subplots() plt.xlabel(r"$x$ [m]") plt.ylabel(r"$y$ [m]") plt.axis("equal") (desired,) = ax.plot([], [], "--C1") (line,) = ax.plot([], [], "C0") (leftwheel,) = ax.fill([], [], color="k") (rightwheel,) = ax.fill([], [], color="k") (body,) = ax.fill([], [], color="C0", alpha=0.5) (castor,) = ax.fill([], [], color="k") time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) def init(): """Function that initializes the animation.""" desired.set_data([], []) line.set_data([], []) leftwheel.set_xy(np.empty([5, 2])) rightwheel.set_xy(np.empty([5, 2])) body.set_xy(np.empty([36, 2])) castor.set_xy(np.empty([36, 2])) time_text.set_text("") return desired, line, leftwheel, rightwheel, body, castor, time_text def movie(k): """Function called at each step of the animation.""" # Draw the desired trajectory desired.set_data(xd[0, 0 : k + 1], xd[1, 0 : k + 1]) # Draw the path followed by the vehicle line.set_data(x[0, 0 : k + 1], x[1, 0 : k + 1]) # Draw the differential drive vehicle X_L, Y_L, X_R, Y_R, X_B, Y_B, X_C, Y_C = self.draw( x[0, k], x[1, k], x[2, k] ) leftwheel.set_xy(np.transpose([X_L, Y_L])) rightwheel.set_xy(np.transpose([X_R, Y_R])) body.set_xy(np.transpose([X_B, Y_B])) castor.set_xy(np.transpose([X_C, Y_C])) # Add the simulation time time_text.set_text(r"$t$ = %.1f s" % (k * T)) # Dynamically set the axis limits ax.set_xlim(x[0, k] - 10 * self.ell, x[0, k] + 10 * self.ell) ax.set_ylim(x[1, k] - 10 * self.ell, x[1, k] + 10 * self.ell) ax.figure.canvas.draw() # Return the objects to animate return desired, line, leftwheel, rightwheel, body, castor, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x[0, :]), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani def animate_estimation( self, x, x_hat, P_hat, alpha, T, save_ani=False, filename="animate_diffdrive.gif", ): """Create an animation of a differential drive vehicle with plots of estimation uncertainty. Returns animation object for array of vehicle positions x with time increments T [s], track ell [m]. To save the animation to a GIF file, set save_ani to True and provide a filename (default 'animate_diffdrive.gif'). """ fig, ax = plt.subplots() plt.xlabel(r"$x$ [m]") plt.ylabel(r"$y$ [m]") plt.axis("equal") (estimated,) = ax.plot([], [], "--C1") (line,) = ax.plot([], [], "C0") (leftwheel,) = ax.fill([], [], color="k") (rightwheel,) = ax.fill([], [], color="k") (body,) = ax.fill([], [], color="C0", alpha=0.5) (castor,) = ax.fill([], [], color="k") time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) s2 = chi2.isf(alpha, 2) def init(): """Function that initializes the animation.""" estimated.set_data([], []) line.set_data([], []) leftwheel.set_xy(np.empty([5, 2])) rightwheel.set_xy(np.empty([5, 2])) body.set_xy(np.empty([36, 2])) castor.set_xy(np.empty([36, 2])) time_text.set_text("") return estimated, line, leftwheel, rightwheel, body, castor, time_text def movie(k): """Function called at each step of the animation.""" # Draw the desired trajectory estimated.set_data(x_hat[0, 0 : k + 1], x_hat[1, 0 : k + 1]) # Draw the path followed by the vehicle line.set_data(x[0, 0 : k + 1], x[1, 0 : k + 1]) # Draw the differential drive vehicle X_L, Y_L, X_R, Y_R, X_B, Y_B, X_C, Y_C = self.draw( x[0, k], x[1, k], x[2, k] ) leftwheel.set_xy(np.transpose([X_L, Y_L])) rightwheel.set_xy(np.transpose([X_R, Y_R])) body.set_xy(np.transpose([X_B, Y_B])) castor.set_xy(np.transpose([X_C, Y_C])) # Compute eigenvalues and eigenvectors to find axes for covariance ellipse W, V = np.linalg.eig(P_hat[0:2, 0:2, k]) # Find the index of the largest and smallest eigenvalues j_max = np.argmax(W) j_min = np.argmin(W) ell = patches.Ellipse( (x_hat[0, k], x_hat[1, k]), 2 * np.sqrt(s2 * W[j_max]), 2 * np.sqrt(s2 * W[j_min]), angle=np.arctan2(V[j_max, 1], V[j_max, 0]) * 180 / np.pi, alpha=0.2, color="C1", ) ax.add_artist(ell) # Add the simulation time time_text.set_text(r"$t$ = %.1f s" % (k * T)) # Dynamically set the axis limits ax.set_xlim(x[0, k] - 10 * self.ell, x[0, k] + 10 * self.ell) ax.set_ylim(x[1, k] - 10 * self.ell, x[1, k] + 10 * self.ell) ax.figure.canvas.draw() # Return the objects to animate return estimated, line, leftwheel, rightwheel, body, castor, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x[0, :]), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani class Tricycle: """Tricycle or planar bicycle vehicle class. Parameters ---------- ell_W : float The wheelbase of the vehicle [m]. ell_T : float The vehicle's track length [m]. """ def __init__(self, ell_W, ell_T): """Constructor method.""" self.ell_W = ell_W self.ell_T = ell_T def f(self, x, u): """Tricycle or planar bicycle vehicle kinematic model. Parameters ---------- x : ndarray of length 4 The vehicle's state (x, y, theta, phi). u : ndarray of length 2 Returns ------- f : ndarray of length 4 The rate of change of the vehicle states. """ f = np.zeros(4) f[0] = u[0] * np.cos(x[2]) f[1] = u[0] * np.sin(x[2]) f[2] = u[0] * 1.0 / self.ell_W * np.tan(x[3]) f[3] = u[1] return f def draw(self, x, y, theta, phi): """Finds points that draw a tricycle vehicle. The centre of the rear wheel axle is (x, y), the body has orientation theta, steering angle phi, wheelbase ell_W and track length ell_T. Returns X_L, Y_L, X_R, Y_R, X_F, Y_F, X_B, Y_B, where L is for the left wheel, R is for the right wheel, F is for the single front wheel, and BD is for the vehicle's body. """ # Left and right back wheels X_L, Y_L = graphics.draw_rectangle( x - 0.5 * self.ell_T * np.sin(theta), y + 0.5 * self.ell_T * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta, ) X_R, Y_R = graphics.draw_rectangle( x + 0.5 * self.ell_T * np.sin(theta), y - 0.5 * self.ell_T * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta, ) # Front wheel X_F, Y_F = graphics.draw_rectangle( x + self.ell_W * np.cos(theta), y + self.ell_W * np.sin(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta + phi, ) # Body X_BD, Y_BD = graphics.draw_rectangle( x + self.ell_W / 2.0 * np.cos(theta), y + self.ell_W / 2.0 * np.sin(theta), 2.0 * self.ell_W, 2.0 * self.ell_T, theta, ) # Return the arrays of points return X_L, Y_L, X_R, Y_R, X_F, Y_F, X_BD, Y_BD def animate( self, x, T, save_ani=False, filename="animate_tricycle.gif", ): """Create an animation of a tricycle vehicle. Returns animation object for array of vehicle positions x with time increments T [s], wheelbase ell_W [m], and track ell_T [m]. To save the animation to a GIF file, set save_ani to True and give a filename (default 'animate_tricycle.gif'). """ fig, ax = plt.subplots() plt.xlabel(r"$x$ [m]") plt.ylabel(r"$y$ [m]") plt.axis("equal") (line,) = ax.plot([], [], "C0") (leftwheel,) = ax.fill([], [], color="k") (rightwheel,) = ax.fill([], [], color="k") (frontwheel,) = ax.fill([], [], color="k") (body,) = ax.fill([], [], color="C0", alpha=0.5) time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) def init(): """A function that initializes the animation.""" line.set_data([], []) leftwheel.set_xy(np.empty([5, 2])) rightwheel.set_xy(np.empty([5, 2])) frontwheel.set_xy(np.empty([5, 2])) body.set_xy(np.empty([5, 2])) time_text.set_text("") return line, leftwheel, rightwheel, frontwheel, body, time_text def movie(k): """The function called at each step of the animation.""" # Draw the path followed by the vehicle line.set_data(x[0, 0 : k + 1], x[1, 0 : k + 1]) # Draw the tricycle vehicle X_L, Y_L, X_R, Y_R, X_F, Y_F, X_B, Y_B = self.draw( x[0, k], x[1, k], x[2, k], x[3, k] ) leftwheel.set_xy(np.transpose([X_L, Y_L])) rightwheel.set_xy(np.transpose([X_R, Y_R])) frontwheel.set_xy(np.transpose([X_F, Y_F])) body.set_xy(np.transpose([X_B, Y_B])) # Add the simulation time time_text.set_text(r"$t$ = %.1f s" % (k * T)) # Dynamically set the axis limits ax.set_xlim(x[0, k] - 10 * self.ell_W, x[0, k] + 10 * self.ell_W) ax.set_ylim(x[1, k] - 10 * self.ell_W, x[1, k] + 10 * self.ell_W) ax.figure.canvas.draw() # Return the objects to animate return line, leftwheel, rightwheel, frontwheel, body, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x[0, :]), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani class Ackermann: """Ackermann steered vehicle class. Parameters ---------- ell_W : float The wheelbase of the vehicle [m]. ell_T : float The vehicle's track length [m]. """ def __init__(self, ell_W, ell_T): """Constructor method.""" self.ell_W = ell_W self.ell_T = ell_T def f(self, x, u): """Ackermann steered vehicle kinematic model. Parameters ---------- x : ndarray of length 4 The vehicle's state (x, y, theta, phi). u : ndarray of length 2 The vehicle's speed and steering angle rate. Returns ------- f : ndarray of length 4 The rate of change of the vehicle states. """ f = np.zeros(4) f[0] = u[0] * np.cos(x[2]) f[1] = u[0] * np.sin(x[2]) f[2] = u[0] * 1.0 / self.ell_W * np.tan(x[3]) f[3] = u[1] return f def ackermann(self, x): """Computes the Ackermann steering angles. Parameters ---------- x : ndarray of length 4 The vehicle's state (x, y, theta, phi). Returns ------- ackermann_angles : ndarray of length 2 The left and right wheel angles (phi_L, phi_R). """ phi_L = np.arctan( 2 * self.ell_W * np.tan(x[3]) / (2 * self.ell_W - self.ell_T * np.tan(x[3])) ) phi_R = np.arctan( 2 * self.ell_W * np.tan(x[3]) / (2 * self.ell_W + self.ell_T * np.tan(x[3])) ) ackermann_angles = np.array([phi_L, phi_R]) return ackermann_angles def draw(self, x, y, theta, phi_L, phi_R): """Finds points that draw an Ackermann steered (car-like) vehicle. The centre of the rear wheel axle is (x, y), the body has orientation theta, effective steering angle phi, wheelbase ell_W and track length ell_T. Returns X_BL, Y_BL, X_BR, Y_BR, X_FL, Y_FL, X_FR, Y_FR, X_BD, Y_BD, where L denotes left, R denotes right, B denotes back, F denotes front, and BD denotes the vehicle's body. """ # Left and right back wheels X_BL, Y_BL = graphics.draw_rectangle( x - 0.5 * self.ell_T * np.sin(theta), y + 0.5 * self.ell_T * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta, ) X_BR, Y_BR = graphics.draw_rectangle( x + 0.5 * self.ell_T * np.sin(theta), y - 0.5 * self.ell_T * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta, ) # Left and right front wheels X_FL, Y_FL = graphics.draw_rectangle( x + self.ell_W * np.cos(theta) - self.ell_T / 2 * np.sin(theta), y + self.ell_W * np.sin(theta) + self.ell_T / 2 * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta + phi_L, ) X_FR, Y_FR = graphics.draw_rectangle( x + self.ell_W * np.cos(theta) + self.ell_T / 2 * np.sin(theta), y + self.ell_W * np.sin(theta) - self.ell_T / 2 * np.cos(theta), 0.5 * self.ell_T, 0.25 * self.ell_T, theta + phi_R, ) # Body X_BD, Y_BD = graphics.draw_rectangle( x + self.ell_W / 2.0 * np.cos(theta), y + self.ell_W / 2.0 * np.sin(theta), 2.0 * self.ell_W, 2.0 * self.ell_T, theta, ) # Return the arrays of points return X_BL, Y_BL, X_BR, Y_BR, X_FL, Y_FL, X_FR, Y_FR, X_BD, Y_BD def animate( self, x, T, phi_L, phi_R, save_ani=False, filename="animate_ackermann.gif", ): """Create an animation of an Ackermann steered (car-like) vehicle. Returns animation object for array of vehicle positions x with time increments T [s], wheelbase ell_W [m], and track ell_T [m]. To save the animation to a GIF file, set save_ani to True and give a filename (default 'animate_ackermann.gif'). """ fig, ax = plt.subplots() plt.xlabel(r"$x$ [m]") plt.ylabel(r"$y$ [m]") plt.axis("equal") (line,) = ax.plot([], [], "C0") (BLwheel,) = ax.fill([], [], color="k") (BRwheel,) = ax.fill([], [], color="k") (FLwheel,) = ax.fill([], [], color="k") (FRwheel,) = ax.fill([], [], color="k") (body,) = ax.fill([], [], color="C0", alpha=0.5) time_text = ax.text(0.05, 0.9, "", transform=ax.transAxes) def init(): """A function that initializes the animation.""" line.set_data([], []) BLwheel.set_xy(np.empty([5, 2])) BRwheel.set_xy(np.empty([5, 2])) FLwheel.set_xy(np.empty([5, 2])) FRwheel.set_xy(np.empty([5, 2])) body.set_xy(np.empty([5, 2])) time_text.set_text("") return line, BLwheel, BRwheel, FLwheel, FRwheel, body, time_text def movie(k): """The function called at each step of the animation.""" # Draw the path followed by the vehicle line.set_data(x[0, 0 : k + 1], x[1, 0 : k + 1]) # Draw the Ackermann steered drive vehicle X_BL, Y_BL, X_BR, Y_BR, X_FL, Y_FL, X_FR, Y_FR, X_BD, Y_BD = self.draw( x[0, k], x[1, k], x[2, k], phi_L[k], phi_R[k] ) BLwheel.set_xy(np.transpose([X_BL, Y_BL])) BRwheel.set_xy(np.transpose([X_BR, Y_BR])) FLwheel.set_xy(np.transpose([X_FL, Y_FL])) FRwheel.set_xy(np.transpose([X_FR, Y_FR])) body.set_xy(np.transpose([X_BD, Y_BD])) # Add the simulation time time_text.set_text(r"$t$ = %.1f s" % (k * T)) # Dynamically set the axis limits ax.set_xlim(x[0, k] - 10 * self.ell_W, x[0, k] + 10 * self.ell_W) ax.set_ylim(x[1, k] - 10 * self.ell_W, x[1, k] + 10 * self.ell_W) ax.figure.canvas.draw() # Return the objects to animate return line, BLwheel, BRwheel, FLwheel, FRwheel, body, time_text # Create the animation ani = animation.FuncAnimation( fig, movie, np.arange(1, len(x[0, :]), max(1, int(1 / T / 10))), init_func=init, interval=T * 1000, blit=True, repeat=False, ) if save_ani == True: ani.save(filename, fps=min(1 / T, 10)) # Return the figure object return ani

