ytzi/the-stack-dedup-python-filtered-docstrings

237cf432cb2a9b8f4203ddc700aff3c83f06d064

1,302

py

Python

deploy/run.py

dato-code/Strata-Now

995ed8851275996ea82131bf6e3d05131c620f60

[ "BSD-3-Clause" ]

4

2015-05-05T16:05:35.000Z

2016-05-19T21:02:25.000Z

deploy/run.py

turi-code/Strata-Now

995ed8851275996ea82131bf6e3d05131c620f60

[ "BSD-3-Clause" ]

1

2015-10-07T18:59:39.000Z

deploy/run.py

dato-code/Strata-Now

995ed8851275996ea82131bf6e3d05131c620f60

[ "BSD-3-Clause" ]

6

2015-05-05T02:11:07.000Z

2016-05-16T19:24:22.000Z

import graphlab as gl from models import * path = "s3://gl-demo-usw2/predictive_service/demolab/ps-1.6" ps = gl.deploy.predictive_service.load(path) # Define dependencies state = {'details_filename': '../data/talks.json', 'speakers_filename': '../data/speakers.json', 'details_sf': '../data/talks.gl', 'speakers_sf': '../data/speakers.gl'} # Data carpentry details = parse_details(state['details_sf']) speakers_dict, speakers = parse_speakers(state['speakers_sf']) details = clean_timing(details) details, talks_per_speaker = join_speaker_data_into_details(details, speakers) details_dict, trimmed = create_details_dict(details) # Create nearest neighbor model and get nearest items nn_model, nearest = build_nn_model(details) # Deploy models as a predictive service upload_list_page(ps, trimmed) upload_speaker(ps, talks_per_speaker) upload_item_sim(ps, details, nn_model, nearest) ######################################################### # Ad hoc testing # Via Python client print ps.query('stratanow_item_sim', input={'item_ids': ['43169'], 'how_many':5}) # Via Curl # !curl -X POST -d '{"api_key": "b9b8dd75-a6d3-4903-b6a7-2dc691d060d8", "data":{"input": {"item_ids":["43750"], "how_many": 5}}}' stratanow-175425062.us-west-2.elb.amazonaws.com/data/item_sim

33.384615

191

0.704301

import graphlab as gl from models import * path = "s3://gl-demo-usw2/predictive_service/demolab/ps-1.6" ps = gl.deploy.predictive_service.load(path) # Define dependencies state = {'details_filename': '../data/talks.json', 'speakers_filename': '../data/speakers.json', 'details_sf': '../data/talks.gl', 'speakers_sf': '../data/speakers.gl'} # Data carpentry details = parse_details(state['details_sf']) speakers_dict, speakers = parse_speakers(state['speakers_sf']) details = clean_timing(details) details, talks_per_speaker = join_speaker_data_into_details(details, speakers) details_dict, trimmed = create_details_dict(details) # Create nearest neighbor model and get nearest items nn_model, nearest = build_nn_model(details) # Deploy models as a predictive service upload_list_page(ps, trimmed) upload_speaker(ps, talks_per_speaker) upload_item_sim(ps, details, nn_model, nearest) ######################################################### # Ad hoc testing # Via Python client print ps.query('stratanow_item_sim', input={'item_ids': ['43169'], 'how_many':5}) # Via Curl # !curl -X POST -d '{"api_key": "b9b8dd75-a6d3-4903-b6a7-2dc691d060d8", "data":{"input": {"item_ids":["43750"], "how_many": 5}}}' stratanow-175425062.us-west-2.elb.amazonaws.com/data/item_sim

0

602796473f89d9acc225140c9078f5ec9c51d423

7,892

py

Python

fuzzylogic.py

bydzen/fuzzy-logic-sc

ccbff1a34bb19048f540244aafd38197cc12318d

[ "MIT" ]

1

2021-08-22T13:52:52.000Z

fuzzylogic.py

bydzen/fuzzy-logic-sc

ccbff1a34bb19048f540244aafd38197cc12318d

[ "MIT" ]

null

fuzzylogic.py

bydzen/fuzzy-logic-sc

ccbff1a34bb19048f540244aafd38197cc12318d

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pandas as pd import numpy as np import matplotlib.pyplot as plt import xlwt #!gdown --id 1bb2irg5nFZhoFkpjWPQHJPBBr8FiK8l7 dataRestoran = pd.read_excel('restoran.xlsx') print(dataRestoran) # akan menghasilkan nilai kelayakan yang lebih bervariasi titikPelayanan = [25, 37, 58, 65, 78, 89, 101] titikMakanan = [3, 5, 8, 11] grafikPelayanan() grafikMakanan() # print(fuzzificationPelayanan(dataRestoran)) # print(fuzzificationMakanan(dataRestoran)) dataFuzzyPelayanan = fuzzificationPelayanan(dataRestoran) dataFuzzyMakanan = fuzzificationMakanan(dataRestoran) # print(inference(dataFuzzyPelayanan, dataFuzzyMakanan)) arrx = [0, 30, 60, 99] arry = [0, 1, 1, 1] fig, ax = plt.subplots(nrows=1, figsize=(10, 4)) plt.xticks([30, 60, 99]) plt.yticks([0, 1]) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.xlabel("Nilai Kelayakan skala [0,100]") plt.ylabel("u") plt.margins(y=0.17) plt.title("FK singleton untuk Nilai Kelayakan") plt.bar(arrx, arry, color=['red', 'red', 'orange', 'green'], width=[ 0.4, 0.4, 0.4, 0.4], label="Runtime CycleSort") rects = ax.patches labels = ["", "rendah", "sedang", "tinggi"] for rect, label in zip(rects, labels): height = rect.get_height() ax.text(rect.get_x() + rect.get_width() / 2, height+0.0088, label, ha='center', va='bottom') plt.show() dataFuzzyRules = inference(dataFuzzyPelayanan, dataFuzzyMakanan) hasilDefuzz = defuzzyfication(dataFuzzyRules) dataRestoran["Result"] = hasilDefuzz hasilAkhir = dataRestoran.sort_values(by="Result", ascending=False)[:10] hasilAkhir print("\nHasil Akhir:\n", hasilAkhir) # Write Peringkat ke file xls. # peringkat = xlwt.Workbook() # ws = peringkat.add_sheet('Output') # ws.write(0, 0, 'Record id') # i = 1 # for j in hasilAkhir["id"]: # ws.write(i, 0, j) # i += 1 # peringkat.save('peringkat.xls')

30.94902

86

0.593512

# -*- coding: utf-8 -*- import pandas as pd import numpy as np import matplotlib.pyplot as plt import xlwt #!gdown --id 1bb2irg5nFZhoFkpjWPQHJPBBr8FiK8l7 dataRestoran = pd.read_excel('restoran.xlsx') print(dataRestoran) # akan menghasilkan nilai kelayakan yang lebih bervariasi titikPelayanan = [25, 37, 58, 65, 78, 89, 101] titikMakanan = [3, 5, 8, 11] def trapesium(x, a, b, c, d): nilaiKeanggotaan = 0 if x <= a or x >= d: nilaiKeanggotaan = 0 elif a < x < b: nilaiKeanggotaan = (x-a)/(b-a) elif b <= x < c: nilaiKeanggotaan = 1 elif c <= x < d: nilaiKeanggotaan = -(x-d)/(d-c) return nilaiKeanggotaan def segitiga(x, a, b, c): nilaiKeanggotaan = 0 if x <= a or x >= c: nilaiKeanggotaan = 0 elif a < x <= b: nilaiKeanggotaan = (x-a)/(b-a) elif b < x <= c: nilaiKeanggotaan = -(x-c)/(c-b) return nilaiKeanggotaan def keanggotaanPelayanan(x): kurang = trapesium(x, -1, 0, titikPelayanan[0], titikPelayanan[1]) cukup = trapesium( x, titikPelayanan[0], titikPelayanan[1], titikPelayanan[2], titikPelayanan[3]) bagus = trapesium( x, titikPelayanan[2], titikPelayanan[3], titikPelayanan[4], titikPelayanan[5]) sangatBagus = trapesium( x, titikPelayanan[4], titikPelayanan[5], titikPelayanan[6], titikPelayanan[6]) return kurang, cukup, bagus, sangatBagus def keanggotaanMakanan(x): kurangEnak = trapesium(x, -1, 0, titikMakanan[0], titikMakanan[1]) enak = segitiga(x, titikMakanan[0], titikMakanan[1], titikMakanan[2]) sangatEnak = trapesium( x, titikMakanan[1], titikMakanan[2], titikMakanan[3], titikMakanan[3]) return kurangEnak, enak, sangatEnak def grafikPelayanan(): x_axis = np.arange(0, 101, 1) len_x = titikPelayanan[len(titikPelayanan)-1] fig, ax = plt.subplots(nrows=1, figsize=(15, 5)) kurang, cukup, bagus, sangatBagus = [], [], [], [] for x in range(len_x): kurang.append(keanggotaanPelayanan(x)[0]) cukup.append(keanggotaanPelayanan(x)[1]) bagus.append(keanggotaanPelayanan(x)[2]) sangatBagus.append(keanggotaanPelayanan(x)[3]) ax.plot(x_axis, kurang, 'g', linewidth=1.5, label='kurang') ax.plot(x_axis, cukup, 'r', linewidth=1.5, label='cukup') ax.plot(x_axis, bagus, 'b', linewidth=1.5, label='bagus') ax.plot(x_axis, sangatBagus, 'c', linewidth=1.5, label='sangat bagus') ax.set_title("Pelayanan") ax.legend() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.get_xaxis().tick_bottom() ax.get_yaxis().tick_left() plt.tight_layout() plt.show() def grafikMakanan(): x_axis = np.arange(0, 11, 1) len_x = titikMakanan[len(titikMakanan)-1] fig, ax = plt.subplots(nrows=1, figsize=(15, 5)) kurangEnak, enak, sangatEnak = [], [], [] for x in range(len_x): kurangEnak.append(keanggotaanMakanan(x)[0]) enak.append(keanggotaanMakanan(x)[1]) sangatEnak.append(keanggotaanMakanan(x)[2]) ax.plot(x_axis, kurangEnak, 'g', linewidth=1.5, label='kurang enak') ax.plot(x_axis, enak, 'r', linewidth=1.5, label='enak') ax.plot(x_axis, sangatEnak, 'b', linewidth=1.5, label='sangat enak') ax.set_title("Makanan") ax.legend() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.get_xaxis().tick_bottom() ax.get_yaxis().tick_left() plt.tight_layout() plt.show() grafikPelayanan() grafikMakanan() def fuzzificationPelayanan(dataRestoran): hasilFuzzy = [] for i in range(len(dataRestoran)): temp = keanggotaanPelayanan(dataRestoran["pelayanan"][i]) hasilFuzzy.append(temp) return hasilFuzzy def fuzzificationMakanan(dataRestoran): hasilFuzzy = [] for i in range(len(dataRestoran)): temp = keanggotaanMakanan(dataRestoran["makanan"][i]) hasilFuzzy.append(temp) return hasilFuzzy # print(fuzzificationPelayanan(dataRestoran)) # print(fuzzificationMakanan(dataRestoran)) def inference(dataFuzzyPelayanan, dataFuzzyMakanan): nilaiKelayakan = [] for i in range(len(dataFuzzyMakanan)): pelayanan = dataFuzzyPelayanan[i] # [0 1.0 0 0] makanan = dataFuzzyMakanan[i] rendah = [] sedang = [] tinggi = [] for j in range(len(pelayanan)): temp = 0 for k in range(len(makanan)): if j == 0 and k == 0: temp = min(pelayanan[j], makanan[k]) rendah.append(temp) if j == 0 and k == 1: temp = min(pelayanan[j], makanan[k]) rendah.append(temp) if j == 0 and k == 2: temp = min(pelayanan[j], makanan[k]) rendah.append(temp) if j == 1 and k == 0: temp = min(pelayanan[j], makanan[k]) rendah.append(temp) if j == 1 and k == 1: temp = min(pelayanan[j], makanan[k]) sedang.append(temp) if j == 1 and k == 2: temp = min(pelayanan[j], makanan[k]) sedang.append(temp) if j == 2 and k == 0: temp = min(pelayanan[j], makanan[k]) rendah.append(temp) if j == 2 and k == 1: temp = min(pelayanan[j], makanan[k]) sedang.append(temp) if j == 2 and k == 2: temp = min(pelayanan[j], makanan[k]) tinggi.append(temp) if j == 3 and k == 0: temp = min(pelayanan[j], makanan[k]) sedang.append(temp) if j == 3 and k == 1: temp = min(pelayanan[j], makanan[k]) tinggi.append(temp) if j == 3 and k == 2: temp = min(pelayanan[j], makanan[k]) tinggi.append(temp) nkRendah = max(rendah) nkSedang = max(sedang) nkTinggi = max(tinggi) nilaiKelayakan.append([nkRendah, nkSedang, nkTinggi]) return nilaiKelayakan dataFuzzyPelayanan = fuzzificationPelayanan(dataRestoran) dataFuzzyMakanan = fuzzificationMakanan(dataRestoran) # print(inference(dataFuzzyPelayanan, dataFuzzyMakanan)) def defuzzyfication(dataFuzzyRules): hasilDefuzz = [] for data in dataFuzzyRules: y = ((data[0]*30) + (data[1]*60) + (data[2]*99)) / \ (data[0] + data[1] + data[2]) hasilDefuzz.append(y) return hasilDefuzz arrx = [0, 30, 60, 99] arry = [0, 1, 1, 1] fig, ax = plt.subplots(nrows=1, figsize=(10, 4)) plt.xticks([30, 60, 99]) plt.yticks([0, 1]) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.xlabel("Nilai Kelayakan skala [0,100]") plt.ylabel("u") plt.margins(y=0.17) plt.title("FK singleton untuk Nilai Kelayakan") plt.bar(arrx, arry, color=['red', 'red', 'orange', 'green'], width=[ 0.4, 0.4, 0.4, 0.4], label="Runtime CycleSort") rects = ax.patches labels = ["", "rendah", "sedang", "tinggi"] for rect, label in zip(rects, labels): height = rect.get_height() ax.text(rect.get_x() + rect.get_width() / 2, height+0.0088, label, ha='center', va='bottom') plt.show() dataFuzzyRules = inference(dataFuzzyPelayanan, dataFuzzyMakanan) hasilDefuzz = defuzzyfication(dataFuzzyRules) dataRestoran["Result"] = hasilDefuzz hasilAkhir = dataRestoran.sort_values(by="Result", ascending=False)[:10] hasilAkhir print("\nHasil Akhir:\n", hasilAkhir) # Write Peringkat ke file xls. # peringkat = xlwt.Workbook() # ws = peringkat.add_sheet('Output') # ws.write(0, 0, 'Record id') # i = 1 # for j in hasilAkhir["id"]: # ws.write(i, 0, j) # i += 1 # peringkat.save('peringkat.xls')

5,765

0

230

deb1cc2b3769ab3932370a34f4aedc4d58995c77

866

py

Python

src/circuitsascode/__init__.py

devbisme/circuitsascode

497ac4650b0da8d65f4b22eceefe918b66f915b4

[ "MIT" ]

3

2021-08-28T15:02:53.000Z

2022-01-17T21:43:31.000Z

src/circuitsascode/__init__.py

devbisme/circuitsascode

497ac4650b0da8d65f4b22eceefe918b66f915b4

[ "MIT" ]

null

src/circuitsascode/__init__.py

devbisme/circuitsascode

497ac4650b0da8d65f4b22eceefe918b66f915b4

[ "MIT" ]

null

# The MIT License (MIT) - Copyright (c) 2021 xesscorp """ Categorized collections of circuits. """ import sys import pint # Create a shortcut name for "circuitsascode". sys.modules["casc"] = sys.modules["circuitsascode"] # For electrical units like ohms, volts, etc. units = pint.UnitRegistry() if sys.version_info[:2] >= (3, 8): # TODO: Import directly (no need for conditional) when `python_requires = >= 3.8` from importlib.metadata import PackageNotFoundError, version # pragma: no cover else: from importlib_metadata import PackageNotFoundError, version # pragma: no cover try: # Change here if project is renamed and does not equal the package name dist_name = __name__ __version__ = version(dist_name) except PackageNotFoundError: # pragma: no cover __version__ = "unknown" finally: del version, PackageNotFoundError

27.935484

85

0.732102

# The MIT License (MIT) - Copyright (c) 2021 xesscorp """ Categorized collections of circuits. """ import sys import pint # Create a shortcut name for "circuitsascode". sys.modules["casc"] = sys.modules["circuitsascode"] # For electrical units like ohms, volts, etc. units = pint.UnitRegistry() if sys.version_info[:2] >= (3, 8): # TODO: Import directly (no need for conditional) when `python_requires = >= 3.8` from importlib.metadata import PackageNotFoundError, version # pragma: no cover else: from importlib_metadata import PackageNotFoundError, version # pragma: no cover try: # Change here if project is renamed and does not equal the package name dist_name = __name__ __version__ = version(dist_name) except PackageNotFoundError: # pragma: no cover __version__ = "unknown" finally: del version, PackageNotFoundError

0

59e413e90db4fea7b7d53b3bfbe74dbdf3fef42a

8,095

py

Python

electrical_simulation/versuch_versuch.py

architecture-building-systems/bipv-tool

3ae73a541754e5215dfd39ef837d72f4b80ef967

[ "MIT" ]

4

2019-02-18T14:10:49.000Z

2021-04-23T09:03:13.000Z

electrical_simulation/versuch_versuch.py

architecture-building-systems/bipv-tool

3ae73a541754e5215dfd39ef837d72f4b80ef967

[ "MIT" ]

1

2018-08-16T13:27:15.000Z

2018-08-16T13:27:16.000Z

electrical_simulation/versuch_versuch.py

architecture-building-systems/bipv-tool

3ae73a541754e5215dfd39ef837d72f4b80ef967

[ "MIT" ]

null

import pandas as pd import numpy as np import json import datetime import miasole_module_two as ps import pvlib.pvsystem as pvsyst #import shaded_miasole as ps import interconnection as connect import matplotlib.pyplot as plt def align_yaxis(ax1, v1, ax2, v2): """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1""" _, y1 = ax1.transData.transform((0, v1)) _, y2 = ax2.transData.transform((0, v2)) inv = ax2.transData.inverted() _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2)) miny, maxy = ax2.get_ylim() ax2.set_ylim(miny+dy, maxy+dy) #This function finds the MPP for the measured data using the lists of I and V of the object if __name__ == "__main__": module_lookup_table_path = 'C:\Users\walkerl\Documents\MA_Local\Electrical_simulation\lookup\MIA_lookup.pkl' lookup_table = pd.read_pickle(module_lookup_table_path) lookup_table = lookup_table.astype('object') number_of_subcells = 5 shading_string = 'completely shaded' #This variable does not change calculations but will app irradiation_path = 'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\meas_irrad.xlsx' time = datetime.datetime(2017,8,15,11,40,16) temp_sensor_name = 'RTD3' ambient_temperature = get_temperature(irradiation_path, time, temp_sensor_name) if time.minute < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour)+ '_0' + str(time.minute) + '_' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" elif time.second < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour) + '_' + str(time.minute) + '_0' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" elif time.second < 10 and time.minute < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour) + '_0' + str(time.minute) + '_0' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" else: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour)+ '_' + str(time.minute) + '_' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" shading_pattern1 = get_shading_pattern(shading_pattern_path) sensor_name1 = "Pyranometer 2 (W/m2)" # sensor_name1 = "DNI (W/m2)" database_path = r'C:\Users\walkerl\Documents\BIPV-planning-tool\BIPV-planning-tool\electrical_simulation\data\sam-library-cec-modules-2015-6-30.csv' module_df = pvsyst.retrieve_sam(path=database_path) irrad_value1 = get_irradiation_value(irradiation_path, time, sensor_name1) irrad1 = create_irradiation_list(irrad_value1, shading_pattern1, partially_shaded_irrad=None) irrad_on_sub_cells_ordered1 = rearrange_shading_pattern(irrad1,number_of_subcells) i_module_sim1, v_module_sim1, lookup_table = ps.partial_shading(irrad_on_sub_cells_ordered1, temperature=ambient_temperature, irrad_temp_lookup_df=lookup_table, module_df=module_df) i_module_meas, v_module_meas = get_measured_iv_curves_from_excel(measurement_data_path) mpp_measured = max(i_module_meas * v_module_meas) mpp_simulated = max(i_module_sim1 * v_module_sim1) print mpp_measured print mpp_simulated plt.plot(v_module_sim1, i_module_sim1, color='blue', linestyle='--') plt.plot(v_module_meas, i_module_meas, color='blue' ) ax = plt.gca() handles, labels = ax.get_legend_handles_labels() ax.legend(handles, labels=['simulated IV ' , 'measured IV'],loc='upper left') ax.set_title('Irradiation: ' + str(int(irrad_value1)) + ", T = " + str(ambient_temperature)+ u"\u00b0" + "C" + '\n Shaded cells: ' + shading_string) ax.set_ylabel('Current I [A]') ax.set_xlabel('Voltage V [V]') ax.set_ylim(0,4) ax.set_xlim(0,105) ax2 = ax.twinx() ax2.set_ylim(0, 50) ax2.set_xlim(0,40) ax2.set_ylabel("Power P [W]") ax2.plot(v_module_sim1, v_module_sim1 * i_module_sim1, color='green', label='PV simulated', linestyle='--') ax2.plot(v_module_meas, i_module_meas*v_module_meas, color='green', label='PV measured' ) handles, labels = ax2.get_legend_handles_labels() ax2.legend(handles, labels=['simulated PV ', 'measured PV']) align_yaxis(ax, 0, ax2, 0) # plt.savefig("F:\Validation_final\Plots_MIA\single_module/" + shading_string + str(int(irrad_value1)) + '.png') plt.show()

43.994565

152

0.664484

import pandas as pd import numpy as np import json import datetime import miasole_module_two as ps import pvlib.pvsystem as pvsyst #import shaded_miasole as ps import interconnection as connect import matplotlib.pyplot as plt def get_irradiation_value(path, time, sensor_name): time = time.replace(second=0) irrad_data_df = pd.read_excel(path) irrad_data_df['Time'] = pd.to_datetime(irrad_data_df['Time']) irrad_data_df.set_index('Time', inplace=True) irrad = irrad_data_df.iloc[irrad_data_df.index.get_loc(time, method='nearest')][sensor_name] return int(round(irrad,0)) def get_temperature(path, time, sensor_name): time = time.replace(second=0) irrad_data_df = pd.read_excel(path) irrad_data_df['Time'] = pd.to_datetime(irrad_data_df['Time']) irrad_data_df.set_index('Time', inplace=True) temp = irrad_data_df.iloc[irrad_data_df.index.get_loc(time, method='nearest')][sensor_name] return round(temp,0) def align_yaxis(ax1, v1, ax2, v2): """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1""" _, y1 = ax1.transData.transform((0, v1)) _, y2 = ax2.transData.transform((0, v2)) inv = ax2.transData.inverted() _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2)) miny, maxy = ax2.get_ylim() ax2.set_ylim(miny+dy, maxy+dy) def create_irradiation_list(irradiation, shade_pattern, partially_shaded_irrad): irradiation_on_cells = [] for i in range(len(shade_pattern)): if shade_pattern[i] ==1: irradiation_on_cells.append(irradiation) elif shade_pattern[i] == 0: irradiation_on_cells.append(1) #not zero because the IV-curve simulator cannot handle 0 elif shade_pattern[i] == 2: irradiation_on_cells.append(partially_shaded_irrad) else: print "The shading pattern hasn't been defined correctly" return irradiation_on_cells def get_measured_iv_curves_from_excel(filepath): iv_df = pd.read_excel(filepath, header=17) v_data = np.array(iv_df['U in V'].tolist()) i_data = np.array(iv_df['I in A'].tolist()) return i_data, v_data def get_shading_pattern(filepath): with open(filepath, 'r')as jfile: return json.load(jfile) def rearrange_shading_pattern(irradiation_pattern, number_of_subcells): # The irradiation pattern is given in a list from module left to right, up to down. # The Miasole module has subcells that are connected in parallel first which requires to rearrange the pattern # e.g: [1,2,3,4, [1,5,9, # 5,6,7,8, as the irradiation on subcells needs to be transformed to the following list: 2,6,10, # 9,10,11,12] 3,7,11, # 4,8,12] new_pattern = [] for i in range(len(irradiation_pattern) / number_of_subcells): for y in range(number_of_subcells): new_pattern.append(irradiation_pattern[y * len(irradiation_pattern) / number_of_subcells + i]) return new_pattern #This function finds the MPP for the measured data using the lists of I and V of the object def get_mpp(self): mpp =0 for counter in range(len(self.v_data)): if self.v_data[counter] * self.i_data[counter] > mpp: mpp = self.v_data[counter] * self.i_data[counter] self.mpp_voltage = self.v_data[counter] self.mpp_current = self.i_data[counter] else: pass self.mpp = mpp if __name__ == "__main__": module_lookup_table_path = 'C:\Users\walkerl\Documents\MA_Local\Electrical_simulation\lookup\MIA_lookup.pkl' lookup_table = pd.read_pickle(module_lookup_table_path) lookup_table = lookup_table.astype('object') number_of_subcells = 5 shading_string = 'completely shaded' #This variable does not change calculations but will app irradiation_path = 'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\meas_irrad.xlsx' time = datetime.datetime(2017,8,15,11,40,16) temp_sensor_name = 'RTD3' ambient_temperature = get_temperature(irradiation_path, time, temp_sensor_name) if time.minute < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour)+ '_0' + str(time.minute) + '_' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" elif time.second < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour) + '_' + str(time.minute) + '_0' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" elif time.second < 10 and time.minute < 10: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour) + '_0' + str(time.minute) + '_0' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" else: measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \ str(time.hour)+ '_' + str(time.minute) + '_' + str(time.second) measurement_data_path = measurement_path + '.XLS' shading_pattern_path = measurement_path + "_shading.json" shading_pattern1 = get_shading_pattern(shading_pattern_path) sensor_name1 = "Pyranometer 2 (W/m2)" # sensor_name1 = "DNI (W/m2)" database_path = r'C:\Users\walkerl\Documents\BIPV-planning-tool\BIPV-planning-tool\electrical_simulation\data\sam-library-cec-modules-2015-6-30.csv' module_df = pvsyst.retrieve_sam(path=database_path) irrad_value1 = get_irradiation_value(irradiation_path, time, sensor_name1) irrad1 = create_irradiation_list(irrad_value1, shading_pattern1, partially_shaded_irrad=None) irrad_on_sub_cells_ordered1 = rearrange_shading_pattern(irrad1,number_of_subcells) i_module_sim1, v_module_sim1, lookup_table = ps.partial_shading(irrad_on_sub_cells_ordered1, temperature=ambient_temperature, irrad_temp_lookup_df=lookup_table, module_df=module_df) i_module_meas, v_module_meas = get_measured_iv_curves_from_excel(measurement_data_path) mpp_measured = max(i_module_meas * v_module_meas) mpp_simulated = max(i_module_sim1 * v_module_sim1) print mpp_measured print mpp_simulated plt.plot(v_module_sim1, i_module_sim1, color='blue', linestyle='--') plt.plot(v_module_meas, i_module_meas, color='blue' ) ax = plt.gca() handles, labels = ax.get_legend_handles_labels() ax.legend(handles, labels=['simulated IV ' , 'measured IV'],loc='upper left') ax.set_title('Irradiation: ' + str(int(irrad_value1)) + ", T = " + str(ambient_temperature)+ u"\u00b0" + "C" + '\n Shaded cells: ' + shading_string) ax.set_ylabel('Current I [A]') ax.set_xlabel('Voltage V [V]') ax.set_ylim(0,4) ax.set_xlim(0,105) ax2 = ax.twinx() ax2.set_ylim(0, 50) ax2.set_xlim(0,40) ax2.set_ylabel("Power P [W]") ax2.plot(v_module_sim1, v_module_sim1 * i_module_sim1, color='green', label='PV simulated', linestyle='--') ax2.plot(v_module_meas, i_module_meas*v_module_meas, color='green', label='PV measured' ) handles, labels = ax2.get_legend_handles_labels() ax2.legend(handles, labels=['simulated PV ', 'measured PV']) align_yaxis(ax, 0, ax2, 0) # plt.savefig("F:\Validation_final\Plots_MIA\single_module/" + shading_string + str(int(irrad_value1)) + '.png') plt.show()

