ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

346900f77d8ee086d65dafb8a1ff78831519c40d

1,188

py

Python

tests/unit/schema/test_links.py

eyadgaran/openapi-core

ff4a6c81eeda0e2274aa9dc03597779c141e5728

[ "BSD-3-Clause" ]

null

tests/unit/schema/test_links.py

eyadgaran/openapi-core

ff4a6c81eeda0e2274aa9dc03597779c141e5728

[ "BSD-3-Clause" ]

null

tests/unit/schema/test_links.py

eyadgaran/openapi-core

ff4a6c81eeda0e2274aa9dc03597779c141e5728

[ "BSD-3-Clause" ]

1

2022-01-19T21:23:56.000Z

import mock import pytest from openapi_core.schema.links.models import Link from openapi_core.schema.servers.models import Server class TestLinks(object): @pytest.fixture def link_factory(self): def link_factory(request_body, server): parameters = { 'par1': mock.sentinel.par1, 'par2': mock.sentinel.par2, } return Link( 'op_id', parameters, request_body, 'Test link', server ) return link_factory servers = [ None, Server("https://bad.remote.domain.net/"), Server("http://localhost") ] request_body_list = [ None, "request", '{"request": "value", "opt": 2}', {"request": "value", "opt": 2} ] @pytest.mark.parametrize("server", servers) @pytest.mark.parametrize("request_body", request_body_list) def test_iteritems(self, link_factory, request_body, server): link = link_factory(request_body, server) for par_name in link.parameters: assert link[par_name] == link.parameters[par_name]

26.4

65

0.564815

1,054

0.887205

0

749

0.630471

0

164

0.138047

3469ba25c1cf2d5686363b9ab8181ddb2f05f798

1,022

py

Python

ex105.py

erikamaylim/Python-CursoemVideo

5a6809818c4c55a02ec52379d95f3d20c833df2e

[ "MIT" ]

null

ex105.py

erikamaylim/Python-CursoemVideo

5a6809818c4c55a02ec52379d95f3d20c833df2e

[ "MIT" ]

null

ex105.py

erikamaylim/Python-CursoemVideo

5a6809818c4c55a02ec52379d95f3d20c833df2e

[ "MIT" ]

null

"""Faça um programa que tenha uma função notas() que pode receber várias notas de alunos e vai retornar um dicionário com as seguintes informações: – Quantidade de notas - A maior nota – A menor nota – A média da turma – A situação (opcional)""" def notas(* num, s=False): """ -> Função para coletar notas dos alunos e retornar informações gerais e a situação da turma. :param num: Notas da turma :param s: Situação (Boa, Razoável ou Ruim) :return: dicionário com informações sobre a turma """ soma = sum(num) qtd = len(num) maior = max(num) menor = min(num) media = soma / qtd if media >= 6: sit = 'Boa' elif media >= 5: sit = 'Razoável' else: sit = 'Ruim' total = {'Quantidade de notas': qtd, 'Maior nota': maior, 'Menor nota': menor, 'Média': media} if s: total['Situação'] = sit return total print(notas(2, 3, 5, 4, 1, 3, s=True)) print(notas(10, 7, 8, 10, s=True)) print(notas(4, 6, 7, 5, 6.5, 7, 5)) help(notas)

26.894737

98

0.61546

0

602

0.570076

346b05d408ff7992b28238f3b72a34e2e2a82c5a

645

py

Python

swigwin-3.0.12/Examples/test-suite/python/swigobject_runme.py

bostich83/atomic_swig

5438f676d690ffddb09d88bbc1b51e3b38fa8c6a

[ "MIT" ]

null

swigwin-3.0.12/Examples/test-suite/python/swigobject_runme.py

bostich83/atomic_swig

5438f676d690ffddb09d88bbc1b51e3b38fa8c6a

[ "MIT" ]

2

2020-03-24T18:19:22.000Z

2020-03-31T11:22:32.000Z

swigwin-3.0.12/Examples/test-suite/python/swigobject_runme.py

bostich83/atomic_swig

5438f676d690ffddb09d88bbc1b51e3b38fa8c6a

[ "MIT" ]

2

2019-11-01T01:28:09.000Z

2020-05-11T05:48:26.000Z

from swigobject import * a = A() a1 = a_ptr(a) a2 = a_ptr(a) if a1.this != a2.this: raise RuntimeError lthis = long(a.this) # match pointer value, but deal with leading zeros on 8/16 bit systems and # different C++ compilers interpretation of %p xstr1 = "%016X" % (lthis,) xstr1 = str.lstrip(xstr1, '0') xstr2 = pointer_str(a) xstr2 = str.replace(xstr2, "0x", "") xstr2 = str.replace(xstr2, "0X", "") xstr2 = str.lstrip(xstr2, '0') xstr2 = str.upper(xstr2) if xstr1 != xstr2: print xstr1, xstr2 raise RuntimeError s = str(a.this) r = repr(a.this) v1 = v_ptr(a) v2 = v_ptr(a) if long(v1) != long(v2): raise RuntimeError

17.916667

74

0.651163

0

145

0.224806

346b88da15ac27f750c964f42e0fb2bd9d1b5698

205

py

Python

com/LimePencil/Q2164/Main.py

LimePencil/baekjoonProblems

61eeeeb875585d165d9e39ecdb3d905b4ba6aa87

[ "MIT" ]

2

2021-07-17T13:05:42.000Z

2021-09-12T09:14:24.000Z

com/LimePencil/Q2164/Main.py

LimePencil/baekjoonProblems

61eeeeb875585d165d9e39ecdb3d905b4ba6aa87

[ "MIT" ]

null

com/LimePencil/Q2164/Main.py

LimePencil/baekjoonProblems

61eeeeb875585d165d9e39ecdb3d905b4ba6aa87

[ "MIT" ]

null

import sys from collections import deque n = int(sys.stdin.readline()) deck = deque(list(range(1, n+1))) for i in range(n-1): deck.popleft() deck.append(deck.popleft()) print(str(deck.pop()))

20.5

33

0.663415

0

346d66976904f2a2e890a78b4c0d64f9e6578329

347

py

Python

contrib/mypy/examples/src/python/mypy_plugin/settings.py

anthonyjpratti/pants

d98e53af6ddd877861231bce8343f8204da0a9d1

[ "Apache-2.0" ]

1

2020-08-26T03:30:31.000Z

contrib/mypy/examples/src/python/mypy_plugin/settings.py

anthonyjpratti/pants

d98e53af6ddd877861231bce8343f8204da0a9d1

[ "Apache-2.0" ]

1

2020-01-21T16:34:02.000Z

contrib/mypy/examples/src/python/mypy_plugin/settings.py

anthonyjpratti/pants

d98e53af6ddd877861231bce8343f8204da0a9d1

[ "Apache-2.0" ]

null

# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from django.urls import URLPattern DEBUG: bool = True DEFAULT_FROM_EMAIL: str = 'webmaster@example.com' SECRET_KEY: str = 'not so secret' MY_SETTING: URLPattern = URLPattern(pattern='foo', callback=lambda: None)

28.916667

73

0.769452

0

172

0.495677

346d8c6aecb6d5c799c07698354cb687ab52ff19

216

py

Python

function/python/brightics/function/statistics/__init__.py

janrenz/studio

a0714ed8dcd9dcd8d024162104d3b4de89ac2b49

[ "Apache-2.0" ]

null

function/python/brightics/function/statistics/__init__.py

janrenz/studio

a0714ed8dcd9dcd8d024162104d3b4de89ac2b49

[ "Apache-2.0" ]

null

function/python/brightics/function/statistics/__init__.py

janrenz/studio

a0714ed8dcd9dcd8d024162104d3b4de89ac2b49

[ "Apache-2.0" ]

null

from .profile_table import profile_table from .correlation import correlation from .pairplot import pairplot from .anova import bartletts_test from .anova import oneway_anova from .anova import tukeys_range_test

36

41

0.842593

0

346dcec89ab4ed1144ede11615065491709f1045

1,637

py

Python

modules/hello-world/dags/hello_world.py

nalin-adhikari/apache-airflow

52b8dd241b9b631d6bad5137bd55c774f1d7beea

[ "MIT" ]

null

modules/hello-world/dags/hello_world.py

nalin-adhikari/apache-airflow

52b8dd241b9b631d6bad5137bd55c774f1d7beea

[ "MIT" ]

null

modules/hello-world/dags/hello_world.py

nalin-adhikari/apache-airflow

52b8dd241b9b631d6bad5137bd55c774f1d7beea

[ "MIT" ]

null

import json from datetime import timedelta, datetime from requests import get from airflow import DAG from airflow.models import Variable from airflow.operators.python_operator import PythonOperator # Config variables # dag_config = Variable.get("hello_world_variables", deserialize_json=True) default_args = { 'owner': 'nalin', 'depends_on_past': True, 'start_date': datetime(2020, 12, 4), # 'end_date': datetime(2018, 12, 5), 'email': ['nalinadhikariofficial@gmail.com'], 'email_on_failure': True, 'email_on_retry': True, 'retries': 2, 'retry_delay': timedelta(minutes=2), } # Set Schedule: Run pipeline once a day. # Use cron to define exact time. Eg. 8:15am would be "15 08 * * *" schedule_interval = "21 1 * * *" # Define DAG: Set ID and assign default args and schedule interval dag = DAG( 'fake_rest_api', default_args=default_args, schedule_interval=schedule_interval ) def hello_world(): print("Hello World") def fetch_data(): url = "https://jsonplaceholder.typicode.com/todos" response = get(url) if response.status_code == 200: print(response.text) else: print(response.status_code) def bye_world(): print("Bye World") t1 = PythonOperator( task_id='print_hello_world', provide_context=False, python_callable=hello_world, dag=dag, ) t2 = PythonOperator( task_id='fetch_data', provide_context=False, python_callable=fetch_data, dag=dag, ) t3 = PythonOperator( task_id='print_bye_world', provide_context=False, python_callable=bye_world, dag=dag, ) t1 >> [t2] t2 >> [t3]

23.056338

75

0.689676

0

584

0.35675

346dfc38e5228510fbb6d8575a372cd8d9bac798

9,074

py

Python

src/thex/apps/utils/signal_utils.py

harris-2374/THEx

04c4f56eb2cf86b8f55ddd6edd3f48029296bf5a

[ "MIT" ]

null

src/thex/apps/utils/signal_utils.py

harris-2374/THEx

04c4f56eb2cf86b8f55ddd6edd3f48029296bf5a

[ "MIT" ]

null

src/thex/apps/utils/signal_utils.py

harris-2374/THEx

04c4f56eb2cf86b8f55ddd6edd3f48029296bf5a

[ "MIT" ]

null

from pathlib import Path import dash_core_components as dcc import dash_bootstrap_components as dbc import dash_html_components as html import plotly import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots # -------------------- Graphing Functions -------------------- def single_chromosome_graph_line( df, chromosome, chosen_template, marker_width, colors, font_size, xaxis_gridlines, yaxis_gridlines, font_family, samples, ): """ Filter out current chromosome and set x- and y-max""" curr_chrom_data = df[df["Chromosome"] == chromosome] y_max = float(curr_chrom_data["Value"].max()) fig = px.line( curr_chrom_data, x='Window', y='Value', category_orders={"Sample": samples}, color='Sample', color_discrete_sequence=colors, height=500, ) fig.update_layout( font=dict( size=font_size, family=font_family, ), legend=dict( itemsizing='trace', orientation="h", xanchor="left", x=0, y=1.02, yanchor="bottom", ), showlegend=True, template=chosen_template, title_x=0.5, ) fig.update_xaxes( title="Position", rangemode='tozero', showgrid=xaxis_gridlines, ) fig.update_yaxes( title="Value", range=[0, y_max], fixedrange=True, showgrid=yaxis_gridlines, ) fig.update_traces( line=dict(width=float(marker_width)), ) return fig def single_chromosome_graph_scatter( df, chromosome, chosen_template, marker_width, colors, font_size, xaxis_gridlines, yaxis_gridlines, font_family, samples, ): """ Filter out current chromosome and set x- and y-max""" curr_chrom_data = df[df["Chromosome"] == chromosome] y_max = float(curr_chrom_data["Value"].max()) fig = px.scatter( curr_chrom_data, x='Window', y='Value', category_orders={"Sample": samples}, color='Sample', color_discrete_sequence=colors, height=500, ) fig.update_layout( font=dict( size=font_size, family=font_family, ), legend=dict( itemsizing='trace', orientation="h", xanchor="left", x=0, y=1.02, yanchor="bottom", ), showlegend=True, template=chosen_template, title_x=0.5, ) fig.update_xaxes( title="Position", rangemode='tozero', showgrid=xaxis_gridlines, ) fig.update_yaxes( title="Value", range=[0, y_max], fixedrange=True, showgrid=yaxis_gridlines, ) fig.update_traces( marker=dict(size=float(marker_width)), ) return fig def whole_genome_line( df, chromosomes, samples, colors, marker_width, template, font_size, y_max, x_max, xaxis_gridlines, yaxis_gridlines, font_family, ): fig = make_subplots( rows=len(chromosomes), cols=1, x_title="Position", y_title="Edit Me!", row_titles=chromosomes, row_heights=[2]*len(chromosomes), ) for n, sample in enumerate(samples): legend_flag = True for row, current_chromosome in enumerate(chromosomes, start=1): filt = (df['Chromosome'] == current_chromosome) & (df["Sample"] == sample) sample_chromosome_data = df[filt] # Make figure fig.add_trace( go.Scatter( x=sample_chromosome_data['Window'], y=sample_chromosome_data['Value'], mode='lines', legendgroup=str(sample), name=sample, line=dict( color=colors[n], width=float(marker_width) ), showlegend=legend_flag, ), row=row, col=1 ) legend_flag = False continue # --- Update Figure --- fig.update_layout( font=dict(size=font_size, family=font_family), height=125*len(chromosomes), hovermode="x unified", legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0, itemsizing='trace', title="", ), margin=dict( l=60, r=50, b=60, t=10, ), template=template, title_x=0.5, font_family="Arial", ) fig.update_xaxes( fixedrange=True, range=[0, x_max], showgrid=xaxis_gridlines, ) fig.update_yaxes( range=[0.0, y_max], fixedrange=True, showgrid=yaxis_gridlines, ) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) # Rotate chromosome names to 0-degrees for annotation in fig['layout']['annotations']: if annotation['text'] == "Edit Me!": continue annotation['textangle']=0 annotation['align']="center" return fig def whole_genome_scatter( df, chromosomes, samples, colors, marker_width, template, font_size, y_max, x_max, xaxis_gridlines, yaxis_gridlines, font_family, ): # fig = make_subplots( # rows=len(chromosomes), # cols=1, # x_title="Position", # y_title="Edit Me!", # row_titles=chromosomes, # row_heights=[2]*len(chromosomes), # ) # for n, sample in enumerate(samples): # legend_flag = True # for row, current_chromosome in enumerate(chromosomes, start=1): # filt = (df['Chromosome'] == current_chromosome) & (df["Sample"] == sample) # sample_chromosome_data = df[filt] # # Make figure # fig.add_trace( # go.Scatter( # x=sample_chromosome_data['Window'], # y=sample_chromosome_data['Value'], # mode='markers', # legendgroup=str(sample), # name=sample, # line=dict( # color=colors[n], # width=float(marker_width) # ), # showlegend=legend_flag, # ), # row=row, # col=1 # ) # legend_flag = False # continue fig = px.scatter( df, x='Window', y='Value', category_orders={"Sample": samples}, color='Sample', color_discrete_sequence=colors, # height=500, facet_row="Chromosome", ) # --- Update Figure --- fig.update_layout( font=dict(size=font_size, family=font_family), height=125*len(chromosomes), hovermode="x unified", legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0, itemsizing='trace', title="", ), margin=dict( l=60, r=50, b=60, t=10, ), template=template, title_x=0.5, font_family=font_family, ) fig.update_xaxes( fixedrange=True, range=[0, x_max], showgrid=xaxis_gridlines, ) fig.update_yaxes( range=[0.0, y_max], fixedrange=True, showgrid=yaxis_gridlines, title='', ) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) fig.update_traces(marker=dict(size=float(marker_width))) # Rotate chromosome names to 0-degrees for annotation in fig['layout']['annotations']: if annotation['text'] == "Edit Me!": continue annotation['textangle']=0 annotation['align']="center" return fig # -------------------- File Validation -------------------- def validate_signal_tracer_headers(df): """Validate that headers are correct""" expected_headers = ["Chromosome", "Window", "Sample", "Value"] try: assert list(df.columns) == expected_headers return True except AssertionError: return False def validate_signal_tracer_values(xlsx_df): """Return False if value column data are not int or float""" try: assert xlsx_df['Value'].dtype != "object" return True except AssertionError: return False def validate_file_type(filename): """Return False if file type is not valid """ valid_filetypes = ['.tsv', '.csv', '.xlsx', '.txt'] filetype = Path(filename).suffix if filetype not in valid_filetypes: return False else: return True

25.925714

88

0.528543

0

2,133

0.235067

346e40e8a2a72d7ea7828ba82b650ee8a97e793a

7,249

py

Python

tests/unit/test_ncit.py

cancervariants/disease-normalization

b4d64c9bf30d600fec4d23b890da669835d93c73

[ "MIT" ]

null

tests/unit/test_ncit.py

cancervariants/disease-normalization

b4d64c9bf30d600fec4d23b890da669835d93c73

[ "MIT" ]

39

2021-02-11T11:33:32.000Z

2022-03-23T13:53:31.000Z

tests/unit/test_ncit.py

cancervariants/disease-normalization

b4d64c9bf30d600fec4d23b890da669835d93c73

[ "MIT" ]

null

"""Test NCIt source.""" import pytest from disease.schemas import MatchType, SourceName from disease.query import QueryHandler @pytest.fixture(scope='module') def ncit(): """Build NCIt ETL test fixture.""" class QueryGetter: def __init__(self): self.query_handler = QueryHandler() def search(self, query_str): response = self.query_handler.search_sources(query_str, keyed=True, incl='ncit') return response['source_matches'][SourceName.NCIT] return QueryGetter() @pytest.fixture(scope='module') def neuroblastoma(): """Build neuroblastoma test fixture.""" return { "label_and_type": "ncit:c3270##identity", "concept_id": "ncit:C3270", "label": "Neuroblastoma", "aliases": [ "Neural Crest Tumor, Malignant", "Neuroblastoma (NBL)", "Neuroblastoma (Schwannian Stroma-poor)", "Neuroblastoma (Schwannian Stroma-Poor)", "NEUROBLASTOMA, MALIGNANT", "Neuroblastoma, NOS", "neuroblastoma" ], "xrefs": [], "associated_with": ["umls:C0027819", "icdo:9500/3"], "src_name": "NCIt" } @pytest.fixture(scope='module') def nsclc(): """Build fixture for non-small cell lung carcinoma""" return { "concept_id": "ncit:C2926", "label": "Lung Non-Small Cell Carcinoma", "aliases": [ "Non Small Cell Lung Cancer NOS", "Non-Small Cell Lung Cancer", "Non-Small Cell Cancer of the Lung", "NSCLC", "non-small cell lung cancer", "Non-Small Cell Carcinoma of the Lung", "Non-Small Cell Cancer of Lung", "Non-small cell lung cancer, NOS", "Non-Small Cell Carcinoma of Lung", "NSCLC - Non-Small Cell Lung Cancer", "Non-Small Cell Lung Carcinoma" ], "xrefs": [], "associated_with": ["umls:C0007131"] } def test_concept_id_match(ncit, neuroblastoma, nsclc, compare_records): """Test that concept ID search resolves to correct record""" response = ncit.search('ncit:C3270') assert response['match_type'] == MatchType.CONCEPT_ID assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('ncit:c2926') assert response['match_type'] == MatchType.CONCEPT_ID assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) response = ncit.search('NCIT:C2926') assert response['match_type'] == MatchType.CONCEPT_ID assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) response = ncit.search('C3270') assert response['match_type'] == MatchType.CONCEPT_ID assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('3270') assert response['match_type'] == MatchType.NO_MATCH def test_label_match(ncit, neuroblastoma, nsclc, compare_records): """Test that label search resolves to correct record.""" response = ncit.search('Neuroblastoma') assert response['match_type'] == MatchType.LABEL assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('NEUROBLASTOMA') assert response['match_type'] == MatchType.LABEL assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('lung non-small cell carcinoma') assert response['match_type'] == MatchType.LABEL assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) response = ncit.search('lung non small cell carcinoma') assert response['match_type'] == MatchType.NO_MATCH def test_alias_match(ncit, neuroblastoma, nsclc, compare_records): """Test that alias search resolves to correct record.""" response = ncit.search('neuroblastoma, nos') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('neuroblastoma (Schwannian Stroma-Poor)') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('Neuroblastoma, Malignant') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('Neural Crest Tumor, Malignant') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('nsclc') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) response = ncit.search('NSCLC - Non-Small Cell Lung Cancer') assert response['match_type'] == MatchType.ALIAS assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) response = ncit.search('neuroblastoma nbl') assert response['match_type'] == MatchType.NO_MATCH def test_associated_with_match(ncit, neuroblastoma, nsclc, compare_records): """Test that associated_with search resolves to correct record.""" response = ncit.search('icdo:9500/3') assert response['match_type'] == MatchType.ASSOCIATED_WITH assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, neuroblastoma) response = ncit.search('umls:C0007131') assert response['match_type'] == MatchType.ASSOCIATED_WITH assert len(response['records']) == 1 actual_disease = response['records'][0] compare_records(actual_disease, nsclc) def test_meta(ncit): """Test that meta field is correct.""" response = ncit.search('neuroblastoma') assert response['source_meta_']['data_license'] == 'CC BY 4.0' assert response['source_meta_']['data_license_url'] == \ 'https://creativecommons.org/licenses/by/4.0/legalcode' assert response['source_meta_']['version'] == '21.01d' assert response['source_meta_']['data_url'] == \ "https://evs.nci.nih.gov/ftp1/NCI_Thesaurus/archive/21.01d_Release/" # noqa: E501 assert response['source_meta_']['rdp_url'] == 'http://reusabledata.org/ncit.html' # noqa: E501 assert response['source_meta_']['data_license_attributes'] == { "non_commercial": False, "share_alike": False, "attribution": True }

37.365979

99

0.66423

346

0.047731

0

1,915

0.264174

0

2,529

0.348876

346e60329f7119de9c6fd91260d085bb215a900f

7,084

py

Python

pytglib/api/types/message.py

iTeam-co/pytglib

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

6

2019-10-30T08:57:27.000Z

2021-02-08T14:17:43.000Z

pytglib/api/types/message.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

1

2021-08-19T05:44:10.000Z

2021-08-19T07:14:56.000Z

pytglib/api/types/message.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

5

2019-12-04T05:30:39.000Z

2021-05-21T18:23:32.000Z

from ..utils import Object class Message(Object): """ Describes a message Attributes: ID (:obj:`str`): ``Message`` Args: id (:obj:`int`): Message identifier, unique for the chat to which the message belongs sender_user_id (:obj:`int`): Identifier of the user who sent the message; 0 if unknownCurrently, it is unknown for channel posts and for channel posts automatically forwarded to discussion group chat_id (:obj:`int`): Chat identifier sending_state (:class:`telegram.api.types.MessageSendingState`): Information about the sending state of the message; may be null scheduling_state (:class:`telegram.api.types.MessageSchedulingState`): Information about the scheduling state of the message; may be null is_outgoing (:obj:`bool`): True, if the message is outgoing can_be_edited (:obj:`bool`): True, if the message can be editedFor live location and poll messages this fields shows whether editMessageLiveLocation or stopPoll can be used with this message by the client can_be_forwarded (:obj:`bool`): True, if the message can be forwarded can_be_deleted_only_for_self (:obj:`bool`): True, if the message can be deleted only for the current user while other users will continue to see it can_be_deleted_for_all_users (:obj:`bool`): True, if the message can be deleted for all users is_channel_post (:obj:`bool`): True, if the message is a channel postAll messages to channels are channel posts, all other messages are not channel posts contains_unread_mention (:obj:`bool`): True, if the message contains an unread mention for the current user date (:obj:`int`): Point in time (Unix timestamp) when the message was sent edit_date (:obj:`int`): Point in time (Unix timestamp) when the message was last edited forward_info (:class:`telegram.api.types.messageForwardInfo`): Information about the initial message sender; may be null reply_to_message_id (:obj:`int`): If non-zero, the identifier of the message this message is replying to; can be the identifier of a deleted message ttl (:obj:`int`): For self-destructing messages, the message's TTL (Time To Live), in seconds; 0 if noneTDLib will send updateDeleteMessages or updateMessageContent once the TTL expires ttl_expires_in (:obj:`float`): Time left before the message expires, in seconds via_bot_user_id (:obj:`int`): If non-zero, the user identifier of the bot through which this message was sent author_signature (:obj:`str`): For channel posts, optional author signature views (:obj:`int`): Number of times this message was viewed media_album_id (:obj:`int`): Unique identifier of an album this message belongs toOnly photos and videos can be grouped together in albums restriction_reason (:obj:`str`): If non-empty, contains a human-readable description of the reason why access to this message must be restricted content (:class:`telegram.api.types.MessageContent`): Content of the message reply_markup (:class:`telegram.api.types.ReplyMarkup`): Reply markup for the message; may be null Returns: Message Raises: :class:`telegram.Error` """ ID = "message" def __init__(self, id, sender_user_id, chat_id, sending_state, scheduling_state, is_outgoing, can_be_edited, can_be_forwarded, can_be_deleted_only_for_self, can_be_deleted_for_all_users, is_channel_post, contains_unread_mention, date, edit_date, forward_info, reply_to_message_id, ttl, ttl_expires_in, via_bot_user_id, author_signature, views, media_album_id, restriction_reason, content, reply_markup, **kwargs): self.id = id # int self.sender_user_id = sender_user_id # int self.chat_id = chat_id # int self.sending_state = sending_state # MessageSendingState self.scheduling_state = scheduling_state # MessageSchedulingState self.is_outgoing = is_outgoing # bool self.can_be_edited = can_be_edited # bool self.can_be_forwarded = can_be_forwarded # bool self.can_be_deleted_only_for_self = can_be_deleted_only_for_self # bool self.can_be_deleted_for_all_users = can_be_deleted_for_all_users # bool self.is_channel_post = is_channel_post # bool self.contains_unread_mention = contains_unread_mention # bool self.date = date # int self.edit_date = edit_date # int self.forward_info = forward_info # MessageForwardInfo self.reply_to_message_id = reply_to_message_id # int self.ttl = ttl # int self.ttl_expires_in = ttl_expires_in # float self.via_bot_user_id = via_bot_user_id # int self.author_signature = author_signature # str self.views = views # int self.media_album_id = media_album_id # int self.restriction_reason = restriction_reason # str self.content = content # MessageContent self.reply_markup = reply_markup # ReplyMarkup @staticmethod def read(q: dict, *args) -> "Message": id = q.get('id') sender_user_id = q.get('sender_user_id') chat_id = q.get('chat_id') sending_state = Object.read(q.get('sending_state')) scheduling_state = Object.read(q.get('scheduling_state')) is_outgoing = q.get('is_outgoing') can_be_edited = q.get('can_be_edited') can_be_forwarded = q.get('can_be_forwarded') can_be_deleted_only_for_self = q.get('can_be_deleted_only_for_self') can_be_deleted_for_all_users = q.get('can_be_deleted_for_all_users') is_channel_post = q.get('is_channel_post') contains_unread_mention = q.get('contains_unread_mention') date = q.get('date') edit_date = q.get('edit_date') forward_info = Object.read(q.get('forward_info')) reply_to_message_id = q.get('reply_to_message_id') ttl = q.get('ttl') ttl_expires_in = q.get('ttl_expires_in') via_bot_user_id = q.get('via_bot_user_id') author_signature = q.get('author_signature') views = q.get('views') media_album_id = q.get('media_album_id') restriction_reason = q.get('restriction_reason') content = Object.read(q.get('content')) reply_markup = Object.read(q.get('reply_markup')) return Message(id, sender_user_id, chat_id, sending_state, scheduling_state, is_outgoing, can_be_edited, can_be_forwarded, can_be_deleted_only_for_self, can_be_deleted_for_all_users, is_channel_post, contains_unread_mention, date, edit_date, forward_info, reply_to_message_id, ttl, ttl_expires_in, via_bot_user_id, author_signature, views, media_album_id, restriction_reason, content, reply_markup)

54.914729

417

0.683512

7,052

0.995483

0

1,721

0.242942

0

4,110

0.580181

346fa28c58b006ee5c9a022eea52649d810fd3de

902

py

Python

trainer/migrations/0023_userpretest.py

tthelen/interpunct

83e2cbd67dcc94c131a3a2b155eefd710636a912

[ "MIT" ]

2

2016-10-21T21:52:08.000Z

2021-10-19T02:19:43.000Z

trainer/migrations/0023_userpretest.py

tthelen/interpunct

83e2cbd67dcc94c131a3a2b155eefd710636a912

[ "MIT" ]

null

trainer/migrations/0023_userpretest.py

tthelen/interpunct

83e2cbd67dcc94c131a3a2b155eefd710636a912

[ "MIT" ]

4

2016-10-24T19:17:48.000Z

2018-05-11T11:53:12.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-05-18 08:55 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('trainer', '0022_auto_20180517_1429'), ] operations = [ migrations.CreateModel( name='UserPretest', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('result', models.BooleanField(default=False)), ('rule', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='link_to_pretests', to='trainer.User')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='link_to_pretest', to='trainer.User')), ], ), ]

34.692308

141

0.640798

713

0.790466

0

207

0.22949

346fda67f8f453b3f930138da3f2c3b926fc2d88

2,352

py

Python

CsvToDynamo.py

cushind/csv-to-dynamodb

5a9cdedde4dfee121efe8efb9bcd61950f242c94

[ "Apache-2.0" ]

2

2018-04-13T21:03:38.000Z

2018-06-25T21:56:02.000Z

CsvToDynamo.py

cushind/csv-to-dynamodb

5a9cdedde4dfee121efe8efb9bcd61950f242c94

[ "Apache-2.0" ]

null

CsvToDynamo.py

cushind/csv-to-dynamodb

5a9cdedde4dfee121efe8efb9bcd61950f242c94

[ "Apache-2.0" ]

1

2018-07-18T10:21:31.000Z

import boto3 import csv import json import argparse ''' You need to have aws configured with access tokens prior to running this script (use aws configure) ''' def batch_create(table, csv_file_name, column_names): ''' Can Handle many puts at one time. Boto3 gives an example of 50, even though max batch size is 25, because batch writer is buffering and sending items behind the scenes. ''' print('Beginning csv to dynamo import...') with table.batch_writer() as batch: with open(csv_file_name, newline='') as csv_file: reader = csv.reader(csv_file) # skip first row which we know is a header row next(reader) count = 0 for row in reader: item = {} for column in range (0, len(column_names)): item[column_names[column]] = row[column] batch.put_item(Item=item) count += 1 if count % 100 == 0: print('Inserted ' + str(count) + ' items...') csv_file.close() print('Finished importing data into dynamo...') def validate(table, csv_file_name, partition_key_col_name, sort_key_col_name): print('Beginning data validation...') with open(csv_file_name, newline='') as csv_file: reader = csv.reader(csv_file) # skip first row which we know is a header row next(reader) for row in reader: key = {partition_key_col_name: row[0], sort_key_col_name: row[1]} try: response = table.get_item(Key=key) assert('Item' in response) except AssertionError: print('Failed to validate data. Key ' + json.dumps(key) + ' does not exist...') csv_file.close() print('Finished data validation...') def main(): csv_file_name = '' table_name = '' region = 'us-west-2' partition_key_col_name = '' sort_key_col_name='' column_names = [partition_key_col_name, sort_key_col_name, 'Column3'] dynamodb_resource = boto3.resource('dynamodb', region_name=region) table = dynamodb_resource.Table(table_name) batch_create(table, csv_file_name, column_names) validate(table, csv_file_name, partition_key_col_name, sort_key_col_name) if __name__ == "__main__": main()

30.545455

99

0.619898

0

653

0.277636

3470757a316e0d017e10fe10eeda40a2177e7aab

5,636

py

Python

qcdb/molecule/parker.py

loriab/qccddb

d9e156ef8b313ac0633211fc6b841f84a3ddde24

[ "BSD-3-Clause" ]

8

2019-03-28T11:54:59.000Z

2022-03-19T03:31:37.000Z

qcdb/molecule/parker.py

loriab/qccddb

d9e156ef8b313ac0633211fc6b841f84a3ddde24

[ "BSD-3-Clause" ]

39

2018-10-31T23:02:18.000Z

2021-12-12T22:11:37.000Z

qcdb/molecule/parker.py

loriab/qccddb

d9e156ef8b313ac0633211fc6b841f84a3ddde24

[ "BSD-3-Clause" ]

9

2018-03-12T20:51:50.000Z

2022-02-28T15:18:34.000Z

import math import numpy as np import qcelemental as qcel BOND_FACTOR = 1.2 # fudge factor for bond length threshold _expected_bonds = { 'H': 1, 'C': 4, 'N': 3, 'O': 2, 'F': 1, 'P': 3, 'S': 2, } def xyz2mol(self): """Returns a string of Molecule formatted for mol2. Written by Trent M. Parker 9 Jun 2014 """ bonds = _bond_profile(self) N = 0 for i in range(self.natom()): if self.Z(i): N += 1 # header text = '%s\n' % (self.name()) text += ' Generated by xyz2mol\n\n' text += '%3i%3i 0 0 0 0 0 0 0 0999 V2000\n' % (N, len(bonds)) # coordinates for i in range(self.natom()): x = self.x(i) * qcel.constants.bohr2angstroms y = self.y(i) * qcel.constants.bohr2angstroms z = self.z(i) * qcel.constants.bohr2angstroms if self.Z(i): text += ' %9.4f %9.4f %9.4f %-2s 0 0 0 0 0\n' % (x, y, z, self.symbol(i)) # bonds for p in range(len(bonds)): text += '%3i%3i%3i' % (bonds[p][0] + 1, bonds[p][1] + 1, bonds[p][2]) text += ' 0 0 0\n' text += 'M END\n' return text def missing_bonds(bonds, bond_tree, at_types): """Determine number of bonds missing for each atom""" n_missing = [] for i in range(len(at_types)): n_bonds_i = 0 for p in range(len(bonds)): at1 = bonds[p][0] at2 = bonds[p][1] if (at1 == i or at2 == i): bond_order = bonds[p][2] n_bonds_i += bond_order n_expect_i = _expected_bonds[at_types[i]] n_missing.append(n_expect_i - n_bonds_i) return n_missing def missing_neighbors(bond_tree, n_missing): """Determine number of neighboring atoms missing bonds for each atom""" missing_neighbors = [] for i in range(len(bond_tree)): N_neighbors = len(bond_tree[i]) missing = 0 for a in range(N_neighbors): j = bond_tree[i][a] if n_missing[j] > 0: missing += 1 missing_neighbors.append(missing) return missing_neighbors def _bond_profile(self): """Obtain bonding topology of molecule""" # determine bond topology from covalent radii bonds = [] for i in range(self.natom()): for j in range(i + 1, self.natom()): try: # qcdb.Molecule dist = np.linalg.norm(self.xyz(j, np_out=True) - self.xyz(i, np_out=True)) except TypeError: # psi4.core.Molecule dist = self.xyz(j).distance(self.xyz(i)) # TOOD check bohr/ang progress bonded_dist = BOND_FACTOR * (qcel.covalentradii.get(self.symbol(i)) + qcel.covalentradii.get(self.symbol(j))) if bonded_dist > dist: bonds.append([i, j, 1]) # determine bond order from number of bonds N_atoms = self.natom() N_bonds = len(bonds) at_types = [self.symbol(i) for i in range(self.natom())] bond_tree = [[] for i in range(N_atoms)] for i in range(N_bonds): at1 = bonds[i][0] at2 = bonds[i][1] bond_tree[at1].append(at2) bond_tree[at2].append(at1) # determine bond order for all bonds from bond tree and element types n_missing = missing_bonds(bonds, bond_tree, at_types) n_neighbors_missing = missing_neighbors(bond_tree, n_missing) # add double / triple bonds if only one neighbor missing bonds N_left = math.floor(sum(n_missing) / 2) N_left_previous = N_left + 1 N_iter = 0 while N_left > 0: N_iter += 1 if N_left == N_left_previous: neighbor_min += 1 else: neighbor_min = 1 N_left_previous = N_left # add a multiple bond to a deficient atom with the fewest number of deficient neighbors BREAK_LOOP = False for i in range(N_atoms): if n_missing[i] > 0 and n_neighbors_missing[i] == neighbor_min: N_neighbors = len(bond_tree[i]) for a in range(N_neighbors): j = bond_tree[i][a] if n_missing[j] > 0: for p in range(N_bonds): at1 = bonds[p][0] at2 = bonds[p][1] if (at1 == i and at2 == j) or (at1 == j and at2 == i): bonds[p][2] += 1 n_missing[i] += -1 n_missing[j] += -1 n_neighbors_missing[i] += -1 n_neighbors_missing[j] += -1 N_left = math.floor(sum(n_missing) / 2) BREAK_LOOP = True if BREAK_LOOP: break if BREAK_LOOP: break # recalculate incomplete bond topology n_missing = missing_bonds(bonds, bond_tree, at_types) n_neighbors_missing = missing_neighbors(bond_tree, n_missing) # break cycle if takes more than given number of iterations max_iter = 100 if N_iter > max_iter: print("""Error: multiple bond determination not complete""") print(""" %i bonds unaccounted for""" % (N_left)) break # bond order is number of bonds between each bonded atom pair bond_order = [] for p in range(N_bonds): bond_order.append(bonds[p][2]) for p in range(len(bond_order)): bonds[p][2] = bond_order[p] return bonds

32.022727

95

0.532825

0

1,124

0.199432

34721682431358cf0bdf09cf841278f4336d58a5

4,050

py

Python

Projects/CS_VQE/UnitaryPartitioning_myriad_on_FULL_H_LCU.py

AlexisRalli/VQE-code

4112d2bba4c327360e95dfd7cb6120b2ce67bf29

[ "MIT" ]

1

2021-04-01T14:01:46.000Z

Projects/CS_VQE/UnitaryPartitioning_myriad_on_FULL_H_LCU.py

AlexisRalli/VQE-code

4112d2bba4c327360e95dfd7cb6120b2ce67bf29

[ "MIT" ]

5

2019-11-13T16:23:54.000Z

2021-04-07T11:03:06.000Z

Projects/CS_VQE/UnitaryPartitioning_myriad_on_FULL_H_LCU.py

AlexisRalli/VQE-code

4112d2bba4c327360e95dfd7cb6120b2ce67bf29

[ "MIT" ]

null

import numpy as np import scipy as sp import ast import os from quchem.Unitary_Partitioning.Graph import Clique_cover_Hamiltonian import quchem.Misc_functions.conversion_scripts as conv_scr from copy import deepcopy from quchem.Unitary_Partitioning.Unitary_partitioning_LCU_method import LCU_linalg_Energy from openfermion import qubit_operator_sparse import pickle import datetime ####### import sys # working_dir = os.getcwd() working_dir = os.path.dirname(os.path.abspath(__file__)) # gets directory where running python file is! Analysis_dir = os.path.join(working_dir, 'Analysis') full_H_results_dir = os.path.join(Analysis_dir, 'SeqRot_LCU_script_A_results') print('start time: {}'.format(datetime.datetime.now().strftime('%Y%b%d-%H%M%S%f'))) print('working directory:', working_dir) ###### IMPORT INITIAL RESULTS ## import LCU results myriad_LCU_results = {} for filename in os.listdir(full_H_results_dir): if (filename.endswith('.pickle') and filename.startswith('LCU_CS_VQE_exp')): file_path = os.path.join(full_H_results_dir, filename) mol_name = filename[40:-8] with open(file_path,'rb') as infile: data = pickle.load(infile) myriad_LCU_results[mol_name] = data ### find anti-commuting sets unitary_paritioning_LCU={} # optional params! commutativity_flag = 'AC' ## <- defines relationship between sets!!! Graph_colouring_strategy='largest_first' check_reduction_LCU = False ######## take commandline arguement to run in parallel mol_num = int(sys.argv[1]) sorted_mol_names = sorted(list(myriad_LCU_results.keys())) mol_key = sorted_mol_names[mol_num-1] # UCL supercomputer indexes from 1, hence minus one here! ######## # for mol_key in tqdm(list(myriad_LCU_results.keys())): # removed loop and used myriad array input! anti_commuting_sets_different_H_LCU_sizes={} for ind_key in myriad_LCU_results[mol_key]: if isinstance(ind_key, str): continue if ind_key==0: # only non-contextual problem anti_commuting_sets_different_H_LCU_sizes[ind_key]= {'AC_sets': {}, 'E':myriad_LCU_results[mol_key][ind_key]['E']} else: ### LCU H_LCU_dict = myriad_LCU_results[mol_key][ind_key]['H'] H_LCU_pruned = {P_key: coeff.real for P_key, coeff in H_LCU_dict.items() if not np.isclose(coeff.real,0)} H_LCU= conv_scr.Get_Openfermion_Hamiltonian(H_LCU_pruned) n_qubits = len(list(H_LCU_dict.keys())[0]) anti_commuting_sets_LCU = Clique_cover_Hamiltonian(list(H_LCU), n_qubits, commutativity_flag, Graph_colouring_strategy) all_zero_Pn_index_dict = {set_key: 0 for set_key in anti_commuting_sets_LCU} E_LCU = LCU_linalg_Energy(anti_commuting_sets_LCU, all_zero_Pn_index_dict, n_qubits, atol=1e-8, rtol=1e-05, check_reduction=check_reduction_LCU) anti_commuting_sets_different_H_LCU_sizes[ind_key]= {'AC_sets': anti_commuting_sets_LCU, 'E':E_LCU} unitary_paritioning_LCU[mol_key]= deepcopy(anti_commuting_sets_different_H_LCU_sizes) del anti_commuting_sets_different_H_LCU_sizes ####### SAVE OUTPUT details unique_file_time = datetime.datetime.now().strftime('%Y%b%d-%H%M%S%f') # output_dir = os.path.join(working_dir, 'Pickle_out') output_dir = os.getcwd() ######## ####### SAVE OUTPUT file_name2 = 'Unitary_Partitinging_LCU_CS_VQE_LCU_exp__{}__{}_.pickle'.format(unique_file_time, mol_key) file_out2=os.path.join(output_dir, file_name2) with open(file_out2, 'wb') as outfile: pickle.dump(unitary_paritioning_LCU, outfile) print('pickle files dumped unqiue time id: {}'.format(unique_file_time)) print('end time: {}'.format(datetime.datetime.now().strftime('%Y%b%d-%H%M%S%f')))

33.75

113

0.678765

0

899

0.221975

3472c5f0edade05adeaae51cedd69cdc00629866

2,925

py

Python

notebooks/00-00-inspect-orig-files.py

will-henney/teresa-pn-ou5

5d127880c1da481f0a1fb3e23abce1d3bf9ccc1a

[ "MIT" ]

null

notebooks/00-00-inspect-orig-files.py

will-henney/teresa-pn-ou5

5d127880c1da481f0a1fb3e23abce1d3bf9ccc1a

[ "MIT" ]

null

notebooks/00-00-inspect-orig-files.py

will-henney/teresa-pn-ou5

5d127880c1da481f0a1fb3e23abce1d3bf9ccc1a

[ "MIT" ]

null

# --- # jupyter: # jupytext: # formats: ipynb,py:light # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.13.0 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # # PN Ou 5: Inspect original files from pathlib import Path from astropy.io import fits from astropy.table import Table dpath = Path("../data/originals/") # Look and see what sort of files we have: data = [] kwds = ["MEZMODE", "DATE-OBS", "FILTER", "RA", "DEC", "PA", "CCDTYPE", "CCDSUM"] for _file in sorted(dpath.glob("*.fits")): hdu = fits.open(_file)[0] thisdata = {"File": _file.stem} for k in kwds: thisdata[k] = hdu.header.get(k) data.append(thisdata) tab = Table(rows=data) tab.show_in_notebook() # So we have 2017 data with 70 micron slit and 2x2 binning, and then 2018, 2019 data with 150 micron slit and 3x3 binning. # Select the image+slit or slit+image files that we will need to do astrometry of m = ["slit" in _ for _ in tab["MEZMODE"]] tab[m] # Write out a list of all the Image+slit files listfile = dpath.parent / "image-list.dat" listfile.write_text("\n".join(tab[m]["File"])) listfile # Check that it worked: listfile.read_text().splitlines() # ## Find the HEALpix coordinates of our source from astropy.coordinates import SkyCoord, ICRS import astropy.units as u # All the positions should be about the same, so we just use the first one. c = SkyCoord(tab[0]["RA"], tab[0]["DEC"], unit=(u.hourangle, u.deg)) c from astropy_healpix import HEALPix # In order to find which data files to download from http://data.astrometry.net/5000/, we need to translate the celestial coordinate to HEALpix index numbers: hp_2 = HEALPix(nside=2, order="nested", frame=ICRS()) hp_1 = HEALPix(nside=1, order="nested", frame=ICRS()) # Levels 0 to 4 use the `nside=2` tiles. hp_2.cone_search_skycoord(c, radius=5 * u.arcminute) # So that means `index500[0-4]-13.fits` hp_1.cone_search_skycoord(c, radius=5 * u.arcminute) # So that means `index500[5-7]-03.fits` # + tags=[] hp_2.cone_search_lonlat(300 * u.deg, 50 * u.deg, 0.1 * u.deg) # - # ## Look at the HEALpix data files # # Something isn't right. I got the 13 series but the program complains that the coordinates are not contained in the tile. hdulist = fits.open(dpath.parent / "astrometry-net" / "index-5004-13.fits") hdulist.info() # Looks like HDU 13 has the original table of stars: hdulist[13].header tstars = Table.read(hdulist[13]) df = tstars.to_pandas() df[["ra", "dec"]].describe() # So no wonder that is not working. I want (318.6, 43.7) but this has an RA range of 270 to 315 tstars2 = Table.read(fits.open(dpath.parent / "astrometry-net" / "index-5004-14.fits")[13]) df2 = tstars2.to_pandas() df2[["ra", "dec"]].describe() # So, it turns out that tile 14 is what I needed, not 13.