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1f42f52e17e69193369fe350fe87f8633f69b781

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py

Python

HelloWorld.py

manikantavasupalli/python-practice

a4504efe3e294e8dfc4dee046778dea7d1c1e528

[ "MIT" ]

null

HelloWorld.py

manikantavasupalli/python-practice

a4504efe3e294e8dfc4dee046778dea7d1c1e528

[ "MIT" ]

null

HelloWorld.py

manikantavasupalli/python-practice

a4504efe3e294e8dfc4dee046778dea7d1c1e528

[ "MIT" ]

null

#Simple Hello World program print("Hello World")

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144

py

Python

ts_eval/viz/components/__init__.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

1

2021-07-12T08:58:07.000Z

ts_eval/viz/components/__init__.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

null

ts_eval/viz/components/__init__.py

vshulyak/ts-eval

2049b1268cf4272f5fa1471851523f8da14dd84c

[ "MIT" ]

null

class BaseComponent: """ Base component """ def compute(self): return None def display(self): return None

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py

Python

src/OOXMLParser/__init__.py

rjsmith/robot-ooxml

3cb957480eda39bd2ffe8659c6f0fa68f17189f5

[ "ECL-2.0", "Apache-2.0" ]

3

2015-03-06T11:07:48.000Z

2021-11-12T17:29:09.000Z

src/OOXMLParser/__init__.py

rjsmith/robot-ooxml

3cb957480eda39bd2ffe8659c6f0fa68f17189f5

[ "ECL-2.0", "Apache-2.0" ]

1

2015-03-06T09:08:02.000Z

2015-03-06T09:34:11.000Z

src/OOXMLParser/__init__.py

rjsmith/robot-ooxml

3cb957480eda39bd2ffe8659c6f0fa68f17189f5

[ "ECL-2.0", "Apache-2.0" ]

null

''' Created on 28 Feb 2014 @author: richardsmith ''' from OOXMLParser.docxreader import DocxReader from OOXMLParser.xlsxreader import XlsxReader

18.25

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null

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0

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c823571d5070355d8bb5ea83e1cc96c7d66cbdf8

699

py

Python

synth/constraint/__init__.py

lummax/switching-lattice-synth

47cf9e64c900cb179c392b46a392049e99dfebab

[ "MIT" ]

null

synth/constraint/__init__.py

lummax/switching-lattice-synth

47cf9e64c900cb179c392b46a392049e99dfebab

[ "MIT" ]

null

synth/constraint/__init__.py

lummax/switching-lattice-synth

47cf9e64c900cb179c392b46a392049e99dfebab

[ "MIT" ]

null

from synth.constraint import cardnet from synth.constraint.sequential_counter import at_most_one from synth.constraint.sequential_counter import at_least_one from synth.constraint.sequential_counter import equals_one def at_most(inputs, p, equivalent=None): if p == 1: yield from at_most_one(inputs, equivalent) else: yield from cardnet.at_most(inputs, p, equivalent) def at_least(inputs, p, equivalent=None): if p == 1: yield from at_least_one(inputs, equivalent) else: yield from cardnet.at_least(inputs, p, equivalent) def equals(inputs, p, equivalent=None): if p == 1: yield from equals_one(inputs, equivalent) else: yield from cardnet.equals(inputs, p, equivalent)

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c8456dd3ec53541f568844c28b5deec50f7881d3

158

py

Python

data/__init__.py

iamVarunAnand/image_classification

58120a2a708e495b598cc97fe297e2071c8c4197

[ "MIT" ]

18

2019-10-17T10:30:40.000Z

2020-12-26T03:59:23.000Z

data/__init__.py

iamVarunAnand/image_classification

58120a2a708e495b598cc97fe297e2071c8c4197

[ "MIT" ]

2

2021-08-25T15:05:01.000Z

2022-02-09T23:41:44.000Z

data/__init__.py

iamVarunAnand/image_classification

58120a2a708e495b598cc97fe297e2071c8c4197

[ "MIT" ]

3

2020-09-18T19:29:13.000Z

2021-12-27T05:17:53.000Z

from .cifargenerator import CifarGenerator, CifarPreprocessor from .mixupcifargenerator import MixUpCifarGenerator from .datadispatcher import DataDispatcher

39.5

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10.846154

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null

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0

1

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6

c85757385b0bcfc653355f569a3fd80306c30376

206

py

Python

students/K33422/Bondarenko_Gleb/NEW/lab_3/candy_project/candy_app/candy_app/admin.py

ArokhTheDragon/ITMO_ICT_WebDevelopment_2021-2022

c0dd905cf6f30c060310f5377b62a1125e697d40

[ "MIT" ]

null

students/K33422/Bondarenko_Gleb/NEW/lab_3/candy_project/candy_app/candy_app/admin.py

ArokhTheDragon/ITMO_ICT_WebDevelopment_2021-2022

c0dd905cf6f30c060310f5377b62a1125e697d40

[ "MIT" ]

null

students/K33422/Bondarenko_Gleb/NEW/lab_3/candy_project/candy_app/candy_app/admin.py

ArokhTheDragon/ITMO_ICT_WebDevelopment_2021-2022

c0dd905cf6f30c060310f5377b62a1125e697d40

[ "MIT" ]

null

from django.contrib import admin from .models import * # Register your models here. admin.site.register(Candies) admin.site.register(Client) admin.site.register(Staff) admin.site.register(Request)

22.888889

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6

c090686d289d2ce9df52481ccf9383891e2377d1

195

py

Python

python/print_string.py

sivasant/python_project

d148a1be177a634b25e571b1fc1b710276f3ef3b

[ "Apache-2.0" ]

null

python/print_string.py

sivasant/python_project

d148a1be177a634b25e571b1fc1b710276f3ef3b

[ "Apache-2.0" ]

null

python/print_string.py

sivasant/python_project

d148a1be177a634b25e571b1fc1b710276f3ef3b

[ "Apache-2.0" ]

null

print('My name is santhosh') print("We'er going to pheonix") print('He said "Hi.."') print('we\'er going to the store') print('Hi'+'you') print('Hi '+'you') print('Hi','you') print('Hi '+str(5))

21.666667

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195

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null

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1

0

1

0

6

c0b95bab04d6dce8ff77a6963e666d2667d4621b

12,872

py

Python

hera_cal/tests/profile_logcal.py

LBJ-Wade/hera_cal

868122b04b8e7f627aa72317427f89ca3eaf7d60

[ "MIT" ]

10

2017-06-22T22:14:23.000Z

2022-03-08T17:33:45.000Z

hera_cal/tests/profile_logcal.py

LBJ-Wade/hera_cal

868122b04b8e7f627aa72317427f89ca3eaf7d60

[ "MIT" ]

610

2017-06-22T22:16:27.000Z

2022-03-31T16:11:34.000Z

hera_cal/tests/profile_logcal.py

LBJ-Wade/hera_cal

868122b04b8e7f627aa72317427f89ca3eaf7d60

[ "MIT" ]