2,901

0

160

d49cc9d9d750d1b39deda00590557e916ce04222

1,231

py

Python

venv/lib/python3.6/site-packages/zmq/sugar/version.py

paxthree/life-dashboard

8c65857d1102331b1e913d8800c61af1f61d9532

[ "CC0-1.0" ]

null

venv/lib/python3.6/site-packages/zmq/sugar/version.py

paxthree/life-dashboard

8c65857d1102331b1e913d8800c61af1f61d9532

[ "CC0-1.0" ]

19

2020-01-28T21:41:50.000Z

2022-03-11T23:17:39.000Z

thunau/lib/python3.6/site-packages/zmq/sugar/version.py

karlburkhart/thunau

900dca0259b30a229cd84f6315a7da9be94cb355

[ "MIT" ]

1

2018-09-19T05:55:27.000Z

"""PyZMQ and 0MQ version functions.""" # Copyright (C) PyZMQ Developers # Distributed under the terms of the Modified BSD License. from zmq.backend import zmq_version_info VERSION_MAJOR = 16 VERSION_MINOR = 0 VERSION_PATCH = 4 VERSION_EXTRA = "" __version__ = '%i.%i.%i' % (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH) if VERSION_EXTRA: __version__ = "%s.%s" % (__version__, VERSION_EXTRA) version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH, float('inf')) else: version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH) __revision__ = '' def pyzmq_version(): """return the version of pyzmq as a string""" if __revision__: return '@'.join([__version__,__revision__[:6]]) else: return __version__ def pyzmq_version_info(): """return the pyzmq version as a tuple of at least three numbers If pyzmq is a development version, `inf` will be appended after the third integer. """ return version_info def zmq_version(): """return the version of libzmq as a string""" return "%i.%i.%i" % zmq_version_info() __all__ = ['zmq_version', 'zmq_version_info', 'pyzmq_version','pyzmq_version_info', '__version__', '__revision__' ]

25.122449

86

0.692933

"""PyZMQ and 0MQ version functions.""" # Copyright (C) PyZMQ Developers # Distributed under the terms of the Modified BSD License. from zmq.backend import zmq_version_info VERSION_MAJOR = 16 VERSION_MINOR = 0 VERSION_PATCH = 4 VERSION_EXTRA = "" __version__ = '%i.%i.%i' % (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH) if VERSION_EXTRA: __version__ = "%s.%s" % (__version__, VERSION_EXTRA) version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH, float('inf')) else: version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH) __revision__ = '' def pyzmq_version(): """return the version of pyzmq as a string""" if __revision__: return '@'.join([__version__,__revision__[:6]]) else: return __version__ def pyzmq_version_info(): """return the pyzmq version as a tuple of at least three numbers If pyzmq is a development version, `inf` will be appended after the third integer. """ return version_info def zmq_version(): """return the version of libzmq as a string""" return "%i.%i.%i" % zmq_version_info() __all__ = ['zmq_version', 'zmq_version_info', 'pyzmq_version','pyzmq_version_info', '__version__', '__revision__' ]

0

4300d28866dc3941f3120411372fade12a422e39

3,846

py

Python

penguin_random_house/parse_scripts/books.py

EvgenyTsydenov/book_recommender

bfbbed52907f979a6ba404f9d1f2ff8cfe142aff

[ "MIT" ]

null

penguin_random_house/parse_scripts/books.py

EvgenyTsydenov/book_recommender

bfbbed52907f979a6ba404f9d1f2ff8cfe142aff

[ "MIT" ]

null

penguin_random_house/parse_scripts/books.py

EvgenyTsydenov/book_recommender

bfbbed52907f979a6ba404f9d1f2ff8cfe142aff

[ "MIT" ]

null

import json import os from tqdm import tqdm def parse_book(book_data: dict) -> dict: """Parse book core data.""" info = {} for param in ['isbn', 'title', 'onsale', 'price', 'language', 'pages', 'publisher']: info[param] = book_data.get(param) info['cover'] = f'https://images.randomhouse.com/cover/{info["isbn"]}' info['format_family'] = book_data.get('formatFamily') info['projected_minutes'] = book_data.get('projectedMinutes') info['series_number'] = book_data.get('seriesNumber') return info def parse_authors(authors_data: list) -> list: """Extract information about contributors.""" authors = [] for author in authors_data: authors.append({ 'author_id': author.get('authorId'), 'first_name': author.get('first'), 'last_name': author.get('last'), 'company': author.get('company'), 'client_source_id': author.get('clientSourceId'), 'role': author.get('contribRoleCode') }) return authors def parse_categories(category_data: list) -> list: """Extract information about categories. Since we downloaded data about categories separately, keep here only category_id and the sequence. """ categories = [] for cat in category_data: # Read PRH docs about sequencing if cat.get('seq', 0) > 0: categories.append({ 'category_id': cat.get('catId'), 'seq': cat.get('seq') }) return categories def parse_series(series_data: list) -> list: """Extract information about series.""" series = [] for item in series_data: series.append({ 'series_id': item.get('seriesCode'), 'name': item.get('seriesName'), 'description': item.get('description'), 'series_count': item.get('seriesCount'), 'is_numbered': item.get('isNumbered'), 'is_kids': item.get('isKids') }) return series def parse_works(works_data: list) -> list: """Extract information about works.""" works = [] for work in works_data: works.append({ 'work_id': work.get('workId'), 'title': work.get('title'), 'author': work.get('author'), 'onsale': work.get('onsale'), 'language': work.get('language'), 'series_number': work.get('seriesNumber') }) return works def parse_content(content_data: dict) -> dict: """Extract long text data.""" content = {} for param in ['flapcopy', 'excerpt']: content[param] = content_data.get(param) return content if __name__ == '__main__': # Paths path_raw_books = os.path.join('..', 'data_raw', 'books.txt') path_parsed_books = os.path.join('..', 'data_interm', 'books.txt') # Parse the file line by line with open(path_raw_books, 'r') as books_raw: with open(path_parsed_books, 'w') as books_parsed: for book in tqdm(books_raw): book_data = json.loads(book) # Get core book data info = parse_book(book_data['titles'][0]) # Parse relative info embeds = {} for embed in book_data['_embeds']: embeds.update(embed) info['authors'] = parse_authors(embeds['authors']) info['categories'] = parse_categories(embeds['categories']) info['series'] = parse_series(embeds['series']) info['works'] = parse_works(embeds['works']) info.update(parse_content(embeds['content'])) # Save data_string = json.dumps(info) books_parsed.write(data_string) books_parsed.write('\n')

32.05

75

0.568903

import json import os from tqdm import tqdm def parse_book(book_data: dict) -> dict: """Parse book core data.""" info = {} for param in ['isbn', 'title', 'onsale', 'price', 'language', 'pages', 'publisher']: info[param] = book_data.get(param) info['cover'] = f'https://images.randomhouse.com/cover/{info["isbn"]}' info['format_family'] = book_data.get('formatFamily') info['projected_minutes'] = book_data.get('projectedMinutes') info['series_number'] = book_data.get('seriesNumber') return info def parse_authors(authors_data: list) -> list: """Extract information about contributors.""" authors = [] for author in authors_data: authors.append({ 'author_id': author.get('authorId'), 'first_name': author.get('first'), 'last_name': author.get('last'), 'company': author.get('company'), 'client_source_id': author.get('clientSourceId'), 'role': author.get('contribRoleCode') }) return authors def parse_categories(category_data: list) -> list: """Extract information about categories. Since we downloaded data about categories separately, keep here only category_id and the sequence. """ categories = [] for cat in category_data: # Read PRH docs about sequencing if cat.get('seq', 0) > 0: categories.append({ 'category_id': cat.get('catId'), 'seq': cat.get('seq') }) return categories def parse_series(series_data: list) -> list: """Extract information about series.""" series = [] for item in series_data: series.append({ 'series_id': item.get('seriesCode'), 'name': item.get('seriesName'), 'description': item.get('description'), 'series_count': item.get('seriesCount'), 'is_numbered': item.get('isNumbered'), 'is_kids': item.get('isKids') }) return series def parse_works(works_data: list) -> list: """Extract information about works.""" works = [] for work in works_data: works.append({ 'work_id': work.get('workId'), 'title': work.get('title'), 'author': work.get('author'), 'onsale': work.get('onsale'), 'language': work.get('language'), 'series_number': work.get('seriesNumber') }) return works def parse_content(content_data: dict) -> dict: """Extract long text data.""" content = {} for param in ['flapcopy', 'excerpt']: content[param] = content_data.get(param) return content if __name__ == '__main__': # Paths path_raw_books = os.path.join('..', 'data_raw', 'books.txt') path_parsed_books = os.path.join('..', 'data_interm', 'books.txt') # Parse the file line by line with open(path_raw_books, 'r') as books_raw: with open(path_parsed_books, 'w') as books_parsed: for book in tqdm(books_raw): book_data = json.loads(book) # Get core book data info = parse_book(book_data['titles'][0]) # Parse relative info embeds = {} for embed in book_data['_embeds']: embeds.update(embed) info['authors'] = parse_authors(embeds['authors']) info['categories'] = parse_categories(embeds['categories']) info['series'] = parse_series(embeds['series']) info['works'] = parse_works(embeds['works']) info.update(parse_content(embeds['content'])) # Save data_string = json.dumps(info) books_parsed.write(data_string) books_parsed.write('\n')

0

b7d7274c982532facf17dec2970f658f11cf429a

3,502

py

Python

scripts/parse_proto.py

JustinGanzer/j2objc

feb43475d70a027c96d5e94874003ef65e6ae34b

[ "Apache-2.0" ]

null

scripts/parse_proto.py

JustinGanzer/j2objc

feb43475d70a027c96d5e94874003ef65e6ae34b

[ "Apache-2.0" ]

null

scripts/parse_proto.py

JustinGanzer/j2objc

feb43475d70a027c96d5e94874003ef65e6ae34b

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # # 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. """Parses metadata from a .proto file Parses metadata from a .proto file including various options and a list of top level messages and enums declared within the proto file. """ import itertools import re import string class ProtoMetadata: """Parses a proto file to extract options and other metadata.""" multiple_files = False package = '' java_package = '' java_api_version = 2 java_alt_api_package = '' outer_class = '' optimize_for = 'SPEED'

25.75

78

0.658195

#!/usr/bin/python3 # # 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. """Parses metadata from a .proto file Parses metadata from a .proto file including various options and a list of top level messages and enums declared within the proto file. """ import itertools import re import string def CamelCase(name): result = [] cap_next = True for ch in name: if cap_next and ch.islower(): result.append(ch.upper()) elif ch.isalnum(): result.append(ch) cap_next = not ch.isalpha() return ''.join(result) class ProtoMetadata: """Parses a proto file to extract options and other metadata.""" multiple_files = False package = '' java_package = '' java_api_version = 2 java_alt_api_package = '' outer_class = '' optimize_for = 'SPEED' def __init__(self): self.messages = [] self.enums = [] def MatchOptions(line, data): # package match = re.match(r'package\s*([\w\.]+)\s*;', line) if match: data.package = match.group(1) # java package match = re.match(r'option\s+java_package\s*=\s*"([^"]+)', line) if match: data.java_package = match.group(1) # outer classname match = re.match(r'option\s+java_outer_classname\s*=\s*"([^"]+)"', line) if match: data.outer_class = match.group(1) # multiple files? match = re.match(r'option\s+java_multiple_files\s*=\s*(\S+)\s*;', line) if match: data.multiple_files = True if match.group(1).lower() == 'true' else False match = re.match(r'option\s+optimize_for\s*=\s*(\S+)\s*;', line) if match: data.optimize_for = match.group(1) def MatchTypes(line, data): # messages and enums match = re.match(r'\s*(message|enum)\s+(\S+)\s+{$', line) if match: if match.group(1) == 'message': data.messages.append(match.group(2)) else: data.enums.append(match.group(2)) def MatchGroups(line, data): match = re.match( r'\s*(required|optional|repeated)\s+group\s+(\S+)\s+=\s+\d+\s+{$', line) if match: data.messages.append(match.group(2)) def SetOuterClass(filename, data): if not data.outer_class: outer_class = CamelCase(filename.rsplit('/', 1)[-1].split('.', 1)[0]) if outer_class in itertools.chain(data.messages, data.enums): outer_class += 'OuterClass' data.outer_class = outer_class def ParseProto(filename): data = ProtoMetadata() with open(filename, 'r') as fh: brace_depth = 0 inside_extend = False for line in fh: line = line.rstrip() MatchOptions(line, data) if brace_depth == 0 and re.match(r'\s*extend\s+\S+\s+{$', line): inside_extend = True brace_depth += 1 continue # never emit anything for nested definitions if brace_depth == 0: MatchTypes(line, data) elif brace_depth == 1 and inside_extend: MatchGroups(line, data) brace_depth += line.count('{') brace_depth -= line.count('}') if brace_depth == 0: inside_extend = False SetOuterClass(filename, data) return data

2,303

0

163

859f22430c4589c36e57592748a354e17b82f62b

33,061

py

Python

tests/test_factors.py

mscarey/AuthoritySpoke

047c30a5f8af8dcfae6d6cd4ec6d80bf646ff33b

[ "FTL", "CNRI-Python", "Apache-1.1" ]

18

2019-05-13T23:04:48.000Z

2022-03-23T00:48:30.000Z

tests/test_factors.py

mscarey/AuthoritySpoke

047c30a5f8af8dcfae6d6cd4ec6d80bf646ff33b

[ "FTL", "CNRI-Python", "Apache-1.1" ]

98

2019-05-07T10:38:03.000Z

2021-10-17T01:54:27.000Z

tests/test_factors.py

mscarey/AuthoritySpoke

047c30a5f8af8dcfae6d6cd4ec6d80bf646ff33b

[ "FTL", "CNRI-Python", "Apache-1.1" ]

1

2020-06-15T17:31:57.000Z

import operator import pytest from nettlesome.terms import ContextRegister, DuplicateTermError from nettlesome.terms import Explanation, TermSequence, means from nettlesome.entities import Entity from nettlesome.groups import FactorGroup from nettlesome.predicates import Predicate from nettlesome.quantities import Comparison, Q_ from authorityspoke.facts import Fact, build_fact