25.884956

158

0.68547

0

1,666

0.569573

3473928efd974d616527ce1198698da2f5b89c17

4,930

py

Python

common/create_public_vn.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

1

2017-06-13T04:42:34.000Z

common/create_public_vn.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

common/create_public_vn.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

import project_test from common.contrail_test_init import ContrailTestInit from common.connections import ContrailConnections import os import fixtures from test import BaseTestCase import time from floating_ip import * from vn_test import * from control_node import * from common import isolated_creds from tcutils.util import Singleton class PublicVn(fixtures.Fixture): __metaclass__ = Singleton def __init__(self, isolated_creds_obj, inputs, ini_file = None ,logger = None, mx_rt = None): self.isolated_creds = isolated_creds_obj self.username = self.isolated_creds.username self.password = self.isolated_creds.password self.inputs = inputs self.ini_file = ini_file self.logger = logger self.public_vn = self.inputs.public_vn self.public_tenant = self.inputs.admin_tenant self.setUp() self.create_public_vn(mx_rt) self.create_floatingip_pool() self.configure_control_nodes() def setUp(self): super(PublicVn, self).setUp() self.project = self.isolated_creds.create_tenant(self.public_tenant) self.inputs = self.isolated_creds.get_inputs(self.project) self.connections = self.isolated_creds.get_connections(self.inputs) if self.isolated_creds.__class__.__name__ == 'AdminIsolatedCreds': # If AdminIsolatedCreds, one could add user to tenant # Else, it is assumed that the administrator has taken # care self.isolated_creds.create_and_attach_user_to_tenant( self.project, self.username, self.password) self.project.set_sec_group_for_allow_all(\ self.public_tenant, 'default') # end setUp def create_public_vn(self,mx_rt = None): if (('MX_GW_TEST' in os.environ) and ( os.environ.get('MX_GW_TEST') == '1')): fip_pool_name = self.inputs.fip_pool_name fvn_name = self.public_vn fip_subnets = [self.inputs.fip_pool] if not mx_rt: mx_rt = self.inputs.mx_rt self.public_vn_fixture = self.useFixture( VNFixture( project_name=self.project.project_name, connections=self.connections, vn_name=fvn_name, inputs=self.inputs, subnets=fip_subnets, router_asn=self.inputs.router_asn, rt_number=mx_rt, router_external=True)) assert self.public_vn_fixture.verify_on_setup() self.logger.info('created public VN:%s' % fvn_name) # end createPublicVN def create_floatingip_pool(self): if (('MX_GW_TEST' in os.environ) and ( os.environ.get('MX_GW_TEST') == '1')): fip_pool_name = self.inputs.fip_pool_name fvn_name = self.public_vn fip_subnets = [self.inputs.fip_pool] self.fip_fixture = self.useFixture( FloatingIPFixture( project_name=self.public_tenant, inputs=self.inputs, connections=self.connections, pool_name=fip_pool_name, vn_id=self.public_vn_fixture.vn_id, option='neutron', vn_name=fvn_name)) assert self.fip_fixture.verify_on_setup() self.logger.info('created FIP Pool:%s under Project:%s' % (self.fip_fixture.pool_name, self.project.project_name)) # end createfloatingip def configure_control_nodes(self): # Configuring all control nodes here if (('MX_GW_TEST' in os.environ) and ( os.environ.get('MX_GW_TEST') == '1')): router_name = self.inputs.ext_routers[0][0] router_ip = self.inputs.ext_routers[0][1] for entry in self.inputs.bgp_ips: hostname = self.inputs.host_data[entry]['name'] entry_control_ip = self.inputs.host_data[ entry]['host_control_ip'] cn_fixture1 = self.useFixture( CNFixture( connections=self.connections, router_name=hostname, router_ip=entry_control_ip, router_type='contrail', inputs=self.inputs)) cn_fixturemx = self.useFixture( CNFixture( connections=self.connections, router_name=router_name, router_ip=router_ip, router_type='mx', inputs=self.inputs)) sleep(10) assert cn_fixturemx.verify_on_setup() # TODO Configure MX. Doing Manually For Now

40.409836

97

0.585598

4,588

0.930629

0

463

0.093915

347441db8bc4affdf62a00833370a2e23025b1d1

996

py

Python

aiotdlib/api/functions/get_voice_chat_available_participants.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

null

aiotdlib/api/functions/get_voice_chat_available_participants.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

null

aiotdlib/api/functions/get_voice_chat_available_participants.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

null

# =============================================================================== # # # # This file has been generated automatically!! Do not change this manually! # # # # =============================================================================== # from __future__ import annotations from pydantic import Field from ..base_object import BaseObject class GetVoiceChatAvailableParticipants(BaseObject): """ Returns list of participant identifiers, which can be used to join voice chats in a chat :param chat_id: Chat identifier :type chat_id: :class:`int` """ ID: str = Field("getVoiceChatAvailableParticipants", alias="@type") chat_id: int @staticmethod def read(q: dict) -> GetVoiceChatAvailableParticipants: return GetVoiceChatAvailableParticipants.construct(**q)

35.571429

92

0.47992

472

0.473896

0

137

0.13755

0

639

0.641566

34779ad96b37a7ece57e092fb2f969041bbfc0ed

27,098

py

Python

tests/test_tree.py

tgragnato/geneva

2fc5b2f2f4766278902cff25af50b753d1d26a76

[ "BSD-3-Clause" ]

1,182

2019-11-15T02:56:47.000Z

2022-03-30T16:09:04.000Z

tests/test_tree.py

Nekotekina/geneva

3eb6b7342f9afd7add1f4aba9e2aadf0b9a5f196

[ "BSD-3-Clause" ]

21

2019-11-15T15:08:02.000Z

2022-01-03T16:22:45.000Z

tests/test_tree.py

Nekotekina/geneva

3eb6b7342f9afd7add1f4aba9e2aadf0b9a5f196

[ "BSD-3-Clause" ]

102

2019-11-15T15:01:07.000Z

2022-03-30T13:52:47.000Z

import logging import os from scapy.all import IP, TCP import actions.tree import actions.drop import actions.tamper import actions.duplicate import actions.utils import layers.packet def test_init(): """ Tests initialization """ print(actions.action.Action.get_actions("out")) def test_count_leaves(): """ Tests leaf count is correct. """ a = actions.tree.ActionTree("out") logger = logging.getLogger("test") assert not a.parse("TCP:reserved:0tamper{TCP:flags:replace:S}-|", logger), "Tree parsed malformed DNA" a.parse("[TCP:reserved:0]-tamper{TCP:flags:replace:S}-|", logger) duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() assert a.count_leaves() == 1 assert a.remove_one() a.add_action(duplicate) assert a.count_leaves() == 1 duplicate.left = duplicate2 assert a.count_leaves() == 1 duplicate.right = drop assert a.count_leaves() == 2 def test_check(): """ Tests action tree check function. """ a = actions.tree.ActionTree("out") logger = logging.getLogger("test") a.parse("[TCP:flags:RA]-tamper{TCP:flags:replace:S}-|", logger) p = layers.packet.Packet(IP()/TCP(flags="A")) assert not a.check(p, logger) p = layers.packet.Packet(IP(ttl=64)/TCP(flags="RA")) assert a.check(p, logger) assert a.remove_one() assert a.check(p, logger) a.parse("[TCP:reserved:0]-tamper{TCP:flags:replace:S}-|", logger) assert a.check(p, logger) a.parse("[IP:ttl:64]-tamper{TCP:flags:replace:S}-|", logger) assert a.check(p, logger) p = layers.packet.Packet(IP(ttl=15)/TCP(flags="RA")) assert not a.check(p, logger) def test_scapy(): """ Tests misc. scapy aspects relevant to strategies. """ a = actions.tree.ActionTree("out") logger = logging.getLogger("test") a.parse("[TCP:reserved:0]-tamper{TCP:flags:replace:S}-|", logger) p = layers.packet.Packet(IP()/TCP(flags="A")) assert a.check(p, logger) packets = a.run(p, logger) assert packets[0][TCP].flags == "S" p = layers.packet.Packet(IP()/TCP(flags="A")) assert a.check(p, logger) a.parse("[TCP:reserved:0]-tamper{TCP:chksum:corrupt}-|", logger) packets = a.run(p, logger) assert packets[0][TCP].chksum assert a.check(p, logger) def test_str(): """ Tests string representation. """ logger = logging.getLogger("test") t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) assert str(a).strip() == "[%s]-|" % str(t) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") assert a.add_action(tamper) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}-|" # Tree will not add a duplicate action assert not a.add_action(tamper) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}-|" assert a.add_action(tamper2) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R},)-|" assert a.add_action(actions.duplicate.DuplicateAction()) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(duplicate,),)-|" drop = actions.drop.DropAction() assert a.add_action(drop) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(duplicate(drop,),),)-|" or \ str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(duplicate(,drop),),)-|" assert a.remove_action(drop) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(duplicate,),)-|" # Cannot remove action that is not present assert not a.remove_action(drop) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(duplicate,),)-|" a = actions.tree.ActionTree("out", trigger=t) orig = "[TCP:urgptr:15963]-duplicate(,drop)-|" a.parse(orig, logger) assert a.remove_one() assert orig != str(a) assert str(a) in ["[TCP:urgptr:15963]-drop-|", "[TCP:urgptr:15963]-duplicate-|"] def test_pretty_print_send(): t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) duplicate = actions.duplicate.DuplicateAction() a.add_action(duplicate) correct_string = "TCP:flags:0\nduplicate\n├── ===> \n└── ===> " assert a.pretty_print() == correct_string def test_pretty_print(logger): """ Print complex tree, although difficult to test """ t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() duplicate3 = actions.duplicate.DuplicateAction() duplicate4 = actions.duplicate.DuplicateAction() duplicate5 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() drop2 = actions.drop.DropAction() drop3 = actions.drop.DropAction() drop4 = actions.drop.DropAction() duplicate.left = duplicate2 duplicate.right = duplicate3 duplicate2.left = tamper duplicate2.right = drop duplicate3.left = duplicate4 duplicate3.right = drop2 duplicate4.left = duplicate5 duplicate4.right = drop3 duplicate5.left = drop4 duplicate5.right = tamper2 a.add_action(duplicate) correct_string = "TCP:flags:0\nduplicate\n├── duplicate\n│ ├── tamper{TCP:flags:replace:S}\n│ │ └── ===> \n│ └── drop\n└── duplicate\n ├── duplicate\n │ ├── duplicate\n │ │ ├── drop\n │ │ └── tamper{TCP:flags:replace:R}\n │ │ └── ===> \n │ └── drop\n └── drop" assert a.pretty_print() == correct_string assert a.pretty_print(visual=True) assert os.path.exists("tree.png") os.remove("tree.png") a.parse("[TCP:flags:0]-|", logger) a.pretty_print(visual=True) # Empty action tree assert not os.path.exists("tree.png") def test_pretty_print_order(): """ Tests the left/right ordering by reading in a new tree """ logger = logging.getLogger("test") a = actions.tree.ActionTree("out") assert a.parse("[TCP:flags:A]-duplicate(tamper{TCP:flags:replace:R}(tamper{TCP:chksum:replace:14239},),duplicate(tamper{TCP:flags:replace:S}(tamper{TCP:chksum:replace:14239},),))-|", logger) correct_pretty_print = "TCP:flags:A\nduplicate\n├── tamper{TCP:flags:replace:R}\n│ └── tamper{TCP:chksum:replace:14239}\n│ └── ===> \n└── duplicate\n ├── tamper{TCP:flags:replace:S}\n │ └── tamper{TCP:chksum:replace:14239}\n │ └── ===> \n └── ===> " assert a.pretty_print() == correct_pretty_print def test_parse(): """ Tests string parsing. """ logger = logging.getLogger("test") t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) base_t = actions.trigger.Trigger("field", "flags", "TCP") base_a = actions.tree.ActionTree("out", trigger=base_t) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") tamper3 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper4 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") a.parse("[TCP:flags:0]-|", logger) assert str(a) == str(base_a) assert len(a) == 0 base_a.add_action(tamper) assert a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}-|", logger) assert str(a) == str(base_a) assert len(a) == 1 assert a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R},)-|", logging.getLogger("test")) base_a.add_action(tamper2) assert str(a) == str(base_a) assert len(a) == 2 base_a.add_action(tamper3) base_a.add_action(tamper4) assert a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R}(tamper{TCP:flags:replace:S}(tamper{TCP:flags:replace:R},),),)-|", logging.getLogger("test")) assert str(a) == str(base_a) assert len(a) == 4 base_t = actions.trigger.Trigger("field", "flags", "TCP") base_a = actions.tree.ActionTree("out", trigger=base_t) duplicate = actions.duplicate.DuplicateAction() assert a.parse("[TCP:flags:0]-duplicate-|", logger) base_a.add_action(duplicate) assert str(a) == str(base_a) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") tamper3 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="A") tamper4 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") duplicate.left = tamper assert a.parse("[TCP:flags:0]-duplicate(tamper{TCP:flags:replace:S},)-|", logger) assert str(a) == str(base_a) duplicate.right = tamper2 assert a.parse("[TCP:flags:0]-duplicate(tamper{TCP:flags:replace:S},tamper{TCP:flags:replace:R})-|", logger) assert str(a) == str(base_a) tamper2.left = tamper3 assert a.parse("[TCP:flags:0]-duplicate(tamper{TCP:flags:replace:S},tamper{TCP:flags:replace:R}(tamper{TCP:flags:replace:A},))-|", logger) assert str(a) == str(base_a) strategy = actions.utils.parse("[TCP:flags:0]-duplicate(tamper{TCP:flags:replace:S},tamper{TCP:flags:replace:R})-| \/", logger) assert strategy assert len(strategy.out_actions[0]) == 3 assert len(strategy.in_actions) == 0 assert not a.parse("[]", logger) # No valid trigger assert not a.parse("[TCP:flags:0]-", logger) # No valid ending "|" assert not a.parse("[TCP:]-|", logger) # invalid trigger assert not a.parse("[TCP:flags:0]-foo-|", logger) # Non-existent action assert not a.parse("[TCP:flags:0]--|", logger) # Empty action assert not a.parse("[TCP:flags:0]-duplicate(,,,)-|", logger) # Bad tree assert not a.parse("[TCP:flags:0]-duplicate()))-|", logger) # Bad tree assert not a.parse("[TCP:flags:0]-duplicate(((()-|", logger) # Bad tree assert not a.parse("[TCP:flags:0]-duplicate(,))))-|", logger) # Bad tree assert not a.parse("[TCP:flags:0]-drop(duplicate,)-|", logger) # Terminal action with children assert not a.parse("[TCP:flags:0]-drop(duplicate,duplicate)-|", logger) # Terminal action with children assert not a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}(,duplicate)-|", logger) # Non-branching action with right child assert not a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}(drop,duplicate)-|", logger) # Non-branching action with children def test_tree(): """ Tests basic tree functionality. """ t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction() tamper2 = actions.tamper.TamperAction() duplicate = actions.duplicate.DuplicateAction() a.add_action(None) a.add_action(tamper) assert a.get_slots() == 1 a.add_action(tamper2) assert a.get_slots() == 1 a.add_action(duplicate) assert a.get_slots() == 2 t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) drop = actions.drop.DropAction() a.add_action(drop) assert a.get_slots() == 0 add_success = a.add_action(tamper) assert not add_success assert a.get_slots() == 0 rep = "" for s in a.string_repr(a.action_root): rep += s assert rep == "drop" print(str(a)) assert a.parse("[TCP:flags:A]-duplicate(tamper{TCP:seq:corrupt},)-|", logging.getLogger("test")) for act in a: print(str(a)) assert len(a) == 2 assert a.get_slots() == 2 for _ in range(100): assert str(a.get_rand_action("out", request="DropAction")) == "drop" def test_remove(): """ Tests remove """ t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction() tamper2 = actions.tamper.TamperAction() tamper3 = actions.tamper.TamperAction() assert not a.remove_action(tamper) a.add_action(tamper) assert a.remove_action(tamper) a.add_action(tamper) a.add_action(tamper2) a.add_action(tamper3) assert a.remove_action(tamper2) assert tamper2 not in a assert tamper.left == tamper3 assert not tamper.right assert len(a) == 2 a = actions.tree.ActionTree("out", trigger=t) duplicate = actions.duplicate.DuplicateAction() tamper = actions.tamper.TamperAction() tamper2 = actions.tamper.TamperAction() tamper3 = actions.tamper.TamperAction() a.add_action(tamper) assert a.action_root == tamper duplicate.left = tamper2 duplicate.right = tamper3 a.add_action(duplicate) assert len(a) == 4 assert a.remove_action(duplicate) assert duplicate not in a assert tamper.left == tamper2 assert not tamper.right assert len(a) == 2 a.parse("[TCP:flags:A]-|", logging.getLogger("test")) assert not a.remove_one(), "Cannot remove one with no action root" def test_len(): """ Tests length calculation. """ t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction() tamper2 = actions.tamper.TamperAction() assert len(a) == 0, "__len__ returned wrong length" a.add_action(tamper) assert len(a) == 1, "__len__ returned wrong length" a.add_action(tamper) assert len(a) == 1, "__len__ returned wrong length" a.add_action(tamper2) assert len(a) == 2, "__len__ returned wrong length" duplicate = actions.duplicate.DuplicateAction() a.add_action(duplicate) assert len(a) == 3, "__len__ returned wrong length" def test_contains(): """ Tests contains method """ t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction() tamper2 = actions.tamper.TamperAction() tamper3 = actions.tamper.TamperAction() assert not a.contains(tamper), "contains incorrect behavior" assert not a.contains(tamper2), "contains incorrect behavior" a.add_action(tamper) assert a.contains(tamper), "contains incorrect behavior" assert not a.contains(tamper2), "contains incorrect behavior" add_success = a.add_action(tamper) assert not add_success, "added duplicate action" assert a.contains(tamper), "contains incorrect behavior" assert not a.contains(tamper2), "contains incorrect behavior" a.add_action(tamper2) assert a.contains(tamper), "contains incorrect behavior" assert a.contains(tamper2), "contains incorrect behavior" a.remove_action(tamper2) assert a.contains(tamper), "contains incorrect behavior" assert not a.contains(tamper2), "contains incorrect behavior" a.add_action(tamper2) assert a.contains(tamper), "contains incorrect behavior" assert a.contains(tamper2), "contains incorrect behavior" remove_success = a.remove_action(tamper) assert remove_success assert not a.contains(tamper), "contains incorrect behavior" assert a.contains(tamper2), "contains incorrect behavior" a.add_action(tamper3) assert a.contains(tamper3), "contains incorrect behavior" assert len(a) == 2, "len incorrect return" remove_success = a.remove_action(tamper2) assert remove_success def test_iter(): """ Tests iterator. """ t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") assert a.add_action(tamper) assert a.add_action(tamper2) assert not a.add_action(tamper) for node in a: print(node) def test_run(): """ Tests running packets through the chain. """ logger = logging.getLogger("test") t = actions.trigger.Trigger(None, None, None) a = actions.tree.ActionTree("out", trigger=t) tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() packet = layers.packet.Packet(IP()/TCP()) a.add_action(tamper) packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 1 assert None not in packets assert packets[0].get("TCP", "flags") == "S" a.add_action(tamper2) print(str(a)) packet = layers.packet.Packet(IP()/TCP()) assert not a.add_action(tamper), "tree added duplicate action" packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 1 assert None not in packets assert packets[0].get("TCP", "flags") == "R" print(str(a)) a.remove_action(tamper2) a.remove_action(tamper) a.add_action(duplicate) packet = layers.packet.Packet(IP()/TCP(flags="RA")) packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 2 assert None not in packets assert packets[0][TCP].flags == "RA" assert packets[1][TCP].flags == "RA" print(str(a)) duplicate.left = tamper duplicate.right = tamper2 packet = layers.packet.Packet(IP()/TCP(flags="RA")) print("ABUT TO RUN") packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 2 assert None not in packets print(str(a)) print(str(packets[0])) print(str(packets[1])) assert packets[0][TCP].flags == "S" assert packets[1][TCP].flags == "R" print(str(a)) tamper.left = duplicate2 packet = layers.packet.Packet(IP()/TCP(flags="RA")) packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 3 assert None not in packets assert packets[0][TCP].flags == "S" assert packets[1][TCP].flags == "S" assert packets[2][TCP].flags == "R" print(str(a)) tamper2.left = drop packet = layers.packet.Packet(IP()/TCP(flags="RA")) packets = a.run(packet, logging.getLogger("test")) assert len(packets) == 2 assert None not in packets assert packets[0][TCP].flags == "S" assert packets[1][TCP].flags == "S" print(str(a)) assert a.remove_action(duplicate2) tamper.left = actions.drop.DropAction() packet = layers.packet.Packet(IP()/TCP(flags="RA")) packets = a.run(packet, logger ) assert len(packets) == 0 print(str(a)) a.parse("[TCP:flags:A]-duplicate(tamper{TCP:flags:replace:R}(tamper{TCP:chksum:replace:14239},),duplicate(tamper{TCP:flags:replace:S},))-|", logger) packet = layers.packet.Packet(IP()/TCP(flags="A")) assert a.check(packet, logger) packets = a.run(packet, logger) assert len(packets) == 3 assert packets[0][TCP].flags == "R" assert packets[1][TCP].flags == "S" assert packets[2][TCP].flags == "A" def test_index(): """ Tests index """ a = actions.tree.ActionTree("out") tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") tamper3 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="F") assert a.add_action(tamper) assert a[0] == tamper assert not a[1] assert a.add_action(tamper2) assert a[0] == tamper assert a[1] == tamper2 assert a[-1] == tamper2 assert not a[10] assert a.add_action(tamper3) assert a[-1] == tamper3 assert not a[-11] def test_mate(): """ Tests mate primitive """ logger = logging.getLogger("test") t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) assert not a.choose_one() tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() other_a = actions.tree.ActionTree("out", trigger=t) assert not a.mate(other_a), "Can't mate empty trees" assert a.add_action(tamper) assert other_a.add_action(tamper2) assert a.choose_one() == tamper assert other_a.choose_one() == tamper2 assert a.get_parent(tamper) == (None, None) assert other_a.get_parent(tamper2) == (None, None) assert a.add_action(duplicate) assert a.get_parent(duplicate) == (tamper, "left") duplicate.right = drop assert a.get_parent(drop) == (duplicate, "right") assert other_a.add_action(duplicate2) # Test mating a full tree with a full tree assert str(a) == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(duplicate(,drop),)-|" assert str(other_a) == "[TCP:flags:0]-tamper{TCP:flags:replace:R}(duplicate,)-|" assert a.swap(duplicate, other_a, duplicate2) assert str(a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(duplicate,)-|" assert str(other_a).strip() == "[TCP:flags:0]-tamper{TCP:flags:replace:R}(duplicate(,drop),)-|" assert len(a) == 2 assert len(other_a) == 3 assert duplicate2 not in other_a assert duplicate not in a assert tamper.left == duplicate2 assert tamper2.left == duplicate assert other_a.get_parent(duplicate) == (tamper2, "left") assert a.get_parent(duplicate2) == (tamper, "left") assert other_a.get_parent(drop) == (duplicate, "right") assert a.get_parent(None) == (None, None) # Test mating two trees with just root nodes t = actions.trigger.Trigger("field", "flags", "TCP") a = actions.tree.ActionTree("out", trigger=t) assert not a.choose_one() tamper = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="S") tamper2 = actions.tamper.TamperAction(field="flags", tamper_type="replace", tamper_value="R") duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() other_a = actions.tree.ActionTree("out", trigger=t) assert not a.mate(other_a) assert a.add_action(duplicate) assert other_a.add_action(duplicate2) assert a.mate(other_a) assert a.action_root == duplicate2 assert other_a.action_root == duplicate assert not duplicate.left and not duplicate.right assert not duplicate2.left and not duplicate2.right # Confirm that no nodes have been aliased or connected between the trees for node in a: for other_node in other_a: assert not node.left == other_node assert not node.right == other_node # Test mating two trees where one is empty assert a.remove_action(duplicate2) # This should swap the duplicate action to be the action root of the other tree assert str(a) == "[TCP:flags:0]-|" assert str(other_a) == "[TCP:flags:0]-duplicate-|" assert a.mate(other_a) assert not other_a.action_root assert a.action_root == duplicate assert len(a) == 1 assert len(other_a) == 0 # Confirm that no nodes have been aliased or connected between the trees for node in a: for other_node in other_a: if other_node: assert not node.left == other_node assert not node.right == other_node assert a.parse("[TCP:flags:0]-tamper{TCP:flags:replace:S}(duplicate(,drop),)-|", logger) drop = a.action_root.left.right assert str(drop) == "drop" # Note that this will return a valid ActionTree, but because it is empty, # it is technically a False-y value, as it's length is 0 assert other_a.parse("[TCP:flags:0]-|", logger) == other_a a.swap(drop, other_a, None) assert other_a.action_root == drop assert not a.action_root.left.right assert str(other_a) == "[TCP:flags:0]-drop-|" assert str(a) == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(duplicate,)-|" other_a.swap(drop, a, a.action_root.left) # Confirm that no nodes have been aliased or connected between the trees for node in a: for other_node in other_a: if other_node: assert not node.left == other_node assert not node.right == other_node assert str(other_a) == "[TCP:flags:0]-duplicate-|" assert str(a) == "[TCP:flags:0]-tamper{TCP:flags:replace:S}(drop,)-|" a.parse("[TCP:flags:0]-drop-|", logger) other_a.parse("[TCP:flags:0]-duplicate(drop,drop)-|", logger) a_drop = a.action_root other_duplicate = other_a.action_root a.swap(a_drop, other_a, other_duplicate) print(str(a)) print(str(other_a)) assert str(other_a) == "[TCP:flags:0]-drop-|" assert str(a) == "[TCP:flags:0]-duplicate(drop,drop)-|" duplicate = actions.duplicate.DuplicateAction() duplicate2 = actions.duplicate.DuplicateAction() drop = actions.drop.DropAction() drop2 = actions.drop.DropAction() drop3 = actions.drop.DropAction() a = actions.tree.ActionTree("out", trigger=t) a.add_action(duplicate) a.add_action(drop) a.add_action(drop2) assert str(a) == "[TCP:flags:0]-duplicate(drop,drop)-|" assert a.get_slots() == 0 other_a = actions.tree.ActionTree("out", trigger=t) other_a.add_action(drop3) a.swap(drop, other_a, drop3) assert str(a) == "[TCP:flags:0]-duplicate(drop,drop)-|" a.swap(drop3, other_a, drop) assert str(a) == "[TCP:flags:0]-duplicate(drop,drop)-|" assert a.mate(other_a) def test_choose_one(): """ Tests choose_one functionality """ a = actions.tree.ActionTree("out") drop = actions.drop.DropAction() assert not a.choose_one() assert a.add_action(drop) assert a.choose_one() == drop assert a.remove_action(drop) assert not a.choose_one() duplicate = actions.duplicate.DuplicateAction() a.add_action(duplicate) assert a.choose_one() == duplicate duplicate.left = drop assert a.choose_one() in [duplicate, drop] # Make sure that both actions get chosen chosen = set() for i in range(0, 10000): act = a.choose_one() chosen.add(act) assert chosen == set([duplicate, drop])

39.501458

315

0.665104

0

7,514

0.275622

3478264c794a3f180ec86805c965604a1b3922e9

2,365

py

Python

l2address/formatter.py

EgorBlagov/l2address

d29970116184bd4bb78a58e789e07ed5009228b4

[ "MIT" ]

1

2021-08-02T07:13:29.000Z

l2address/formatter.py

EgorBlagov/l2address

d29970116184bd4bb78a58e789e07ed5009228b4

[ "MIT" ]

null

l2address/formatter.py

EgorBlagov/l2address

d29970116184bd4bb78a58e789e07ed5009228b4

[ "MIT" ]

null

import re from abc import ABC, abstractmethod from .utils import parse_hex, per_join class Formatter(ABC): def _to_clean_str(self, value, max_value): value_str = str(hex(value))[2:] full_mac_str = '0' * (self._hex_digits_count(max_value) - len(value_str)) + value_str return full_mac_str def _hex_digits_count(self, value): return len(hex(value)[2:]) def _common_regex(self, max_value, delimeter, step): return "^" + per_join([r'[\da-fA-f]' for _ in range(self._hex_digits_count(max_value))], delimeter, step) + "$" @abstractmethod def format(self, value, max_value): pass def parse(self, _str, max_value): m = re.match(self._get_validator_regexp(_str, max_value), _str) if m is None: raise ValueError('Invalid MAC address format') else: return self._parse_value_from_str(_str) def _parse_value_from_str(self, _str): return parse_hex(_str) @abstractmethod def _get_validator_regexp(self, _str, max_value): pass class ColonFormatter(Formatter): def format(self, value, max_value): return per_join(self._to_clean_str(value, max_value), ':', 2) def _get_validator_regexp(self, _str, max_value): return self._common_regex(max_value, r'\:', 2) class PeriodFormatter(Formatter): def __init__(self, step=2): super().__init__() self.step = step def format(self, value, max_value): return per_join(self._to_clean_str(value, max_value), '.', self.step) def _get_validator_regexp(self, _str, max_value): return self._common_regex(max_value, r'\.', self.step) class HyphenFormatter(Formatter): def format(self, value, max_value): return per_join(self._to_clean_str(value, max_value), '-', 2) def _get_validator_regexp(self, _str, max_value): return self._common_regex(max_value, r'\-', 2) class CleanFormatter(Formatter): def format(self, value, max_value): return self._to_clean_str(value, max_value) def _get_validator_regexp(self, _str, max_value): return self._common_regex(max_value, '', 2) DEFAULT_FORMATTERS = [ ColonFormatter(), PeriodFormatter(2), PeriodFormatter(3), PeriodFormatter(4), HyphenFormatter(), CleanFormatter() ]

28.493976

119

0.662579

2,099

0.887526

0

150

0.063425

0

76

0.032135

347843e7209d1e94b13bc470e5fc335542cf2f21

5,142

py

Python

pytests/ent_backup_restore/provider/s3.py

couchbaselabs/testrunner-bharath

96af90070da2140cc11c549db7403f5ea3b76d34

[ "Apache-2.0" ]

null

pytests/ent_backup_restore/provider/s3.py

couchbaselabs/testrunner-bharath

96af90070da2140cc11c549db7403f5ea3b76d34

[ "Apache-2.0" ]

null

pytests/ent_backup_restore/provider/s3.py

couchbaselabs/testrunner-bharath

96af90070da2140cc11c549db7403f5ea3b76d34

[ "Apache-2.0" ]

null

#!/usr/bin/python3 import json import re import boto3 import botocore from . import provider class S3(provider.Provider): def __init__(self, access_key_id, bucket, cacert, endpoint, no_ssl_verify, region, secret_access_key, staging_directory): """Create a new S3 provider which allows interaction with S3 masked behind the common 'Provider' interface. All required parameters should be those parsed from the ini. """ super().__init__(access_key_id, bucket, cacert, endpoint, no_ssl_verify, region, secret_access_key, staging_directory) # boto3 will raise an exception if given an empty string as the endpoint_url so we must construct a kwargs # dictionary and conditionally populate it. kwargs = {} if self.access_key_id: kwargs['aws_access_key_id'] = self.access_key_id if self.cacert: kwargs['verify'] = self.cacert if self.endpoint != '': kwargs['endpoint_url'] = self.endpoint if self.no_ssl_verify: # Supplying no_ssl_verify will override the cacert value if supplied e.g. they are mutually exclusive kwargs['verify'] = False if self.region: kwargs['region_name'] = self.region if self.secret_access_key: kwargs['aws_secret_access_key'] = self.secret_access_key self.resource = boto3.resource('s3', **kwargs) def schema_prefix(self): """See super class""" return 's3://' def setup(self): """See super class""" configuration = {} if self.region: configuration['LocationConstraint'] = self.region try: self.resource.create_bucket(Bucket=self.bucket, CreateBucketConfiguration=configuration) except botocore.exceptions.ClientError as error: error_code = error.response['Error']['Code'] if error_code != 'BucketAlreadyExists': raise error def teardown(self, info, remote_client): """See super class""" bucket = self.resource.Bucket(self.bucket) # Delete all the remaining objects try: for obj in bucket.objects.all(): obj.delete() except botocore.exceptions.ClientError as error: error_code = error.response['Error']['Code'] if error_code == 'NoSuchBucket': # Some tests remove the bucket after it's created/cleaned, if the bucket doesn't exist then all we need # to do is clean the staging directory. self._remove_staging_directory(info, remote_client) return raise error_code # Abort all the remaining multipart uploads. We ignore any 'NoSuchUpload' errors because we don't care if the # upload doesn't exist; we are trying to remove it. for upload in bucket.multipart_uploads.all(): try: upload.abort() except botocore.exceptions.ClientError as error: error_code = error.response['Error']['Code'] if error_code != "NoSuchUpload": raise error # Remove the staging directory because cbbackupmgr has validation to ensure that are unique to each archive self._remove_staging_directory(info, remote_client) def remove_bucket(self): """See super class""" self.resource.Bucket(self.bucket).delete() def get_json_object(self, key): """See super class""" obj = None try: obj = json.loads(self.resource.Object(self.bucket, key).get()['Body'].read()) except botocore.exceptions.ClientError as error: error_code = error.response['Error']['Code'] if error_code not in ('NoSuchKey', 'KeyNotFound'): raise error_code return obj def list_objects(self, prefix=None): """See super class""" keys = [] kwargs = {} if prefix: kwargs['Prefix'] = prefix for obj in self.resource.Bucket(self.bucket).objects.filter(**kwargs): keys.append(obj.key) return keys def delete_objects(self, prefix): """See super class""" kwargs = {} if prefix: kwargs['Prefix'] = prefix for obj in self.resource.Bucket(self.bucket).objects.filter(**kwargs): obj.delete() def list_backups(self, archive, repo): """See super class""" pattern = re.compile("([0-9]+)-(0[1-9]|1[012])-(0[1-9]|[12][0-9]|3[01])[Tt]([01][0-9]|2[0-3])_([0-5][0-9])_([0-5][0-9]|60)(\.[0-9]+)?(([Zz])|([\+|\-]([01][0-9]|2[0-3])_[0-5][0-9]))") backups = [] for obj in self.resource.Bucket(self.bucket).objects.filter(Prefix=f"{archive}/{repo}"): res = pattern.search(obj.key) if res and res.group() not in backups: backups.append(res.group()) return backups def num_multipart_uploads(self): return sum(1 for _ in self.resource.Bucket(self.bucket).multipart_uploads.all()) provider.Provider.register(S3)

36.211268

190

0.606573

5,012

0.974718

0

1,511

0.293855

34792d446f26e4e27a5343d406f40c7fae30afe3

1,059

py

Python

pythonProject/05al137Random/rd.py

D-Wolter/PycharmProjects

c8d6144efa30261bff72a3e0414a0d80f6730f9b

[ "MIT" ]

null

pythonProject/05al137Random/rd.py

D-Wolter/PycharmProjects

c8d6144efa30261bff72a3e0414a0d80f6730f9b

[ "MIT" ]

null

pythonProject/05al137Random/rd.py

D-Wolter/PycharmProjects

c8d6144efa30261bff72a3e0414a0d80f6730f9b

[ "MIT" ]

null

import random import string # Gera um número inteiro entra A e B # inteiro = random.randint(10, 20) # Gera um número de ponto flutuante entra A e B # flutuante = random.uniform(10, 20) # Gera um número de ponto flutuante entre 0.0 e 1.0 flutuante = random.random() # Gerar um número aleatório usando a função range() # de ate pulando de 10 inteiro = random.randrange(900, 1000, 10) lista = ['Luiz', 'Otávio', 'Maria', 'Rose', 'Jenny', 'Danilo', 'Felipe'] # Seleciona aleatóriamente valores de uma lista sorteio = random.sample(lista, 2) ##sorteia dois nomes diferentes aleatorios por vez # sorteio = random.choices(lista, k=2) #sorteia dois nomes aleatorios por vez porem pode repetir o mesmo nome # sorteio = random.choice(lista) #sorteia um nome na lista # Embaralha a lista random.shuffle(lista)# embaralhar a lista # Gera senha aleatória letras = string.ascii_letters digitos = string.digits caracteres = '!@#$%&*._-' geral = letras + digitos + caracteres senha = "".join(random.choices(geral, k=20)) print(senha)

28.621622

109

0.708215

0

706

0.66043

347a6f99a4de31c46f4ac69d858282e197897fe5

1,651

py

Python

chris_backend/users/tests/test_serializers.py

PintoGideon/ChRIS_ultron_backEnd

3c094c90f45c64e279c6d78d9accc357679fb37b

[ "MIT" ]

null

chris_backend/users/tests/test_serializers.py

PintoGideon/ChRIS_ultron_backEnd

3c094c90f45c64e279c6d78d9accc357679fb37b

[ "MIT" ]

null

chris_backend/users/tests/test_serializers.py

PintoGideon/ChRIS_ultron_backEnd

3c094c90f45c64e279c6d78d9accc357679fb37b

[ "MIT" ]

null

import logging from django.test import TestCase from rest_framework import serializers from users.serializers import UserSerializer class UserSerializerTests(TestCase): """ Generic user view tests' setup and tearDown """ def setUp(self): # avoid cluttered console output (for instance logging all the http requests) logging.disable(logging.CRITICAL) self.username = 'cube' self.password = 'cubepass' self.email = 'dev@babymri.org' def tearDown(self): # re-enable logging logging.disable(logging.DEBUG) def test_create(self): """ Test whether overriden create method takes care of the password hashing. """ user_serializer = UserSerializer() validated_data = {'username': self.username, 'password': self.password, 'email': self.email} user = user_serializer.create(validated_data) self.assertEqual(user.username, self.username) self.assertEqual(user.email, self.email) self.assertNotEqual(user.password, self.password) self.assertTrue(user.check_password(self.password)) def test_validate_username(self): """ Test whether overriden validate_username method raises a serializers.ValidationError when the username contains forward slashes. """ user_serializer = UserSerializer() with self.assertRaises(serializers.ValidationError): user_serializer.validate_username('user/') username = user_serializer.validate_username(self.username) self.assertEqual(username, self.username)

32.372549

85

0.675348

1,512

0.915809

0

478

0.289522

347c3d0645e2c613d5b9e0808de4a8ec3f9718f3

11,036

py

Python

pegtree/optimizer.py

Caterpie-poke/pegtree

81c37a743d4bccc2eac05ebb10803224785f55c0

[ "MIT" ]

1

2022-03-05T13:33:55.000Z

pegtree/optimizer.py

Caterpie-poke/pegtree

81c37a743d4bccc2eac05ebb10803224785f55c0

[ "MIT" ]

6

2020-10-06T23:57:10.000Z

2021-05-11T22:16:46.000Z

pegtree/optimizer.py

Caterpie-poke/pegtree

81c37a743d4bccc2eac05ebb10803224785f55c0

[ "MIT" ]

5

2020-02-18T12:00:32.000Z

2020-05-13T06:35:23.000Z

from .peg import * # # PRange Utilities def bitsetRange(chars, ranges): cs = 0 for c in chars: cs |= 1 << ord(c) r = ranges while len(r) > 1: for c in range(ord(r[0]), ord(r[1])+1): cs |= 1 << c r = r[2:] return cs def stringfyRange(bits): c = 0 s = None p = None chars = [] ranges = [] while bits > 0: if bits & 1 == 1: if s is None: s = c p = c elif p + 1 == c: p = c else: _appendRange(s, p, chars, ranges) s = c p = c bits >>= 1 c += 1 if s is not None: _appendRange(s, p, chars, ranges) return ''.join(chars), ''.join(ranges) def _appendRange(s, p, chars, ranges): if s == p: chars.append(chr(s)) elif s+1 == p: chars.append(chr(s)) chars.append(chr(p)) else: ranges.append(chr(s)) ranges.append(chr(p)) def uniqueRange(chars, ranges): bits = bitsetRange(chars, ranges) newchars, newranges = stringfyRange(bits) checkbits = bitsetRange(chars, ranges) assert bits == checkbits return newchars, newranges # # Inlining # def isCharOrRange(pe): return isinstance(pe, PChar) or isinstance(pe, PRange) def inline(pe: PExpr, filter=isCharOrRange): start = pe while isinstance(pe, PRef) or isinstance(pe, PName): pe = pe.deref() if filter(pe): if(pe != start): logger.info('INLINE', start, '=>', pe) return pe return start # # make Minimum Rules # def makeMinimumRules(pe: PExpr, visited: dict, rules: list): if isinstance(pe, PName): pe.deref() pe = pe.e if isinstance(pe, PRef): uname = pe.uname() if uname not in visited: visited[uname] = pe makeMinimumRules(pe.deref(), visited, rules) rules.append(pe) return rules if isinstance(pe, PUnary) or isinstance(pe, PTuple): for e in pe: makeMinimumRules(e, visited, rules) return rules # # Sorting Rules # def sortRules(refs): newrefs = [] unsolved = [] for ref in refs: names = set([]) _makeSortingRefs(ref.deref(), names) if len(names) == 0: newrefs.append(ref) else: unsolved.append((ref, set(names))) return _solveSortingRefs(newrefs, unsolved) def _makeSortingRefs(e, names): if isinstance(e, PTuple): for e2 in e: _makeSortingRefs(e2, names) elif hasattr(e, 'e'): _makeSortingRefs(e.e, names) elif isinstance(e, PRef): names.add(e.uname()) def _removeSolvedName(unsolved, uname): removed = False for _, names in unsolved: if uname in names: removed = True names.remove(uname) return removed def _solveSortingRefs(refs, unsolved): removed = False # print(refs) for ref in refs: removed |= _removeSolvedName(unsolved, ref.uname()) max = 0 while max < 10: removed = True while removed: removed = False newrefs = [] stillUnsolved = [] for ref, names in unsolved: if len(names) <= max: refs.append(ref) newrefs.append(ref) else: stillUnsolved.append((ref, names)) unsolved = stillUnsolved #print(max, newrefs) for ref in newrefs: removed |= _removeSolvedName(unsolved, ref.uname()) if removed: max = 0 max += 1 for ref, _ in unsolved: refs.append(ref) return refs ### def flattenSeq(pe: PExpr, ps: list, conv=lambda x: x): if isinstance(pe, PSeq): for e in pe: ps.append(conv(e)) else: ps.append(conv(pe)) return ps def appendSeq(ps: list, pe: PExpr): if pe == EMPTY or (isinstance(pe, PChar) and len(pe.text) == 0): return if isinstance(pe, PSeq): for e in pe: appendSeq(ps, e) return if len(ps) == 0: ps.append(pe) return e0 = ps[-1] if isinstance(pe, PChar) and isinstance(e0, PChar): ps[-1] = PChar(e0.text+pe.text) return ps.append(pe) def newSeq(ps: list): optimized = [] for e in ps: appendSeq(optimized, e) if len(optimized) == 0: return EMPTY if len(optimized) == 1: return optimized[0] return PSeq(*optimized) # # Ore # def flattenOre(pe: PExpr, ps: list, conv=lambda x: x): if isinstance(pe, POre): for e in pe: ps.append(conv(e)) else: ps.append(conv(pe)) return ps def mergeRange(e, e2): if isAny(e) or isAny(e2): return ANY chars = '' ranges = '' if isinstance(e, PChar): chars += e.text if isinstance(e2, PChar): chars += e2.text if isinstance(e, PRange): chars += e.chars ranges += e.ranges if isinstance(e2, PRange): chars += e2.chars ranges += e2.ranges chars, ranges = uniqueRange(chars, ranges) return PRange.new(chars, ranges) def prefixChar(pe): if isinstance(pe, PChar) and len(pe.text) > 0: return pe.text[0] if isinstance(pe, PSeq) and isinstance(pe.es[0], PChar) and len(pe.es[0].text) > 0: return pe.es[0].text[0] return None def dc(pe): if isinstance(pe, PChar) and len(pe.text) > 0: return PChar.new(pe.text[1:]) if isinstance(pe, PSeq) and isinstance(pe.es[0], PChar) and len(pe.es[0].text) > 0: first = PChar.new(pe.es[0].text[1:]) return PSeq(first, *pe.es[1:]) return FAIL def appendOre(ps: list, pe, cmap=None, deref=False): start = pe while deref and (isinstance(pe, PRef) or isinstance(pe, PName)): pe = pe.deref() if isinstance(pe, POre): for e in pe: appendOre(ps, e, cmap, deref) return if len(ps) > 0: e0 = ps[-1] if isEmpty(e0): return if isSingleCharacter(e0) and isSingleCharacter(pe): ps[-1] = mergeRange(e0, pe) return c = prefixChar(pe) if c is not None and cmap != None: if c not in cmap: cmap[c] = len(ps) ps.append([pe]) else: nested_choice = ps[cmap[c]] nested_choice.append(pe) return ps.append(start) def newOre(ps: list, cmap=None, deref=False): optimized = [] for e in ps: appendOre(optimized, e, cmap, deref) for i in range(len(optimized)): if not isinstance(optimized[i], list): continue nested_choice = optimized[i] if len(nested_choice) == 1: optimized[i] = nested_choice[0] else: first = PChar(prefixChar(nested_choice[0])) nested = [] for ne in nested_choice: ne = dc(ne) appendOre(nested, ne) optimized[i] = PSeq.new(first, newOre(nested, {})) ps = optimized optimized = [] for e in ps: appendOre(optimized, e, None, False) if len(optimized) == 0: return FAIL if len(optimized) == 1: return optimized[0] if len(optimized) == 2 and isEmpty(optimized[1]): return POption(optimized[0]) return POre(*optimized) # # Out of order execution # def fixedSize(e: PExpr): if isinstance(e, PRange) or isinstance(e, PAny): return 1 if isinstance(e, PChar): return len(e.text) if isinstance(e, PAnd) or isinstance(e, PNot): return 0 return -1 def splitFixed(e, conv=lambda x: x): ps = flattenSeq(e, [], conv) if fixedSize(ps[0]) == -1: return None, -1, e shift = 0 fixed = [] for e in ps: size = fixedSize(e) if size == -1: break shift += size fixed.append(e) #print('@', fixed, shift, ps[len(fixed):]) return fixed, shift, ps[len(fixed):] ### class Optimizer(object): def visit(self, pe: PExpr): pe = inline(pe) cname = pe.cname() if not hasattr(self, cname): return pe f = getattr(self, cname) optimized = f(pe) return optimized def PRef(self, pe): return pe def PName(self, pe): return PName(self.visit(pe.e), pe.name, pe.tree, pe.isLeftRec) def PAnd(self, pe): return PAnd(self.visit(pe.e)) def PNot(self, pe): return PNot(self.visit(pe.e)) def PMany(self, pe): return PMany(self.visit(pe.e)) def POneMany(self, pe): return POneMany(self.visit(pe.e)) def POption(self, pe): return POption(self.visit(pe.e)) def PSeq(self, pe): ps = flattenSeq(pe, [], lambda e: self.visit(e)) return newSeq(ps) # Ore def POre(self, pe: POre): if pe.isDict() and len(pe.es) > 8: return pe ps = flattenOre(pe, [], lambda e: self.visit(e)) return newOre(ps, {}) # Tree Construction def PNode(self, pe: PNode): e = self.visit(pe.e) fixed, shift, ves = splitFixed(e) if fixed is None: # or not self.Ooox: return PNode(e, pe.tag, pe.shift) fixed.append(PNode(newSeq(ves), pe.tag, pe.shift - shift)) return newSeq(fixed) def PEdge(self, pe): e = self.visit(pe.e) fixed, shift, ves = splitFixed(e) if fixed is None: return PEdge(pe.edge, e, pe.shift) fixed.append(PEdge(pe.edge, newSeq(ves), pe.shift - shift)) return newSeq(fixed) def PFold(self, pe): e = self.visit(pe.e) fixed, shift, ves = splitFixed(e) if fixed is None: # or not self.Ooox: return PFold(pe.edge, e, pe.tag, pe.shift) fixed.append(PFold(pe.edge, newSeq(ves), pe.tag, pe.shift - shift)) return newSeq(fixed) def PAbs(self, pe): return PAbs(self.visit(pe.e)) def PAction(self, pe): return PAction(self.visit(pe.e), pe.func, pe.params, pe.ptree) optimizer = Optimizer() def default_optimizer(e): return optimizer.visit(e) # def default_optimizer(e): # return e def optimize(e): return optimizer.visit(e) def prepare(peg: Grammar, start_name=None, optimize_function=default_optimizer): #peg = peg if start_name is None: start_name = peg.start() start_ref = peg.newRef(start_name) refs = makeMinimumRules(start_ref, {}, []) refs = sortRules(refs) rules = {} memos = [] for ref in refs: uname = ref.uname(peg) rules[uname] = optimize_function(ref.deref()) memos.append(uname) # if str(rules[uname]) != str(ref.deref()): # print('OPTIMIZE', ref.deref(), '\n\t=>', rules[uname]) if 'packrat' not in peg: memos.clear() return start_ref, refs, rules, memos

24.415929

87

0.545216

2,074

0.18793

0

429

0.038873

347c4640275f1ffebdd61eb9ff26c587f84bd59e

911

py

Python

lib/ipc/async_emitter.py

stevenman42/architus

86f0c065172e27ae02603d6ef4e7d4eedfb6dbe4

[ "MIT" ]

null

lib/ipc/async_emitter.py

stevenman42/architus

86f0c065172e27ae02603d6ef4e7d4eedfb6dbe4

[ "MIT" ]

null

lib/ipc/async_emitter.py

stevenman42/architus

86f0c065172e27ae02603d6ef4e7d4eedfb6dbe4

[ "MIT" ]

null

import json from aio_pika import Message, DeliveryMode, ExchangeType from lib.ipc.util import poll_for_async_connection class Emitter: def __init__(self): self.connection = None self.event_exchange = None async def connect(self, loop): # Perform connection self.connection = await poll_for_async_connection(loop) channel = await self.connection.channel() self.event_exchange = await channel.declare_exchange( 'events', ExchangeType.TOPIC ) return self async def close(self): await self.connection.close() async def emit(self, routing_key, body): message = Message( json.dumps(body).encode(), delivery_mode=DeliveryMode.PERSISTENT ) # Sending the message await self.event_exchange.publish( message, routing_key=routing_key )

23.973684

63

0.644347

787

0.863886

0

665

0.729967

49

0.053787

347c814616bfce0ed2381cbdbfdc2ba0cb647a74

72

py

Python

pys/classes/annotations.py

Xithrius/Examples

d29fe9510f1c62a807e09f9707d0b2f6de9ffeed

[ "MIT" ]

null

pys/classes/annotations.py

Xithrius/Examples

d29fe9510f1c62a807e09f9707d0b2f6de9ffeed

[ "MIT" ]

null

pys/classes/annotations.py

Xithrius/Examples

d29fe9510f1c62a807e09f9707d0b2f6de9ffeed

[ "MIT" ]

null

import typing as t def test0(a: t.Union[str, int]) -> t.Any: pass

12

41

0.611111

0

347d4a00184ea7c2c1c24dd55efd4162c15dc9d8

7,727

py

Python

day_4/day_4_improvements/tests/test_user_edit.py

dmchu/Pytest_REST_API_with_Allure

a41fbbcc04304a89a99ece7b06148de668a7b696

[ "Apache-2.0" ]

null

day_4/day_4_improvements/tests/test_user_edit.py

dmchu/Pytest_REST_API_with_Allure

a41fbbcc04304a89a99ece7b06148de668a7b696

[ "Apache-2.0" ]

null

day_4/day_4_improvements/tests/test_user_edit.py

dmchu/Pytest_REST_API_with_Allure

a41fbbcc04304a89a99ece7b06148de668a7b696

[ "Apache-2.0" ]

null

import allure from day_4.day_4_improvements.lib.base_case import BaseCase from day_4.day_4_improvements.lib.assersions import Assertions as AS from day_4.day_4_improvements.lib.my_requests import MyRequests as MR from day_4.day_4_improvements.lib.helpers import Helpers as HP @allure.epic("User Profile Edit cases") class TestUserEdit(BaseCase): BASE_URI: str = "/user/" URI_LOGIN: str = BASE_URI + "login" @allure.feature("User Profile Edit") @allure.story("positive - Edit profile of just created user") @allure.description("Verifiying that user profile of just created user can be edited") def test_edit_just_created_user(self): registered_user: dict = HP.register_user(HP) user_email = registered_user.get("user_email") user_password = registered_user.get("user_password") user_id = registered_user.get("user_id") login_user_response = HP.authorize_user(HP, user_email, user_password) URI_USER = self.BASE_URI + str(user_id) new_name = "Changed Name" headers = { 'x-csrf-token': login_user_response.get("token") } cookies = { 'auth_sid': login_user_response.get("auth_sid") } edit_data = { 'firstName': new_name } # Edit user data response3 = MR.put(URI_USER, headers=headers, cookies=cookies, data=edit_data) AS.assert_code_status(response3, 200) # Get updated user data response4 = MR.get(URI_USER, headers=headers, cookies=cookies) AS.assert_json_value_by_name(response4, "firstName", new_name, "Wrong name of user after update") @allure.feature("User Profile Edit") @allure.story("negative - Edit profile of user without authorization") @allure.description("Verifiying that user profile can not be edited without authorization") def test_edit_existing_user_without_authorization(self): registered_user: dict = HP.register_user(HP) user_id = registered_user.get("user_id") URI_USER = self.BASE_URI + str(user_id) new_name = "Changed Name2" headers = { 'x-csrf-token': "" } cookies = { 'auth_sid': "" } edit_data = { 'firstName': new_name } # Edit user data response = MR.put(URI_USER, headers=headers, cookies=cookies, data=edit_data) AS.assert_code_status(response, 400) AS.assert_response_text(response, "Auth token not supplied") @allure.feature("User Profile Edit") @allure.story("negative - Edit profile of user with another user authorization") @allure.description("Verifiying that user profile can not be edited with another user authorization") def test_edit_existing_user_with_authorization_by_another_user(self): registered_user: dict = HP.register_user(HP) correct_user_email = registered_user.get("user_email") correct_user_password = registered_user.get("user_password") user_id = registered_user.get("user_id") user_email = "vinkotov@example.com" user_password = "1234" # Authorization with another user login_data = { 'email': user_email, 'password': user_password } response = MR.post(self.URI_LOGIN, data=login_data) auth_sid = self.get_cookie(response, "auth_sid") token = self.get_header(response, "x-csrf-token") URI_USER = self.BASE_URI + str(user_id) new_name = "Changed Name3" headers = { 'x-csrf-token': token } cookies = { 'auth_sid': auth_sid } edit_data = { 'email': new_name } # Try to edit user data response2 = MR.put(URI_USER, headers=headers, cookies=cookies, data=edit_data) AS.assert_code_status(response2, 400) # Authorization with correct user login_user_response = HP.authorize_user(HP, correct_user_email, correct_user_password) URI_USER = self.BASE_URI + str(user_id) new_name = "Changed Name" headers_2 = { 'x-csrf-token': login_user_response.get("token") } cookies_2 = { 'auth_sid': login_user_response.get("auth_sid") } # Get user data and verify that changes was not made response4 = MR.get(URI_USER, headers=headers_2, cookies=cookies_2) response_data = response4.json() user_first_name = response_data.get("firstName") assert user_first_name != new_name, \ "First name should not be changed by user with another authenticated user, but it did" @allure.feature("User Profile Edit") @allure.story("negative - Edit user 'email' with wrong email format") @allure.description("Verifiying that user 'email' can not be edited with wrong email format") def test_edit_user_email_with_wrong_format(self): registered_user: dict = HP.register_user(HP) user_email = registered_user.get("user_email") user_password = registered_user.get("user_password") user_id = registered_user.get("user_id") login_user_response = HP.authorize_user(HP, user_email, user_password) URI_USER = self.BASE_URI + str(user_id) new_email = user_email.replace("@", ".") headers = { 'x-csrf-token': login_user_response.get("token") } cookies = { 'auth_sid': login_user_response.get("auth_sid") } edit_data = { 'email': new_email } # Edit user data response3 = MR.put(URI_USER, headers=headers, cookies=cookies, data=edit_data) AS.assert_code_status(response3, 400) AS.assert_response_text(response3, "Invalid email format") # Get updated user data response4 = MR.get(URI_USER, headers=headers, cookies=cookies) response_data = response4.json() user_email = response_data.get("email") assert user_email != new_email, \ "Email should not be changed by user to email with wrong format, but it did" @allure.feature("User Profile Edit") @allure.story("negative - Edit user 'first name' with one character") @allure.description("Verifiying that user 'first name' can not be edited with one character") def test_edit_user_first_name_with_one_character(self): registered_user: dict = HP.register_user(HP) user_email = registered_user.get("user_email") user_password = registered_user.get("user_password") user_id = registered_user.get("user_id") login_user_response = HP.authorize_user(HP, user_email, user_password) URI_USER = self.BASE_URI + str(user_id) new_email = user_email.replace("@", ".") headers = { 'x-csrf-token': login_user_response.get("token") } cookies = { 'auth_sid': login_user_response.get("auth_sid") } edit_data = { 'firstName': "V" } # Edit user data response3 = MR.put(URI_USER, headers=headers, cookies=cookies, data=edit_data) AS.assert_code_status(response3, 400) AS.assert_json_value_by_name(response3, "error", "Too short value for field firstName", "The error message is not as expected") # Get updated user data response4 = MR.get(URI_USER, headers=headers, cookies=cookies) response_data = response4.json() user_first_name = response_data.get("firstName") assert user_first_name != "V", \ "First name should not be changed by user to very short name, but it did"