8

2017-10-30T18:16:19.000Z

2021-04-01T09:20:18.000Z

from __future__ import print_function import hera_cal.redcal as om import hera_cal.omni import omnical.calib import numpy as np import unittest import time from copy import deepcopy import pstats import cProfile np.random.seed(0) # SHAPE = (1,1024) SHAPE = (60, 1024) # SHAPE = (2,1024) NANTS = 18 def build_linear_array(nants, sep=14.7): antpos = {i: np.array([sep * i, 0, 0]) for i in range(nants)} return antpos def build_hex_array(hexNum, sep=14.7): antpos, i = {}, 0 for row in range(hexNum - 1, -(hexNum), -1): for col in range(2 * hexNum - abs(row) - 1): xPos = ((-(2 * hexNum - abs(row)) + 2) / 2.0 + col) * sep yPos = row * sep * 3**.5 / 2 antpos[i] = np.array([xPos, yPos, 0]) i += 1 return antpos antpos = build_linear_array(NANTS) reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') info = om.RedundantCalibrator(reds) gains, true_vis, d = om.sim_red_data(reds, shape=SHAPE, gain_scatter=.0099999) d = {key: value + 1e-3 * om.noise(value.shape) for key, value in d.items()} d = {key: value.astype(np.complex64) for key, value in d.items()} w = dict([(k, 1.) for k in d.keys()]) class TestRedundantCalibrator(unittest.TestCase): def setUp(self): self.pr = cProfile.Profile() self.pr.enable() def tearDown(self): p = pstats.Stats(self.pr) p.strip_dirs() p.sort_stats('cumtime') p.print_stats(20) def test_logcal(self): NANTS = 18 antpos = build_linear_array(NANTS) reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') info = om.RedundantCalibrator(reds) gains, true_vis, d = om.sim_red_data(reds, shape=SHAPE, gain_scatter=.05) d = {key: value.astype(np.complex64) for key, value in d.items()} w = dict([(k, 1.) for k in d.keys()]) t0 = time.time() for i in xrange(1): sol = info.logcal(d) # print('logcal', time.time() - t0) for i in xrange(NANTS): self.assertEqual(sol[(i, 'x')].shape, SHAPE) for bls in reds: ubl = sol[bls[0]] self.assertEqual(ubl.shape, SHAPE) for bl in bls: d_bl = d[bl] mdl = sol[(bl[0], 'x')] * sol[(bl[1], 'x')].conj() * ubl # np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 10) # np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 10) np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 5) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 5) def test_omnilogcal(self): NANTS = 18 antpos = build_linear_array(NANTS) hcreds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') pols = ['x'] antpos_ideal = np.array(antpos.values()) xs, ys, zs = antpos_ideal.T layout = np.arange(len(xs)) antpos = -np.ones((NANTS * len(pols), 3)) for ant, x, y in zip(layout.flatten(), xs.flatten(), ys.flatten()): for z, pol in enumerate(pols): z = 2**z # exponential ensures diff xpols aren't redundant w/ each other i = hera_cal.omni.Antpol(ant, pol, NANTS) antpos[int(i), 0], antpos[int(i), 1], antpos[int(i), 2] = x, y, z reds = hera_cal.omni.compute_reds(NANTS, pols, antpos[:NANTS], tol=.01) # reds = hera_cal.omni.filter_reds(reds, **kwargs) info = hera_cal.omni.RedundantInfo(NANTS) info.init_from_reds(reds, antpos_ideal) # info = om.RedundantCalibrator(reds) # gains, true_vis, d = om.sim_red_data(hcreds, shape=SHAPE, gain_scatter=.05) data = {} for key in d.keys(): if not data.has_key(key[:2]): data[key[:2]] = {} data[key[:2]][key[-1]] = d[key].astype(np.complex64) t0 = time.time() for i in xrange(1): m1, g1, v1 = omnical.calib.logcal(data, info) # print('omnilogcal', time.time() - t0) for i in xrange(NANTS): self.assertEqual(g1['x'][i].shape, SHAPE) for bls in reds: ubl = v1['xx'][(int(bls[0][0]), int(bls[0][1]))] self.assertEqual(ubl.shape, SHAPE) for bl in bls: d_bl = data[(int(bl[0]), int(bl[1]))]['xx'] mdl = g1['x'][bl[0]] * g1['x'][bl[1]].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 5) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 5) def test_lincal(self): NANTS = 18 antpos = build_linear_array(NANTS) reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') info = om.RedundantCalibrator(reds) gains, true_vis, d = om.sim_red_data(reds, shape=SHAPE, gain_scatter=.0099999) d = {key: value.astype(np.complex64) for key, value in d.items()} w = dict([(k, 1.) for k in d.keys()]) sol0 = dict([(k, np.ones_like(v)) for k, v in gains.items()]) sol0.update(info.compute_ubls(d, sol0)) sol0 = {k: v.astype(np.complex64) for k, v in sol0.items()} # sol0 = info.logcal(d) # for k in sol0: sol0[k] += .01*capo.oqe.noise(sol0[k].shape) meta, sol = info.lincal(d, sol0) for i in xrange(NANTS): self.assertEqual(sol[(i, 'x')].shape, SHAPE) for bls in reds: ubl = sol[bls[0]] self.assertEqual(ubl.shape, SHAPE) for bl in bls: d_bl = d[bl] mdl = sol[(bl[0], 'x')] * sol[(bl[1], 'x')].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 5) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 5) def test_omnical(self): NANTS = 18 antpos = build_linear_array(NANTS) reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') info = om.RedundantCalibrator(reds) # gains, true_vis, d = om.sim_red_data(reds, shape=SHAPE, gain_scatter=.0099999) # d = {key:value.astype(np.complex64) for key,value in d.items()} w = dict([(k, 1.) for k in d.keys()]) sol0 = dict([(k, np.ones_like(v)) for k, v in gains.items()]) sol0.update(info.compute_ubls(d, sol0)) sol0 = {k: v.astype(np.complex64) for k, v in sol0.items()} meta, sol = info.omnical(d, sol0, gain=.5, maxiter=500, check_after=30, check_every=6) # meta, sol = info.omnical(d, sol0, gain=.5, maxiter=50, check_after=1, check_every=1) # print(meta) for i in xrange(NANTS): self.assertEqual(sol[(i, 'x')].shape, SHAPE) for bls in reds: ubl = sol[bls[0]] self.assertEqual(ubl.shape, SHAPE) for bl in bls: d_bl = d[bl] mdl = sol[(bl[0], 'x')] * sol[(bl[1], 'x')].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 5) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 5) def test_omnical_original(self): NANTS = 18 antpos = build_linear_array(NANTS) hcreds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') pols = ['x'] antpos_ideal = np.array(antpos.values()) xs, ys, zs = antpos_ideal.T layout = np.arange(len(xs)) antpos = -np.ones((NANTS * len(pols), 3)) for ant, x, y in zip(layout.flatten(), xs.flatten(), ys.flatten()): for z, pol in enumerate(pols): z = 2**z # exponential ensures diff xpols aren't redundant w/ each other i = hera_cal.omni.Antpol(ant, pol, NANTS) antpos[int(i), 0], antpos[int(i), 1], antpos[int(i), 2] = x, y, z reds = hera_cal.omni.compute_reds(NANTS, pols, antpos[:NANTS], tol=.01) # reds = hera_cal.omni.filter_reds(reds, **kwargs) info = hera_cal.omni.RedundantInfo(NANTS) info.init_from_reds(reds, antpos_ideal) # info = om.RedundantCalibrator(reds) # gains, true_vis, d = om.sim_red_data(hcreds, shape=SHAPE, gain_scatter=.0099999) data = {} for key in d.keys(): if not data.has_key(key[:2]): data[key[:2]] = {} data[key[:2]][key[-1]] = d[key].astype(np.complex64) t0 = time.time() for i in xrange(1): m1, g1, v1 = omnical.calib.lincal(data, info, maxiter=50) # print('omnilincal', time.time() - t0) for i in xrange(NANTS): self.assertEqual(g1['x'][i].shape, SHAPE) for bls in reds: ubl = v1['xx'][(int(bls[0][0]), int(bls[0][1]))] self.assertEqual(ubl.shape, SHAPE) for bl in bls: d_bl = data[(int(bl[0]), int(bl[1]))]['xx'] mdl = g1['x'][bl[0]] * g1['x'][bl[1]].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 5) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 5) ''' def test_lincal_hex_end_to_end_1pol_with_remove_degen_and_firstcal(self): antpos = build_hex_array(3) reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol') rc = om.RedundantCalibrator(reds) freqs = np.linspace(.1, .2, 10) gains, true_vis, d = om.sim_red_data(reds, gain_scatter=.1, shape=(1, len(freqs))) fc_delays = {ant: 100 * np.random.randn() for ant in gains.keys()} # in ns fc_gains = {ant: np.reshape(np.exp(-2.0j * np.pi * freqs * delay), (1, len(freqs))) for ant, delay in fc_delays.items()} for ant1, ant2, pol in d.keys(): d[(ant1, ant2, pol)] *= fc_gains[(ant1, pol[0])] * np.conj(fc_gains[(ant2, pol[1])]) for ant in gains.keys(): gains[ant] *= fc_gains[ant] w = dict([(k, 1.) for k in d.keys()]) sol0 = rc.logcal(d, sol0=fc_gains, wgts=w) meta, sol = rc.lincal(d, sol0, wgts=w) np.testing.assert_array_less(meta['iter'], 50 * np.ones_like(meta['iter'])) np.testing.assert_almost_equal(meta['chisq'], np.zeros_like(meta['chisq']), decimal=10) np.testing.assert_almost_equal(meta['chisq'], 0, 10) for i in xrange(len(antpos)): self.assertEqual(sol[(i, 'x')].shape, (1, len(freqs))) for bls in reds: ubl = sol[bls[0]] self.assertEqual(ubl.shape, (1, len(freqs))) for bl in bls: d_bl = d[bl] mdl = sol[(bl[0], 'x')] * sol[(bl[1], 'x')].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 10) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 10) sol_rd = rc.remove_degen(antpos, sol) g, v = om.get_gains_and_vis_from_sol(sol_rd) ants = [key for key in sol_rd.keys() if len(key) == 2] gainSols = np.array([sol_rd[ant] for ant in ants]) meanSqAmplitude = np.mean([np.abs(g[key1] * g[key2]) for key1 in g.keys() for key2 in g.keys() if key1[1] == 'x' and key2[1] == 'x' and key1[0] != key2[0]], axis=0) np.testing.assert_almost_equal(meanSqAmplitude, 1, 10) #np.testing.assert_almost_equal(np.mean(np.angle(gainSols), axis=0), 0, 10) for bls in reds: ubl = sol_rd[bls[0]] self.assertEqual(ubl.shape, (1, len(freqs))) for bl in bls: d_bl = d[bl] mdl = sol_rd[(bl[0], 'x')] * sol_rd[(bl[1], 'x')].conj() * ubl np.testing.assert_almost_equal(np.abs(d_bl), np.abs(mdl), 10) np.testing.assert_almost_equal(np.angle(d_bl * mdl.conj()), 0, 10) sol_rd = rc.remove_degen(antpos, sol, degen_sol=gains) g, v = om.get_gains_and_vis_from_sol(sol_rd) meanSqAmplitude = np.mean([np.abs(g[key1] * g[key2]) for key1 in g.keys() for key2 in g.keys() if key1[1] == 'x' and key2[1] == 'x' and key1[0] != key2[0]], axis=0) degenMeanSqAmplitude = np.mean([np.abs(gains[key1] * gains[key2]) for key1 in g.keys() for key2 in g.keys() if key1[1] == 'x' and key2[1] == 'x' and key1[0] != key2[0]], axis=0) np.testing.assert_almost_equal(meanSqAmplitude, degenMeanSqAmplitude, 10) #np.testing.assert_almost_equal(np.mean(np.angle(gainSols), axis=0), 0, 10) for key, val in sol_rd.items(): if len(key) == 2: np.testing.assert_almost_equal(val, gains[key], 10) if len(key) == 3: np.testing.assert_almost_equal(val, true_vis[key], 10) rc.pol_mode = 'unrecognized_pol_mode' with self.assertRaises(ValueError): sol_rd = rc.remove_degen(antpos, sol) ''' if __name__ == '__main__': unittest.main()

44.850174

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0.556868

1,938

12,872

3.575851

0.114551

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null

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1

0

1

0

null

0

6

c0d16240c73e0424fa9b7307485b93f72168d941

2,474

py

Python

tests/functions/energies/test_anisotropy_energy.py

jdalzatec/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

4

2019-09-02T19:18:55.000Z

2021-05-05T15:04:54.000Z

tests/functions/energies/test_anisotropy_energy.py

lufvelasquezgo/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

116

2020-02-09T05:19:52.000Z

2022-03-27T18:47:17.000Z

tests/functions/energies/test_anisotropy_energy.py

lufvelasquezgo/llg

c0acd728d29a9a821ebadc4f1e17e0327d7e238c

[ "MIT" ]

null

from llg.functions import energy import pytest import numpy def compute_anisotropy_energy(num_sites, state, anisotropy_constant, anisotropy_vector): total = 0 for i in range(num_sites): total -= anisotropy_constant[i] * numpy.dot(state[i], anisotropy_vector[i]) ** 2 return total @pytest.mark.repeat(10) def test_anisotropy_energy_null_anisotropy_constant( random_state_spins, build_sample, random_anisotropy_vector ): num_sites, _, _, _ = build_sample anisotropy_constant = numpy.zeros(shape=num_sites) expected = compute_anisotropy_energy( num_sites, random_state_spins, anisotropy_constant, random_anisotropy_vector ) total = energy.compute_anisotropy_energy( random_state_spins, anisotropy_constant, random_anisotropy_vector ) assert numpy.allclose(expected, total) @pytest.mark.repeat(10) def test_anisotropy_energy_null_anisotropy_vector( random_state_spins, build_sample, random_anisotropy_constant ): num_sites, _, _, _ = build_sample anisotropy_vector = numpy.zeros(shape=(num_sites, 3)) expected = compute_anisotropy_energy( num_sites, random_state_spins, random_anisotropy_constant, anisotropy_vector ) total = energy.compute_anisotropy_energy( random_state_spins, random_anisotropy_constant, anisotropy_vector ) assert numpy.allclose(expected, total) @pytest.mark.repeat(10) def test_anisotropy_energy_random_anisotropy_constant( random_state_spins, build_sample, random_anisotropy_constant ): num_sites, _, _, _ = build_sample anisotropy_vector = numpy.ones(shape=(num_sites, 3)) expected = compute_anisotropy_energy( num_sites, random_state_spins, random_anisotropy_constant, anisotropy_vector ) total = energy.compute_anisotropy_energy( random_state_spins, random_anisotropy_constant, anisotropy_vector ) assert numpy.allclose(expected, total) @pytest.mark.repeat(10) def test_anisotropy_energy_random_anisotropy_vector( random_state_spins, build_sample, random_anisotropy_vector ): num_sites, _, _, _ = build_sample anisotropy_constants = numpy.ones(shape=num_sites) expected = compute_anisotropy_energy( num_sites, random_state_spins, anisotropy_constants, random_anisotropy_vector ) total = energy.compute_anisotropy_energy( random_state_spins, anisotropy_constants, random_anisotropy_vector ) assert numpy.allclose(expected, total)

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py

Python

sentence_transformers/losses/__init__.py

coastalcph/sentence-transformers-for-analogies

e05e73d59c8f5840de8075e0e8c09a7c309fc3a2

[ "Apache-2.0" ]

2

2021-11-01T14:40:19.000Z

2021-11-01T14:57:29.000Z

sentence_transformers/losses/__init__.py

coastalcph/sentence-transformers-for-analogies

e05e73d59c8f5840de8075e0e8c09a7c309fc3a2

[ "Apache-2.0" ]

1

2021-11-19T15:37:15.000Z

2022-02-24T14:11:40.000Z

sentence_transformers/losses/__init__.py

coastalcph/sentence-transformers-for-analogies

e05e73d59c8f5840de8075e0e8c09a7c309fc3a2

[ "Apache-2.0" ]

null

from .CosineSimilarityLoss import * from .SoftmaxLoss import * from .BatchHardTripletLoss import * from .MultipleNegativesRankingLoss import * from .TripletLoss import * from .MSELoss import * from .AnalogyMSELoss import * from .AnalogyBatchHardTripletLoss import * from .AnalogyCosineLoss import *

33.111111

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2396c51005ef790157514cc8c9dfb46cb469f682

35

py

Python

pete/runner/__init__.py

ColCarroll/pete

066bc5c6e5706bc215038f835202d8fb5162033f

[ "MIT" ]

1

2020-10-28T03:39:56.000Z

pete/runner/__init__.py

ColCarroll/pete

066bc5c6e5706bc215038f835202d8fb5162033f

[ "MIT" ]

2

2016-07-10T17:09:05.000Z

2021-04-20T17:58:12.000Z

pete/runner/__init__.py

ColCarroll/pete

066bc5c6e5706bc215038f835202d8fb5162033f

[ "MIT" ]

1

2016-08-15T13:33:15.000Z

from .runner import Runner # noqa

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py

Python

wallhaven/api/__init__.py

lucasshiva/wallhaven

444a04d3e574579b08cf1bc0fa7879b41621d916

[ "MIT" ]

5

2020-04-24T12:14:02.000Z

2021-06-21T15:29:37.000Z

wallhaven/api/__init__.py

lucasshiva/wallhaven

444a04d3e574579b08cf1bc0fa7879b41621d916

[ "MIT" ]

null

wallhaven/api/__init__.py

lucasshiva/wallhaven

444a04d3e574579b08cf1bc0fa7879b41621d916

[ "MIT" ]