42.169643

91

0.669641

import operator import pytest from nettlesome.terms import ContextRegister, DuplicateTermError from nettlesome.terms import Explanation, TermSequence, means from nettlesome.entities import Entity from nettlesome.groups import FactorGroup from nettlesome.predicates import Predicate from nettlesome.quantities import Comparison, Q_ from authorityspoke.facts import Fact, build_fact class TestFacts: def test_default_terms_for_fact(self, make_entity, make_predicate, watt_mentioned): e = make_entity f1 = build_fact(make_predicate["p1"], case_factors=watt_mentioned) assert f1.terms == [e["motel"]] def test_no_terms_for_fact(self): predicate = Predicate(content="context was included", truth=False) fact = Fact(predicate=predicate, terms=[]) assert str(fact) == "the fact it was false that context was included" def test_build_fact(self, make_predicate, watt_mentioned): """ Check that terms is created as a (hashable) tuple, not list """ shooting = build_fact( make_predicate["p_shooting"], (2, 3), case_factors=watt_mentioned, standard_of_proof="preponderance of evidence", ) assert isinstance(shooting.terms, list) def test_terms_from_case_factor_indices( self, make_entity, make_predicate, watt_mentioned ): """ If you pass in integers instead of Factor objects to fill the blanks in the Predicate (which was the only way to do things in the first version of the Fact class's __init__ method), then the integers you pass in should be used as indices to select Factor objects from case_factors. """ e = make_entity f2 = build_fact( make_predicate["p2"], indices=(1, 0), case_factors=watt_mentioned ) assert f2.terms == [e["watt"], e["motel"]] def test_correct_factors_from_indices_in_build_fact( self, make_entity, make_predicate, watt_mentioned ): e = make_entity f2 = build_fact( make_predicate["p2"], indices=(1, 2), case_factors=watt_mentioned, ) assert f2.terms == [e["watt"], e["trees"]] def test_wrong_type_in_terms_in_init( self, make_entity, make_predicate, watt_mentioned ): e = make_entity with pytest.raises(TypeError): f2 = build_fact( make_predicate["p1"], indices=("nonsense"), case_factors=watt_mentioned, ) def test_invalid_index_for_case_factors_in_init(self, make_predicate, make_entity): with pytest.raises(IndexError): _ = build_fact( make_predicate["p1"], indices=2, case_factors=make_entity["watt"], ) def test_convert_int_terms_to_tuple(self, make_predicate, watt_mentioned): f = build_fact(make_predicate["p_irrelevant_1"], 3, case_factors=watt_mentioned) assert f.terms == [watt_mentioned[3]] def test_string_representation_of_factor(self, watt_factor): assert "<Hideaway Lodge> was a motel" in str(watt_factor["f1"]) assert "absence of the fact" in str(watt_factor["f3_absent"]).lower() def test_string_no_truth_value(self, watt_factor): factor = watt_factor["f2_no_truth"] assert "whether" in str(factor) def test_repeating_entity_string(self, make_predicate): """I'm not convinced that a model of a Fact ever needs to include multiple references to the same Entity just because the name of the Entity appears more than once in the Predicate.""" with pytest.raises(DuplicateTermError): Fact( predicate=make_predicate["p_three_entities"], terms=[Entity(name="Al"), Entity(name="Bob"), Entity(name="Al")], ) def test_string_representation_with_concrete_entities(self, watt_factor): """ "Hideaway Lodge" is still a string representation of an Entity object, but it's not in angle brackets because it can't be replaced by another Entity object without changing the meaning of the Fact. """ assert "Hideaway Lodge was a motel" in str(watt_factor["f1_specific"]) def test_string_for_fact_with_identical_terms(self): devon = Entity(name="Devon", generic=True) elaine = Entity(name="Elaine", generic=True) opened_account = Fact( predicate=Predicate( content="$applicant opened a bank account for $applicant and $cosigner" ), terms=(devon, elaine), ) assert "<Devon> opened a bank account for <Devon> and <Elaine>" in str( opened_account ) def test_str_with_concrete_context(self, make_opinion_with_holding): holding = list(make_opinion_with_holding["cardenas_majority"].holdings)[1] longer_str = holding.inputs[0].str_with_concrete_context assert "the exhibit in the form testimony" in longer_str.lower() assert "the exhibit in the form testimony" not in str(holding.inputs[0]).lower() def test_complex_fact_no_line_break_in_predicate(self, make_opinion_with_holding): """ Tests that the string representation of this Holding's only input Fact does not contain indented new lines, except in the "SPECIFIC CONTEXT" part, if present. The representation of the Exhibit mentioned in the Fact should not introduce any indented lines inside the Fact's string. """ holding = list(make_opinion_with_holding["cardenas_majority"].holdings)[1] fact_text = str(holding.inputs[0]) if "SPECIFIC CONTEXT" in fact_text: fact_text = fact_text.split("SPECIFIC CONTEXT")[0].strip() assert "\n " not in fact_text def test_new_context_replace_fact(self, make_entity, watt_factor): changes = ContextRegister.from_lists( [make_entity["watt"], watt_factor["f2"]], [Entity(name="Darth Vader"), watt_factor["f10"]], ) assert "was within the curtilage of <Hideaway Lodge>" in ( watt_factor["f2"].new_context(changes).short_string ) def test_get_factor_from_recursive_search(self, make_opinion_with_holding): holding_list = list(make_opinion_with_holding["cardenas_majority"].holdings) factor_list = list(holding_list[0].recursive_terms.values()) assert any( factor.compare_keys(Entity(name="parole officer")) and factor.name == "parole officer" for factor in factor_list ) def test_no_duplicate_comma_in_evidence(self, make_opinion_with_holding): holdings = list(make_opinion_with_holding["cardenas_majority"].holdings) assert "addicted to heroin, in showing the fact" in str(holdings[0].outputs[0]) def test_new_concrete_context(self, make_entity, watt_factor): register = ContextRegister.from_lists( to_replace=[make_entity["watt"], make_entity["motel"]], replacements=[Entity(name="Darth Vader"), Entity(name="Death Star")], ) different = watt_factor["f2"].new_context(register) assert "<Darth Vader> operated" in str(different) def test_type_of_terms(self, watt_factor): assert isinstance(watt_factor["f1"].term_sequence, TermSequence) def test_concrete_to_abstract(self, make_entity, make_predicate): motel = make_entity["motel_specific"] d = make_entity["watt"] fact = Fact(predicate=make_predicate["p2"], terms=(d, motel)) assert "<Wattenburg> operated and lived at Hideaway Lodge" in str(fact) assert "<Wattenburg> operated and lived at Hideaway Lodge>" in str( fact.make_generic() ) def test_entity_slots_as_length_of_factor(self, watt_factor): assert len(watt_factor["f1"].predicate) == 1 assert len(watt_factor["f1"]) == 1 def test_predicate_with_entities(self, make_entity, watt_factor): assert "<Hideaway Lodge> was a motel" in str(watt_factor["f1"]) def test_factor_terms_do_not_match_predicate(self, make_predicate, watt_mentioned): """ make_predicate["p1"] has only one slot for context factors, but this tells it to look for three. """ with pytest.raises(ValueError): _ = build_fact(make_predicate["p1"], (0, 1, 2), case_factors=watt_mentioned) def test_reciprocal_with_wrong_number_of_entities(self, make_entity, watt_factor): with pytest.raises(ValueError): watt_factor["f1"].predicate._content_with_terms( (make_entity["motel"], make_entity["watt"]) ) def test_entity_and_human_in_predicate(self, make_entity, watt_factor): assert "<Wattenburg> operated and lived at <Hideaway Lodge>" in watt_factor[ "f2" ].predicate._content_with_terms((make_entity["watt"], make_entity["motel"])) def test_standard_of_proof_must_be_listed(self, make_predicate, watt_mentioned): with pytest.raises(ValueError): _ = build_fact( make_predicate["p2"], case_factors=watt_mentioned, standard_of_proof="probably so", ) def test_standard_of_proof_in_str(self, watt_factor): factor = watt_factor["f2_preponderance_of_evidence"] assert factor.standard_of_proof in factor.short_string def test_case_factors_deleted_from_fact(self, watt_factor): """This attribute should have been deleted during Fact.__post_init__""" predicate = Predicate(content="some things happened") factor = build_fact(predicate) assert not hasattr(factor, "case_factors") def test_repeated_placeholder_in_fact(self, make_opinion_with_holding): holding = make_opinion_with_holding["lotus_majority"].holdings[9] fact = holding.inputs[1] assert fact.short_string == ( "the fact it was false that the precise formulation " "of <Lotus 1-2-3>'s code was necessary for <Lotus 1-2-3> to work" ) assert len(fact.terms) == 1 class TestSameMeaning: def test_equality_factor_from_same_predicate(self, watt_factor): assert watt_factor["f1"].means(watt_factor["f1b"]) def test_equality_factor_from_equal_predicate(self, watt_factor): assert watt_factor["f1"].means(watt_factor["f1c"]) def test_equality_because_factors_are_generic_entities(self, watt_factor): assert watt_factor["f1"].means(watt_factor["f1_different_entity"]) def test_unequal_because_a_factor_is_not_generic(self, watt_factor): assert not watt_factor["f9_swap_entities_4"].means(watt_factor["f9"]) def test_generic_terms_equal(self, watt_factor): assert watt_factor["f2_generic"].means(watt_factor["f2_false_generic"]) assert watt_factor["f2_generic"].means(watt_factor["f3_generic"]) def test_equal_referencing_diffent_generic_terms(self, make_factor): assert make_factor["f_murder"].means(make_factor["f_murder_craig"]) def test_generic_and_specific_factors_unequal(self, watt_factor): assert not watt_factor["f2"].means(watt_factor["f2_generic"]) def test_factor_unequal_due_to_repeated_term(self, watt_factor): assert not watt_factor["f2"].means(watt_factor["f2_reflexive"]) def test_factor_different_predicate_truth_unequal(self, watt_factor): assert not watt_factor["f7"].means(watt_factor["f7_opposite"]) def test_unequal_because_one_factor_is_absent(self, watt_factor): assert not watt_factor["f8"].means(watt_factor["f8_absent"]) def test_copies_of_identical_factor(self, make_factor): """ Even if the two factors have different entity markers in self.terms, I expect them to evaluate equal because the choice of entity markers is arbitrary. """ f = make_factor assert f["f_irrelevant_3"].means(f["f_irrelevant_3"]) assert f["f_irrelevant_3"].means(f["f_irrelevant_3_new_context"]) def test_equal_with_different_generic_subfactors(self, make_complex_fact): assert make_complex_fact["f_relevant_murder"].means( make_complex_fact["f_relevant_murder_craig"] ) def test_reciprocal_context_register(self, watt_factor): """ This test describes two objects with the same meaning that have been made in two different ways, each with a different id and repr. """ assert watt_factor["f7"].means(watt_factor["f7_swap_entities"]) def test_interchangeable_concrete_terms(self): """Detect that placeholders differing only by a final digit are interchangeable.""" ann = Entity(name="Ann", generic=False) bob = Entity(name="Bob", generic=False) ann_and_bob_were_family = Fact( predicate=Predicate( content="$relative1 and $relative2 both were members of the same family" ), terms=(ann, bob), ) bob_and_ann_were_family = Fact( predicate=Predicate( content="$relative1 and $relative2 both were members of the same family" ), terms=(bob, ann), ) assert ann_and_bob_were_family.means(bob_and_ann_were_family) def test_unequal_to_enactment(self, watt_factor, e_copyright): assert not watt_factor["f1"].means(e_copyright) with pytest.raises(TypeError): e_copyright.means(watt_factor["f1"]) def test_standard_of_proof_inequality(self, watt_factor): f = watt_factor assert not f["f2_clear_and_convincing"].means(f["f2_preponderance_of_evidence"]) assert not f["f2_clear_and_convincing"].means(f["f2"]) assert f["f2_clear_and_convincing"].wrapped_string.endswith( "\n by the STANDARD clear and convincing" ) def test_means_despite_plural(self): directory = Entity(name="Rural's telephone directory", plural=False) listings = Entity(name="Rural's telephone listings", plural=True) directory_original = Fact( predicate=Predicate(content="$thing was original"), terms=directory ) listings_original = Fact( predicate=Predicate(content="$thing were original"), terms=listings ) assert directory_original.means(listings_original) def test_same_meaning_no_terms(self, make_predicate): no_context = Fact(predicate=make_predicate["p_no_context"], terms=[]) assert no_context.means(no_context) class TestImplication: def test_fact_implies_none(self, watt_factor): assert watt_factor["f1"].implies(None) def test_no_implication_of_rule(self, watt_factor, make_rule): assert not watt_factor["f1"].implies(make_rule["h1"]) def test_fact_does_not_imply_holding(self, watt_factor, make_holding): assert not watt_factor["f1"].implies(make_holding["h1"]) def test_specific_factor_implies_generic(self, watt_factor): assert watt_factor["f2"] > watt_factor["f2_generic"] assert not watt_factor["f2_generic"] > watt_factor["f2"] def test_specific_factor_implies_generic_explain(self, watt_factor): answer = watt_factor["f2"].explain_implication(watt_factor["f2_generic"]) assert ( str(watt_factor["f2"]), watt_factor["f2_generic"], ) in answer.context.items() def test_specific_implies_generic_form_of_another_fact(self, watt_factor): assert watt_factor["f2"] > watt_factor["f3_generic"] def test_specific_fact_does_not_imply_generic_entity( self, make_entity, watt_factor ): assert not watt_factor["f2"] > make_entity["motel"] def test_factor_does_not_imply_predicate(self, make_predicate, watt_factor): with pytest.raises(TypeError): assert not watt_factor["f8_meters"] > make_predicate["p8"] def test_factor_implies_because_of_quantity(self, watt_factor): assert watt_factor["f8_meters"] > watt_factor["f8"] assert watt_factor["f8_higher_int"] > watt_factor["f8_float"] assert watt_factor["f8_int"] < watt_factor["f8_higher_int"] def test_factor_implies_no_truth_value(self, watt_factor): assert watt_factor["f2"] > watt_factor["f2_no_truth"] assert not watt_factor["f2_no_truth"] > watt_factor["f2"] def test_comparison_implies_no_truth_value(self, watt_factor): assert watt_factor["f8"] > watt_factor["f8_no_truth"] assert not watt_factor["f8_no_truth"] > watt_factor["f8"] def test_implication_standard_of_proof(self, make_factor): assert ( not make_factor["f_shooting_craig_poe"] > make_factor["f_shooting_craig_brd"] ) assert make_factor["f_shooting_craig_brd"] > make_factor["f_shooting_craig_poe"] def test_factor_implies_because_of_exact_quantity(self, watt_factor): assert watt_factor["f8_exact"] > watt_factor["f7"] assert watt_factor["f8_exact"] >= watt_factor["f8"] def test_no_implication_pint_quantity_and_int(self, watt_factor): assert not watt_factor["f8"] > watt_factor["f8_int"] assert not watt_factor["f8"] < watt_factor["f8_int"] def test_absent_factor_implies_absent_factor_with_lesser_quantity( self, watt_factor ): assert watt_factor["f9_absent_miles"] > watt_factor["f9_absent"] def test_equal_factors_not_gt(self, watt_factor): f = watt_factor assert f["f7"] >= f["f7"] assert f["f7"] <= f["f7"] assert not f["f7"] > f["f7"] def test_standard_of_proof_comparison(self, watt_factor): f = watt_factor assert f["f2_clear_and_convincing"].implies(f["f2_preponderance_of_evidence"]) assert f["f2_beyond_reasonable_doubt"] >= f["f2_clear_and_convincing"] def test_no_implication_between_factors_with_and_without_standards( self, watt_factor ): f = watt_factor assert not f["f2_clear_and_convincing"] > f["f2"] assert not f["f2"] > f["f2_preponderance_of_evidence"] def test_implication_complex(self, make_complex_fact): assert ( make_complex_fact["f_relevant_murder"] > make_complex_fact["f_relevant_murder_whether"] ) def test_context_register_text(self, make_context_register): assert str(make_context_register) == ( "ContextRegister(<Alice> is like <Craig>, and <Bob> is like <Dan>)" ) def test_implication_complex_explain( self, make_complex_fact, make_context_register ): complex_true = make_complex_fact["f_relevant_murder"] complex_whether = make_complex_fact["f_relevant_murder_whether"].new_context( make_context_register ) explanation = complex_true.explain_implication(complex_whether) assert explanation.context[Entity(name="Alice").key].compare_keys( Entity(name="Craig") ) def test_implication_explain_keys_only_from_left( self, make_complex_fact, make_context_register ): """ Check that when implies provides a ContextRegister as an "explanation", it uses elements only from the left as keys and from the right as values. """ complex_true = make_complex_fact["f_relevant_murder"] complex_whether = make_complex_fact["f_relevant_murder_whether"] new = complex_whether.new_context(make_context_register) explanations = list(complex_true.explanations_implication(new)) explanation = explanations.pop() assert explanation.context.get("<Craig>") != Entity(name="Alice") assert explanation.context.get("<Alice>").compare_keys(Entity(name="Craig")) def test_context_registers_for_complex_comparison(self, make_complex_fact): gen = make_complex_fact["f_relevant_murder_nested_swap"]._context_registers( make_complex_fact["f_relevant_murder"], operator.ge ) register = next(gen) assert register.matches.get("<Alice>").compare_keys(Entity(name="Bob")) def test_no_implication_complex(self, make_complex_fact): left = make_complex_fact["f_relevant_murder"] right = make_complex_fact["f_relevant_murder_alice_craig"] assert not left >= right assert left.explain_implication(right) is None def test_implied_by(self, make_complex_fact): assert make_complex_fact["f_relevant_murder_whether"].implied_by( make_complex_fact["f_relevant_murder"] ) def test_explanation_implied_by(self, make_complex_fact): explanation = make_complex_fact["f_relevant_murder_whether"].explain_implied_by( make_complex_fact["f_relevant_murder"] ) assert explanation def test_explain_not_implied_by(self, make_complex_fact): left = make_complex_fact["f_relevant_murder"] right = make_complex_fact["f_relevant_murder_whether"] assert left.explain_implied_by(right) is None def test_not_implied_by_none(self, make_complex_fact): left = make_complex_fact["f_relevant_murder"] assert not left.implied_by(None) def test_some_interchangeable_entities(self): hit = Fact( predicate="$person1 hit $target1 and $target2", terms=[Entity(name="Moe"), Entity(name="Curly"), Entity(name="Larry")], ) hit2 = Fact( predicate="$person1 hit $target1 and $target2", terms=[ Entity(name="Joker"), Entity(name="Batman"), Entity(name="Superman"), ], ) assert hit.means(hit2) def test_interchangeable_entities_in_group(self): fought = Fact( predicate="$person1, $person2, and $person3 fought each other", terms=[Entity(name="Larry"), Entity(name="Moe"), Entity(name="Curly")], ) hit = Fact( predicate="$person1 hit $target1 and $target2", terms=[Entity(name="Moe"), Entity(name="Curly"), Entity(name="Larry")], ) fought2 = Fact( predicate="$person1, $person2, and $person3 fought each other", terms=[ Entity(name="Superman"), Entity(name="Batman"), Entity(name="Joker"), ], ) hit2 = Fact( predicate="$person1 hit $target1 and $target2", terms=[ Entity(name="Joker"), Entity(name="Batman"), Entity(name="Superman"), ], ) assert FactorGroup([fought, hit]).implies(FactorGroup([fought2, hit2])) class TestContradiction: def test_factor_different_predicate_truth_contradicts(self, watt_factor): assert watt_factor["f7"].contradicts(watt_factor["f7_opposite"]) assert watt_factor["f7_opposite"].contradicts(watt_factor["f7"]) def test_same_predicate_true_vs_false(self, watt_factor): assert watt_factor["f10"].contradicts(watt_factor["f10_false"]) assert watt_factor["f10"].truth != watt_factor["f10_false"].truth def test_factor_does_not_contradict_predicate(self, make_predicate, watt_factor): with pytest.raises(TypeError): _ = watt_factor["f7"].contradicts(make_predicate["p7_true"]) def test_factor_contradiction_absent_predicate(self, watt_factor): assert watt_factor["f3"].contradicts(watt_factor["f3_absent"]) assert watt_factor["f3_absent"].contradicts(watt_factor["f3"]) def test_absences_of_contradictory_facts_consistent(self, watt_factor): assert not watt_factor["f8_absent"].contradicts(watt_factor["f8_less_absent"]) def test_factor_no_contradiction_no_truth_value(self, watt_factor): assert not watt_factor["f2"].contradicts(watt_factor["f2_no_truth"]) assert not watt_factor["f2_no_truth"].contradicts(watt_factor["f2_false"]) def test_absent_factor_contradicts_broader_quantity_statement(self, watt_factor): assert watt_factor["f8_absent"].contradicts(watt_factor["f8_meters"]) assert watt_factor["f8_meters"].contradicts(watt_factor["f8_absent"]) def test_less_specific_absent_contradicts_more_specific(self, watt_factor): assert watt_factor["f9_absent_miles"].contradicts(watt_factor["f9"]) assert watt_factor["f9"].contradicts(watt_factor["f9_absent_miles"]) def test_no_contradiction_with_more_specific_absent(self, watt_factor): assert not watt_factor["f9_absent"].contradicts(watt_factor["f9_miles"]) assert not watt_factor["f9_miles"].contradicts(watt_factor["f9_absent"]) def test_contradiction_complex(self, make_complex_fact): assert make_complex_fact["f_irrelevant_murder"].contradicts( make_complex_fact["f_relevant_murder_craig"] ) def test_no_contradiction_complex(self, make_complex_fact): assert not make_complex_fact["f_irrelevant_murder"].contradicts( make_complex_fact["f_relevant_murder_alice_craig"] ) def test_no_contradiction_of_None(self, watt_factor): assert not watt_factor["f1"].contradicts(None) def test_contradicts_if_present_both_present(self, watt_factor): """ Test a helper function that checks whether there would be a contradiction if neither Factor was "absent". """ assert watt_factor["f2"]._contradicts_if_present( watt_factor["f2_false"], explanation=Explanation.from_context() ) def test_contradicts_if_present_one_absent(self, watt_factor): assert watt_factor["f2"]._contradicts_if_present( watt_factor["f2_false_absent"], explanation=Explanation.from_context() ) def test_false_does_not_contradict_absent(self): absent_fact = Fact( predicate=Predicate( content="${rural_s_telephone_directory} was copyrightable", truth=True ), terms=(Entity(name="Rural's telephone directory")), absent=True, ) false_fact = Fact( predicate=Predicate( content="${the_java_api} was copyrightable", truth=False ), terms=(Entity(name="the Java API", generic=True, plural=False)), absent=False, ) assert not false_fact.contradicts(absent_fact) assert not absent_fact.contradicts(false_fact) def test_inconsistent_statements_about_different_entities(self): """ Alice and Bob are both generics. So it's possible to reach a contradiction if you assume they correspond to one another. """ p_small_weight = Comparison( content="the amount of gold $person possessed was", sign="<", expression=Q_("1 gram"), ) p_large_weight = Comparison( content="the amount of gold $person possessed was", sign=">=", expression=Q_("100 kilograms"), ) alice = Entity(name="Alice") bob = Entity(name="Bob") alice_rich = Fact(predicate=p_large_weight, terms=alice) bob_poor = Fact(predicate=p_small_weight, terms=bob) assert alice_rich.contradicts(bob_poor) def test_inconsistent_statements_about_corresponding_entities(self): """ Even though Alice and Bob are both generics, it's known that Alice in the first context corresponds with Alice in the second. So there's no contradiction. """ p_small_weight = Comparison( content="the amount of gold $person possessed was", sign="<", expression=Q_("1 gram"), ) p_large_weight = Comparison( content="the amount of gold $person possessed was", sign=">=", expression=Q_("100 kilograms"), ) alice = Entity(name="Alice") bob = Entity(name="Bob") alice_rich = Fact(predicate=p_large_weight, terms=alice) bob_poor = Fact(predicate=p_small_weight, terms=bob) register = ContextRegister() register.insert_pair(alice, alice) assert not alice_rich.contradicts(bob_poor, context=register) def test_copy_with_foreign_context(self, watt_mentioned, watt_factor): w = watt_mentioned assert ( watt_factor["f1"] .new_context(ContextRegister.from_lists([w[0]], [w[2]])) .means(watt_factor["f1_different_entity"]) ) def test_check_entity_consistency_true(self, make_entity, make_factor): left = make_factor["f_irrelevant_3"] right = make_factor["f_irrelevant_3_new_context"] e = make_entity easy_register = ContextRegister.from_lists([e["dan"]], [e["craig"]]) easy_update = left.update_context_register( right, easy_register, comparison=means ) harder_register = ContextRegister.from_lists( to_replace=[e["alice"], e["bob"], e["craig"], e["dan"], e["circus"]], replacements=[e["bob"], e["alice"], e["dan"], e["craig"], e["circus"]], ) harder_update = left.update_context_register( right, context=harder_register, comparison=means, ) assert any(register is not None for register in easy_update) assert any(register is not None for register in harder_update) def test_check_entity_consistency_false(self, make_entity, make_factor): context = ContextRegister() context.insert_pair(make_entity["circus"], make_entity["alice"]) update = make_factor["f_irrelevant_3"].update_context_register( make_factor["f_irrelevant_3_new_context"], comparison=means, context=context ) assert not any(register is not None for register in update) def test_entity_consistency_identity_not_equality(self, make_entity, make_factor): register = ContextRegister() register.insert_pair(make_entity["dan"], make_entity["dan"]) update = make_factor["f_irrelevant_3"].update_context_register( make_factor["f_irrelevant_3_new_context"], context=register, comparison=means, ) assert not any(register is not None for register in update) def test_check_entity_consistency_type_error( self, make_entity, make_factor, make_predicate ): """ There would be no TypeError if it used "means" instead of .gt. The comparison would just return False. """ update = make_factor["f_irrelevant_3"].update_context_register( make_predicate["p2"], {str(make_entity["dan"]): make_entity["dan"]}, operator.gt, ) with pytest.raises(TypeError): any(register is not None for register in update) class TestConsistent: def test_contradictory_facts_about_same_entity(self, watt_factor): left = watt_factor["f8_less"] right = watt_factor["f8_meters"] register = ContextRegister() register.insert_pair(left.generic_terms()[0], right.generic_terms()[0]) assert not left.consistent_with(right, register) assert left.explain_consistent_with(right, register) is None def test_explanations_consistent_with(self, watt_factor): left = watt_factor["f8_less"] right = watt_factor["f8_meters"] register = ContextRegister() register.insert_pair(left.generic_terms()[0], right.generic_terms()[0]) explanations = list(left.explanations_consistent_with(right, context=register)) assert not explanations def test_factor_consistent_with_none(self, make_exhibit): assert make_exhibit["no_shooting_testimony"].consistent_with( make_exhibit["no_shooting_witness_unknown_testimony"] ) class TestAddition: @pytest.mark.parametrize( "left, right, expected", [ ("f_shooting_craig_poe", "f_shooting_craig_brd", "f_shooting_craig_brd"), ("f_irrelevant_3", "f_irrelevant_3_new_context", "f_irrelevant_3"), ( "f_irrelevant_3_new_context", "f_irrelevant_3", "f_irrelevant_3_new_context", ), ], ) def test_addition(self, make_factor, left, right, expected): answer = make_factor[left] + make_factor[right] assert answer.means(make_factor[expected]) def test_add_unrelated_factors(self, make_factor): assert make_factor["f_murder"] + make_factor["f_crime"] is None def test_cant_add_enactment_to_fact(self, watt_factor, e_search_clause): with pytest.raises(TypeError): print(watt_factor["f3"] + e_search_clause)

21,389

11,064

218

74d5c07e6a219370de5020bdd900eea843a2ba4f

165,553

py

Python

icbd/compiler/compiler_types.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

7

2015-04-06T15:17:13.000Z

2020-10-21T04:57:00.000Z

icbd/compiler/compiler_types.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

null

icbd/compiler/compiler_types.py

kmod/icbd

9636564eb3993afa07c6220d589bbd1991923d74

[ "MIT" ]

4

2016-05-16T17:53:08.000Z

2020-11-28T17:18:50.000Z

import _ast import inspect import re import sys import traceback from . import closure_analyzer from .code_emitter import CodeEmitter DEBUG_CHECKS = True BINOP_MAP = { _ast.Add:"__add__", _ast.Sub:"__sub__", _ast.Mult:"__mul__", _ast.BitOr:"__or__", _ast.BitXor:"__xor__", _ast.BitAnd:'__and__', _ast.LShift:'__lshift__', _ast.RShift:'__rshift__', _ast.Mod:'__mod__', _ast.Div:'__div__', _ast.Pow:'__pow__', } COMPARE_MAP = { _ast.Lt:"__lt__", _ast.Gt:"__gt__", _ast.LtE:"__le__", _ast.GtE:"__ge__", _ast.Eq:"__eq__", _ast.NotEq:"__ne__", _ast.In:"__contains__", } COMPARE_REFLECTIONS = { _ast.Lt:_ast.Gt, _ast.Gt:_ast.Lt, _ast.LtE:_ast.GtE, _ast.GtE:_ast.LtE, _ast.Eq:_ast.Eq, _ast.NotEq:_ast.NotEq, } # The same thing as an AttributeError, but for the compiled code rather than the compiler code # The same thing as an TypeError, but for the compiled code rather than the compiler code # AN error that you tried to instantiate an object that has no first-class representation (such as a polymorphic function, or module) class AttributeAccessType(object): """ An enum of the possible ways that an object attribute will be generated. """ # A member variable of the object, that has a persistent memory location FIELD = "field" # A field that the object has implicitly, and is generated on access (such as __class__) IMPLICIT_FIELD = "implicit_field" # A class-level method of the object that doesn't change, which is instantiated into an instancemethod on access CONST_METHOD = "attr_const_method" # convert everything to IEEE754 format to make sure we don't lose any precision in serialization _cached_templates = {} _cached_ctemplates = {} Variable._ok_code = list(func.func_code for name, func in inspect.getmembers(Variable, inspect.ismethod)) # All types default to raised types Slice = singleton(SliceMT) Slice.initialized = ("attrs", "write") StrConstant = singleton(StrConstantMT) StrConstant.initialized = ("attrs", "write") None_ = singleton(NoneMT) None_.initialized = ("attrs", "write") Len = singleton(LenMT) StrFunc = singleton(StrFuncMT) ReprFunc = singleton(ReprFuncMT) Nref = singleton(NrefMT) TypeFunc = singleton(TypeFuncMT) BoolFunc = singleton(BoolFuncMT) Isinstance = singleton(IsinstanceMT) Cast = singleton(CastMT) StrFormat = singleton(StrFormatMT) MapFunc = singleton(MapFuncMT) ReduceFunc = singleton(ReduceFuncMT) Enumerate = singleton(EnumerateMT) MinFunc = MinFuncMT("min") MaxFunc = MinFuncMT("max") ListFunc = Parametric1ArgCtorFuncMT(ListMT.make_list, "append") SetFunc = Parametric1ArgCtorFuncMT(SetMT.make_set, "add") DequeFunc = Parametric1ArgCtorFuncMT(DequeMT.make_deque, "append") DictFunc = singleton(DictFuncMT) ObjectClass = ClassMT(None, "object", "object") Object = ObjectClass._instance IntClass = ClassMT(ObjectClass, "int", "int", llvm_type="i64") Int = IntClass._instance FloatClass = ClassMT(ObjectClass, "float", "float", llvm_type="double") Float = FloatClass._instance StrClass = ClassMT(ObjectClass, "str", "str", llvm_type="%string*") Str = StrClass._instance BoolClass = ClassMT(ObjectClass, "bool", "bool", "i1") Bool = BoolClass._instance TypeClass = ClassMT(ObjectClass, "type", "type") Type = TypeClass._instance FileClass = ClassMT(ObjectClass, "file", "file") File = FileClass._instance # TODO there is a lot of duplication between this and stuff like closures class PtrMT(MT): """ An MT to represent the type of a stored pointer to an object. They should only exist as a compiler implementation detail. """ Underlying = singleton(_UnderlyingMT) _made_supertypes = {} # Some type classes for stdlib stuff: STDLIB_TYPES = [] _IntIterator, _IntIterable = _make_iterable(Int) _FloatIterator, _FloatIterable = _make_iterable(Float) _Boolable = BoxedMT([_FakeMT({ "__class__": (Type, AttributeAccessType.IMPLICIT_FIELD), "__incref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__decref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__nonzero__": (CallableMT.make_callable([], 0, Bool), AttributeAccessType.CONST_METHOD), })]) STDLIB_TYPES.append(_Boolable) _BoolableIterator, _BoolableIterable = _make_iterable(_Boolable) BUILTINS = { "True":Variable(Bool, 1, 1, False), "False":Variable(Bool, 0, 1, False), "len":Variable(Len, (), 1, False), "str":Variable(StrClass, (), 1, False), "repr":Variable(ReprFunc, (), 1, False), "type":Variable(TypeClass, (), 1, False), "map":Variable(MapFunc, (), 1, False), "reduce":Variable(ReduceFunc, (), 1, False), "nrefs":Variable(Nref, (), 1, False), "bool":Variable(BoolClass, (), 1, False), "list":Variable(ListFunc, (), 1, False), "dict":Variable(DictFunc, (), 1, False), "set":Variable(SetFunc, (), 1, False), "isinstance":Variable(Isinstance, (), 1, False), "__cast__":Variable(Cast, (), 1, False), "enumerate":Variable(Enumerate, (), 1, False), "chr":Variable(UnboxedFunctionMT(None, None, [Int], Str), ("@chr", [], None), 1, False), "ord":Variable(UnboxedFunctionMT(None, None, [Str], Int), ("@ord", [], None), 1, False), # "open":Variable(UnboxedFunctionMT(None, None, [Str], File), ("@file_open", [], None), 1, True), "open":Variable(UnboxedFunctionMT(None, None, [Str, Str], File, ndefaults=1), ("@file_open2", [Variable(Str, "@.str_r", 1, True)], None), 1, False), "int":Variable(IntClass, (), 1, False), "min":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False), Variable(MinFunc, (), 1, False), ]), "max":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_max", [], None), 1, False), Variable(MaxFunc, (), 1, False), ]), "float":Variable(FloatClass, (), 1, False), "file":Variable(FileClass, (), 1, False), "abs":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int], Int), ("@int_abs", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False)]), "None":Variable(None_, "null", 1, False), "object":Variable(ObjectClass, (), 1, False), "sum":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [_IntIterable], Int), ("@sum_int", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [_FloatIterable], Float), ("@sum_float", [], None), 1, False), ]), "any":Variable(UnboxedFunctionMT(None, None, [_BoolableIterable], Bool), ("@any", [], None), 1, False), } BUILTIN_MODULES = { "time":Variable(ModuleMT({ 'time':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_time", [], None), 1, False), 'clock':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_clock", [], None), 1, False), 'sleep':Variable(UnboxedFunctionMT(None, None, [Float], None_), ("@time_sleep", [], None), 1, False), }), 1, 1, False), "sys":Variable(ModuleMT({ 'stdin':Variable(File, "@sys_stdin", 1, False), 'stdout':Variable(File, "@sys_stdout", 1, False), 'stderr':Variable(File, "@sys_stderr", 1, False), }), 1, 1, False), "math":Variable(ModuleMT({ 'sqrt':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sqrt", [], None), 1, False), 'tan':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@tan", [], None), 1, False), 'sin':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sin", [], None), 1, False), 'cos':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@cos", [], None), 1, False), 'ceil':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@ceil", [], None), 1, False), 'pi':Variable(Float, format_float(3.141592653589793), 1, False), }), 1, 1, False), "collections":Variable(ModuleMT({ 'deque':Variable(DequeFunc, (), 1, False), }), 1, 1, False), # Interopability library: "hax":Variable(ModuleMT({ "ftoi":Variable(UnboxedFunctionMT(None, None, [Float], Int), ("@hax_ftoi", [], None), 1, False), "itof":Variable(UnboxedFunctionMT(None, None, [Int], Float), ("@hax_itof", [], None), 1, False), "min":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False), "max":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False), "fmin":Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False), "abs":Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False), "initvideo":Variable(UnboxedFunctionMT(None, None, [Int, Int], None_), ("@hax_initvideo", [], None), 1, False), "plot":Variable(UnboxedFunctionMT(None, None, [Int, Int, Int, Int, Int], None_), ("@hax_plot", [], None), 1, False), }), 1, 1, False), } SliceMT.setup_class_methods() NoneMT.setup_class_methods() setup_int() setup_float() setup_string() setup_bool() setup_type() setup_file()