38.635

115

0.64734

7,408

0.958716

0

7,448

0.963893

0

2,040

0.264009

347d886b920493c8af185f7166a5e1089d5bdb0f

23

py

Python

test/example/__init__.py

fieldOfView/izzyPythonPlugin

deb0dca2a4294cd74aa18ab1228de10eceba1266

[ "MIT" ]

6

2019-11-06T00:37:43.000Z

2021-12-22T02:39:45.000Z

example/__init__.py

Submanifold/cmake-cpp-pybind11

f22893e87977de8c619d6033b7ca5bb240451f3d

[ "MIT" ]

null

example/__init__.py

Submanifold/cmake-cpp-pybind11

f22893e87977de8c619d6033b7ca5bb240451f3d

[ "MIT" ]

null

from .example import *

11.5

22

0.73913

0

347e054992d27cb2ec4c3396370171a573169533

64

py

Python

backend/models/__init__.py

Hori1234/gastech-project

dc89e546fce9b588fc8038cf63b7d82ad3eafff9

[ "MIT" ]

null

backend/models/__init__.py

Hori1234/gastech-project

dc89e546fce9b588fc8038cf63b7d82ad3eafff9

[ "MIT" ]

null

backend/models/__init__.py

Hori1234/gastech-project

dc89e546fce9b588fc8038cf63b7d82ad3eafff9

[ "MIT" ]

null

from .users import User __all__ = [ 'User']

9.142857

24

0.46875

0

6

0.09375

347e6ea9930d9b92685144c3238b1acc36f5d915

6,005

py

Python

bandit_github_formatter/formatter.py

epsylabs/action-bandit

626214a0b7ee3fa22ac81b0d8d5f14da61b41345

[ "MIT" ]

8

2021-06-29T07:12:33.000Z

2022-02-15T22:10:07.000Z

bandit_github_formatter/formatter.py

epsylabs/action-bandit

626214a0b7ee3fa22ac81b0d8d5f14da61b41345

[ "MIT" ]

null

bandit_github_formatter/formatter.py

epsylabs/action-bandit

626214a0b7ee3fa22ac81b0d8d5f14da61b41345

[ "MIT" ]

1

2022-02-13T20:09:01.000Z

r""" ============== GitHub Formatter ============== This formatter outputs the issues as plain text. :Example: .. code-block:: none >> Issue: [B301:blacklist_calls] Use of unsafe yaml load. Allows instantiation of arbitrary objects. Consider yaml.safe_load(). Severity: Medium Confidence: High Location: examples/yaml_load.py:5 More Info: https://bandit.readthedocs.io/en/latest/ 4 ystr = yaml.dump({'a' : 1, 'b' : 2, 'c' : 3}) 5 y = yaml.load(ystr) 6 yaml.dump(y) .. versionadded:: 0.9.0 """ from __future__ import print_function import logging import json import os import requests from bandit.core import constants from bandit.core import docs_utils from bandit.core import test_properties LOG = logging.getLogger(__name__) def get_verbose_details(manager): # TODO: re-format the output for verbose details bits = [] bits.append(u'Files in scope (%i):' % len(manager.files_list)) tpl = u"\t%s (score: {SEVERITY: %i, CONFIDENCE: %i})" bits.extend([tpl % (item, sum(score['SEVERITY']), sum(score['CONFIDENCE'])) for (item, score) in zip(manager.files_list, manager.scores)]) bits.append(u'Files excluded (%i):' % len(manager.excluded_files)) bits.extend([u"\t%s" % fname for fname in manager.excluded_files]) return '\n'.join([bit for bit in bits]) def get_metrics(manager): bits = [] bits.append("\n### Run metrics:\n") bits.append(f"| | {'|'.join(constants.RANKING)}|") bits.append("|:-:|:-:|:-:|:-:|:-:|") severity_list = [str(int(manager.metrics.data['_totals'][f'SEVERITY.{rank}'])) for rank in constants.RANKING] bits.append(f"|SEVERITY|{'|'.join(severity_list)}|") confidence_list = [str(int(manager.metrics.data['_totals'][f'CONFIDENCE.{rank}'])) for rank in constants.RANKING] bits.append(f"|CONFIDENCE|{'|'.join(confidence_list)}|") return '\n'.join([bit for bit in bits]) def _output_issue_str(issue, indent, show_lineno=True, show_code=True, lines=-1): # returns a list of lines that should be added to the existing lines list bits = [] bits.append("<details>") bits.append("<summary>[%s:%s] %s</summary>\n \n" % ( issue.test_id, issue.test, issue.text)) bits.append("|Severity| %s |\n|:-:|:-:|\n|Confidence| %s |" % ( issue.severity.capitalize(), issue.confidence.capitalize())) bits.append("|Location| %s:%s:%s |" % ( issue.fname, issue.lineno if show_lineno else "", "")) bits.append("|More Info| %s |\n" % ( docs_utils.get_url(issue.test_id))) if show_code: bits.append(" \n\n```python") bits.extend([indent + line for line in issue.get_code(lines, True).split('\n')]) bits.append("```\n") bits.append("</details>") return '\n'.join([bit for bit in bits]) def get_results(manager, sev_level, conf_level, lines): bits = [] issues = manager.get_issue_list(sev_level, conf_level) baseline = not isinstance(issues, list) candidate_indent = ' ' * 10 if not len(issues): return u"\tNo issues identified." for issue in issues: # if not a baseline or only one candidate we know the issue if not baseline or len(issues[issue]) == 1: bits.append(_output_issue_str(issue, "", lines=lines)) # otherwise show the finding and the candidates else: bits.append(_output_issue_str(issue, "", show_lineno=False, show_code=False)) bits.append(u'\n-- Candidate Issues --') for candidate in issues[issue]: bits.append(_output_issue_str(candidate, candidate_indent, lines=lines)) bits.append('\n') return '\n'.join([bit for bit in bits]) def comment_on_pr(message): token = os.getenv("INPUT_GITHUB_TOKEN") if not token: print(message) return if os.getenv("GITHUB_EVENT_NAME") == "pull_request": with open(os.getenv("GITHUB_EVENT_PATH")) as json_file: event = json.load(json_file) headers_dict = { "Accept": "application/vnd.github.v3+json", "Authorization": f"token {token}" } request_path = ( f"https://api.github.com/repos/{event['repository']['full_name']}/issues/{event['number']}/comments") requests.post(request_path, headers=headers_dict, json={"body": message}) @test_properties.accepts_baseline def report(manager, fileobj, sev_level, conf_level, lines=-1): """Prints discovered issues in the text format :param manager: the bandit manager object :param fileobj: The output file object, which may be sys.stdout :param sev_level: Filtering severity level :param conf_level: Filtering confidence level :param lines: Number of lines to report, -1 for all """ bits = [] if manager.results_count(sev_level, conf_level): if manager.verbose: bits.append(get_verbose_details(manager)) bits.append("## Bandit results:") bits.append('Total lines of code: %i' % (manager.metrics.data['_totals']['loc'])) bits.append('Total lines skipped (#nosec): %i' % (manager.metrics.data['_totals']['nosec'])) bits.append(get_metrics(manager)) bits.append("<details><summary>📋 Click here to see the all possible security issues</summary>\n \n") bits.append(get_results(manager, sev_level, conf_level, lines)) bits.append("</details>") result = '\n'.join([bit for bit in bits]) + '\n' comment_on_pr(result)

34.119318

117

0.602498

0

1,237

0.205892

0

2,308

0.384154

347e90ac805f9147fa4f7a2a02dbf307daa9c5ca

3,685

py

Python

usecase/usecase-cordova-android-tests/samples/SharedModeLibraryDownload4.x/res/test.py

JianfengXu/crosswalk-test-suite

6fb6ef9d89235743ee8b867fd2541c5bdf388786

[ "BSD-3-Clause" ]

null

usecase/usecase-cordova-android-tests/samples/SharedModeLibraryDownload4.x/res/test.py

JianfengXu/crosswalk-test-suite

6fb6ef9d89235743ee8b867fd2541c5bdf388786

[ "BSD-3-Clause" ]

null

usecase/usecase-cordova-android-tests/samples/SharedModeLibraryDownload4.x/res/test.py

JianfengXu/crosswalk-test-suite

6fb6ef9d89235743ee8b867fd2541c5bdf388786

[ "BSD-3-Clause" ]

null

import os import commands import sys import json from optparse import OptionParser global CROSSWALK_VERSION with open("../../tools/VERSION", "rt") as pkg_version_file: pkg_version_raw = pkg_version_file.read() pkg_version_file.close() pkg_version_json = json.loads(pkg_version_raw) CROSSWALK_VERSION = pkg_version_json["main-version"] try: usage = "Usage: ./test.py -u [http://host/XWalkRuntimeLib.apk]" opts_parser = OptionParser(usage=usage) opts_parser.add_option( "-u", "--url", dest="url", help="specify the url, e.g. http://host/XWalkRuntimeLib.apk") global BUILD_PARAMETERS (BUILD_PARAMETERS, args) = opts_parser.parse_args() except Exception as e: print "Got wrong options: %s, exit ..." % e sys.exit(1) if not BUILD_PARAMETERS.url: print "Please add the -u parameter for the url of XWalkRuntimeLib.apk" sys.exit(1) version_parts = CROSSWALK_VERSION.split('.') if len(version_parts) < 4: print "The crosswalk version is not configured exactly!" sys.exit(1) versionType = version_parts[3] if versionType == '0': username = commands.getoutput("echo $USER") repository_aar_path = "/home/%s/.m2/repository/org/xwalk/xwalk_shared_library/%s/" \ "xwalk_shared_library-%s.aar" % \ (username, CROSSWALK_VERSION, CROSSWALK_VERSION) repository_pom_path = "/home/%s/.m2/repository/org/xwalk/xwalk_shared_library/%s/" \ "xwalk_shared_library-%s.pom" % \ (username, CROSSWALK_VERSION, CROSSWALK_VERSION) if not os.path.exists(repository_aar_path) or not os.path.exists(repository_pom_path): wget_cmd = "wget https://download.01.org/crosswalk/releases/crosswalk/" \ "android/canary/%s/crosswalk-shared-%s.aar" % \ (CROSSWALK_VERSION, CROSSWALK_VERSION) install_cmd = "mvn install:install-file -DgroupId=org.xwalk " \ "-DartifactId=xwalk_shared_library -Dversion=%s -Dpackaging=aar " \ "-Dfile=crosswalk-shared-%s.aar -DgeneratePom=true" % \ (CROSSWALK_VERSION, CROSSWALK_VERSION) os.system(wget_cmd) os.system(install_cmd) library_url = BUILD_PARAMETERS.url library_url = library_url.replace("/", "\\/") if os.path.exists("SharedModeLibraryDownload"): os.system("rm -rf SharedModeLibraryDownload") os.system("cordova create SharedModeLibraryDownload com.example.sharedModeLibraryDownload SharedModeLibraryDownload") os.chdir("./SharedModeLibraryDownload") os.system('sed -i "s/<widget/<widget android-activityName=\\"SharedModeLibraryDownload\\"/g" config.xml') os.system('sed -i "s/<\/widget>/ <allow-navigation href=\\"*\\" \/>\\n<\/widget>/g" config.xml') os.system("cordova platform add android") add_plugin_cmd = "cordova plugin add ../../../tools/cordova-plugin-crosswalk-webview" \ " --variable XWALK_VERSION=\"%s\" --variable XWALK_MODE=\"shared\"" % CROSSWALK_VERSION print add_plugin_cmd os.system(add_plugin_cmd) os.system('sed -i "s/android:supportsRtl=\\"true\\">/android:supportsRtl=\\"true\\">\\n <meta-data android:name=\\"xwalk_apk_url\\" android:value=\\"' + library_url + '\\" \\/>/g" platforms/android/AndroidManifest.xml') os.system("cordova build android") os.system("cordova run") lsstatus = commands.getstatusoutput("ls ./platforms/android/build/outputs/apk/*.apk") if lsstatus[0] == 0: print "Build Package Successfully" else: print "Build Package Error" pmstatus = commands.getstatusoutput("adb shell pm list packages |grep com.example.sharedModeLibraryDownload") if pmstatus[0] == 0: print "Package Name Consistent" else: print "Package Name Inconsistent"

44.39759

226

0.699322

0

1,780

0.483039

347ea510032b5f75cc03eb21b308b8c9b11d3a97

1,176

py

Python

examples/python/simple_triangulation_3.py

chrisidefix/cgal-bindings

ddd8551f1ded7354c82a9690c06fcbc6512b6604

[ "BSL-1.0" ]

33

2015-04-14T22:03:37.000Z

2022-01-23T00:01:23.000Z

examples/python/simple_triangulation_3.py

chrisidefix/cgal-bindings

ddd8551f1ded7354c82a9690c06fcbc6512b6604

[ "BSL-1.0" ]

4

2017-07-23T18:02:32.000Z

2020-02-17T18:31:37.000Z

examples/python/simple_triangulation_3.py

chrisidefix/cgal-bindings

ddd8551f1ded7354c82a9690c06fcbc6512b6604

[ "BSL-1.0" ]

6

2015-06-10T10:51:11.000Z

2019-04-16T10:24:53.000Z

from CGAL.CGAL_Kernel import Point_3 from CGAL.CGAL_Triangulation_3 import Delaunay_triangulation_3 from CGAL.CGAL_Triangulation_3 import Delaunay_triangulation_3_Cell_handle from CGAL.CGAL_Triangulation_3 import Delaunay_triangulation_3_Vertex_handle from CGAL.CGAL_Triangulation_3 import Ref_Locate_type_3 from CGAL.CGAL_Triangulation_3 import VERTEX from CGAL.CGAL_Kernel import Ref_int L=[] L.append( Point_3(0,0,0) ) L.append( Point_3(1,0,0) ) L.append( Point_3(0,1,0) ) T=Delaunay_triangulation_3(L) n=T.number_of_vertices() V=[] V.append( Point_3(0,0,1) ) V.append( Point_3(1,1,1) ) V.append( Point_3(2,2,2) ) n = n + T.insert(V) assert n==6 assert T.is_valid() lt=Ref_Locate_type_3() li=Ref_int() lj=Ref_int() p=Point_3(0,0,0) c = T.locate(p, lt, li, lj) assert lt.object() == VERTEX assert c.vertex(li.object()).point() == p v = c.vertex( (li.object()+1)&3 ) nc = c.neighbor(li.object()) nli=Ref_int() assert nc.has_vertex( v, nli ) T.write_to_file("output",14) T1 = Delaunay_triangulation_3() T1.read_from_file("output") assert T1.is_valid() assert T1.number_of_vertices() == T.number_of_vertices() assert T1.number_of_cells() == T.number_of_cells()

21.777778

76

0.754252

0

16

0.013605

347f4781f31fad4fabc2967828da3263fd07f923

17,298

py

Python

lims/inventory/views.py

sqilz/LIMS-Backend

b64e1fa512f89e4492803d44c6b8c35e4d4724cc

[ "MIT" ]

12

2017-03-01T10:39:36.000Z

2022-01-04T06:17:19.000Z

lims/inventory/views.py

sqilz/LIMS-Backend

b64e1fa512f89e4492803d44c6b8c35e4d4724cc

[ "MIT" ]

29

2017-04-25T14:05:08.000Z

2021-06-21T14:41:53.000Z

lims/inventory/views.py

sqilz/LIMS-Backend

b64e1fa512f89e4492803d44c6b8c35e4d4724cc

[ "MIT" ]

4

2017-10-11T16:22:53.000Z

2021-02-23T15:45:21.000Z

import io import json from django.core.exceptions import ObjectDoesNotExist from pint import UnitRegistry import django_filters from rest_framework import viewsets from rest_framework.response import Response from rest_framework.decorators import detail_route, list_route from rest_framework import serializers from rest_framework.parsers import FormParser, MultiPartParser from rest_framework.filters import (OrderingFilter, SearchFilter, DjangoFilterBackend) from lims.permissions.permissions import (IsInAdminGroupOrRO, ViewPermissionsMixin, ExtendedObjectPermissions, ExtendedObjectPermissionsFilter) from lims.shared.mixins import StatsViewMixin, AuditTrailViewMixin from lims.filetemplate.models import FileTemplate from lims.projects.models import Product from .models import Set, Item, ItemTransfer, ItemType, Location, AmountMeasure from .serializers import (AmountMeasureSerializer, ItemTypeSerializer, LocationSerializer, ItemSerializer, DetailedItemSerializer, SetSerializer, ItemTransferSerializer) from .providers import InventoryItemPluginProvider # Define as module level due to issues with file locking # when calling a function requiring it multiple times ureg = UnitRegistry() class LeveledMixin(AuditTrailViewMixin): """ Provide a display value for a heirarchy of elements """ def _to_leveled(self, obj): level = getattr(obj, obj._mptt_meta.level_attr) if level == 0: display_value = obj.name else: display_value = '{} {}'.format('--' * level, obj.name) return { 'display_value': display_value, 'value': obj.name, 'root': obj.get_root().name } class MeasureViewSet(AuditTrailViewMixin, viewsets.ModelViewSet): queryset = AmountMeasure.objects.all() serializer_class = AmountMeasureSerializer permission_classes = (IsInAdminGroupOrRO,) search_fields = ('symbol', 'name',) class ItemTypeViewSet(viewsets.ModelViewSet, LeveledMixin): queryset = ItemType.objects.all() serializer_class = ItemTypeSerializer permission_classes = (IsInAdminGroupOrRO,) search_fields = ('name', 'parent__name',) def destroy(self, request, *args, **kwargs): instance = self.get_object() if instance.has_children(): return Response({'message': 'Cannot delete ItemType with children'}, status=400) self.perform_destroy(instance) return Response(status=204) class LocationViewSet(viewsets.ModelViewSet, LeveledMixin): queryset = Location.objects.all() serializer_class = LocationSerializer permission_classes = (IsInAdminGroupOrRO,) search_fields = ('name', 'parent__name') def filter_queryset(self, queryset): queryset = super(LocationViewSet, self).filter_queryset(queryset) # Set ordering explicitly as django-filter borks the defaults return queryset.order_by('tree_id', 'lft') def destroy(self, request, *args, **kwargs): instance = self.get_object() if instance.has_children(): return Response({'message': 'Cannot delete Location with children'}, status=400) self.perform_destroy(instance) return Response(status=204) class InventoryFilterSet(django_filters.FilterSet): """ Filter for inventory items """ class Meta: model = Item fields = { 'id': ['exact'], 'name': ['exact', 'icontains'], 'added_by__username': ['exact'], 'identifier': ['exact'], 'barcode': ['exact'], 'description': ['icontains'], 'item_type__name': ['exact'], 'location__name': ['exact'], 'in_inventory': ['exact'], 'amount_measure__symbol': ['exact'], 'amount_available': ['exact', 'lt', 'lte', 'gt', 'gte'], 'concentration_measure__symbol': ['exact'], 'concentration': ['exact', 'lt', 'lte', 'gt', 'gte'], 'added_on': ['exact', 'lt', 'lte', 'gt', 'gte'], 'last_updated_on': ['exact', 'lt', 'lte', 'gt', 'gte'], 'properties__name': ['exact', 'icontains'], 'properties__value': ['exact', 'icontains'], } class InventoryViewSet(LeveledMixin, StatsViewMixin, ViewPermissionsMixin, viewsets.ModelViewSet): queryset = Item.objects.all() serializer_class = ItemSerializer permission_classes = (ExtendedObjectPermissions,) filter_backends = (SearchFilter, DjangoFilterBackend, OrderingFilter, ExtendedObjectPermissionsFilter,) search_fields = ('name', 'identifier', 'item_type__name', 'location__name', 'location__parent__name') filter_class = InventoryFilterSet def get_serializer_class(self): if self.action == 'list': return self.serializer_class return DetailedItemSerializer def get_object(self): instance = super().get_object() plugins = [p(instance) for p in InventoryItemPluginProvider.plugins] for p in plugins: p.view() return instance def perform_create(self, serializer): serializer, permissions = self.clean_serializer_of_permissions(serializer) instance = serializer.save(added_by=self.request.user) self.assign_permissions(instance, permissions) plugins = [p(instance) for p in InventoryItemPluginProvider.plugins] for p in plugins: p.create() def perform_update(self, serializer): instance = serializer.save() plugins = [p(instance) for p in InventoryItemPluginProvider.plugins] for p in plugins: p.update() @list_route(methods=['POST'], parser_classes=(FormParser, MultiPartParser,)) def importitems(self, request): """ Import items from a CSV file Expects: file_template: The ID of the file template to use to parse the file items_file: The CSV file to parse permissions: Standard permissions format ({"name": "rw"}) to give to all items """ file_template_id = request.data.get('filetemplate', None) uploaded_file = request.data.get('items_file', None) permissions = request.data.get('permissions', '{}') response_data = {} if uploaded_file and file_template_id: try: filetemplate = FileTemplate.objects.get(id=file_template_id) except FileTemplate.DoesNotExist: return Response({'message': 'File template does not exist'}, status=404) encoding = 'utf-8' if request.encoding is None else request.encoding f = io.TextIOWrapper(uploaded_file.file, encoding=encoding) items_to_import = filetemplate.read(f, as_list=True) saved = [] rejected = [] if items_to_import: for item_data in items_to_import: item_data['assign_groups'] = json.loads(permissions) if 'properties' not in item_data: item_data['properties'] = [] ''' I'm not actually sure what this was supposed to do! Properties are already list so this shouldn't be required. else: item_data['properties'] = ast.literal_eval(item_data['properties']) ''' item = DetailedItemSerializer(data=item_data) if item.is_valid(): saved.append(item_data) item, parsed_permissions = self.clean_serializer_of_permissions(item) item.validated_data['added_by'] = request.user instance = item.save() self.assign_permissions(instance, parsed_permissions) if 'product' in item_data: try: prod = item_data['product'] product = Product.objects.get(product_identifier=prod) except: pass else: product.linked_inventory.add(instance) else: item_data['errors'] = item.errors rejected.append(item_data) else: return Response({'message': 'File is format is incorrect'}, status=400) response_data = { 'saved': saved, 'rejected': rejected } return Response(response_data) @list_route(methods=['POST']) def export_items(self, request): # The ID of the file template file_template_id = request.data.get('filetemplate', None) # The ID's of items to get selected = request.data.get('selected', None) if file_template_id: if selected: ids = selected.strip(',').split(',') items = Item.objects.filter(pk__in=ids) else: # The query used to get the results # Query params in URL used NOT in .data items = self.filter_queryset(self.get_queryset()) serializer = DetailedItemSerializer(items, many=True) try: file_template = FileTemplate.objects.get(pk=file_template_id) except: return Response({'message': 'File template does not exist'}, status=404) with io.StringIO() as output_file: output_file = file_template.write(output_file, serializer.data) output_file.seek(0) return Response(output_file.read(), content_type='text/csv') return Response({'Please supply a file template and data to export'}, status=400) @detail_route(methods=['POST']) def transfer(self, request, pk=None): """ Either create or complete an item transfer. """ tfr_id = request.query_params.get('id', None) complete_transfer = request.query_params.get('complete', False) transfer_details = request.data if tfr_id and complete_transfer: try: tfr = ItemTransfer.objects.get(pk=tfr_id) except ObjectDoesNotExist: return Response({'message': 'No item transfer exists with that ID'}, status=404) tfr.transfer_complete = True tfr.save() return Response({'message': 'Transfer {} complete'.format(tfr_id)}) elif transfer_details: item = self.get_object() raw_amount = float(transfer_details.get('amount', 0)) raw_measure = transfer_details.get('measure', item.amount_measure.symbol) addition = transfer_details.get('is_addition', False) # Booleanise them is_complete = False is_addition = False if addition: is_addition = True is_complete = True if transfer_details.get('transfer_complete', False): is_complete = True try: measure = AmountMeasure.objects.get(symbol=raw_measure) except AmountMeasure.DoesNotExist: raise serializers.ValidationError({'message': 'Measure {} does not exist'.format(raw_measure) }) tfr = ItemTransfer( item=item, amount_taken=raw_amount, amount_measure=measure, barcode=transfer_details.get('barcode', ''), coordinates=transfer_details.get('coordinates', ''), transfer_complete=is_complete, is_addition=is_addition ) transfer_status = tfr.check_transfer() if transfer_status[0] is True: tfr.save() tfr.do_transfer(ureg) else: return Response( {'message': 'Inventory item {} ({}) is short of amount by {}'.format( item.identifier, item.name, transfer_status[1])}, status=400) return Response({'message': 'Transfer {} created'.format(tfr.id)}) return Response({'message': 'You must provide a transfer ID'}, status=400) @detail_route(methods=['POST']) def cancel_transfer(self, request, pk=None): """ Cancel an active transfer, adding the amount back """ tfr_id = request.query_params.get('id', None) if tfr_id: try: tfr = ItemTransfer.objects.get(pk=tfr_id, transfer_complete=False) except ObjectDoesNotExist: return Response({'message': 'No item transfer exists with that ID'}, status=404) tfr.is_addition = True tfr.do_transfer(ureg) tfr.delete() return Response({'message': 'Transfer cancelled'}) return Response({'message': 'You must provide a transfer ID'}, status=400) class SetViewSet(AuditTrailViewMixin, viewsets.ModelViewSet, ViewPermissionsMixin): queryset = Set.objects.all() serializer_class = SetSerializer permission_classes = (ExtendedObjectPermissions,) search_fields = ('name',) filter_fields = ('is_partset',) filter_backends = (SearchFilter, DjangoFilterBackend, OrderingFilter, ExtendedObjectPermissionsFilter,) def perform_create(self, serializer): serializer, permissions = self.clean_serializer_of_permissions(serializer) instance = serializer.save() self.assign_permissions(instance, permissions) @detail_route() def items(self, request, pk=None): limit_to = request.query_params.get('limit_to', None) item = self.get_object() if limit_to: queryset = [o for o in item.items.all() if o.item_type.name == limit_to] else: queryset = item.items.all() serializer = ItemSerializer(queryset, many=True) return Response(serializer.data) @detail_route(methods=['POST']) def add(self, request, pk=None): item_id = request.query_params.get('id', None) inventoryset = self.get_object() if item_id: try: item = Item.objects.get(pk=item_id) except Item.DoesNotExist: raise serializers.ValidationError({'message': 'Item {} does not exist'.format(item_id)}) item.sets.add(inventoryset) return Response(status=201) return Response( {'message': 'The id of the item to add to the inventory is required'}, status=400) @detail_route(methods=['DELETE']) def remove(self, request, pk=None): item_id = request.query_params.get('id', None) inventoryset = self.get_object() if item_id: try: item = inventoryset.items.get(pk=item_id) except Item.DoesNotExist: raise serializers.ValidationError({'message': 'Item {} does not exist'.format(item_id)}) inventoryset.items.remove(item) return Response(status=204) return Response( {'message': 'The id of the item to add to the inventory is required'}, status=400) class ItemTransferViewSet(AuditTrailViewMixin, viewsets.ReadOnlyModelViewSet, ViewPermissionsMixin): queryset = ItemTransfer.objects.all() serializer_class = ItemTransferSerializer search_fields = ('item__name', 'item__identifier', 'barcode',) filter_fields = ('transfer_complete', 'barcode',) filter_backends = (SearchFilter, DjangoFilterBackend, OrderingFilter,) def get_queryset(self): return ItemTransfer.objects.filter(transfer_complete=False) @list_route(methods=['GET']) def grouped(self, request): """ Group transfers under the same barcode e.g. as if they where in plates. Limit allows to set how many barcodes are fetched. """ limit = int(request.query_params.get('limit', 10)) qs = (ItemTransfer.objects.filter(transfer_complete=False) .distinct('barcode') .order_by('barcode', '-date_created')[:limit]) barcodes = [i.barcode for i in qs] transfers = (ItemTransfer.objects.filter(transfer_complete=False, barcode__in=barcodes) .order_by('barcode', 'coordinates')) serializer = ItemTransferSerializer(transfers, many=True) groups = {} for t in serializer.data: if t['barcode'] not in groups: groups[t['barcode']] = [] groups[t['barcode']].append(t) return Response(groups)

41.482014

100

0.594693

15,863

0.917042

0

10,165

0.58764

0

3,296

0.190542

34827671e1dfddc2f889fbb286b2c0765d7735fe

7,047

py

Python

python/xfr/models/vggface.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

52

2020-08-04T11:33:09.000Z

2021-12-05T14:16:22.000Z

python/xfr/models/vggface.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

10

2020-08-24T08:34:15.000Z

2021-12-05T06:59:50.000Z

python/xfr/models/vggface.py

rwe0214/xfr

a49d9e80a1bc45c25c72c394c60f6274599321aa

[ "MIT" ]

12

2020-08-05T03:11:56.000Z

2021-12-14T23:52:50.000Z

# Copyright 2019 Systems & Technology Research, LLC # Use of this software is governed by the license.txt file. import os import numpy as np import torch import torch.nn as nn import torchvision.transforms as transforms import torch.nn.functional as F from PIL import ImageFilter def prepare_vggface_image(img): """ Convert an RGB byte image to a FloatTensor suitable for processing with the network. This function assumes the image has already been resized, cropped, jittered, etc. """ # Convert to BGR img_bgr = np.array(img)[...,[2,1,0]] # Subtract mean pixel value img_bgr_fp = img_bgr - np.array((93.5940, 104.7624, 129.1863)) # Permute dimensions so output is 3xRxC img_bgr_fp = np.rollaxis(img_bgr_fp, 2, 0) return torch.from_numpy(img_bgr_fp).float() def generate_random_blur(blur_radius, blur_prob): def random_blur(img): if np.random.random() < blur_prob: return img.filter(ImageFilter.GaussianBlur(radius=blur_radius)) else: return img return random_blur """ Function suitable for transform argument of datasets.ImageFolder """ def vggface_preprocess(jitter=False, blur_radius=None, blur_prob=1.0): transform_list = [transforms.Resize(256),] if jitter: transform_list.append(transforms.RandomCrop((224,224))) transform_list.append(transforms.RandomHorizontalFlip()) #transform_list.append(transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)) else: transform_list.append(transforms.CenterCrop((224,224))) if blur_radius is not None and blur_prob > 0: transform_list.append(transforms.Lambda(generate_random_blur(blur_radius, blur_prob))) # finally, convert PIL RGB image to FloatTensor transform_list.append(transforms.Lambda(prepare_vggface_image)) return transforms.Compose(transform_list) class VGGFace(nn.Module): """ The VGGFace network (VGG_VD_16) mode can be one of ['encode', 'classify', 'both'] """ def __init__(self, mode='encode', num_classes=2622): super(VGGFace, self).__init__() valid_modes = {'encode','classify','both'} if mode not in valid_modes: raise Exception('mode should be one of ' + str(valid_modes)) self.mode = mode self.fc_outputs = num_classes # layers with stored weights self.conv1_1 = nn.Conv2d(3,64,(3, 3),(1, 1),(1, 1)) self.conv1_2 = nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1)) self.conv2_1 = nn.Conv2d(64,128,(3, 3),(1, 1),(1, 1)) self.conv2_2 = nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1)) self.conv3_1 = nn.Conv2d(128,256,(3, 3),(1, 1),(1, 1)) self.conv3_2 = nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)) self.conv3_3 = nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)) self.conv4_1 = nn.Conv2d(256,512,(3, 3),(1, 1),(1, 1)) self.conv4_2 = nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)) self.conv4_3 = nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)) self.conv5_1 = nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)) self.conv5_2 = nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)) self.conv5_3 = nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)) self.fc6 = nn.Linear(25088,4096) self.fc7 = nn.Linear(4096,4096) self.fc8 = nn.Linear(4096, self.fc_outputs) # layers with no weights self.nonlin = nn.ReLU() self.maxpool = nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True) self.dropout = nn.Dropout(0.5) def forward(self, input): """ Run the network. Input should be Nx3x224x224. Based on self.mode, return output of fc7, fc8, or both. """ assert len(input.size()) == 4 e1_1 = self.nonlin(self.conv1_1(input)) e1_2 = self.maxpool(self.nonlin(self.conv1_2(e1_1))) e2_1 = self.nonlin(self.conv2_1(e1_2)) e2_2 = self.maxpool(self.nonlin(self.conv2_2(e2_1))) e3_1 = self.nonlin(self.conv3_1(e2_2)) e3_2 = self.nonlin(self.conv3_2(e3_1)) e3_3 = self.maxpool(self.nonlin(self.conv3_3(e3_2))) e4_1 = self.nonlin(self.conv4_1(e3_3)) e4_2 = self.nonlin(self.conv4_2(e4_1)) e4_3 = self.maxpool(self.nonlin(self.conv4_3(e4_2))) e5_1 = self.nonlin(self.conv5_1(e4_3)) e5_2 = self.nonlin(self.conv5_2(e5_1)) e5_3 = self.maxpool(self.nonlin(self.conv5_3(e5_2))) e5_3_flat = e5_3.view(e5_3.size(0), -1) e6 = self.nonlin(self.fc6(e5_3_flat)) # use encoding prior to nonlinearity e7_pre = self.fc7(self.dropout(e6)) e7 = self.nonlin(e7_pre) # return e7, e8, or both depending on self.mode if self.mode == 'encode': return e7 else: e8 = self.fc8(self.dropout(e7)) if self.mode == 'classify': return e8 elif self.mode == 'both': return e7,e8 else: raise Exception('Invalid mode: ' + mode) def set_fc_outputs(self, new_fc_outputs): self.fc_outputs = new_fc_outputs self.fc8 = nn.Linear(4096, self.fc_outputs) class VGGFace_Custom(VGGFace): """Inherit VGGFace() and override the forward pass to normalize the output. Don't care about classification """ def forward(self, input, nrm=True): """ Run the network. Input should be Nx3x224x224. Based on self.mode, return output of fc7, fc8, or both. """ assert len(input.size()) == 4 e1_1 = self.nonlin(self.conv1_1(input)) e1_2 = self.maxpool(self.nonlin(self.conv1_2(e1_1))) e2_1 = self.nonlin(self.conv2_1(e1_2)) e2_2 = self.maxpool(self.nonlin(self.conv2_2(e2_1))) e3_1 = self.nonlin(self.conv3_1(e2_2)) e3_2 = self.nonlin(self.conv3_2(e3_1)) e3_3 = self.maxpool(self.nonlin(self.conv3_3(e3_2))) e4_1 = self.nonlin(self.conv4_1(e3_3)) e4_2 = self.nonlin(self.conv4_2(e4_1)) e4_3 = self.maxpool(self.nonlin(self.conv4_3(e4_2))) e5_1 = self.nonlin(self.conv5_1(e4_3)) e5_2 = self.nonlin(self.conv5_2(e5_1)) e5_3 = self.maxpool(self.nonlin(self.conv5_3(e5_2))) e5_3_flat = e5_3.view(e5_3.size(0), -1) e6 = self.nonlin(self.fc6(e5_3_flat)) # use encoding prior to nonlinearity e7_pre = self.fc7(self.dropout(e6)) e7 = self.nonlin(e7_pre) """Override code here: Want to normalize the output and return the encoding. Don't care about classification. """ if nrm is False: return e7 #print torch.div(e7,torch.norm(e7)) #print e7.size() xnorm = F.normalize(e7, p=2, dim=1) return xnorm #return torch.div(e7,torch.norm(e7)) def vgg16(model_filename=None): """ Constructs a VGG-16 model """ model = VGGFace_Custom() if model_filename is not None: model.load_state_dict(torch.load(model_filename)) return model

34.208738

109

0.616858

4,917

0.697744

0

1,624

0.230453

348349d381d9db8465fa237f65e4440d7cf40299

1,400

py

Python

easy/1356-Sort Integers by The Number of 1 Bits.py

Davidxswang/leetcode

d554b7f5228f14c646f726ddb91014a612673e06

[ "Apache-2.0" ]

2

2020-05-08T02:17:17.000Z

2020-05-17T04:55:56.000Z

easy/1356-Sort Integers by The Number of 1 Bits.py

Davidxswang/leetcode

d554b7f5228f14c646f726ddb91014a612673e06

[ "Apache-2.0" ]

null

easy/1356-Sort Integers by The Number of 1 Bits.py

Davidxswang/leetcode

d554b7f5228f14c646f726ddb91014a612673e06

[ "Apache-2.0" ]

null

""" https://leetcode.com/problems/sort-integers-by-the-number-of-1-bits/ Given an integer array arr. You have to sort the integers in the array in ascending order by the number of 1's in their binary representation and in case of two or more integers have the same number of 1's you have to sort them in ascending order. Return the sorted array. Example 1: Input: arr = [0,1,2,3,4,5,6,7,8] Output: [0,1,2,4,8,3,5,6,7] Explantion: [0] is the only integer with 0 bits. [1,2,4,8] all have 1 bit. [3,5,6] have 2 bits. [7] has 3 bits. The sorted array by bits is [0,1,2,4,8,3,5,6,7] Example 2: Input: arr = [1024,512,256,128,64,32,16,8,4,2,1] Output: [1,2,4,8,16,32,64,128,256,512,1024] Explantion: All integers have 1 bit in the binary representation, you should just sort them in ascending order. Example 3: Input: arr = [10000,10000] Output: [10000,10000] Example 4: Input: arr = [2,3,5,7,11,13,17,19] Output: [2,3,5,17,7,11,13,19] Example 5: Input: arr = [10,100,1000,10000] Output: [10,100,10000,1000] Constraints: 1 <= arr.length <= 500 0 <= arr[i] <= 10^4 """ # time complexity: O(nlogn), space complexity: O(n) class Solution: def sortByBits(self, arr: List[int]) -> List[int]: scale = 1e5 for i in range(len(arr)): arr[i] = [bin(arr[i]).count('1')*scale+arr[i],arr[i]] arr.sort(key=lambda x:x[0]) return list(num[1] for num in arr)

26.923077

247

0.660714

269

0.192143

0

1,129

0.806429

ca9e9bd0ad838e0babbbf679e67b3ceb8ca12cb5

959

py

Python

server/plugins/machine_detail_ard_info/scripts/ard_info.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

2

2019-11-01T20:50:35.000Z

2021-01-13T22:02:55.000Z

server/plugins/machine_detail_ard_info/scripts/ard_info.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

null

server/plugins/machine_detail_ard_info/scripts/ard_info.py

gregneagle/sal

74c583fb1c1b33d3201b308b147376b3dcaca33f

[ "Apache-2.0" ]

null

#!/usr/bin/python import os import sys sys.path.append("/usr/local/munki/munkilib") import FoundationPlist RESULTS_PATH = "/usr/local/sal/plugin_results.plist" def main(): ard_path = "/Library/Preferences/com.apple.RemoteDesktop.plist" if os.path.exists(ard_path): ard_prefs = FoundationPlist.readPlist(ard_path) else: ard_prefs = {} sal_result_key = "ARD_Info_{}" prefs_key_prefix = "Text{}" data = { sal_result_key.format(i): ard_prefs.get(prefs_key_prefix.format(i), "") for i in xrange(1, 5)} formatted_results = { "plugin": "ARD_Info", "historical": False, "data": data} if os.path.exists(RESULTS_PATH): plugin_results = FoundationPlist.readPlist(RESULTS_PATH) else: plugin_results = [] plugin_results.append(formatted_results) FoundationPlist.writePlist(plugin_results, RESULTS_PATH) if __name__ == "__main__": main()

21.795455

79

0.663191

0

202

0.210636

ca9f36676f8900dd5fc1fa8ab6463b0c5873344a

12,476

py

Python

biquad_filter_original.py

ignaciodsimon/optimised_biquad_filter

0d85dc42033e767eeb55107e72dba98417377686

[ "MIT" ]

null

biquad_filter_original.py

ignaciodsimon/optimised_biquad_filter

0d85dc42033e767eeb55107e72dba98417377686

[ "MIT" ]

null

biquad_filter_original.py

ignaciodsimon/optimised_biquad_filter

0d85dc42033e767eeb55107e72dba98417377686

[ "MIT" ]

null

''' Standard implementation of a biquad filter Joe Simon 2018. ''' import math from enum import Enum class BiquadFilterCoefficients(): def __init__(self, b0=1.0, b1=0, b2=0, a0=0, a1=0, a2=0): self.b0 = b0 self.b1 = b1 self.b2 = b2 self.a0 = a0 self.a1 = a1 self.a2 = a2 class BiquadFilterType(Enum): BPF_NORMALIZED = 1 HPF = 2 BPF = 3 LPF = 4 NOTCH = 5 APF = 6 LOW_SHELVING = 7 HIGH_SHELVING = 8 PEAK = 9 class BiquadFilterParameters: def __init__(self, sampleRate=48000.0, filterType=BiquadFilterType.APF, filterf0=1000.0, filterQ=1.0, filterGain=0.0): self.filterf0 = filterf0 self.filterQ = filterQ self.filterGain = filterGain self.filterType = filterType self.sampleRate = sampleRate class BiquadFilter: def __init__(self, filterParameters=None, sampleRate=None): # Filter parameters (initialize to a default value if not provided) if filterParameters is None: filterParameters = BiquadFilterParameters() self.filterf0 = filterParameters.filterf0 self.filterQ = filterParameters.filterQ self.filterGain = filterParameters.filterGain self.filterType = filterParameters.filterType self._sampleRate = filterParameters.sampleRate if not sampleRate is None: self._sampleRate = sampleRate # Initilize filter states self.x1 = 0.0 self.x2 = 0.0 self.y1 = 0.0 self.y2 = 0.0 # Generate filter coefficients self.filterCoefficients = BiquadFilterCoefficients() self.generateBiQuadCoefficients(filterType=self.filterType, filterf0=self.filterf0, filterQ=self.filterQ, filterGain=self.filterGain) def __str__(self): return "f0:%10.3f Hz, Q: %.3f, Gain: %.3f (%5.1f dB), Type: %s" % (self.filterf0, self.filterQ, 10**(self.filterGain/20.0), self.filterGain, str(self.filterType)) def processSample(self, inputSample, outputSample=None): # return inputSample # # Calculate new output sample # _output = (inputSample * self.filterCoefficients.b0) + \ # (self.x1 * self.filterCoefficients.b1) + \ # (self.x2 * self.filterCoefficients.b2) - \ # (self.y1 * self.filterCoefficients.a1) - \ # (self.y2 * self.filterCoefficients.a2) # _output = _output / self.filterCoefficients.a0 # return _output if outputSample is None: inputSample = float(inputSample) self._output = (inputSample * self.filterCoefficients.b0) self._output += (self.x1 * self.filterCoefficients.b1) self._output += (self.x2 * self.filterCoefficients.b2) self._output -= (self.y1 * self.filterCoefficients.a1) self._output -= (self.y2 * self.filterCoefficients.a2) self._output /= self.filterCoefficients.a0 # Rotate states self.x2 = self.x1 self.x1 = inputSample self.y2 = self.y1 self.y1 = self._output # Return new output return self._output else: # Calculate new output sample # inputSample[0] = float(inputSample[0]) outputSample[0] = (float(inputSample[0]) * self.filterCoefficients.b0) outputSample[0] += (self.x1 * self.filterCoefficients.b1) outputSample[0] += (self.x2 * self.filterCoefficients.b2) outputSample[0] -= (self.y1 * self.filterCoefficients.a1) outputSample[0] -= (self.y2 * self.filterCoefficients.a2) outputSample[0] /= self.filterCoefficients.a0 # Rotate states self.x2 = self.x1 self.x1 = inputSample[0] self.y2 = self.y1 self.y1 = outputSample[0] def generateBiQuadCoefficients(self, filterType, filterf0, filterQ, filterGain=0): ''' This example generates the coefficients for a bandpass filter with the peak gain at 0 dB http://shepazu.github.io/Audio-EQ-Cookbook/audio-eq-cookbook.html ''' # print("Generating filter coefficients for parameters: Type=%s, f0=%.1f [Hz], Q=%.2f, Gain=%.1f[dB]" % (str(filterType), filterf0, filterQ, filterGain)) if filterf0 > self._sampleRate / 2.0: _limitFreq = 0.99 * self._sampleRate / 2.0 print("Warning: Filter's f0 was set to %.1f [Hz], limiting it f0 to: %.1f" % (filterf0, _limitFreq)) filterf0 = _limitFreq _omega0 = 2 * math.pi * filterf0 / self._sampleRate _alpha = math.sin(_omega0) / (2 * filterQ) if filterType == BiquadFilterType.BPF_NORMALIZED: # BPF: _b0 = _alpha _b1 = 0 _b2 = -_alpha _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.HPF: # HPF: _b0 = (1 + math.cos(_omega0)) / 2.0 _b1 = - (1 + math.cos(_omega0)) _b2 = _b0 _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.BPF: # BPF-2: _b0 = filterQ * _alpha _b1 = 0 _b2 = -filterQ * _alpha _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.LPF: _b0 = (1 - math.cos(_omega0)) / 2.0 _b1 = 1 - math.cos(_omega0) _b2 = _b0 _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.NOTCH: _b0 = 1 _b1 = -2 * math.cos(_omega0) _b2 = 1 _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.APF: _b0 = 1 - _alpha _b1 = -2 * math.cos(_omega0) _b2 = 1 + _alpha _a0 = 1 + _alpha _a1 = -2 * math.cos(_omega0) _a2 = 1 - _alpha elif filterType == BiquadFilterType.PEAK: A = 10**(filterGain / 40.0) _b0 = 1 + (_alpha * A) _b1 = -2 * math.cos(_omega0) _b2 = 1 - (_alpha * A) _a0 = 1 + (_alpha / A) _a1 = -2 * math.cos(_omega0) _a2 = 1 - (_alpha / A) elif filterType == BiquadFilterType.LOW_SHELVING: A = 10**(filterGain / 40.0) _b0 = A * ((A + 1) - (A - 1)*math.cos(_omega0) + 2*math.sqrt(A)*_alpha) _b1 = 2 * A * ((A - 1) - (A + 1)*math.cos(_omega0)) _b2 = A * ((A + 1) - (A - 1)*math.cos(_omega0) - 2*math.sqrt(A)*_alpha) _a0 = (A + 1) + (A - 1)*math.cos(_omega0) + 2*math.sqrt(A)*_alpha _a1 = -2 * ((A - 1) + (A + 1)*math.cos(_omega0)) _a2 = (A + 1) + (A - 1)*math.cos(_omega0) - 2*math.sqrt(A)*_alpha elif filterType == BiquadFilterType.HIGH_SHELVING: A = 10**(filterGain / 40.0) _b0 = A * ((A + 1) + (A - 1)*math.cos(_omega0) + 2*math.sqrt(A)*_alpha) _b1 = -2 * A * ((A - 1) + (A + 1)*math.cos(_omega0)) _b2 = A * ((A + 1) + (A - 1)*math.cos(_omega0) - 2*math.sqrt(A)*_alpha) _a0 = (A + 1) - (A - 1)*math.cos(_omega0) + 2*math.sqrt(A)*_alpha _a1 = 2 * ((A - 1) - (A + 1)*math.cos(_omega0)) _a2 = (A + 1) - (A - 1)*math.cos(_omega0) - 2*math.sqrt(A)*_alpha else: # Unknown type of filter: _b0 = 1 _b1 = 0 _b2 = 0 _a0 = 0 _a1 = 0 _a2 = 0 # Using numpy types is slower, so for real-time applications, better use floats self.filterCoefficients.b0 = float(_b0) self.filterCoefficients.b1 = float(_b1) self.filterCoefficients.b2 = float(_b2) self.filterCoefficients.a0 = float(_a0) self.filterCoefficients.a1 = float(_a1) self.filterCoefficients.a2 = float(_a2) def computeBiquadFilterResponse(filterCoefficients, normalizedFreqBins): if filterCoefficients is None: return if not len(filterCoefficients) == 6: return b0 = filterCoefficients[0] b1 = filterCoefficients[1] b2 = filterCoefficients[2] a0 = filterCoefficients[3] a1 = filterCoefficients[4] a2 = filterCoefficients[5] alpha = b0 import math import numpy as np w0 = np.abs(math.acos(-a1 / 2.0)) Q = np.sin(w0) / (2.0 * alpha) e = np.exp pi = np.pi _freqResponse = [(b0 + (b1*e(-1j*2*pi*f)) + (b2 * e(-2j*2*pi*f))) / (a0 + (a1*e(-1j*2*pi*f)) + (a2*e(-2j*2*pi*f))) for f in normalizedFreqBins] return _freqResponse def computeBiquadFilterIR(filterCoefficients, IRLength): _freqResponse = computeBiquadFilterResponse(filterCoefficients, [i / IRLength for i in range(int(IRLength))]) return np.real(np.fft.ifft(_freqResponse)) def getFilterSpectrumModule(filter): _coeffs = [filter.filterCoefficients.b0, filter.filterCoefficients.b1, filter.filterCoefficients.b2, \ filter.filterCoefficients.a0, filter.filterCoefficients.a1, filter.filterCoefficients.a2] # Compute complex frequency response sampleRate = 48000 _normalizedFreqBins = [i/sampleRate for i in range(int(sampleRate/2.0))] _freqResponse = computeBiquadFilterResponse(_coeffs, _normalizedFreqBins) _freqResponseMod = 20.0 * np.log10(np.abs(_freqResponse)) _freqResponsePha = np.angle(_freqResponse) / np.pi * 180.0 _ir = computeBiquadFilterIR(_coeffs, len(_normalizedFreqBins)) return np.multiply(_normalizedFreqBins, sampleRate), _freqResponseMod, _freqResponsePha, _ir if __name__ == "__main__": import numpy as np import matplotlib.pyplot as plot # Real-time filtering of an MLS signal and IR calculation _filter = BiquadFilter() _filter.generateBiQuadCoefficients(filterf0=1000, filterQ=0.125, filterType=BiquadFilterType.BPF_NORMALIZED) _inputSignal = [0.0 for i in range(48000)] _inputSignal[0] = 1.0 _outputSignal = [_filter.processSample(_inputSignal[i]) for i in range(len(_inputSignal))] _ir = _outputSignal _spec = 20 * np.log10(np.abs(np.fft.fft(_ir))) _spec = _spec[0 : round(len(_spec)/2.0)] _freqBins = [i / len(_spec) * 48000.0 / 2.0 for i in range(len(_spec))] plot.semilogx(_freqBins, _spec) plot.grid(1) plot.ylim([-60, 10]) plot.xlim([2, 20000]) plot.show() # quit() # Real-time filtering of an input signal _filter = BiquadFilter() _filter.generateBiQuadCoefficients(filterf0=1000, filterQ=10.0, filterType=BiquadFilterType.NOTCH) _inputSignal = [np.sin(2 * np.pi * 1000 * i / 48000) for i in range(1000)] _outputSignal = [_filter.processSample(_inputSignal[i]) for i in range(len(_inputSignal))] plot.plot(_inputSignal) plot.plot(_outputSignal) plot.legend(['Input', 'Output']) plot.grid(1) plot.show() # quit() # Spectrum plotting of a couple of filters _filter = BiquadFilter() _filterSpecs = [[1000, 0.3, BiquadFilterType.NOTCH], [1000, 30, BiquadFilterType.NOTCH]] for _specs in _filterSpecs: _filter.generateBiQuadCoefficients(filterf0=_specs[0], filterQ=_specs[1], filterType=_specs[2]) _bins, _mod, _pha, _ir = getFilterSpectrumModule(_filter) plot.subplot(3,1,1) plot.semilogx(_bins, _mod) plot.grid(1) plot.ylim([-60, 10]) plot.subplot(3,1,2) plot.semilogx(_bins, _pha) plot.ylim([-180, 180]) plot.grid(1) plot.subplot(3,1,3) plot.plot(_ir) plot.grid(1) plot.grid(1) plot.show()