2

2020-11-21T20:56:07.000Z

2021-06-12T14:03:07.000Z

# flake8: noqa from wallhaven.api.endpoints import API_ENDPOINTS from wallhaven.api.wallhaven import Wallhaven

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23f5c05e0ebd27bf1be93d08b4f5525a594e55ac

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py

Python

src/ctc/toolbox/twap_utils/twap_data_sources.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

94

2022-02-15T19:34:49.000Z

2022-03-26T19:26:22.000Z

src/ctc/toolbox/twap_utils/twap_data_sources.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

7

2022-03-03T02:58:47.000Z

2022-03-11T18:41:05.000Z

src/ctc/toolbox/twap_utils/twap_data_sources.py

fei-protocol/checkthechain

ec838f3d0d44af228f45394d9ba8d8eb7f677520

[ "MIT" ]

7

2022-02-15T17:53:07.000Z

2022-03-17T19:14:17.000Z

from __future__ import annotations import typing from ctc import spec from . import twap_spec async def async_get_chainlink_data( data_source: twap_spec.DataSource, start_block: typing.Optional[spec.BlockNumberReference] = None, end_block: typing.Optional[spec.BlockNumberReference] = None, provider: spec.ProviderSpec = None, ) -> spec.Series: from ctc.protocols import chainlink_utils feed = data_source.get('feed') composite_feed = data_source.get('composite_feed') invert = data_source.get('invert') if invert is None: invert = False normalize = data_source.get('normalize') if normalize is None: normalize = False if feed is not None: return await chainlink_utils.async_get_feed_data( feed=feed, invert=invert, normalize=normalize, start_block=start_block, end_block=end_block, interpolate=True, fields='answer', provider=provider, ) elif composite_feed is not None: return await chainlink_utils.async_get_composite_feed_data( composite_feed=composite_feed, invert=invert, start_block=start_block, end_block=end_block, provider=provider, ) else: raise Exception('must specify feed or composite_feed') # async def async_get_uniswap_v2_data( # data_source: twap_spec.DataSource, # start_block: typing.Optional[spec.BlockNumberReference] = None, # end_block: typing.Optional[spec.BlockNumberReference] = None, # provider: spec.ProviderSpec = None, # ) -> spec.Series: # from ctc.protocols import uniswap_v2_utils # feed = data_source.get('feed') # composite_feed = data_source.get('composite_feed') # invert = data_source.get('invert') # normalize = data_source.get('normalize') # if feed is not None: # return await uniswap_v2_utils.async_get_feed_data( # feed=feed, # invert=invert, # normalize=normalize, # start_block=start_block, # end_block=end_block, # interpolate=True, # provider=provider, # ) # elif composite_feed is not None: # return await uniswap_v2_utils.async_get_composite_feed_data( # composite_feed=composite_feed, # invert=invert, # normalize=normalize, # start_block=start_block, # end_block=end_block, # provider=provider, # ) # else: # raise Exception('must specify feed or composite_feed')

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f1d5114d561f8656216b6c66641f6d28da8c2e96

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py

Python

tests/test_network_patch_api.py

vaporyproject/pytest-asyncio-network-simulator

7a7ee136c8e47cde751c1a5af9739b1099810608

[ "MIT" ]

4

2019-06-05T23:53:04.000Z

2021-11-04T14:24:21.000Z

tests/test_network_patch_api.py

vaporyproject/pytest-asyncio-network-simulator

7a7ee136c8e47cde751c1a5af9739b1099810608

[ "MIT" ]

5

2018-07-20T20:34:04.000Z

2019-04-26T23:02:40.000Z

tests/test_network_patch_api.py

vaporyproject/pytest-asyncio-network-simulator

7a7ee136c8e47cde751c1a5af9739b1099810608

[ "MIT" ]

4

2018-08-23T07:43:12.000Z

2020-10-01T03:00:27.000Z

import asyncio import pytest from pytest_asyncio_network_simulator.network import ( Network, ) class sentinal_a: pass class sentinal_b: pass @pytest.fixture(autouse=True) def _pre_patch(monkeypatch): monkeypatch.setattr(asyncio, 'start_server', sentinal_a) monkeypatch.setattr(asyncio, 'open_connection', sentinal_b) def test_network_patch_asyncio_context_manager_api(): network = Network('test', None, 'localhost') assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b with network.patch_asyncio(): assert asyncio.start_server == network.start_server assert asyncio.open_connection == network.open_connection assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b def test_network_patch_asyncio_context_manager_api_with_error(): network = Network('test', None, 'localhost') assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b with pytest.raises(AssertionError): with network.patch_asyncio(): assert asyncio.start_server == network.start_server assert asyncio.open_connection == network.open_connection raise AssertionError('failed') assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b def test_explicit_patching(): network = Network('test', None, 'localhost') assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b network.patch_asyncio() assert asyncio.start_server == network.start_server assert asyncio.open_connection == network.open_connection # assert we can idempotently call this multiple times network.patch_asyncio() assert asyncio.start_server == network.start_server assert asyncio.open_connection == network.open_connection network.unpatch_asynio() assert asyncio.start_server == sentinal_a assert asyncio.open_connection == sentinal_b with pytest.raises(RuntimeError): # should raise now that it has been unpatched. network.unpatch_asynio() def test_cannot_unpatch_if_never_patched(): network = Network('test', None, 'localhost') with pytest.raises(RuntimeError): # should raise now that it has been unpatched. network.unpatch_asynio()

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null

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f1deb642a8987ae4cb18087b9fe8b023c2ecc7a6

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py

Python

api/handlers/google.py

LostLuma/repo-import-test

45273fc3543d21366ed3cc5007dc5680b1e3e546

[ "MIT" ]

1

2020-01-27T17:42:30.000Z

api/handlers/google.py

LostLuma/repo-import-test

45273fc3543d21366ed3cc5007dc5680b1e3e546

[ "MIT" ]

59

2021-11-17T08:21:59.000Z

2022-03-29T08:29:55.000Z

api/handlers/google.py

SpoopySite/SpoopySite

da68e454eee2a242e3df2ae8ef31bf1e50da571b

[ "MIT" ]

3

2020-01-26T23:19:24.000Z

2021-09-25T07:07:59.000Z

import urllib.parse from urllib.parse import ParseResult def google(parsed: ParseResult): if "url" in urllib.parse.parse_qs(parsed.query): return urllib.parse.parse_qs(parsed.query).get("url")[0]

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1

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9e4679793090dba80590974ea169a6b1458e6f70

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py

Python

ambra_sdk/service/entrypoints/npi.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

ambra_sdk/service/entrypoints/npi.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

ambra_sdk/service/entrypoints/npi.py

dyens/sdk-python

24bf05268af2832c70120b84fd53bf44862cffec

[ "Apache-2.0" ]

null

from ambra_sdk.service.entrypoints.generated.npi import Npi as GNpi class Npi(GNpi): """Npi."""

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6

9e6f34ed2e2acb79719987855982f7a1fc4d43a1

83

py

Python

01-basics/app-strings-esc.py

vitoOmbero/python-notes

062c05c216d8a59678868d62dd5aacea6249e0b2

[ "Unlicense" ]

null

01-basics/app-strings-esc.py

vitoOmbero/python-notes

062c05c216d8a59678868d62dd5aacea6249e0b2

[ "Unlicense" ]

null

01-basics/app-strings-esc.py

vitoOmbero/python-notes

062c05c216d8a59678868d62dd5aacea6249e0b2

[ "Unlicense" ]

null

msg = 'Python "New string"' print(msg) msg2 = "Python \"New string\"" print(msg2)

13.833333

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9eb1161de0edaef257758a92b4c4827735532701

78

py

Python

cfscan/__init__.py

cloudhound-io/cftest

d26eb06ea274b20a91f121d726c0696d5c5fe910

[ "BSD-2-Clause" ]

5

2018-02-26T21:24:59.000Z

2018-03-07T19:49:50.000Z

cfscan/__init__.py

cloudhound-io/cftest

d26eb06ea274b20a91f121d726c0696d5c5fe910

[ "BSD-2-Clause" ]

null

cfscan/__init__.py

cloudhound-io/cftest

d26eb06ea274b20a91f121d726c0696d5c5fe910

[ "BSD-2-Clause" ]

1

2020-10-29T21:22:03.000Z

from scanner import Scanner, test, PASS, FAIL from cfscanner import CFScanner

26

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7b467a336099359bcd58b14e903a9a3f9aeca01e

46

py

Python

src/cho_util/math/__init__.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

src/cho_util/math/__init__.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

src/cho_util/math/__init__.py

yycho0108/cho-util

331efc7aac8cddfb4258620b80cb7fc5f0688d1f

[ "MIT" ]

null

from .common import * from . import transform

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7b634839466ca4a147780f7a1bd356ef63e14374

25,870

py

Python

code/prolate_ellipsoid.py

pinga-lab/magnetic-ellipsoid

2907042175d1a4b4b9864408671ddcfd4097dab4

[ "BSD-3-Clause" ]

14

2017-03-20T17:58:01.000Z

2021-06-30T13:18:15.000Z

code/prolate_ellipsoid.py

pinga-lab/magnetic-ellipsoid

2907042175d1a4b4b9864408671ddcfd4097dab4

[ "BSD-3-Clause" ]

4

2017-03-20T20:52:46.000Z

2017-05-11T20:08:39.000Z

code/prolate_ellipsoid.py

pinga-lab/magnetic-ellipsoid

2907042175d1a4b4b9864408671ddcfd4097dab4

[ "BSD-3-Clause" ]