41.669519

331

0.570228

import _ast import inspect import re import sys import traceback from . import closure_analyzer from .code_emitter import CodeEmitter DEBUG_CHECKS = True BINOP_MAP = { _ast.Add:"__add__", _ast.Sub:"__sub__", _ast.Mult:"__mul__", _ast.BitOr:"__or__", _ast.BitXor:"__xor__", _ast.BitAnd:'__and__', _ast.LShift:'__lshift__', _ast.RShift:'__rshift__', _ast.Mod:'__mod__', _ast.Div:'__div__', _ast.Pow:'__pow__', } COMPARE_MAP = { _ast.Lt:"__lt__", _ast.Gt:"__gt__", _ast.LtE:"__le__", _ast.GtE:"__ge__", _ast.Eq:"__eq__", _ast.NotEq:"__ne__", _ast.In:"__contains__", } COMPARE_REFLECTIONS = { _ast.Lt:_ast.Gt, _ast.Gt:_ast.Lt, _ast.LtE:_ast.GtE, _ast.GtE:_ast.LtE, _ast.Eq:_ast.Eq, _ast.NotEq:_ast.NotEq, } # The same thing as an AttributeError, but for the compiled code rather than the compiler code class UserAttributeError(Exception): pass # The same thing as an TypeError, but for the compiled code rather than the compiler code class UserTypeError(Exception): pass # AN error that you tried to instantiate an object that has no first-class representation (such as a polymorphic function, or module) class CantInstantiateException(Exception): pass class AttributeAccessType(object): """ An enum of the possible ways that an object attribute will be generated. """ # A member variable of the object, that has a persistent memory location FIELD = "field" # A field that the object has implicitly, and is generated on access (such as __class__) IMPLICIT_FIELD = "implicit_field" # A class-level method of the object that doesn't change, which is instantiated into an instancemethod on access CONST_METHOD = "attr_const_method" # convert everything to IEEE754 format to make sure we don't lose any precision in serialization def format_float(f): import struct import binascii s = struct.pack('d', f) return "0x" + binascii.hexlify(s[::-1]) def is_emitter(e): from .codegen import CodeEmitter return e is None or isinstance(e, CodeEmitter) _cached_templates = {} def get_template(name): if name not in _cached_templates: _cached_templates[name] = open("templates/%s.tll" % name).read() return _cached_templates[name] def convert_none_to_void_ll(code): code = code.replace("define %none* ", "define void ") code = code.replace("declare %none* ", "declare void ") code = re.sub("ret (%none\*|void) [^ \n]*", "ret void", code) def repl(m): return "%s = inttoptr i64 0 to %%none*\n call void " % (m.group(1),) code = re.sub("([^ ]*) = call (%none\*|void) ", repl, code) code = code.replace("%none* (", "void (") # function pointers return code _cached_ctemplates = {} def get_ctemplate(name): if name not in _cached_ctemplates: _cached_ctemplates[name] = open("templates/%s.tc" % name).read() return _cached_ctemplates[name] def eval_ctemplate(name, em, locals_): assert "em" not in locals_ template = get_ctemplate(name) return _eval_template(name, template, em, locals_, output="c") def eval_template(name, em, locals_): assert "em" not in locals_ template = get_template(name) return _eval_template(name, template, em, locals_, output="llvm") def _make_typeobj(em, llvm_name, display_name=None): display_name = display_name or llvm_name nameptr = em.get_str_ptr(display_name) return ("""@%(n)s_typename = global %%string {i64 1, i64 %(len)s, i8* %(nameptr)s, [0 x i8] zeroinitializer}\n""" """@%(n)s_typeobj = global %%type {i64 1, %%string* @%(n)s_typename, %%type* null}""") % { 'n': llvm_name, 'nameptr': nameptr, 'len': len(display_name) } def _eval_template(name, template, _em, locals_, output): lines = template.split('\n') newlines = [] locals_['make_typeobj'] = _make_typeobj assert isinstance(_em, CodeEmitter) if_state = [] def convert(em, lineno, l): ignore = not all(if_state) locals_['em'] = em get_written = lambda: em.get_llvm().replace('\n', '\n ') if output == "llvm" else em.get_c() assert not get_written(), get_written() if '///' in l: return convert(em, lineno, l[:l.find('///')]) m = re.search(r"\$+\(", l) if not m: if ignore: return "" return l ndollar = len(m.group()) - 1 assert ndollar in (1, 2, 3) nopen = 1 quote = None for i in xrange(m.end(), len(l)): if l[i] == '\\': assert l[i+1] in "nt", l[i+1] if quote: if l[i] == quote: quote = None continue if l[i] in '\'\"': quote = l[i] continue if l[i] == '(': nopen += 1 elif l[i] == ')': nopen -= 1 if nopen == 0: end = i break else: raise Exception("Unclosed parethesis at line %d" % (lineno+1,)) to_eval = l[m.end():end] if to_eval.startswith("if "): if ignore: conditional = False else: conditional = eval(to_eval[3:], globals(), locals_) if_state.append(conditional) evaled = "" elif to_eval == "endif": if_state.pop() evaled = "" elif ndollar == 1: if ignore: evaled = "" else: evaled = str(eval(to_eval, globals(), locals_)) assert not get_written() else: if not ignore: exec to_eval in globals(), locals_ evaled = get_written() em = CodeEmitter(_em) if ndollar == 3: end += 3 # assert m.start() == 0, l # evaled = " " + evaled assert not get_written(), get_written() if ignore: return convert(em, lineno, l[end + 1:]) else: return l[:m.start()] + evaled + convert(em, lineno, l[end + 1:]) in_3dollar = False for_3dollar = [] for lineno, l in enumerate(lines): if "$$$(" in l: assert not in_3dollar, "can't nest $$$ sections" in_3dollar = True if in_3dollar: for_3dollar.append(l) if ")$$$" in l: # Strip of leading writespace: for i, c in enumerate(for_3dollar[0]): if c != ' ': break for_3dollar = [s[i:] for s in for_3dollar] l = '\n'.join(for_3dollar) for_3dollar = [] in_3dollar = False else: continue try: newlines.append(convert(CodeEmitter(_em), lineno, l)) if len(newlines) >= 2 and newlines[-1] == newlines[-2] == '': newlines.pop() except Exception: print >>sys.stderr, "failed to convert line %d of %s template '%s', args %s" % (lineno + 1, output, name, locals_) # print l raise assert not in_3dollar, "unterminated $$$ section" return '\n'.join(newlines) def raw_func_name(llvm_func_name): if llvm_func_name.startswith('@'): return llvm_func_name[1:] elif llvm_func_name.startswith("#!"): return llvm_func_name else: raise Exception(llvm_func_name) class Variable(object): def __init__(self, t, v, nrefs, marked): assert isinstance(t, MT) if isinstance(t, UnboxedFunctionMT): assert t.ndefaults == len(v[1]) if t is Float: # Make sure that the compiler doesn't use any potentially-lossy float representations assert not isinstance(v, float), v if isinstance(v, str): assert not re.match("\d*\.?\d*$", v), v if isinstance(t, (StrConstantMT, UnboxedInstanceMethod, _SpecialFuncMT, UnboxedFunctionMT, ClassMT, UnboxedTupleMT, PolymorphicFunctionMT)): assert isinstance(v, tuple), (t, v) if isinstance(t, UnboxedFunctionMT): assert len(v) == 3 assert not marked else: if isinstance(t, ClosureMT): assert isinstance(v, str) or v is None else: assert isinstance(v, (int, float, str)), (t, v) assert isinstance(nrefs, int) assert isinstance(marked, bool) self.t = t self.v = v self.nrefs = nrefs self.marked = marked # def mkwrap(n): # _f = getattr(self.t, n) # def inner(c, *args): # return _f(c, self.v, *args) # return inner # TODO shouldnt be forgetting about owned references? # def __del__(self): # if self.owned: # import traceback # traceback.print_stack() if DEBUG_CHECKS: def __getattribute__(self, n): # Allow Variable methods, but only those, to operate on things with 0 nrefs if sys._getframe(1).f_code not in Variable._ok_code: assert object.__getattribute__(self, "nrefs") > 0, n return object.__getattribute__(self, n) def equiv(self, rhs): assert self.nrefs > 0 # hax: d1 = dict(self.__dict__) d2 = dict(rhs.__dict__) v1 = d1.pop('v') v2 = d2.pop('v') if isinstance(v1, tuple): assert isinstance(v2, tuple) assert len(v1) == len(v2) for i in xrange(len(v1)): if isinstance(v1[i], Variable): if not v1[i].equiv(v2[i]): return False elif isinstance(v1[i], list): assert isinstance(v2[i], list) l1 = v1[i] l2 = v2[i] if len(l1) != len(l2): return False for j in xrange(len(l1)): if not l1[j].equiv(l2[j]): return False elif v1[i] != v2[i]: return False else: if v1 != v2: return False return d1 == d2 # return self.t == rhs.t and self.v == rhs.v and self.nrefs == rhs.nrefs and def split(self, em): """ Split off a copy of this variable, and ensure it's marked """ assert is_emitter(em), em """ assert self.nrefs > 0 if not self.marked: r = self.t.incref_llvm(em, self.v) if r: em.pl(r) else: # Optimization: if this was marked, just "transfer" the mark self.marked = False return Variable(self.t, self.v, 1, True) """ assert self.nrefs > 0 r = self.t.incref_llvm(em, self.v) if r: em.pl(r + " ; split") r = self.t.incref_c(em, self.v) if r: em.pc(r + "; // split") return Variable(self.t, self.v, 1, True) def dup(self, dup_cache): """ Makes a copy of this variable object. Only makes sense at a meta level, ie we want to distribute copies of this variable to all successor nodes. """ assert self.nrefs > 0 if self not in dup_cache: dup_cache[self] = Variable(self.t, self.t.dup(self.v, dup_cache), self.nrefs, self.marked) return dup_cache[self] def incvref(self, em): assert is_emitter(em), em assert self.nrefs > 0 self.nrefs += 1 # print "incvref %s" % self, self.nrefs # em.pl("; inc: %s now has %d vrefs" % (self.v, self.nrefs)) def decvref(self, em, note=None): assert is_emitter(em) # print; import traceback; traceback.print_stack() assert self.nrefs > 0, (self, self.t) self.nrefs -= 1 # print "decvref %s" % self, self.nrefs # em.pl("; dec: %s now has %d vrefs" % (self.v, self.nrefs)) if self.nrefs == 0: if isinstance(self.v, tuple): assert not self.marked self.t.free(em, self.v) elif self.marked: self.nrefs += 2 # for the getattr; add 2 since we don't want it to hit zero again f = self.getattr(em, "__decref__") f.call(em, []) self.nrefs -= 1 # for the extra +1 nrefs assert self.nrefs == 0, self.nrefs # This shouldn't matter but it's safer: self.marked = False def getattr(self, em, attr, clsonly=False, decvref=True, **kw): assert is_emitter(em) assert self.nrefs > 0 try: r = self.t.getattr(em, self, attr, clsonly=clsonly, **kw) finally: if decvref: self.decvref(em, "getattr") return r def getattrptr(self, em, attr): assert is_emitter(em) assert self.nrefs > 0 try: r = self.t.getattrptr(em, self, attr) finally: self.decvref(em, "getattrptr") return r def setattr(self, em, attr, var): assert is_emitter(em) assert self.nrefs > 0 assert isinstance(var, Variable) r = self.t.setattr(em, self.v, attr, var) self.decvref(em, "setattr") return r def call(self, em, args, expected_type=None): assert is_emitter(em) assert self.nrefs > 0 try: r = self.t.call(em, self.v, args, expected_type=expected_type) finally: self.decvref(em, "call [was func]") return r def convert_to(self, em, t): assert is_emitter(em) assert self.nrefs > 0 assert isinstance(t, MT) return self.t.convert_to(em, self, t) Variable._ok_code = list(func.func_code for name, func in inspect.getmembers(Variable, inspect.ismethod)) class MT(object): def __init__(self): self.initialized = set() self.initializing = set() self.__st = ''.join(traceback.format_stack()) # TODO take this out once I'm done debugging def __check_initialized(self, stage): if stage not in self.initialized: print self.__st raise Exception(self) def c_type(self): l = self.llvm_type() if l.startswith('%'): return l[1:] assert l in ('i1', 'i64', "double") return l def incref_llvm(self, em, v): assert is_emitter(em) emitter = CodeEmitter(em) Variable(self, v, 1, False).getattr(emitter, "__incref__").call(emitter, []) return emitter.get_llvm() def incref_c(self, em, v): assert is_emitter(em) emitter = CodeEmitter(em) Variable(self, v, 1, False).getattr(emitter, "__incref__").call(emitter, []) return emitter.get_c() def decref_llvm(self, em, v): assert is_emitter(em) emitter = CodeEmitter(em) Variable(self, v, 1, False).getattr(emitter, "__decref__").call(emitter, []) return emitter.get_llvm() def decref_c(self, em, v): assert is_emitter(em) emitter = CodeEmitter(em) Variable(self, v, 1, False).getattr(emitter, "__decref__").call(emitter, []) return emitter.get_c() # All types default to raised types def get_instantiated(self): return self def dup(self, v, dup_cache): assert isinstance(v, (float, int, str)), (self, repr(v)) return v def initialize(self, em, stage): assert stage in ("attrs", "write") if stage == "write": self.initialize(em, "attrs") assert stage not in self.initializing if stage not in self.initialized: self.initializing.add(stage) self._initialize(em, stage) self.initializing.remove(stage) self.initialized.add(stage) def get_attr_types(self): self.__check_initialized("attrs") if hasattr(self, "class_methods"): r = {} for name, v in self.class_methods.iteritems(): if name in r: continue r[name] = (UnboxedInstanceMethod(self, v.t), AttributeAccessType.CONST_METHOD) if hasattr(self, "typeobj_name"): r["__class__"] = (Type, AttributeAccessType.IMPLICIT_FIELD) return r raise NotImplementedError(type(self)) def getattr(self, em, v, attr, clsonly): self.__check_initialized("attrs") if attr == "__class__" and hasattr(self, "typeobj_name"): return Variable(Type, self.typeobj_name, 1, False) if hasattr(self, "class_methods"): if attr not in self.class_methods: raise UserAttributeError((self, attr)) r = self.class_methods[attr] r.incvref(em) return UnboxedInstanceMethod.make(em, v, r) raise NotImplementedError((type(self), attr)) def hasattr(self, attr): self.__check_initialized("attrs") if hasattr(self, "class_methods"): return attr in self.class_methods raise NotImplementedError(type(self)) def get_method_name(self, em, name): self.__check_initialized("attrs") fake_var = Variable(None_, "null", 1, False) attr = self.getattr(em, fake_var, name, clsonly=True) assert isinstance(attr.t, UnboxedInstanceMethod) f = attr.v[1] assert isinstance(f, Variable) if isinstance(f.t, PolymorphicFunctionMT): f = f.v[0][0] assert isinstance(f.t, UnboxedFunctionMT), f.t assert len(f.v) == 3 assert f.v[2] is None r = f.v[0] attr.decvref(em) return raw_func_name(r) def _can_convert_to(self, t): return False def can_convert_to(self, t): if t is self: return True if isinstance(t, BoxedMT): return t.can_convert_from(self) return self._can_convert_to(t) def _convert_to(self, em, var, t): if isinstance(t, BoxedMT): return t.convert_from(em, var) raise UserTypeError("Don't know how to convert %s to %s" % (self, t)) def convert_to(self, em, var, t): self.initialize(em, "write") assert var.t is self if t is self: return var r = self._convert_to(em, var, t) assert r.t is t, (self, t, r.t.__dict__, t.__dict__) return r def call(self, em, v, args, expected_type=None): self.initialize(em, "write") return self.getattr(em, Variable(self, v, 1, False), "__call__", clsonly=True).call(em, args, expected_type=expected_type) def singleton(cls): r = cls() cls.__init__ = None return r class SliceMT(MT): def llvm_type(self): return "%slice*" @staticmethod def setup_class_methods(): SliceMT.class_methods = { "__incref__": Variable(UnboxedFunctionMT(None, None, [Slice], None_), ("@slice_incref", [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(None, None, [Slice], None_), ("@slice_decref", [], None), 1, False), } def _can_convert_to(self, t): return False @staticmethod def create(em, start, end, step): assert start is None or isinstance(start, Variable) assert end is None or isinstance(end, Variable) assert step is None or isinstance(step, Variable) flags = 0 if start: flags |= 1 if end: flags |= 2 name = "%" + em.mkname() start_s = start.v if start else 0 end_s = end.v if end else 0 step_s = step.v if step else 1 em.pl("%s = call %%slice* @slice_alloc(i64 %d, i64 %s, i64 %s, i64 %s)" % (name, flags, start_s, end_s, step_s)) em.pc("#error unimplemented 0") for arg in start, end, step: if arg: arg.decvref(em) return Variable(Slice, name, 1, True) Slice = singleton(SliceMT) Slice.initialized = ("attrs", "write") class StrConstantMT(MT): def dup(self, v, dup_cache): return v def free(self, em, v): pass def getattr(self, em, v, attr, clsonly): if attr == "__mod__": return UnboxedInstanceMethod.make(em, v, Variable(StrFormat, (), 1, False)) # Returning a method that takes a str will cause this StrConstant # to get upconverted on every call to the method; not most optimized # but it works out: return Str.getattr(em, v, attr, clsonly) def get_instantiated(self): return Str def _can_convert_to(self, t): return t is Str or Str._can_convert_to(t) __converted = {} def _convert_to(self, em, var, t): if t is not Str: str_v = self._convert_to(em, var, Str) return Str.convert_to(em, str_v, t) assert t is Str, t [s] = var.v if s not in StrConstantMT.__converted: r = "@" + em.mkname("str") ptr = em.get_str_ptr(s) em.llvm_tail.write("%s = global %%string {i64 1, i64 %d, i8* %s, [0 x i8] zeroinitializer}\n" % (r, len(s), ptr)) StrConstantMT.__converted[s] = r var.decvref(em) return Variable(Str, StrConstantMT.__converted[s], 1, False) StrConstant = singleton(StrConstantMT) StrConstant.initialized = ("attrs", "write") class NoneMT(MT): def llvm_type(self): return "%none*" @staticmethod def setup_class_methods(): em = None NoneMT.class_methods = { "__incref__": Variable(UnboxedFunctionMT(em, None, [None_], None_), ("@none_incref", [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [None_], None_), ("@none_decref", [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [None_], Str), ("@none_repr", [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [None_, None_], Bool), ("@none_eq", [], None), 1, False), } NoneMT.class_methods["__str__"] = NoneMT.class_methods["__repr__"] def c_type(self): return "void*" def dup(self, v, dup_cache): return v def _can_convert_to(self, t): return t not in (Int, Float, Bool, StrConstantMT, UnboxedTupleMT, UnboxedInstanceMethod, UnboxedFunctionMT) def _convert_to(self, em, var, t): assert t is not None_ var.decvref(em) return Variable(t, "null", 1, True) None_ = singleton(NoneMT) None_.initialized = ("attrs", "write") class _SpecialFuncMT(MT): def __init__(self): super(_SpecialFuncMT, self).__init__() self.initialized = ("attrs", "write") def call(self, em, v, args, expected_type=None): raise NotImplementedError() def dup(self, v, dup_cache): assert not v return v def free(self, em, v): assert v == () return None def _can_convert_to(self, t): return False class LenMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args return _v.getattr(em, "__len__", clsonly=True).call(em, []) Len = singleton(LenMT) class StrFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args return _v.getattr(em, "__str__", clsonly=True).call(em, []) StrFunc = singleton(StrFuncMT) class ReprFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args return _v.getattr(em, "__repr__", clsonly=True).call(em, []) ReprFunc = singleton(ReprFuncMT) class NrefMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args return _v.getattr(em, "__nrefs__", clsonly=True).call(em, []) Nref = singleton(NrefMT) class TypeFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args return _v.getattr(em, "__class__") TypeFunc = singleton(TypeFuncMT) class BoolFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 1 [_v] = args r = _v.getattr(em, "__nonzero__", clsonly=True).call(em, []) assert r.t in (Int, Bool), "__nonzero__ should return bool or int, returned %s" % (r.t,) if r.t is Int: new_name = "%" + em.mkname() em.pl("%s = trunc i64 %s to i1" % (new_name, r.v)) em.pc("#error unimplemented 1") r.decvref(em) r = Variable(Bool, new_name, 1, False) return r BoolFunc = singleton(BoolFuncMT) class IsinstanceMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 2 obj, cls = args assert isinstance(cls.t, ClassMT) if isinstance(obj.t, InstanceMT): r = obj.t.cls is cls.t obj.decvref(em) cls.decvref(em) return Variable(Bool, str(int(r)), 1, False) elif isinstance(obj.t, BoxedMT): realclass = obj.getattr(em, "__class__") r = "%" + em.mkname() thisclass = cls.t.get_typeobj(em) assert realclass.t is Type assert thisclass.t is Type em.pl("%s = icmp eq %s %s, %s" % (r, Type.llvm_type(), realclass.v, thisclass.v)) em.pc("#error unimplemented 2") cls.decvref(em) return Variable(Bool, r, 1, True) else: raise Exception(obj.t) Isinstance = singleton(IsinstanceMT) class CastMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert not v assert len(args) == 2 obj, cls = args assert isinstance(cls.t, ClassMT) instance_t = cls.t._instance if isinstance(obj.t, BoxedMT): underlying = obj.t._struct.get(em, obj.v, BoxedMT.UNDERLYING_FIELD_NAME, skip_incref=True) name = "%" + em.mkname() if instance_t is Float: em.pl("%s = call %s @ptr_to_float(%s %s)" % (name, instance_t.llvm_type(), underlying.t.llvm_type(), underlying.v)) else: llvm_cmd = "bitcast" if instance_t is Int or instance_t is Bool: llvm_cmd = "ptrtoint" em.pl("%s = %s %s %s to %s" % (name, llvm_cmd, underlying.t.llvm_type(), underlying.v, instance_t.llvm_type())) em.pc("#error unimplemented 3") return Variable(instance_t, name, 1, False) else: raise Exception(obj.t) Cast = singleton(CastMT) class StrFormatMT(_SpecialFuncMT): def _extract_format_chars(self, s): cur = 0 rtn = [] while cur < len(s): if s[cur] != '%': cur += 1 continue cur += 1 while s[cur] in " 0123456789+-.#*": cur += 1 c = s[cur] cur += 1 if c == '%': continue assert c.isalpha() rtn.append(c) return rtn def can_call(self, args): return True def call(self, em, v, args, expected_type=None): assert len(args) == 2 assert args[0].t is StrConstant, args[0].t [fmt] = args[0].v if isinstance(args[1].t, UnboxedTupleMT): data = list(args[1].v) elif isinstance(args[1].t, TupleMT): raise NotImplementedError() else: data = [args[1]] chars = self._extract_format_chars(fmt) assert len(chars) == len(data), "Wrong number of format arguments specified (need %d but got %d)" % (len(chars), len(data)) for i in xrange(len(chars)): ch = chars[i] if ch == 'd': assert data[i].t is Int elif ch == 's': if data[i].t is not Str: data[i] = StrFunc.call(em, (), [data[i]]) assert data[i].t is Str elif ch == 'f': assert data[i].t is Float else: raise Exception("Unsupported format character: '%s'" % ch) s = args[0].convert_to(em, Str) name = '%' + em.mkname() em.pl("%s = call %%string* (%%string*, ...)* @str_format(%s)" % (name, ', '.join(["%s %s" % (v.t.llvm_type(), v.v) for v in [s] + data]))) em.pc("#error unimplemented 4") for d in [s] + data: d.decvref(em) return Variable(Str, name, 1, True) StrFormat = singleton(StrFormatMT) class MapFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert len(args) == 2 args[0] = args[0].convert_to(em, args[0].t.get_instantiated()) assert isinstance(args[0].t, CallableMT), (args[0].t,) assert isinstance(args[1].t, ListMT) f = args[0] l = args[1] assert len(f.t.arg_types) >= 1 and len(f.t.arg_types) - f.t.ndefaults <= 1, (len(f.t.arg_types), f.t.ndefaults) assert f.t.arg_types[0] is l.t.elt_type name = MapFuncMT.get_name(em, f.t.arg_types[0], f.t.rtn_type) callable_type = CallableMT.make_callable([f.t.arg_types[0]], 0, f.t.rtn_type) f = args[0] = f.convert_to(em, callable_type) r = '%' + em.mkname() r_type = ListMT.make_list(f.t.rtn_type) r_type.initialize(em, "write") em.pl('%s = call %s @%s(%s %s, %s %s)' % (r, r_type.llvm_type(), name, f.t.llvm_type(), f.v, l.t.llvm_type(), l.v)) em.pc("#error unimplemented 5") for a in args: a.decvref(em) return Variable(r_type, r, 1, True) __made_maps = {} @staticmethod def get_name(em, arg_type, ret_type): mem_key = (arg_type, ret_type) if mem_key not in MapFuncMT.__made_maps: name = "map%d" % len(MapFuncMT.__made_maps) template = eval_ctemplate("map", em, { 'input_type':arg_type, 'output_type':ret_type, 'name':name, }) em.c_head.write(template) MapFuncMT.__made_maps[mem_key] = name ret_list = ListMT.make_list(ret_type) ret_list.initialize(em, "write") arg_list = ListMT.make_list(arg_type) arg_list.initialize(em, "write") callable_type = CallableMT.make_callable([arg_type], 0, ret_type) callable_type.initialize(em, "write") em.llvm_head.write("declare %s @%s(%s, %s)\n" % (ListMT.make_list(ret_type).llvm_type(), name, callable_type.llvm_type(), ListMT.make_list(arg_type).llvm_type())) return MapFuncMT.__made_maps[mem_key] MapFunc = singleton(MapFuncMT) class ReduceFuncMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert len(args) == 3 f, l, initial = args f = f.convert_to(em, f.t.get_instantiated()) assert isinstance(f.t, CallableMT), (f.t,) assert isinstance(l.t, ListMT) accum_type = make_common_supertype([f.t.rtn_type, initial.t]) print accum_type, f.t.rtn_type, initial.t elt_type = l.t.elt_type assert len(f.t.arg_types) == 2 assert accum_type.can_convert_to(f.t.arg_types[0]), (accum_type, f.t.arg_types[0]) assert elt_type.can_convert_to(f.t.arg_types[1]) name = ReduceFuncMT.get_name(em, accum_type, elt_type) callable_type = CallableMT.make_callable([accum_type, elt_type], 0, accum_type) converted_f = f.convert_to(em, callable_type) converted_initial = initial.convert_to(em, accum_type) r = '%' + em.mkname() em.pl('%s = call %s @%s(%s %s, %s %s, %s %s)' % (r, accum_type.llvm_type(), name, converted_f.t.llvm_type(), converted_f.v, l.t.llvm_type(), l.v, accum_type.llvm_type(), converted_initial.v)) em.pc("#error unimplemented 5") for a in (converted_f, l, converted_initial): a.decvref(em) return Variable(accum_type, r, 1, True) __made_reduces = {} @staticmethod def get_name(em, accum_type, elt_type): mem_key = (accum_type, elt_type) if mem_key not in ReduceFuncMT.__made_reduces: name = "reduce%d" % len(ReduceFuncMT.__made_reduces) arg_list = ListMT.make_list(elt_type) arg_list.initialize(em, "write") callable_type = CallableMT.make_callable([accum_type, elt_type], 0, accum_type) callable_type.initialize(em, "write") template = eval_ctemplate("reduce", em, { 'accum_type':accum_type, 'elt_type':elt_type, 'list_type':arg_list, 'callable_type':callable_type, 'name':name, }) em.c_head.write(template) ReduceFuncMT.__made_reduces[mem_key] = name em.llvm_head.write("declare %s @%s(%s, %s, %s)\n" % (accum_type.llvm_type(), name, callable_type.llvm_type(), ListMT.make_list(elt_type).llvm_type(), accum_type.llvm_type())) return ReduceFuncMT.__made_reduces[mem_key] ReduceFunc = singleton(ReduceFuncMT) class EnumerateMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert len(args) == 1 args[0] = args[0].convert_to(em, args[0].t.get_instantiated()) assert isinstance(args[0].t, ListMT), (args[0].t,) l = args[0] name = EnumerateMT.get_name(em, l.t) rtn_type = ListMT.make_list(TupleMT.make_tuple([Int, l.t.elt_type])) r = "%" + em.mkname() em.pl("%s = call %s @%s(%s %s)" % (r, rtn_type.llvm_type(), name, l.t.llvm_type(), l.v)) em.pc("#error unimplemented 6") l.decvref(em) return Variable(rtn_type, r, 1, True) __made_names = {} @staticmethod def get_name(em, l): assert isinstance(l, ListMT) mem_key = (l,) if mem_key not in EnumerateMT.__made_names: name = "enumerate%d" % len(EnumerateMT.__made_names) rtn_elt_type = TupleMT.make_tuple([Int, l.elt_type]) rtn_elt_type.initialize(em, "write") rtn_type = ListMT.make_list(rtn_elt_type) rtn_type.initialize(em, "write") template = eval_ctemplate("enumerate", em, { 'input_type':l, 'return_type':rtn_type, 'name':name, }) em.c_head.write(template) EnumerateMT.__made_names[mem_key] = name em.llvm_head.write("declare %s @%s(%s)\n" % (rtn_type.llvm_type(), name, l.llvm_type())) return EnumerateMT.__made_names[mem_key] Enumerate = singleton(EnumerateMT) class MinFuncMT(_SpecialFuncMT): def __init__(self, type): self._type = type def can_call(self, args): return len(args) == 1 def call(self, em, v, args, expected_type=None): assert len(args) == 1 args[0] = args[0].convert_to(em, args[0].t.get_instantiated()) l = args[0] name = self._type + str(MinFuncMT.get_num(em, l.t)) rtn_type = l.t.get_attr_types()['__iter__'][0].get_instantiated().rtn_type.get_attr_types()['next'][0].get_instantiated().rtn_type r = "%" + em.mkname() em.pl("%s = call %s @%s(%s %s)" % (r, rtn_type.llvm_type(), name, l.t.llvm_type(), l.v)) em.pc("#error unimplemented 21") l.decvref(em) return Variable(rtn_type, r, 1, True) __made_names = {} @staticmethod def get_num(em, t): mem_key = (t,) if mem_key not in MinFuncMT.__made_names: num = len(MinFuncMT.__made_names) it_type = t.get_attr_types()['__iter__'][0].get_instantiated().rtn_type elt_type = it_type.get_attr_types()['next'][0].get_instantiated().rtn_type template = eval_ctemplate("min", em, { 't':t, 'it':it_type, 'et':elt_type, 'num':num, }) em.c_head.write(template) MinFuncMT.__made_names[mem_key] = num em.llvm_head.write("declare %s @min%s(%s)\n" % (elt_type.llvm_type(), num, t.llvm_type())) em.llvm_head.write("declare %s @max%s(%s)\n" % (elt_type.llvm_type(), num, t.llvm_type())) return MinFuncMT.__made_names[mem_key] MinFunc = MinFuncMT("min") MaxFunc = MinFuncMT("max") class UnboxedFunctionMT(MT): def __init__(self, em, closure_type, arg_types, rtn_type, ndefaults=0): super(UnboxedFunctionMT, self).__init__() self.closure_type = closure_type self.arg_types = [a.get_instantiated() for a in arg_types] self.rtn_type = rtn_type.get_instantiated() self.ndefaults = ndefaults def _initialize(self, em, stage): self.get_instantiated().initialize(em, stage) def dup(self, v, dup_cache): func_name, defaults, closure = v assert len(defaults) == self.ndefaults return func_name, [d.dup(dup_cache) for d in defaults], closure.dup(dup_cache) if closure else None def free(self, em, v): func_name, defaults, closure = v assert len(defaults) == self.ndefaults for d in defaults: d.decvref(em) return None def _argtype_str(self): return ", ".join(([self.closure_type.llvm_type()] if self.closure_type else []) + [a.llvm_type() for a in self.arg_types]) def llvm_type(self): ret_type = self.rtn_type.llvm_type() if self.rtn_type is not None_ else "void" return "%s (%s)*" % (ret_type, self._argtype_str()) def can_call(self, args): # TODO should pass in defaults_types to this type if len(args) != len(self.arg_types): return False for i in xrange(len(args)): if not args[i].can_convert_to(self.arg_types[i]): return False return True def call(self, em, v, args, expected_type=None): func_name, defaults, closure = v assert len(defaults) == self.ndefaults assert len(defaults) <= len(self.arg_types) for d in defaults: assert isinstance(d, Variable) assert len(self.arg_types) - len(defaults) <= len(args) <= len(self.arg_types), (args, self.arg_types, defaults) args = list(args) # we're going to modify it for n in xrange(len(args), len(self.arg_types)): arg = defaults[n - len(self.arg_types)] assert isinstance(arg, Variable) arg.incvref(em) args.append(arg) # args.append(Variable(Int, arg, 1, False)) assert len(self.arg_types) == len(args) for i in xrange(len(args)): args[i] = args[i].convert_to(em, self.arg_types[i]) if [_v.t for _v in args] != self.arg_types: raise UserTypeError([_v.t for _v in args], self.arg_types) prologue = "" if self.rtn_type is not None_: name = "%" + em.mkname() prologue = "%s = " % name args_plus_closure = ([closure] + args) if closure else args rtn_type = self.rtn_type.llvm_type() if self.rtn_type is not None_ else "void" em.pl("%scall %s (%s)* %s(%s)" % (prologue, rtn_type, self._argtype_str(), func_name, ", ".join("%s %s" % (_v.t.llvm_type(), _v.v) for _v in args_plus_closure))) if self.rtn_type is not None_: assert prologue.startswith("%") prologue = "%s " % self.rtn_type.c_type() + prologue[1:] if func_name.startswith('%'): em.pc("#error unimplemented 20") else: def local(n): n = str(n) if n.startswith('%'): return n[1:] return n em.pc("%s %s(%s);" % (prologue, raw_func_name(func_name), ", ".join(local(_v.v) for _v in args_plus_closure))) for _v in args: _v.decvref(em) if self.rtn_type is not None_: return Variable(self.rtn_type, name, 1, True) else: return Variable(self.rtn_type, "null", 1, False) def get_instantiated(self): return CallableMT.make_callable(self.arg_types, self.ndefaults, self.rtn_type) def _can_convert_to(self, t): if not isinstance(t, CallableMT): return False return t.arg_types == self.arg_types and t.rtn_type == self.rtn_type def _convert_to(self, em, var, t): (func_name, defaults, closure) = var.v assert len(defaults) == self.ndefaults if isinstance(t, CallableMT): if t.arg_types == self.arg_types and t.rtn_type == self.rtn_type: assert (closure is None) == (self.closure_type is None) assert t.ndefaults <= len(defaults) defaults = defaults[:t.ndefaults] r = SimpleFunction.make(em, self.arg_types, defaults, self.rtn_type, closure, func_name) var.decvref(em) return r raise UserTypeError((self.arg_types, self.rtn_type, var.v[1], t.arg_types, t.rtn_type)) raise UserTypeError(t) class PolymorphicFunctionMT(MT): def __init__(self, func_types): super(PolymorphicFunctionMT, self).