37.920973

162

0.558913

8,539

0.684434

0

1,562

0.1252

ca9fc7fc056114c1fa1f918327e46761a7db8549

5,065

py

Python

util/training.py

NeuralVFX/facial-pose-estimation-pytorch-v2

20af370409e8508a4a5e1e78341f66c5c4f9c1c2

[ "MIT" ]

5

2021-02-03T21:55:55.000Z

2022-03-01T15:52:14.000Z

util/training.py

NeuralVFX/facial-pose-estimation-pytorch-v2

20af370409e8508a4a5e1e78341f66c5c4f9c1c2

[ "MIT" ]

null

util/training.py

NeuralVFX/facial-pose-estimation-pytorch-v2

20af370409e8508a4a5e1e78341f66c5c4f9c1c2

[ "MIT" ]

1

2021-12-08T02:48:08.000Z

import math import numpy as np import torch ############################################################################ # Learning Rate ############################################################################ def set_lr_sched(epochs, iters, mult): """ Learning rate schedule, using cyclical learning rate Args: epochs (int): how many cycles to build iters (int): iter count for first epoch mult (float): how much longer each epoch will be Returns: list: float values representing learning rate during training """ mult_iter = iters iter_stack = [] for a in range(epochs): iter_stack += [math.cos((x / mult_iter) * 3.14) * .5 + .5 for x in (range(int(mult_iter)))] mult_iter *= mult return iter_stack def set_opt_lr(opt, lr): """ Sets optimizer learning rate, using different values for each sub group Args: opt (torch.optimizer): optimizer to set learning rate on lr (np.array): array of learning rate values """ opt.param_groups[0]['lr'] = lr[0] opt.param_groups[1]['lr'] = lr[1] opt.param_groups[2]['lr'] = lr[2] ############################################################################ # Training loop ############################################################################ def train(train_dict, x, y, lr, lr_lookup, current_iter): """ Train network for one iter Args: train_dict (dict): dictionary containing network, optimizer, loss and loader x (torch.tensor): input image from dataset y (torch.tensor): actual y value from dataset lr (float): base learning rate overall multiplier lr_lookup (list): float values representing learning rate during training current_iter (int): which iteration we are on, used to lookup learning rate Returns: torch.tensor: mse loss torch.tensor: point loss """ lr_mult = lr_lookup[current_iter] set_opt_lr(train_dict["opt"], lr * lr_mult) train_dict["opt"].zero_grad() yhat = train_dict["net"](x) mse_loss = train_dict["mse_crit"](yhat, y.float()) point_loss = train_dict["point_crit"](yhat, y.float()) # Testing with no point loss, works better in some situations loss = mse_loss # + point_loss # might not help loss.backward() train_dict["opt"].step() return mse_loss, point_loss def test(train_dict, x, y): """ Test network for one iter Args: train_dict (dict): dictionary containing network, optimizer, loss and loader x (torch.tensor): input image from dataset y (torch.tensor): actual y value from dataset Returns: torch.tensor: mse loss torch.tensor: point loss """ yhat = train_dict["net"](x) mse_loss = train_dict["mse_crit"](yhat, y.float()) point_loss = train_dict["point_crit"](yhat, y.float()) return mse_loss, point_loss def one_run(train_dict, freeze, lr_list, lr_array): """ Run through one cycle of cyclical learning Args: train_dict (dict): dictionary containing network, optimizer, loss and loader freeze (int): how many layers to freeze on the model lr_list (float): float values representing learning rate during training lr_array (np.array): array of learning rate values """ current_iter = 0 current_epoch = 0 done = False train_dict["net"].set_freeze(freeze) while not done: epoch_test_mse_loss = [] epoch_train_mse_loss = [] epoch_test_point_loss = [] epoch_train_point_loss = [] # TRAIN LOOP train_dict["net"].train() for x, y in train_dict["train_loader"]: if current_iter > len(lr_list) - 1: done = True break x, y = x.cuda(), y.cuda() mse_loss, point_loss = train(train_dict, x, y, lr_array, lr_list, current_iter) epoch_train_mse_loss.append(torch.mean(mse_loss).cpu().detach().numpy()) epoch_train_point_loss.append(torch.mean(point_loss.float()).cpu().detach().numpy()) current_iter += 1 # TEST LOOP train_dict["net"].eval() for x, y in train_dict["test_loader"]: x, y = x.cuda(), y.cuda() mse_loss, point_loss = test(train_dict, x, y) epoch_test_mse_loss.append(torch.mean(mse_loss).cpu().detach().numpy()) epoch_test_point_loss.append(torch.mean(point_loss.float()).cpu().detach().numpy()) print( f'train mse_loss: {np.array(epoch_train_mse_loss).mean()}' f' train point_loss: {np.array(epoch_train_point_loss).mean()}') print( f'test mse_loss: {np.array(epoch_test_mse_loss).mean()}' f' test point_loss: {np.array(epoch_test_point_loss).mean()}') current_epoch += 1 print('Done')

31.855346

99

0.569793

0

2,514

0.496347

caa060579f980710bf47cd0784773c22fd517be1

10,754

py

Python

components/fatfs/test_fatfsgen/test_fatfsparse.py

fbucafusco/esp-idf

c2ccc383dae2a47c2c2dc8c7ad78175a3fd11361

[ "Apache-2.0" ]

null

components/fatfs/test_fatfsgen/test_fatfsparse.py

fbucafusco/esp-idf

c2ccc383dae2a47c2c2dc8c7ad78175a3fd11361

[ "Apache-2.0" ]

null

components/fatfs/test_fatfsgen/test_fatfsparse.py

fbucafusco/esp-idf

c2ccc383dae2a47c2c2dc8c7ad78175a3fd11361

[ "Apache-2.0" ]

null

#!/usr/bin/env python # SPDX-FileCopyrightText: 2021-2022 Espressif Systems (Shanghai) CO LTD # SPDX-License-Identifier: Apache-2.0 import os import shutil import sys import unittest from subprocess import STDOUT, run from test_utils import compare_folders, fill_sector, generate_local_folder_structure, generate_test_dir_2 sys.path.append(os.path.join(os.path.dirname(__file__), '..')) import fatfsgen # noqa E402 # pylint: disable=C0413 class FatFSGen(unittest.TestCase): def setUp(self) -> None: os.makedirs('output_data') generate_test_dir_2() def tearDown(self) -> None: shutil.rmtree('output_data', ignore_errors=True) shutil.rmtree('Espressif', ignore_errors=True) shutil.rmtree('testf', ignore_errors=True) if os.path.exists('fatfs_image.img'): os.remove('fatfs_image.img') @staticmethod def test_gen_parse() -> None: run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'output_data/tst_str' ], stderr=STDOUT) run(['python', '../fatfsgen.py', 'output_data/tst_str'], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert set(os.listdir('Espressif')) == {'TEST', 'TESTFILE'} with open('Espressif/TESTFILE', 'rb') as in_: assert in_.read() == b'ahoj\n' assert set(os.listdir('Espressif/TEST')) == {'TEST', 'TESTFIL2'} with open('Espressif/TEST/TESTFIL2', 'rb') as in_: assert in_.read() == b'thisistest\n' assert set(os.listdir('Espressif/TEST/TEST')) == {'LASTFILE.TXT'} with open('Espressif/TEST/TEST/LASTFILE.TXT', 'rb') as in_: assert in_.read() == b'deeptest\n' @staticmethod def test_file_chaining() -> None: fatfs = fatfsgen.FATFS() fatfs.create_file('WRITEF', extension='TXT') fatfs.write_content(path_from_root=['WRITEF.TXT'], content=4096 * b'a' + b'a') fatfs.write_filesystem('fatfs_image.img') run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) with open('Espressif/WRITEF.TXT', 'rb') as in_: assert in_.read() == 4097 * b'a' @staticmethod def test_full_two_sectors_folder() -> None: fatfs = fatfsgen.FATFS(size=2 * 1024 * 1024) fatfs.create_directory('TESTFOLD') for i in range((2 * 4096) // 32): fatfs.create_file(f'A{str(i).upper()}', path_from_root=['TESTFOLD']) fatfs.write_content(path_from_root=['TESTFOLD', 'A253'], content=b'later') fatfs.write_content(path_from_root=['TESTFOLD', 'A255'], content=b'last') fatfs.write_filesystem('fatfs_image.img') run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert set(os.listdir('Espressif')) == {'TESTFOLD'} assert set(os.listdir('Espressif/TESTFOLD')) == {f'A{str(i).upper()}' for i in range(256)} with open('Espressif/TESTFOLD/A253', 'rb') as in_: assert in_.read() == b'later' with open('Espressif/TESTFOLD/A255', 'rb') as in_: assert in_.read() == b'last' @staticmethod def test_empty_fat16() -> None: fatfs = fatfsgen.FATFS(size=17 * 1024 * 1024) fatfs.write_filesystem('fatfs_image.img') run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) @staticmethod def test_chaining_fat16() -> None: fatfs = fatfsgen.FATFS(size=17 * 1024 * 1024) fatfs.create_file('WRITEF', extension='TXT') fatfs.write_content(path_from_root=['WRITEF.TXT'], content=4096 * b'a' + b'a') fatfs.write_filesystem('fatfs_image.img') run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) with open('Espressif/WRITEF.TXT', 'rb') as in_: assert in_.read() == 4097 * b'a' @staticmethod def test_full_sector_folder_fat16() -> None: fatfs = fatfsgen.FATFS(size=17 * 1024 * 1024) fatfs.create_directory('TESTFOLD') fill_sector(fatfs) fatfs.write_content(path_from_root=['TESTFOLD', 'A0'], content=b'first') fatfs.write_content(path_from_root=['TESTFOLD', 'A126'], content=b'later') fatfs.write_filesystem('fatfs_image.img') run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert set(os.listdir('Espressif')) == {'TESTFOLD'} assert set(os.listdir('Espressif/TESTFOLD')) == {f'A{str(i).upper()}' for i in range(128)} with open('Espressif/TESTFOLD/A0', 'rb') as in_: assert in_.read() == b'first' with open('Espressif/TESTFOLD/A126', 'rb') as in_: assert in_.read() == b'later' @staticmethod def file_(x: str, content_: str = 'hey this is a test') -> dict: return { 'type': 'file', 'name': x, 'content': content_ } def test_e2e_file(self) -> None: struct_: dict = { 'type': 'folder', 'name': 'testf', 'content': [self.file_('NEWF')] } generate_local_folder_structure(struct_, path_='.') run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'testf' ], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert compare_folders('testf', 'Espressif') def test_e2e_deeper(self) -> None: folder_ = { 'type': 'folder', 'name': 'XYZ', 'content': [ self.file_('NEWFLE'), self.file_('NEW.TXT'), self.file_('NEWE.TXT'), self.file_('NEW4.TXT'), self.file_('NEW5.TXT'), ] } struct_: dict = { 'type': 'folder', 'name': 'testf', 'content': [ self.file_('MY_NEW'), folder_ ] } generate_local_folder_structure(struct_, path_='.') run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'testf' ], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert compare_folders('testf', 'Espressif') def test_e2e_deeper_large(self) -> None: folder_ = { 'type': 'folder', 'name': 'XYZ', 'content': [ self.file_('NEWFLE', content_=4097 * 'a'), self.file_('NEW.TXT', content_=2 * 4097 * 'a'), self.file_('NEWE.TXT'), self.file_('NEW4.TXT'), self.file_('NEW5.TXT'), ] } folder2_ = { 'type': 'folder', 'name': 'XYZ3', 'content': [ self.file_('NEWFLE', content_=4097 * 'a'), self.file_('NEW.TXT', content_=2 * 4097 * 'a'), self.file_('NEWE.TXT'), self.file_('NEW4.TXT'), self.file_('NEW5.TXT'), ] } folder3_ = { 'type': 'folder', 'name': 'XYZ2', 'content': [self.file_(f'A{i}') for i in range(50)] } struct_: dict = { 'type': 'folder', 'name': 'testf', 'content': [ self.file_('MY_NEW'), folder_, folder2_, folder3_ ] } generate_local_folder_structure(struct_, path_='.') run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'testf' ], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert compare_folders('testf', 'Espressif') def test_e2e_very_deep(self) -> None: folder_ = { 'type': 'folder', 'name': 'XYZ', 'content': [ self.file_('NEWFLE', content_=4097 * 'a'), self.file_('NEW.TXT', content_=2 * 4097 * 'a'), self.file_('NEWE.TXT'), self.file_('NEW4.TXT'), self.file_('NEW5.TXT'), ] } folder2_ = { 'type': 'folder', 'name': 'XYZ3', 'content': [ self.file_('NEWFLE', content_=4097 * 'a'), self.file_('NEW.TXT', content_=2 * 4097 * 'a'), self.file_('NEWE.TXT'), self.file_('NEW4.TXT'), self.file_('NEW5.TXT'), folder_, ] } folder3_ = { 'type': 'folder', 'name': 'XYZ2', 'content': [self.file_(f'A{i}') for i in range(50)] + [folder2_] } struct_: dict = { 'type': 'folder', 'name': 'testf', 'content': [ self.file_('MY_NEW'), folder_, folder2_, folder3_ ] } generate_local_folder_structure(struct_, path_='.') run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'testf' ], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img'], stderr=STDOUT) assert compare_folders('testf', 'Espressif') def test_e2e_very_deep_long(self) -> None: folder_ = { 'type': 'folder', 'name': 'veryveryverylong111', 'content': [ self.file_('myndewveryverylongfile1.txt', content_=4097 * 'a'), self.file_('mynewveryverylongfile22.txt', content_=2 * 4097 * 'a'), self.file_('mynewveryverylongfile333.txt' * 8), self.file_('mynewveryverylongfile4444.txt' * 8), self.file_('mynewveryverylongfile5555.txt'), self.file_('SHORT.TXT'), ] } struct_: dict = { 'type': 'folder', 'name': 'testf', 'content': [ self.file_('mynewveryverylongfile.txt' * 5), folder_, ] } generate_local_folder_structure(struct_, path_='.') run([ 'python', f'{os.path.join(os.path.dirname(__file__), "..", "fatfsgen.py")}', 'testf', '--long_name_support' ], stderr=STDOUT) run(['python', '../fatfsparse.py', 'fatfs_image.img', '--long-name-support'], stderr=STDOUT) assert compare_folders('testf', 'Espressif') if __name__ == '__main__': unittest.main()

35.846667

105

0.521666

10,258

0.953878

0

4,047

0.376325

0

3,275

0.304538

caa067b7ced84067ccfd44ea02b97cd1e99a10d1

7,121

py

Python

tests/test_tasks.py

rzuris/python-harvest_apiv2

1a4915c2772aa9d27b74a545b14138d418566832

[ "MIT" ]

null

tests/test_tasks.py

rzuris/python-harvest_apiv2

1a4915c2772aa9d27b74a545b14138d418566832

[ "MIT" ]

null

tests/test_tasks.py

rzuris/python-harvest_apiv2

1a4915c2772aa9d27b74a545b14138d418566832

[ "MIT" ]

1

2022-03-28T10:47:37.000Z

# Copyright 2020 Bradbase import os, sys import unittest import configparser from dataclasses import asdict from requests_oauthlib import OAuth2Session from oauthlib.oauth2 import MobileApplicationClient, WebApplicationClient import httpretty import warnings from dacite import from_dict import json sys.path.insert(0, sys.path[0]+"/..") import harvest from harvest.harvestdataclasses import * """ There is a sample test config. Copy it, name it test_config.ini and fill it out with your test details. tests/test_config.ini is already in .gitignore Just in case, the test config file looks like this: [PERSONAL ACCESS TOKEN] url = https://api.harvestapp.com/api/v2 put_auth_in_header = True personal_token = Bearer 1234567.pt.somebunchoflettersandnumbers account_id = 1234567 [OAuth2 Implicit Code Grant] uri = https://api.harvestapp.com/api/v2 client_id = aclientid auth_url = https://id.getharvest.com/oauth2/authorize [OAuth2 Authorization Code Grant] uri = https://api.harvestapp.com/api/v2 client_id = aclientid client_secret = itsmysecret auth_url = https://id.getharvest.com/oauth2/authorize token_url = https://id.getharvest.com/api/v2/oauth2/token account_id = 1234567 """ """ Those who tread this path:- These tests currently really only test that the default URL has been formed correctly and that the datatype that gets returned can be typed into the dataclass. Probably enough but a long way from "comprehensive". """ class TestTasks(unittest.TestCase): def setUp(self): personal_access_token = PersonalAccessToken('ACCOUNT_NUMBER', 'PERSONAL_ACCESS_TOKEN') self.harvest = harvest.Harvest('https://api.harvestapp.com/api/v2', personal_access_token) warnings.filterwarnings("ignore", category=ResourceWarning, message="unclosed.*") # There's a bug in httpretty ATM. httpretty.enable() def teardown(self): httpretty.reset() httpretty.disable() def test_tasks(self): task_8083800_dict = { "id":8083800, "name":"Business Development", "billable_by_default":False, "default_hourly_rate":0.0, "is_default":False, "is_active":True, "created_at":"2017-06-26T22:08:25Z", "updated_at":"2017-06-26T22:08:25Z" } task_8083369_dict = { "id":8083369, "name":"Research", "billable_by_default":False, "default_hourly_rate":0.0, "is_default":True, "is_active":True, "created_at":"2017-06-26T20:41:00Z", "updated_at":"2017-06-26T21:53:34Z" } task_8083368_dict = { "id":8083368, "name":"Project Management", "billable_by_default":True, "default_hourly_rate":100.0, "is_default":True, "is_active":True, "created_at":"2017-06-26T20:41:00Z", "updated_at":"2017-06-26T21:14:10Z" } task_8083366_dict = { "id":8083366, "name":"Programming", "billable_by_default":True, "default_hourly_rate":100.0, "is_default":True, "is_active":True, "created_at":"2017-06-26T20:41:00Z", "updated_at":"2017-06-26T21:14:07Z" } task_8083365_dict = { "id":8083365, "name":"Graphic Design", "billable_by_default":True, "default_hourly_rate":100.0, "is_default":True, "is_active":True, "created_at":"2017-06-26T20:41:00Z", "updated_at":"2017-06-26T21:14:02Z" } task_8083782_dict = { "id":8083782, "name":"New Task Name", "billable_by_default":True, "default_hourly_rate":0.0, # TODO: this is supposed to be an int. Something isn't casting int to float. "is_default":False, "is_active":True, "created_at":"2017-06-26T22:04:31Z", "updated_at":"2017-06-26T22:04:31Z" } tasks_dict = { "tasks":[task_8083800_dict, task_8083369_dict, task_8083368_dict, task_8083366_dict, task_8083365_dict], "per_page":100, "total_pages":1, "total_entries":5, "next_page":None, "previous_page":None, "page":1, "links":{ "first":"https://api.harvestapp.com/v2/tasks?page=1&per_page=100", "next":None, "previous":None, "last":"https://api.harvestapp.com/v2/tasks?page=1&per_page=100" } } # tasks httpretty.register_uri(httpretty.GET, "https://api.harvestapp.com/api/v2/tasks?page=1&per_page=100", body=json.dumps(tasks_dict), status=200 ) tasks = from_dict(data_class=Tasks, data=tasks_dict) requested_tasks = self.harvest.tasks() self.assertEqual(requested_tasks, tasks) # get_task httpretty.register_uri(httpretty.GET, "https://api.harvestapp.com/api/v2/tasks/8083800", body=json.dumps(task_8083800_dict), status=200 ) task = from_dict(data_class=Task, data=task_8083800_dict) requested_task = self.harvest.get_task(task_id= 8083800) self.assertEqual(requested_task, task) # create_task httpretty.register_uri(httpretty.POST, "https://api.harvestapp.com/api/v2/tasks", body=json.dumps(task_8083782_dict), status=201 ) new_task = from_dict(data_class=Task, data=task_8083782_dict) requested_new_task = self.harvest.create_task(name= "New Task Name", default_hourly_rate= 120.0) # Harvest doco is wrong. they use hourly_rate not default_hourly_rate self.assertEqual(requested_new_task, new_task) # update_task task_8083782_dict["is_default"] = True httpretty.register_uri(httpretty.PATCH, "https://api.harvestapp.com/api/v2/tasks/8083782", body=json.dumps(task_8083782_dict), status=200 ) updated_task = from_dict(data_class=Task, data=task_8083782_dict) requested_updated_task = self.harvest.update_task(task_id=8083782, is_default=True) self.assertEqual(requested_updated_task, updated_task) # delete_task httpretty.register_uri(httpretty.DELETE, "https://api.harvestapp.com/api/v2/tasks/8083782", status=200 ) requested_deleted_task = self.harvest.delete_task(task_id=8083782) self.assertEqual(requested_deleted_task, None) httpretty.reset()

35.078818

174

0.592052

5,675

0.796939

0

2,857

0.401208

caa0e695442da8dc639ed3c9061223b76d6ae4f6

579

py

Python

db_models/deckentry.py

Teplitsa/false-security-1

9e5cc23c8bf324d923965bb2624cac4994891154

[ "MIT" ]

1

2020-10-01T17:44:26.000Z

db_models/deckentry.py

Teplitsa/false-security-1

9e5cc23c8bf324d923965bb2624cac4994891154

[ "MIT" ]

null

db_models/deckentry.py

Teplitsa/false-security-1

9e5cc23c8bf324d923965bb2624cac4994891154

[ "MIT" ]

1

2021-10-05T12:09:07.000Z

from globals import db import db_models.game from db_models.card import Card class DeckEntry(db.Model): #__table_args__ = {'extend_existing': True} __tablename__ = 'deckentry' id = db.Column(db.Integer, primary_key=True) # TODO: Undo nullable cardId = db.Column(db.Integer, db.ForeignKey('card.id'), nullable=True) gameId = db.Column(db.Integer, db.ForeignKey('game.id'), nullable=False) card = db.relationship('Card', lazy=False) game = db.relationship('Game', back_populates='deck', lazy=False) order = db.Column(db.Integer, nullable=False)

41.357143

76

0.713299

501

0.865285

0

111

0.19171

caa108c99289e504df2d4967b77c333b2d533a6d

1,482

py

Python

fisher_py/data/file_error.py

abdelq/fisher_py

befb98732ba7c4e57858d158c68cda09ed829d66

[ "MIT" ]

3

2021-11-03T20:55:45.000Z

2022-02-01T10:11:47.000Z

fisher_py/data/file_error.py

abdelq/fisher_py

befb98732ba7c4e57858d158c68cda09ed829d66

[ "MIT" ]

2

2022-01-28T02:04:21.000Z

2022-01-29T01:29:14.000Z

fisher_py/data/file_error.py

abdelq/fisher_py

befb98732ba7c4e57858d158c68cda09ed829d66

[ "MIT" ]

1

2022-01-26T23:30:37.000Z

from fisher_py.net_wrapping import NetWrapperBase class FileError(NetWrapperBase): @property def has_error(self) -> bool: """ Gets a value indicating whether this file has detected an error. If this is false: Other error properties in this interface have no meaning. Applications should not continue with processing data from any file which indicates an error. """ return self._get_wrapped_object_().HasError @property def has_warning(self) -> bool: """ Gets a value indicating whether this file has detected a warning. If this is false: Other warning properties in this interface have no meaning. """ return self._get_wrapped_object_().HasWarning @property def error_code(self) -> int: """ Gets the error code number. Typically this is a windows system error number. The lowest valid windows error is: 0x00030200 Errors detected within our files will have codes below 100. """ return self._get_wrapped_object_().ErrorCode @property def error_message(self) -> str: """ Gets the error message. For "unknown exceptions" this may include a stack trace. """ return self._get_wrapped_object_().ErrorMessage @property def warning_message(self) -> str: """ Gets the warning message. """ return self._get_wrapped_object_().WarningMessage

32.217391

90

0.654521

1,428

0.963563

0

1,362

0.919028

0

824

0.556005

caa1562b36eda4a5d00b900f7d291109468dbc07

1,839

py

Python

05-data_acquisition/scrap.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

05-data_acquisition/scrap.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

05-data_acquisition/scrap.py

sachinpr0001/data_science

d028233ff7bbcbbb6b26f01806d1c5ccf788df9a

[ "MIT" ]

null

import bs4 import requests import os str = input() input_str = str str = str.replace(" ", "&20") url = "https://www.snapdeal.com/search?keyword={}&santizedKeyword=&catId=&categoryId=0&suggested=false&vertical=&noOfResults=20&searchState=&clickSrc=go_header&lastKeyword=&prodCatId=&changeBackToAll=false&foundInAll=false&categoryIdSearched=&cityPageUrl=&categoryUrl=&url=&utmContent=&dealDetail=&sort=rlvncy".format(str) response = requests.get(url) soup = bs4.BeautifulSoup(response.content) picture_element = soup.findAll('picture') count = 0 try: os.mkdir(input_str) for i, picture in enumerate(picture_element): count = i with open('{}/{}-{}.jpg'.format(input_str, input_str, i), 'wb') as file: try: img_url = picture.img.attrs['src'] response = requests.get(img_url) file.write(response.content) except KeyError: img_url = picture.img.attrs['data-src'] response = requests.get(img_url) file.write(response.content) except FileExistsError: print("The search keyword is same to a previously searched keyword. Therefore, deleting old files.") for f in os.listdir(input_str): os.remove(os.path.join(input_str, f)) for i, picture in enumerate(picture_element): count = i with open('{}/{}-{}.jpg'.format(input_str, input_str, i), 'wb') as file: try: img_url = picture.img.attrs['src'] response = requests.get(img_url) file.write(response.content) except KeyError: img_url = picture.img.attrs['data-src'] response = requests.get(img_url) file.write(response.content) print(count, "new files are saved in the newly created folder")

42.767442

322

0.633496

0

529

0.287656

caa39db7232070832f9bd3b29c7411a08b85d2e9

980

py

Python

slixmpp/__init__.py

marconfus/slixmpp

bcf186f42dc31d360e0a0af8a4b3aaf1e0b212aa

[ "BSD-3-Clause" ]

null

slixmpp/__init__.py

marconfus/slixmpp

bcf186f42dc31d360e0a0af8a4b3aaf1e0b212aa

[ "BSD-3-Clause" ]

null

slixmpp/__init__.py

marconfus/slixmpp

bcf186f42dc31d360e0a0af8a4b3aaf1e0b212aa

[ "BSD-3-Clause" ]

null

""" Slixmpp: The Slick XMPP Library Copyright (C) 2010 Nathanael C. Fritz This file is part of Slixmpp. See the file LICENSE for copying permission. """ import logging logging.getLogger(__name__).addHandler(logging.NullHandler()) import asyncio # Required for python < 3.7 to use the old ssl implementation # and manage to do starttls as an unintended side effect asyncio.sslproto._is_sslproto_available = lambda: False from slixmpp.stanza import Message, Presence, Iq from slixmpp.jid import JID, InvalidJID from slixmpp.xmlstream.stanzabase import ET, ElementBase, register_stanza_plugin from slixmpp.xmlstream.handler import * from slixmpp.xmlstream import XMLStream from slixmpp.xmlstream.matcher import * from slixmpp.xmlstream.asyncio import asyncio, future_wrapper from slixmpp.basexmpp import BaseXMPP from slixmpp.clientxmpp import ClientXMPP from slixmpp.componentxmpp import ComponentXMPP from slixmpp.version import __version__, __version_info__

33.793103

80

0.812245

0

287

0.292857

caa3dd046c62cf7f7f653e5201f7bf879ec9032d

164

py

Python

model_wrappers/errors.py

SelfHacked/django-model-wrappers

2aa0bb773d61c6b5c794126b1dc4f06d622ff079

[ "MIT" ]

null

model_wrappers/errors.py

SelfHacked/django-model-wrappers

2aa0bb773d61c6b5c794126b1dc4f06d622ff079

[ "MIT" ]

null

model_wrappers/errors.py

SelfHacked/django-model-wrappers

2aa0bb773d61c6b5c794126b1dc4f06d622ff079

[ "MIT" ]

1

2021-06-03T12:04:22.000Z

class FieldDoesNotExist(Exception): def __init__(self, **kwargs): super().__init__(f"{self.__class__.__name__}: {kwargs}") self.kwargs = kwargs

32.8

64

0.664634

163

0.993902

0

38

0.231707

caa57ab541795d55d1fd7fa6b28f28f0dd754bf4

761

py

Python

ois_api_client/v3_0/deserialization/deserialize_user_header.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

7

2020-10-22T08:15:29.000Z

2022-01-27T07:59:39.000Z

ois_api_client/v3_0/deserialization/deserialize_user_header.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

ois_api_client/v3_0/deserialization/deserialize_user_header.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

from typing import Optional import xml.etree.ElementTree as ET from ...xml.XmlReader import XmlReader as XR from ..namespaces import COMMON from ..dto.UserHeader import UserHeader from .deserialize_crypto import deserialize_crypto def deserialize_user_header(element: ET.Element) -> Optional[UserHeader]: if element is None: return None result = UserHeader( login=XR.get_child_text(element, 'login', COMMON), password_hash=deserialize_crypto( XR.find_child(element, 'passwordHash', COMMON) ), tax_number=XR.get_child_text(element, 'taxNumber', COMMON), request_signature=deserialize_crypto( XR.find_child(element, 'requestSignature', COMMON) ), ) return result

30.44

73

0.70565

0

50

0.065703

caa5955124e72ef0bcafe3a20466c885857de706

5,172

py

Python

src/test.py

kevin3314/gcn_ppi

39b0e618bbb592f9cb8d37edf28deeb7c0987dad

[ "MIT" ]

null

src/test.py

kevin3314/gcn_ppi

39b0e618bbb592f9cb8d37edf28deeb7c0987dad

[ "MIT" ]

1

2021-12-08T02:47:10.000Z

src/test.py

kevin3314/gcn_ppi

39b0e618bbb592f9cb8d37edf28deeb7c0987dad

[ "MIT" ]

null

from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional import hydra import numpy as np import pandas as pd from omegaconf import DictConfig, OmegaConf from pytorch_lightning import ( Callback, LightningDataModule, LightningModule, Trainer, seed_everything, ) from pytorch_lightning.loggers import LightningLoggerBase from sklearn.model_selection import KFold from src.train import PrepareTmpFile from src.utils import utils log = utils.get_logger(__name__) def test(config: DictConfig, datamodule: Optional[LightningDataModule] = None) -> Optional[float]: """Contains training pipeline. Instantiates all PyTorch Lightning objects from config. Args: config (DictConfig): Configuration composed by Hydra. Returns: Optional[float]: Metric score for hyperparameter optimization. """ # Set seed for random number generators in pytorch, numpy and python.random if config.get("seed"): seed_everything(config.seed, workers=True) # Init lightning datamodule if datamodule is None: log.info(f"Instantiating datamodule <{config.datamodule._target_}>") datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule) # Init lightning model log.info(f"Instantiating model <{config.model._target_}>") model_cls = utils._locate(config.model._target_) checkpoint_path: Path = Path(config.work_dir) / config.load_checkpoint model: LightningModule = model_cls.load_from_checkpoint(checkpoint_path) # Init lightning callbacks callbacks: List[Callback] = [] if "callbacks" in config: for _, cb_conf in config.callbacks.items(): if "_target_" in cb_conf: log.info(f"Instantiating callback <{cb_conf._target_}>") callbacks.append(hydra.utils.instantiate(cb_conf)) # Init lightning loggers logger: List[LightningLoggerBase] = [] if "logger" in config: for _, lg_conf in config.logger.items(): if "_target_" in lg_conf: log.info(f"Instantiating logger <{lg_conf._target_}>") logger.append(hydra.utils.instantiate(lg_conf)) # Init lightning trainer log.info(f"Instantiating trainer <{config.trainer._target_}>") trainer: Trainer = hydra.utils.instantiate(config.trainer, callbacks=callbacks, logger=logger, _convert_="partial") # Send some parameters from config to all lightning loggers log.info("Logging hyperparameters!") utils.log_hyperparameters( config=config, model=model, datamodule=datamodule, trainer=trainer, callbacks=callbacks, logger=logger, ) # Evaluate model on test set, using the best model achieved during training log.info("Starting testing!") result: List[Dict[str, float]] = trainer.test(model=model, datamodule=datamodule) # Make sure everything closed properly log.info("Finalizing!") utils.finish( config=config, model=model, datamodule=datamodule, trainer=trainer, callbacks=callbacks, logger=logger, ) return result def test_cv(config: OmegaConf, df: pd.DataFrame): # Filter run log.debug("Filtering") log.debug(f"Length: {len(df)}") for name, d in [("model", config.model), ("dataset", config.datamodule), ("trainer", config.trainer)]: for k, v in d.items(): if len(df) == 1: break df = df[df[f"{name}_{k}"] == v] log.debug(f"{name}_{k}={v}") log.debug(f"Length: {len(df)}") index = df.index assert len(index) == 1 run_name = index[0] log.info(f"Run name: {run_name}") checkpoint_paths = df.filter(regex="^best_checkpoint") result_dict = defaultdict(list) # Load csv df = pd.read_csv(config.datamodule.csv_path) kf = KFold(n_splits=config["folds"], shuffle=True, random_state=config.seed) datamodule_params = dict(config.datamodule) datamodule_cls = utils._locate(datamodule_params.pop("_target_")) datamodule_params.pop("csv_path") # remove csv_path from params for i, (checkpoint_path, (train_idx, test_idx)) in enumerate( zip(checkpoint_paths.values[0], kf.split(df)), start=1 ): log.info(f"Start {i}th fold out of {kf.n_splits} folds") train_df = df.iloc[train_idx] test_df = df.iloc[test_idx] valid_df, test_df = np.array_split(test_df, 2) log.info(checkpoint_path) config.load_checkpoint = checkpoint_path # Init lightning datamodule log.info(f"Instantiating datamodule <{config.datamodule._target_}>") with PrepareTmpFile(train_df, valid_df, test_df) as (ft, fv, fe): datamodule: LightningDataModule = datamodule_cls(ft.name, fv.name, fe.name, **datamodule_params) result: List[Dict[str, float]] = test(config, datamodule) print(result) assert len(result) == 1 result = result[0] for k, v in result.items(): result_dict[k].append(v) utils.log_cv_result(run_name, config, result_dict)

35.424658

119

0.67208

0

1,342

0.259474

caa5f1ac1b06136391a9d7d4dbe4508ef117d016

1,234

py

Python

source_code/main.py

Sehannnnnnn/shortest-path

a815cbde365a58c1c4b3a0b60293f77dfc89f3df

[ "MIT" ]

null

source_code/main.py

Sehannnnnnn/shortest-path

a815cbde365a58c1c4b3a0b60293f77dfc89f3df

[ "MIT" ]

null

source_code/main.py

Sehannnnnnn/shortest-path

a815cbde365a58c1c4b3a0b60293f77dfc89f3df

[ "MIT" ]

null

from agent import Qnet from agent import ReplayBuffer from agent import train q = Qnet() q_target = Qnet() q_target.load_state_dict(q.state_dict()) memory = ReplayBuffer() print_interval = 20 score = 0.0 optimizer = optim.Adam(q.parameters(), lr=learning_rate) score_history= [] for n_epi in range(3000): epsilon = max(0.01, 0.08 - 0.01*(n_epi/200)) #Linear annealing from 8% to 1% s = env.reset(random_init=True) done = False n_step =0 while not done: n_step +=1 a = q.sample_action(torch.from_numpy(np.array(s)).float(), epsilon) s_prime, r, done = env.transition(a) done_mask = 0.0 if done else 1.0 memory.put((s,a,r,s_prime, done_mask)) score += r if done: break s = s_prime if memory.size()>2000: train(q, q_target, memory, optimizer) if n_epi%print_interval==0 and n_epi!=0: q_target.load_state_dict(q.state_dict()) print("n_episode :{}, score : {:.1f}, n_buffer : {}, eps : {:.1f}%, n_step:{}".format(n_epi, score/print_interval, memory.size(), epsilon*100, n_step)) score_history.append(score/print_interval) score = 0.0

31.641026

160

0.604538

0

104

0.084279

caa6662e2795f3a211e91c2d833e2f7ae98a4ebc

9,094

py

Python

ide/tests/test_import_archive.py

Ramonrlb/cloudpebble

20b5408724aa810ce1626552d9f1062f1094fc3b

[ "MIT" ]

147

2015-01-11T04:33:10.000Z

2021-08-12T18:22:52.000Z

ide/tests/test_import_archive.py

Ramonrlb/cloudpebble

20b5408724aa810ce1626552d9f1062f1094fc3b

[ "MIT" ]

155

2015-01-02T12:54:30.000Z

2020-11-06T19:17:09.000Z

ide/tests/test_import_archive.py

gfunkmonk/cloudpebble-1

c5b63483ac26ae0d60ac7ef1bf9e803400188e91

[ "MIT" ]

105

2015-01-01T21:04:36.000Z

2021-01-22T22:10:38.000Z

""" These tests check basic operation of ide.tasks.archive.do_import_archive """ import mock from django.core.exceptions import ValidationError from ide.tasks.archive import do_import_archive, InvalidProjectArchiveException from ide.utils.cloudpebble_test import CloudpebbleTestCase, make_package, make_appinfo, build_bundle, override_settings from ide.models.project import Project from utils.fakes import FakeS3 __author__ = 'joe' fake_s3 = FakeS3() @mock.patch('ide.models.s3file.s3', fake_s3) class TestImportArchive(CloudpebbleTestCase): def setUp(self): self.login() @staticmethod def make_resource_spec(name='IMAGE_BLAH'): return { 'resources': { 'media': [{ 'file': 'images/blah.png', 'name': name, 'type': 'bitmap' }] } } def test_import_basic_bundle_with_appinfo(self): """ Check that a minimal bundle imports without error """ bundle = build_bundle({ 'src/main.c': '', 'appinfo.json': make_appinfo() }) do_import_archive(self.project_id, bundle) def test_throws_with_invalid_appinfo(self): """ Check that appinfo validation is performed with a few invalid values """ invalid_things = [ ('projectType', 'invalid'), ('sdkVersion', '1'), ('versionLabel', '01.0'), ] for k, v in invalid_things: bundle = build_bundle({ 'src/main.c': '', 'appinfo.json': make_appinfo({k: v}) }) with self.assertRaises(ValidationError): do_import_archive(self.project_id, bundle) def test_import_basic_bundle_with_npm_manifest(self): """ Check that archives with package.json can be imported """ bundle = build_bundle({ 'src/main.c': '', 'package.json': make_package(package_options={'name': 'myproject'}) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(project.app_long_name, 'test') self.assertEqual(project.app_short_name, 'myproject') def test_import_package_with_dependencies(self): """ Check that dependencies in a package.json file are imported into the database """ deps = { 'some_package': '3.14.15', 'another': 'http://blah.com/package.git', } bundle = build_bundle({ 'src/main.c': '', 'package.json': make_package(package_options={ 'dependencies': deps }) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) actual_deps = {d.name: d.version for d in project.dependencies.all()} self.assertDictEqual(actual_deps, deps) def test_import_package_with_keywords(self): """ Check that keywords in a package.json file are imported into the database """ keywords = ['pebbles', 'watch', 'bunnies'] bundle = build_bundle({ 'src/main.c': '', 'package.json': make_package(package_options={ 'keywords': keywords }) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(set(keywords), set(project.keywords)) def test_import_appinfo_with_resources(self): """ Check that a resource can be imported in an appinfo.json project """ bundle = build_bundle({ 'src/main.c': '', 'resources/images/blah.png': 'contents!', 'appinfo.json': make_appinfo(options=self.make_resource_spec()) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(project.resources.get().variants.get().get_contents(), 'contents!') def test_import_package_with_resources(self): """ Check that a resource can be imported in an package.json project """ bundle = build_bundle({ 'src/main.c': '', 'resources/images/blah.png': 'contents!', 'package.json': make_package(pebble_options=self.make_resource_spec()) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(project.resources.get().variants.get().get_contents(), 'contents!') def test_throws_with_local_file_dependencies(self): """ Throw if any dependencies reference local files """ bad_versions = [ 'file:security/breach', '/security/breach', './security/breach', '../security/breach', '~/security/breach' ] for version in bad_versions: bundle = build_bundle({ 'src/main.c': '', 'package.json': make_package(package_options={ 'dependencies': {'some_package': version} }) }) with self.assertRaises(ValidationError): do_import_archive(self.project_id, bundle) def test_throws_if_sdk2_project_has_array_appkeys(self): """ Throw when trying to import an sdk 2 project with array appkeys """ bundle = build_bundle({ 'src/main.c': '', 'appinfo.json': make_appinfo(options={'appKeys': [], 'sdkVersion': '2'}) }) with self.assertRaises(ValidationError): do_import_archive(self.project_id, bundle) def test_invalid_resource_id(self): """ Check that invalid characters are banned from resource IDs """ bundle = build_bundle({ 'src/main.c': '', 'resources/images/blah.png': 'contents!', 'package.json': make_package(pebble_options=self.make_resource_spec("<>")) }) with self.assertRaises(ValidationError): do_import_archive(self.project_id, bundle) def test_import_json_file(self): """ Check that json files are correctly imported """ bundle = build_bundle({ 'src/js/test.json': '{}', 'src/main.c': '', 'package.json': make_package() }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(project.source_files.filter(file_name='test.json').count(), 1) def test_import_rocky(self): """ Check that json files are correctly imported """ bundle = build_bundle({ 'src/rocky/index.js': '', 'src/common/lib.js': '', 'src/pkjs/app.js': '', 'package.json': make_package(pebble_options={'projectType': 'rocky'}) }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertEqual(project.source_files.filter(file_name='index.js', target='app').count(), 1) self.assertEqual(project.source_files.filter(file_name='lib.js', target='common').count(), 1) self.assertEqual(project.source_files.filter(file_name='app.js', target='pkjs').count(), 1) @mock.patch('ide.models.s3file.s3', fake_s3) class TestImportLibrary(CloudpebbleTestCase): def setUp(self): self.login(type='package') def test_import_basic_library(self): """ Try importing a basic library """ bundle = build_bundle({ 'include/my-lib.h': '', 'package.json': make_package(pebble_options={'projectType': 'package'}), 'src/c/my-lib.c': '', 'src/c/my-priv.h': '', }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) files = {f.file_name: f for f in project.source_files.all()} self.assertSetEqual(set(files.keys()), {'my-lib.h', 'my-lib.c', 'my-priv.h'}) self.assertEqual(files['my-lib.h'].target, 'public') self.assertEqual(files['my-lib.c'].target, 'app') self.assertEqual(files['my-priv.h'].target, 'app') def test_import_library_with_resources(self): """ Try importing a basic library with resources """ bundle = build_bundle({ 'package.json': make_package(pebble_options={ 'projectType': 'package', 'resources': {'media': [{ 'type': 'bitmap', 'name': 'MY_RES1', 'file': 'res1.png' }, { 'type': 'bitmap', 'name': 'MY_RES2', 'file': 'res2.png' }]} }), 'src/resources/res1.png': '', 'src/resources/res2.png': '', }) do_import_archive(self.project_id, bundle) project = Project.objects.get(pk=self.project_id) self.assertSetEqual({f.file_name for f in project.resources.all()}, {'res1.png', 'res2.png'})

40.238938

119

0.591819

8,542

0.939301

0

8,632

0.949197

0

2,462

0.270728

caa7b5af1d7c721d26af5a7a4217743395012765

2,419

py

Python

src/features/build_features.py

weasysolutions/skin-lesion-dataset-cookiecutter

f3788b273f9c0caca21867f4846d6efd3683f646

[ "MIT" ]

null

src/features/build_features.py

weasysolutions/skin-lesion-dataset-cookiecutter

f3788b273f9c0caca21867f4846d6efd3683f646

[ "MIT" ]

null

src/features/build_features.py

weasysolutions/skin-lesion-dataset-cookiecutter

f3788b273f9c0caca21867f4846d6efd3683f646

[ "MIT" ]

null

# -*- coding: utf-8 -*- import click import os import logging import sys import pandas as pd import os, sys, inspect cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"images_in_features_subdirs"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"non_duplicate_lesion_id"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) cmd_subfolder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],"training_and_validation_sets"))) if cmd_subfolder not in sys.path: sys.path.append(cmd_subfolder) from images_in_features_subdirs import images_in_features_subdirs from non_duplicate_lesion_id import non_duplicate_lesion_id from training_and_validation_sets import training_and_validation_sets def build_features(**kwargs): metadata_csv = kwargs['metadata_csv'] train_dir = kwargs['train_dir'] images_dir = kwargs['images_dir'] val_dir = kwargs['val_dir'] """ Takes data in ../data/interim. Splits training and validation data. Stores splitted sets in ../data/processed/base_dir/train_dir and ../data/processed/base_dir/val_dir """ #load meta-data-set df = pd.read_csv(metadata_csv) #Create non_duplicate column. # Make a new df with unique_ids df, df_unique_id = non_duplicate_lesion_id(df) #split in training and validation dataframes df, df_train, df_val = training_and_validation_sets(df,df_unique_id) #place images in named attributes directories images_in_features_subdirs(df, images_dir = images_dir, train_dir = train_dir, val_dir = val_dir \ ) logger = logging.getLogger(__name__) logger.info('Features added. Data is ready for modelling.') if __name__ == '__main__': import json log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_fmt) data=json.loads(argv[1]) build_features(data)

32.253333

155

0.667218

0

628

0.259611

caaa9bfd17d71dc9c4b5c2733db7f41b398c87ca

5,897

py

Python

dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/hsa/hlc/hlc.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

8

2019-10-07T16:33:47.000Z

2020-12-07T03:59:58.000Z

dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/hsa/hlc/hlc.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

1

2017-12-21T23:31:59.000Z

2017-12-29T16:56:05.000Z

dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/hsa/hlc/hlc.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