6

2018-01-18T14:17:52.000Z

2021-04-22T06:33:40.000Z

r""" The potential fields of a homogeneous prolate ellipsoid. """ from __future__ import division, absolute_import import numpy as np from fatiando.constants import CM, T2NT from fatiando import utils def tf(xp, yp, zp, ellipsoids, F, inc, dec, demag=True, pmag=None): r""" The total-field anomaly produced by prolate ellipsoids. .. math:: \Delta T = |\mathbf{T}| - |\mathbf{F}|, where :math:`\mathbf{T}` is the measured field and :math:`\mathbf{F}` is the local-geomagnetic field. The anomaly of a homogeneous ellipsoid can be calculated as: .. math:: \Delta T \approx \hat{\mathbf{F}}\cdot\mathbf{B}. where :math:`\mathbf{B}` is the magnetic induction produced by the ellipsoid. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> North, y -> East and z -> Down. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated. * ellipsoids : list of :class:`mesher.ProlateEllipsoid` The ellipsoids. Ellipsoids must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. Ellipsoids that are ``None`` will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of calculating the magnetization of the ellipsoid. Use this, e.g., for sensitivity matrix building. Returns: * tf : array The total-field anomaly References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ fx, fy, fz = utils.dircos(inc, dec) Bx = bx(xp, yp, zp, ellipsoids, F, inc, dec, demag, pmag) By = by(xp, yp, zp, ellipsoids, F, inc, dec, demag, pmag) Bz = bz(xp, yp, zp, ellipsoids, F, inc, dec, demag, pmag) return fx*Bx + fy*By + fz*Bz def bx(xp, yp, zp, ellipsoids, F, inc, dec, demag=True, pmag=None): r""" The x component of the magnetic induction produced by prolate ellipsoids. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> North, y -> East and z -> Down. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoids : list of :class:`mesher.ProlateEllipsoid` The ellipsoids. Ellipsoids must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. Ellipsoids that are ``None`` will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoids. Use this, e.g., for sensitivity matrix building. Returns: * bx: array The x component of the magnetic induction References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ res = 0 for ellipsoid in ellipsoids: if ellipsoid is None: continue b1 = _bx(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b2 = _by(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b3 = _bz(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) res += ellipsoid.transf_matrix[0, 0]*b1 \ + ellipsoid.transf_matrix[0, 1]*b2 \ + ellipsoid.transf_matrix[0, 2]*b3 return res def by(xp, yp, zp, ellipsoids, F, inc, dec, demag=True, pmag=None): r""" The y component of the magnetic induction produced by prolate ellipsoids. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> North, y -> East and z -> Down. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoids : list of :class:`mesher.ProlateEllipsoid` The ellipsoids. Ellipsoids must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. Ellipsoids that are ``None`` will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoids. Use this, e.g., for sensitivity matrix building. Returns: * by: array The y component of the magnetic induction References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ res = 0 for ellipsoid in ellipsoids: if ellipsoid is None: continue b1 = _bx(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b2 = _by(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b3 = _bz(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) res += ellipsoid.transf_matrix[1, 0]*b1 \ + ellipsoid.transf_matrix[1, 1]*b2 \ + ellipsoid.transf_matrix[1, 2]*b3 return res def bz(xp, yp, zp, ellipsoids, F, inc, dec, demag=True, pmag=None): r""" The z component of the magnetic induction produced by prolate ellipsoids. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> North, y -> East and z -> Down. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoids : list of :class:`mesher.ProlateEllipsoid` The ellipsoids. Ellipsoids must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. Ellipsoids that are ``None`` will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoids. Use this, e.g., for sensitivity matrix building. Returns: * bz: array The z component of the magnetic induction References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ res = 0 for ellipsoid in ellipsoids: if ellipsoid is None: continue b1 = _bx(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b2 = _by(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) b3 = _bz(xp, yp, zp, ellipsoid, F, inc, dec, demag, pmag) res += ellipsoid.transf_matrix[2, 0]*b1 \ + ellipsoid.transf_matrix[2, 1]*b2 \ + ellipsoid.transf_matrix[2, 2]*b3 return res def _bx(xp, yp, zp, ellipsoid, F, inc, dec, demag=True, pmag=None): r""" The x component of the magnetic induction produced by prolate ellipsoids in the ellipsoid system. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> semi-axis a, y -> semi-axis b and z -> semi-axis c. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. The ellipsoid. It must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. If the ellipsoid is ``None``, it will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoid. Use this, e.g., for sensitivity matrix building. Returns: * bx : array The x component of the magnetic induction in the ellipsoid system. References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ if pmag is None: mx, my, mz = magnetization(ellipsoid, F, inc, dec, demag) else: assert demag is not True, 'the use of a forced magnetization \ impedes the computation of self-demagnetization' mx, my, mz = pmag # Transform the magnetization to the local coordinate system V = ellipsoid.transf_matrix mx_local = V[0, 0]*mx + V[1, 0]*my + V[2, 0]*mz my_local = V[0, 1]*mx + V[1, 1]*my + V[2, 1]*mz mz_local = V[0, 2]*mx + V[1, 2]*my + V[2, 2]*mz x1, x2, x3 = x1x2x3(xp, yp, zp, ellipsoid) lamb = _lamb(x1, x2, x3, ellipsoid) denominator = _dlamb_aux(x1, x2, x3, ellipsoid, lamb) dlamb = _dlamb(x1, x2, x3, ellipsoid, lamb, denominator, deriv='x') h1 = _hv(ellipsoid, lamb, v='x') h2 = _hv(ellipsoid, lamb, v='y') h3 = _hv(ellipsoid, lamb, v='z') g = _gv(ellipsoid, lamb, v='x') res = dlamb*(h1*x1*mx_local + h2*x2*my_local + h3*x3*mz_local) res += g*mx_local a = ellipsoid.large_axis b = ellipsoid.small_axis volume = 4*np.pi*a*b*b/3 res *= -1.5*volume*CM*T2NT return res def _by(xp, yp, zp, ellipsoid, F, inc, dec, demag=True, pmag=None): r""" The y component of the magnetic induction produced by prolate ellipsoids in the ellipsoid system. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> semi-axis a, y -> semi-axis b and z -> semi-axis c. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoid : element of :class:`mesher.ProlateEllipsoid` The ellipsoid. It must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. If the ellipsoid is ``None``, it will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoid. Use this, e.g., for sensitivity matrix building. Returns: * by : array The y component of the magnetic induction in the ellipsoid system. References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ if pmag is None: mx, my, mz = magnetization(ellipsoid, F, inc, dec, demag) else: assert demag is not True, 'the use of a forced magnetization \ impedes the computation of self-demagnetization' mx, my, mz = pmag # Transform the magnetization to the local coordinate system V = ellipsoid.transf_matrix mx_local = V[0, 0]*mx + V[1, 0]*my + V[2, 0]*mz my_local = V[0, 1]*mx + V[1, 1]*my + V[2, 1]*mz mz_local = V[0, 2]*mx + V[1, 2]*my + V[2, 2]*mz x1, x2, x3 = x1x2x3(xp, yp, zp, ellipsoid) lamb = _lamb(x1, x2, x3, ellipsoid) denominator = _dlamb_aux(x1, x2, x3, ellipsoid, lamb) dlamb = _dlamb(x1, x2, x3, ellipsoid, lamb, denominator, deriv='y') h1 = _hv(ellipsoid, lamb, v='x') h2 = _hv(ellipsoid, lamb, v='y') h3 = _hv(ellipsoid, lamb, v='z') g = _gv(ellipsoid, lamb, v='y') res = dlamb*(h1*x1*mx_local + h2*x2*my_local + h3*x3*mz_local) res += g*my_local a = ellipsoid.large_axis b = ellipsoid.small_axis volume = 4*np.pi*a*b*b/3 res *= -1.5*volume*CM*T2NT return res def _bz(xp, yp, zp, ellipsoid, F, inc, dec, demag=True, pmag=None): r""" The z component of the magnetic induction produced by prolate ellipsoids in the ellipsoid system. This code follows the approach presented by Emerson et al. (1985). The coordinate system of the input parameters is x -> semi-axis a, y -> semi-axis b and z -> semi-axis c. Input units should be SI. Output is in nT. Parameters: * xp, yp, zp : arrays The x, y, and z coordinates where the anomaly will be calculated * ellipsoid : element of :class:`mesher.ProlateEllipsoid` The ellipsoid. It must have the physical properties ``'principal susceptibilities'`` and ``'susceptibility angles'`` as prerequisite to calculate the self-demagnetization. If the ellipsoid is ``None``, it will be ignored. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. * pmag : [mx, my, mz] or None A magnetization vector. If not None, will use this value instead of the resultant magnetization of the ellipsoid. Use this, e.g., for sensitivity matrix building. Returns: * bz : array The z component of the magnetic induction in the ellipsoid system. References: Emerson, D. W., Clark, D., and Saul, S.: Magnetic exploration models incorporating remanence, demagnetization and anisotropy: HP 41C handheld computer algorithms, Exploration Geophysics, 16, 1-122, 1985. """ if pmag is None: mx, my, mz = magnetization(ellipsoid, F, inc, dec, demag) else: assert demag is not True, 'the use of a forced magnetization \ impedes the computation of self-demagnetization' mx, my, mz = pmag # Transform the magnetization to the local coordinate system V = ellipsoid.transf_matrix mx_local = V[0, 0]*mx + V[1, 0]*my + V[2, 0]*mz my_local = V[0, 1]*mx + V[1, 1]*my + V[2, 1]*mz mz_local = V[0, 2]*mx + V[1, 2]*my + V[2, 2]*mz x1, x2, x3 = x1x2x3(xp, yp, zp, ellipsoid) lamb = _lamb(x1, x2, x3, ellipsoid) denominator = _dlamb_aux(x1, x2, x3, ellipsoid, lamb) dlamb = _dlamb(x1, x2, x3, ellipsoid, lamb, denominator, deriv='z') h1 = _hv(ellipsoid, lamb, v='x') h2 = _hv(ellipsoid, lamb, v='y') h3 = _hv(ellipsoid, lamb, v='z') g = _gv(ellipsoid, lamb, v='z') res = dlamb*(h1*x1*mx_local + h2*x2*my_local + h3*x3*mz_local) res += g*mz_local a = ellipsoid.large_axis b = ellipsoid.small_axis volume = 4*np.pi*a*b*b/3 res *= -1.5*volume*CM*T2NT return res def x1x2x3(xp, yp, zp, ellipsoid): ''' Calculates the x, y and z coordinates referred to the ellipsoid coordinate system. Parameters: * xp, yp, zp: numpy arrays 1D x, y and z coordinates of points referred to the main system (in meters). * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. Returns: * x1, x2, x3: numpy arrays 1D x, y and z coordinates of points referred to the ellipsoid system (in meters). ''' assert xp.size == yp.size == zp.size, \ 'xp, yp and zp must have the same size' dx = xp - ellipsoid.x dy = yp - ellipsoid.y dz = zp - ellipsoid.z x1 = ellipsoid.transf_matrix[0, 0]*dx + \ ellipsoid.transf_matrix[1, 0]*dy + \ ellipsoid.transf_matrix[2, 0]*dz x2 = ellipsoid.transf_matrix[0, 1]*dx + \ ellipsoid.transf_matrix[1, 1]*dy + \ ellipsoid.transf_matrix[2, 1]*dz x3 = ellipsoid.transf_matrix[0, 2]*dx + \ ellipsoid.transf_matrix[1, 2]*dy + \ ellipsoid.transf_matrix[2, 2]*dz return x1, x2, x3 def _lamb(x1, x2, x3, ellipsoid): ''' Calculates the parameter lambda for a prolate ellipsoid. The parameter lambda is defined as the largest root of the quadratic equation defining the surface of the prolate ellipsoid. Parameters: * x1, x2, x3: numpy arrays 1D x, y and z coordinates of points referred to the ellipsoid system (in meters). * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. Returns: * lamb: numpy array 1D Parameter lambda for each point in the ellipsoid system. ''' a = ellipsoid.large_axis b = ellipsoid.small_axis # auxiliary variables (http://mathworld.wolfram.com/QuadraticFormula.html) p1 = a*a + b*b - x1*x1 - x2*x2 - x3*x3 p0 = a*a*b*b - b*b*x1*x1 - a*a*(x2*x2 + x3*x3) delta = np.sqrt(p1*p1 - 4*p0) lamb = (-p1 + delta)/2. return lamb def _dlamb(x1, x2, x3, ellipsoid, lamb, denominator, deriv='x'): ''' Calculates the spatial derivative of the parameter lambda with respect to the coordinates x, y or z in the ellipsoid system. Parameters: * x1, x2, x3: numpy arrays 1D x, y and z coordinates of points referred to the ellipsoid system (in meters). * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. * lamb: numpy array 1D Parameter lambda for each point in the ellipsoid system. * denominator: numpy array 1D Denominator of the expression used to calculate the spatial derivative of the parameter lambda. * deriv: string Defines the coordinate with respect to which the derivative will be calculated. It must be 'x', 'y' or 'z'. Returns: * dlamb_dv: numpy array 1D Derivative of lambda with respect to the coordinate v = x, y, z in the ellipsoid system. ''' assert deriv in ['x', 'y', 'z'], 'deriv must represent a coordinate \ x, y or z' assert denominator.size == lamb.size == x1.size == x2.size == x3.size, \ 'x1, x2, x3, lamb and denominator must have the same size' a = ellipsoid.large_axis b = ellipsoid.small_axis if deriv is 'x': dlamb_dv = (2*x1/(a*a + lamb))/denominator if deriv is 'y': dlamb_dv = (2*x2/(b*b + lamb))/denominator if deriv is 'z': dlamb_dv = (2*x3/(b*b + lamb))/denominator return dlamb_dv def _dlamb_aux(x1, x2, x3, ellipsoid, lamb): ''' Calculates an auxiliary variable used to calculate the spatial derivatives of the parameter lambda with respect to the coordinates x, y and z in the ellipsoid system. Parameters: * x1, x2, x3: numpy arrays 1D x, y and z coordinates of points referred to the ellipsoid system (in meters). * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. * lamb: numpy array 1D Parameter lambda for each point in the ellipsoid system. Returns: * aux: numpy array 1D Denominator of the expression used to calculate the spatial derivative of the parameter lambda. ''' a = ellipsoid.large_axis b = ellipsoid.small_axis aux1 = x1/(a*a + lamb) aux2 = x2/(b*b + lamb) aux3 = x3/(b*b + lamb) aux = aux1*aux1 + aux2*aux2 + aux3*aux3 return aux def demag_factors(ellipsoid): ''' Calculates the demagnetizing factors n11 and n22. Parameters: * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. Returns: * n11, n22: floats Demagnetizing factors (in SI) along, respectively, the large and small axes of the prolate ellipsoid. ''' a = ellipsoid.large_axis b = ellipsoid.small_axis m = a/b aux1 = m*m - 1 aux2 = np.sqrt(aux1) n11 = (1/aux1)*((m/aux2)*np.log(m + aux2) - 1) n22 = 0.5*(1 - n11) return n11, n22 def magnetization(ellipsoid, F, inc, dec, demag): ''' Calculates the resultant magnetization corrected from demagnetizing in the main system. Parameters: * ellipsoid: element of :class:`mesher.ProlateEllipsoid`. * F, inc, dec : floats The intensity (in nT), inclination and declination (in degrees) of the local-geomagnetic field. * demag : boolean If True, will include the self-demagnetization. Returns: * resultant_mag: numpy array 1D Resultant magnetization (in A/m) in the main system. ''' # Remanent magnetization if 'remanent magnetization' in ellipsoid.props: intensity = ellipsoid.props['remanent magnetization'][0] inclination = ellipsoid.props['remanent magnetization'][1] declination = ellipsoid.props['remanent magnetization'][2] remanent_mag = utils.ang2vec(intensity, inclination, declination) else: remanent_mag = np.zeros(3) suscep = ellipsoid.susceptibility_tensor # Induced magnetization if suscep is not None: geomag_field = utils.ang2vec(F/(4*np.pi*100), inc, dec) induced_mag = np.dot(suscep, geomag_field) else: induced_mag = np.zeros(3) # Self-demagnetization if demag is True: assert suscep is not None, 'self-demagnetization requires a \ susceptibility tensor' n11, n22 = demag_factors(ellipsoid) coord_transf_matrix = ellipsoid.transf_matrix suscep_tilde = np.dot(np.dot(coord_transf_matrix.T, suscep), coord_transf_matrix) aux = np.linalg.inv(np.identity(3) + np.dot(suscep_tilde, np.diag([n11, n22, n22]))) Lambda = np.dot(np.dot(coord_transf_matrix, aux), coord_transf_matrix.T) # resultant magnetization in the main system resultant_mag = np.dot(Lambda, induced_mag + remanent_mag) else: assert (suscep is not None) or ('remanent magnetization' in ellipsoid.props), 'neglecting \ self-demagnetization requires a susceptibility tensor or a rem\ anent magnetization' # resultant magnetization in the main system resultant_mag = induced_mag + remanent_mag return resultant_mag def _hv(ellipsoid, lamb, v='x'): ''' Calculates an auxiliary variable used to calculate the depolarization tensor outside the ellipsoidal body. Parameters: * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. * lamb: numpy array 1D Parameter lambda for each point in the ellipsoid system. * v: string Defines the coordinate with respect to which the variable hv will be calculated. It must be 'x', 'y' or 'z'. Returns: * hv: numpy array 1D Auxiliary variable. ''' assert v in ['x', 'y', 'z'], "v must be 'x', 'y' or 'z'" a = ellipsoid.large_axis b = ellipsoid.small_axis aux1 = a*a + lamb aux2 = b*b + lamb if v is 'x': hv = -1./(np.sqrt(aux1*aux1*aux1)*aux2) if v is 'y' or 'z': hv = -1./(np.sqrt(aux1)*aux2*aux2) return hv def _gv(ellipsoid, lamb, v='x'): ''' Diagonal term of the depolarization tensor defined outside the ellipsoidal body. Parameters: * ellipsoid : element of :class:`mesher.ProlateEllipsoid`. * lamb: numpy array 1D Parameter lambda for each point in the ellipsoid system. * v: string Defines the coordinate with respect to which the variable gv will be calculated. It must be 'x', 'y' or 'z'. Returns: * gv: numpy array 1D Diagonal term of the depolarization tensor calculated for each lambda. ''' assert v in ['x', 'y', 'z'], "v must be 'x', 'y' or 'z'" a = ellipsoid.large_axis b = ellipsoid.small_axis log = np.log((np.sqrt(a*a-b*b)+np.sqrt(a*a+lamb))/np.sqrt(b*b+lamb)) aux1 = 1./np.sqrt((a*a - b*b)*(a*a - b*b)*(a*a - b*b)) if v is 'x': aux2 = np.sqrt((a*a - b*b)/(a*a + lamb)) gv = 2*aux1*(log - aux2) if v is 'y' or 'z': aux2 = np.sqrt((a*a - b*b)*(a*a + lamb))/(b*b + lamb) gv = aux1*(aux2 - log) return gv