__init__() assert isinstance(func_types, (list, tuple)) assert len(func_types) > 0 self.func_types = func_types self.initialized = ("attrs", "write") def dup(self, v, dup_cache): funcs, = v return ([f.dup(dup_cache) for f in funcs],) def free(self, em, v): funcs, = v assert len(funcs) == len(self.func_types) for i in xrange(len(funcs)): assert funcs[i].t is self.func_types[i] for f in funcs: f.decvref(em) def call(self, em, v, args, expected_type=None): funcs, = v assert len(funcs) == len(self.func_types) for i in xrange(len(funcs)): assert funcs[i].t is self.func_types[i] for f in funcs: if f.t.can_call([a.t for a in args]): f.incvref(em) return f.call(em, args) raise UserTypeError("Can't call with args %s" % (args,)) def convert_to(self, em, var, t): funcs, = var.v assert len(funcs) == len(self.func_types) for i in xrange(len(funcs)): assert funcs[i].t is self.func_types[i] for f in funcs: if f.t.can_convert_to(t): f.incvref(em) var.decvref(em) return f.convert_to(em, t) raise UserTypeError() def get_instantiated(self): # TODO this just heuristically picks the first function, but it'd be nice to be able to pick # the "right" one (is that the same thing as supporting rank-2 polymorphism?) return self.func_types[0].get_instantiated() @staticmethod def make(funcs): for f in funcs: assert isinstance(f, Variable) # This isn't completely necessary but I haven't thought through how it would work assert isinstance(f.t, (UnboxedFunctionMT, _SpecialFuncMT)) t = PolymorphicFunctionMT([f.t for f in funcs]) return Variable(t, (funcs,), 1, False) class UnboxedInstanceMethod(MT): def __init__(self, obj_type, func_type): super(UnboxedInstanceMethod, self).__init__() assert isinstance(obj_type, MT) assert isinstance(func_type, MT) self._obj_type = obj_type self._func_type = func_type def _initialize(self, em, stage): try: self.get_instantiated().initialize(em, stage) except CantInstantiateException: pass @staticmethod def make(em, o, f): assert is_emitter(em) assert isinstance(o, Variable) assert isinstance(f, Variable) assert isinstance(f.t, (UnboxedFunctionMT, PolymorphicFunctionMT, _SpecialFuncMT)), f.t o.incvref(em) f.incvref(em) t = UnboxedInstanceMethod(o.t, f.t) t.initialize(em, "write") return Variable(t, (o, f), 1, False) def dup(self, v, dup_cache): (o, f) = v return o.dup(dup_cache), f.dup(dup_cache) def can_call(self, args): return self._func_type.can_call([self._obj_type] + args) def call(self, em, v, args, expected_type=None): (o, f) = v o.incvref(em) # being passed into the function, f.incvref(em) # which consumes one vref return f.call(em, [o] + args) def free(self, em, v): (o, f) = v # em.pl("; UIM.decref") o.decvref(em) f.decvref(em) # em.pl("; UIM.decref, done") def get_instantiated(self): if isinstance(self._func_type, UnboxedFunctionMT): return CallableMT.make_callable(self._func_type.arg_types[1:], 0, self._func_type.rtn_type) elif isinstance(self._func_type, CallableMT): return CallableMT.make_callable(self._func_type.arg_types[1:], 0, self._func_type.rtn_type) else: raise CantInstantiateException(self._func_type) def _can_convert_to(self, t): return self.get_instantiated().can_convert_to(t) def _convert_to(self, em, var, t): if isinstance(t, CallableMT): assert isinstance(self._func_type, UnboxedFunctionMT), "unimplemented" if t.arg_types == self._func_type.arg_types[1:] and t.rtn_type == self._func_type.rtn_type: assert t == self.get_instantiated() (o, f) = var.v o.incvref(em) if o.t != o.t.get_instantiated(): o = o.convert_to(em, o.t.get_instantiated()) r = InstanceMethod.make(em, o, f) o.decvref(em, "raised for UIM->IM") var.decvref(em, "UIM raised to IM") return r return var.convert_to(em, self.get_instantiated()).convert_to(em, t) class InstanceMethod(object): __made_funcs = {} def __init__(self): assert 0 @staticmethod def make(em, o, f): assert o.t is o.t.get_instantiated() assert isinstance(f.t, UnboxedFunctionMT) name = InstanceMethod.get_name(em, o.t, f.t) args = f.t.arg_types[1:] ret = f.t.rtn_type func = CallableMT.make_callable(args, 0, ret) func.initialize(em, "write") r = "%" + em.mkname() o = o.split(em) if not isinstance(f.t, UnboxedFunctionMT): f = f.split(em) raise Exception("test this") em.pl("%s = call %s (%s, %s)* @%s_ctor(%s %s, %s %s)" % (r, func.llvm_type(), o.t.llvm_type(), f.t.llvm_type(), name, o.t.llvm_type(), o.v, f.t.llvm_type(), f.v[0])) em.pc("#error unimplemented 7") return Variable(func, r, 1, True) @staticmethod def get_name(em, ot, ft): assert isinstance(ot, MT) assert isinstance(ft, UnboxedFunctionMT) assert ot is ot.get_instantiated() mem_key = (ot, tuple(ft.arg_types), ft.rtn_type) ft.initialize(em, "write") ot.initialize(em, "attrs") if mem_key not in InstanceMethod.__made_funcs: name = "im_%d" % (len(InstanceMethod.__made_funcs),) InstanceMethod.__made_funcs[mem_key] = name bound_args = ft.arg_types[1:] ret = ft.rtn_type ret_type = ret.llvm_type() callable_mt = CallableMT.make_callable(bound_args, 0, ret) callable_mt.initialize(em, "write") callable_type = callable_mt.llvm_type() o_decref = ot.decref_llvm(em, "%o") or "" real_call_type = "%s (%s)*" % (ret_type, ", ".join(["%" + name + "*"] + [a.llvm_type() for a in bound_args])) call_type = "%s (%s)*" % (ret_type, ", ".join([callable_type] + [a.llvm_type() for a in bound_args])) func_type = ft.llvm_type() arg_string = ", ".join(["%s %%o" % ot.llvm_type()] + ["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(bound_args)]) def_args = ", ".join(["%%%s* %%self" % name] + ["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(bound_args)]) evaluated = eval_template("im", em, { 'n':name, 'r':ret_type, 'ft':ft, 'ot':ot, 'bound_args':bound_args, "callable_type":callable_type, "call_type":call_type, "real_call_type":real_call_type, "arg_string":arg_string, "func_type":func_type, "def_args":def_args, "obj_type":ot.llvm_type(), "f_decref":"", # Only supporting UnboxedFunctionMT for now "o_decref":o_decref, "vtable_t":callable_type[:-1] + "_vtable", "alloc_name":em.get_str_ptr(name), }) evaluated = convert_none_to_void_ll(evaluated) em.llvm_tail.write(evaluated) return InstanceMethod.__made_funcs[mem_key] class CallableMT(MT): __made_funcs = {} def __init__(self, args, ndefaults, ret, name): super(CallableMT, self).__init__() for a in args: assert isinstance(a, MT) assert isinstance(ret, MT) assert isinstance(ndefaults, (int, long)) assert ret.get_instantiated() is ret for a in args: assert a.get_instantiated() is a, (a, a.get_instantiated()) self.arg_types = args self.ndefaults = ndefaults self.rtn_type = ret self.name = name def _initialize(self, em, stage): if stage == "attrs": self.class_methods = { "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % self.name, [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % self.name, [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_eq" % self.name, [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % self.name, [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % self.name, [], None), 1, False), } self.class_methods["__str__"] = self.class_methods["__repr__"] elif stage == "write": for a in self.arg_types: a.initialize(em, "attrs") self.rtn_type.initialize(em, "attrs") ret_type = self.rtn_type.llvm_type() arg_type_strings = [] arg_strings = [] call_types = [] for i in xrange(len(self.arg_types) - self.ndefaults, len(self.arg_types) + 1): arg_type_string = "%s" % (", ".join(["%" + self.name + "*"] + [a.llvm_type() for a in self.arg_types[:i]])) arg_string = ", ".join(["%%%s* %%f" % self.name] + ["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(self.arg_types[:i])]) call_type = "%s (%s)" % (ret_type, arg_type_string) arg_type_strings.append(arg_type_string) arg_strings.append(arg_string) call_types.append(call_type) del arg_type_string, arg_string, call_type evaluated = eval_template("callable", em, { 'n':self.name, 'call_types': ", ".join(["%s*" % ct for ct in call_types]), "alloc_name":em.get_str_ptr(self.name), }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) for i in xrange(self.ndefaults + 1): this_nargs = len(self.arg_types) - self.ndefaults + i evaluated = eval_template("callable_call", em, { 'CHECK_NONE':'', 'n':self.name, 'this_nargs': this_nargs, 'call_ptr_idx': i + 1, 'r':ret_type, "this_call_type":call_types[i], "this_arg_string":arg_strings[i], }) evaluated = convert_none_to_void_ll(evaluated) em.llvm_tail.write(evaluated) ctemplate = eval_ctemplate("callable", em, { 'n':self.name, 'nargs': len(self.arg_types), 'ndefaults': self.ndefaults, }) em.c_head.write(ctemplate) else: raise Exception(stage) def llvm_type(self): return "%%%s*" % (self.name,) def can_call(self, args): if not (len(self.arg_types) - self.ndefaults <= len(args) <= len(self.arg_types)): return False for i in xrange(len(args)): if not args[i].can_convert_to(self.arg_types[i]): return False return True def call(self, em, v, args, expected_type=None): assert len(self.arg_types) - self.ndefaults <= len(args) <= len(self.arg_types), (args, self.arg_types, self.ndefaults) args = list(args) for i in xrange(len(args)): args[i] = args[i].convert_to(em, self.arg_types[i]) assert [_v.t for _v in args] == self.arg_types[:len(args)], ([_v.t for _v in args], self.arg_types) prologue = "" if self.rtn_type is not None_: rtn_name = "%" + em.mkname() prologue = "%s = " % rtn_name ret_type = self.rtn_type.llvm_type() # TODO duplication else: ret_type = "void" type_string = "%s (%s)" % (ret_type, ", ".join([self.llvm_type()] + [a.llvm_type() for a in self.arg_types[:len(args)]])) arg_string = ", ".join(["%s %s" % (self.llvm_type(), v)] + ["%s %s" % (a.t.llvm_type(), a.v) for a in args]) em.pl("%scall %s* @%s_call%d(%s)" % (prologue, type_string, self.name, len(args), arg_string)) em.pc("#error unimplemented 8") for _v in args: _v.decvref(em) if self.rtn_type is not None_: return Variable(self.rtn_type, rtn_name, 1, True) else: return Variable(self.rtn_type, "null", 1, False) def _can_convert_to(self, t): if not isinstance(t, CallableMT): return False if t.arg_types != self.arg_types: return False if not self.rtn_type.can_convert_to(t.rtn_type): return False return True @staticmethod def make_callable(args, ndefaults, ret): for a in args: assert isinstance(a, MT) assert isinstance(ret, MT) mem_key = (tuple(args), ndefaults, ret) if mem_key not in CallableMT.__made_funcs: name = "c_%d" % (len(CallableMT.__made_funcs),) CallableMT.__made_funcs[mem_key] = CallableMT(args, ndefaults, ret, name) return CallableMT.__made_funcs[mem_key] def _convert_to(self, em, var, t): assert isinstance(t, CallableMT) if t.ndefaults <= self.ndefaults and len(t.arg_types) - t.ndefaults == len(self.arg_types) - self.ndefaults: if self.arg_types[:len(t.arg_types)] == t.arg_types and self.rtn_type == t.rtn_type: name = "%" + em.mkname() em.pl("%s = bitcast %s %s to %s" % (name, self.llvm_type(), var.v, t.llvm_type())) rtn = Variable(t, name, 1, True) rtn.incvref(em) rtn.getattr(em, "__incref__").call(em, []) var.decvref(em) return rtn return _Reboxer.make(em, var, t) def vtable_type(self): return self.llvm_type()[:-1] + "_vtable" class _Reboxer(object): __made_reboxers = {} __init__ = None @staticmethod def make(em, var, new_type): assert is_emitter(em) assert isinstance(var.t, CallableMT) assert isinstance(new_type, CallableMT) key = (var.t, new_type) if key not in _Reboxer.__made_reboxers: name = em.mkname(prefix="reboxer_") call_type = UnboxedFunctionMT(em, None, [new_type] + new_type.arg_types, new_type.rtn_type) _Reboxer.__made_reboxers[key] = name evaluated = eval_template("reboxer", em, { 'n':name, 'orig_type':var.t, 'new_type':new_type, 'call_type':call_type, }) em.llvm_tail.write(evaluated) name = _Reboxer.__made_reboxers[key] em.pc("#error unimplemented 9") r = "%" + em.mkname() em.pl("%s = call %s @%s_ctor(%s %s)" % (r, new_type.llvm_type(), name, var.t.llvm_type(), var.v)) var.decvref(em) return Variable(new_type, r, 1, True) class SimpleFunction(object): __made_funcs = {} __init__ = None @staticmethod def make(em, arg_types, defaults, ret_type, closure, func_name): ndefaults = len(defaults) for i in xrange(ndefaults): arg_idx = len(arg_types) - ndefaults + i defaults[i].incvref(em) defaults[i] = defaults[i].convert_to(em, arg_types[arg_idx]) callable_type = CallableMT.make_callable(arg_types, ndefaults, ret_type) callable_type.initialize(em, "write") unboxed = UnboxedFunctionMT(em, closure.t if closure else None, arg_types, ret_type) func_type = unboxed.llvm_type() default_types = ''.join(", %s" % (d.t.llvm_type(),) for d in defaults) mem_key = (tuple(arg_types), ndefaults, ret_type, closure.t if closure else None) closure_def_type = ", %s" % closure.t.llvm_type() if closure else "" if mem_key not in SimpleFunction.__made_funcs: name = "f_%d" % (len(SimpleFunction.__made_funcs),) vtable_t = callable_type.llvm_type()[:-1] + "_vtable" ret_name = ret_type.llvm_type() if ret_type is not None_ else "void" call_types = [] for i in xrange(len(arg_types) - ndefaults, len(arg_types) + 1): arg_type_string = "%s" % (", ".join([callable_type.llvm_type()] + [a.llvm_type() for a in arg_types[:i]])) call_type = "%s (%s)" % (ret_name, arg_type_string) call_types.append(call_type) del arg_type_string, call_type defaults_argstr = ''.join(", %s %%default%d" % (d.t.llvm_type(), i) for (i, d) in enumerate(defaults)) call_funcs = ", ".join("%s* @%s_call%d" % (call_types[i], name, len(arg_types) - ndefaults + i) for i in xrange(ndefaults + 1)) defaults_start = 4 if closure else 3 store_defaults = "\n ".join("%%dptr%d = getelementptr inbounds %%%s* %%made, i32 0, i32 %d\n store %s %%default%d, %s* %%dptr%d\n %s" % (i, name, i + defaults_start, defaults[i].t.llvm_type(), i, defaults[i].t.llvm_type(), i, defaults[i].t.incref_llvm(em, "%%default%d" % (i,))) for i in xrange(len(defaults))) decref_defaults = "\n ".join("%%dptr%d = getelementptr inbounds %%%s* %%self, i32 0, i32 %d\n %%default%d = load %s* %%dptr%d\n %s" % (i, name, i + defaults_start, i, defaults[i].t.llvm_type(), i, defaults[i].t.decref_llvm(em, "%%default%d" % (i,))) for i in xrange(len(defaults))) evaluated = eval_template("function", em, { 'n': name, 'vtable_t': vtable_t, 'callable_type': callable_type.llvm_type(), 'call_types': ', '.join(call_types), 'closure_type': closure.t.llvm_type() if closure else "X", 'closure_def_type': closure_def_type, 'default_types': default_types, 'func_type': func_type, 'IFCLOSURE': '' if closure else ';', 'defaults_argstr': defaults_argstr, 'closure_incref': closure.t.incref_llvm(em, "%closure") if closure else 'closure_incref', 'closure_decref': closure.t.decref_llvm(em, "%closure") if closure else 'closure_decref', 'call_funcs': call_funcs, 'store_defaults': store_defaults, 'decref_defaults': decref_defaults, 'alloc_name': em.get_str_ptr(name), }) em.llvm_tail.write(evaluated) for i in xrange(ndefaults + 1): this_nargs = len(arg_types) - ndefaults + i this_defaults = len(arg_types) - this_nargs call_args = ", ".join(["%s %%self" % callable_type.llvm_type()] + ["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(arg_types[:this_nargs])]) func_args = ", ".join(["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(arg_types[:this_nargs])]) if func_args: if closure: func_args = ", " + func_args if this_defaults: func_args += ", " elif closure and this_defaults: func_args = "," load_defaults = "\n ".join("%%dptr%d = getelementptr inbounds %%%s* %%f, i32 0, i32 %d\n %%default%d = load %s* %%dptr%d\n ;%s" % (i, name, i + defaults_start, i, defaults[i].t.llvm_type(), i, defaults[i].t.incref_llvm(em, "%%default%d" % (i,))) for i in xrange(ndefaults - this_defaults, ndefaults)) decref_defaults = "\n ".join(";%s" % (defaults[i].t.decref_llvm(em, "%%default%d" % (i,)), ) for i in xrange(ndefaults - this_defaults, ndefaults)) defaults_argstr = ', '.join("%s %%default%d" % (defaults[i].t.llvm_type(), i) for i in xrange(ndefaults - this_defaults, ndefaults)) evaluated = eval_template("function_call", em, { 'n':name, 'this_nargs': this_nargs, 'r':ret_name, 'func_type': func_type, "call_args": call_args, "func_args": func_args, 'closure_type': closure.t.llvm_type() if closure else "X", 'IFCLOSURE': '' if closure else ';', "load_defaults": load_defaults, "decref_defaults": decref_defaults, "defaults_args": defaults_argstr, 'callable_type': callable_type.llvm_type(), }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) SimpleFunction.__made_funcs[mem_key] = name name = SimpleFunction.__made_funcs[mem_key] r = '%' + em.mkname() # TODO upconvert here closure_args = ", %s %s" % (closure.t.llvm_type(), closure.v) if closure else "" defaults_args = "".join(", %s %s" % (defaults[i].t.llvm_type(), defaults[i].v) for i in xrange(ndefaults)) em.pl("%s = call %s (%s %s %s)* @%s_ctor(%s %s %s %s)" % (r, callable_type.llvm_type(), func_type, closure_def_type, default_types, name, func_type, func_name, closure_args, defaults_args)) em.pc("#error unimplemented 10") for i in xrange(ndefaults): defaults[i].decvref(em) return Variable(callable_type, r, 1, True) class UnboxedTupleMT(MT): def __init__(self, elt_types): super(UnboxedTupleMT, self).__init__() for e in elt_types: assert isinstance(e, MT) self.elt_types = tuple(elt_types) self.initialized = ("attrs", "write") def dup(self, v, dup_cache): return tuple([e.dup(dup_cache) for e in v]) def getattr(self, em, v, attr, clsonly): if attr == "__getitem__": return UnboxedInstanceMethod.make(em, v, Variable(UnboxedTupleMT.Getitem, (), 1, False)) return self.get_instantiated().getattr(em, v, attr, clsonly) def free(self, em, v): assert isinstance(v, tuple) for e in v: e.decvref(em) class GetitemMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert len(args) == 2 assert isinstance(args[0].t, UnboxedTupleMT) assert args[1].t is Int idx = args[1].v assert isinstance(idx, int) assert 0 <= idx < len(args[0].t.elt_types) assert isinstance(args[0].v, tuple) assert len(args[0].t.elt_types) == len(args[0].v) r = args[0].v[idx] args[0].decvref(em) args[1].decvref(em) r.incvref(em) return r Getitem = GetitemMT() def get_instantiated(self): return TupleMT.make_tuple([e.get_instantiated() for e in self.elt_types]) def _can_convert_to(self, t): if not isinstance(t, TupleMT) or len(t.elt_types) != len(self.elt_types): return False for i in xrange(len(self.elt_types)): if not self.elt_types[i].can_convert_to(t.elt_types[i]): return False return True def _convert_to(self, em, var, t): if isinstance(t, TupleMT) and len(t.elt_types) == len(self.elt_types): elts = [] for i, e in enumerate(var.v): e.incvref(em) elts.append(e.convert_to(em, t.elt_types[i])) r = t.alloc(em, elts) for e in elts: e.decvref(em) var.decvref(em) return r var.incvref(em) return var.convert_to(em, self.get_instantiated()).convert_to(em, t) class TupleMT(MT): def __init__(self, name, elt_types): super(TupleMT, self).__init__() for e in elt_types: assert isinstance(e, MT) assert e == e.get_instantiated() self.elt_types = tuple(elt_types) self.name = name def _initialize(self, em, stage): if stage == "attrs": for e in self.elt_types: e.initialize(em, "attrs") self.class_methods = { "__getitem__": Variable(TupleMT.Getitem, (), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % (self.name,), [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_len" % (self.name,), [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % (self.name,), [], None), 1, False), "__lt__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_lt" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), } self.class_methods["__str__"] = self.class_methods["__repr__"] self.typeobj_name = "@%s_typeobj" % self.name if not all(e.hasattr("__eq__") for e in self.elt_types): del self.class_methods["__eq__"] del self.class_methods["__lt__"] elif not all(e.hasattr("__lt__") for e in self.elt_types): del self.class_methods["__lt__"] elif stage == "write": type_str = ", ".join(["i64"] + [e.llvm_type() for e in self.elt_types]) arg_str = ", ".join(["%s %%arg%d" % (e.llvm_type(), i) for i, e in enumerate(self.elt_types)]) carg_str = ", ".join(["%s" % (e.c_type(),) for i, e in enumerate(self.elt_types)]) assign_all = "" for i in xrange(len(self.elt_types)): e = self.elt_types[i] assign_all += "%%ptr%d = getelementptr inbounds %s %s, i64 0, i32 %d\n" % (i, self.llvm_type(), "%rtn", i + 1) inc = self.elt_types[i].incref_llvm(em, "%%arg%d" % i) if inc: assign_all += inc + '\n' assign_all += "store %s %%arg%d, %s* %%ptr%d\n" % (e.llvm_type(), i, e.llvm_type(), i) assign_all = assign_all.replace('\n', '\n ') decref_all = "" for i in xrange(len(self.elt_types)): e = self.elt_types[i] d = e.decref_llvm(em, "%%elt%d" % (i,)) if not d: continue decref_all += "%%ptr%d = getelementptr inbounds %s %%self, i64 0, i32 %d\n" % (i, self.llvm_type(), i + 1) decref_all += "%%elt%d = load %s* %%ptr%d\n" % (i, e.llvm_type(), i) decref_all += d + '\n' decref_all = decref_all.replace('\n', '\n ') add_all_str = "" for i in xrange(len(self.elt_types)): e = self.elt_types[i] assert e == e.get_instantiated() if i > 0: add_all_str += "call void @list_string_append(%%list_string* %%list, %%string* %%commaspace)\n" % () add_all_str += "%%ptr%d = getelementptr inbounds %s %%self, i64 0, i32 %d\n" % (i, self.llvm_type(), i + 1) add_all_str += "%%elt%d = load %s* %%ptr%d\n" % (i, e.llvm_type(), i) emitter = CodeEmitter(em) elt_repr_func = Variable(e, "%%elt%d" % i, 1, False).getattr(emitter, "__repr__", clsonly=True) repr_r = elt_repr_func.call(emitter, []) assert repr_r.t is Str add_all_str += emitter.get_llvm() + '\n' add_all_str += "call void @list_string_append(%%list_string* %%list, %%string* %s)\n" % (repr_r.v,) add_all_str += "call void @str_decref(%%string* %s)\n" % (repr_r.v,) if len(self.elt_types) == 1: add_all_str += "call void @list_string_append(%%list_string* %%list, %%string* %%comma)\n" % () add_all_str = add_all_str.replace('\n', '\n ') em.llvm_tail.write(eval_template("tuple", em, { 'n':self.name, 'type_str':type_str, 'arg_str':arg_str, 'assign_all':assign_all, 'decref_all':decref_all, 'add_all_str':add_all_str, 'DEBUG_CHECKS':' ' if DEBUG_CHECKS else ';', 'NO_DEBUG_CHECKS':' ' if not DEBUG_CHECKS else ';', 'len':len(self.elt_types), 'alloc_name':em.get_str_ptr(self.name), })) ctemplate = eval_ctemplate("tuple", em, { 't':self, 'n':self.name, 'args':carg_str, }) em.c_head.write(ctemplate) else: raise Exception(stage) def llvm_type(self): return "%%%s*" % self.name def get_ctor_name(self): return "@%s_ctor" % (self.name,) def alloc(self, em, elts): assert self.elt_types == tuple([e.t for e in elts]), (self.elt_types, tuple([e.t for e in elts])) name = '%' + em.mkname() em.pl("%s = call %s %s(%s)" % (name, self.llvm_type(), self.get_ctor_name(), ", ".join(["%s %s" % (v.t.llvm_type(), v.v) for v in elts]))) em.pc("#error unimplemented 11") return Variable(self, name, 1, True) def _can_convert_to(self, t): return False # TODO merge this with the Unboxed version class GetitemMT(_SpecialFuncMT): def call(self, em, v, args, expected_type=None): assert len(args) == 2 assert isinstance(args[0].t, TupleMT) assert args[1].t is Int idx = args[1].v assert isinstance(idx, int) assert 0 <= idx < len(args[0].t.elt_types) assert isinstance(args[0].v, str) t = args[0].t.elt_types[idx] pl = '%' + em.mkname() r = '%' + em.mkname() em.pl("%s = getelementptr inbounds %s %s, i64 0, i32 %d" % (pl, args[0].t.llvm_type(), args[0].v, idx + 1)) em.pl("%s = load %s* %s" % (r, t.llvm_type(), pl)) inc = t.incref_llvm(em, r) if inc: em.pl(inc + " ; tuple getitem") em.pc("#error unimplemented 12") args[0].decvref(em) args[1].decvref(em) return Variable(t, r, 1, True) Getitem = GetitemMT() __tuples = {} @staticmethod def make_tuple(elt_types): elt_types = tuple(elt_types) for e in elt_types: assert isinstance(e, MT) if elt_types not in TupleMT.__tuples: name = "_".join(["tuple%d" % len(elt_types)] + [e.llvm_type().replace('%', '').replace('*', '') for e in elt_types]) if len(name) > 40: name = "tuple_%d" % len(TupleMT.__tuples) TupleMT.__tuples[elt_types] = TupleMT(name, elt_types) t = TupleMT.__tuples[elt_types] return t class ListMT(MT): def __init__(self, elt_type): super(ListMT, self).__init__() assert isinstance(elt_type, MT) self.elt_type = elt_type self.name = ListMT.get_name(elt_type) self.iter_type = ListMT.ListIteratorMT(self) def _initialize(self, em, stage): if stage == "attrs": self.elt_type.initialize(em, "attrs") self.class_methods = { "__add__": Variable(UnboxedFunctionMT(em, None, [self, self], self), ("@%s_add" % (self.name,), [], None), 1, False), "append": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], None_), ("@%s_append" % (self.name,), [], None), 1, False), "__contains__": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], Bool), ("@%s_contains" % (self.name,), [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % (self.name,), [], None), 1, False), "extend": Variable(UnboxedFunctionMT(em, None, [self, self], None_), ("@%s_extend" % (self.name,), [], None), 1, False), "__getitem__": PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(em, None, [self, Int], self.elt_type), ("@%s_getitem" % (self.name,), [], None), 1, False), Variable(UnboxedFunctionMT(em, None, [self, Slice], self), ("@%s_getitem_slice" % (self.name,), [], None), 1, False)]), "__iadd__": Variable(UnboxedFunctionMT(em, None, [self, self], self), ("@%s_iadd" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "insert": Variable(UnboxedFunctionMT(em, None, [self, Int, self.elt_type], None_), ("@%s_insert" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [self], self.iter_type), ("@%s_iter" % (self.name,), [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_len" % (self.name,), [], None), 1, False), "__mul__": Variable(UnboxedFunctionMT(em, None, [self, Int], self), ("@%s_mul" % (self.name,), [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_nonzero" % (self.name,), [], None), 1, False), "__nrefs__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_nrefs" % (self.name,), [], None), 1, False), "pop": Variable(UnboxedFunctionMT(em, None, [self, Int], self.elt_type), ("@%s_pop" % (self.name,), [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % (self.name,), [], None), 1, False), "reverse": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_reverse" % (self.name,), [], None), 1, False), "sort": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_sort" % (self.name,), [], None), 1, False), "__setitem__": PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(em, None, [self, Int, self.elt_type], None_), ("@%s_setitem" % (self.name,), [], None), 1, False), Variable(UnboxedFunctionMT(em, None, [self, Slice, self], None_), ("@%s_setitem_slice" % (self.name,), [], None), 1, False)]), } self.class_methods["__str__"] = self.class_methods["__repr__"] self.typeobj_name = "@%s_typeobj" % self.name if not self.elt_type.hasattr("__eq__"): del self.class_methods["__eq__"] del self.class_methods["__contains__"] if not self.elt_type.hasattr("__lt__"): del self.class_methods["sort"] elif stage == "write": initial_size = 10 elt_incref = self.elt_type.incref_llvm(em, "%elt") elt_decref = self.elt_type.decref_llvm(em, "%gone_elt") emitter = CodeEmitter(em) elt_repr_func = Variable(self.elt_type, "%gone_elt", 1, False).getattr(emitter, "__repr__", clsonly=True) repr_r = elt_repr_func.call(emitter, []) elt_repr = emitter.get_llvm() evaluated = eval_template("list", em, { 'n':self.name, 'in_':self.iter_type.name, 'e':self.elt_type.llvm_type(), 'elt_repr':elt_repr, 'repr_r':repr_r.v, 'initial_size':initial_size, 'elt_incref':elt_incref, 'elt_decref':elt_decref, 'DEBUG_CHECKS':' ' if DEBUG_CHECKS else ';', 'NO_DEBUG_CHECKS':' ' if not DEBUG_CHECKS else ';', 'alloc_name':em.get_str_ptr(self.name), }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) ctemplate = eval_ctemplate("list", em, { 'et':self.elt_type, 'n':self.name, 'in_':self.iter_type.name, }) em.c_head.write(ctemplate) else: raise Exception(stage) def llvm_type(self): return "%%%s*" % (self.name,) def get_ctor_name(self): return "@%s_ctor" % (self.name,) def _can_convert_to(self, t): if isinstance(t, ListMT): return self.elt_type.can_convert_to(t.elt_type) return False def __make_conversion(self, em, t): assert isinstance(t, ListMT) em2 = CodeEmitter(em) func_name = "@" + em.mkname("_convert_list") em2.pl("define %s %s(%s %%l) {" % (t.llvm_type(), func_name, self.llvm_type())) em2.pl("start:") starting_block = "start" isnull_name = "%" + em2.mkname() idx_name = "%" + em2.mkname() next_idx_name = "%" + em2.mkname() done_name = "%" + em2.mkname() newlist_name = "%" + em2.mkname() rtn_name = "%" + em2.mkname() start_label = em2.mkname(prefix="label") cond_label = em2.mkname(prefix="label") loop_label = em2.mkname(prefix="label") back_label = em2.mkname(prefix="backedge") done_label = em2.mkname(prefix="label") em2.indent(4) em2.pl("; Starting conversion from %s to %s" % (self.llvm_type(), t.llvm_type())) em2.pl("%s = icmp eq %s %%l, null" % (isnull_name, self.llvm_type())) em2.pl("br i1 %s, label %%%s, label %%%s" % (isnull_name, done_label, start_label)) em2.indent(-4) em2.pl() em2.pl("%s:" % (start_label,)) em2.indent(4) em2.blockname = start_label var = Variable(self, "%l", 1, False) len_v = var.getattr(em2, "__len__", clsonly=True).call(em2, []) del var ctor_name = t.get_ctor_name() em2.pl("%s = call %s %s()" % (newlist_name, t.llvm_type(), ctor_name)) rtn = Variable(t, newlist_name, 1, True) em2.pl("br label %%%s" % (cond_label,)) em2.indent(-4) em2.pl() em2.pl("%s:" % (cond_label,)) em2.indent(4) em2.blockname = cond_label em2.pl("%s = phi i64 [0, %%%s], [%s, %%%s]" % (idx_name, start_label, next_idx_name, back_label)) em2.pl("%s = icmp sge i64 %s, %s" % (done_name, idx_name, len_v.v)) em2.pl("br i1 %s, label %%%s, label %%%s" % (done_name, done_label, loop_label)) em2.indent(-4) em2.pl() em2.pl("%s:" % (loop_label,)) em2.indent(4) em2.blockname = loop_label var = Variable(self, "%l", 1, False) gotten = var.getattr(em2, "__getitem__", clsonly=True).call(em2, [Variable(Int, idx_name, 1, False)]) del var gotten = gotten.convert_to(em2, t.elt_type) rtn.incvref(em2) # for the following call rtn.getattr(em2, "append").call(em2, [gotten]) em2.pl("%s = add i64 %s, 1" % (next_idx_name, idx_name)) em2.pl("br label %%%s" % (back_label,)) # We use this trampoline to give the backedge a predictable name, # so that we can generate the phi instruction beforehand em2.indent(-4) em2.pl() em2.pl("%s:" % (back_label,)) em2.indent(4) em2.blockname = back_label em2.pl("br label %%%s" % (cond_label,)) em2.indent(-4) em2.pl() em2.pl("%s:" % (done_label,)) em2.indent(4) em2.blockname = done_label em2.pl("%s = phi %s [null, %%%s], [%s, %%%s]" % (rtn_name, t.llvm_type(), starting_block, newlist_name, cond_label)) em2.pl("; Done with conversion from %s to %s" % (self.llvm_type(), t.llvm_type())) em2.pl("ret %s %s" % (t.llvm_type(), rtn_name)) em2.indent(-4) em2.pl("}") em.llvm_tail.write(em2.get_llvm() + '\n') return Variable(UnboxedFunctionMT(em, None, [self], t), (func_name, [], None), 1, False) # return Variable(t, rtn_name, 1, True) def _convert_to(self, em, var, t): if isinstance(t, BoxedMT): return t.convert_from(em, var) if not isinstance(t, ListMT) or not self.elt_type.can_convert_to(t.elt_type): raise UserTypeError(t) # TODO don't write out a new function if we already did for this type of conversion f = self.__make_conversion(em, t) return f.call(em, [var]) @staticmethod def get_name(elt_type): return "list_%s" % elt_type.llvm_type().replace('%', '').replace('*', '') __lists = {} @staticmethod def make_list(elt_type): assert isinstance(elt_type, MT) if elt_type not in ListMT.