5

2020-08-27T20:44:18.000Z

2021-08-21T22:54:11.000Z

# A temporary wrapper to connect to the HLC LLVM binaries. # Currently, connect to commandline interface. from __future__ import print_function, absolute_import import sys from subprocess import check_call import tempfile import os from collections import namedtuple from numba import config from .utils import adapt_llvm_version from .config import BUILTIN_PATH _real_check_call = check_call def check_call(*args, **kwargs): print('CMD: ' + ';'.join(args), file=sys.stdout) return _real_check_call(*args, **kwargs) class CmdLine(object): CMD_OPT = ("$HSAILBIN/opt " "-O3 " # "-gpu " # "-whole " "-verify " "-S " "-o {fout} " "{fin}") CMD_VERIFY = ("$HSAILBIN/opt " "-verify " "-S " "-o {fout} " "{fin}") CMD_GEN_HSAIL = ("$HSAILBIN/llc -O2 " "-march=hsail64 " "-filetype=asm " "-o {fout} " "{fin}") CMD_GEN_BRIG = ("$HSAILBIN/llc -O2 " "-march=hsail64 " "-filetype=obj " "-o {fout} " "{fin}") CMD_LINK_BUILTINS = ("$HSAILBIN/llvm-link " # "-prelink-opt " "-S " "-o {fout} " "{fin} " "{lib}") CMD_LINK_LIBS = ("$HSAILBIN/llvm-link " # "-prelink-opt " "-S " "-o {fout} " "{fin} ") def verify(self, ipath, opath): check_call(self.CMD_VERIFY.format(fout=opath, fin=ipath), shell=True) def optimize(self, ipath, opath): check_call(self.CMD_OPT.format(fout=opath, fin=ipath), shell=True) def generate_hsail(self, ipath, opath): check_call(self.CMD_GEN_HSAIL.format(fout=opath, fin=ipath), shell=True) def generate_brig(self, ipath, opath): check_call(self.CMD_GEN_BRIG.format(fout=opath, fin=ipath), shell=True) def link_builtins(self, ipath, opath): cmd = self.CMD_LINK_BUILTINS.format(fout=opath, fin=ipath, lib=BUILTIN_PATH) check_call(cmd, shell=True) def link_libs(self, ipath, libpaths, opath): cmdline = self.CMD_LINK_LIBS.format(fout=opath, fin=ipath) cmdline += ' '.join(["{0}".format(lib) for lib in libpaths]) check_call(cmdline, shell=True) class Module(object): def __init__(self): """ Setup """ self._tmpdir = tempfile.mkdtemp() self._tempfiles = [] self._linkfiles = [] self._cmd = CmdLine() self._finalized = False def __del__(self): return self.close() def close(self): # Remove all temporary files for afile in self._tempfiles: os.unlink(afile) # Remove directory os.rmdir(self._tmpdir) def _create_temp_file(self, name, mode='wb'): path = self._track_temp_file(name) fobj = open(path, mode=mode) return fobj, path def _track_temp_file(self, name): path = os.path.join(self._tmpdir, "{0}-{1}".format(len(self._tempfiles), name)) self._tempfiles.append(path) return path def _preprocess(self, llvmir): return adapt_llvm_version(llvmir) def load_llvm(self, llvmir): """ Load LLVM with HSAIL SPIR spec """ # Preprocess LLVM IR # Because HLC does not handle dot in LLVM variable names llvmir = self._preprocess(llvmir) # Create temp file to store the input file tmp_llvm_ir, fin = self._create_temp_file("dump-llvm-ir") with tmp_llvm_ir: tmp_llvm_ir.write(llvmir.encode('ascii')) # Create temp file for optimization fout = self._track_temp_file("verified-llvm-ir") self._cmd.verify(ipath=fin, opath=fout) if config.DUMP_OPTIMIZED: with open(fout, 'rb') as fin_opt: print(fin_opt.read().decode('ascii')) self._linkfiles.append(fout) def finalize(self): """ Finalize module and return the HSAIL code """ assert not self._finalized, "Module finalized already" # Link dependencies libraries llvmfile = self._linkfiles[0] pre_builtin_path = self._track_temp_file("link-dep") libpaths = self._linkfiles[1:] self._cmd.link_libs(ipath=llvmfile, libpaths=libpaths, opath=pre_builtin_path) # Link library with the builtin modules linked_path = self._track_temp_file("linked-path") self._cmd.link_builtins(ipath=pre_builtin_path, opath=linked_path) # Optimize opt_path = self._track_temp_file("optimized-llvm-ir") self._cmd.optimize(ipath=linked_path, opath=opt_path) if config.DUMP_OPTIMIZED: with open(opt_path, 'rb') as fin: print(fin.read().decode('ascii')) # Finalize the llvm to HSAIL hsail_path = self._track_temp_file("finalized-hsail") self._cmd.generate_hsail(ipath=opt_path, opath=hsail_path) # Finalize the llvm to BRIG brig_path = self._track_temp_file("finalized-brig") self._cmd.generate_brig(ipath=opt_path, opath=brig_path) self._finalized = True # Read HSAIL with open(hsail_path, 'rb') as fin: hsail = fin.read().decode('ascii') # Read BRIG with open(brig_path, 'rb') as fin: brig = fin.read() if config.DUMP_ASSEMBLY: print(hsail) return namedtuple('FinalizerResult', ['hsail', 'brig'])(hsail, brig)

30.713542

80

0.559267

5,363

0.909445

0

1,250

0.211972

caad98cebdae9619248fd033d10604a27d5863a1

5,851

py

Python

lensit/ffs_iterators/bfgs.py

Sebastian-Belkner/LensIt

3e746ceeaa53b2845af31cc8372cd897e34ad53f

[ "MIT" ]

null

lensit/ffs_iterators/bfgs.py

Sebastian-Belkner/LensIt

3e746ceeaa53b2845af31cc8372cd897e34ad53f

[ "MIT" ]

null

lensit/ffs_iterators/bfgs.py

Sebastian-Belkner/LensIt

3e746ceeaa53b2845af31cc8372cd897e34ad53f

[ "MIT" ]

null

from __future__ import print_function import numpy as np import os class BFGS_Hessian(object): """ Class to evaluate the update to inverse Hessian matrix in the L-BFGS scheme. (see wikipedia article if nothing else). H is B^-1 form that article. B_k+1 = B + yy^t / (y^ts) - B s s^t B / (s^t Bk s)) (all k on the RHS) H_k+1 = (1 - sy^t / (y^t s) ) H (1 - ys^t / (y^ts))) + ss^t / (y^t s). Determinant of B: ln det Bk+1 = ln det Bk + ln( s^ty / s^t B s). For quasi Newton, s_k = x_k1 - x_k = - alpha_k Hk grad_k with alpha_k newton step-length. --> s^t B s at k is alpha_k^2 g_k H g_k s^t y is - alpha_k (g_k+1 - g_k) H g_k This leads to ln|B_k + 1| = ln |B_k| + ln(1 - 1/alpha_k g_k+1 H g_k / (gk H gk)) """ def __init__(self, lib_dir, apply_H0k, paths2ys, paths2ss, L=100000, apply_B0k=None, verbose=True): """ :param apply_H0k: user supplied function(x,k), applying a zeroth order estimate of the inverse Hessian to x at iter k. :param paths2ys: dictionary of paths to the y vectors. y_k = grad_k+1 - grad_k :param paths2ss: dictionary of paths to the s vectors. s_k = x_k+1 - xk_k :return: H is inverse Hessian, not Hessian. """ self.lib_dir = lib_dir self.paths2ys = paths2ys self.paths2ss = paths2ss self.L = L self.applyH0k = apply_H0k self.applyB0k = apply_B0k self.verbose = verbose def y(self, n): return np.load(self.paths2ys[n], mmap_mode='r') def s(self, n): return np.load(self.paths2ss[n], mmap_mode='r') def add_ys(self, path2y, path2s, k): assert os.path.exists(path2y), path2y assert os.path.exists(path2s), path2s self.paths2ys[k] = path2y self.paths2ss[k] = path2s if self.verbose: print('Linked y vector ', path2y, ' to Hessian') print('Linked s vector ', path2s, ' to Hessian') def _save_alpha(self, alpha, i): fname = os.path.join(self.lib_dir, 'temp_alpha_%s.npy' % i) np.save(fname, alpha) return def _load_alpha(self, i): """ Loads, and remove, alpha from disk. :param i: :return: """ fname = os.path.join(self.lib_dir, 'temp_alpha_%s.npy' % i) assert os.path.exists(fname) ret = np.load(fname) os.remove(fname) return ret def applyH(self, x, k, _depth=0): """ Recursive calculation of H_k x, for any x. This uses the product form update H_new = (1 - rho s y^t) H (1 - rho y s^t) + rho ss^t :param x: vector to apply the inverse Hessian to :param k: iter level. Output is H_k x. :param _depth : internal, for internal bookkeeping. :return: """ if k <= 0 or _depth >= self.L or self.L == 0: return self.applyH0k(x, k) s = self.s(k - 1) y = self.y(k - 1) rho = 1. / np.sum(s * y) Hv = self.applyH(x - rho * y * np.sum(x * s), k - 1, _depth=_depth + 1) return Hv - s * (rho * np.sum(y * Hv)) + rho * s * np.sum(s * x) def get_gk(self, k, alpha_k0): """ Reconstruct gradient at xk, given the first newton step length at step max(0,k-L) ! this is very badly behaved numerically. """ assert self.applyB0k is not None ret = -self.applyB0k(self.s(max(0, k - self.L)),max(0,k-self.L)) / alpha_k0 for j in range(max(0, k - self.L), k): ret += self.y(j) return ret def get_sBs(self, k, alpha_k, alpha_k0): """ Reconstruct s^Bs at x_k, given the first newton step length at step max(0,k-L) and current step alpha_k. """ return - alpha_k * np.sum(self.s(k) * self.get_gk(k, alpha_k0)) def get_lndet_update(self, k, alpha_k, alpha_k0): """ Return update to B log determinant, lndet B_k+1 = lndet B_k + output. """ return np.log(np.sum(self.y(k) * self.s(k)) / self.get_sBs(k, alpha_k, alpha_k0)) def get_mHkgk(self, gk, k, output_fname=None): """ Obtains - H_k g_k with L-BFGS two-loop recursion. :param gk: grad f(x_k) :param k: iterate index :return: - H_k g_k according to L-BFGS. If output_fname is set then output is saved in file and nothing is returned. Should be fine with k == 0 """ q = gk.copy() rho = lambda i: 1. / np.sum(self.s(i) * self.y(i)) for i in range(k - 1, np.max([-1, k - self.L - 1]), -1): alpha_i = rho(i) * np.sum(self.s(i) * q) q -= alpha_i * self.y(i) self._save_alpha(alpha_i, i) r = self.applyH0k(q, k) for i in range(np.max([0, k - self.L]), k): beta = rho(i) * np.sum(self.y(i) * r) r += self.s(i) * (self._load_alpha(i) - beta) if output_fname is None: return -r np.save(output_fname, -r) return def sample_Gaussian(self, k, x_0, rng_state=None): """ sample from a MV zero-mean Gaussian with covariance matrix H, at iteration level k, given input x_0 random vector with covariance H_0. Since H is the inverse Hessian, then H is roughly the covariance matrix of the parameters in a line search. :param k: :param x_0: :return: """ ret = x_0.copy() rho = lambda i: 1. / np.sum(self.s(i) * self.y(i)) if rng_state is not None: np.random.set_state(rng_state) eps = np.random.standard_normal((len(range(np.max([0, k - self.L]), k)), 1)) for idx, i in enumerate(range(np.max([0, k - self.L]), k)): ret = ret - self.s(i) * np.sum(self.y(i) * ret) * rho(i) + np.sqrt(rho(i)) * self.s(i) * eps[idx] return ret

38.24183

118

0.56093

5,780

0.987865

0

2,620

0.447787

caaf2c40188789d0575a2ef98deb50c6eded3c8b

464

py

Python

annotations/migrations/0015_organization_description.py

alexliyihao/auto-annotation-web

391bd2c4a8ea1d2d3aba92a13cd7c41dd77a609d

[ "MIT" ]

1

2021-11-17T15:34:33.000Z

annotations/migrations/0015_organization_description.py

alexliyihao/auto-annotation-web

391bd2c4a8ea1d2d3aba92a13cd7c41dd77a609d

[ "MIT" ]

null

annotations/migrations/0015_organization_description.py

alexliyihao/auto-annotation-web

391bd2c4a8ea1d2d3aba92a13cd7c41dd77a609d

[ "MIT" ]

null

# Generated by Django 3.2.8 on 2021-11-11 02:06 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('annotations', '0014_auto_20211110_2007'), ] operations = [ migrations.AddField( model_name='organization', name='description', field=models.CharField(default='test description', max_length=400), preserve_default=False, ), ]

23.2

79

0.62069

371

0.799569

0

130

0.280172

caaf463e68f987c64b55b2231633fe5be3a6307b

5,536

py

Python

paper/figures/make_photometry_table.py

abostroem/asassn15oz

ade090096b61b155c86108d1945bb7b4522365b8

[ "BSD-3-Clause" ]

null

paper/figures/make_photometry_table.py

abostroem/asassn15oz

ade090096b61b155c86108d1945bb7b4522365b8

[ "BSD-3-Clause" ]

3

2019-02-24T23:24:33.000Z

2019-02-24T23:25:12.000Z

paper/figures/make_photometry_table.py

abostroem/asassn15oz

ade090096b61b155c86108d1945bb7b4522365b8

[ "BSD-3-Clause" ]

null

# coding: utf-8 # Creates a table of all imaging observations in the database for paper: # * lc_obs.tex # In[1]: import numpy as np from astropy import table from astropy.table import Table from astropy.time import Time from utilities_az import supernova, connect_to_sndavis # In[2]: db, cursor = connect_to_sndavis.get_cursor() # In[3]: sn15oz = supernova.LightCurve2('asassn-15oz') # In[4]: query_str = ''' SELECT DISTINCT mag, magerr, BINARY(filter), jd, source FROM photometry WHERE targetid = 322 ORDER BY jd''' # In[5]: cursor.execute(query_str) results = cursor.fetchall() # In[6]: loc_dict = { 1: {'short':'OGG 2m', 'long': 'Haleakala Observatory - 2m'}, #ogg2m001-fs02 2m0 2: {'short':'COJ 2m', 'long': 'Siding Springs Observatory - 2m'}, #coj2m002-fs03 3: {'short':'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m003-kb71 4: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m004-kb77 1m0 5: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m005-kb78 1m0 8: {'short':'ELP 1m', 'long': 'McDonald Observatory - 1m'},#elp1m008-kb74 1m0 9: {'short':'LSC 1m', 'long': 'CTIO - Region IV'}, #lsc1m009-fl03 1m0 10: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m010 11: {'short': 'COJ 1m', 'long': 'Siding Springs Observatory - 1m'}, #coj1m011-kb05 1m0 12: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m'}, #cpt1m012-kb75 1m0 13: {'short': 'CPT 1m', 'long': 'SAAO - Sutherland Facilities - 1m '},#cpt1m013-kb76 1m0 88: {'short': 'Swift', 'long': 'Swift'}, #Swift -88: {'short': 'Swift', 'long': 'Swift'}} #Swift; non-detections in the DB have negative sources # In[7]: band = [] jd = [] date = [] mag = [] mag_err = [] source = [] phase = [] for iresult in results: #rename the Swift filters to their proper names ifilter = iresult['BINARY(filter)'] if ifilter in [b'us', b'bs', b'vs']: band.append(ifilter.decode('utf-8')[0]) elif ifilter in [b'uw1', b'uw2', b'um2']: band.append('uv'+ifilter.decode('utf-8')[1:]) else: band.append(ifilter) jd.append(iresult['jd']) mag.append(iresult['mag']) mag_err.append(iresult['magerr']) if ifilter in [b'J', b'H', b'K']: source.append('NTT') else: source.append(loc_dict[iresult['source']]['short']) date.append(Time(iresult['jd'], format='jd', out_subfmt='date').iso) phase.append((Time(iresult['jd'], format='jd') - Time(sn15oz.jdexpl, format='jd')).value) tbdata = Table([date, jd, phase, mag, mag_err, band, source], names=['Date-Obs','JD', 'Phase (Day)', 'Apparent Magnitude', 'Apparent Magnitude Error', 'Filter', 'Source']) tbdata.sort(keys=['JD', 'Filter']) # In[8]: #tbdata.write('../lc_obs.tex', format='aastex', # formats={'JD':'%8.2f', # 'Phase (Day)':'%4.2f', # 'Apparent Magnitude':'%2.2f', # 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, # latexdict={'preamble':r'\centering', # 'caption':r'Imaging Observations of ASASSN-15oz.\label{tab:LcObs}', # 'data_start':r'\hline'}) # In[9]: tbdata_short = tbdata[0:5].copy() tbdata_short.write('../lc_obs_short.tex', format='latex', formats={'JD':'%8.2f', 'Phase (Day)':'%4.2f', 'Apparent Magnitude':'%2.2f', 'Apparent Magnitude Error': '%1.2f'}, overwrite=True, latexdict={'preamble':r'\centering', 'caption':r'Sample of Imaging Observations of ASASSN-15oz. Full table available on-line.\label{tab:LcObs}', 'data_start':r'\hline', 'data_end':r'\hline', 'header_start':r'\hline', 'tabletype': 'table*'}) # In[10]: #tbdata.write('../lc_obs.dat', overwrite=True, format='ascii') #ofile = open('../lc_obs.dat', 'r') #all_lines = ofile.readlines() #ofile.close() #header = '''#Photometric observations of ASASSN-15oz. ##Columns: ##Date-Obs: (str) Human readable date of observation ##JD: (float) Julian Date of observation ##Phase: (float) Days since explosion, where explosion is defined as {} ##Apparent Magnitude: (float) ##Apparent Magntidue Error: (float) ##Filter: (str) Filter used for observation ##Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n #'''.format(sn15oz.jdexpl) #ofile = open('../asassn15oz_lc_obs.dat', 'w') #ofile.write(header) #for iline in all_lines[1:]: # ofile.write(iline) #ofile.close() # In[11]: tbdata.write('../lc_obs.csv', overwrite=True) ofile = open('../lc_obs.csv', 'r') all_lines = ofile.readlines() ofile.close() header = '''#Photometric observations of ASASSN-15oz. #Columns: #Date-Obs: (str) Human readable date of observation #JD: (float) Julian Date of observation #Phase: (float) Days since explosion, where explosion is defined as {} #Apparent Magnitude: (float) #Apparent Magntidue Error: (float) #Filter: (str) Filter used for observation #Source: (str) Observatory used to take the data. OGG, COJ, LSC, ELP, and CPT are Las Cumbres Observatory Telescopes.\n '''.format(sn15oz.jdexpl) ofile = open('../asassn15oz_lc_obs.csv', 'w') ofile.write(header) for iline in all_lines[1:]: ofile.write(iline) ofile.close()

27.405941

130

0.604408

0

3,489

0.630238

caaf9b2d174b67f74ceb1ce325fcdf36e83f2b9b

632

py

Python

flask_resources/parsers/__init__.py

inveniosoftware/flask-resources

f9d926a959f392b66811a10ee8faaa2f77e6d528

[ "MIT" ]

2

2021-09-04T22:36:26.000Z

2021-12-06T22:02:37.000Z

flask_resources/parsers/__init__.py

inveniosoftware/flask-resources

f9d926a959f392b66811a10ee8faaa2f77e6d528

[ "MIT" ]

62

2020-06-09T09:09:28.000Z

2021-03-31T16:32:51.000Z

flask_resources/parsers/__init__.py

inveniosoftware/flask-resources

f9d926a959f392b66811a10ee8faaa2f77e6d528

[ "MIT" ]

6

2020-04-28T08:23:55.000Z

2021-04-09T07:41:15.000Z

# -*- coding: utf-8 -*- # # Copyright (C) 2020-2021 CERN. # Copyright (C) 2020-2021 Northwestern University. # # Flask-Resources is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """Request parser for the body, headers, query string and view args.""" from .base import RequestParser from .body import RequestBodyParser from .decorators import request_body_parser, request_parser from .schema import MultiDictSchema __all__ = ( "MultiDictSchema", "request_body_parser", "request_parser", "RequestBodyParser", "RequestParser", )

27.478261

76

0.740506

0

413

0.653481

caaff8629778159017275b2997472982be0e80e4

3,253

py

Python

analyze_tagged_corpus.py

kevincobain2000/nltk-trainer

5c0b53ccecb7a5042d5af6c4325e134f7d83cb45

[ "Apache-2.0" ]

1

2021-10-08T11:40:09.000Z

analyze_tagged_corpus.py

kevincobain2000/nltk-trainer

5c0b53ccecb7a5042d5af6c4325e134f7d83cb45

[ "Apache-2.0" ]

null

analyze_tagged_corpus.py

kevincobain2000/nltk-trainer

5c0b53ccecb7a5042d5af6c4325e134f7d83cb45

[ "Apache-2.0" ]

null

#!/usr/bin/python import argparse import nltk.corpus from nltk.corpus.util import LazyCorpusLoader from nltk.probability import FreqDist from nltk.tag.simplify import simplify_wsj_tag from nltk_trainer import load_corpus_reader ######################################## ## command options & argument parsing ## ######################################## parser = argparse.ArgumentParser(description='Analyze a part-of-speech tagged corpus', formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('corpus', help='''The name of a tagged corpus included with NLTK, such as treebank, brown, cess_esp, floresta, or the root path to a corpus directory, which can be either an absolute path or relative to a nltk_data directory.''') parser.add_argument('--trace', default=1, type=int, help='How much trace output you want, defaults to %(default)d. 0 is no trace output.') corpus_group = parser.add_argument_group('Corpus Reader Options') corpus_group.add_argument('--reader', default=None, help='''Full module path to a corpus reader class, such as nltk.corpus.reader.tagged.TaggedCorpusReader''') corpus_group.add_argument('--fileids', default=None, help='Specify fileids to load from corpus') corpus_group.add_argument('--simplify_tags', action='store_true', default=False, help='Use simplified tags') sort_group = parser.add_argument_group('Tag Count Sorting Options') sort_group.add_argument('--sort', default='tag', choices=['tag', 'count'], help='Sort key, defaults to %(default)s') sort_group.add_argument('--reverse', action='store_true', default=False, help='Sort in revere order') args = parser.parse_args() ################### ## corpus reader ## ################### tagged_corpus = load_corpus_reader(args.corpus, reader=args.reader, fileids=args.fileids) if not tagged_corpus: raise ValueError('%s is an unknown corpus') if args.trace: print 'loading %s' % args.corpus ############## ## counting ## ############## wc = 0 tag_counts = FreqDist() taglen = 7 word_set = set() if args.simplify_tags and args.corpus not in ['conll2000', 'switchboard']: kwargs = {'simplify_tags': True} else: kwargs = {} for word, tag in tagged_corpus.tagged_words(fileids=args.fileids, **kwargs): if len(tag) > taglen: taglen = len(tag) if args.corpus in ['conll2000', 'switchboard'] and args.simplify_tags: tag = simplify_wsj_tag(tag) wc += 1 # loading corpora/treebank/tagged with ChunkedCorpusReader produces None tags if not isinstance(tag, basestring): tag = str(tag) tag_counts.inc(tag) word_set.add(word) ############ ## output ## ############ print '%d total words\n%d unique words\n%d tags\n' % (wc, len(word_set), len(tag_counts)) if args.sort == 'tag': sort_key = lambda (t, c): t elif args.sort == 'count': sort_key = lambda (t, c): c else: raise ValueError('%s is not a valid sort option' % args.sort) countlen = max(len(str(tag_counts[tag_counts.max()])) + 2, 9) # simple reSt table format print ' '.join(['Tag'.center(taglen), 'Count'.center(countlen)]) print ' '.join(['='*taglen, '='*(countlen)]) for tag, count in sorted(tag_counts.items(), key=sort_key, reverse=args.reverse): print ' '.join([tag.ljust(taglen), str(count).rjust(countlen)]) print ' '.join(['='*taglen, '='*(countlen)])

32.53

89

0.694436

0

1,316

0.40455

cab0e7355c94993a3d9aee2b860a711d2c4abfa3

5,562

py

Python

service/example_config.py

w1kke/example-service

4c845796ae314f18b886bd62dcf42f75a40601d0

[ "MIT" ]

null

service/example_config.py

w1kke/example-service

4c845796ae314f18b886bd62dcf42f75a40601d0

[ "MIT" ]

null

service/example_config.py

w1kke/example-service

4c845796ae314f18b886bd62dcf42f75a40601d0

[ "MIT" ]

null

# Copyright 2018 Ocean Protocol Foundation # SPDX-License-Identifier: Apache-2.0 import logging import os import sys from squid_py import Config def get_variable_value(variable): if os.getenv(variable) is None: logging.error(f'you should provide a {variable}') sys.exit(1) else: return os.getenv(variable) class ExampleConfig: _local_aqua_url = "http://172.15.0.15:5000" _local_brizo_url = "http://localhost:8030" _duero_aqua_url = "https://aquarius.compute.duero.dev-ocean.com" _duero_brizo_url = "https://brizo.compute.duero.dev-ocean.com" # _duero_aqua_url = "http://localhost:5000" # _duero_brizo_url = "http://localhost:8030" _pacific_aqua_url = "https://aquarius.pacific.dev-ocean.com" _pacific_brizo_url = "https://brizo.pacific.dev-ocean.com" # _nile_aqua_url = "http://172.15.0.15:5000" # _nile_aqua_url = "https://nginx-aquarius.dev-ocean.com" # _nile_brizo_url = "https://nginx-brizo.dev-ocean.com" # _nile_aqua_url = "https://nginx-aquarius.dev-ocean.com" _nile_aqua_url = "https://aquarius.marketplace.dev-ocean.com" # _nile_aqua_url = "http://172.15.0.15:5000" _nile_brizo_url = "https://brizo.marketplace.dev-ocean.com" # _nile_brizo_url = "http://localhost:8030" _duero_secret_store_url = "https://secret-store.duero.dev-ocean.com" _nile_secret_store_url = "https://secret-store.dev-ocean.com" _pacific_secret_store_url = "https://secret-store.pacific.oceanprotocol.com" # _nile_secret_store_url = "https://secret-store.marketplace.dev-ocean.com" _kovan_keeper_url = "http://localhost:8545" _remote_keeper_url = "https://%s.dev-ocean.com" _parity_url = "http://localhost:8545" _net_to_services_url = { 'duero': {'aquarius': _duero_aqua_url, 'brizo': _duero_brizo_url}, 'nile': {'aquarius': _nile_aqua_url, 'brizo': _nile_brizo_url}, 'kovan': {'aquarius': _local_aqua_url, 'brizo': _local_brizo_url}, 'pacific': {'aquarius': _pacific_aqua_url, 'brizo': _pacific_brizo_url}, } _net_name_map = { 'duero': 'duero', 'duero_local': 'duero', 'nile': 'nile', 'nile_local': 'nile', 'kovan': 'kovan', 'kovan_local': 'kovan', 'pacific': 'pacific', 'pacific_local': 'pacific' } _net_to_env_name = { 'nile': 'TEST_NILE', 'nile_local': 'TEST_LOCAL_NILE', 'duero': 'TEST_DUERO', 'duero_local': 'TEST_LOCAL_DUERO', 'spree': 'TEST_LOCAL_SPREE', 'kovan': 'TEST_KOVAN', 'kovan_local': 'TEST_LOCAL_KOVAN', 'pacific': 'TEST_PACIFIC', 'pacific_local': 'TEST_LOCAL_PACIFIC' } @staticmethod def get_config_net(): return os.environ.get('TEST_NET', 'spree') @staticmethod def get_env_name(): net = ExampleConfig.get_config_net() return ExampleConfig._net_to_env_name.get(net) @staticmethod def get_base_config(): config = { "keeper-contracts": { "keeper.url": "http://localhost:8545", "keeper.path": "artifacts", "secret_store.url": "http://localhost:12001", "parity.url": "http://localhost:8545", }, "resources": { "aquarius.url": "http://172.15.0.15:5000", # "aquarius.url": "http://localhost:5000", # "brizo.url": "http://172.15.0.17:8030", "brizo.url": "http://localhost:8030", "storage.path": "squid_py.db", "downloads.path": "consume-downloads" } } return config @staticmethod def _get_config(local_node=True, net_key=''): config = ExampleConfig.get_base_config() net_name = ExampleConfig._net_name_map.get(net_key) if net_name == 'kovan': config['keeper-contracts']['keeper.url'] = ExampleConfig._kovan_keeper_url elif net_name == 'pacific': config['keeper-contracts']['keeper.url'] = 'https://pacific.oceanprotocol.com' config['keeper-contracts']['parity.url'] = 'https://pacific.oceanprotocol.com' elif not local_node: config['keeper-contracts']['keeper.url'] = ExampleConfig._remote_keeper_url % net_name config['keeper-contracts']['parity.url'] = ExampleConfig._remote_keeper_url % net_name if net_name: config['keeper-contracts']['secret_store.url'] = \ ExampleConfig._duero_secret_store_url if net_name == 'duero' \ else ExampleConfig._nile_secret_store_url service_url = ExampleConfig._net_to_services_url[net_name] config['resources']['aquarius.url'] = service_url['aquarius'] config['resources']['brizo.url'] = service_url['brizo'] # parity_url maybe different than the keeper_url # config['keeper-contracts']['parity.url'] = ExampleConfig._parity_url return config @staticmethod def get_config_dict(): test_net = ExampleConfig.get_config_net() local_node = not test_net or test_net in ( 'nile_local', 'duero_local', 'spree', 'kovan_local') config_dict = ExampleConfig._get_config(local_node, test_net) return config_dict @staticmethod def get_config(): logging.debug("Configuration loaded for environment '{}'" .format(ExampleConfig.get_config_net())) return Config(options_dict=ExampleConfig.get_config_dict())

38.895105

98

0.627652

5,215

0.937612

0

2,828

0.50845

0

2,491

0.44786

cab12a2e4b4e5bc1f20b3d34222016965f6e7990

352

py

Python

02_Variables/Variable types/tests.py

dannymeijer/level-up-with-python

1bd1169aafd0fdc124984c30edc7f0153626cf06

[ "MIT" ]

null

02_Variables/Variable types/tests.py

dannymeijer/level-up-with-python

1bd1169aafd0fdc124984c30edc7f0153626cf06

[ "MIT" ]

null

02_Variables/Variable types/tests.py

dannymeijer/level-up-with-python

1bd1169aafd0fdc124984c30edc7f0153626cf06

[ "MIT" ]

null

from lessons.test_helper import run_common_tests, get_answer_placeholders, passed, failed def test_type_used(): window = get_answer_placeholders()[0] if "type" in window and "float_number" in window: passed() else: failed("Use the type() function") if __name__ == '__main__': run_common_tests() test_type_used()

23.466667

89

0.696023

0

55

0.15625

cab19f6521b5afec4f623e6c04e0f97d6d1dbb36

579

py

Python

rssexample.py

nhtnhan/CMPUT404-Web-Mining

ed9d847a1e3636b9d40c3a29e80aae44b97a0247

[ "MIT" ]

1

2016-04-01T19:18:16.000Z

rssexample.py

nhtnhan/CMPUT404-Web-Mining

ed9d847a1e3636b9d40c3a29e80aae44b97a0247

[ "MIT" ]

null

rssexample.py

nhtnhan/CMPUT404-Web-Mining

ed9d847a1e3636b9d40c3a29e80aae44b97a0247

[ "MIT" ]

3

2015-02-14T18:11:50.000Z

2021-04-18T06:48:07.000Z

import feedparser import difflib import json cbc = feedparser.parse("http://rss.cbc.ca/lineup/topstories.xml") print(json.dumps(cbc)) print("\n\n################################################\n\n") cnn = feedparser.parse("http://rss.cnn.com/rss/cnn_topstories.rss") print(json.dumps(cnn)) print("\n\n################################################\n\n") cbc_titles = [x['title'] for x in cbc.get('entries')] cnn_titles = [x['title'] for x in cnn.get('entries')] res = [(x,difflib.get_close_matches(x,cbc_titles,1,0.01)) for x in cnn_titles] print(json.dumps(res))

38.6

67

0.582038

0

232

0.400691

cab1bebb90bfc4565d2855b792d6d2d579ca6ab9

3,271

py

Python

dump_to_json.py

saqib-nadeem/sample_python_scripts

35054816b262a3860ab9db383ab5d9e11ac0ce87

[ "MIT" ]

null

dump_to_json.py

saqib-nadeem/sample_python_scripts

35054816b262a3860ab9db383ab5d9e11ac0ce87

[ "MIT" ]

null

dump_to_json.py

saqib-nadeem/sample_python_scripts

35054816b262a3860ab9db383ab5d9e11ac0ce87

[ "MIT" ]

null

""" Usage Example: cat imesh_sample.txt | python dump_to_json.py -o imesh.json -e imesh_hashes.json """ import sys import json import argparse import traceback from os.path import dirname, abspath project_folder = dirname(dirname(abspath('.'))) if project_folder not in sys.path: sys.path.append(project_folder) from breaches.lib.data_record import ValidationError def eprint(*args, **kwargs): # pylint: disable=w0621 print(*args, file=sys.stderr, **kwargs) if '__main__' == __name__: parser = argparse.ArgumentParser() parser.add_argument("-o", "--output_filename", help="File to write json data info for breaches index") parser.add_argument("-e", "--hash_filename", help="File to write json data info for hashes index") args = parser.parse_args() if args.output_filename is None: parser.print_help() sys.exit(1) package_base = dirname(dirname(dirname(abspath(__file__)))) if package_base not in sys.path: sys.path.insert(0, package_base) from breaches.imesh.imesh import ImeshImporter importer = ImeshImporter() dump_type = 'hashed' output_file = open(args.output_filename, "w", encoding='utf8') hash_file = None if args.hash_filename: hash_file = open(args.hash_filename, "w") processed_count = 0 error_count = 0 line_num = 1 for line in sys.stdin: try: hash_record = {} line_num += 1 record = importer.process_record(line.rstrip(), dump_type, for_import=True) if record is None: eprint("Skipping: " + line.rstrip()) continue if hash_file and hasattr(record, 'hash') and record.hash is not None: hash_record["hash"] = record.hash if hasattr(record, 'password') and record.password is not None: hash_record["password"] = record.password if hasattr(record, 'salt') and record.salt is not None: hash_record["salt"] = record.salt hash_record["hashtype"] = record.hashtype hash_file.write(json.dumps(hash_record) + '\n') # Delete any fields that are in the dump's ignore list if importer._import_ignore_fields: for fname in importer._import_ignore_fields: if hasattr(record, fname): delattr(record, fname) processed_count += 1 output_file.write(record.to_json() + '\n') if 0 == processed_count % 100000: eprint("Processed %i, Errors: %i" % (processed_count, error_count)) except ValidationError as vexp: error_count += 1 eprint("ValidationError %r\n while processing line number %i\n %s" % (vexp, line_num, line)) except Exception as exp: error_count += 1 eprint("Error %r\n while processing line number %i\n %s" % (exp, line_num, line)) traceback.print_exc(file=sys.stderr) print("Processed %i, Errors: %i" % (processed_count, error_count)) output_file.close() if hash_file: hash_file.close()

30.858491

106

0.602874

0

588

0.179762

cab39dac3498266e965d7a8495df886ca0322b45

31,860

py

Python

src/setprogramoptions/unittests/test_SetProgramOptionsCMake.py

sandialabs/SetProgramOptions

c0b3cec0e2309d726f450b449c080a33e7df3092

[ "BSD-3-Clause" ]

1

2021-12-21T21:53:26.000Z

src/setprogramoptions/unittests/test_SetProgramOptionsCMake.py

sandialabs/SetProgramOptions

c0b3cec0e2309d726f450b449c080a33e7df3092

[ "BSD-3-Clause" ]

null

src/setprogramoptions/unittests/test_SetProgramOptionsCMake.py

sandialabs/SetProgramOptions

c0b3cec0e2309d726f450b449c080a33e7df3092

[ "BSD-3-Clause" ]

3

2021-12-27T18:29:04.000Z

2022-01-31T14:08:41.000Z

#!/usr/bin/env python3 # -*- mode: python; py-indent-offset: 4; py-continuation-offset: 4 -*- #=============================================================================== # # License (3-Clause BSD) # ---------------------- # Copyright 2021 National Technology & Engineering Solutions of Sandia, # LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, # the U.S. Government retains certain rights in this software. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON # ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #=============================================================================== """ """ from __future__ import print_function import sys sys.dont_write_bytecode = True import contextlib import io import os sys.path.insert(1, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from pprint import pprint import unittest from unittest import TestCase # Coverage will always miss one of these depending on the system # and what is available. try: # pragma: no cover import unittest.mock as mock # pragma: no cover except: # pragma: no cover import mock # pragma: no cover from mock import Mock from mock import MagicMock from mock import patch import filecmp from textwrap import dedent try: from cStringIO import StringIO except ImportError: from io import StringIO from setprogramoptions import * from .common import * # =============================================================================== # # General Utility Data # # =============================================================================== global_gen_new_ground_truth_files = False # global_gen_new_ground_truth_files = True # comment this out for production. class DEFAULT_VALUE(object): pass # =============================================================================== # # General Utility Functions # # =============================================================================== # =============================================================================== # # Mock Helpers # # =============================================================================== # =============================================================================== # # Tests # # =============================================================================== class SetProgramOptionsTestCMake(TestCase): """ Main test driver for the SetProgramOptions class """ def setUp(self): print("") self.maxDiff = None self._filename = find_config_ini(filename="config_test_setprogramoptions.ini") # Get the location of the unit testing scripts (for file writing tests) unit_test_path = os.path.realpath(__file__) self.unit_test_file = os.path.basename(unit_test_path) self.unit_test_path = os.path.dirname(unit_test_path) def test_SetProgramOptionsCMake_Template(self): """ Basic template test for SetProgramOptions. This test doesn't really validate any output -- it just runs a basic check. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "CMAKE_GENERATOR_NINJA" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_property_inifilepath(self): """ Runs a check that loads the filename using `inifilepath` property rather than the parameter in the c'tor. """ parser = self._create_standard_parser(filename=None) parser.inifilepath = self._filename print("-----[ TEST BEGIN ]----------------------------------------") # parse all sections print("-" * 40) print("Execute Parser") print("-" * 40) sections = parser.configparserenhanceddata.sections(parse=False) self.assertGreater(len(sections), 2) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_bash(self): """ Test the ``gen_option_list`` method using the ``bash`` generator. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TRILINOS_CONFIGURATION_ALPHA" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) print("-" * 40) print("Option List") print("-" * 40) option_list_expect = [ 'cmake', '-G=Ninja', '-DTrilinos_ENABLE_COMPLEX:BOOL=ON', '-DTrilinos_ENABLE_THREAD_SAFE:BOOL=ON', # '-DTrilinos_PARALLEL_COMPILE_JOBS_LIMIT=20', # '-DTrilinos_PARALLEL_LINK_JOBS_LIMIT=4', '-DTrilinos_ENABLE_Kokkos:BOOL=ON', '-DTrilinos_ENABLE_KokkosCore:BOOL=ON', '-DTrilinos_ENABLE_KokkosKernels:BOOL=ON', '-DKokkosKernels_ENABLE_EXAMPLES:BOOL=ON', '-DTrilinos_ENABLE_Tpetra:BOOL=ON', '-DTpetra_INST_DOUBLE:BOOL=ON', '/path/to/source/dir' ] option_list_actual = parser.gen_option_list(section, generator="bash") pprint(option_list_actual, width=200) self.assertListEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_bash_expandvars(self): """ Test the ``gen_option_list`` method using the ``bash`` generator. """ parser = self._create_standard_parser() # parser.exception_control_compact_warnings = True print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_01" print("Section : {}".format(section)) self._execute_parser(parser, section) print("-" * 40) print("Option List") print("-" * 40) option_list_expect = [ 'cmake', '-DCMAKE_CXX_FLAGS:STRING="${LDFLAGS} -foo"', ] option_list_actual = parser.gen_option_list(section, generator="bash") pprint(option_list_actual, width=200) self.assertListEqual(option_list_expect, option_list_actual) print("OK") print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") # Update 03 will generate the update option section = "TEST_VAR_EXPANSION_UPDATE_03" print("Section : {}".format(section)) self._execute_parser(parser, section) print("-" * 40) print("Option List") print("-" * 40) option_list_expect = [ 'cmake', '-DCMAKE_CXX_FLAGS:STRING="${LDFLAGS} -foo"', '-DCMAKE_CXX_FLAGS:STRING="${LDFLAGS} -foo -bif"', ] option_list_actual = parser.gen_option_list(section, generator="bash") pprint(option_list_actual, width=200) self.assertListEqual(option_list_expect, option_list_actual) print("OK") print("-----[ TEST END ]------------------------------------------") return 0 def test_SetProgramOptionsCMake_gen_option_list_bash_expandvars_with_unknown_cmake_var_ecl3(self): """ Test the ``gen_option_list`` method using the ``bash`` generator when the ECL for ExpandVarsInTextCMake is set to 3 or lower. This should generate a WARNING. """ parser = self._create_standard_parser() parser.exception_control_compact_warnings = False parser.exception_control_level = 3 print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_02" print("Section : {}".format(section)) # parse the section self._execute_parser(parser, section) # Generate a BASH script representing the instructions in the section. # what answer do we EXPECT: option_list_expect = [ 'cmake', '-DCMAKE_CXX_FLAGS:STRING="${LDFLAGS} -foo"', '-DCMAKE_F90_FLAGS:STRING=" -baz"' ] # Generate the BASH entries: option_list_actual = parser.gen_option_list(section, generator="bash") # Verify the results: self.assertListEqual(option_list_actual, option_list_expect) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_bash_expandvars_with_unknown_cmake_var_ecl4(self): """ Test the ``gen_option_list`` method using the ``bash`` generator when the ECL for ExpandVarsInTextCMake is set to 4 or higher. This should raise a ``ValueError``. """ parser = self._create_standard_parser() parser.exception_control_level = 5 print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_02" print("Section : {}".format(section)) # parse the section self._execute_parser(parser, section) # Generate a BASH script representing the instructions in the section. with self.assertRaises(ValueError): parser.gen_option_list(section, generator="bash") print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_cmake_fragment(self): """ Test the ``gen_option_list`` method using the ``cmake_fragment`` generator. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TRILINOS_CONFIGURATION_ALPHA" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) print("-" * 40) print("Option List") print("-" * 40) option_list_expect = [ 'set(Trilinos_ENABLE_COMPLEX ON CACHE BOOL "from .ini configuration")', 'set(Trilinos_ENABLE_THREAD_SAFE ON CACHE BOOL "from .ini configuration")', 'set(Trilinos_PARALLEL_COMPILE_JOBS_LIMIT 20)', 'set(Trilinos_PARALLEL_LINK_JOBS_LIMIT 4)', 'set(Trilinos_ENABLE_Kokkos ON CACHE BOOL "from .ini configuration")', 'set(Trilinos_ENABLE_KokkosCore ON CACHE BOOL "from .ini configuration")', 'set(Trilinos_ENABLE_KokkosKernels ON CACHE BOOL "from .ini configuration")', 'set(KokkosKernels_ENABLE_EXAMPLES ON CACHE BOOL "from .ini configuration")', 'set(Trilinos_ENABLE_Tpetra ON CACHE BOOL "from .ini configuration")', 'set(Tpetra_INST_DOUBLE ON CACHE BOOL "from .ini configuration")' ] option_list_actual = parser.gen_option_list(section, generator="cmake_fragment") pprint(option_list_actual, width=200) self.assertListEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_cmake_fragment_expandvars(self): """ Test the ``gen_option_list`` method using the ``bash`` generator. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_02" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) parser.gen_option_list(section, generator="cmake_fragment") option_list_expect = [ 'set(CMAKE_CXX_FLAGS "$ENV{LDFLAGS} -foo" CACHE STRING "from .ini configuration")', 'set(CMAKE_F90_FLAGS "${CMAKE_F90_FLAGS} -baz" CACHE STRING "from .ini configuration")' ] option_list_actual = parser.gen_option_list(section, generator="cmake_fragment") print("Expected Output:\n{}\n".format("\n".join(option_list_expect))) print("Actual Output:\n{}\n".format("\n".join(option_list_actual))) self.assertListEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") # Test that the CMake generator will generate a sequence of operations # that don't include a FORCE option on an update of an existing CACHE # value. As far as SPOCM is concerned, it'll generate the CMake as # defined in the .ini file. print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_01" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) parser.gen_option_list(section, generator="cmake_fragment") option_list_expect = [ 'set(CMAKE_CXX_FLAGS "$ENV{LDFLAGS} -foo" CACHE STRING "from .ini configuration")', 'set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -bar" CACHE STRING "from .ini configuration")', ] option_list_actual = parser.gen_option_list(section, generator="cmake_fragment") print("Expected Output:\n{}\n".format("\n".join(option_list_expect))) print("Actual Output:\n{}\n".format("\n".join(option_list_actual))) self.assertListEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") # Test that the CMake generator will generate a sequence of operations # that do include a FORCE option on an update of an existing CACHE # value. As far as SPOCM is concerned w/rt to CMake fragments, we will # generate what the .ini file tells us to do and respect that the CMake # engine will operate as the CMake engine does. print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_VAR_EXPANSION_UPDATE_03" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) parser.gen_option_list(section, generator="cmake_fragment") option_list_expect = [ # Sets CMAKE_CXX_FLAGS the _first_ time, CMAKE_CXX_FLAGS would be set. 'set(CMAKE_CXX_FLAGS "$ENV{LDFLAGS} -foo" CACHE STRING "from .ini configuration")', # Tries to update CMAKE_CXX_FLAGS the _second_ time without FORCE. # CMake will not save this. 'set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -bar" CACHE STRING "from .ini configuration")', # Tries to update CMAKE_CXX_FLAGS again but this time uses FORCE. # CMake will save this updated value. 'set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -bif" CACHE STRING "from .ini configuration" FORCE)', ] option_list_actual = parser.gen_option_list(section, generator="cmake_fragment") print("Expected Output:\n{}\n".format("\n".join(option_list_expect))) print("Actual Output:\n{}\n".format("\n".join(option_list_actual))) self.assertListEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_param_order_01(self): """ """ parser = self._create_standard_parser() section = "TEST_CMAKE_CACHE_PARAM_ORDER" print("Section : {}".format(section)) self._execute_parser(parser, section) print("-----[ TEST BEGIN ]----------------------------------------") option_list_bash_expect = [ '-DCMAKE_VAR_A:STRING="ON"', '-DCMAKE_VAR_C:BOOL=ON', '-DCMAKE_VAR_D:BOOL=ON', '-DCMAKE_VAR_E:BOOL=ON', ] option_list_bash_actual = parser.gen_option_list(section, generator="bash") self.assertListEqual(option_list_bash_expect, option_list_bash_actual) option_list_cmake_fragment_expect = [ 'set(CMAKE_VAR_A ON CACHE STRING "from .ini configuration" FORCE)', 'set(CMAKE_VAR_B ON PARENT_SCOPE)', 'set(CMAKE_VAR_C ON CACHE BOOL "from .ini configuration")', 'set(CMAKE_VAR_D ON CACHE BOOL "from .ini configuration" FORCE)', 'set(CMAKE_VAR_E ON CACHE BOOL "from .ini configuration" FORCE)', 'set(CMAKE_VAR_F ON CACHE BOOL "from .ini configuration" PARENT_SCOPE)', 'set(CMAKE_VAR_G ON CACHE BOOL "from .ini configuration" PARENT_SCOPE)', ] option_list_cmake_fragment_actual = parser.gen_option_list(section, generator="cmake_fragment") self.assertListEqual(option_list_cmake_fragment_expect, option_list_cmake_fragment_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_param_order_02(self): """ Tests that we correctly generate output if extra flags are provided such as something to uniqueify a .ini option entry. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_CMAKE_CACHE_PARAM_TEST_02" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) option_list_bash_expect = ['-DCMAKE_VAR_A:STRING="ON"'] option_list_bash_actual = parser.gen_option_list(section, generator="bash") self.assertListEqual(option_list_bash_expect, option_list_bash_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_bash_generator_ignores_PARENT_SCOPE(self): """ Verify that the bash generator will not add a ``-D`` entry for a ``opt-set-cmake-var`` that has the ``PARENT_SCOPE`` flag since that will always force CMake to create a type-1 (non-cache) var assignment. """ parser = self._create_standard_parser() section = "TEST_CMAKE_PARENT_SCOPE_NOT_BASH" print("Section : {}".format(section)) print("-----[ TEST BEGIN ]----------------------------------------") option_list_bash_expect = [] option_list_bash_actual = parser.gen_option_list(section, generator="bash") self.assertListEqual(option_list_bash_expect, option_list_bash_actual) print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") option_list_cmake_fragment_expect = [ 'set(FOO_VAR_A "FOO_VAL A" PARENT_SCOPE)', 'set(FOO_VAR_B "FOO_VAL B" CACHE STRING "from .ini configuration" PARENT_SCOPE)' ] option_list_cmake_fragment_actual = parser.gen_option_list(section, generator="cmake_fragment") self.assertListEqual(option_list_cmake_fragment_expect, option_list_cmake_fragment_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_fail_on_FORCE_and_PARENT_SCOPE(self): """ Tests the case that both PARENT_SCOPE and FORCE are provided. This will cause a CMake error beacuse the existence of PARENT_SCOPE forces CMake to use a Type-1 set operation, i.e. a NON-CACHEd variable. However ``FORCE`` is only valid for a CACHED variable (Type-2). These two options are mutually exclusive and CMake will fail. In this case SetProgramOptionsCMake should raise a CATASTROPHIC error because the operation provided is invalid. """ parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_CMAKE_FAIL_ON_PARENT_SCOPE_AND_FORCE" print("Section : {}".format(section)) # parse a section self._execute_parser(parser, section) with self.assertRaises(ValueError): parser.gen_option_list(section, generator="bash") print("-----[ TEST END ]------------------------------------------") print("OK") return def test_SetProgramOptionsCMake_test_STRING_value_surrounded_by_double_quotes(self): """ Test STRING values are surrounded by double quotes. """ print("\n") print("Load file: {}".format(self._filename)) parser = self._create_standard_parser() print("-----[ TEST BEGIN ]----------------------------------------") section = "TEST_STRING_DOUBLE_QUOTES" print("Section : {}".format(section)) option_list_expect = ['-DFOO:STRING="foo::bar::baz<Type>"', '-DBAR:STRING="600"'] option_list_actual = parser.gen_option_list(section, generator="bash") print("-" * 40) print("Options List Expect") print("-" * 40) pprint(option_list_expect, width=120) print("") print("Options List Actual") print("-" * 40) pprint(option_list_actual, width=120) self.assertEqual(option_list_expect, option_list_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_opt_remove(self): """ This test validates that `opt-remove` will correctly remove a CMake var that was created using `opt-set-cmake-var` """ parser = self._create_standard_parser() section = "TEST_CMAKE_VAR_REMOVE" print("Section : {}".format(section)) print("-----[ TEST BEGIN ]----------------------------------------") option_list_bash_actual = parser.gen_option_list(section, 'bash') option_list_bash_expect = ['-DBAR_TEST:STRING="BAR"', '-DBAZ_TEST:STRING="BAZ"'] self.assertListEqual(option_list_bash_expect, option_list_bash_actual) print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") option_list_cmake_fragment_actual = parser.gen_option_list(section, 'cmake_fragment') option_list_cmake_fragment_expect = [ 'set(BAR_TEST BAR CACHE STRING "from .ini configuration")', 'set(BAZ_TEST BAZ CACHE STRING "from .ini configuration")' ] self.assertListEqual(option_list_cmake_fragment_expect, option_list_cmake_fragment_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_FORCE_only_for_bash(self): """ Test that an ``opt-set-cmake-var`` that has a FORCE but does not specify a TYPE will be assigned STRING by default and will generate the appropriate ``-D`` entry. [TEST_CMAKE_VAR_FORCE_ONLY] opt-set-cmake-var FOO FORCE : "BAR" should generate: -DFOO:STRING="BAR" """ parser = self._create_standard_parser() section = "TEST_CMAKE_VAR_FORCE_ONLY" print("Section : {}".format(section)) print("-----[ TEST BEGIN ]----------------------------------------") option_list_bash_actual = parser.gen_option_list(section, 'bash') option_list_bash_expect = [ '-DFOO:STRING="BAR"', ] self.assertListEqual(option_list_bash_expect, option_list_bash_actual) print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") option_list_cmake_fragment_actual = parser.gen_option_list(section, 'cmake_fragment') option_list_cmake_fragment_expect = [ 'set(FOO BAR CACHE STRING "from .ini configuration" FORCE)', ] self.assertListEqual(option_list_cmake_fragment_expect, option_list_cmake_fragment_actual) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def test_SetProgramOptionsCMake_gen_option_list_bash_unresolved_cmake_var_01(self): """ Tests what we do with an unresolved cmake variable encountered in the bash generator. The hitch is that if we replace the unresolved cmake var with an empty string we may be allowing a ``cmake-fragment`` and a ``bash command`` to diverge sicne the cmake fragment would have additional context of pre-existing variables that *might exist* versus the bash command where a cmake variable *definitely will not exist*. """ parser = self._create_standard_parser() section = "TEST_CMAKE_VAR_IN_BASH_GENERATOR" print("Section : {}".format(section)) print("-----[ TEST BEGIN ]----------------------------------------") # Test 1: Validate exception is raised when `exception_control_level` # is the default (4). with self.assertRaises(ValueError): option_list_actual = parser.gen_option_list(section, generator='bash') print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") # Test 2: Reduce the `exception_control_level` so that the exception is # not generated. # - Sets `exception_control_level` to 3 # - Sets `exception_control_compact_warnings` to False # Note: This test is sensitive to formatting changes to `ExceptionControl` # if this is a big problem we may need to change this in the future # to be less sensitive to stdout. option_list_expect = [ '-DFOO_VAR:STRING="FOO"', '-DFOO_VAR:STRING="BAR "' ] parser.exception_control_level = 3 parser.exception_control_compact_warnings = False with io.StringIO() as m_stdout: with contextlib.redirect_stdout(m_stdout): option_list_actual = parser.gen_option_list(section, generator='bash') # Check that the output matches self.assertListEqual(option_list_expect, option_list_actual) # Check that the exception-control warning message gets printed self.assertIn("EXCEPTION SKIPPED", m_stdout.getvalue()) self.assertIn("Event Type : MINOR", m_stdout.getvalue()) self.assertIn("Exception : ValueError", m_stdout.getvalue()) print("-----[ TEST END ]------------------------------------------") print("-----[ TEST BEGIN ]----------------------------------------") # Test 2: Repeat the previous test but with *compact* warnings from # `exception_control_compact_warnings` set to True to enable # compact warnings. # - Sets `exception_control_level` to 3 # - Sets `exception_control_compact_warnings` to True # Note: This test is sensitive to formatting changes to `ExceptionControl` # if this is a big problem we may need to change this in the future # to be less sensitive to stdout. option_list_expect = [ '-DFOO_VAR:STRING="FOO"', '-DFOO_VAR:STRING="BAR "' ] parser.exception_control_level = 3 parser.exception_control_compact_warnings = True with io.StringIO() as m_stdout: with contextlib.redirect_stdout(m_stdout): option_list_actual = parser.gen_option_list(section, generator='bash') # Check that the output matches self.assertListEqual(option_list_expect, option_list_actual) # Check that the exception-control warning message gets printed self.assertIn("EXCEPTION SKIPPED", m_stdout.getvalue()) self.assertIn("(MINOR : ValueError)", m_stdout.getvalue()) print("-----[ TEST END ]------------------------------------------") print("OK") return 0 def _create_standard_parser( self, filename=DEFAULT_VALUE(), debug_level=5, ece_level=4, ece_compact=False ): if isinstance(filename, DEFAULT_VALUE): filename = self._filename output = None if filename is not None: print("\n") print("filename: {}".format(filename)) output = SetProgramOptionsCMake(filename) else: output = SetProgramOptionsCMake() output.debug_level = debug_level output.exception_control_level = ece_level output.exception_control_compact_warnings = ece_compact return output def _execute_parser(self, parser, section): output = None # parse a section print("-" * 40) print("Execute Parser") print("-" * 40) output = parser.parse_section(section) # pretty print the output print("-" * 40) print("Output") print("-" * 40) pprint(output, width=120) # pretty print the loginfo print("-" * 40) print("LogInfo") print("-" * 40) parser._loginfo_print() return output