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sumankumar0091/gdplot

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__init__.py

sumankumar0091/gdplot

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sumankumar0091/gdplot

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geo_prior/geo_prior/SpatialRelationEncoder.py

gengchenmai/space2vec

a29793336e6a1ebdb497289c286a0b4d5a83079f

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2022-03-29T04:17:18.000Z

geo_prior/geo_prior/SpatialRelationEncoder.py

gengchenmai/space2vec

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2020-10-23T20:01:06.000Z

geo_prior/geo_prior/SpatialRelationEncoder.py

gengchenmai/space2vec

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2020-03-12T12:09:35.000Z

2021-12-19T08:08:54.000Z

import torch import torch.nn as nn from torch.nn import init import torch.nn.functional as F import numpy as np import math from module import * from data_utils import * """ A Set of position encoder """ def _cal_freq_list(freq_init, frequency_num, max_radius, min_radius): if freq_init == "random": # the frequence we use for each block, alpha in ICLR paper # freq_list shape: (frequency_num) freq_list = np.random.random(size=[frequency_num]) * max_radius elif freq_init == "geometric": # freq_list = [] # for cur_freq in range(frequency_num): # base = 1.0/(np.power(max_radius, cur_freq*1.0/(frequency_num-1))) # freq_list.append(base) # freq_list = np.asarray(freq_list) log_timescale_increment = (math.log(float(max_radius) / float(min_radius)) / (frequency_num*1.0 - 1)) timescales = min_radius * np.exp( np.arange(frequency_num).astype(float) * log_timescale_increment) freq_list = 1.0/timescales return freq_list class GridCellSpatialRelationEncoder(nn.Module): """ Given a list of (deltaX,deltaY), encode them using the position encoding function """ def __init__(self, spa_embed_dim, coord_dim = 2, frequency_num = 16, max_radius = 10000, min_radius = 10, freq_init = "geometric", ffn=None, device = "cuda"): """ Args: spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other frequency_num: the number of different sinusoidal with different frequencies/wavelengths max_radius: the largest context radius this model can handle """ super(GridCellSpatialRelationEncoder, self).__init__() self.spa_embed_dim = spa_embed_dim self.coord_dim = coord_dim self.frequency_num = frequency_num self.freq_init = freq_init self.max_radius = max_radius self.min_radius = min_radius # the frequence we use for each block, alpha in ICLR paper self.cal_freq_list() self.cal_freq_mat() self.input_embed_dim = self.cal_input_dim() self.ffn = ffn self.device = device def cal_elementwise_angle(self, coord, cur_freq): ''' Args: coord: the deltaX or deltaY cur_freq: the frequency ''' return coord/(np.power(self.max_radius, cur_freq*1.0/(self.frequency_num-1))) def cal_coord_embed(self, coords_tuple): embed = [] for coord in coords_tuple: for cur_freq in range(self.frequency_num): embed.append(math.sin(self.cal_elementwise_angle(coord, cur_freq))) embed.append(math.cos(self.cal_elementwise_angle(coord, cur_freq))) # embed: shape (input_embed_dim) return embed def cal_input_dim(self): # compute the dimention of the encoded spatial relation embedding return int(self.coord_dim * self.frequency_num * 2) def cal_freq_list(self): # if self.freq_init == "random": # # the frequence we use for each block, alpha in ICLR paper # # self.freq_list shape: (frequency_num) # self.freq_list = np.random.random(size=[self.frequency_num]) * self.max_radius # elif self.freq_init == "geometric": # self.freq_list = [] # for cur_freq in range(self.frequency_num): # base = 1.0/(np.power(self.max_radius, cur_freq*1.0/(self.frequency_num-1))) # self.freq_list.append(base) # self.freq_list = np.asarray(self.freq_list) self.freq_list = _cal_freq_list(self.freq_init, self.frequency_num, self.max_radius, self.min_radius) def cal_freq_mat(self): # freq_mat shape: (frequency_num, 1) freq_mat = np.expand_dims(self.freq_list, axis = 1) # self.freq_mat shape: (frequency_num, 2) self.freq_mat = np.repeat(freq_mat, 2, axis = 1) def make_input_embeds(self, coords): if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for GridCellSpatialRelationEncoder") # coords_mat: shape (batch_size, num_context_pt, 2) coords_mat = np.asarray(coords).astype(float) batch_size = coords_mat.shape[0] num_context_pt = coords_mat.shape[1] # coords_mat: shape (batch_size, num_context_pt, 2, 1) coords_mat = np.expand_dims(coords_mat, axis = 3) # coords_mat: shape (batch_size, num_context_pt, 2, 1, 1) coords_mat = np.expand_dims(coords_mat, axis = 4) # coords_mat: shape (batch_size, num_context_pt, 2, frequency_num, 1) coords_mat = np.repeat(coords_mat, self.frequency_num, axis = 3) # coords_mat: shape (batch_size, num_context_pt, 2, frequency_num, 2) coords_mat = np.repeat(coords_mat, 2, axis = 4) # spr_embeds: shape (batch_size, num_context_pt, 2, frequency_num, 2) spr_embeds = coords_mat * self.freq_mat # make sinuniod function # sin for 2i, cos for 2i+1 # spr_embeds: (batch_size, num_context_pt, 2*frequency_num*2=input_embed_dim) spr_embeds[:, :, :, :, 0::2] = np.sin(spr_embeds[:, :, :, :, 0::2]) # dim 2i spr_embeds[:, :, :, :, 1::2] = np.cos(spr_embeds[:, :, :, :, 1::2]) # dim 2i+1 # (batch_size, num_context_pt, 2*frequency_num*2) spr_embeds = np.reshape(spr_embeds, (batch_size, num_context_pt, -1)) return spr_embeds def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ spr_embeds = self.make_input_embeds(coords) # # loop over all batches # spr_embeds = [] # for cur_batch in coords: # # loop over N context points # cur_embeds = [] # for coords_tuple in cur_batch: # cur_embeds.append(self.cal_coord_embed(coords_tuple)) # spr_embeds.append(cur_embeds) # spr_embeds: shape (batch_size, num_context_pt, input_embed_dim) spr_embeds = torch.FloatTensor(spr_embeds).to(self.device) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) # sprenc = torch.einsum("bnd,dk->bnk", (spr_embeds, self.post_mat)) # sprenc = self.f_act(self.dropout(self.post_linear(spr_embeds))) # return sprenc if self.ffn is not None: return self.ffn(spr_embeds) else: return spr_embeds class HexagonGridCellSpatialRelationEncoder(nn.Module): """ Given a list of (deltaX,deltaY), encode them using the position encoding function """ def __init__(self, spa_embed_dim, coord_dim = 2, frequency_num = 16, max_radius = 10000, dropout = 0.5, f_act = "sigmoid", device = "cuda"): """ Args: spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other frequency_num: the number of different sinusoidal with different frequencies/wavelengths max_radius: the largest context radius this model can handle """ super(HexagonGridCellSpatialRelationEncoder, self).__init__() self.frequency_num = frequency_num self.coord_dim = coord_dim self.max_radius = max_radius self.spa_embed_dim = spa_embed_dim self.input_embed_dim = self.cal_input_dim() self.post_linear = nn.Linear(self.input_embed_dim, self.spa_embed_dim) nn.init.xavier_uniform(self.post_linear.weight) self.dropout = nn.Dropout(p=dropout) # self.dropout_ = nn.Dropout(p=dropout) # self.post_mat = nn.Parameter(torch.FloatTensor(self.input_embed_dim, self.spa_embed_dim)) # init.xavier_uniform_(self.post_mat) # self.register_parameter("spa_postmat", self.post_mat) self.f_act = get_activation_function(f_act, "HexagonGridCellSpatialRelationEncoder") self.device = device def cal_elementwise_angle(self, coord, cur_freq): ''' Args: coord: the deltaX or deltaY cur_freq: the frequency ''' return coord/(np.power(self.max_radius, cur_freq*1.0/(self.frequency_num-1))) def cal_coord_embed(self, coords_tuple): embed = [] for coord in coords_tuple: for cur_freq in range(self.frequency_num): embed.append(math.sin(self.cal_elementwise_angle(coord, cur_freq))) embed.append(math.sin(self.cal_elementwise_angle(coord, cur_freq) + math.pi*2.0/3)) embed.append(math.sin(self.cal_elementwise_angle(coord, cur_freq) + math.pi*4.0/3)) # embed: shape (input_embed_dim) return embed def cal_input_dim(self): # compute the dimention of the encoded spatial relation embedding return int(self.coord_dim * self.frequency_num * 3) def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for GridCellSpatialRelationEncoder") # loop over all batches spr_embeds = [] for cur_batch in coords: # loop over N context points cur_embeds = [] for coords_tuple in cur_batch: cur_embeds.append(self.cal_coord_embed(coords_tuple)) spr_embeds.append(cur_embeds) # spr_embeds: shape (batch_size, num_context_pt, input_embed_dim) spr_embeds = torch.FloatTensor(spr_embeds).to(self.device) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) # sprenc = torch.einsum("bnd,dk->bnk", (spr_embeds, self.post_mat)) sprenc = self.f_act(self.dropout(self.post_linear(spr_embeds))) return sprenc """ The theory based Grid cell spatial relation encoder, See https://openreview.net/forum?id=Syx0Mh05YQ Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion """ class TheoryGridCellSpatialRelationEncoder(nn.Module): """ Given a list of (deltaX,deltaY), encode them using the position encoding function """ def __init__(self, spa_embed_dim, coord_dim = 2, frequency_num = 16, max_radius = 10000, min_radius = 1000, freq_init = "geometric", ffn = None, device = "cuda"): """ Args: spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other frequency_num: the number of different sinusoidal with different frequencies/wavelengths max_radius: the largest context radius this model can handle """ super(TheoryGridCellSpatialRelationEncoder, self).__init__() self.frequency_num = frequency_num self.coord_dim = coord_dim self.max_radius = max_radius self.min_radius = min_radius self.spa_embed_dim = spa_embed_dim self.freq_init = freq_init # the frequence we use for each block, alpha in ICLR paper self.cal_freq_list() self.cal_freq_mat() # there unit vectors which is 120 degree apart from each other self.unit_vec1 = np.asarray([1.0, 0.0]) # 0 self.unit_vec2 = np.asarray([-1.0/2.0, math.sqrt(3)/2.0]) # 120 degree self.unit_vec3 = np.asarray([-1.0/2.0, -math.sqrt(3)/2.0]) # 240 degree self.input_embed_dim = self.cal_input_dim() # self.f_act = get_activation_function(f_act, "TheoryGridCellSpatialRelationEncoder") # self.dropout = nn.Dropout(p=dropout) # self.use_post_mat = use_post_mat # if self.use_post_mat: # self.post_linear_1 = nn.Linear(self.input_embed_dim, 64) # nn.init.xavier_uniform(self.post_linear_1.weight) # self.post_linear_2 = nn.Linear(64, self.spa_embed_dim) # nn.init.xavier_uniform(self.post_linear_2.weight) # self.dropout_ = nn.Dropout(p=dropout) # else: # self.post_linear = nn.Linear(self.input_embed_dim, self.spa_embed_dim) # nn.init.xavier_uniform(self.post_linear.weight) self.ffn = ffn self.device = device def cal_freq_list(self): self.freq_list = _cal_freq_list(self.freq_init, self.frequency_num, self.max_radius, self.min_radius) def cal_freq_mat(self): # freq_mat shape: (frequency_num, 1) freq_mat = np.expand_dims(self.freq_list, axis = 1) # self.freq_mat shape: (frequency_num, 6) self.freq_mat = np.repeat(freq_mat, 6, axis = 1) def cal_input_dim(self): # compute the dimention of the encoded spatial relation embedding return int(6 * self.frequency_num) def make_input_embeds(self, coords): if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for GridCellSpatialRelationEncoder") # (batch_size, num_context_pt, coord_dim) coords_mat = np.asarray(coords).astype(float) batch_size = coords_mat.shape[0] num_context_pt = coords_mat.shape[1] # compute the dot product between [deltaX, deltaY] and each unit_vec # (batch_size, num_context_pt, 1) angle_mat1 = np.expand_dims(np.matmul(coords_mat, self.unit_vec1), axis = -1) # (batch_size, num_context_pt, 1) angle_mat2 = np.expand_dims(np.matmul(coords_mat, self.unit_vec2), axis = -1) # (batch_size, num_context_pt, 1) angle_mat3 = np.expand_dims(np.matmul(coords_mat, self.unit_vec3), axis = -1) # (batch_size, num_context_pt, 6) angle_mat = np.concatenate([angle_mat1, angle_mat1, angle_mat2, angle_mat2, angle_mat3, angle_mat3], axis = -1) # (batch_size, num_context_pt, 1, 6) angle_mat = np.expand_dims(angle_mat, axis = -2) # (batch_size, num_context_pt, frequency_num, 6) angle_mat = np.repeat(angle_mat, self.frequency_num, axis = -2) # (batch_size, num_context_pt, frequency_num, 6) angle_mat = angle_mat * self.freq_mat # (batch_size, num_context_pt, frequency_num*6) spr_embeds = np.reshape(angle_mat, (batch_size, num_context_pt, -1)) # make sinuniod function # sin for 2i, cos for 2i+1 # spr_embeds: (batch_size, num_context_pt, frequency_num*6=input_embed_dim) spr_embeds[:, :, 0::2] = np.sin(spr_embeds[:, :, 0::2]) # dim 2i spr_embeds[:, :, 1::2] = np.cos(spr_embeds[:, :, 1::2]) # dim 2i+1 return spr_embeds def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ spr_embeds = self.make_input_embeds(coords) # spr_embeds: (batch_size, num_context_pt, input_embed_dim) spr_embeds = torch.FloatTensor(spr_embeds).to(self.device) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) # sprenc = torch.einsum("bnd,dk->bnk", (spr_embeds, self.post_mat)) # if self.use_post_mat: # sprenc = self.post_linear_1(spr_embeds) # sprenc = self.post_linear_2(self.dropout(sprenc)) # sprenc = self.f_act(self.dropout(sprenc)) # else: # sprenc = self.post_linear(spr_embeds) # sprenc = self.f_act(self.dropout(sprenc)) if self.ffn is not None: return self.ffn(spr_embeds) else: return spr_embeds """ The theory based Grid cell spatial relation encoder, See https://openreview.net/forum?id=Syx0Mh05YQ Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion We retrict the linear layer is block diagonal """ class TheoryDiagGridCellSpatialRelationEncoder(nn.Module): """ Given a list of (deltaX,deltaY), encode them using the position encoding function """ def __init__(self, spa_embed_dim, coord_dim = 2, frequency_num = 16, max_radius = 10000, min_radius = 10, dropout = 0.5, f_act = "sigmoid", freq_init = "geometric", use_layn=False, use_post_mat = False, device = "cuda"): """ Args: spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other frequency_num: the number of different sinusoidal with different frequencies/wavelengths max_radius: the largest context radius this model can handle """ super(TheoryDiagGridCellSpatialRelationEncoder, self).