__lists: ListMT.__lists[elt_type] = ListMT(elt_type) return ListMT.__lists[elt_type] class ListIteratorMT(MT): def __init__(self, lst): super(ListMT.ListIteratorMT, self).__init__() assert isinstance(lst, ListMT) self._lst = lst assert lst.name.startswith("list") self.name = "listiterator" + lst.name[4:] self.class_methods = { "__decref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "hasnext": Variable(UnboxedFunctionMT(None, None, [self], Bool), ("@%s_hasnext" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "next": Variable(UnboxedFunctionMT(None, None, [self], self._lst.elt_type), ("@%s_next" % (self.name,), [], None), 1, False), } self.typeobj_name = "@%s_typeobj" % self.name self.initialized = ("attrs", "write") def llvm_type(self): return "%%%s*" % (self.name) class SetMT(MT): def __init__(self, elt_type): super(SetMT, self).__init__() assert isinstance(elt_type, MT) self.elt_type = elt_type self.name = SetMT.get_name(elt_type) self.iter_type = SetMT.SetIteratorMT(self) def _initialize(self, em, stage): if stage == "attrs": self.class_methods = { "add": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], None_), ("@%s_add" % (self.name,), [], None), 1, False), "__contains__": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], Bool), ("@%s_contains" % (self.name,), [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [self], self.iter_type), ("@%s_iter" % (self.name,), [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_len" % (self.name,), [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_nonzero" % (self.name,), [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % (self.name,), [], None), 1, False), } self.class_methods["__str__"] = self.class_methods["__repr__"] self.typeobj_name = "@%s_typeobj" % self.name elif stage == "write": name = SetMT.get_name(self.elt_type) emitter = CodeEmitter(em) evaluated = eval_template("set", emitter, { 'n':name, 'elt_type':self.elt_type, 'e':self.elt_type.llvm_type(), 'iter_name':self.iter_type.name, }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) ctemplate = eval_ctemplate("set", em, { 'et':self.elt_type, 'n':name, 'i':self.iter_type.name, }) em.c_head.write(ctemplate) else: raise Exception(stage) def __repr__(self): return "<SetMT %r>" % (self.elt_type,) @staticmethod def get_name(elt_type): return "set_%s" % elt_type.llvm_type().replace('%', '').replace('*', '') def llvm_type(self): return "%%%s*" % (self.name,) def get_ctor_name(self): return "@%s_ctor" % (self.name,) __sets = {} @staticmethod def make_set(elt_type): assert isinstance(elt_type, MT) if elt_type not in SetMT.__sets: SetMT.__sets[elt_type] = SetMT(elt_type) return SetMT.__sets[elt_type] class SetIteratorMT(MT): def __init__(self, st): super(SetMT.SetIteratorMT, self).__init__() assert isinstance(st, SetMT) self._st = st assert st.name.startswith("set") self.name = "setiterator" + st.name[4:] self.class_methods = { "__decref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "hasnext": Variable(UnboxedFunctionMT(None, None, [self], Bool), ("@%s_hasnext" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "next": Variable(UnboxedFunctionMT(None, None, [self], self._st.elt_type), ("@%s_next" % (self.name,), [], None), 1, False), } self.initialized = ("attrs", "write") def llvm_type(self): return "%%%s*" % (self.name) class DequeMT(MT): def __init__(self, elt_type): super(DequeMT, self).__init__() assert isinstance(elt_type, MT) self.elt_type = elt_type self.name = DequeMT.get_name(elt_type) self.iter_type = DequeMT.DequeIteratorMT(self) def _initialize(self, em, stage): if stage == "attrs": self.class_methods = { "append": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], None_), ("@%s_append" % (self.name,), [], None), 1, False), "appendleft": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], None_), ("@%s_appendleft" % (self.name,), [], None), 1, False), "__contains__": Variable(UnboxedFunctionMT(em, None, [self, self.elt_type], Bool), ("@%s_contains" % (self.name,), [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [self], self.iter_type), ("@%s_iter" % (self.name,), [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_len" % (self.name,), [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_nonzero" % (self.name,), [], None), 1, False), "pop": Variable(UnboxedFunctionMT(em, None, [self], self.elt_type), ("@%s_pop" % (self.name,), [], None), 1, False), "popleft": Variable(UnboxedFunctionMT(em, None, [self], self.elt_type), ("@%s_popleft" % (self.name,), [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % (self.name,), [], None), 1, False), } self.class_methods["__str__"] = self.class_methods["__repr__"] self.typeobj_name = "@%s_typeobj" % self.name elif stage == "write": name = DequeMT.get_name(self.elt_type) emitter = CodeEmitter(em) evaluated = eval_template("deque", emitter, { 'n':self.name, 'elt_type':self.elt_type, 'e':self.elt_type.llvm_type(), 'iter_name':self.iter_type.name, }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) ctemplate = eval_ctemplate("deque", em, { 'et':self.elt_type, 'n':name, 'i':self.iter_type.name, }) em.c_head.write(ctemplate) else: raise Exception(stage) @staticmethod def get_name(elt_type): return "deque_%s" % elt_type.llvm_type().replace('%', '').replace('*', '') def llvm_type(self): return "%%%s*" % (self.name,) def get_ctor_name(self): return "@%s_ctor" % (self.name,) __deques = {} @staticmethod def make_deque(elt_type): assert isinstance(elt_type, MT) if elt_type not in DequeMT.__deques: DequeMT.__deques[elt_type] = DequeMT(elt_type) return DequeMT.__deques[elt_type] class DequeIteratorMT(MT): def __init__(self, st): super(DequeMT.DequeIteratorMT, self).__init__() assert isinstance(st, DequeMT) self._st = st assert st.name.startswith("deque") self.name = "dequeiterator" + st.name[4:] self.class_methods = { "__decref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "hasnext": Variable(UnboxedFunctionMT(None, None, [self], Bool), ("@%s_hasnext" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "next": Variable(UnboxedFunctionMT(None, None, [self], self._st.elt_type), ("@%s_next" % (self.name,), [], None), 1, False), } self.initialized = ("attrs", "write") def llvm_type(self): return "%%%s*" % (self.name) class Parametric1ArgCtorFuncMT(_SpecialFuncMT): def __init__(self, make_type, add_func_name): super(Parametric1ArgCtorFuncMT, self).__init__() self._make_type = make_type self._add_func_name = add_func_name self.__made = {} def _get_converter(self, em, iter_type): if iter_type not in self.__made: next_type = iter_type.get_attr_types()['next'][0] elt_type = next_type.get_instantiated().rtn_type rtn_type = self._make_type(elt_type) rtn_type.initialize(em, "write") func_name = "@" + em.mkname(prefix="%s_convert" % (rtn_type.name,)) evaluated = eval_template("list_convert", em, { "iter_type":iter_type, "elt_type":elt_type, "rtn_type":rtn_type, "func_name":func_name, "add_func_name":self._add_func_name, }) em.llvm_tail.write(evaluated) self.__made[iter_type] = Variable(UnboxedFunctionMT(em, None, [iter_type], rtn_type), (func_name, [], None), 1, False) return self.__made[iter_type] def call(self, em, v, args, expected_type=None): if len(args) == 1: arg, = args iter = arg.getattr(em, "__iter__").call(em, []) f = self._get_converter(em, iter.t) f.incvref(em) return f.call(em, [iter]) elif not args: assert isinstance(expected_type, MT), repr(expected_type) name = "%" + em.mkname() em.pl("%s = call %s %s()" % (name, expected_type.llvm_type(), expected_type.get_ctor_name())) return Variable(expected_type, name, 1, True) else: raise Exception(len(args)) ListFunc = Parametric1ArgCtorFuncMT(ListMT.make_list, "append") SetFunc = Parametric1ArgCtorFuncMT(SetMT.make_set, "add") DequeFunc = Parametric1ArgCtorFuncMT(DequeMT.make_deque, "append") class DictMT(MT): def __init__(self, key_type, value_type): super(DictMT, self).__init__() self.key_type = key_type self.value_type = value_type self.item_type = TupleMT.make_tuple([self.key_type, self.value_type]) self.name = DictMT.get_name(key_type, value_type) self.key_iter_type = DictMT.DictIteratorMT(self, "key", self.key_type) self.value_iter_type = DictMT.DictIteratorMT(self, "value", self.value_type) self.item_iter_type = DictMT.DictIteratorMT(self, "item", self.item_type) def _initialize(self, em, stage): if stage == "attrs": self.class_methods = { "__contains__": Variable(UnboxedFunctionMT(em, None, [self, self.key_type], Bool), ("@%s_contains" % (self.name,), [], None), 1, False), "__decref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(None, None, [self, self], Bool), ("@%s_eq" % (self.name,), [], None), 1, False), "get": Variable(UnboxedFunctionMT(None, None, [self, self.key_type, self.value_type], self.value_type), ("@%s_get" % self.name, [], None), 1, False), "__getitem__": Variable(UnboxedFunctionMT(em, None, [self, self.key_type], self.value_type), ("@%s_getitem" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "items": Variable(UnboxedFunctionMT(em, None, [self], ListMT.make_list(self.item_type)), ("@%s_items" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [self], self.key_iter_type), ("@%s_iter" % (self.name,), [], None), 1, False), "iteritems": Variable(UnboxedFunctionMT(em, None, [self], self.item_iter_type), ("@%s_iteritems" % (self.name,), [], None), 1, False), "itervalues": Variable(UnboxedFunctionMT(em, None, [self], self.value_iter_type), ("@%s_itervalues" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [self], self.key_iter_type), ("@%s_iter" % (self.name,), [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [self], Int), ("@%s_len" % (self.name,), [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_nonzero" % (self.name,), [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % (self.name,), [], None), 1, False), "setdefault": Variable(UnboxedFunctionMT(em, None, [self, self.key_type, self.value_type], self.value_type), ("@%s_setdefault" % (self.name,), [], None), 1, False), "__setitem__": Variable(UnboxedFunctionMT(em, None, [self, self.key_type, self.value_type], None_), ("@%s_set" % (self.name,), [], None), 1, False), "values": Variable(UnboxedFunctionMT(em, None, [self], ListMT.make_list(self.value_type)), ("@%s_values" % (self.name,), [], None), 1, False), } self.class_methods["__str__"] = self.class_methods["__repr__"] self.class_methods["iterkeys"] = self.class_methods["__iter__"] self.typeobj_name = "@%s_typeobj" % self.name elif stage == "write": ListMT.make_list(self.key_type).initialize(em, "write") ListMT.make_list(self.value_type).initialize(em, "write") ListMT.make_list(self.item_type).initialize(em, "write") self.key_type.initialize(em, "write") self.value_type.initialize(em, "write") self.item_type.initialize(em, "write") template = eval_ctemplate("dict", em, { 't':self, 'n':self.name, }) em.c_head.write(template) evaluated = eval_template("dict", em, { 'n':self.name, 'k':self.key_type.llvm_type(), 'v':self.value_type.llvm_type(), 'i':self.item_type.llvm_type(), 'lv':ListMT.make_list(self.value_type).llvm_type(), 'li':ListMT.make_list(self.item_type).llvm_type(), 'key_iter_name':self.key_iter_type.name, 'key_iter':self.key_iter_type.llvm_type(), 'value_iter_name':self.value_iter_type.name, 'value_iter':self.value_iter_type.llvm_type(), 'item_iter_name':self.item_iter_type.name, 'item_iter':self.item_iter_type.llvm_type(), }) em.llvm_tail.write(convert_none_to_void_ll(evaluated)) else: raise Exception(stage) def llvm_type(self): return "%%%s*" % (self.name,) def get_ctor_name(self): return "@%s_ctor" % (self.name,) @staticmethod def get_name(key_type, value_type): return "dict_%s_%s" % (key_type.llvm_type().replace('%', '').replace('*', ''), value_type.llvm_type().replace('%', '').replace('*', '')) __made_dicts = {} @staticmethod def make_dict(key_type, value_type): assert isinstance(key_type, MT) assert isinstance(value_type, MT) mem_key = (key_type, value_type) if mem_key not in DictMT.__made_dicts: d = DictMT(key_type, value_type) DictMT.__made_dicts[mem_key] = d return DictMT.__made_dicts[mem_key] class DictIteratorMT(MT): def __init__(self, d, type_name, rtn_type): super(DictMT.DictIteratorMT, self).__init__() assert isinstance(d, DictMT) self._d = d assert d.name.startswith("dict") self.name = "dict%siterator%s" % (type_name, d.name[4:]) self.class_methods = { "__decref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_decref" % (self.name,), [], None), 1, False), "hasnext": Variable(UnboxedFunctionMT(None, None, [self], Bool), ("@%s_hasnext" % (self.name,), [], None), 1, False), "__incref__": Variable(UnboxedFunctionMT(None, None, [self], None_), ("@%s_incref" % (self.name,), [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(None, None, [self], self), ("@%s_iter" % (self.name,), [], None), 1, False), "next": Variable(UnboxedFunctionMT(None, None, [self], rtn_type), ("@%s_next" % (self.name,), [], None), 1, False), } self.typeobj_name = "@%s_typeobj" % self.name self.initialized = ("attrs", "write") def llvm_type(self): return "%%%s*" % (self.name) class DictFuncMT(_SpecialFuncMT): __made = {} @staticmethod def get_converter(em, iter_type): if iter_type not in DictFuncMT.__made: next_type = iter_type.get_attr_types()['next'][0] elt_type = next_type.get_instantiated().rtn_type assert isinstance(elt_type, TupleMT), elt_type assert len(elt_type.elt_types) == 2 key_type, value_type = elt_type.elt_types rtn_type = DictMT.make_dict(key_type, value_type) rtn_type.initialize(em, "write") func_name = "@" + em.mkname(prefix="%s_convert" % (rtn_type.name,)) evaluated = eval_template("dict_convert", em, { "iter_type":iter_type, "elt_type":elt_type, "key_type":key_type, "value_type":value_type, "rtn_type":rtn_type, "func_name":func_name, }) em.llvm_tail.write(evaluated) DictFuncMT.__made[iter_type] = Variable(UnboxedFunctionMT(em, None, [iter_type], rtn_type), (func_name, [], None), 1, False) return DictFuncMT.__made[iter_type] def call(self, em, v, args, expected_type=None): assert len(args) == 1 arg, = args if isinstance(arg.t, DictMT): iter = arg.getattr(em, "iteritems").call(em, []) else: iter = arg.getattr(em, "__iter__").call(em, []) f = DictFuncMT.get_converter(em, iter.t) f.incvref(em) return f.call(em, [iter]) DictFunc = singleton(DictFuncMT) class _StructWriter(object): def __init__(self, em, type_name, fields, constants, dealloc_maker=None, function_types=None): self.type_name = type_name self.fields = fields self.constants = constants self._function_types = function_types or {} self._function_placeholders = {} assert all(isinstance(v, UnboxedFunctionMT) for v in self._function_types.values()) self.dealloc_maker = dealloc_maker or _StructWriter.make_struct_dealloc if not type_name: assert not fields return def write(self, em): if not self.type_name: assert not self.fields return field_positions = {} for i, (n, t) in enumerate(self.fields): t.initialize(em, "attrs") field_positions[n] = (i+1, t) dealloc = self.dealloc_maker(em, self.type_name, field_positions) em.llvm_tail.write(eval_template("struct", em, { 'n':self.type_name, 'type_str':", ".join(['i64'] + [type.llvm_type() for (name, type) in self.fields]), 'DEBUG_CHECKS':' ' if DEBUG_CHECKS else ';', 'NO_DEBUG_CHECKS':' ' if not DEBUG_CHECKS else ';', 'dealloc':dealloc, 'alloc_name':em.get_str_ptr(self.type_name), })) @staticmethod def make_struct_dealloc(em, type_name, field_positions): deallocs = [] for n, (i, t) in sorted(field_positions.items()): ptr_name = "%" + em.mkname() name = "%" + em.mkname() d = t.decref_llvm(em, name) if d is None: continue deallocs.append("%s = getelementptr %%%s* %%self, i64 0, i32 %d\n %s = load %s* %s\n %s" % (ptr_name, type_name, i, name, t.llvm_type(), ptr_name, d)) dealloc = '\n'.join(s for s in deallocs if s) return dealloc def get(self, em, v, name, skip_incref=False): if name in self.constants: if name in self._function_types and self.constants[name] is None: assert name not in self._function_placeholders placeholder = em.get_placeholder() self.constants[name] = Variable(self._function_types[name], (placeholder, [], None), 1, False) self._function_placeholders[name] = placeholder del self._function_types[name] assert self.constants[name], name self.constants[name].incvref(em) return self.constants[name] # return self.constants[name].dup({}) assert isinstance(v, str), v offset = 1 for i, (n, t) in enumerate(self.fields): if n == name: assert v assert em p_name = '%' + em.mkname() rtn_name = '%' + em.mkname() em.pl("%s = getelementptr inbounds %%%s* %s, i64 0, i32 %d" % (p_name, self.type_name, v, i + offset)) em.pl("%s = load %s* %s" % (rtn_name, t.llvm_type(), p_name)) em.pc("#error unimplemented 13") if not skip_incref: inc = t.incref_llvm(em, rtn_name) if inc: em.pl(inc + " ; struct get") marked = not skip_incref return Variable(t, rtn_name, 1, marked) raise UserAttributeError("struct doesn't have field %r" % (name,)) def set(self, em, v, name, val, skip_decref_prev=False, skip_incref=False): assert v is None or isinstance(v, str), v if name in self.constants: if self.constants[name] is not None: orig = self.constants[name] if isinstance(val.t, (UserModuleMT, ClassMT, ModuleMT)): assert val.t is orig.t elif isinstance(val.t, UnboxedFunctionMT): assert name in self._function_placeholders assert name not in self._function_types, "should have been deleted by the get that created the placeholder" em.register_replacement(self._function_placeholders[name], val.v[0]) del self._function_placeholders[name] else: raise Exception(name, orig.t) self.constants[name] = val return assert is_emitter(em) and em, em assert v offset = 1 for i, (n, t) in enumerate(self.fields): if n == name: p_name = '%' + em.mkname() em.pl("%s = getelementptr inbounds %%%s* %s, i64 0, i32 %d" % (p_name, self.type_name, v, i + offset)) val = val.convert_to(em, t) assert val.t == t if not skip_decref_prev: old_val = '%' + em.mkname() d = t.decref_llvm(em, old_val) if d: em.pl("%s = load %s* %s" % (old_val, t.llvm_type(), p_name)) em.pl(d) em.pl("store %s %s, %s* %s" % (t.llvm_type(), val.v, t.llvm_type(), p_name)) if not skip_incref: em.pl(t.incref_llvm(em, val.v)) val.decvref(em) em.pc("#error unimplemented 14") return raise UserAttributeError("struct doesn't have field %r" % (name,)) def has(self, name): return self.has_constant(name) or any(name == n for (n, t) in self.fields) def has_constant(self, name): return name in self.constants def alloc(self, em): name = "%" + em.mkname() em.pl("%s = call %%%s* @%s_alloc()" % (name, self.type_name, self.type_name)) em.pc("#error unimplemented 15") return name class ClosureMT(MT): PARENT_FIELD_NAME = " __parent__" def __init__(self, em, name, parent_type, parent_obj, cells, functions, classes, modules): super(ClosureMT, self).__init__() assert (parent_type is None) or isinstance(parent_type, ClosureMT) assert isinstance(parent_obj, bool) if not parent_type: assert not parent_obj self.inlined = not cells if self.inlined: # Just to make sure the name is invalid and we don't try to use it: assert name == '' if parent_obj: # Ensure that chains of just inlined closures dont get objects assert (not parent_type.inlined) or (parent_type.inlined and parent_type.parent_obj) self.name = name self.parent_type = parent_type # ClosureMT of the parent (or None) self.parent_obj = parent_obj # Whether this closure has a reference to the parent object # self.cells = cells # list of [(name, type)] # self.functions = functions # mapping of name -> UnboxedFunctionMT # self.classes = classes # mapping of name -> ClassMT # self.modules = modules # mapping of name -> ModuleMT struct_fields = cells if cells and parent_obj: struct_fields.insert(0, (ClosureMT.PARENT_FIELD_NAME, parent_type.instantiated_type())) assert not set(functions).intersection(classes) assert not set(classes).intersection(modules) assert not set(modules).intersection(functions) constants = {} constants.update(functions) constants.update(classes) constants.update(modules) self._struct = _StructWriter(em, name, struct_fields, constants) self._struct.write(em) self.initialized = ("attrs", "write") def instantiated_type(self): if self.inlined: return self.parent_type.instantiated_type() return self def llvm_type(self): if self.inlined: assert self.parent_type, "shouldnt want the llvm type of this because it's not going to be instantiated" return self.parent_type.llvm_type() return "%%%s*" % (self.name,) def dup(self, v, dup_cache): assert v is None or isinstance(v, str) return v def alloc(self, em, parent=None): assert (parent is None) == (not self.parent_obj), (parent, self.parent_obj) if self.inlined: if parent: r = parent.split(em) r.t = self return r else: return Variable(self, None, 1, False) if parent: assert parent.t == self.parent_type name = self._struct.alloc(em) assert isinstance(name, str) if parent: self._struct.set(em, name, ClosureMT.PARENT_FIELD_NAME, parent, skip_decref_prev=True) return Variable(self, name, 1, True) def getattr(self, em, v, attr, clsonly): first_real = self while first_real.inlined: first_real = first_real.parent_type assert first_real if attr == "__incref__": return UnboxedInstanceMethod.make(em, v, Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % first_real.name, [], None), 1, False)) if attr == "__decref__": return UnboxedInstanceMethod.make(em, v, Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % first_real.name, [], None), 1, False)) raise Exception("couldn't find '%s' attribute on %s" % (attr, self)) def has(self, name, include_parents=False): if self._struct.has(name): return True if not include_parents: return False if self.parent_type: if self.parent_obj: return self.parent_type.has(name) else: return self.parent_type.has_constant(name) return False def has_constant(self, name, include_parents=False): if self._struct.has_constant(name): return True if not include_parents: return False return self.parent_type and self.parent_type.has_constant(name) def get(self, em, v, name): assert isinstance(name, str) assert (v is None) or isinstance(v, str) if self._struct.has_constant(name): return self._struct.get(em, None, name) if v is None: return self.parent_type.get(None, None, name) if self._struct.has(name): return self._struct.get(em, v, name) assert self.parent_type if self.inlined: return self.parent_type.get(em, v, name) if self.parent_obj: parent = self._struct.get(em, v, ClosureMT.PARENT_FIELD_NAME) r = self.parent_type.get(em, parent.v, name) parent.decvref(em) return r else: return self.parent_type.get(None, None, name) def set(self, em, v, name, val): assert isinstance(val, Variable) self._struct.set(em, v, name, val) def _can_convert_to(self, t): return False @staticmethod def create(em, node, parent_type, parent_obj, closure_info, type_info): assert is_emitter(em) assert (parent_type is None) or (isinstance(parent_type, ClosureMT)), parent_type assert isinstance(closure_info, closure_analyzer.ClosureResults) assert isinstance(parent_obj, bool) if parent_obj: assert parent_type cells = [] for name in closure_info.used_in_nested: try: cells.append((name, type_info.get_scope_type(em, node, name))) except: print "failed on", name raise # Probably don't need to always create the global closure? # if not cells and not isinstance(node, _ast.Module): if not cells: return ClosureMT(em, '', parent_type, parent_obj, cells, dict([(n, None) for n in closure_info.functions]), dict([(n, None) for n in closure_info.classes]), dict([(n, None) for n in closure_info.modules])) type_name = em.mkname(prefix="closure") return ClosureMT( em, type_name, parent_type, parent_obj, cells, dict([(n, None) for n in closure_info.functions]), dict([(n, None) for n in closure_info.classes]), dict([(n, None) for n in closure_info.modules]), ) class ClassMT(MT): def __init__(self, base, type_name, displayname, llvm_type=None): super(ClassMT, self).__init__() if type_name != "object": assert base assert isinstance(base, ClassMT), base self.base = base self._name = type_name self._displayname = displayname if llvm_type is None: llvm_type = "%%%s*" % type_name self._llvm_type = llvm_type self._ctor = None self._instance = InstanceMT(self) self._clsattr_types = {} self._clsattrs = {} self._instattr_types = [] def _initialize(self, em, stage): if stage == "attrs": pass elif stage == "write": type_string = ", ".join(['i64'] + [type.llvm_type() for (name, type) in self._instattr_types]) decref_all = "" for i in xrange(len(self._instattr_types)): attr_type = self._instattr_types[i][1] attr_type.initialize(em, "attrs") d = attr_type.decref_llvm(em, "%%attr%d" % (i,)) if not d: continue decref_all += "%%ptr%d = getelementptr inbounds %s %%self, i64 0, i32 %d\n" % (i, self._instance.llvm_type(), i + 1) decref_all += "%%attr%d = load %s* %%ptr%d\n" % (i, attr_type.llvm_type(), i) decref_all += d + '\n' decref_all = decref_all.replace('\n', '\n ') if self._ctor: # TODO Hacky that it still writes out the __new__ function but doesn't use it new_args = "" init = "" elif not self.has_classattr("__init__"): new_args = "" init = "" self._ctor = Variable(UnboxedFunctionMT(None, None, [], self._instance), ("@%s_new" % self._name, [], None), 1, False) else: init_fn = self.getattr(em, Variable(self, (), 1, False), "__init__", False) init_fn.t.initialize(em, "write") init_fn.incvref(em) new_em = CodeEmitter(em) args = [Variable(self._instance, "%rtn", 1, False)] for i, a in enumerate(init_fn.t.arg_types[1:]): args.append(Variable(a, "%%v%d" % i, 1, False)) init_fn.call(new_em, args) init = new_em.get_llvm() new_t = UnboxedFunctionMT(None, None, init_fn.t.arg_types[1:], self._instance, ndefaults=init_fn.t.ndefaults) assert isinstance(init_fn.t, UnboxedFunctionMT) defaults = init_fn.v[1] self._ctor = Variable(new_t, ("@%s_new" % self._name, defaults, None), 1, False) new_args = ", ".join(["%s %%v%d" % (a.llvm_type(), i) for i, a in enumerate(init_fn.t.arg_types[1:])]) strname_ptr = em.get_str_ptr(self._name) strname_str = "@" + em.mkname("str") em.llvm_tail.write("%s = global %%string {i64 1, i64 %d, i8* %s, [0 x i8] zeroinitializer}\n" % (strname_str, len(self._name), strname_ptr)) typeobj = "{i64 1, %%string* %s, %%type* null}" % (strname_str,) em.llvm_tail.write(eval_template("instance", em, { 'n':self._name, 'type_str':type_string, 'DEBUG_CHECKS':' ' if DEBUG_CHECKS else ';', 'NO_DEBUG_CHECKS':' ' if not DEBUG_CHECKS else ';', 'decref_all':decref_all, 'new_args':new_args, 'init':init, 'alloc_name':em.get_str_ptr(self._name), 'typeobj': typeobj, 'displayname': self._displayname, })) em.c_head.write(eval_ctemplate("instance", em, {'n':self._name})) else: raise Exception(stage) def get_typeobj(self, em): return Variable(Type, "@%s_typeobj" % self._name, 1, False) def set_clsattr_type(self, name, t): assert not self.initialized assert name not in ("__nrefs__", "__incref__", "__decref__") self._clsattr_types[name] = t def setattr(self, em, v, attr, val): assert not v assert isinstance(val.t, UnboxedFunctionMT) self.set_clsattr_value(attr, val, em=em) def set_clsattr_value(self, name, v, _init=False, em=None, force=False): if not force: assert name not in ("__nrefs__", "__incref__", "__decref__") assert isinstance(v.t, (UnboxedFunctionMT, PolymorphicFunctionMT)) assert isinstance(v.v, tuple), (v.v, "probably not a constant and we don't allocate actual storage for class attrs") assert _init or em if _init or (em and name in self._clsattr_types and name not in self._clsattrs): if _init: assert name not in self._clsattr_types assert name not in self._clsattrs self._clsattr_types[name] = v.t self._clsattrs[name] = v.dup({}) else: assert name in self._clsattr_types assert name in self._clsattrs placeholder = self._clsattrs[name] assert v.t.can_convert_to(self._clsattr_types[name]), (v.t, self._clsattr_types[name]) assert isinstance(v.t, UnboxedFunctionMT) placeholder_defaults = placeholder.v[1] new_defaults = v.v[1] assert len(new_defaults) == len(placeholder_defaults) for i in xrange(len(placeholder_defaults)): assert new_defaults[i].t == placeholder_defaults[i].t print placeholder_defaults[i].v, new_defaults[i].v em.register_replacement(placeholder_defaults[i].v, str(new_defaults[i].v)) em.register_replacement(placeholder.v[0], v.v[0]) def set_instattr_type(self, name, t): assert not self.initialized assert name not in ("__nrefs__", "__incref__", "__decref__") assert not any(name == _n for (_n, _t) in self._instattr_types) self._instattr_types.append((name, t)) def has_classattr(self, attr): return attr in self._clsattr_types or (self.base and self.base.has_classattr(attr)) def getattr(self, em, var, attr, clsonly): # TODO this should just use _StructWriter; there's some compilcation with builtin classes though assert not clsonly if attr not in self._clsattr_types: assert self.base.has_classattr(attr) return self.base.getattr(em, var, attr, clsonly) if attr in self._clsattr_types and attr not in self._clsattrs: t = self._clsattr_types[attr] assert isinstance(t, CallableMT), (name, t) new_t = UnboxedFunctionMT(None, None, t.arg_types, t.rtn_type, ndefaults=t.ndefaults) defaults = [Variable(t.arg_types[len(t.arg_types) - t.ndefaults + i], em.get_placeholder(), 1, False) for i in xrange(t.ndefaults)] self._clsattrs[attr] = Variable(new_t, (em.get_placeholder(), defaults, None), 1, False) assert attr in self._clsattrs v = self._clsattrs[attr].dup({}) assert isinstance(v.v, tuple), (v.v, "probably not a constant and we don't allocate actual storage for class attrs") assert not v.marked, "should probably have incref'd in this case?" return v def free(self, em, v): assert v == () for v in self._clsattrs.itervalues(): print object.__getattribute__(v, "__dict__") v.decvref(em) def dup(self, v, dup_cache): assert v == () return v def call(self, em, v, args, expected_type=None): assert v == () self._ctor.incvref(em) return self._ctor.call(em, args) def get_instance(self): return self._instance __nclasses = 0 @staticmethod def create(base, displayname): type_name = "cls%d" % ClassMT.__nclasses ClassMT.__nclasses += 1 return ClassMT(base, type_name, displayname) def llvm_type(self): assert 0, "shouldnt do this" def get_instantiated(self): return self._ctor.t.get_instantiated() def _can_convert_to(self, t): return self._ctor.t.can_convert_to(t) def _convert_to(self, em, var, t): if t is Type: return self.get_typeobj(em) self._ctor.incvref(em) return self._ctor.convert_to(em, t) class InstanceMT(MT): def __init__(self, cls): super(InstanceMT, self).