40.227273

177

0.570088

28,120

0.882611

0

16,242

0.509793

cab3e0c73d1808d2ec171a56c51c18d7eab9c9eb

603

py

Python

accelRF/rep/base.py

nexuslrf/Accel-RF

6e1034c27ea2a3f51093a6dcb10310e7fae04e30

[ "MIT" ]

null

accelRF/rep/base.py

nexuslrf/Accel-RF

6e1034c27ea2a3f51093a6dcb10310e7fae04e30

[ "MIT" ]

null

accelRF/rep/base.py

nexuslrf/Accel-RF

6e1034c27ea2a3f51093a6dcb10310e7fae04e30

[ "MIT" ]

null

from typing import Tuple import torch.nn as nn from torch import Tensor class Explicit3D(nn.Module): # corner_points: Tensor center_points: Tensor center2corner: Tensor n_voxels: int n_corners: int grid_shape: Tensor voxel_size: float occupancy: Tensor def __init__(self): super().__init__() def ray_intersect( self, rays_o: Tensor, rays_d: Tensor ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: NotImplemented def pruning(self, keep: Tensor): NotImplemented def splitting(self): NotImplemented

22.333333

51

0.648425

530

0.878939

0

23

0.038143

cab43f37667bf4a445a87188b43c261b7be4262e

1,250

py

Python

URLSHORT/lib.py

its-mr-monday/Url-Shortener

bca0f9c1a8d6c5a686704d7a410351b9ce31439b

[ "MIT" ]

null

URLSHORT/lib.py

its-mr-monday/Url-Shortener

bca0f9c1a8d6c5a686704d7a410351b9ce31439b

[ "MIT" ]

null

URLSHORT/lib.py

its-mr-monday/Url-Shortener

bca0f9c1a8d6c5a686704d7a410351b9ce31439b

[ "MIT" ]

null

import random import string import requests def SQL_SYNTAX_CHECK(input: str) -> bool: bad_char = ['*',';','SELECT ',' FROM ', ' TRUE ', ' WHERE '] for char in bad_char: if char in input: return False return True def validateRegistration(name, uname, email, password, confirm): if len(name) < 1 or len(name) > 45: return "Error invalid name" if len(uname) < 1 or len(uname) > 20: return "Error invalid username" if len(email) < 1 or len(email) > 100: return "Error invalid email" if len(password) < 1: return "Error invalid password" if password != confirm: return "Error passwords do not match" return "Success" def validate_email(email: str): at_counter = 0 for x in email: if x == "@": at_counter+=1 if at_counter == 1: return True else: return False def validate_link(link: str): req = requests.get('http://www.example.com') if req.status_code == 200: return True else: return False def generate_link() -> str: letters = string.ascii_lowercase+string.ascii_uppercase+"0123456789" return (''.join(random.choice(letters) for i in range(10)))

24.509804

72

0.604

0

209

0.1672

cab5b79870681508e9b3e9a34436132f63a674f8

3,985

py

Python

main.py

hamolicious/Python-Word-Search-Generator

9a8ce3f54afd1e2dd286b89bdb9052bb03023ccf

[ "Apache-2.0" ]

null

main.py

hamolicious/Python-Word-Search-Generator

9a8ce3f54afd1e2dd286b89bdb9052bb03023ccf

[ "Apache-2.0" ]

null

main.py

hamolicious/Python-Word-Search-Generator

9a8ce3f54afd1e2dd286b89bdb9052bb03023ccf

[ "Apache-2.0" ]

null

from random import choice, randint import os def generate_grid(w, h): global width, height alphabet = 'qwertyuiopasdfghjklzxcvbnm' grid = [] for i in range(h): row = [] for j in range(w): row.append(' ') grid.append(row) return grid def populate_grid(words, grid): for word in words: done = False tries = 10 while not done: try: start_x = randint(0, len(grid[0]) - len(word)) start_y = randint(0, len(grid) - len(word)) vel = choice([(1, 0), (0, 1), (1, 1)]) except ValueError: done = True break valid_spot = True x, y = start_x, start_y for i in range(len(word)): if grid[y][x] == ' ' or grid[y][x] == word[i]: pass else: valid_spot = False tries -= 1 break x += vel[0] y += vel[1] if tries <= 0: done = True if valid_spot: x, y = start_x, start_y for i in range(len(word)): grid[y][x] = word[i].upper() x += vel[0] y += vel[1] done = True alphabet = 'qwertyuiopasdfghjklzxcvbnm'.upper() for i in range(len(grid)): for j in range(len(grid[0])): if grid[i][j] == ' ': grid[i][j] = choice(alphabet) return grid def draw_grid(grid): screen = '' for row in grid: screen += '\n' do_once = True for tile in row: if do_once: screen += '|' do_once = False screen += tile + '|' print(screen) def use_random(): path = 'words.txt' if os.path.exists(path): with open(path, 'r') as file: temp = file.readlines() class wrd(): def __init__(self, word): self.word = word self.index = randint(0, 1000) def __lt__(self, other): return self.index < other.index words = [] word_count = 5 for word in temp: w = word.replace('\n', '') words.append(wrd(w)) words.sort() temp = [] for i in range(word_count): temp.append(words[i].word) return temp def get_details(): # width while True: w = input('\nWhat is the width of the grid in characters?\n[>> ') if w.isdigit(): w = int(w) break # height while True: h = input('\nWhat is the height of the grid in characters?\n[>> ') if h.isdigit(): h = int(h) break # words words = [] while True: wrd = input('Please add the words to the bank, you can press ENTER to use random words and press "q" when you\'re finished.\n[>> ').strip().lower() if wrd == '': words = use_random() break if wrd == 'q': break for let in wrd: if let not in 'qwertyuiopasdfghjklzxcvbnm': print('Invalid word, words can only contain the following letters:', ''.join(i for i in sorted('qwertyuiopasdfghjklzxcvbnm'))) break else: words.append(wrd) return w, h, words def clear(): print('\n' * 50) width, height, words = get_details() while True: grid = generate_grid(width, height) grid = populate_grid(words, grid) clear() draw_grid(grid) input('>')

20.863874

156

0.430866

221

0.055458

0

480

0.120452

cab84df34eb13ac7d2ea53e10575d0f1080c819c

4,416

py

Python

Scripts/HLS_Stream_Dowloader/hls_dowloader.py

WilliamMokoena/portfolio

8176d81e669279510af9fc7ff98aa769603cff60

[ "MIT" ]

null

Scripts/HLS_Stream_Dowloader/hls_dowloader.py

WilliamMokoena/portfolio

8176d81e669279510af9fc7ff98aa769603cff60

[ "MIT" ]

null

Scripts/HLS_Stream_Dowloader/hls_dowloader.py

WilliamMokoena/portfolio

8176d81e669279510af9fc7ff98aa769603cff60

[ "MIT" ]

null

import sys, os, asyncio, shutil import wget from ffmpeg import FFmpeg # Func calls wget to download the file given in url arg def webget(url): wget.download(url) # Fuc calls ffmpeg to transcode .m3u8 to .mp4 def transcode(ffmpeg): @ffmpeg.on('stderr') def on_stderr(line): print(line) @ffmpeg.on('progress') def on_progress(progress): print(progress) @ffmpeg.on('completed') def on_completed(): print('\nCompleted') @ffmpeg.on('error') def on_error(code): print('Error:', code) loop = asyncio.get_event_loop() loop.run_until_complete(ffmpeg.execute()) loop.close() def parse_m3u8_url(input_url): parse_url = input_url.split('/') input_m3u8 = parse_url[len(parse_url) - 1] base_url = input_url[:-len(input_m3u8)] if '?' in input_m3u8: input_m3u8 = input_m3u8.split('?')[0] return base_url, input_m3u8 def create_manifest(input_m3u8): with open(f'./{input_m3u8}', 'r') as f: file = f.readlines() manifest = [] for el in file: el = el[:-1] if 'http' in el and '?' in el and '=' in el: manifest.append(el) elif 'https' in el: el = el.split('/') el = el[len(el)-1] manifest.append(el) else: manifest.append(el) with open('./manifest.m3u8', 'a') as f: for elm in manifest: f.write(elm+'\n') def cleanup_working_dir(input_m3u8, storage_folder): try: # Create folder given in arg os.mkdir(storage_folder) except FileExistsError: print('\nWARNING: Output folder exists') cwd = os.getcwd() files = os.listdir() print(f'\nMESSAGE: Cleaning up and Packaging things nicely') os.mkdir(f'{storage_folder}/{storage_folder}') for f in files: # Logic for moving the output file if f[-3:] == 'mp4': original = f'{cwd}/{f}' target = f'{cwd}/{storage_folder}' # Moving the output file print(f'\nMESSAGE: Moving {input_m3u8} to {storage_folder}') shutil.move(original,target) if f[-4:] == 'm3u8': original = f'{os.getcwd()}/{f}' target = f'{os.getcwd()}/{storage_folder}/{storage_folder}' shutil.move(original,target) if f[-2:] == 'ts': original = f'{os.getcwd()}/{f}' target = f'{os.getcwd()}/{storage_folder}/{storage_folder}' shutil.move(original,target) # Read cli args : 'hls-downloader.py ["m3u8_url"] ["mp4_output_name"] ["storage_folder"]' input_url = sys.argv[1] output_filename = sys.argv[2] storage_folder = "./site/media" base_url, input_m3u8 = parse_m3u8_url(input_url) # Call wget to download files if input_m3u8 in os.listdir(): print(f'WARNING: {input_m3u8} already exists') else: print(f'MESSAGE: Downloading m3u8 file') webget(input_url) print(f'\nMESSAGE: Creating manifest.m3u8') create_manifest(input_m3u8) print(f'\nMESSAGE: Reading {input_m3u8}') data = None if 'movcloud' in input_url: with open('playlist.m3u8', 'r') as f: data = f.read() elif 'manifest.m3u8' in os.listdir(): with open('manifest.m3u8', 'r') as f: data = f.read() if data != None: contents = data.split('\n') print(f'\nMESSAGE: Attempting to download items from {input_m3u8}') for item in contents: if item in os.listdir(): continue if 'http' in item and '?' in item and '=' in item: webget(item) if 'movcloud' in item: item_sp = item.split('/') if item_sp[len(item_sp)-1] in os.listdir(): continue else: webget(item) else: stxt = item[0:5] entxt = item[-2:] if stxt == 'https': l = item.split('/') name = item[len(l)-1] webget(item) elif entxt == 'ts': cut = slice(0,-len(input_m3u8)) webget(input_url[cut] + item) # Configuring ffmpeg ## ffmpeg -i "./folder/file.m3u8" -c copy file.mp4 _ffmpeg = FFmpeg().option('n').input('./manifest.m3u8').output(output_filename,{'c': 'copy'}) print(f'\n\nMESSAGE: Running command: ffmpeg -i ./manifest.m3u8 -c copy {output_filename}') transcode(_ffmpeg) cleanup_working_dir(input_m3u8, storage_folder)

25.976471

93

0.584013

0

291

0.065897

0

1,306

0.295743

cab89ee2fce3fceaa9fcc3a842fbaa80ba1da5df

2,532

py

Python

fairlearn/metrics/__init__.py

alliesaizan/fairlearn

846ce6cdaf188e32a545d3f90197515a4a5bc471

[ "MIT" ]

1,142

2019-10-14T18:05:46.000Z

2022-03-30T06:56:54.000Z

fairlearn/metrics/__init__.py

alliesaizan/fairlearn

846ce6cdaf188e32a545d3f90197515a4a5bc471

[ "MIT" ]

623

2019-10-14T17:11:25.000Z

2022-03-31T17:46:54.000Z

fairlearn/metrics/__init__.py

alliesaizan/fairlearn

846ce6cdaf188e32a545d3f90197515a4a5bc471

[ "MIT" ]

299

2019-10-15T00:09:53.000Z

2022-03-30T12:35:27.000Z

# Copyright (c) Microsoft Corporation and Fairlearn contributors. # Licensed under the MIT License. """Functionality for computing metrics, with a particular focus on disaggregated metrics. For our purpose, a metric is a function with signature ``f(y_true, y_pred, ....)`` where ``y_true`` are the set of true values and ``y_pred`` are values predicted by a machine learning algorithm. Other arguments may be present (most often sample weights), which will affect how the metric is calculated. This module provides the concept of a *disaggregated metric*. This is a metric where in addition to ``y_true`` and ``y_pred`` values, the user provides information about group membership for each sample. For example, a user could provide a 'Gender' column, and the disaggregated metric would contain separate results for the subgroups 'male', 'female' and 'nonbinary' indicated by that column. The underlying metric function is evaluated for each of these three subgroups. This extends to multiple grouping columns, calculating the metric for each combination of subgroups. """ import sys as _sys from ._metric_frame import MetricFrame # noqa: F401 from ._make_derived_metric import make_derived_metric # noqa: F401 from ._generated_metrics import _generated_metric_dict from ._disparities import ( # noqa: F401 demographic_parity_difference, demographic_parity_ratio, equalized_odds_difference, equalized_odds_ratio) from ._extra_metrics import ( # noqa: F401 true_positive_rate, true_negative_rate, false_positive_rate, false_negative_rate, _balanced_root_mean_squared_error, mean_prediction, selection_rate, _mean_overprediction, _mean_underprediction, count) # Add the generated metrics of the form and # `<metric>_{difference,ratio,group_min,group_max` _module_obj = _sys.modules[__name__] for _name, _func in _generated_metric_dict.items(): setattr(_module_obj, _name, _func) # ============================================ # Build list of items to be listed in the docs _core = [ "MetricFrame", "make_derived_metric" ] _disparities = [ "demographic_parity_difference", "demographic_parity_ratio", "equalized_odds_difference", "equalized_odds_ratio" ] _extra_metrics = [ "true_positive_rate", "true_negative_rate", "false_positive_rate", "false_negative_rate", "mean_prediction", "selection_rate", "count" ] __all__ = _core + _disparities + _extra_metrics + list(sorted(_generated_metric_dict.keys()))

30.142857

93

0.75079

0

1,566

0.618483

cab923dc066cf5ab57a2921c1652c8204411aadb

4,360

py

Python

detector/ssd/ssd.py

Senyaaa/detection-experiments

5e80dd458e886ca27db5420d25ade8f9d74ae5a8

[ "Apache-2.0" ]

5

2020-06-08T08:21:03.000Z

2021-03-03T21:54:06.000Z

detector/ssd/ssd.py

Senyaaa/detection-experiments

5e80dd458e886ca27db5420d25ade8f9d74ae5a8

[ "Apache-2.0" ]

3

2021-02-06T20:21:02.000Z

2021-06-06T18:46:27.000Z

detector/ssd/ssd.py

Senyaaa/detection-experiments

5e80dd458e886ca27db5420d25ade8f9d74ae5a8

[ "Apache-2.0" ]

2

2020-06-08T08:21:05.000Z

2021-02-06T11:44:04.000Z

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from detector.ssd.utils import box_utils from nn.separable_conv_2d import SeparableConv2d from fpn.extension import Extension from detector.ssd.to_predictions import ToPredictions class SSD(nn.Module): def __init__(self, num_classes, backbone, arch_name, batch_size=None, config=None): """Compose a SSD model using the given components. """ super(SSD, self).__init__() self.num_classes = num_classes self.backbone = backbone self.arch_name = arch_name self.batch_size = batch_size # to ease the inference model feature_channels = self.backbone.feature_channels() self.extras = Extension( bootstrap_channels=feature_channels[-1], out_channels=[512, 256, 256, 64], conv=SeparableConv2d) self.classification_headers = nn.ModuleList([ SeparableConv2d(in_channels=feature_channels[-2], out_channels=6 * num_classes, kernel_size=3, padding=1), SeparableConv2d(in_channels=feature_channels[-1], out_channels=6 * num_classes, kernel_size=3, padding=1), SeparableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1), SeparableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1), SeparableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1), nn.Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1), ]) self.regression_headers = nn.ModuleList([ SeparableConv2d(in_channels=feature_channels[-2], out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False), SeparableConv2d(in_channels=feature_channels[-1], out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False), SeparableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False), SeparableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False), SeparableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False), nn.Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1), ]) self.config = config def forward(self, x): confidences = [] locations = [] cs = self.backbone.forward(x) cs = cs[-2:] for i, c in enumerate(cs): confidence, location = self.compute_header(i, c) x = c confidences.append(confidence) locations.append(location) extra_x = self.extras.forward(x) header_index = i + 1 for ex in extra_x: confidence, location = self.compute_header(header_index, ex) header_index += 1 confidences.append(confidence) locations.append(location) confidences = torch.cat(confidences, 1) locations = torch.cat(locations, 1) return confidences, locations def compute_header(self, i, x): batch_size = self.batch_size or x.size(0) confidence = self.classification_headers[i](x) confidence = confidence.permute(0, 2, 3, 1).contiguous() confidence = confidence.reshape(batch_size, -1, self.num_classes) location = self.regression_headers[i](x) location = location.permute(0, 2, 3, 1).contiguous() location = location.reshape(batch_size, -1, 4) return confidence, location def load_backbone_weights(self, path): self.backbone.load_state_dict( torch.load(path, map_location=lambda storage, loc: storage), strict=True) def freeze_backbone(self): for p in self.backbone.parameters(): p.requires_grad = False class SSDInference(SSD): def __init__(self, num_classes, backbone, arch_name, batch_size=None, config=None): super(SSDInference, self).__init__(num_classes, backbone, arch_name, batch_size, config) self.to_predictions = ToPredictions(self.config.priors, self.config.center_variance, self.config.size_variance) def forward(self, x): confidences, locations = super(SSDInference, self).forward(x) confidences, boxes = self.to_predictions.forward(confidences, locations) return confidences, boxes

32.296296

74

0.682339

4,084

0.936697

0

85

0.019495

caba47926befb920d795eb25c2284edc52eb5271

1,044

py

Python

commands/serverinfo.py

Stereo528/Osmium

cd17912b2cd559238b25c75f2d53785c72452aca

[ "MIT" ]

2

2021-12-15T10:50:45.000Z

2022-02-15T13:15:30.000Z

commands/serverinfo.py

Stereo528/Osmium

cd17912b2cd559238b25c75f2d53785c72452aca

[ "MIT" ]

6

2021-02-23T17:08:09.000Z

2021-02-23T23:33:55.000Z

commands/serverinfo.py

Stereo528/Osmium

cd17912b2cd559238b25c75f2d53785c72452aca

[ "MIT" ]

null

import discord from discord.ext import commands from main import getAlias class Util(commands.Cog): def __init__(self, client): self.client = client @commands.command(aliases=getAlias("serverinfo")) async def serverinfo(self, ctx): embed = discord.Embed( title=f"Info for {ctx.guild.name}", description=f"Emoji Limit: {ctx.guild.emoji_limit} \nFilesize Limit: {round(ctx.guild.filesize_limit/1000000)} megabytes", color=discord.Color.blurple() ) embed.set_thumbnail(url=ctx.guild.icon_url) embed.add_field(name="Current Members", value=ctx.guild.member_count, inline=True) embed.add_field(name="Other Data", value=f"Nitro Level: {ctx.guild.premium_tier}, Nitro Boosters: {ctx.guild.premium_subscription_count}", inline=False) embed.add_field(name="Owner Info", value=f"Server Owner: {ctx.guild.owner} \nOwner ID: {ctx.guild.owner_id}", inline=False) await ctx.send(embed=embed) def setup(client): client.add_cog(Util(client))

45.391304

160

0.694444

916

0.877395

0

824

0.789272

770

0.737548

353

0.338123

cababbb0c22d7ff63e14475c4594f1e5800c07e0

6,019

py

Python

sklearn_pipeline_enhancements/shared/transformers.py

Kgoetsch/sklearn_pipeline_enhancements

9afb9e03d762c3d8ecd19639f3b84f38cb33a71d

[ "MIT" ]

11

2017-04-18T00:19:05.000Z

2020-04-06T22:16:35.000Z

sklearn_pipeline_enhancements/shared/transformers.py

OlliePage/sklearn_pipeline_enhancements

9afb9e03d762c3d8ecd19639f3b84f38cb33a71d

[ "MIT" ]

null

sklearn_pipeline_enhancements/shared/transformers.py

OlliePage/sklearn_pipeline_enhancements

9afb9e03d762c3d8ecd19639f3b84f38cb33a71d

[ "MIT" ]

5

2017-12-05T20:14:55.000Z

2021-03-29T12:25:36.000Z

import numpy as np import pandas as pd from patsy.highlevel import dmatrix from sklearn.base import TransformerMixin, BaseEstimator from sklearn.pipeline import _name_estimators, Pipeline __author__ = 'kgoetsch' def make_dataframeunion(steps): return DataFrameUnion(_name_estimators(steps)) class FactorExtractor(TransformerMixin, BaseEstimator): """ In: pd.DataFrame Column in that Frame Out: pd.Series """ def __init__(self, factor): self.factor = factor def transform(self, data): return data[self.factor] def fit(self, *_): return self class RenameField(TransformerMixin, BaseEstimator): """ In: pd.DataFrame Column in that Frame Out: pd.Series """ def __init__(self, new_name): self.new_name = new_name def transform(self, data): data.name = self.new_name return data def fit(self, *_): return self class FillNA(TransformerMixin, BaseEstimator): """ In: pd.Series Out: pd.Series """ def __init__(self, na_replacement=None): if na_replacement is not None: self.NA_replacement = na_replacement else: self.NA_replacement = 'missing' def transform(self, data): return data.fillna(self.NA_replacement) def fit(self, *_): return self class DataFrameUnion(TransformerMixin, BaseEstimator): """ In: list of (string, transformer) tuples : Out: pd.DataFrame """ def __init__(self, transformer_list): self.feature_names = None self.transformer_list = transformer_list # (string, Transformer)-tuple list def __getitem__(self, attrib): return self.__dict__[attrib] def transform(self, X): """Transform X separately by each transformer, concatenate results. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data to be transformed. Returns ------- X_t : array-like or sparse matrix, shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. """ Xs = (self._transform_one(trans, X) for name, trans in self.transformer_list) df_merged_result = self._merge_results(Xs) return df_merged_result def fit(self, X, y=None): """Fit all transformers using X. Parameters ---------- :param X: pd.DataFrame Input data, used to fit transformers. :param y: """ transformers = (self._fit_one_transformer(trans, X, y) for name, trans in self.transformer_list) self._update_transformer_list(transformers) return self def _merge_results(self, transformed_result_generator): df_merged_result = '' for transformed in transformed_result_generator: if isinstance(transformed, pd.Series): transformed = pd.DataFrame(data=transformed) if not isinstance(df_merged_result, pd.DataFrame): df_merged_result = transformed else: df_merged_result = pd.concat([df_merged_result, transformed], axis=1) if self.feature_names is None: self.feature_names = df_merged_result.columns elif (len(self.feature_names) != len(df_merged_result.columns)) or \ ((self.feature_names != df_merged_result.columns).any()): custom_dataframe = pd.DataFrame(data=0, columns=self.feature_names, index=df_merged_result.index) custom_dataframe.update(df_merged_result) df_merged_result = custom_dataframe return df_merged_result def _update_transformer_list(self, transformers): self.transformer_list[:] = [ (name, new) for ((name, old), new) in zip(self.transformer_list, transformers) ] def _fit_one_transformer(self, transformer, X, y): return transformer.fit(X, y) def _transform_one(self, transformer, X): return transformer.transform(X) def extract_and_denull(var, na=0): return Pipeline([ ('extract', FactorExtractor(var)), ('fill_na', FillNA(na)) ]) class ConvertToArray(TransformerMixin, BaseEstimator): """ In: pd.Dataframe Out: np.array """ def transform(self, data): return np.ascontiguousarray(data.values) def fit(self, *_): return self class CategoricalDummifier(TransformerMixin, BaseEstimator): """ In: pd.Series Out: pd.DataFrame """ def transform(self, data): return dmatrix(formula_like=str(data.name), data=pd.DataFrame(data.apply(str)), return_type='dataframe', NA_action='raise').drop('Intercept', axis=1) def fit(self, *_): return self class WeekdayExtraction(TransformerMixin, BaseEstimator): """ In: pd.DataFrame Out: pd.Series """ def transform(self, data): return_data = pd.Series(data.index.weekday, index=data.index, name='weekday') return return_data def fit(self, *_): return self class LengthofField(TransformerMixin, BaseEstimator): """ In: pd.Series Out: pd.Series """ def transform(self, data): return_value = data.apply(len) return return_value def fit(self, *_): return self class InsertIntercept(TransformerMixin, BaseEstimator): """ In: pd.DataFrame Out: pd.Series = 1 of the same length """ def transform(self, data): return pd.DataFrame(data=1, index=data.index, columns=['Intercept']) def fit(self, *_): return self if __name__ == '__main__': target = make_dataframeunion([extract_and_denull('years'), extract_and_denull('kitten')]) for step in target.transformer_list: print step

25.943966

112

0.632663

5,370

0.892175

0

1,382

0.229606

cabbf72f74049f95a62204023ec60f67c663a913

1,130

py

Python

exercises/exercise7.py

AsBeeb/DistributedExercisesAAU

b84343d5d5b86ccb4750d47a8594a428ecbd83ce

[ "MIT" ]

4

2021-09-16T12:52:04.000Z

2022-01-09T15:44:49.000Z

exercises/exercise7.py

AsBeeb/DistributedExercisesAAU

b84343d5d5b86ccb4750d47a8594a428ecbd83ce

[ "MIT" ]

null

exercises/exercise7.py

AsBeeb/DistributedExercisesAAU

b84343d5d5b86ccb4750d47a8594a428ecbd83ce

[ "MIT" ]

21

2021-09-06T09:39:18.000Z

2022-03-08T12:18:23.000Z

import math import random import threading import time from emulators.Medium import Medium from emulators.Device import Device from emulators.MessageStub import MessageStub class Vote(MessageStub): def __init__(self, sender: int, destination: int, vote: int, decided: bool): super().__init__(sender, destination) self._vote = vote self._decided = decided def vote(self): return self._vote def decided(self): return self._decided def __str__(self): return f'Vote: {self.source} -> {self.destination}, voted for {self._vote}, decided? {self._decided}' class Bully(Device): def __init__(self, index: int, number_of_devices: int, medium: Medium): super().__init__(index, number_of_devices, medium) self._leader = None self._shut_up = False self._election = False def largest(self): return self.index() == max(self.medium().ids()) def run(self): """TODO""" def start_election(self): """TODO""" def print_result(self): print(f'Leader seen from {self._id} is {self._leader}')

24.565217

109

0.652212

951

0.841593

0

162

0.143363

cabc29877369a0222f319b94559f9ee8817e0fb8

4,036

py

Python

pait/util/_pydantic_util.py

so1n/pa

bf43a20f54b6b7e9f1bd6531c49f4f39deb68399

[ "Apache-2.0" ]

19

2020-08-26T13:46:33.000Z

2022-02-22T07:48:29.000Z

pait/util/_pydantic_util.py

so1n/pa

bf43a20f54b6b7e9f1bd6531c49f4f39deb68399

[ "Apache-2.0" ]

1

2021-06-06T17:45:54.000Z

pait/util/_pydantic_util.py

so1n/pa

bf43a20f54b6b7e9f1bd6531c49f4f39deb68399

[ "Apache-2.0" ]

1

2022-01-21T20:25:33.000Z

from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Type, Union from pydantic.schema import ( default_ref_template, get_flat_models_from_model, get_long_model_name, get_model, get_schema_ref, model_process_schema, normalize_name, ) if TYPE_CHECKING: from pydantic import BaseModel from pydantic.dataclasses import Dataclass from pydantic.schema import TypeModelOrEnum, TypeModelSet global_name_model_map = {} global_conflicting_names: Set[str] = set() def get_model_global_name(model: "TypeModelOrEnum") -> str: return pait_get_model_name_map({model})[model] def pait_get_model_name_map(unique_models: "TypeModelSet") -> Dict["TypeModelOrEnum", str]: """ Process a set of models and generate unique names for them to be used as keys in the JSON Schema definitions. By default the names are the same as the class name. But if two models in different Python modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be based on the Python module path for those conflicting models to prevent name collisions. :param unique_models: a Python set of models :return: dict mapping models to names """ global global_name_model_map global global_conflicting_names for model in unique_models: model_name = normalize_name(model.__name__) if model_name in global_conflicting_names: model_name = get_long_model_name(model) global_name_model_map[model_name] = model elif model_name in global_name_model_map: global_conflicting_names.add(model_name) conflicting_model = global_name_model_map.pop(model_name) global_name_model_map[get_long_model_name(conflicting_model)] = conflicting_model global_name_model_map[get_long_model_name(model)] = model else: global_name_model_map[model_name] = model return {v: k for k, v in global_name_model_map.items()} def pait_model_schema( model: Union[Type["BaseModel"], Type["Dataclass"]], by_alias: bool = True, ref_prefix: Optional[str] = None, ref_template: str = default_ref_template, ) -> Dict[str, Any]: """ Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level JSON key. :param model: a Pydantic model (a class that inherits from BaseModel) :param by_alias: generate the schemas using the aliases defined, if any :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the top-level key ``definitions``, so you can extract them from there. But all the references will have the set prefix. :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. :return: dict with the JSON Schema for the passed ``model`` """ model = get_model(model) flat_models = get_flat_models_from_model(model) model_name_map = pait_get_model_name_map(flat_models) model_name = model_name_map[model] m_schema, m_definitions, nested_models = model_process_schema( model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template ) if model_name in nested_models: # model_name is in Nested models, it has circular references m_definitions[model_name] = m_schema m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False) if m_definitions: m_schema.update({"definitions": m_definitions}) return m_schema

42.93617

116

0.724727

0

1,777

0.440287

cabd038328a350d6730ea789068223bf2e852a25

526

py

Python

experimental/plotlyDelaunay3D.py

FYP-DES5/deepscan-core

b6ce70ae69577fbdf5b80b30c4e83c7ee9cf6942

[ "MIT" ]

null

experimental/plotlyDelaunay3D.py

FYP-DES5/deepscan-core

b6ce70ae69577fbdf5b80b30c4e83c7ee9cf6942

[ "MIT" ]

null

experimental/plotlyDelaunay3D.py

FYP-DES5/deepscan-core

b6ce70ae69577fbdf5b80b30c4e83c7ee9cf6942

[ "MIT" ]

null

import plotly.plotly as py from plotly.graph_objs import * import numpy as np import matplotlib.cm as cm from scipy.spatial import Delaunay u=np.linspace(0,2*np.pi, 24) v=np.linspace(-1,1, 8) u,v=np.meshgrid(u,v) u=u.flatten() v=v.flatten() #evaluate the parameterization at the flattened u and v tp=1+0.5*v*np.cos(u/2.) x=tp*np.cos(u) y=tp*np.sin(u) z=0.5*v*np.sin(u/2.) #define 2D points, as input data for the Delaunay triangulation of U points2D=np.vstack([u,v]).T tri = Delaunay(points2D)#triangulate the rectangle U

22.869565

68

0.731939

0

151

0.287072

cabe8c977597c3348430dae0fd1a0dcbabe5b972

3,184

py

Python

src/sardana/taurus/qt/qtcore/tango/sardana/pool.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

43

2016-11-25T15:21:23.000Z

2021-08-20T06:09:40.000Z

src/sardana/taurus/qt/qtcore/tango/sardana/pool.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

1,263

2016-11-25T15:58:37.000Z

2021-11-02T22:23:47.000Z

src/sardana/taurus/qt/qtcore/tango/sardana/pool.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

58

2016-11-21T11:33:55.000Z

2021-09-01T06:21:21.000Z

#!/usr/bin/env python ############################################################################## ## # This file is part of Sardana ## # http://www.sardana-controls.org/ ## # Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## # Sardana is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. ## # Sardana 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 Lesser General Public License for more details. ## # You should have received a copy of the GNU Lesser General Public License # along with Sardana. If not, see <http://www.gnu.org/licenses/>. ## ############################################################################## """Device pool extension for taurus Qt""" __all__ = ["QPool", "QMeasurementGroup", "registerExtensions"] import json from taurus.external.qt import Qt from taurus.core.taurusbasetypes import TaurusEventType from taurus.core.tango import TangoDevice CHANGE_EVTS = TaurusEventType.Change, TaurusEventType.Periodic class QPool(Qt.QObject, TangoDevice): def __init__(self, name='', qt_parent=None, **kw): self.call__init__(TangoDevice, name, **kw) self.call__init__wo_kw(Qt.QObject, qt_parent) class QMeasurementGroup(Qt.QObject, TangoDevice): configurationChanged = Qt.pyqtSignal() def __init__(self, name='', qt_parent=None, **kw): self.call__init__(TangoDevice, name, **kw) self.call__init__wo_kw(Qt.QObject, qt_parent) self._config = None self.__configuration = self.getAttribute("Configuration") self.__configuration.addListener(self._configurationChanged) def __getattr__(self, name): try: return Qt.QObject.__getattr__(self, name) except AttributeError: return TangoDevice.__getattr__(self, name) def _configurationChanged(self, s, t, v): if t == TaurusEventType.Config: return if TaurusEventType.Error: self._config = None else: self._config = json.loads(v.value) self.configurationChanged.emit() def getConfiguration(self, cache=True): if self._config is None or not cache: try: v = self.read_attribute("configuration") self._config = json.loads(v.value) except: self._config = None return self._config def setConfiguration(self, config): self.write_attribute("configuration", json.dumps(config)) def registerExtensions(): """Registers the pool extensions in the :class:`taurus.core.tango.TangoFactory`""" import taurus #import sardana.taurus.core.tango.sardana.pool # sardana.taurus.core.tango.sardana.pool.registerExtensions() factory = taurus.Factory() #factory.registerDeviceClass('Pool', QPool) factory.registerDeviceClass('MeasurementGroup', QMeasurementGroup)

33.166667

86

0.659548

1,484

0.46608

0

1,327

0.416771

cac1340a7afa2d15888e2ebc2d6dd2802d2bd8de

85

py

Python

basics_data_structure/queue.py

corenel/algorithm-exercises

f3f31f709e289e590c98247c019d36fc9cc44faf

[ "MIT" ]

null

basics_data_structure/queue.py

corenel/algorithm-exercises

f3f31f709e289e590c98247c019d36fc9cc44faf

[ "MIT" ]

null

basics_data_structure/queue.py

corenel/algorithm-exercises

f3f31f709e289e590c98247c019d36fc9cc44faf

[ "MIT" ]

null

""" Queue https://algorithm.yuanbin.me/zh-hans/basics_data_structure/queue.html """

14.166667

69

0.752941

0

84

0.988235

cac8d19cf2fbd34c80ea8f4c9aeb60a84c12ab4c

1,542

py

Python

after/trans.py

Windsooon/Comments

47a6077e3bf46743a8da3d59ea8ebcd5601c9fe9

[ "MIT" ]

1

2020-07-08T06:17:54.000Z

after/trans.py

Windsooon/Comments

47a6077e3bf46743a8da3d59ea8ebcd5601c9fe9

[ "MIT" ]

null

after/trans.py

Windsooon/Comments

47a6077e3bf46743a8da3d59ea8ebcd5601c9fe9

[ "MIT" ]

null

import os import csv import numpy as np import pandas as pd import matplotlib.pyplot as plt from comments.base import DATA_DIR, cut_words, sigmoid class Trans: def __init__(self): self.trans_matrix = pd.read_excel(os.path.join(DATA_DIR, 'trans.xls'), index_col=0) # add one to every value self.trans_matrix += 1 def calculate_pro(self, sentence): ''' TODO ''' res = cut_words(sentence, postag=True) # Get all the POS pos = [r[1] for r in res] possibility = 0 pre = 'begin' for i in range(len(pos)-1): try: possibility += np.log(self.trans_matrix.loc[pre, : ][pos[i]] * 10 / sum(self.trans_matrix.loc[pre, : ])) except KeyError: pass else: pre = pos[i] return possibility def pro(self, csv_file): data = pd.read_csv(os.path.join(DATA_DIR, csv_file), skipinitialspace=True) data['pro'] = data['comments'].apply(self.calculate_pro) data['pro'].to_csv('output_' + csv_file) def show(self, csv_file): axes = plt.axes() axes.set_xlim([-200, 200]) d = pd.read_csv(csv_file, skipinitialspace=True, names=['pro']) plt.hist(d['pro'], color='blue', edgecolor='black', bins = 300) plt.title('Log Pro') plt.xlabel('log_pro') plt.ylabel('number') plt.tight_layout() plt.show() t = Trans() # t.pro('fin_useless.csv') t.show('output_fin_useless.csv')

29.09434

120

0.576524

1,318

0.854734

0

215

0.139429

cac957160334299ea86d99379b7a64786fa17120

2,071

py

Python

titan/api_pkg/mutation/props.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/api_pkg/mutation/props.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

titan/api_pkg/mutation/props.py

mnieber/gen

65f8aa4fb671c4f90d5cbcb1a0e10290647a31d9

[ "MIT" ]

null

from moonleap import u0 from moonleap.typespec.field_spec import FieldSpec, FormFieldSpec from moonleap.typespec.type_spec_store import TypeSpec, type_spec_store from titan.api_pkg.mutation.default_outputs_type_spec import default_outputs_type_spec def _field_spec_from_item(self, item): return FormFieldSpec( name=item.item_name + "Form", required=False, private=False, field_type="form", field_type_attrs=dict(target=item.item_type.name), ) def _field_spec_from_deleted_item_list(self, item_list): return FieldSpec( name=item_list.item_name + "Ids", required=True, private=False, field_type="idList", field_type_attrs=dict(target=item_list.item_type.name), ) def _default_inputs_type_spec(self, name): return TypeSpec( type_name=name, field_specs=[ *[_field_spec_from_item(self, item) for item in self.items_posted], *[ _field_spec_from_deleted_item_list(self, item_list) for item_list in self.item_lists_deleted ], ], ) def inputs_type_spec(self): type_spec_name = f"{u0(self.name)}Inputs" type_spec = type_spec_store().get(type_spec_name, None) if not type_spec: type_spec = _default_inputs_type_spec(self, type_spec_name) type_spec_store().setdefault(type_spec_name, type_spec) return type_spec_store().get(type_spec_name) def outputs_type_spec(self): name = f"{u0(self.name)}Outputs" if not type_spec_store().has(name): type_spec_store().setdefault(name, default_outputs_type_spec(self, name)) return type_spec_store().get(name) def posts_item(self, item_name): return [x for x in self.items_posted if x.item_name == item_name] def graphql_api_item_types_posted(self): result = list() for mutation in self.mutations: for item_posted in mutation.items_posted: if item_posted.item_type not in result: result.append(item_posted.item_type) return result

30.455882

86

0.695316

0

74

0.035732

cac979ac83238ac6883853f4946f27c46d7d2e84

10,337

py

Python

api/opentrons/instruments/pipette_config.py

heyoni/opentrons

abb4c742091d800541ed27273c6865fd5ba3aeac

[ "Apache-2.0" ]

null

api/opentrons/instruments/pipette_config.py

heyoni/opentrons

abb4c742091d800541ed27273c6865fd5ba3aeac

[ "Apache-2.0" ]

null

api/opentrons/instruments/pipette_config.py

heyoni/opentrons

abb4c742091d800541ed27273c6865fd5ba3aeac

[ "Apache-2.0" ]

null

import logging import os import json from collections import namedtuple from opentrons.config import get_config_index FILE_DIR = os.path.abspath(os.path.dirname(__file__)) log = logging.getLogger(__name__) def pipette_config_path(): index = get_config_index() return index.get('pipetteConfigFile', './settings.json') pipette_config = namedtuple( 'pipette_config', [ 'plunger_positions', 'pick_up_current', 'aspirate_flow_rate', 'dispense_flow_rate', 'ul_per_mm', 'channels', 'name', 'model_offset', 'plunger_current', 'drop_tip_current', 'tip_length' # TODO (andy): remove from pipette, move to tip-rack ] ) def _create_config_from_dict(cfg: dict, model: str) -> pipette_config: def _dict_key_to_config_attribute(key: str) -> str: ''' Converts the JSON key syntax (eg: "plungerPositions"), to the format used in the namedtuple `plunger_config` (eg: "plunger_positions") ''' return ''.join([ '_{}'.format(c.lower()) if c.isupper() else c for c in key ]) def _load_config_value(config_dict: dict, key: str): ''' Retrieves a given key from the loaded JSON config dict. If that key is not present in the dictionary, it falls back to the value from the namedtuple `plunger_config`, named "fallback" ''' nonlocal model fallback_cfg = fallback_configs.get(model) fallback_key = _dict_key_to_config_attribute(key) fallback_value = getattr(fallback_cfg, fallback_key) return config_dict.get(key, fallback_value) res = None try: plunger_pos = _load_config_value(cfg, 'plungerPositions') res = pipette_config( plunger_positions={ 'top': plunger_pos['top'], 'bottom': plunger_pos['bottom'], 'blow_out': plunger_pos.get( 'blowOut', plunger_pos.get('blow_out')), 'drop_tip': plunger_pos.get( 'dropTip', plunger_pos.get('drop_tip')), }, pick_up_current=_load_config_value(cfg, 'pickUpCurrent'), aspirate_flow_rate=_load_config_value( cfg, 'aspirateFlowRate'), dispense_flow_rate=_load_config_value( cfg, 'dispenseFlowRate'), ul_per_mm=_load_config_value(cfg, 'ulPerMm'), channels=_load_config_value(cfg, 'channels'), name=model, model_offset=_load_config_value(cfg, 'modelOffset'), plunger_current=_load_config_value(cfg, 'plungerCurrent'), drop_tip_current=_load_config_value(cfg, 'dropTipCurrent'), tip_length=_load_config_value(cfg, 'tipLength') ) except (KeyError, json.decoder.JSONDecodeError) as e: log.error('Error when loading pipette config: {}'.format(e)) return res def _load_config_dict_from_file(pipette_model: str) -> dict: config_file = pipette_config_path() cfg = {} if os.path.exists(config_file): with open(config_file) as conf: all_configs = json.load(conf) cfg = all_configs[pipette_model] return cfg # ------------------------- deprecated data --------------------------- # This section is left in as a fall-back until the settings file is # available on all robots. Currently, getting the settings file onto # the robots requires a Resin push, which involves some pain to users # because it restarts the robot--even if a protocol run is in progress. # The preferred solution is to implement a server endpoint that will # accept a data packet and save it in the robot, the same way that API # server updates are currently done. Once that is in place, the app can # ship the required data to the robot and this fallback data can be # removed from server code. Delete from here to "end deprecated data" # below, and remove the `select_config` call from the `config` dict # comprehension. DISTANCE_BETWEEN_NOZZLES = 9 NUM_MULTI_CHANNEL_NOZZLES = 8 MULTI_LENGTH = (NUM_MULTI_CHANNEL_NOZZLES - 1) * DISTANCE_BETWEEN_NOZZLES Y_OFFSET_MULTI = MULTI_LENGTH / 2 Z_OFFSET_MULTI = -25.8 Z_OFFSET_P10 = -13 # longest single-channel pipette Z_OFFSET_P50 = 0 Z_OFFSET_P300 = 0 Z_OFFSET_P1000 = 20 # shortest single-channel pipette DEFAULT_ASPIRATE_SECONDS = 2 DEFAULT_DISPENSE_SECONDS = 1 # TODO (ben 20180511): should we read these values from # TODO /shared-data/robot-data/pipette-config.json ? Unclear, # TODO because this is the backup in case that behavior fails, # TODO but we could make it more reliable if we start bundling # TODO config data into the wheel file perhaps. Needs research. p10_single = pipette_config( plunger_positions={ 'top': 19, 'bottom': 2.5, 'blow_out': -0.5, 'drop_tip': -4 }, pick_up_current=0.1, aspirate_flow_rate=10 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=10 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=0.77, channels=1, name='p10_single_v1', model_offset=[0.0, 0.0, Z_OFFSET_P10], plunger_current=0.3, drop_tip_current=0.5, tip_length=33 ) p10_multi = pipette_config( plunger_positions={ 'top': 19, 'bottom': 4, 'blow_out': 1, 'drop_tip': -4.5 }, pick_up_current=0.2, aspirate_flow_rate=10 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=10 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=0.77, channels=8, name='p10_multi_v1', model_offset=[0.0, Y_OFFSET_MULTI, Z_OFFSET_MULTI], plunger_current=0.5, drop_tip_current=0.5, tip_length=33 ) p50_single = pipette_config( plunger_positions={ 'top': 19, 'bottom': 2.5, 'blow_out': 2, 'drop_tip': -5 }, pick_up_current=0.1, aspirate_flow_rate=50 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=50 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=3.35, channels=1, name='p50_single_v1', model_offset=[0.0, 0.0, Z_OFFSET_P50], plunger_current=0.3, drop_tip_current=0.5, tip_length=51.7 ) p50_multi = pipette_config( plunger_positions={ 'top': 19, 'bottom': 2.5, 'blow_out': 2, 'drop_tip': -4 }, pick_up_current=0.3, aspirate_flow_rate=50 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=50 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=3.35, channels=8, name='p50_multi_v1', model_offset=[0.0, Y_OFFSET_MULTI, Z_OFFSET_MULTI], plunger_current=0.5, drop_tip_current=0.5, tip_length=51.7 ) p300_single = pipette_config( plunger_positions={ 'top': 19, 'bottom': 2.5, 'blow_out': 1, 'drop_tip': -5 }, pick_up_current=0.1, aspirate_flow_rate=300 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=300 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=18.7, channels=1, name='p300_single_v1', model_offset=[0.0, 0.0, Z_OFFSET_P300], plunger_current=0.3, drop_tip_current=0.5, tip_length=51.7 ) p300_multi = pipette_config( plunger_positions={ 'top': 19, 'bottom': 3, 'blow_out': 1, 'drop_tip': -3.5 }, pick_up_current=0.3, aspirate_flow_rate=300 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=300 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=19, channels=8, name='p300_multi_v1', model_offset=[0.0, Y_OFFSET_MULTI, Z_OFFSET_MULTI], plunger_current=0.5, drop_tip_current=0.5, tip_length=51.7 ) p1000_single = pipette_config( plunger_positions={ 'top': 19, 'bottom': 3, 'blow_out': 1, 'drop_tip': -5 }, pick_up_current=0.1, aspirate_flow_rate=1000 / DEFAULT_ASPIRATE_SECONDS, dispense_flow_rate=1000 / DEFAULT_DISPENSE_SECONDS, ul_per_mm=65, channels=1, name='p1000_single_v1', model_offset=[0.0, 0.0, Z_OFFSET_P1000], plunger_current=0.5, drop_tip_current=0.5, tip_length=76.7 ) fallback_configs = { 'p10_single_v1': p10_single, 'p10_multi_v1': p10_multi, 'p50_single_v1': p50_single, 'p50_multi_v1': p50_multi, 'p300_single_v1': p300_single, 'p300_multi_v1': p300_multi, 'p1000_single_v1': p1000_single } def select_config(model: str): cfg_dict = _load_config_dict_from_file(model) cfg = _create_config_from_dict(cfg_dict, model) if not cfg: cfg = fallback_configs.get(model) return cfg # ----------------------- end deprecated data ------------------------- # Notes: # - multi-channel pipettes share the same dimensional offsets # - single-channel pipettes have different lengths # - Default number of seconds to aspirate/dispense a pipette's full volume, # and these times were chosen to mimic normal human-pipetting motions. # However, accurate speeds are dependent on environment (ex: liquid # viscosity), therefore a pipette's flow-rates (ul/sec) should be set by # protocol writer # model-specific ID's, saved with each Pipette's memory # used to identifiy what model pipette is currently connected to machine PIPETTE_MODEL_IDENTIFIERS = { 'single': { '10': 'p10_single_v1', '50': 'p50_single_v1', '300': 'p300_single_v1', '1000': 'p1000_single_v1' }, 'multi': { '10': 'p10_multi_v1', '50': 'p50_multi_v1', '300': 'p300_multi_v1', } } configs = { model: select_config(model) for model in [ 'p10_single_v1', 'p10_multi_v1', 'p50_single_v1', 'p50_multi_v1', 'p300_single_v1', 'p300_multi_v1', 'p1000_single_v1']} def load(pipette_model: str) -> pipette_config: """ Lazily loads pipette config data from disk. This means that changes to the configuration data should be picked up on newly instantiated objects without requiring a restart. If :param pipette_model is not in the top- level keys of the "pipette-config.json" file, this function will raise a KeyError :param pipette_model: a pipette model string corresponding to a top-level key in the "pipette-config.json" file :return: a `pipette_config` instance """ return select_config(pipette_model)