__init__() self.frequency_num = frequency_num self.coord_dim = coord_dim self.max_radius = max_radius self.min_radius = min_radius self.spa_embed_dim = spa_embed_dim self.freq_init = freq_init self.device = device # the frequence we use for each block, alpha in ICLR paper self.cal_freq_list() self.cal_freq_mat() # there unit vectors which is 120 degree apart from each other self.unit_vec1 = np.asarray([1.0, 0.0]) # 0 self.unit_vec2 = np.asarray([-1.0/2.0, math.sqrt(3)/2.0]) # 120 degree self.unit_vec3 = np.asarray([-1.0/2.0, -math.sqrt(3)/2.0]) # 240 degree self.input_embed_dim = self.cal_input_dim() assert self.spa_embed_dim % self.frequency_num == 0 # self.post_linear = nn.Linear(self.frequency_num, 6, self.spa_embed_dim//self.frequency_num) # a block diagnal matrix self.post_mat = nn.Parameter(torch.FloatTensor(self.frequency_num, 6, self.spa_embed_dim//self.frequency_num).to(device)) init.xavier_uniform_(self.post_mat) self.register_parameter("spa_postmat", self.post_mat) self.dropout = nn.Dropout(p=dropout) self.use_post_mat = use_post_mat if self.use_post_mat: self.post_linear = nn.Linear(self.spa_embed_dim, self.spa_embed_dim) self.dropout_ = nn.Dropout(p=dropout) self.f_act = get_activation_function(f_act, "TheoryDiagGridCellSpatialRelationEncoder") def cal_freq_list(self): # if self.freq_init == "random": # # the frequence we use for each block, alpha in ICLR paper # # self.freq_list shape: (frequency_num) # self.freq_list = np.random.random(size=[self.frequency_num]) * self.max_radius # elif self.freq_init == "geometric": # self.freq_list = [] # for cur_freq in range(self.frequency_num): # base = 1.0/(np.power(self.max_radius, cur_freq*1.0/(self.frequency_num-1))) # self.freq_list.append(base) # self.freq_list = np.asarray(self.freq_list) self.freq_list = _cal_freq_list(self.freq_init, self.frequency_num, self.max_radius, self.min_radius) def cal_freq_mat(self): # freq_mat shape: (frequency_num, 1) freq_mat = np.expand_dims(self.freq_list, axis = 1) # self.freq_mat shape: (frequency_num, 6) self.freq_mat = np.repeat(freq_mat, 6, axis = 1) def cal_input_dim(self): # compute the dimention of the encoded spatial relation embedding return int(6 * self.frequency_num) def make_input_embeds(self, coords): if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for GridCellSpatialRelationEncoder") # (batch_size, num_context_pt, coord_dim) coords_mat = np.asarray(coords).astype(float) batch_size = coords_mat.shape[0] num_context_pt = coords_mat.shape[1] # compute the dot product between [deltaX, deltaY] and each unit_vec # (batch_size, num_context_pt, 1) angle_mat1 = np.expand_dims(np.matmul(coords_mat, self.unit_vec1), axis = -1) # (batch_size, num_context_pt, 1) angle_mat2 = np.expand_dims(np.matmul(coords_mat, self.unit_vec2), axis = -1) # (batch_size, num_context_pt, 1) angle_mat3 = np.expand_dims(np.matmul(coords_mat, self.unit_vec3), axis = -1) # (batch_size, num_context_pt, 6) angle_mat = np.concatenate([angle_mat1, angle_mat1, angle_mat2, angle_mat2, angle_mat3, angle_mat3], axis = -1) # (batch_size, num_context_pt, 1, 6) angle_mat = np.expand_dims(angle_mat, axis = -2) # (batch_size, num_context_pt, frequency_num, 6) angle_mat = np.repeat(angle_mat, self.frequency_num, axis = -2) # (batch_size, num_context_pt, frequency_num, 6) spr_embeds = angle_mat * self.freq_mat # make sinuniod function # sin for 2i, cos for 2i+1 # spr_embeds: (batch_size, num_context_pt, frequency_num, 6) spr_embeds[:, :, :, 0::2] = np.sin(spr_embeds[:, :, :, 0::2]) # dim 2i spr_embeds[:, :, :, 1::2] = np.cos(spr_embeds[:, :, :, 1::2]) # dim 2i+1 return spr_embeds def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ # (batch_size, num_context_pt, coord_dim) coords_mat = np.asarray(coords).astype(float) batch_size = coords_mat.shape[0] num_context_pt = coords_mat.shape[1] spr_embeds = self.make_input_embeds(coords) # spr_embeds: (batch_size, num_context_pt, frequency_num, 6) spr_embeds = torch.FloatTensor(spr_embeds).to(self.device) # sprenc: shape (batch_size, num_context_pt, frequency_num, spa_embed_dim//frequency_num) sprenc = torch.einsum("bnfs,fsd->bnfd", (spr_embeds, self.post_mat)) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) sprenc = sprenc.contiguous().view(batch_size, num_context_pt, self.spa_embed_dim) if self.use_post_mat: sprenc = self.dropout(sprenc) sprenc = self.f_act(self.dropout_(self.post_linear(sprenc))) else: # print(sprenc.size()) sprenc = self.f_act(self.dropout(sprenc)) return sprenc class NaiveSpatialRelationEncoder(nn.Module): """ Given a list of (deltaX,deltaY), encode them using the position encoding function """ def __init__(self, spa_embed_dim, extent, coord_dim = 2, ffn = None, device = "cuda"): """ Args: spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other extent: (x_min, x_max, y_min, y_max) """ super(NaiveSpatialRelationEncoder, self).__init__() self.spa_embed_dim = spa_embed_dim self.coord_dim = coord_dim self.extent = extent # self.post_linear = nn.Linear(self.coord_dim, self.spa_embed_dim) # nn.init.xavier_uniform(self.post_linear.weight) # self.dropout = nn.Dropout(p=dropout) # self.f_act = get_activation_function(f_act, "NaiveSpatialRelationEncoder") self.ffn = ffn self.device = device def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for GridCellSpatialRelationEncoder") coords_mat = coord_normalize(coords, self.extent) # spr_embeds: shape (batch_size, num_context_pt, coord_dim) spr_embeds = torch.FloatTensor(coords_mat).to(self.device) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) # sprenc = torch.einsum("bnd,dk->bnk", (spr_embeds, self.post_mat)) # sprenc = self.f_act(self.dropout(self.post_linear(spr_embeds))) if self.ffn is not None: return self.ffn(spr_embeds) else: return spr_embeds # return sprenc class RBFSpatialRelationEncoder(nn.Module): """ Given a list of (X,Y), compute the distance from each pt to each RBF anchor points Feed into a MLP This is for global position encoding or relative/spatial context position encoding """ def __init__(self, model_type, train_locs, spa_embed_dim, coord_dim = 2, num_rbf_anchor_pts = 100, rbf_kernal_size = 10e2, rbf_kernal_size_ratio = 0.0, max_radius = 10000, ffn = None, rbf_anchor_pt_ids = None, device = "cuda"): """ Args: train_locs: np.arrary, [batch_size, 2], location data spa_embed_dim: the output spatial relation embedding dimention coord_dim: the dimention of space, 2D, 3D, or other num_rbf_anchor_pts: the number of RBF anchor points rbf_kernal_size: the RBF kernal size The sigma in https://en.wikipedia.org/wiki/Radial_basis_function_kernel rbf_kernal_size_ratio: if not None, (only applied on relative model) different anchor pts have different kernal size : dist(anchot_pt, origin) * rbf_kernal_size_ratio + rbf_kernal_size max_radius: the relative spatial context size in spatial context model """ super(RBFSpatialRelationEncoder, self).__init__() self.model_type = model_type self.train_locs = train_locs self.spa_embed_dim = spa_embed_dim self.coord_dim = coord_dim self.num_rbf_anchor_pts = num_rbf_anchor_pts self.rbf_kernal_size = rbf_kernal_size self.rbf_kernal_size_ratio = rbf_kernal_size_ratio self.max_radius = max_radius self.rbf_anchor_pt_ids = rbf_anchor_pt_ids # calculate the coordinate matrix for each RBF anchor points self.cal_rbf_anchor_coord_mat() self.input_embed_dim = self.num_rbf_anchor_pts # self.use_layn = use_layn # self.use_post_mat = use_post_mat # if self.use_post_mat: # self.post_linear1 = nn.Linear(self.input_embed_dim, 64) # self.post_linear2 = nn.Linear(64, self.spa_embed_dim) # nn.init.xavier_uniform(self.post_linear1.weight) # nn.init.xavier_uniform(self.post_linear2.weight) # else: # self.post_linear = nn.Linear(self.input_embed_dim, self.spa_embed_dim) # nn.init.xavier_uniform(self.post_linear.weight) # self.dropout = nn.Dropout(p=dropout) # self.f_act = get_activation_function(f_act, "RBFSpatialRelationEncoder") self.ffn = ffn self.device = device def _random_sampling(self, item_tuple, num_sample): ''' poi_type_tuple: (Type1, Type2,...TypeM) ''' type_list = list(item_tuple) if len(type_list) > num_sample: return list(np.random.choice(type_list, num_sample, replace=False)) elif len(type_list) == num_sample: return item_tuple else: return list(np.random.choice(type_list, num_sample, replace=True)) def cal_rbf_anchor_coord_mat(self): if self.model_type == "global": assert self.rbf_kernal_size_ratio == 0 # If we do RBF on location/global model, # we need to random sample M RBF anchor points from training point dataset if self.rbf_anchor_pt_ids == None: self.rbf_anchor_pt_ids = self._random_sampling(np.arange(len(self.train_locs)), self.num_rbf_anchor_pts) # coords = [] # for pid in rbf_anchor_pt_ids: # coord = list(self.pointset.pt_dict[pid].coord) # coords.append(coord) # self.rbf_coords: (num_rbf_anchor_pts, 2) # self.rbf_coords_mat = np.asarray(coords).astype(float) self.rbf_coords_mat = self.train_locs[self.rbf_anchor_pt_ids] elif self.model_type == "relative": # If we do RBF on spatial context/relative model, # We just random sample M-1 RBF anchor point in the relative spatial context defined by max_radius # The (0,0) is also an anchor point x_list = np.random.uniform(-self.max_radius, self.max_radius, self.num_rbf_anchor_pts) x_list[0] = 0.0 y_list = np.random.uniform(-self.max_radius, self.max_radius, self.num_rbf_anchor_pts) y_list[0] = 0.0 # self.rbf_coords: (num_rbf_anchor_pts, 2) self.rbf_coords_mat = np.transpose(np.stack([x_list, y_list], axis=0)) if self.rbf_kernal_size_ratio > 0: dist_mat = np.sqrt(np.sum(np.power(self.rbf_coords_mat, 2), axis = -1)) # rbf_kernal_size_mat: (num_rbf_anchor_pts) self.rbf_kernal_size_mat = dist_mat * self.rbf_kernal_size_ratio + self.rbf_kernal_size def make_input_embeds(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt=1, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, input_embed_dim) """ if type(coords) == np.ndarray: assert self.coord_dim == np.shape(coords)[2] coords = list(coords) elif type(coords) == list: assert self.coord_dim == len(coords[0][0]) else: raise Exception("Unknown coords data type for RBFSpatialRelationEncoder") # coords_mat: shape (batch_size, num_context_pt, 2) coords_mat = np.asarray(coords).astype(float) batch_size = coords_mat.shape[0] num_context_pt = coords_mat.shape[1] # coords_mat: shape (batch_size, num_context_pt, 1, 2) coords_mat = np.expand_dims(coords_mat, axis = 2) # coords_mat: shape (batch_size, num_context_pt, num_rbf_anchor_pts, 2) coords_mat = np.repeat(coords_mat, self.num_rbf_anchor_pts, axis = 2) # compute (deltaX, deltaY) between each point and each RBF anchor points # coords_mat: shape (batch_size, num_context_pt, num_rbf_anchor_pts, 2) coords_mat = coords_mat - self.rbf_coords_mat # coords_mat: shape (batch_size, num_context_pt, num_rbf_anchor_pts=input_embed_dim) coords_mat = np.sum(np.power(coords_mat, 2), axis = 3) if self.rbf_kernal_size_ratio > 0: spr_embeds = np.exp((-1*coords_mat)/(2.0 * np.power(self.rbf_kernal_size_mat, 2))) else: # spr_embeds: shape (batch_size, num_context_pt, num_rbf_anchor_pts=input_embed_dim) spr_embeds = np.exp((-1*coords_mat)/(2.0 * np.power(self.rbf_kernal_size, 2))) return spr_embeds def forward(self, coords): """ Given a list of coords (deltaX, deltaY), give their spatial relation embedding Args: coords: a python list with shape (batch_size, num_context_pt=1, coord_dim) Return: sprenc: Tensor shape (batch_size, num_context_pt, spa_embed_dim) """ spr_embeds = self.make_input_embeds(coords) # spr_embeds: shape (batch_size, num_context_pt, input_embed_dim) spr_embeds = torch.FloatTensor(spr_embeds).to(self.device) # sprenc: shape (batch_size, num_context_pt, spa_embed_dim) # sprenc = torch.einsum("bnd,dk->bnk", (spr_embeds, self.post_mat)) # if self.use_post_mat: # spr_embeds = self.dropout(self.post_linear1(spr_embeds)) # spr_embeds = self.post_linear2(spr_embeds) # sprenc = self.f_act(spr_embeds) # else: # sprenc = self.f_act(self.dropout(self.post_linear(spr_embeds))) if self.ffn is not None: return self.ffn(spr_embeds) else: return spr_embeds