__init__() assert isinstance(cls, ClassMT) self.cls = cls self._name = cls._name self._llvm_type = cls._llvm_type def _initialize(self, em, stage): # Careful: calling initailize with the same stage has the potential to # introduce cyclic dependencies, but the class should only call instance.initialize # with an earlier stage, so it should be ok: self.cls.initialize(em, stage) def llvm_type(self): return self._llvm_type def getattrptr(self, em, var, attr): assert isinstance(var, Variable) offset = 1 for i, (name, t) in enumerate(self.cls._instattr_types): if name == attr: pl = '%' + em.mkname() em.pl("%s = getelementptr %s %s, i64 0, i32 %d" % (pl, self.llvm_type(), var.v, i + offset)) em.pc("#error unimplemented 15") return Variable(PtrMT(t), pl, 1, False) raise UserAttributeError(attr) def hasattr(self, attr): return attr in self.cls._clsattr_types or attr in [name for (name, t) in self.cls._instattr_types] def getattr(self, em, var, attr, clsonly): self.cls._MT__check_initialized("attrs") assert isinstance(var, Variable) if not clsonly: if attr == "__class__": return self.cls.get_typeobj(em) offset = 1 for i, (name, t) in enumerate(self.cls._instattr_types): if name == attr: pl = '%' + em.mkname() rtn = '%' + em.mkname() em.pl("%s = getelementptr %s %s, i64 0, i32 %d" % (pl, self.llvm_type(), var.v, i + offset)) em.pl("%s = load %s* %s" % (rtn, t.llvm_type(), pl)) em.pc("#error unimplemented 16") i = t.incref_llvm(em, rtn) if i: em.pl(i) return Variable(t, rtn, 1, True) m = self._get_clsmethod(em, attr) return UnboxedInstanceMethod.make(em, var, m) def get_attr_types(self): if self.cls.base: r = self.cls.base._instance.get_attr_types() else: r = {} for name, t in self.cls._instattr_types: r[name] = (t, AttributeAccessType.FIELD) for name, t in self.cls._clsattr_types.iteritems(): if name in r: continue r[name] = (UnboxedInstanceMethod(self, t), AttributeAccessType.CONST_METHOD) # TODO default functions like __repr__ r["__incref__"] = (UnboxedInstanceMethod(self, UnboxedFunctionMT(None, None, [self], None_)), AttributeAccessType.CONST_METHOD) r["__decref__"] = (UnboxedInstanceMethod(self, UnboxedFunctionMT(None, None, [self], None_)), AttributeAccessType.CONST_METHOD) if "__repr__" not in r: r["__repr__"] = (UnboxedInstanceMethod(self, UnboxedFunctionMT(None, None, [self], Str)), AttributeAccessType.CONST_METHOD) r["__class__"] = (Type, AttributeAccessType.IMPLICIT_FIELD) return r def _get_clsmethod(self, em, attr): self.cls._MT__check_initialized("attrs") if not self.cls.has_classattr(attr): if attr == "__str__": return self._get_clsmethod(em, "__repr__") if attr == "__repr__": return Variable(UnboxedFunctionMT(em, None, [self], Str), ("@%s_repr" % self._name, [], None), 1, False) if attr == "__nonzero__": return Variable(UnboxedFunctionMT(em, None, [self], Bool), ("@%s_nonzero" % self._name, [], None), 1, False) if attr == "__eq__": return Variable(UnboxedFunctionMT(em, None, [self, self], Bool), ("@%s_eq" % self._name, [], None), 1, False) if attr == "__incref__": return Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % self._name, [], None), 1, False) if attr == "__decref__": return Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % self._name, [], None), 1, False) raise UserAttributeError("no such attribute '%s' on %s" % (attr, self)) return self.cls.getattr(em, Variable(self.cls, (), 1, False), attr, False) def setattr(self, em, v, attr, val): assert isinstance(v, str) assert isinstance(val, Variable) offset = 1 for i, (name, t) in enumerate(self.cls._instattr_types): if name == attr: val = val.convert_to(em, t) assert t == val.t, (t, val.t) inc = val.t.incref_llvm(em, val.v) if inc: em.pl(inc) pl = '%' + em.mkname() old_val = '%' + em.mkname() em.pl("%s = getelementptr %s %s, i64 0, i32 %d" % (pl, self.llvm_type(), v, i + offset)) d = t.decref_llvm(em, old_val) if d: em.pl("%s = load %s* %s" % (old_val, t.llvm_type(), pl)) em.pl(d) em.pl("store %s %s, %s* %s" % (t.llvm_type(), val.v, t.llvm_type(), pl)) em.pc("#error unimplemented 17") val.decvref(em) return raise Exception("doesn't have attribute '%s'" % (attr,)) def convert_to(self, em, var, t): if t is self: return var assert isinstance(t, BoxedMT), (self, t) return t.convert_from(em, var) def _can_convert_to(self, t): # TODO this is wrong return False def __repr__(self): return "<InstanceMT %r (%s)>" % (self.llvm_type(), self.cls._displayname) ObjectClass = ClassMT(None, "object", "object") Object = ObjectClass._instance IntClass = ClassMT(ObjectClass, "int", "int", llvm_type="i64") Int = IntClass._instance FloatClass = ClassMT(ObjectClass, "float", "float", llvm_type="double") Float = FloatClass._instance StrClass = ClassMT(ObjectClass, "str", "str", llvm_type="%string*") Str = StrClass._instance BoolClass = ClassMT(ObjectClass, "bool", "bool", "i1") Bool = BoolClass._instance TypeClass = ClassMT(ObjectClass, "type", "type") Type = TypeClass._instance FileClass = ClassMT(ObjectClass, "file", "file") File = FileClass._instance # TODO there is a lot of duplication between this and stuff like closures class UserModuleMT(MT): LOADED_FIELD_NAME = " __loaded__" def __init__(self, em, module_name, module_fn, type_name, vars, constants, function_types): super(UserModuleMT, self).__init__() self.module_name = module_name self._type_name = type_name self.fn = module_fn self._struct = _StructWriter(em, type_name, vars, constants, function_types=function_types) self._struct.write(em) self.initialized = ("attrs", "write") def llvm_type(self): return "%%%s*" % (self._type_name) def has(self, name): return self._struct.has(name) def has_constant(self, name): return self._struct.has_constant(name) # Have this to mimic closure objects, since this can be a closure def set(self, em, v, name, val): return self.setattr(em, v, name, val) def getattr(self, em, v, name, clsonly=False, skip_incref=False): assert not clsonly return self._struct.get(em, v.v, name, skip_incref=skip_incref) def setattr(self, em, v, attr, val): return self._struct.set(em, v, attr, val) def load(self, em): em.pc("#error unimplemented 18") em.pl("call void @%s_init()" % self.module_name) def load_modules(self, em, cg, closure_info, ts_module, type_info): for n in closure_info.modules: assert self._struct.constants[n] is None # TODO this is pretty hacky submodule = ts_module.get_name(n) assert len(submodule.types()) == 1 submodule, = submodule.types() m = cg.import_module(em, submodule.name) self._struct.constants[n] = m @staticmethod def make(em, module_name, module, module_fn, closure_info, ts_module, type_info): assert is_emitter(em) assert isinstance(module, _ast.Module) assert isinstance(closure_info, closure_analyzer.ClosureResults) vars = [] vars.append((UserModuleMT.LOADED_FIELD_NAME, Bool)) for name in closure_info.used_in_nested: try: vars.append((name, type_info.get_scope_type(em, module, name))) except: print "failed on", name raise constants = {} function_types = {} for n in closure_info.functions: u = ts_module.get_name(n) assert len(u.types()) == 1 if u.types()[0].is_dead(): print "Not adding %s since it's dead" % (n,) continue try: function_types[n] = type_info._convert_type(em, u) except Exception: print >>sys.stderr, "Failed when converting attribute %s of module %s [%s]" % (n, module_name, ts_module.get_name(n).display()) raise for n, c in function_types.iteritems(): assert n not in constants function_types[n] = UnboxedFunctionMT(em, None, c.arg_types, c.rtn_type) constants[n] = None for n in closure_info.classes: assert n not in constants cls = type_info._convert_type(em, ts_module.get_name(n)) assert isinstance(cls, ClassMT) constants[n] = Variable(cls, (), 1, False) for n in closure_info.modules: assert n not in constants constants[n] = None # We will fill this in later, to allow for # cycles in the import graph type_name = em.mkname(prefix="mod_%s" % filter(lambda c: not c.isdigit(), module_name)) t = UserModuleMT( em, module_name, module_fn, type_name, vars, constants, function_types, ) module_obj_name = "@" + em.mkname(prefix="module") module_obj = Variable(t, module_obj_name, 1, False) em.llvm_tail.write(eval_template("module", em, { 'module_obj':module_obj, 'type_name':type_name, 'module_name':module_name, 't':t, })) return module_obj class ModuleMT(MT): def __init__(self, attrs): super(ModuleMT, self).__init__() self._attrs = attrs self.initialized = ("attrs", "write") def getattr(self, em, var, attr, clsonly): assert not clsonly if attr not in self._attrs: raise UserAttributeError(attr) v = self._attrs[attr].dup({}) # TODO to support setting things, this should assert marked, but then we need to put it somewhere assert not v.marked, "should probably have incref'd in this case?" return v def llvm_type(self): assert 0, "shouldnt do this" def get_instantiated(self): assert 0, "dont support this yet" # Don't support raising these yet return None def _can_convert_to(self, t): return False class PtrMT(MT): """ An MT to represent the type of a stored pointer to an object. They should only exist as a compiler implementation detail. """ def __init__(self, referent_type): super(PtrMT, self).__init__() self.referent_type = referent_type self.initialized = ("attrs", "write") def llvm_type(self): return self.referent_type.llvm_type() + "*" def incref_llvm(self, em, name): return None def decref_llvm(self, em, name): return None class FuncPtrMT(MT): def __init__(self, func_type): super(FuncPtrMT, self).__init__() self.func_type = func_type self.initialized = ("attrs", "write") def llvm_type(self): return self.func_type.llvm_type() def incref_llvm(self, em, name): return None def decref_llvm(self, em, name): return None def getattr(self, em, var, attr, clsonly=False): raise UserAttributeError(attr) class _UnderlyingMT(MT): def __init__(self): super(_UnderlyingMT, self).__init__() self.initialized = ("attrs", "write") def llvm_type(self): return "%underlying*" def incref_llvm(self, em, v): assert False, "shouldn't be calling this" def decref_llvm(self, em, v): assert False, "shouldn't be calling this" Underlying = singleton(_UnderlyingMT) class BoxedMT(MT): class StorageStrategy(object): CONST_METHOD = "method" # store a reference to the method, and create the instancemethod on access PTR = "ptr" # store a pointer to the field in the object VALUE = "value" # store the value of the field in the boxed object UNDERLYING_FIELD_NAME = " __underlying" @staticmethod def make_struct_dealloc(em, type_name, field_positions): deallocs = [] decref_name = None underlying_name = None for n, (i, t) in sorted(field_positions.items()): ptr_name = "%" + em.mkname() name = "%" + em.mkname() deallocs.append("%s = getelementptr %%%s* %%self, i64 0, i32 %d\n %s = load %s* %s" % (ptr_name, type_name, i, name, t.llvm_type(), ptr_name)) if n == BoxedMT.UNDERLYING_FIELD_NAME: underlying_name = name continue if n == "__decref__": decref_name = name d = t.decref_llvm(em, name) if d is None: continue deallocs.append(d) assert decref_name assert underlying_name deallocs.append("call void %s(%%underlying* %s)" % (decref_name, underlying_name)) dealloc = '\n '.join(s for s in deallocs if s) return dealloc __nboxes = 0 def __init__(self, types): super(BoxedMT, self).__init__() self._name = "boxed%d" % (BoxedMT.__nboxes,) BoxedMT.__nboxes += 1 self.types = types def _initialize(self, em, stage): if stage == "attrs": for t in self.types: t.initialize(em, "attrs") all_attrs = [t.get_attr_types() for t in self.types] all_attr_names = set() for i in xrange(len(self.types)): d = all_attrs[i] all_attr_names.update(d) assert "__class__" in d, (d, self.types[i]) for n, (t, at) in d.items(): try: d[n] = (t.get_instantiated(), at) except CantInstantiateException: del d[n] attrs = {} for n in all_attr_names: if any(n not in d for d in all_attrs): continue attr_types = [d[n] for d in all_attrs] if any(at[1] != attr_types[0][1] for at in attr_types): continue types = [at[0] for at in attr_types] if attr_types[0][1] == AttributeAccessType.FIELD and len(set(types)) > 1: continue sup = make_common_supertype(types) if sup is not None: # TODO this next initialize should somehow be taken care of before the # enclosing _initialize is even started # sup.initialize(em) attrs[n] = sup, attr_types[0][1] assert "__incref__" in attrs assert "__decref__" in attrs assert "__class__" in attrs attrs.pop("__init__", None) converted_attrs = [] for n, (t, at) in attrs.iteritems(): ss = BoxedMT._storage_strategy(n, t, at) converted_attrs.append((n, t, ss)) self.attrs = {} struct_fields = [] struct_fields.append((BoxedMT.UNDERLYING_FIELD_NAME, Underlying)) for n, t, ss in converted_attrs: st = self._storage_type(em, t, ss) # print n, t, st struct_fields.append((n, st)) self.attrs[n] = (t, ss, st) self._struct = _StructWriter(em, self._name, struct_fields, {}, dealloc_maker=BoxedMT.make_struct_dealloc) self.__converters = {} em.c_head.write("struct %s;" % self._name) elif stage == "write": self._struct.write(em) else: raise Exception(stage) def llvm_type(self): return "%" + self._name + "*" def c_type(self): return "struct " + self._name + "*" def hasattr(self, attr): return self._struct.has(attr) def setattr(self, em, v, attr, val): assert self.initialized if attr not in self.attrs: raise UserAttributeError("boxed object does not contain settable %s" % attr) t, ss, st = self.attrs[attr] assert ss == BoxedMT.StorageStrategy.PTR, ss ptr = self._struct.get(em, v, attr) assert isinstance(ptr.t, PtrMT) assert ptr.t.referent_type is val.t prev = "%" + em.mkname() dec = val.t.decref_llvm(em, prev) if dec: em.pl("%s = load %s* %s" % (prev, val.t.llvm_type(), ptr.v)) em.pl(dec) em.pl("store %s %s, %s* %s" % (val.t.llvm_type(), val.v, val.t.llvm_type(), ptr.v)) def getattr(self, em, v, attr, clsonly): assert self.initialized # Handle these specially, since they hit the boxed object, not the underlying: if attr == "__incref__": return UnboxedInstanceMethod.make(em, v, Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_incref" % self._name, [], None), 1, False)) if attr == "__decref__": return UnboxedInstanceMethod.make(em, v, Variable(UnboxedFunctionMT(em, None, [self], None_), ("@%s_decref" % self._name, [], None), 1, False)) assert attr not in ("__incref__", "__decref__") stored = self._struct.get(em, v.v, attr) t, ss, st = self.attrs[attr] if ss == BoxedMT.StorageStrategy.CONST_METHOD: raise Exception("Pretty sure this isn't right, since we should incref the underlying as well") o = self._struct.get(em, v, BoxedMT.UNDERLYING_FIELD_NAME) assert isinstance(stored.t, FuncPtrMT) return UnboxedInstanceMethod.make(em, o, stored) elif ss == BoxedMT.StorageStrategy.VALUE: return stored elif ss == BoxedMT.StorageStrategy.PTR: assert isinstance(stored.t, PtrMT) rtn_type = st.referent_type r = '%' + em.mkname() em.pl("%s = load %s* %s" % (r, rtn_type.llvm_type(), stored.v)) inc = rtn_type.incref_llvm(em, r) if inc: em.pl(inc + " ; getting boxed attr by ptr") em.pc("#error unimplemented 19") return Variable(rtn_type, r, 1, True) raise Exception(ss) def _storage_type(self, em, attr_type, storage_strategy): if storage_strategy == BoxedMT.StorageStrategy.CONST_METHOD: return FuncPtrMT(UnboxedFunctionMT(em, None, [Underlying] + attr_type.arg_types, attr_type.rtn_type)) elif storage_strategy == BoxedMT.StorageStrategy.VALUE: return attr_type elif storage_strategy == BoxedMT.StorageStrategy.PTR: return PtrMT(attr_type) else: raise Exception(storage_strategy) def can_convert_from(self, t): if t is None_: return True attr_types = t.get_attr_types() for attr_name, (_t, ss, st) in self.attrs.iteritems(): if attr_name not in attr_types or not attr_types[attr_name][0].can_convert_to(_t): return False return True def convert_from(self, em, var): self._MT__check_initialized("attrs") # TODO assert size of var object is 64 bits # TODO return None if the input was None if var.t not in self.__converters: converter_name = "@%s_from_%s" % (self._name, var.t.llvm_type().replace("*", "_").replace("%", "")) self.__converters[var.t] = Variable(UnboxedFunctionMT(None, None, [var.t], self), (converter_name, [], None), 1, False) evaluated = eval_template("boxer", em, { 'bt': self, 'ot': var.t, 'converter_name':converter_name, }) em.llvm_tail.write(evaluated) converter = self.__converters[var.t] converter.incvref(em) return converter.call(em, [var]) @staticmethod def _storage_strategy(name, t, at): if name in ("__incref__", "__decref__"): return BoxedMT.StorageStrategy.CONST_METHOD if at in (AttributeAccessType.CONST_METHOD, AttributeAccessType.IMPLICIT_FIELD): return BoxedMT.StorageStrategy.VALUE if at == AttributeAccessType.FIELD: return BoxedMT.StorageStrategy.PTR raise Exception(at) _made_supertypes = {} def make_common_supertype(types): assert all(isinstance(t, MT) for t in types) types = tuple(sorted(set(types))) if len(types) == 1: return types[0] if types == tuple(sorted([Int, Float])): return Float if types in _made_supertypes: return _made_supertypes[types] if all(isinstance(t, ClassMT) for t in types): return Type if all(isinstance(t, CallableMT) for t in types): for t in types[1:]: if t.arg_types != types[0].arg_types: return None ret_type = make_common_supertype([t.rtn_type for t in types]) if ret_type is None: return None return CallableMT.make_callable(types[0].arg_types, 0, ret_type) if any(isinstance(t, CallableMT) for t in types): # Callables aren't mixable with non-callables, at least for now. # Probably can/should return the common_supertype of all the __call__ attributes? return None rtn = BoxedMT(types) _made_supertypes[types] = rtn return rtn def common_subtype(em, types): raise NotImplementedError() class _FakeMT(MT): def __init__(self, attrs): super(_FakeMT, self).__init__() self._attrs = attrs def _initialize(self, em, stage): pass def get_attr_types(self): return self._attrs # Some type classes for stdlib stuff: STDLIB_TYPES = [] def _make_iterable(elt_type): iterator = BoxedMT([_FakeMT({ "__class__": (Type, AttributeAccessType.IMPLICIT_FIELD), "__incref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__decref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "hasnext": (CallableMT.make_callable([], 0, Bool), AttributeAccessType.CONST_METHOD), "next": (CallableMT.make_callable([], 0, elt_type), AttributeAccessType.CONST_METHOD), })]) iterable = BoxedMT([_FakeMT({ "__class__": (Type, AttributeAccessType.IMPLICIT_FIELD), "__incref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__decref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__iter__": (CallableMT.make_callable([], 0, iterator), AttributeAccessType.CONST_METHOD), })]) STDLIB_TYPES.append(iterator) STDLIB_TYPES.append(iterable) return iterator, iterable _IntIterator, _IntIterable = _make_iterable(Int) _FloatIterator, _FloatIterable = _make_iterable(Float) _Boolable = BoxedMT([_FakeMT({ "__class__": (Type, AttributeAccessType.IMPLICIT_FIELD), "__incref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__decref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD), "__nonzero__": (CallableMT.make_callable([], 0, Bool), AttributeAccessType.CONST_METHOD), })]) STDLIB_TYPES.append(_Boolable) _BoolableIterator, _BoolableIterable = _make_iterable(_Boolable) BUILTINS = { "True":Variable(Bool, 1, 1, False), "False":Variable(Bool, 0, 1, False), "len":Variable(Len, (), 1, False), "str":Variable(StrClass, (), 1, False), "repr":Variable(ReprFunc, (), 1, False), "type":Variable(TypeClass, (), 1, False), "map":Variable(MapFunc, (), 1, False), "reduce":Variable(ReduceFunc, (), 1, False), "nrefs":Variable(Nref, (), 1, False), "bool":Variable(BoolClass, (), 1, False), "list":Variable(ListFunc, (), 1, False), "dict":Variable(DictFunc, (), 1, False), "set":Variable(SetFunc, (), 1, False), "isinstance":Variable(Isinstance, (), 1, False), "__cast__":Variable(Cast, (), 1, False), "enumerate":Variable(Enumerate, (), 1, False), "chr":Variable(UnboxedFunctionMT(None, None, [Int], Str), ("@chr", [], None), 1, False), "ord":Variable(UnboxedFunctionMT(None, None, [Str], Int), ("@ord", [], None), 1, False), # "open":Variable(UnboxedFunctionMT(None, None, [Str], File), ("@file_open", [], None), 1, True), "open":Variable(UnboxedFunctionMT(None, None, [Str, Str], File, ndefaults=1), ("@file_open2", [Variable(Str, "@.str_r", 1, True)], None), 1, False), "int":Variable(IntClass, (), 1, False), "min":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False), Variable(MinFunc, (), 1, False), ]), "max":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_max", [], None), 1, False), Variable(MaxFunc, (), 1, False), ]), "float":Variable(FloatClass, (), 1, False), "file":Variable(FileClass, (), 1, False), "abs":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [Int], Int), ("@int_abs", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False)]), "None":Variable(None_, "null", 1, False), "object":Variable(ObjectClass, (), 1, False), "sum":PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(None, None, [_IntIterable], Int), ("@sum_int", [], None), 1, False), Variable(UnboxedFunctionMT(None, None, [_FloatIterable], Float), ("@sum_float", [], None), 1, False), ]), "any":Variable(UnboxedFunctionMT(None, None, [_BoolableIterable], Bool), ("@any", [], None), 1, False), } BUILTIN_MODULES = { "time":Variable(ModuleMT({ 'time':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_time", [], None), 1, False), 'clock':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_clock", [], None), 1, False), 'sleep':Variable(UnboxedFunctionMT(None, None, [Float], None_), ("@time_sleep", [], None), 1, False), }), 1, 1, False), "sys":Variable(ModuleMT({ 'stdin':Variable(File, "@sys_stdin", 1, False), 'stdout':Variable(File, "@sys_stdout", 1, False), 'stderr':Variable(File, "@sys_stderr", 1, False), }), 1, 1, False), "math":Variable(ModuleMT({ 'sqrt':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sqrt", [], None), 1, False), 'tan':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@tan", [], None), 1, False), 'sin':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sin", [], None), 1, False), 'cos':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@cos", [], None), 1, False), 'ceil':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@ceil", [], None), 1, False), 'pi':Variable(Float, format_float(3.141592653589793), 1, False), }), 1, 1, False), "collections":Variable(ModuleMT({ 'deque':Variable(DequeFunc, (), 1, False), }), 1, 1, False), # Interopability library: "hax":Variable(ModuleMT({ "ftoi":Variable(UnboxedFunctionMT(None, None, [Float], Int), ("@hax_ftoi", [], None), 1, False), "itof":Variable(UnboxedFunctionMT(None, None, [Int], Float), ("@hax_itof", [], None), 1, False), "min":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False), "max":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False), "fmin":Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False), "abs":Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False), "initvideo":Variable(UnboxedFunctionMT(None, None, [Int, Int], None_), ("@hax_initvideo", [], None), 1, False), "plot":Variable(UnboxedFunctionMT(None, None, [Int, Int, Int, Int, Int], None_), ("@hax_plot", [], None), 1, False), }), 1, 1, False), } SliceMT.setup_class_methods() NoneMT.setup_class_methods() def setup_int(): IntClass._ctor = Variable(UnboxedFunctionMT(None, None, [Str, Int], Int, ndefaults=1), ("@int_", [Variable(Int, 10, 1, False)], None), 1, False) def _int_can_convert_to(t): return t is Float def _int_convert_to(em, var, t): if t is Int: return var if t is Float: name = '%' + em.mkname() em.pl('%s = sitofp i64 %s to double' % (name, var.v)) em.pc("#error unimplemented") var.decvref(em) return Variable(Float, name, 1, True) if isinstance(t, BoxedMT): return t.convert_from(em, var) raise UserTypeError(t) Int._can_convert_to = _int_can_convert_to Int.convert_to = _int_convert_to int_class_methods = { "__add__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_add", [], None), 1, False), "__and__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_and", [], None), 1, False), "__or__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_or", [], None), 1, False), "__div__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_div", [], None), 1, False), "__lshift__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_lshift", [], None), 1, False), "__mod__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_mod", [], None), 1, False), "__mul__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_mul", [], None), 1, False), "__neg__": Variable(UnboxedFunctionMT(None, None, [Int], Int), ("@int_neg", [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(None, None, [Int], Bool), ("@int_nonzero", [], None), 1, False), "__pow__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_pow", [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(None, None, [Int], Str), ("@int_repr", [], None), 1, False), "__rshift__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_rshift", [], None), 1, False), "__sub__": Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_sub", [], None), 1, False), # "__incref__": Variable(UnboxedFunctionMT(None, None, [Int], None_), ("@int_incref", [], None), 1, False), # "__decref__": Variable(UnboxedFunctionMT(None, None, [Int], None_), ("@int_decref", [], None), 1, False), } int_class_methods["__str__"] = int_class_methods["__repr__"] for attr in COMPARE_MAP.values(): int_class_methods[attr] = Variable(UnboxedFunctionMT(None, None, [Int, Int], Bool), ("@int_" + attr[2:-2], [], None), 1, False) for n, v in int_class_methods.iteritems(): IntClass.set_clsattr_value(n, v, _init=True) IntClass.initialized = ("attrs", "write") Int.initialized = ("attrs", "write") setup_int() def setup_float(): FloatClass._ctor = Variable(UnboxedFunctionMT(None, None, [Int], Float), ("@float_", [], None), 1, False) float_class_methods = { "__add__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_add", [], None), 1, False), "__div__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_div", [], None), 1, False), "__mod__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_mod", [], None), 1, False), "__mul__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_mul", [], None), 1, False), "__neg__": Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_neg", [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(None, None, [Float], Bool), ("@float_nonzero", [], None), 1, False), "__pow__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_pow", [], None), 1, False), "__rdiv__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_rdiv", [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(None, None, [Float], Str), ("@float_repr", [], None), 1, False), "__rsub__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_rsub", [], None), 1, False), "__str__": Variable(UnboxedFunctionMT(None, None, [Float], Str), ("@float_str", [], None), 1, False), "__sub__": Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_sub", [], None), 1, False), # "__incref__": Variable(UnboxedFunctionMT(None, None, [Float], None_), ("@float_incref", [], None), 1, False), # "__decref__": Variable(UnboxedFunctionMT(None, None, [Float], None_), ("@float_decref", [], None), 1, False), } float_class_methods["__radd__"] = float_class_methods["__add__"] float_class_methods["__rmul__"] = float_class_methods["__mul__"] for attr in COMPARE_MAP.values(): float_class_methods[attr] = Variable(UnboxedFunctionMT(None, None, [Float, Float], Bool), ("@float_" + attr[2:-2], [], None), 1, False) for n, v in float_class_methods.iteritems(): FloatClass.set_clsattr_value(n, v, _init=True) FloatClass.initialized = ("attrs", "write") Float.initialized = ("attrs", "write") setup_float() def setup_string(): StrIteratorClass = ClassMT(ObjectClass, "striterator", "striterator") StrIterator = StrIteratorClass._instance StrClass._ctor = Variable(StrFunc, (), 1, False) em = None string_class_methods = { "__add__": Variable(UnboxedFunctionMT(em, None, [Str, Str], Str), ("@str_add", [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [Str, Str], Bool), ("@str_eq", [], None), 1, False), "__getitem__": PolymorphicFunctionMT.make([ Variable(UnboxedFunctionMT(em, None, [Str, Int], Str), ("@str_getitem", [], None), 1, False), Variable(UnboxedFunctionMT(em, None, [Str, Slice], Str), ("@str_getitem_slice", [], None), 1, False)]), "join": Variable(UnboxedFunctionMT(em, None, [Str, ListMT.make_list(Str)], Str), ("@str_join", [], None), 1, False), "__len__": Variable(UnboxedFunctionMT(em, None, [Str], Int), ("@str_len", [], None), 1, False), "__le__": Variable(UnboxedFunctionMT(em, None, [Str, Str], Bool), ("@str_le", [], None), 1, False), "__lt__": Variable(UnboxedFunctionMT(em, None, [Str, Str], Bool), ("@str_lt", [], None), 1, False), "__mul__": Variable(UnboxedFunctionMT(em, None, [Str, Int], Str), ("@str_mul", [], None), 1, False), "__ne__": Variable(UnboxedFunctionMT(em, None, [Str, Str], Bool), ("@str_ne", [], None), 1, False), "__nonzero__": Variable(UnboxedFunctionMT(em, None, [Str], Bool), ("@str_nonzero", [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [Str], Str), ("@str_repr", [], None), 1, False), "split": Variable(UnboxedFunctionMT(em, None, [Str, Str], ListMT.make_list(Str), ndefaults=1), ("@str_split", [Variable(Str, "null", 1, False)], None), 1, False), "__str__": Variable(UnboxedFunctionMT(em, None, [Str], Str), ("@str_str", [], None), 1, False), # "__incref__": Variable(UnboxedFunctionMT(em, None, [Str], None_), ("@str_incref", [], None), 1, False), # "__decref__": Variable(UnboxedFunctionMT(em, None, [Str], None_), ("@str_decref", [], None), 1, False), "strip": Variable(UnboxedFunctionMT(em, None, [Str], Str), ("@str_strip", [], None), 1, False), "lstrip": Variable(UnboxedFunctionMT(em, None, [Str], Str), ("@str_lstrip", [], None), 1, False), "rstrip": Variable(UnboxedFunctionMT(em, None, [Str], Str), ("@str_rstrip", [], None), 1, False), "__iter__": Variable(UnboxedFunctionMT(em, None, [Str], StrIterator), ("@str_iter", [], None), 1, False), } for n, v in string_class_methods.iteritems(): StrClass.set_clsattr_value(n, v, _init=True) StrClass.initialized = ("attrs", "write") Str.initialized = ("attrs", "write") StrIteratorClass.set_clsattr_value("hasnext", Variable(UnboxedFunctionMT(em, None, [StrIterator], Bool), ("@striterator_hasnext", [], None), 1, False), _init=True) StrIteratorClass.set_clsattr_value("next", Variable(UnboxedFunctionMT(em, None, [StrIterator], Str), ("@striterator_next", [], None), 1, False), _init=True) StrIteratorClass.set_instattr_type(" str", Str) StrIteratorClass.set_instattr_type(" pos", Int) STDLIB_TYPES.append(StrIteratorClass) setup_string() def setup_bool(): BoolClass._ctor = Variable(BoolFunc, (), 1, False) em = None bool_class_methods = { "__nonzero__": Variable(UnboxedFunctionMT(em, None, [Bool], Bool), ("@bool_nonzero", [], None), 1, False), "__repr__": Variable(UnboxedFunctionMT(em, None, [Bool], Str), ("@bool_repr", [], None), 1, False), "__eq__": Variable(UnboxedFunctionMT(em, None, [Bool, Bool], Bool), ("@bool_eq", [], None), 1, False), } bool_class_methods["__str__"] = bool_class_methods["__repr__"] def _bool_can_convert_to(self, t): return t in (Int,) for n, v in bool_class_methods.iteritems(): BoolClass.set_clsattr_value(n, v, _init=True) BoolClass.initialized = ("attrs", "write") Bool.initialized = ("attrs", "write") setup_bool() def setup_type(): TypeClass._ctor = Variable(TypeFunc, (), 1, False) type_class_methods = { "__repr__": Variable(UnboxedFunctionMT(None, None, [Type], Str), ("@type_repr_", [], None), 1, False), } type_class_methods["__str__"] = type_class_methods["__repr__"] for n, v in type_class_methods.iteritems(): TypeClass.set_clsattr_value(n, v, _init=True) TypeClass.set_clsattr_value("__incref__", Variable(UnboxedFunctionMT(None, None, [Type], None_), ("@type_incref_", [], None), 1, False), _init=True, force=True) TypeClass.set_clsattr_value("__decref__", Variable(UnboxedFunctionMT(None, None, [Type], None_), ("@type_decref_", [], None), 1, False), _init=True, force=True) TypeClass.set_instattr_type("__name__", Str) TypeClass.set_instattr_type("__base__", Type) # TypeClass.initialized = ("attrs", "write") # Type.initialized = ("attrs", "write") setup_type() def setup_file(): FileClass.set_clsattr_value("__init__", Variable(UnboxedFunctionMT(None, None, [File, Str], None_), ("@file_init", [], None), 1, False), _init=True) FileClass.set_clsattr_value("read", Variable(UnboxedFunctionMT(None, None, [File, Int], Str), ("@file_read", [], None), 1, False), _init=True) FileClass.set_clsattr_value("write", Variable(UnboxedFunctionMT(None, None, [File, Str], None_), ("@file_write", [], None), 1, False), _init=True) FileClass.set_clsattr_value("readline", Variable(UnboxedFunctionMT(None, None, [File], Str), ("@file_readline", [], None), 1, False), _init=True) FileClass.set_clsattr_value("close", Variable(UnboxedFunctionMT(None, None, [File], None_), ("@file_close", [], None), 1, False), _init=True) FileClass.set_clsattr_value("flush", Variable(UnboxedFunctionMT(None, None, [File], None_), ("@file_flush", [], None), 1, False), _init=True) FileClass.set_clsattr_value("__enter__", Variable(UnboxedFunctionMT(None, None, [File], File), ("@file_enter", [], None), 1, False), _init=True) FileClass.set_clsattr_value("__exit__", Variable(UnboxedFunctionMT(None, None, [File, None_, None_, None_], None_), ("@file_exit", [], None), 1, False), _init=True) FileClass.set_instattr_type("closed", Bool) FileClass.set_instattr_type("fd", Int) FileClass.initialized = ("attrs", "write") File.initialized = ("attrs", "write") setup_file()