30.764881

79

0.65164

0

3,967

0.383767

cacadf76d959296c2be37d92baf26148aaa927bb

4,417

py

Python

pysnmp/Unisphere-Products-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

11

2021-02-02T16:27:16.000Z

2021-08-31T06:22:49.000Z

pysnmp/Unisphere-Products-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

75

2021-02-24T17:30:31.000Z

2021-12-08T00:01:18.000Z

pysnmp/Unisphere-Products-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module Unisphere-Products-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Unisphere-Products-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 21:26:09 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") SingleValueConstraint, ValueSizeConstraint, ValueRangeConstraint, ConstraintsIntersection, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ValueSizeConstraint", "ValueRangeConstraint", "ConstraintsIntersection", "ConstraintsUnion") NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance") iso, NotificationType, Counter64, Gauge32, ModuleIdentity, Counter32, IpAddress, Integer32, Unsigned32, TimeTicks, ObjectIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, MibIdentifier, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "iso", "NotificationType", "Counter64", "Gauge32", "ModuleIdentity", "Counter32", "IpAddress", "Integer32", "Unsigned32", "TimeTicks", "ObjectIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "MibIdentifier", "Bits") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") unisphere, = mibBuilder.importSymbols("Unisphere-SMI", "unisphere") usProducts = ModuleIdentity((1, 3, 6, 1, 4, 1, 4874, 1)) usProducts.setRevisions(('2001-12-07 15:36', '2001-10-15 18:29', '2001-03-01 15:27', '2000-05-24 00:00', '1999-12-13 19:36', '1999-11-16 00:00', '1999-09-28 00:00',)) if mibBuilder.loadTexts: usProducts.setLastUpdated('200112071536Z') if mibBuilder.loadTexts: usProducts.setOrganization('Unisphere Networks, Inc.') productFamilies = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1)) unisphereProductFamilies = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1)) usErx = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 1)) usEdgeRoutingSwitch1400 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 1, 1)) usEdgeRoutingSwitch700 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 1, 2)) usEdgeRoutingSwitch1440 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 1, 3)) usEdgeRoutingSwitch705 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 1, 4)) usMrx = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 2)) usMrxRoutingSwitch16000 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 2, 1)) usMrxRoutingSwitch32000 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 2, 2)) usSmx = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 3)) usServiceMediationSwitch2100 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 3, 1)) usSrx = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 4)) usServiceReadySwitch3000 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 4, 1)) usUmc = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 5)) usUmcSystemManagement = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 1, 5, 1)) oemProductFamilies = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2)) marconiProductFamilies = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2, 1)) usSsx = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2, 1, 1)) usSsx1400 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2, 1, 1, 1)) usSsx700 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2, 1, 1, 2)) usSsx1440 = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 1, 1, 2, 1, 1, 3)) mibBuilder.exportSymbols("Unisphere-Products-MIB", usSsx1400=usSsx1400, usErx=usErx, usServiceMediationSwitch2100=usServiceMediationSwitch2100, oemProductFamilies=oemProductFamilies, usServiceReadySwitch3000=usServiceReadySwitch3000, usUmc=usUmc, usSmx=usSmx, usSsx1440=usSsx1440, unisphereProductFamilies=unisphereProductFamilies, usEdgeRoutingSwitch705=usEdgeRoutingSwitch705, usMrxRoutingSwitch16000=usMrxRoutingSwitch16000, usSsx=usSsx, usProducts=usProducts, usEdgeRoutingSwitch700=usEdgeRoutingSwitch700, usSsx700=usSsx700, usUmcSystemManagement=usUmcSystemManagement, marconiProductFamilies=marconiProductFamilies, productFamilies=productFamilies, usEdgeRoutingSwitch1440=usEdgeRoutingSwitch1440, usMrx=usMrx, usMrxRoutingSwitch32000=usMrxRoutingSwitch32000, usEdgeRoutingSwitch1400=usEdgeRoutingSwitch1400, PYSNMP_MODULE_ID=usProducts, usSrx=usSrx)

105.166667

856

0.737152

0

1,076

0.243604

cacbc63aebd384ee4e0145c5118d6ffecda40a4b

5,006

py

Python

src/main.py

MauroLuzzatto/algorithmic-explanations

4a362ae9576cc68ecf4b61dd6bad2105ff62bf57

[ "Apache-2.0" ]

null

src/main.py

MauroLuzzatto/algorithmic-explanations

4a362ae9576cc68ecf4b61dd6bad2105ff62bf57

[ "Apache-2.0" ]

null

src/main.py

MauroLuzzatto/algorithmic-explanations

4a362ae9576cc68ecf4b61dd6bad2105ff62bf57

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Nov 28 17:16:29 2020 @author: mauro """ import logging import os from explanation import ( CounterfactualExplanation, PermutationExplanation, ShapleyExplanation, SurrogateModelExplanation, ControlGroupExplanation ) from src.model.config import path_base from src.model.DataConfig import DataConfig from src.model.utils import ( average_the_ratings, get_dataset, load_pickle, map_index_to_sample, shuffle_in_unison, experiment_setup, create_treatment_dataframe ) from src.explanation.surrogate_manual import run logger = logging.getLogger(__file__) logger.setLevel(logging.DEBUG) path_config = os.path.join(path_base, "src", "resources") path_load = os.path.join(path_base, "dataset", "training") path_model_base = os.path.join(path_base, "model") data = DataConfig(path_config) data_config = data.load_config() def print_output(sample, output): score_text, method_text, explanation_text = output separator = '---' * 20 print(sample) print(separator) print(score_text) print(separator) print(method_text) print(separator) print(explanation_text) print("\n") def find_winner(X, y): y_pred = model.predict(X.values) y_winner = y.copy() y_winner['y_pred'] = y_pred y_winner.reset_index(inplace=True) index_winner = y_winner['y_pred'].argmax() df_winner = y_winner.iloc[index_winner] return df_winner for field in ['all']: model_name = [name for name in os.listdir(path_model_base) if field in name][-1] print(model_name) path_model = os.path.join(path_model_base, model_name) path_save = os.path.join(os.path.dirname(os.getcwd()), "reports", field) config = data_config[field] config["folder"] = field model = load_pickle( path_model=path_model, model_name="XGBRegressor.pickle", ) X, y = get_dataset( path_load=path_load, name=data_config["dataset"], target=config["target"], features=config["features"], ) new_name = f"{field}.player.rating" y = average_the_ratings(y, list(y), new_name) df_winner = find_winner(X, y) df_winner.to_csv( os.path.join(path_save, 'winner.csv'), sep=";", encoding="utf-8-sig" ) print(X.loc[df_winner['Entry ID']].tolist()) # remove winner X.drop(df_winner['Entry ID'], inplace=True) y.drop(df_winner['Entry ID'], inplace=True) X, y = shuffle_in_unison(X, y) samples_dict = experiment_setup(X) df_treatment = create_treatment_dataframe(samples_dict) df_treatment.to_csv( os.path.join(path_save, 'treatment_groups.csv'), sep=";", encoding="utf-8-sig", ) # control group for samples, sparse, show_rating in samples_dict["control_group"]: control = ControlGroupExplanation(X, y, model, sparse, show_rating, config) for sample in samples: sample_index = map_index_to_sample(X, sample) output = control.main(sample_index, sample) print(sparse, show_rating) print_output(sample, output) # Global, Non-contrastive for samples, sparse, show_rating in samples_dict["permutation"]: permutation = PermutationExplanation( X, y, model, sparse, show_rating, config ) for sample in samples: sample_index = map_index_to_sample(X, sample) output = permutation.main(sample_index, sample) print(sparse, show_rating) print_output(sample, output) # Local, Non-contrastive for samples, sparse, show_rating in samples_dict["shapley"]: shapely = ShapleyExplanation(X, y, model, sparse, show_rating, config) for sample in samples: sample_index = map_index_to_sample(X, sample) output = shapely.main(sample_index, sample) print(sparse, show_rating) print_output(sample, output) # Global, Contrastive for samples, sparse, show_rating in samples_dict["surrogate"]: surrogate = SurrogateModelExplanation( X, y, model, sparse, show_rating, config ) for sample in samples: sample_index = map_index_to_sample(X, sample) output = surrogate.main(sample_index, sample) print(sparse, show_rating) print_output(sample, output) # Local, Contrastive for samples, sparse, show_rating in samples_dict["counterfactual"]: counterfactual = CounterfactualExplanation( X, y, model, sparse, show_rating, config, y_desired=8. ) for sample in samples: sample_index = map_index_to_sample(X, sample) output = counterfactual.main(sample_index, sample) print(sparse, show_rating) print_output(sample, output)

28.605714

84

0.646025

0

519

0.103676

cacbc9b4cfe03469a1b46747898d31856ab1f581

3,752

py

Python

det.py

styler00dollar/CenDetect

eba84c3702c5196b40e994515f399ae0db2e43fb

[ "Apache-2.0" ]

3

2021-07-19T16:49:18.000Z

2021-07-19T19:24:26.000Z

det.py

styler00dollar/CenDetect

eba84c3702c5196b40e994515f399ae0db2e43fb

[ "Apache-2.0" ]

null

det.py

styler00dollar/CenDetect

eba84c3702c5196b40e994515f399ae0db2e43fb

[ "Apache-2.0" ]

null

import asyncio from argparse import ArgumentParser from mmdet.apis import (async_inference_detector, inference_detector, init_detector, show_result_pyplot) from mmcv.runner import wrap_fp16_model import cv2 import numpy as np from tqdm import tqdm import os import glob import torch # dont print deprication warnings import warnings warnings.filterwarnings("ignore") def parse_args(): parser = ArgumentParser() parser.add_argument('--fp16', action='store_true', required=False, help='Enables FP16') parser.add_argument('--input_path', type=str, default="input", required=False, help='Path to a folder for input') parser.add_argument('--output_path', type=str, default="output", required=False, help='Path to a folder for output') # manually selecting config #parser.add_argument('--config_path', type=str, default="mask_rcnn_r50_fpn_2x_coco.py", required=False, help='Config file to build the model') #parser.add_argument('--model_path', type=str, default="epoch_3.pth", required=False, help='The path to the pth model itself') parser.add_argument('--model_directory', type=str, default="models", required=False, help='Folder path to configs and models') parser.add_argument('--model', type=str, default="mask_rcnn_r50_fpn", required=False, help='Seleting a model. [mask_rcnn_r50_fpn, mask_rcnn_r101_fpn, point_rend_r50_fpn, cascade_mask_rcnn_r50_fpn_dconv]') parser.add_argument('--device', default=None, help='Device used for inference') parser.add_argument('--confidence', type=float, default=0.3, required=False, help='Confidence thresh for detections (Values between 0 and 1)') args = parser.parse_args() return args def main(args): # manually select model and config path #model = init_detector(args.config_path, args.model_path, device=args.device) # selection config_path = os.path.join(args.model_directory, args.model + ".py") model_path = os.path.join(args.model_directory, args.model + ".pth") if args.device == None: device = 'cuda' if torch.cuda.is_available() else 'cpu' else: device = args.device print(f"Using device: {device}") model = init_detector(config_path, model_path, device=device) # detect all files from input folder files = glob.glob(args.input_path + '/**/*.png', recursive=True) files_jpg = glob.glob(args.input_path + '/**/*.jpg', recursive=True) files_jpeg = glob.glob(args.input_path + '/**/*.jpeg', recursive=True) files_webp = glob.glob(args.input_path + '/**/*.webp', recursive=True) files.extend(files_jpg) files.extend(files_jpeg) files.extend(files_webp) if args.fp16 == True: wrap_fp16_model(model) model.half() # test a single image for i in tqdm(files): image = cv2.imread(i, cv2.IMREAD_COLOR) result = inference_detector(model, i) # creating mask from detection counter = 0 mask = np.zeros((image.shape[0], image.shape[1])).astype(bool) # bar for f in result[0][0]: if f[4] > args.confidence: mask = mask | result[1][0][counter] counter += 1 counter = 0 # mosaic for f in result[0][1]: if f[4] > args.confidence: mask = mask | result[1][1][counter] counter += 1 # only save the mask #cv2.imwrite(os.path.join(output_path, os.path.splitext(os.path.basename(i))[0] + ".png"), np.array(mask).astype(np.uint8)*255) # save with image image[mask]= [0, 255, 0] cv2.imwrite(os.path.join(args.output_path, os.path.splitext(os.path.basename(i))[0] + ".png"), np.array(image).astype(np.uint8)) if __name__ == '__main__': args = parse_args() main(args)

36.427184

208

0.679104

0

1,283

0.341951

cacd5c042e61c49091f95d611b580f1100626117

3,357

py

Python

command/sub8_alarm/alarm_handlers/kill.py

bhostetler18/SubjuGator

2f56231d821bc802988264d0eab3005f8286a597

[ "MIT" ]

null

command/sub8_alarm/alarm_handlers/kill.py

bhostetler18/SubjuGator

2f56231d821bc802988264d0eab3005f8286a597

[ "MIT" ]

null

command/sub8_alarm/alarm_handlers/kill.py

bhostetler18/SubjuGator

2f56231d821bc802988264d0eab3005f8286a597

[ "MIT" ]

null

import rospy from ros_alarms import HandlerBase, Alarm from actionlib import SimpleActionClient, TerminalState from mil_msgs.msg import BagOnlineAction, BagOnlineGoal import os class Kill(HandlerBase): alarm_name = 'kill' initally_raised = True def __init__(self): # Alarm server wil set this as the intial state of kill alarm (starts killed) self.initial_alarm = Alarm(self.alarm_name, True, node_name='alarm_server', problem_description='Initial kill') self._killed = False self._last_mission_killed = False self.bag_client = SimpleActionClient('/online_bagger/bag', BagOnlineAction) self.first = True def raised(self, alarm): self._killed = True self.bagger_dump() self.first = False def cleared(self, alarm): self._killed = False def _bag_done_cb(self, status, result): if status == 3: rospy.loginfo('KILL BAG WRITTEN TO {}'.format(result.filename)) else: rospy.logwarn('KILL BAG {}, status: {}'.format(TerminalState.to_string(status), result.status)) def bagger_dump(self): """Call online_bagger/dump service""" if self.first: return if 'BAG_ALWAYS' not in os.environ or 'bag_kill' not in os.environ: rospy.logwarn('BAG_ALWAYS or BAG_KILL not set. Not making kill bag.') return goal = BagOnlineGoal(bag_name='kill.bag') goal.topics = os.environ['BAG_ALWAYS'] + ' ' + os.environ['bag_kill'] self.bag_client.send_goal(goal, done_cb=self._bag_done_cb) def meta_predicate(self, meta_alarm, sub_alarms): ignore = [] # Stay killed until user clears if self._killed: return True if sub_alarms["pause-kill"].raised and sub_alarms["pause-kill"].severity == 5: return True ignore.append("pause-kill") # Battery too low if sub_alarms["bus-voltage"].raised and sub_alarms["bus-voltage"].severity == 5: return True ignore.append("bus-voltage") if sub_alarms["odom-kill"].raised and sub_alarms["odom-kill"].severity == 5: return True ignore.append("odom-kill") # If we lose network but don't want to go autonomous if sub_alarms["network-loss"].raised and not rospy.get_param("/autonomous", False): return True ignore.append("network-loss") # Severity level of 3 means too many thrusters out (3 thrusters out) if sub_alarms["thruster-out"].raised and sub_alarms["thruster-out"].severity == 3: return True ignore.append("thruster-out") # If a mission wants us to kill, go ahead and kill if sub_alarms["mission-kill"].raised: self._last_mission_killed = True return True elif self._last_mission_killed: self._last_mission_killed = False # If we weren't killed by another source, clear the kill if not self._killed: return False ignore.append("mission-kill") # Raised if any alarms besides the two above are raised return any([alarm.raised for name, alarm in sub_alarms.items() if name not in ignore])

36.096774

107

0.619303

3,177

0.946381

0

876

0.260947

cacea4a58d86e5f652adf524e84090333af95605

12,094

py

Python

Projects/Healthcare/breast-cancer/src/data_loading/augmentations.py

DanielMabadeje/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

7adab3877fc1d3f1d5f57e6c1743dae8f76f72c5

[ "Apache-2.0" ]

3,266

2017-08-06T16:51:46.000Z

2022-03-30T07:34:24.000Z

Projects/Healthcare/breast-cancer/src/data_loading/augmentations.py

Yasin-Shah/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

243a2a744ced81b69438e08e981249d7629a1f03

[ "Apache-2.0" ]

150

2017-08-28T14:59:36.000Z

2022-03-11T23:21:35.000Z

Projects/Healthcare/breast-cancer/src/data_loading/augmentations.py

sourcecode369/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

372874b93b0d054abc499887cba39e93a00f10a9

[ "Apache-2.0" ]

1,449

2017-08-06T17:40:59.000Z

2022-03-31T12:03:24.000Z

# Copyright (C) 2019 Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, # Stanisław Jastrzębski, Thibault Févry, Joe Katsnelson, Eric Kim, Stacey Wolfson, Ujas Parikh, # Sushma Gaddam, Leng Leng Young Lin, Kara Ho, Joshua D. Weinstein, Beatriu Reig, Yiming Gao, # Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, Krystal Airola, Eralda Mema, # Stephanie Chung, Esther Hwang, Naziya Samreen, S. Gene Kim, Laura Heacock, Linda Moy, # Kyunghyun Cho, Krzysztof J. Geras # # This file is part of breast_cancer_classifier. # # breast_cancer_classifier is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # breast_cancer_classifier 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 Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with breast_cancer_classifier. If not, see <http://www.gnu.org/licenses/>. # ============================================================================== import cv2 import numpy as np from src.constants import VIEWS def shift_window_inside_image(start, end, image_axis_size, input_axis_size): """ If the window goes outside the bound of the image, then shifts it to fit inside the image. """ if start < 0: start = 0 end = start + input_axis_size elif end > image_axis_size: end = image_axis_size start = end - input_axis_size return start, end def zero_pad_and_align_window(image_axis_size, input_axis_size, max_crop_and_size_noise, bidirectional): """ Adds Zero padding to the image if cropped image is smaller than required window size. """ pad_width = input_axis_size - image_axis_size + max_crop_and_size_noise * (2 if bidirectional else 1) assert (pad_width >= 0) if bidirectional: pad_front = int(pad_width / 2) start = max_crop_and_size_noise else: start, pad_front = 0, 0 pad_back = pad_width - pad_front end = start + input_axis_size return start, end, pad_front, pad_back def simple_resize(image_to_resize, size): """ Resizes image to the required size """ image_resized = cv2.resize(image_to_resize, (size[1], size[0]), interpolation=cv2.INTER_CUBIC) if len(image_to_resize.shape) == 3 and len(image_resized.shape) == 2 and image_to_resize.shape[2] == 1: image_resized = np.expand_dims(image_resized, 2) return image_resized def crop_image(image, input_size, borders): """ Crops image to the required size using window location """ cropped_image = image[borders[0]: borders[1], borders[2]: borders[3]] if ((borders[1] - borders[0]) != input_size[0]) or ((borders[3] - borders[2]) != input_size[1]): cropped_image = simple_resize(cropped_image, input_size) return cropped_image def window_location_at_center_point(input_size, center_y, center_x): """ Calculates window location (top, bottom, left, right) given center point and size of augmentation window """ half_height = input_size[0] // 2 half_width = input_size[1] // 2 top = center_y - half_height bottom = center_y + input_size[0] - half_height left = center_x - half_width right = center_x + input_size[1] - half_width return top, bottom, left, right def sample_crop_best_center(image, input_size, random_number_generator, max_crop_noise, max_crop_size_noise, best_center, view): """ Crops using the best center point and ideal window size. Pads small images to have enough room for crop noise and size noise. Applies crop noise in location of the window borders. """ max_crop_noise = np.array(max_crop_noise) crop_noise_multiplier = np.zeros(2, dtype=np.float32) if max_crop_noise.any(): # there is no point in sampling crop_noise_multiplier if it's going to be multiplied by (0, 0) crop_noise_multiplier = random_number_generator.uniform(low=-1.0, high=1.0, size=2) center_y, center_x = best_center # get the window around the center point. The window might be outside of the image. top, bottom, left, right = window_location_at_center_point(input_size, center_y, center_x) pad_y_top, pad_y_bottom, pad_x_right = 0, 0, 0 if VIEWS.is_cc(view): if image.shape[0] < input_size[0] + (max_crop_noise[0] + max_crop_size_noise) * 2: # Image is smaller than window size + noise margin in y direction. # CC view: pad at both top and bottom top, bottom, pad_y_top, pad_y_bottom = zero_pad_and_align_window(image.shape[0], input_size[0], max_crop_noise[0] + max_crop_size_noise, True) elif VIEWS.is_mlo(view): if image.shape[0] < input_size[0] + max_crop_noise[0] + max_crop_size_noise: # Image is smaller than window size + noise margin in y direction. # MLO view: only pad at the bottom top, bottom, _, pad_y_bottom = zero_pad_and_align_window(image.shape[0], input_size[0], max_crop_noise[0] + max_crop_size_noise, False) else: raise KeyError("Unknown view", view) if image.shape[1] < input_size[1] + max_crop_noise[1] + max_crop_size_noise: # Image is smaller than window size + noise margin in x direction. left, right, _, pad_x_right = zero_pad_and_align_window(image.shape[1], input_size[1], max_crop_noise[1] + max_crop_size_noise, False) # Pad image if necessary by allocating new memory and copying contents over if pad_y_top > 0 or pad_y_bottom > 0 or pad_x_right > 0: new_zero_array = np.zeros(( image.shape[0] + pad_y_top + pad_y_bottom, image.shape[1] + pad_x_right, image.shape[2]), dtype=image.dtype) new_zero_array[pad_y_top: image.shape[0] + pad_y_top, 0: image.shape[1]] = image image = new_zero_array # if window is drawn outside of image, shift it to be inside the image. top, bottom = shift_window_inside_image(top, bottom, image.shape[0], input_size[0]) left, right = shift_window_inside_image(left, right, image.shape[1], input_size[1]) if top == 0: # there is nowhere to shift upwards, we only apply noise downwards crop_noise_multiplier[0] = np.abs(crop_noise_multiplier[0]) elif bottom == image.shape[0]: # there is nowhere to shift down, we only apply noise upwards crop_noise_multiplier[0] = -np.abs(crop_noise_multiplier[0]) # else: we do nothing to the noise multiplier if left == 0: # there is nowhere to shift left, we only apply noise to move right crop_noise_multiplier[1] = np.abs(crop_noise_multiplier[1]) elif right == image.shape[1]: # there is nowhere to shift right, we only apply noise to move left crop_noise_multiplier[1] = -np.abs(crop_noise_multiplier[1]) # else: we do nothing to the noise multiplier borders = np.array((top, bottom, left, right), dtype=np.int32) # Calculate maximum amount of how much the window can move for cropping noise top_margin = top bottom_margin = image.shape[0] - bottom left_margin = left right_margin = image.shape[1] - right if crop_noise_multiplier[0] >= 0: vertical_margin = bottom_margin else: vertical_margin = top_margin if crop_noise_multiplier[1] >= 0: horizontal_margin = right_margin else: horizontal_margin = left_margin if vertical_margin < max_crop_noise[0]: max_crop_noise[0] = vertical_margin if horizontal_margin < max_crop_noise[1]: max_crop_noise[1] = horizontal_margin crop_noise = np.round(max_crop_noise * crop_noise_multiplier) crop_noise = np.array((crop_noise[0], crop_noise[0], crop_noise[1], crop_noise[1]), dtype=np.int32) borders = borders + crop_noise # this is to make sure that the cropping window isn't outside of the image assert (borders[0] >= 0) and (borders[1] <= image.shape[0]) and (borders[2] >= 0) and (borders[3] <= image.shape[ 1]), "Centre of the crop area is sampled such that the borders are outside of the image. Borders: " + str( borders) + ', image shape: ' + str(image.shape) # return the padded image and cropping window information return image, borders def sample_crop(image, input_size, borders, random_number_generator, max_crop_size_noise): """ Applies size noise of the window borders. """ size_noise_multiplier = random_number_generator.uniform(low=-1.0, high=1.0, size=4) top_margin = borders[0] bottom_margin = image.shape[0] - borders[1] left_margin = borders[2] right_margin = image.shape[1] - borders[3] max_crop_size_noise = min(max_crop_size_noise, top_margin, bottom_margin, left_margin, right_margin) if input_size[0] >= input_size[1]: max_crop_size_vertical_noise = max_crop_size_noise max_crop_size_horizontal_noise = np.round(max_crop_size_noise * (input_size[1] / input_size[0])) elif input_size[0] < input_size[1]: max_crop_size_vertical_noise = np.round(max_crop_size_noise * (input_size[0] / input_size[1])) max_crop_size_horizontal_noise = max_crop_size_noise else: raise RuntimeError() max_crop_size_noise = np.array((max_crop_size_vertical_noise, max_crop_size_vertical_noise, max_crop_size_horizontal_noise, max_crop_size_horizontal_noise), dtype=np.int32) size_noise = np.round(max_crop_size_noise * size_noise_multiplier) size_noise = np.array(size_noise, dtype=np.int32) borders = borders + size_noise # this is to make sure that the cropping window isn't outside of the image assert (borders[0] >= 0) and (borders[1] <= image.shape[0]) and (borders[2] >= 0) and (borders[3] <= image.shape[ 1]), "Center of the crop area is sampled such that the borders are outside of the image. Borders: " + str( borders) + ', image shape: ' + str(image.shape) # Sanity check. make sure that the top is above the bottom assert borders[1] > borders[0], "Bottom above the top. Top: " + str(borders[0]) + ', bottom: ' + str(borders[1]) # Sanity check. make sure that the left is left to the right assert borders[3] > borders[2], "Left on the right. Left: " + str(borders[2]) + ', right: ' + str(borders[3]) return borders def random_augmentation_best_center(image, input_size, random_number_generator, max_crop_noise=(0, 0), max_crop_size_noise=0, auxiliary_image=None, best_center=None, view=""): """ Crops augmentation window from a given image by applying noise in location and size of the window. """ joint_image = np.expand_dims(image, 2) if auxiliary_image is not None: joint_image = np.concatenate([joint_image, auxiliary_image], axis=2) joint_image, borders = sample_crop_best_center(joint_image, input_size, random_number_generator, max_crop_noise, max_crop_size_noise, best_center, view) borders = sample_crop(joint_image, input_size, borders, random_number_generator, max_crop_size_noise) sampled_joint_image = crop_image(joint_image, input_size, borders) if auxiliary_image is None: return sampled_joint_image[:, :, 0], None else: return sampled_joint_image[:, :, 0], sampled_joint_image[:, :, 1:]

44.792593

117

0.670415

0

3,811

0.315037

caceb51d93c36ec68a44bf085fefaa6d893e959c

2,609

py

Python

ooobuild/dyn/awt/field_unit.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/dyn/awt/field_unit.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/dyn/awt/field_unit.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Const Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.awt from enum import IntEnum from typing import TYPE_CHECKING from ooo.oenv.env_const import UNO_ENVIRONMENT, UNO_RUNTIME _DYNAMIC = False if (not TYPE_CHECKING) and UNO_RUNTIME and UNO_ENVIRONMENT: _DYNAMIC = True if not TYPE_CHECKING and _DYNAMIC: from com.sun.star.awt import FieldUnit as FieldUnit if hasattr(FieldUnit, '_constants') and isinstance(FieldUnit._constants, dict): FieldUnit._constants['__ooo_ns__'] = 'com.sun.star.awt' FieldUnit._constants['__ooo_full_ns__'] = 'com.sun.star.awt.FieldUnit' FieldUnit._constants['__ooo_type_name__'] = 'const' def build_enum(): global FieldUnitEnum ls = [f for f in dir(FieldUnit) if not callable(getattr(FieldUnit, f)) and not f.startswith('__')] _dict = {} for name in ls: _dict[name] = getattr(FieldUnit, name) FieldUnitEnum = IntEnum('FieldUnitEnum', _dict) build_enum() else: from ...lo.awt.field_unit import FieldUnit as FieldUnit class FieldUnitEnum(IntEnum): """ Enum of Const Class FieldUnit specifies attributes for the MetricField map units. IMPORTANT: These constants have to be disjunct with constants in util/MeasureUnit. """ FUNIT_NONE = FieldUnit.FUNIT_NONE FUNIT_MM = FieldUnit.FUNIT_MM FUNIT_CM = FieldUnit.FUNIT_CM FUNIT_M = FieldUnit.FUNIT_M FUNIT_KM = FieldUnit.FUNIT_KM FUNIT_TWIP = FieldUnit.FUNIT_TWIP FUNIT_POINT = FieldUnit.FUNIT_POINT FUNIT_PICA = FieldUnit.FUNIT_PICA FUNIT_INCH = FieldUnit.FUNIT_INCH FUNIT_FOOT = FieldUnit.FUNIT_FOOT FUNIT_MILE = FieldUnit.FUNIT_MILE FUNIT_CUSTOM = FieldUnit.FUNIT_CUSTOM FUNIT_PERCENT = FieldUnit.FUNIT_PERCENT FUNIT_100TH_MM = FieldUnit.FUNIT_100TH_MM __all__ = ['FieldUnit', 'FieldUnitEnum']

37.811594

106

0.708317

842

0.322729

0

1,086

0.416251

cad072e6da4ed8b99fa019319f2a53f7231fa953

7,202

py

Python

utils.py

zrongcheng/srcnn

33942c0a54fbdc3c59c4ab796c98a64eef10db77

[ "Apache-2.0" ]

null

utils.py

zrongcheng/srcnn

33942c0a54fbdc3c59c4ab796c98a64eef10db77

[ "Apache-2.0" ]

null

utils.py

zrongcheng/srcnn

33942c0a54fbdc3c59c4ab796c98a64eef10db77

[ "Apache-2.0" ]

null

""" Scipy version > 0.18 is needed, due to 'mode' option from scipy.misc.imread function """ import os import glob import h5py import random import matplotlib.pyplot as plt from PIL import Image # for loading images as YCbCr format import Scipy.misc import Scipy.ndimage import numpy as np import tensorflow as tf FLAGS = tf.app.flags.FLAGS#全局变量 def read_data(path): """ Read h5 format data file Args: path: file path of desired file所需文件 data: '.h5' file format that contains train data values label: '.h5' file format that contains train label values """ with h5py.File(path, 'r') as hf: data = np.array(hf.get('data')) label = np.array(hf.get('label')) return data, label def preprocess(path, scale=3): """ Preprocess single image file (1) Read original image as YCbCr format (and grayscale as default) (2) Normalize (3) Apply image file with bicubic interpolation 唯一的预处理操作 Args: path: file path of desired file input_: image applied bicubic interpolation (low-resolution) label_: image with original resolution (high-resolution) """ image = imread(path, is_grayscale=True)#？？为什么灰度处理 label_ = modcrop(image, scale) # Must be normalized #image = image / 255. label_ = label_ / 255. #两次为了降低精度 input_ = Scipy.ndimage.interpolation.zoom(label_, zoom=(1. / scale), prefilter=False)#一次 input_ = Scipy.ndimage.interpolation.zoom(input_, zoom=(scale / 1.), prefilter=False)#二次，bicubic #imsave(input_,r'F:\tf_py\srcnn\sample\test1.png') #imsave(label_, r'F:\tf_py\srcnn\sample\test2.png') return input_, label_ def prepare_data(dataset): """ Args: dataset: choose train dataset or test dataset For train dataset, output data would be ['.../t1.bmp', '.../t2.bmp', ..., '.../t99.bmp'] """ if FLAGS.is_train: #filenames = os.listdir(dataset) data_dir = os.path.join(os.getcwd(), dataset) # glob.glob()获取当前目录或相对路径所有文件的路径，输出一个list，读取字符中的*(通配符) data = glob.glob(os.path.join(data_dir, "*.bmp")) else: data_dir = os.path.join(os.sep, (os.path.join(os.getcwd(), dataset)), "Set5") data = glob.glob(os.path.join(data_dir, "*.bmp")) return data def make_data(data, label): """ Make input data as h5 file format Depending on 'is_train' (flag value), savepath would be changed. """ if FLAGS.is_train: savepath = os.path.join(os.getcwd(), 'checkpoint/train.h5') else: savepath = os.path.join(os.getcwd(), 'checkpoint/test.h5') with h5py.File(savepath, 'w') as hf: hf.create_dataset('data', data=data) hf.create_dataset('label', data=label) def imread(path, is_grayscale=True): """ Read image using its path. Default value is gray-scale, and image is read by YCbCr format as the paper said. """ if is_grayscale: return Scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)#将图像转灰度 else: return Scipy.misc.imread(path, mode='YCbCr').astype(np.float)#默认为false def modcrop(image, scale=3): """ To scale down and up the original image, first thing to do is to have no remainder while scaling operation. We need to find modulo of height (and width) and scale factor. Then, subtract the modulo from height (and width) of original image size. There would be no remainder even after scaling operation. 要缩放原始图像, 首先要做的是在缩放操作时没有余数。我们需要找到高度 (和宽度) 和比例因子的模。然后, 减去原始图像大小的高度 (和宽度) 的模。即使在缩放操作之后, 也不会有余数。 """ if len(image.shape) == 3:#彩色 800*600*3 h, w, _ = image.shape h = h - np.mod(h, scale) w = w - np.mod(w, scale) image = image[0:h, 0:w, :] else:#灰度 800*600 h, w = image.shape h = h - np.mod(h, scale) w = w - np.mod(w, scale) image = image[0:h, 0:w] return image def input_setup(config): """ Read image files and make their sub-images and saved them as a h5 file format. """ # Load data path if config.is_train: data = prepare_data(dataset="Train") else: data = prepare_data(dataset="Test") sub_input_sequence = [] sub_label_sequence = [] padding = abs(config.image_size - config.label_size) // 2 # 6 填充 if config.is_train: for i in range(len(data)): input_, label_ = preprocess(data[i], config.scale)#data[i]为数据目录 if len(input_.shape) == 3: h, w, _ = input_.shape else: h, w = input_.shape #。。。 for x in range(0, h-config.image_size+1, config.stride): for y in range(0, w-config.image_size+1, config.stride): sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33] sub_label = label_[x+padding:x+padding+config.label_size, y+padding:y+padding+config.label_size] # [21 x 21] # Make channel value,颜色通道1 sub_input = sub_input.reshape([config.image_size, config.image_size, 1]) sub_label = sub_label.reshape([config.label_size, config.label_size, 1]) sub_input_sequence.append(sub_input) sub_label_sequence.append(sub_label) # Make list to numpy array. With this transform arrdata = np.asarray(sub_input_sequence) # [?, 33, 33, 1] arrlabel = np.asarray(sub_label_sequence) # [?, 21, 21, 1] make_data(arrdata, arrlabel) # 把处理好的数据进行存储，路径为checkpoint/.. else: input_, label_ = preprocess(data[4], config.scale) if len(input_.shape) == 3: h, w, _ = input_.shape else: h, w = input_.shape input = input_.reshape([h,w,1]) label=label_[6:h-6,6:w-6] label=label.reshape([h-12,w-12,1]) sub_input_sequence.append(input) sub_label_sequence.append(label) input1 = np.asarray(sub_input_sequence) label1 = np.asarray(sub_label_sequence) #label=label_.reshape([height,weight,1]) return input1,label1,h,w # # Numbers of sub-images in height and width of image are needed to compute merge operation. # nx = ny = 0 # for x in range(0, h-config.image_size+1, config.stride): # nx += 1; ny = 0 # for y in range(0, w-config.image_size+1, config.stride): # ny += 1 # sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33] # sub_label = label_[x+padding:x+padding+config.label_size, # y+padding:y+padding+config.label_size] # [21 x 21] # # sub_input = sub_input.reshape([config.image_size, config.image_size, 1]) # sub_label = sub_label.reshape([config.label_size, config.label_size, 1]) # # sub_input_sequence.append(sub_input) # sub_label_sequence.append(sub_label) """ len(sub_input_sequence) : the number of sub_input (33 x 33 x ch) in one image (sub_input_sequence[0]).shape : (33, 33, 1) """ # if not config.is_train: # return nx, ny def imsave(image, path): return Scipy.misc.imsave(path, image) # def merge(images, size): # h, w = images.shape[1], images.shape[2]#21*21 # p,q,j=0,0,0 # img = np.zeros((14*(size[0]-1)+21, 14*(size[1]-1)+21, 1)) # for idx, image in enumerate(images):#image.shape=(21,21,1) # i = idx % size[1]#余数 # t=j # j = idx // size[1]#商 # if (j-t)==1: # p=p+14 # q=0 # #img[0:21,0:21,:]=image # img[p:p+h, q:q+w, :] = image # # q=q+14 # # return img

31.177489

109

0.650097

0

3,963

0.522134

cad1115bd177c78411f2be817f4464e97774f362

826

py

Python

db/mongodb/save_all.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

13

2018-12-17T23:30:54.000Z

2021-12-29T14:31:43.000Z

db/mongodb/save_all.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

36

2018-06-07T21:34:13.000Z

2022-03-13T21:01:43.000Z

db/mongodb/save_all.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

2

2021-01-03T11:47:20.000Z

2021-12-29T14:31:49.000Z

import os import json from pymongo import MongoClient with open('keys.json', 'r') as file: keys = json.loads(file.read()) db_all = MongoClient( username=keys['login'], password=keys['password'], authSource='admin', authMechanism='SCRAM-SHA-1' ) for db_name in db_all.list_database_names(): if db_name in ('admin', 'config', 'local'): continue print('---', db_name, '---') os.mkdir('db/{}'.format(db_name)) db = db_all[db_name] collections = [collection['name'] for collection in db.list_collections()] for collection_name in collections: print('{}'.format(collection_name), end=' ') with open('db/{}/{}.txt'.format(db_name, collection_name), 'w') as file: for i in db[collection_name].find(): del i['_id'] print(json.dumps(i, ensure_ascii=False), file=file) # , indent='\t' print('✅')

22.324324

75

0.670702

0

145

0.175121

cad1619bf5ab33d3d587d63fa05c0c21b4bc2a86

797

py

Python

setup.py

saksham/nepse-crawler

adee65ebc86e269c2941d48f91018b8e3263e14e

[ "MIT" ]

1

2019-10-25T18:41:24.000Z

setup.py

saksham/nepse-crawler

adee65ebc86e269c2941d48f91018b8e3263e14e

[ "MIT" ]

null

setup.py

saksham/nepse-crawler

adee65ebc86e269c2941d48f91018b8e3263e14e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from distutils.core import setup from codecs import open from os import path here = path.abspath(path.dirname(__file__)) def read_file(filename): with open(path.join(here, filename)) as f: return f.read() setup(name='nepse-crawler', version='0.0.1', description='Crawls websites for Nepal Stock Exchange and loads them to SQLite3 database', long_description=read_file('README.md'), url='https://github.com/saksham/nepse-crawler', install_requires=read_file('requirements.txt'), author='Saksham', author_email='saksham@no-reply.github.com', license='MIT', entry_points={ 'console_scripts': [ 'crawl-nepse=crawler.__main__:main' ] })

27.482759

96

0.644918

0

309

0.387704

cad2fe1cdd6d86eceead5fb008c5cfdfa6b6f198

2,678

py

Python

backend/backend.py

alexnbferreira/CryptoTracker

7071fab27200c59288eade6616e6c4f5e2eaa5ad

[ "MIT" ]

null

backend/backend.py

alexnbferreira/CryptoTracker

7071fab27200c59288eade6616e6c4f5e2eaa5ad

[ "MIT" ]

null

backend/backend.py

alexnbferreira/CryptoTracker

7071fab27200c59288eade6616e6c4f5e2eaa5ad

[ "MIT" ]

null

from flask import Flask, Response, render_template import requests import json import logging from pandas import read_json from decimal import Decimal app = Flask(__name__) exchanges = ["BNB", "BTX", "BFX"] token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6ImRldkBnbG92b2FwcC5jb20iLCJpZCI6IjVhNTcyZGEyNTM4OWMzNzZiZWZlNjY1NCIsImlhdCI6MTUxNTY2MjgyMn0.a6homMOumqLBxwfX9nOwbBaxmSx-srkS8dISSPCPPYE" logging.basicConfig() logger = logging.getLogger(__name__) base_currencies = None @app.route("/products", methods=["GET"]) def get_products(): #This end points fetches the list of pairs for each exchange, #updates the global list "common_pairs" with currency pairs common in all 3 exchanges #and sends it as a json headers = {"Authorization": "Bearer {}".format(token)} api_endpoint = "https://api.moneeda.com/api/exchanges/{}/products" common_pairs = None for exchange in exchanges: # Fetch products for each exchange and store the pairs try: resp = requests.get(api_endpoint.format(exchange), headers=headers).text pairs_df = read_json(resp) pairs = pairs_df["id"].tolist() # Create the set of pairs that appear on all exchanges if common_pairs == None: common_pairs = set(pairs) else: common_pairs = common_pairs & set(pairs) except requests.exceptions.ConnectionError as e: logger.error("No response from server") logger.error(e) continue return Response(json.dumps(list(common_pairs)), mimetype="application/json") @app.route("/products/<pair>/prices", methods=["GET"]) def get_product_prices(pair): #This endpoint fetches the price for a given <pair> and returns it's price for each of the listed exchanges in a json object headers = {"Authorization": "Bearer {}".format(token)} api_endpoint = "https://api.moneeda.com/api/exchanges/{exchange}/ticker?product={pair}" prices = {} for exchange in exchanges: endp = api_endpoint.format(exchange=exchange, pair=pair) try: ticker = requests.get(endp, headers=headers).text ticker_json = json.loads(ticker, parse_float=Decimal) prices[exchange] = str(ticker_json["price"]) except requests.exceptions.ConnectionError as e: logger.error("Error fetching prices") logger.error(e) continue return Response(json.dumps(prices), mimetype="application/json") @app.route('/', defaults={'path': ''}) @app.route('/<path:path>') def catch_all(path): return render_template("index.html") app.run(debug=True)

38.257143

194

0.685213

0

2,164

0.808066

0

955

0.356609

cad3c7ef16ebb65b6d272b2d40089713a3ce54ff

11,375

py

Python

data_pipeline/_kafka_producer.py

poros/data_pipeline

e143a4031b0940e17b22cdf36db0b677b46e3975

[ "Apache-2.0" ]

110

2016-11-17T18:32:25.000Z

2022-01-03T17:27:58.000Z

data_pipeline/_kafka_producer.py

poros/data_pipeline

e143a4031b0940e17b22cdf36db0b677b46e3975

[ "Apache-2.0" ]

12

2016-11-18T00:00:37.000Z

2018-01-14T00:31:37.000Z

data_pipeline/_kafka_producer.py

poros/data_pipeline

e143a4031b0940e17b22cdf36db0b677b46e3975

[ "Apache-2.0" ]

25

2016-11-18T15:00:16.000Z

2020-10-01T13:42:47.000Z

# -*- coding: utf-8 -*- # Copyright 2016 Yelp Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from __future__ import absolute_import from __future__ import unicode_literals import time from collections import defaultdict from collections import namedtuple from contextlib import contextmanager from cached_property import cached_property from kafka import create_message from kafka import KafkaClient from kafka.common import ProduceRequest from data_pipeline._position_data_tracker import PositionDataTracker from data_pipeline._producer_retry import RetryHandler from data_pipeline._retry_util import ExpBackoffPolicy from data_pipeline._retry_util import MaxRetryError from data_pipeline._retry_util import Predicate from data_pipeline._retry_util import retry_on_condition from data_pipeline._retry_util import RetryPolicy from data_pipeline.config import get_config from data_pipeline.envelope import Envelope _EnvelopeAndMessage = namedtuple("_EnvelopeAndMessage", ["envelope", "message"]) logger = get_config().logger # prepare needs to be in the module top level so it can be serialized for # multiprocessing def _prepare(envelope_and_message): try: kwargs = {} if envelope_and_message.message.keys: kwargs['key'] = envelope_and_message.message.encoded_keys return create_message( envelope_and_message.envelope.pack(envelope_and_message.message), **kwargs ) except: logger.exception('Prepare failed') raise class KafkaProducer(object): """The KafkaProducer deals with buffering messages that need to be published into Kafka, preparing them for publication, and ultimately publishing them. Args: producer_position_callback (function): The producer position callback is called when the KafkaProducer is instantiated, and every time messages are published to notify the producer of current position information of successfully published messages. dry_run (Optional[bool]): When dry_run mode is on, the producer won't talk to real KafKa topic, nor to real Schematizer. Default to False. """ @cached_property def envelope(self): return Envelope() def __init__(self, producer_position_callback, dry_run=False): self.producer_position_callback = producer_position_callback self.dry_run = dry_run self.kafka_client = KafkaClient(get_config().cluster_config.broker_list) self.position_data_tracker = PositionDataTracker() self._reset_message_buffer() self.skip_messages_with_pii = get_config().skip_messages_with_pii self._publish_retry_policy = RetryPolicy( ExpBackoffPolicy(with_jitter=True), max_retry_count=get_config().producer_max_publish_retry_count ) self._automatic_flush_enabled = True @contextmanager def disable_automatic_flushing(self): """Prevents the producer from flushing automatically (e.g. for timeouts or batch size) while the context manager is open. """ try: self._automatic_flush_enabled = False yield finally: self._automatic_flush_enabled = True def wake(self): """Should be called periodically if we're not otherwise waking up by publishing, to ensure that messages are actually published. """ # if we haven't woken up in a while, we may need to flush messages self._flush_if_necessary() def publish(self, message): if message.contains_pii and self.skip_messages_with_pii: logger.info( "Skipping a PII message - " "uuid hex: {0}, " "schema_id: {1}, " "timestamp: {2}, " "type: {3}".format( message.uuid_hex, message.schema_id, message.timestamp, message.message_type.name ) ) return self._add_message_to_buffer(message) self.position_data_tracker.record_message_buffered(message) self._flush_if_necessary() def flush_buffered_messages(self): produce_method = (self._publish_produce_requests_dry_run if self.dry_run else self._publish_produce_requests) produce_method(self._generate_produce_requests()) self._reset_message_buffer() def close(self): self.flush_buffered_messages() self.kafka_client.close() def _publish_produce_requests(self, requests): """It will try to publish all the produce requests for topics, and retry a number of times until either all the requests are successfully published or it can no longer retry, in which case, the exception will be thrown. Each time the requests that are successfully published in the previous round will be removed from the requests and won't be published again. """ unpublished_requests = list(requests) retry_handler = RetryHandler(self.kafka_client, unpublished_requests) def has_requests_to_be_sent(): return bool(retry_handler.requests_to_be_sent) retry_handler = retry_on_condition( retry_policy=self._publish_retry_policy, retry_conditions=[Predicate(has_requests_to_be_sent)], func_to_retry=self._publish_requests, use_previous_result_as_param=True, retry_handler=retry_handler ) if retry_handler.has_unpublished_request: raise MaxRetryError(last_result=retry_handler) def _publish_requests(self, retry_handler): """Main function to publish message requests. This function is wrapped with retry function and will be retried based on specified retry policy Args: retry_handler: :class:`data_pipeline._producer_retry.RetryHandler` that determines which messages should be retried next time. """ if not retry_handler.requests_to_be_sent: return retry_handler responses = self._try_send_produce_requests( retry_handler.requests_to_be_sent ) retry_handler.update_requests_to_be_sent( responses, self.position_data_tracker.topic_to_kafka_offset_map ) self._record_success_requests(retry_handler.success_topic_stats_map) return retry_handler def _try_send_produce_requests(self, requests): # Either it throws exceptions and none of them succeeds, or it returns # responses of all the requests (success or fail response). try: return self.kafka_client.send_produce_request( payloads=requests, acks=get_config().kafka_client_ack_count, fail_on_error=False ) except Exception: # Exceptions like KafkaUnavailableError, LeaderNotAvailableError, # UnknownTopicOrPartitionError, etc., are not controlled by # `fail_on_error` flag and could be thrown from the kafka client, # and fail all the requests. We will retry all the requests until # either all of them are successfully published or it exceeds the # maximum retry criteria. return [] def _record_success_requests(self, success_topic_stats_map): for topic_partition, stats in success_topic_stats_map.iteritems(): topic = topic_partition.topic_name assert stats.message_count == len(self.message_buffer[topic]) self.position_data_tracker.record_messages_published( topic=topic, offset=stats.original_offset, message_count=stats.message_count ) self.message_buffer.pop(topic) def _publish_produce_requests_dry_run(self, requests): for request in requests: self._publish_single_request_dry_run(request) def _publish_single_request_dry_run(self, request): topic = request.topic message_count = len(request.messages) self.position_data_tracker.record_messages_published( topic, -1, message_count ) def _is_ready_to_flush(self): time_limit = get_config().kafka_producer_flush_time_limit_seconds return (self._automatic_flush_enabled and ( (time.time() - self.start_time) >= time_limit or self.message_buffer_size >= get_config().kafka_producer_buffer_size )) def _flush_if_necessary(self): if self._is_ready_to_flush(): self.flush_buffered_messages() def _add_message_to_buffer(self, message): topic = message.topic message = self._prepare_message(message) self.message_buffer[topic].append(message) self.message_buffer_size += 1 def _generate_produce_requests(self): return [ ProduceRequest(topic=topic, partition=0, messages=messages) for topic, messages in self._generate_prepared_topic_and_messages() ] def _generate_prepared_topic_and_messages(self): return self.message_buffer.iteritems() def _prepare_message(self, message): return _prepare(_EnvelopeAndMessage(envelope=self.envelope, message=message)) def _reset_message_buffer(self): if not hasattr(self, 'message_buffer_size') or self.message_buffer_size > 0: self.producer_position_callback(self.position_data_tracker.get_position_data()) self.start_time = time.time() self.message_buffer = defaultdict(list) self.message_buffer_size = 0 class LoggingKafkaProducer(KafkaProducer): def _publish_produce_requests(self, requests): logger.info( "Flushing buffered messages - requests={0}, messages={1}".format( len(requests), self.message_buffer_size ) ) try: super(LoggingKafkaProducer, self)._publish_produce_requests(requests) logger.info("All messages published successfully") except MaxRetryError as e: logger.exception( "Failed to publish all produce requests. {0}".format(repr(e)) ) raise def _reset_message_buffer(self): logger.info("Resetting message buffer for success requests.") super(LoggingKafkaProducer, self)._reset_message_buffer() def _publish_single_request_dry_run(self, request): super(LoggingKafkaProducer, self)._publish_single_request_dry_run(request) logger.debug("dry_run mode: Would have published {0} messages to {1}".format( len(request.messages), request.topic ))