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c88f2474838ca64f3d74402d8ea422556407e473

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py

Python

__init__.py

SDL-player/Rede_Neural_Numpy

4bb088c19573db24834ca2f3d46841248eb4debf

[ "Apache-2.0" ]

1

2021-07-24T14:00:53.000Z

__init__.py

SDL-player/Rede_Neural_Numpy

4bb088c19573db24834ca2f3d46841248eb4debf

[ "Apache-2.0" ]

null

__init__.py

SDL-player/Rede_Neural_Numpy

4bb088c19573db24834ca2f3d46841248eb4debf

[ "Apache-2.0" ]

null

from model import Model from layer import Layer

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6

cda2158dabec0abae86539d3941ba7ce0be9ae31

126

py

Python

page_velocity.py

philers/stringer-1

6e172a0d815f90774b9035bf5ab7a7df89d71918

[ "MIT" ]

null

page_velocity.py

philers/stringer-1

6e172a0d815f90774b9035bf5ab7a7df89d71918

[ "MIT" ]

null

page_velocity.py

philers/stringer-1

6e172a0d815f90774b9035bf5ab7a7df89d71918

[ "MIT" ]

null

import streamlit as st def page_velocity(): st.text('page_velocity') st.subheader('Work in Progress!') return

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cda93de2e581be278f8a7bb6f769fb54708d1c2c

120

py

Python

server/exception/BaseException.py

ue007/3d-model-convert-to-gltf

fcbea97d5979b67769df12da813587f194fc8f34

[ "Apache-2.0" ]

null

server/exception/BaseException.py

ue007/3d-model-convert-to-gltf

fcbea97d5979b67769df12da813587f194fc8f34

[ "Apache-2.0" ]

null

server/exception/BaseException.py

ue007/3d-model-convert-to-gltf

fcbea97d5979b67769df12da813587f194fc8f34

[ "Apache-2.0" ]

null

class BaseException(Exception): def __init__(self, err="Program exec error"): Exception.__init__(self, err)

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a832b79d2c782634287284e7ef8d221f16ee73c8

232

py

Python

nxbt/controller/__init__.py

zfghterb721/nxbt

f27f9aec539199aad92a0edd2bf875649b31ea5b

[ "MIT" ]

257

2020-09-16T05:29:05.000Z

2022-03-31T07:38:01.000Z

nxbt/controller/__init__.py

zfghterb721/nxbt

f27f9aec539199aad92a0edd2bf875649b31ea5b

[ "MIT" ]

48

2020-10-18T00:52:13.000Z

2022-03-27T02:02:21.000Z

nxbt/controller/__init__.py

zfghterb721/nxbt

f27f9aec539199aad92a0edd2bf875649b31ea5b

[ "MIT" ]

37

2020-09-16T05:50:05.000Z

2022-03-31T21:39:55.000Z

from .server import ControllerServer from .controller import ControllerTypes from .controller import Controller from .protocol import ControllerProtocol from .protocol import SwitchReportParser from .protocol import SwitchResponses

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0

6

b53fe72e3cfd4e0b1dee61a0b62d801e5aa718fc

232

py

Python

semeval2020/preprocessor/umap_preprocessor.py

DavidRother/semeval2020-task1

715f82afb8b282669d59ff610b63714d19db4618

[ "MIT" ]

8

2020-12-02T23:18:59.000Z

2021-12-19T11:19:28.000Z

semeval2020/preprocessor/umap_preprocessor.py

DavidRother/semeval2020-task1

715f82afb8b282669d59ff610b63714d19db4618

[ "MIT" ]

1

2020-05-24T15:22:26.000Z

2020-05-25T08:08:07.000Z

semeval2020/preprocessor/umap_preprocessor.py

DavidRother/semeval2020-task1

715f82afb8b282669d59ff610b63714d19db4618

[ "MIT" ]

null

from semeval2020.factory_hub import preprocessor_factory import umap preprocessor_factory.register("UMAP", umap.UMAP) preprocessor_factory.register("UMAP_AE", umap.UMAP) preprocessor_factory.register("UMAP_AE_Language", umap.UMAP)

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b541e34106cccb7a89be7796f23c102b2ea32b6e

30

py

Python

mt3d/mt3d/__init__.py

ExtensionEngineArchive/xblock_3d_viewer

ca688a042819c68f196e84f863bd803093efac8c

[ "MIT" ]

3

2015-03-09T23:38:22.000Z

2019-04-24T00:05:07.000Z

mt3d/mt3d/__init__.py

ExtensionEngineArchive/xblock_3d_viewer

ca688a042819c68f196e84f863bd803093efac8c

[ "MIT" ]

1

2017-05-11T17:25:10.000Z

mt3d/mt3d/__init__.py

ExtensionEngine/xblock_3d_viewer

ca688a042819c68f196e84f863bd803093efac8c

[ "MIT" ]

4

2015-01-17T16:59:06.000Z

2018-07-27T16:58:08.000Z

from .mt3d import ModelViewer

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b55351e26ed2656924c0c1393dcf8924d6b5772d

53

py

Python

tests/test_cache.py

hzdg/django-combocache

2d4ee482c2cf60f18e9a44262a8eac644b89919b

[ "MIT" ]

1

2019-01-21T11:20:18.000Z

tests/test_cache.py

hzdg/django-combocache

2d4ee482c2cf60f18e9a44262a8eac644b89919b

[ "MIT" ]

null

tests/test_cache.py

hzdg/django-combocache

2d4ee482c2cf60f18e9a44262a8eac644b89919b

[ "MIT" ]

null

# TODO: add tests! def test_cache(): assert True

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b579e2eb0663ec25a15f36ea1bc2012c3a8d6224

184

py

Python

basetestcase/__init__.py

Spleeding1/django-basetestcase

341bb0921c9eb3699f44ca59b6d0b1dbfa32bd00

[ "MIT" ]

1

2019-07-30T12:55:47.000Z

basetestcase/__init__.py

Spleeding1/django-basetestcase

341bb0921c9eb3699f44ca59b6d0b1dbfa32bd00

[ "MIT" ]

null

basetestcase/__init__.py

Spleeding1/django-basetestcase

341bb0921c9eb3699f44ca59b6d0b1dbfa32bd00

[ "MIT" ]

null

name = 'basetestcase' __version__ = '1.1.8' from .base_FormTestCase import * from .base_FunctionalTestCase import * from .base_ModelTestCase import * from .base_ViewTestCase import *

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1

0

6

b5962682bf3988b31d330d931b2c6173fd91eebf

311

py

Python

test.py

chq-matteo/Ropper

b8171300096b086495b6d17f9e046b9aee6f829d

[ "BSD-3-Clause" ]

1

2019-09-20T05:16:19.000Z

test.py

chq-matteo/Ropper

b8171300096b086495b6d17f9e046b9aee6f829d

[ "BSD-3-Clause" ]

null

test.py

chq-matteo/Ropper

b8171300096b086495b6d17f9e046b9aee6f829d

[ "BSD-3-Clause" ]

1

2019-09-20T05:16:15.000Z

from testcases.test_general import * from testcases.test_x86_64 import * from testcases.test_x86 import * from testcases.test_arm import * from testcases.test_arm64 import * from testcases.test_mips import * from testcases.test_ppc import * from testcases.test_console import * import unittest unittest.main()

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6

a923a37da16c3fc1767a6bcd63a60b29f678be1a

95

py

Python

tests/lambda/hello_world.py

ebridges/lgw

c5a0b51bb6d3e5e9d6c1fa10ba186ba7f56c8de4

[ "Apache-2.0" ]

1

2020-05-25T19:01:26.000Z

tests/lambda/hello_world.py

ebridges/lgw

c5a0b51bb6d3e5e9d6c1fa10ba186ba7f56c8de4

[ "Apache-2.0" ]

5

2019-12-05T10:55:56.000Z

2020-06-05T17:48:12.000Z

tests/lambda/hello_world.py

ebridges/lgw

c5a0b51bb6d3e5e9d6c1fa10ba186ba7f56c8de4

[ "Apache-2.0" ]

null

from botocore.vendored import requests def handler(event, context): return 'hello world'

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0

6

a9386206b77198c97af693d4bc381af69a2bf3a0

46

py

Python

flow_vae/__init__.py

artsobolev/IWHVI

3a8b5631fe5b08587c594bd0aac43f84dc261579

[ "MIT" ]

7

2019-10-18T21:21:15.000Z

2021-03-08T15:27:09.000Z

flow_vae/__init__.py

artsobolev/IWHVI

3a8b5631fe5b08587c594bd0aac43f84dc261579

[ "MIT" ]

null

flow_vae/__init__.py

artsobolev/IWHVI

3a8b5631fe5b08587c594bd0aac43f84dc261579

[ "MIT" ]

1

2020-03-15T09:24:27.000Z

from .model import FlowVAE from . import utils

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6

a94e21841fedcd06f6eee8ba841517e0a5a060ca

35

py

Python

gmail_sender/__init__.py

Pydevoleper/mail-sender-program

7abfab94c4af6135fa771719f1b6a91287292e3d

[ "MIT" ]

2

2021-06-16T17:00:29.000Z

2021-09-29T09:31:01.000Z

gmail_sender/__init__.py

Pydevoleper/mail-sender-program

7abfab94c4af6135fa771719f1b6a91287292e3d

[ "MIT" ]

null

gmail_sender/__init__.py

Pydevoleper/mail-sender-program

7abfab94c4af6135fa771719f1b6a91287292e3d

[ "MIT" ]

null

from .mail_sender_program import *

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