39.359862

91

0.683868

9,351

0.822066

334

0.029363

420

0.036923

0

3,292

0.289407

cad56f6e99929dcb2621454332e576da53bc0b1f

25,097

py

Python

morphsed/images.py

RuancunLi/MorphSED

e386db49305d120841c61e8bea194257b4abd89b

[ "MIT" ]

null

morphsed/images.py

RuancunLi/MorphSED

e386db49305d120841c61e8bea194257b4abd89b

[ "MIT" ]

null

morphsed/images.py

RuancunLi/MorphSED

e386db49305d120841c61e8bea194257b4abd89b

[ "MIT" ]

1

2021-01-04T10:54:03.000Z

import numpy as np import matplotlib.pyplot as plt from astropy import units as u from astropy.coordinates import SkyCoord from astropy.io import fits from astropy.nddata import CCDData from astropy.nddata import Cutout2D from astropy.stats import sigma_clipped_stats from astropy.wcs.utils import proj_plane_pixel_scales from .plot import plot_image from .instrument_info import get_zp from .utils import get_wcs_rotation from astropy.visualization import simple_norm, make_lupton_rgb from .math import Maskellipse,polynomialfit,cross_match from photutils.segmentation import deblend_sources from astropy.convolution import Gaussian2DKernel from astropy.stats import gaussian_fwhm_to_sigma from photutils import detect_threshold from photutils import detect_sources from photutils import source_properties from astropy.table import Table, Column, join, join_skycoord from astropy.wcs import WCS from astropy.nddata import NDData from photutils.psf import extract_stars import matplotlib.colors as colors from photutils import EPSFBuilder __all__ = ['image', 'image_atlas'] class image(object): ''' A single image object. Functions --------- * Read from fits file use CCDData. * get_size : Get the image size. * plot : Plot the image. * sigma_clipped_stats : Calculate the basic statistics of the image. * set_data : Load from numpy array. * set_mask : Set image mask. * set_pixel_scales : Set the pixel scales along two axes. * set_zero_point : Set magnitude zero point. ''' def __init__(self, filename=None, hdu=0, unit=None, zero_point=None, pixel_scales=None, wcs_rotation=None, mask=None, verbose=True): ''' Parameters ---------- filename (optional) : string FITS file name of the image. hdu : int (default: 0) The number of extension to load from the FITS file. unit (optional) : string Unit of the image flux for CCDData. zero_point (optional) : float Magnitude zero point. pixel_scales (optional) : tuple Pixel scales along the first and second directions, units: arcsec. wcs_rotation (optional) : float WCS rotation, east of north, units: radian. mask (optional) : 2D bool array The image mask. verbose : bool (default: True) Print out auxiliary data. ''' if filename is None: self.data = None else: self.data = CCDData.read(filename, hdu=hdu, unit=unit, mask=mask) if self.data.wcs and (pixel_scales is None): pixel_scales = proj_plane_pixel_scales(self.data.wcs) * u.degree.to('arcsec') self.zero_point = zero_point if pixel_scales is None: self.pixel_scales = None else: self.pixel_scales = (pixel_scales[0]*u.arcsec, pixel_scales[1]*u.arcsec) if self.data.wcs and (wcs_rotation is None): self.wcs_rotation = get_wcs_rotation(self.data.wcs) elif wcs_rotation is not None: self.wcs_rotation = wcs_rotation * u.radian else: self.wcs_rotation = None self.sources_catalog = None self.sigma_image = None self.sources_skycord = None self.ss_data = None self.PSF = None def get_size(self, units='pixel'): ''' Get the size of the image. Parameters ---------- units : string Units of the size (pixel or angular units). Returns ------- x, y : float Size along X and Y axes. ''' nrow, ncol = self.data.shape if units == 'pixel': x = ncol y = nrow else: x = ncol * self.pixel_scales[0].to(units).value y = nrow * self.pixel_scales[1].to(units).value return (x, y) def get_size(self, units='pixel'): ''' Get the size of the image. Parameters ---------- units : string Units of the size (pixel or angular units). Returns ------- x, y : float Size along X and Y axes. ''' nrow, ncol = self.data.shape if units == 'pixel': x = ncol y = nrow else: x = ncol * self.pixel_scales[0].to(units).value y = nrow * self.pixel_scales[1].to(units).value return (x, y) def get_data_info(self): ''' Data information to generate model image. Returns ------- d : dict shape : (ny, nx) Image array shape. pixel_scale : (pixelscale_x, pixelscale_y), default units: arcsec Pixel scales. wcs_rotation : angle, default units: radian WCS rotation, east of north. ''' d = dict(shape=self.data.shape, pixel_scale=self.pixel_scale, wcs_rotation=self.wcs_rotation) return d def sigma_clipped_stats(self, **kwargs): ''' Run astropy.stats.sigma_clipped_stats to get the basic statistics of the image. Parameters ---------- All of the parameters go to astropy.stats.sigma_clipped_stats(). Returns ------- mean, median, stddev : float The mean, median, and standard deviation of the sigma-clipped data. ''' return sigma_clipped_stats(self.data.data, mask=self.data.mask, **kwargs) def plot(self, stretch='asinh', units='arcsec', vmin=None, vmax=None, a=None, ax=None, plain=False, **kwargs): ''' Plot an image. Parameters ---------- stretch : string (default: 'asinh') Choice of stretch: asinh, linear, sqrt, log. units : string (default: 'arcsec') Units of pixel scale. vmin (optional) : float Minimal value of imshow. vmax (optional) : float Maximal value of imshow. a (optional) : float Scale factor of some stretch function. ax (optional) : matplotlib.Axis Axis to plot the image. plain : bool (default: False) If False, tune the image. **kwargs : Additional parameters goes into plt.imshow() Returns ------- ax : matplotlib.Axis Axis to plot the image. ''' assert self.data is not None, 'Set data first!' ax = plot_image(self.data, self.pixel_scales, stretch=stretch, units=units, vmin=vmin, vmax=vmax, a=a, ax=ax, plain=plain, **kwargs) if plain is False: ax.set_xlabel(r'$\Delta X$ ({0})'.format(units), fontsize=24) ax.set_ylabel(r'$\Delta Y$ ({0})'.format(units), fontsize=24) return ax def plot_direction(self, ax, xy=(0, 0), len_E=None, len_N=None, color='k', fontsize=20, linewidth=2, frac_len=0.1, units='arcsec', backextend=0.05): ''' Plot the direction arrow. Only applied to plots using WCS. Parameters ---------- ax : Axis Axis to plot the direction. xy : (x, y) Coordinate of the origin of the arrows. length : float Length of the arrows, units: pixel. units: string (default: arcsec) Units of xy. ''' xlim = ax.get_xlim() len_total = np.abs(xlim[1] - xlim[0]) pixelscale = self.pixel_scales[0].to('degree').value if len_E is None: len_E = len_total * frac_len / pixelscale if len_N is None: len_N = len_total * frac_len / pixelscale wcs = self.data.wcs header = wcs.to_header() d_ra = len_E * pixelscale d_dec = len_N * pixelscale ra = [header['CRVAL1'], header['CRVAL1']+d_ra, header['CRVAL1']] dec = [header['CRVAL2'], header['CRVAL2'], header['CRVAL2']+d_dec] ra_pix, dec_pix = wcs.all_world2pix(ra, dec, 1) d_arrow1 = [ra_pix[1]-ra_pix[0], dec_pix[1]-dec_pix[0]] d_arrow2 = [ra_pix[2]-ra_pix[0], dec_pix[2]-dec_pix[0]] l_arrow1 = np.sqrt(d_arrow1[0]**2 + d_arrow1[1]**2) l_arrow2 = np.sqrt(d_arrow2[0]**2 + d_arrow2[1]**2) d_arrow1 = np.array(d_arrow1) / l_arrow1 * len_E * pixelscale d_arrow2 = np.array(d_arrow2) / l_arrow2 * len_N * pixelscale def sign_2_align(sign): ''' Determine the alignment of the text. ''' if sign[0] < 0: ha = 'right' else: ha = 'left' if sign[1] < 0: va = 'top' else: va = 'bottom' return ha, va ha1, va1 = sign_2_align(np.sign(d_arrow1)) ha2, va2 = sign_2_align(np.sign(d_arrow2)) xy_e = (xy[0] - d_arrow1[0] * backextend, xy[1] - d_arrow1[1] * backextend) ax.annotate('E', xy=xy_e, xycoords='data', fontsize=fontsize, xytext=(d_arrow1[0]+xy[0], d_arrow1[1]+xy[1]), color=color, arrowprops=dict(color=color, arrowstyle="<-", lw=linewidth), ha=ha1, va=va1) xy_n = (xy[0] - d_arrow2[0] * backextend, xy[1] - d_arrow2[1] * backextend) ax.annotate('N', xy=xy_n, xycoords='data', fontsize=fontsize, xytext=(d_arrow2[0]+xy[0], d_arrow2[1]+xy[1]), color=color, arrowprops=dict(color=color, arrowstyle="<-", lw=linewidth), ha=ha2, va=va2) def set_data(self, data, unit): ''' Parameters ---------- data : 2D array Image data. unit : string Unit for CCDData. ''' self.data = CCDData(data, unit=unit) def source_detection_individual(self, psfFWHM, nsigma=3.0, sc_key=''): ''' Parameters ---------- psfFWHM : float FWHM of the imaging point spread function nsigma : float source detection threshold ''' data = np.array(self.data.copy()) psfFWHMpix = psfFWHM / self.pixel_scales[0].value thresholder = detect_threshold(data, nsigma=nsigma) sigma = psfFWHMpix * gaussian_fwhm_to_sigma kernel = Gaussian2DKernel(sigma, x_size=5, y_size=5) kernel.normalize() segm = detect_sources(data, thresholder, npixels=5, filter_kernel=kernel) props = source_properties(data, segm) tab = Table(props.to_table()) self.sources_catalog = tab srcPstradec = self.data.wcs.all_pix2world(tab['xcentroid'], tab['ycentroid'],1) sc = SkyCoord(srcPstradec[0], srcPstradec[1], unit='deg') sctab = Table([sc,np.arange(len(sc))],names=['sc','sloop_{0}'.format(sc_key)]) self.sources_skycord = sctab def make_mask(self,sources=None,magnification=3.): ''' make mask for the extension. Parameters ---------- sources : a to-be masked source table (can generate from photutils source detection) if None, will use its own source catalog magnification : expand factor to generate mask ''' mask=np.zeros_like(self.data, dtype=bool) mask[np.isnan(self.data)] = True mask[np.isinf(self.data)] = True if sources is None: sources = self.sources_catalog for loop in range(len(sources)): position = (sources['xcentroid'][loop],sources['ycentroid'][loop]) a = sources['semimajor_axis_sigma'][loop] b = sources['semiminor_axis_sigma'][loop] theta = sources['orientation'][loop]*180./np.pi mask=Maskellipse(mask,position,magnification*a,(1-b/a),theta) self.data.mask = mask if self.ss_data is not None: self.ss_data.mask = mask def set_mask(self, mask): ''' Set mask for the extension. Parameters ---------- mask : 2D array The mask. ''' assert self.data.shape == mask.shape, 'Mask shape incorrect!' self.data.mask = mask if self.ss_data is not Nont: self.ss_data.mask = mask def set_pixel_scales(self, pixel_scales): ''' Parameters ---------- pixel_scales (optional) : tuple Pixel scales along the first and second directions, units: arcsec. ''' self.pixel_scales = (pixel_scales[0]*u.arcsec, pixel_scales[1]*u.arcsec) def set_zero_point(self, zp): ''' Set magnitude zero point. ''' self.zero_point = zp def sky_subtraction(self, order=3 , filepath = None): ''' Do polynomial-fitting sky subtraction Parameters ---------- order (optional) : int order of the polynomial ''' data = np.array(self.data.copy()) maskplus = self.data.mask.copy() backR=polynomialfit(data,maskplus.astype(bool),order=order) background=backR['bkg'] self.ss_data = CCDData(data-background, unit=self.data.unit) self.ss_data.mask = maskplus if filepath is not None: hdu_temp = fits.PrimaryHDU(data-background) hdu_temp.writeto(filepath, overwrite=True) def read_ss_image(self,filepath): ''' read sky subtracted image from "filepath" ''' hdu = fits.open(filepath) self.ss_data = CCDData(hdu[0].data, unit=self.data.unit) self.ss_data.mask = self.data.mask.copy() def cal_sigma_image(self,filepath=None): ''' Construct sigma map following the same procedure as Galfit (quadruture sum of sigma at each pixel from source and sky background). Note ---------- 'GAIN' keyword must be available in the image header and ADU x GAIN = electron Parameters ---------- filepath: Whether and where to save sigma map ''' GAIN = self.data.header['CELL.GAIN'] if self.ss_data is None: raise ValueError(" Please do sky subtration first !!!") data = np.array(self.ss_data.copy()) mask = self.ss_data.mask.copy() bkgrms = np.nanstd(data[~mask.astype(bool)]) data[~mask.astype(bool)] = 0. sigmap = np.sqrt(data/GAIN+bkgrms**2) self.sigma_image = sigmap if filepath is not None: hdu_temp = fits.PrimaryHDU(sigmap) hdu_temp.writeto(filepath, overwrite=True) def read_sigmap(self, filepath): ''' read sigma image from "filepath" ''' hdu = fits.open(filepath) self.sigma_image = hdu[0].data def read_PSF(self, filepath): ''' read PSF image from "filepath" ''' hdu = fits.open(filepath) self.PSF = hdu[0].data class image_atlas(object): ''' Many images. ''' def __init__(self, image_list=None, zp_list=None, band_list=None, psfFWHM_list=None): ''' Parameters ---------- image_list (optional) : List List of `image`. zp_list (optional) : List List of magnitude zeropoint. band_list (optional) : List List of band name. Check `instrument_info` for band names. ''' if image_list is None: self.image_list = [] else: self.image_list = image_list if band_list is None: self.band_list = [] else: self.band_list = band_list if (zp_list is None) and (band_list is not None): zp_list = [] for b in band_list: zp_list.append(get_zp(b)) for loop, img in enumerate(self.image_list): img.set_zero_point(zp_list[loop]) if psfFWHM_list is None: self.psfFWHM_list = [] else: self.psfFWHM_list = psfFWHM_list self.__length = len(image_list) self.common_catalog = None def __getitem__(self, key): ''' Get the image data using the filter name or number index. ''' if type(key) is str: idx = self.band_list.index(key) elif type(key) is int: idx = key return self.image_list[idx] def __len__(self): ''' Get the length of the data list. ''' return self.__length def source_detection(self,nsigma=3.0): ''' Do multi-band source detection Parameters ---------- nsigma : float, or a array with same size as image_atlas source detection threshold ''' if type(nsigma) == float: nsigma = nsigma * np.ones(self.__length,dtype=float) for loop in range(self.__length): self.image_list[loop].source_detection_individual(self.psfFWHM_list[loop],nsigma=nsigma[loop],sc_key=loop+1) def make_common_catalog(self,CM_separation=2.5,magnification=3.0,applylist=None): ''' Do multi-band source detection Parameters ---------- CM_separation : float angular separation used to do sky coordinates crossmatching, unit in deg magnification : float, or a array with same size as image_atlas magnification for generating mask foe each image applylist : [list of index] None for all images ''' if type(magnification) == float: magnification = magnification * np.ones(self.__length,dtype=float) if applylist is None: applylist = np.arange(self.__length) cats = [] for loop in applylist: cats.append(self.image_list[loop].sources_skycord) comc = cross_match(cats,angular_sep = 2.5) lencc = len(comc) master_a = np.zeros(lencc, dtype = float) master_b = np.zeros(lencc, dtype = float) for loop in range(len(comc)): a = [] b = [] for loop2 in applylist: a.append(self.image_list[loop2].sources_catalog['semimajor_axis_sigma'][comc['sloop_{0}'.format(loop2+1)][loop]] *magnification[loop2]*self.image_list[loop2].pixel_scales[0].value) b.append(self.image_list[loop2].sources_catalog['semiminor_axis_sigma'][comc['sloop_{0}'.format(loop2+1)][loop]] *magnification[loop2]*self.image_list[loop2].pixel_scales[0].value) master_a[loop] = np.max(np.array(a)) master_b[loop] = np.max(np.array(b)) comc.add_column(Column(master_a, name = 'master_a')) comc.add_column(Column(master_b, name = 'master_b')) self.common_catalog = comc def sky_subtraction(self,order=3,filepaths=None): ''' Do multi-band sky subtration Parameters ---------- order (optional) : int order of the polynomial filepaths : filepath to store the sky subtracted images ''' if type(order) == int: order = order * np.ones(self.__length,dtype=int) for loop in range(self.__length): if filepaths is None: self.image_list[loop].sky_subtraction(order[loop]) else: self.image_list[loop].sky_subtraction(order[loop],filepath=filepaths[loop]) def master_mask(self, magnification=3.0, applylist=None): ''' Do multi-band source masking Parameters ---------- magnification : float, or a array with same size as image_atlas magnification for generating mask foe each image applylist : [list of index] None for all images ''' if type(magnification) == float: magnification = magnification * np.ones(self.__length,dtype=float) if applylist is None: applylist = np.arange(self.__length) comc = self.common_catalog.copy() commonsourcelist = [] for loop2 in applylist: newsc = self.image_list[loop2].sources_catalog.copy() for loop in range(len(comc)): self.image_list[loop2].sources_catalog['semimajor_axis_sigma'][comc['sloop_{0}'.format(loop2+1)][loop]] = comc['master_a'][loop]/(magnification[loop2]*self.image_list[loop2].pixel_scales[0].value) self.image_list[loop2].sources_catalog['semiminor_axis_sigma'][comc['sloop_{0}'.format(loop2+1)][loop]] = comc['master_b'][loop]/(magnification[loop2]*self.image_list[loop2].pixel_scales[0].value) indexes = np.delete(np.arange(len(self.image_list[loop2].sources_catalog)), comc['sloop_{0}'.format(loop2+1)]) newsc.remove_rows(indexes) commonsourcelist.append(newsc) for loop2 in range(self.__length): self.image_list[loop2].make_mask(sources=commonsourcelist[loop2],magnification=magnification[loop2]) def generate_PSFs(self, equivalent_radius=2., size = 20.,oversampling=1, plot=None, filepaths=None): ''' Generate effective point spread fuctions (ePSFs) for each image Parameters ---------- equivalent_radius : float, unit arcsec radius criteria to indentify star size : float, unit pixel use what size box to extract stars oversampling : int oversample the ePSF plot : None for not plot stars & ePSF list like [1,2,3] to plot rgb image filepaths : filepath to store the ePSFs ''' stars = self.common_catalog.copy() remolist = [] for loop in range(len(stars)): for loop2 in range(self.__length): a = (self.image_list[loop2].sources_catalog['equivalent_radius'][stars['sloop_{0}'.format(loop2+1)][loop]])*self.image_list[loop2].pixel_scales[0].value if (a > equivalent_radius): remolist.append(loop) break stars.remove_rows(remolist) star_images = [] PSFs = [] for loop2 in range(self.__length): newsc = self.image_list[loop2].sources_catalog.copy() indexes = np.delete(np.arange(len(self.image_list[loop2].sources_catalog)), stars['sloop_{0}'.format(loop2+1)]) newsc.remove_rows(indexes) stars_tbl = Table() stars_tbl['x']=np.array(newsc['maxval_xpos']) stars_tbl['y']=np.array(newsc['maxval_ypos']) nddata = NDData(data=np.array(self.image_list[loop2].ss_data)) Tstar = extract_stars(nddata, stars_tbl, size=size) epsf_builder = EPSFBuilder(oversampling=oversampling, maxiters=15,progress_bar=False) epsf, fitted_stars = epsf_builder(Tstar) self.image_list[loop2].PSF = epsf.data if filepaths is not None: hdu = fits.PrimaryHDU(epsf.data.astype('float32')) After = fits.HDUList([hdu]) After.writeto(filepaths[loop2],overwrite= True) if plot is not None: star_images.append(Tstar) PSFs.append(epsf.data) if plot is not None: tlens = len(stars) if (((tlens//5)+1)*5-tlens) < (((tlens//4)+1)*4-tlens): ncols = 5 nrows = (tlens//5)+1 else: ncols = 4 nrows = (tlens//4)+1 fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(3*ncols, 3*nrows),squeeze=True) ax = ax.ravel() for i in range(tlens): if len(plot) > 2: star_b = star_images[plot[0]][i].data*100./np.sum(star_images[plot[0]][i].data) star_g = star_images[plot[1]][i].data*100./np.sum(star_images[plot[1]][i].data) star_r = star_images[plot[2]][i].data*100./np.sum(star_images[plot[2]][i].data) norm = simple_norm(star_b, 'log', percent=99.) image = make_lupton_rgb(star_r, star_g, star_b, Q=10) else: image = star_images[plot[0]][i].data norm = simple_norm(image, 'log', percent=99.) ax[i].imshow(image,norm=norm ,origin='lower') plt.show() fig=plt.figure(figsize=(10,10)) if len(plot) > 2: star_b = PSFs[plot[0]]*100./np.sum(PSFs[plot[0]]) star_g = PSFs[plot[1]]*100./np.sum(PSFs[plot[1]]) star_r = PSFs[plot[2]]*100./np.sum(PSFs[plot[2]]) norm = simple_norm(star_b, 'log', percent=99.) image = make_lupton_rgb(star_r, star_g, star_b, Q=10) else: image = PSFs[plot[0]] norm = simple_norm(image, 'log', percent=99.) plt.imshow(image,norm=norm ,origin='lower') plt.show()

37.853695

212

0.5721

24,018

0.957007

0

8,260

0.329123

cad6d4d9039f032ee0647fb24456c499c4ecede6

1,108

py

Python

tests/test_kfold.py

pal-16/preprocessy

6577143d3397a47bd69c125c8de46ebc5ad2ddae

[ "MIT" ]

null

tests/test_kfold.py

pal-16/preprocessy

6577143d3397a47bd69c125c8de46ebc5ad2ddae

[ "MIT" ]

null

tests/test_kfold.py

pal-16/preprocessy

6577143d3397a47bd69c125c8de46ebc5ad2ddae

[ "MIT" ]

null

import numpy as np import pytest from preprocessy.resampling import KFold class TestKFold: def test_kfold(self): with pytest.raises(ValueError): KFold(n_splits=2.3) with pytest.raises(ValueError): KFold(n_splits=0) with pytest.raises(ValueError): KFold(shuffle=1) with pytest.raises(ValueError): KFold(shuffle=True, random_state=4.5) with pytest.raises(ValueError): KFold(shuffle=False, random_state=69) def test_split(self): with pytest.raises(ValueError): arr = np.arange(10) kFold = KFold(n_splits=20) for train_indices, test_indices in kFold.split(arr): print( f"Train indices: {train_indices}\nTest indices:" f" {test_indices}" ) arr = np.arange(12) kFold = KFold(n_splits=3, shuffle=True, random_state=69) for train_indices, test_indices in kFold.split(arr): assert len(train_indices) == 8 assert len(test_indices) == 4

27.7

68

0.583935

1,030

0.929603

0

66

0.059567

cad6f407f7e2ec628f292a0cf8cb6230d6d12b29

337

py

Python

srforge_tests/unit_tests/utilities_tests/__init__.py

alunduil/srforge

ef498b0c2e327ae14613bd369f45940005ad8b1f

[ "MIT" ]

null

srforge_tests/unit_tests/utilities_tests/__init__.py

alunduil/srforge

ef498b0c2e327ae14613bd369f45940005ad8b1f

[ "MIT" ]

null

srforge_tests/unit_tests/utilities_tests/__init__.py

alunduil/srforge

ef498b0c2e327ae14613bd369f45940005ad8b1f

[ "MIT" ]

null

# Copyright (C) 2016 srforge project developers. # # See the COPYRIGHT file at the top-level directory of this distribution and at # https://github.com/alunduil/srforge/blob/master/COPYRIGHT # # srforge is freely distributable under the terms of an MIT-style license. # See LICENSE or http://www.opensource.org/licenses/mit-license.php.

42.125

79

0.780415

0

330

0.979228

cad795ae9dd6135d51053b4f8de0331e4efd6df4

422

py

Python

prices/admin.py

KazuruK/FilmGetter

fd84bcaddf17d4b89ad6e5d27095535346c5f4a9

[ "BSD-3-Clause" ]

1

2021-06-23T13:06:11.000Z

prices/admin.py

KazuruK/FilmGetter

fd84bcaddf17d4b89ad6e5d27095535346c5f4a9

[ "BSD-3-Clause" ]

1

2021-06-23T21:21:52.000Z

prices/admin.py

KazuruK/FilmGetter

fd84bcaddf17d4b89ad6e5d27095535346c5f4a9

[ "BSD-3-Clause" ]

1

2021-06-28T19:14:19.000Z

from django.contrib import admin # Register your models here. from prices.models import IdDB class IdDBAdmin(admin.ModelAdmin): list_display = ( 'kinopoisk_id', 'title', 'title_en', 'year', 'date_created', 'price',) search_fields = ('kinopoisk_id', 'title',) list_filter = ('year',) empty_value_display = '-пусто-' admin.site.register(IdDB, IdDBAdmin)

19.181818

46

0.620853

290

0.679157

0

127

0.297424

cad84f636cd4b82b3be3bfeb9a329a3e645a1011

2,166

py

Python

tapetracker.py

kemfic/pumavision2019

b14a566ce63629a90f31fd70d5f62d9efd61bdff

[ "MIT" ]

2

2019-03-05T21:59:06.000Z

2019-06-18T03:58:00.000Z

tapetracker.py

kemfic/pumavision2019

b14a566ce63629a90f31fd70d5f62d9efd61bdff

[ "MIT" ]

null

tapetracker.py

kemfic/pumavision2019

b14a566ce63629a90f31fd70d5f62d9efd61bdff

[ "MIT" ]

null

import cv2 import numpy as np class TapeTracker(object): min_thresh = np.array( [80,0,0] ) max_thresh = np.array( [90, 255, 255] ) def __init_(self): self.img = np.zeros((500,500)) def pipeline(self, img): self.img = cv2.resize(img, (300,300), cv2.INTER_NEAREST) self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2HLS) self.mask = cv2.inRange(self.img, self.min_thresh, self.max_thresh) kernel = np.ones((5,5), np.uint8) #self.mask = cv2.dilate(self.mask,kernel, iterations=2) self.cnt, self.hier = cv2.findContours(self.mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) self.ret = np.copy(self.img) self.cnt_f = [] self.cnt = sorted(self.cnt, key=cv2.contourArea, reverse=True)[:2] # get largest contour for cnt in self.cnt: x,y,w,h = cv2.boundingRect(cnt) if w < 0.6*h and cv2.contourArea(cnt) > 10: cv2.rectangle(self.ret, (x,y), (x+w, y+h), (0,255,0), 2) self.cnt_f.append(cnt) M_1 = cv2.moments(self.cnt_f[0]) cx_1 = int(M_1['m10']/M_1['m00']) cy_1 = int(M_1['m01']/M_1['m00']) M_2 = cv2.moments(self.cnt_f[1]) cx_2 = int(M_2['m10']/M_2['m00']) cy_2 = int(M_2['m01']/M_2['m00']) midpoint = ((cx_1+cx_2)//2, (cy_1+cy_2)//2) self.error = midpoint[0] - self.img.shape[0] print(self.error) #cy = int(M['m01']/M['m00']) #print(cx - self.img.shape[0]//2) #print(cx) self.ret = cv2.drawContours(self.ret, self.cnt_f, -1, (150, 150, 255), 2) self.ret = cv2.circle(self.ret, (cx_1, cy_1), 2, (150, 155, 255)) self.ret = cv2.circle(self.ret, (cx_2, cy_2), 2, (150, 155, 255)) self.ret = cv2.circle(self.ret, midpoint, 2, (150, 255, 255)) if __name__ == "__main__": ct = TapeTracker() img = cv2.imread('img/1.jpg') ct.pipeline(img) cv2.imshow('output', cv2.resize(cv2.cvtColor(ct.img, cv2.COLOR_HLS2BGR), (500, 500), cv2.INTER_NEAREST)) cv2.imshow('mask', cv2.resize(ct.mask, (500,500), cv2.INTER_NEAREST)) cv2.imshow('contour', cv2.resize(cv2.cvtColor(ct.ret, cv2.COLOR_HLS2BGR), (500, 500), cv2.INTER_NEAREST)) k = cv2.waitKey(0) & 0xFF if k == 27: cv2.destroyAllWindows()

34.935484

107

0.617729

1,675

0.773315

0

232

0.10711

cad9516cda873ad5c7a80d3614252cdbc50f0241

3,241

py

Python

Engine/Tools.py

NoNotCar/SpaceX

803390fdf0ed3deee18d8f0d101ae575cf9b55e1

[ "MIT" ]

null

Engine/Tools.py

NoNotCar/SpaceX

803390fdf0ed3deee18d8f0d101ae575cf9b55e1

[ "MIT" ]

null

Engine/Tools.py

NoNotCar/SpaceX

803390fdf0ed3deee18d8f0d101ae575cf9b55e1

[ "MIT" ]

null

from .Items import Item from Lib import Img import pygame from Game import Registry,Research from Objects import War error=Img.sndget("error") bsnd=Img.sndget("break") class Pickaxe(Item): img=Img.imgx("Tools/Pickaxe") last_used=0 MAX_CONTINUOUS=200 last_mined=None stack_size = 1 singular = True continuous = True def use(self,area,tpos,tr,p): for l in reversed(area.layers): o=l[tpos] if o and o.hardness: if o is self.last_mined and pygame.time.get_ticks()-self.last_used<self.MAX_CONTINUOUS: self.prog+=1 if self.prog==o.hardness: item=o.mined() o.on_mine(area,tpos) if item is None or p.inv.add(item,1): bsnd.play() elif area.clear("Items",tpos): area.spawn_item(item,tpos) bsnd.play() else: o.on_mine(area, tpos,True) error.play() self.prog=0 self.prog_max=0 self.last_mined=None else: self.prog=0 self.prog_max=o.hardness self.last_mined=o break self.last_used=pygame.time.get_ticks() class Bridger(Item): img=Img.imgx("Tools/BridgeBuilder") def use(self,area,tpos,tr,p): t=area.get("Tiles",tpos) if t and not t.support: area.set_tile("Bridge",tpos) return True class ChainSaw(Item): img=Img.imgx("Tools/ChainSaw") stack_size = 1 name="Chainsaw" def use(self,area,tpos,tr,p): tree=area.get("Objects",tpos) if tree and tree.name=="Tree" and p.inv.add(tree.mined()): area.dobj(tree,tpos) class FireFlower(Item): img=Img.imgstripx("Plants/FireFlower")[-1] def use(self,area,tpos,tr,p): if area.clear("Objects",tpos): area.spawn_new(War.Fireball,tpos,p.d) return True return False class Wrench(Item): img=Img.imgx("Tools/Wrench") def use(self,area,tpos,tr,p): o=area.get("Objects",tpos) if o: o.wrench(p,p.coords.pos,tpos,area) from Game import Boxes class EntangledPlacer(Item): imgs=Img.imgstripx("Tools/EntangledPlacer") img=imgs[0] singular = True stack_size = 1 phase=0 e1=None name="EntangledPlacer" def use(self,area,tpos,tr,p): for l in Boxes.EntangledBox1.layers: if not area.clear(l,tpos): return False if self.phase: area.spawn_new(Boxes.EntangledBox2,tpos,self.e1.area,self.e1.dummy) return True else: self.e1=Boxes.EntangledBox1(None) area.spawn(self.e1,tpos) self.phase=1 self.img=self.imgs[1] Registry.add_recipe({"Iron":3,"Brick":5},Bridger()) Registry.add_recipe({"Iron":3},Wrench()) Research.add_recipesearch({"Circuit":5,"Steel":3},ChainSaw(),[1],20) Research.add_recipesearch({"StdBox":1,"ChaosCrystal":3},EntangledPlacer(),[1],50)

33.412371

103

0.54983

2,798

0.863314

0

261

0.080531

cadb7857fc43538c2164847a75f460688bce44f4

879

py

Python

tests/max/test_rmc.py

realead/rmc

6dafa2a4b5ab7199e86e86a4c10388bc8e472bb6

[ "MIT" ]

null

tests/max/test_rmc.py

realead/rmc

6dafa2a4b5ab7199e86e86a4c10388bc8e472bb6

[ "MIT" ]

null

tests/max/test_rmc.py

realead/rmc

6dafa2a4b5ab7199e86e86a4c10388bc8e472bb6

[ "MIT" ]

null

import os import exetest as ex import exetest.decorator as dec import RMCTester @dec.to_unit_tests class Tester(RMCTester.RMCTester): #setting up the test case my_path = os.path.dirname(__file__) program_name = "max" exe = os.path.join(my_path, RMCTester.RMCTester.EXE_NAME) default_parameters = {ex.EXIT_CODE: 0, ex.STDERR: "", ex.INPUT: ""} casedata_both_nulls = {ex.OPTIONS: ["2", "0", "0"], ex.STDOUT: "0\n"} casedata_both_ones = {ex.OPTIONS: ["2", "1", "1"], ex.STDOUT: "1\n"} casedata_max_first= {ex.OPTIONS: ["2", "5", "1"], ex.STDOUT: "5\n"} casedata_max_second = {ex.OPTIONS: ["2", "5", "6"], ex.STDOUT: "6\n"}

25.114286

61

0.486917

773

0.879408

0

792

0.901024

0

90

0.102389

cadb919713f442cb696ef20531e176f4131df704

1,925

py

Python

ceilometer_infoblox/pollsters/__init__.py

mwinslow7/ceilometer-infoblox

c183fec8d12d6957156485684759e064fc43bbcd

[ "Apache-2.0" ]

4

2015-09-22T22:26:00.000Z

2018-12-27T07:16:24.000Z

ceilometer_infoblox/pollsters/__init__.py

mwinslow7/ceilometer-infoblox

c183fec8d12d6957156485684759e064fc43bbcd

[ "Apache-2.0" ]

1

2017-05-03T00:34:03.000Z

2017-05-03T00:37:28.000Z

ceilometer_infoblox/pollsters/__init__.py

mwinslow7/ceilometer-infoblox

c183fec8d12d6957156485684759e064fc43bbcd

[ "Apache-2.0" ]

9

2015-10-22T15:55:29.000Z

2019-09-02T07:47:11.000Z

# Copyright 2015 Infoblox, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import abc from oslo_utils import timeutils import six from ceilometer.hardware.pollsters import generic from ceilometer import sample @six.add_metaclass(abc.ABCMeta) class BaseNIOSPollster(generic.GenericHardwareDeclarativePollster): def __init__(self): super(BaseNIOSPollster, self).__init__() meter = generic.MeterDefinition(self.meter_dict) self._update_meter_definition(meter) @property def default_discovery(self): return 'nios_instances' def generate_samples(self, host_url, data): """Generate a list of Sample from the data returned by inspector :param host_url: host url of the endpoint :param data: list of data returned by the corresponding inspector """ samples = [] definition = self.meter_definition for (value, metadata, extra) in data: s = sample.Sample( name=definition.name, type=definition.type, unit=definition.unit, volume=value, user_id=extra['user_id'], project_id=extra['tenant_id'], resource_id=extra['resource_id'], timestamp=timeutils.utcnow().isoformat(), resource_metadata=extra, ) samples.append(s) return samples

32.627119

75

0.668052

1,173

0.609351

0

1,205

0.625974

0

816

0.423896

cadf0bfc3589bccc805fdee98c4cd79597ee146b

2,309

py

Python

waitercaller.py

salaikumar/waitercaller

83ad2f8a477fc196c665db1ce1159a042d69fe8e

[ "Apache-2.0" ]

null

waitercaller.py

salaikumar/waitercaller

83ad2f8a477fc196c665db1ce1159a042d69fe8e

[ "Apache-2.0" ]

null

waitercaller.py

salaikumar/waitercaller

83ad2f8a477fc196c665db1ce1159a042d69fe8e

[ "Apache-2.0" ]

null

from flask import Flask from flask import request from flask import render_template # Login Extension from flask_login import LoginManager from flask_login import login_required from flask_login import login_user from flask_login import logout_user from mockdbhelper import MockDBHelper as DBHelper from user import User from flask import redirect from flask import url_for # Password Helper imports from passwordhelper import PasswordHelper # DB Helper instance DB = DBHelper() # Password Helper instance PH = PasswordHelper() # Creating a Flask App instance app = Flask(__name__) # Set a secret key for you application app.secret_key = 'tPXJY3X37Qybz4QykV+hOyUxVQeEXf1Ao2C8upz+fGQXKsM' login_manager = LoginManager(app) @app.route("/") def home(): return render_template("home.html") @app.route("/account") @login_required def account(): return "You're logged in" @login_manager.user_loader def load_user(user_id): user_password = DB.get_user(user_id) if user_password: return User(user_id) @app.route("/login", methods=["POST"]) def login(): email = request.form.get("email") password = request.form.get("password") stored_user = DB.get_user(email) if stored_user and PH.validate_password(password, stored_user['salt'], stored_user['hashed']): user = User(email) login_user(user, remember=True) return redirect(url_for('account')) # user_password = DB.get_user(email) # if user_password and user_password == password: # user = User(email) # login_user(user) # return redirect(url_for('account')) # return account() return home() # Register function @app.route("/register" , methods=["POST"]) def register(): email = request.form.get("email") password = request.form.get("password") confirmpass = request.form.get("password2") if not password == confirmpass: return redirect(url_for('home')) if DB.get_user(email): return redirect(url_for('home')) salt = PH.get_salt() hashed = PH.get_hash(password + salt) DB.add_user(email, salt, hashed) return redirect(url_for('home')) @app.route("/logout") def logout(): logout_user() return redirect(url_for("home")) if __name__ == '__main__': app.run(port=5000, debug=True)

27.164706

98

0.708099

0

1,481

0.641403

0

604

0.261585

cadff6a5947df261c22029325d9e900afbcb18eb

348

py

Python

Part_3_advanced/m03_date_and_time/date_today/example_1/main.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m03_date_and_time/date_today/example_1/main.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m03_date_and_time/date_today/example_1/main.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

from book_book import books_directory, user_interface def run_example(): user_interface.add_new_book() print("Data dodania: ", books_directory.available_books[-1].added_at_date) user_interface.add_new_book() print("Data dodania: ", books_directory.available_books[-1].added_at_date) if __name__ == "__main__": run_example()

26.769231

78

0.755747

0

42

0.12069

cae0c802208358b65003e5f7be5cda37037a9c79

1,602

py

Python

examples/example1.py

inverseproblem/pyMag2Dpoly

d4210faa691c1ca96677e4a416ae6571d53970ac

[ "MIT" ]

4

2021-01-31T00:41:43.000Z

2021-09-08T05:01:13.000Z

examples/example1.py

inverseproblem/pyMag2Dpoly

d4210faa691c1ca96677e4a416ae6571d53970ac

[ "MIT" ]

null

examples/example1.py

inverseproblem/pyMag2Dpoly

d4210faa691c1ca96677e4a416ae6571d53970ac

[ "MIT" ]

1

2021-03-28T07:19:26.000Z

import sys # in this case local import sys.path.append("../") import mag2dpoly as mag import numpy as np # induced magnetization Jind = mag.MagnetizVector(mod=4.9,Ideg=90.0,Ddeg=45.0) # remanent magnetization Jrem = mag.MagnetizVector(mod=3.1,Ideg=45.0,Ddeg=0.0) # angle with the North axis northxax = 90.0 # number of observation Nobs = 101 xzobs = np.transpose(np.vstack(( np.linspace(0.0,100.0,Nobs), -1.0*np.ones(Nobs)))) # vertices of the poligonal bodies vertices = np.array([ [35.0, 50.0], [65.0, 50.0], [80.0, 35.0], [65.0, 20.0], [35.0, 20.0], [20.0, 35.0] ]) # indices of vertices for the body nbod = 1 bodyindices = np.empty(shape=(nbod,), dtype=np.object) inds = range(6) bodyindices[0] = np.array(inds) # construct the poligonal body object pbody = mag.MagPolyBodies2D(bodyindices,vertices) # type of forward algorithm forwardtype = "talwani" # compute total field # make Jind and Jrem arrays of objects (as many as there are bodies) Jindv = np.array([Jind]) # we have one single body in this case Jremv = np.array([Jrem]) # we have one single body in this case tmag = mag.tmagpolybodies2Dgen(xzobs,Jindv,Jremv,northxax,pbody,forwardtype) ## plot import matplotlib.pyplot as plt plt.figure() plt.subplot(211) plt.title("Magnetic anomaly") plt.plot(xzobs[:,0],tmag,"o-") plt.subplot(212) plt.title("Polygonal body") x = np.append(pbody.bo[0].ver1[:,0],pbody.bo[0].ver1[0,0]) y = np.append(pbody.bo[0].ver1[:,1],pbody.bo[0].ver1[0,1]) plt.plot(x,y,"o-") plt.show()

25.03125

83

0.655431

0

484

0.302122

cae2b6216f4f43c83ea66c2eb2462e3bd35c9bfd

1,607

py

Python

stats.py

warppoint42/Mahjong221

dac82c726927730e11112e2a62b500587717b7ed

[ "MIT" ]

null

stats.py

warppoint42/Mahjong221

dac82c726927730e11112e2a62b500587717b7ed

[ "MIT" ]

null

stats.py

warppoint42/Mahjong221

dac82c726927730e11112e2a62b500587717b7ed

[ "MIT" ]

null

import csv stats = dict() with open('unifiedroundlog.csv', 'r') as csvfile: reader = csv.DictReader(csvfile, ('AI', 'gameID', 'end', 'win', 'feed', 'riichi')) for row in reader: if row['AI'] not in stats: stats[row['AI']] = dict() stats[row['AI']].setdefault('n', 0) stats[row['AI']].setdefault('end', 0) stats[row['AI']].setdefault('win', 0) stats[row['AI']].setdefault('feed', 0) stats[row['AI']]['n'] += 1 if row['end'] == '1': stats[row['AI']]['end'] += 1 if row['win'] == '1': stats[row['AI']]['win'] += 1 if row['feed'] == '1': stats[row['AI']]['feed'] += 1 with open('owari.csv', 'r') as csvfile: reader = csv.DictReader(csvfile, ('AI', 'gameID', '1', '2', '3', '4')) for row in reader: stats[row['AI']].setdefault('1', 0) stats[row['AI']].setdefault('2', 0) stats[row['AI']].setdefault('3', 0) stats[row['AI']].setdefault('4', 0) stats[row['AI']].setdefault('totp', 0) stats[row['AI']].setdefault('totn', 0) stats[row['AI']]['totn'] += 1 if 'Name' in row['1']: stats[row['AI']]['1'] += 1 stats[row['AI']]['totp'] += 1 if 'Name' in row['2']: stats[row['AI']]['2'] += 1 stats[row['AI']]['totp'] += 2 if 'Name' in row['3']: stats[row['AI']]['3'] += 1 stats[row['AI']]['totp'] += 3 if 'Name' in row['4']: stats[row['AI']]['4'] += 1 stats[row['AI']]['totp'] += 4 print(stats)

32.14

86

0.450529

0

363

0.225887

cae4acfde1f01b9cb0259fd0db2f62854a8254cc

4,544

py

Python

fabfile.py

yanikou19/pymatgen

8ee0d9ff35a9c2fa4f00da5d423e536ed8914e31

[ "MIT" ]

null

fabfile.py

yanikou19/pymatgen

8ee0d9ff35a9c2fa4f00da5d423e536ed8914e31

[ "MIT" ]

null

fabfile.py

yanikou19/pymatgen

8ee0d9ff35a9c2fa4f00da5d423e536ed8914e31

[ "MIT" ]

null

""" Deployment file to facilitate releases of pymatgen. Note that this file is meant to be run from the root directory of the pymatgen repo. """ __author__ = "Shyue Ping Ong" __email__ = "ongsp@ucsd.edu" __date__ = "Sep 1, 2014" import glob import os import json import webbrowser import requests import re import subprocess from fabric.api import local, lcd from pymatgen import __version__ as ver def make_doc(): with open("CHANGES.rst") as f: contents = f.read() toks = re.split("\-{3,}", contents) n = len(toks[0].split()[-1]) changes = [toks[0]] changes.append("\n" + "\n".join(toks[1].strip().split("\n")[0:-1])) changes = ("-" * n).join(changes) with open("docs/latest_changes.rst", "w") as f: f.write(changes) with lcd("examples"): local("ipython nbconvert --to html *.ipynb") local("mv *.html ../docs/_static") with lcd("docs"): local("cp ../CHANGES.rst change_log.rst") local("sphinx-apidoc -d 6 -o . -f ../pymatgen") local("rm pymatgen.*.tests.rst") for f in glob.glob("docs/*.rst"): if f.startswith('docs/pymatgen') and f.endswith('rst'): newoutput = [] suboutput = [] subpackage = False with open(f, 'r') as fid: for line in fid: clean = line.strip() if clean == "Subpackages": subpackage = True if not subpackage and not clean.endswith("tests"): newoutput.append(line) else: if not clean.endswith("tests"): suboutput.append(line) if clean.startswith("pymatgen") and not clean.endswith("tests"): newoutput.extend(suboutput) subpackage = False suboutput = [] with open(f, 'w') as fid: fid.write("".join(newoutput)) local("make html") local("cp _static/* _build/html/_static") #This makes sure pymatgen.org works to redirect to the Gihub page local("echo \"pymatgen.org\" > _build/html/CNAME") #Avoid ths use of jekyll so that _dir works as intended. local("touch _build/html/.nojekyll") def publish(): local("python setup.py release") def setver(): local("sed s/version=.*,/version=\\\"{}\\\",/ setup.py > newsetup" .format(ver)) local("mv newsetup setup.py") def update_doc(): make_doc() with lcd("docs/_build/html/"): local("git add .") local("git commit -a -m \"Update dev docs\"") local("git push origin gh-pages") def merge_stable(): local("git commit -a -m \"v%s release\"" % ver) local("git push") local("git checkout stable") local("git pull") local("git merge master") local("git push") local("git checkout master") def release_github(): with open("CHANGES.rst") as f: contents = f.read() toks = re.split("\-+", contents) desc = toks[1].strip() payload = { "tag_name": "v" + ver, "target_commitish": "master", "name": "v" + ver, "body": desc, "draft": False, "prerelease": False } response = requests.post( "https://api.github.com/repos/materialsproject/pymatgen/releases", data=json.dumps(payload), headers={"Authorization": "token " + os.environ["GITHUB_RELEASES_TOKEN"]}) print response.text def update_changelog(): output = subprocess.check_output(["git", "log", "--pretty=format:%s", "v%s..HEAD" % ver]) lines = ["* " + l for l in output.strip().split("\n")] with open("CHANGES.rst") as f: contents = f.read() toks = contents.split("==========") toks.insert(-1, "\n\n" + "\n".join(lines)) with open("CHANGES.rst", "w") as f: f.write("==========".join(toks)) def log_ver(): filepath = os.path.join(os.environ["HOME"], "Dropbox", "Public", "pymatgen", ver) with open(filepath, "w") as f: f.write("Release") def release(skip_test=False): setver() if not skip_test: local("nosetests") publish() log_ver() update_doc() merge_stable() release_github() def open_doc(): pth = os.path.abspath("docs/_build/html/index.html") webbrowser.open("file://" + pth)

29.316129

92

0.541813

0

1,487

0.327245