143,686

8,264

3,719

cb9d202d11aad5d50b66f4a9f03ebef480d92465

435

py

Python

app/models/item.py

yudjinn/backend-starlite-postgres

2cb42a9f85f902e2bdfb6cb99d306401e6b23e7f

[ "MIT" ]

null

app/models/item.py

yudjinn/backend-starlite-postgres

2cb42a9f85f902e2bdfb6cb99d306401e6b23e7f

[ "MIT" ]

null

app/models/item.py

yudjinn/backend-starlite-postgres

2cb42a9f85f902e2bdfb6cb99d306401e6b23e7f

[ "MIT" ]

2

2022-02-06T17:29:15.000Z

2022-03-04T12:46:39.000Z

import uuid from sqlalchemy import Column, ForeignKey, String from sqlalchemy.dialects.postgresql import UUID from sqlalchemy.orm import relationship from .base import Base from .mixins import DateFieldsMixins

27.1875

75

0.770115

import uuid from sqlalchemy import Column, ForeignKey, String from sqlalchemy.dialects.postgresql import UUID from sqlalchemy.orm import relationship from .base import Base from .mixins import DateFieldsMixins class Item(DateFieldsMixins, Base): name: str = Column(String(64), nullable=False) owner_id: uuid.UUID = Column(UUID(as_uuid=True), ForeignKey("user.id")) owner = relationship("User", back_populates="items")

0

199

23

1df90d6b5a21d880a74025dd9aa1c5f1ebfced00

7,715

py

Python

addons/io_scene_gltf2/io/exp/gltf2_io_get.py

dtysky/glTF-Blender-IO

5488adce3d496c6db7b2fff121d4fb46962a02b2

[ "Apache-2.0" ]

null

addons/io_scene_gltf2/io/exp/gltf2_io_get.py

dtysky/glTF-Blender-IO

5488adce3d496c6db7b2fff121d4fb46962a02b2

[ "Apache-2.0" ]

null

addons/io_scene_gltf2/io/exp/gltf2_io_get.py

dtysky/glTF-Blender-IO

5488adce3d496c6db7b2fff121d4fb46962a02b2

[ "Apache-2.0" ]

null

# Copyright (c) 2017 The Khronos Group 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. # # Imports # import os from ...io.com.gltf2_io_debug import * # # Globals # # # Functions # def get_material_requires_texcoords(glTF, index): """ Query function, if a material "needs" texture coordinates. This is the case, if a texture is present and used. """ if glTF.get('materials') is None: return False materials = glTF['materials'] if index < 0 or index >= len(materials): return False material = materials[index] # General if material.get('emissiveTexture') is not None: return True if material.get('normalTexture') is not None: return True if material.get('occlusionTexture') is not None: return True # Metallic roughness if material.get('baseColorTexture') is not None: return True if material.get('metallicRoughnessTexture') is not None: return True # Specular glossiness if material.get('diffuseTexture') is not None: return True if material.get('specularGlossinessTexture') is not None: return True # Unlit Material if material.get('baseColorTexture') is not None: return True if material.get('diffuseTexture') is not None: return True # Displacement if material.get('displacementTexture') is not None: return True return False def get_material_requires_normals(glTF, index): """ Query function, if a material "needs" normals. This is the case, if a texture is present and used. At point of writing, same function as for texture coordinates. """ return get_material_requires_texcoords(glTF, index) def get_material_index(glTF, name): """ Return the material index in the glTF array. """ if name is None: return -1 if glTF.get('materials') is None: return -1 index = 0 for material in glTF['materials']: if material['name'] == name: return index index += 1 return -1 def get_mesh_index(glTF, name): """ Return the mesh index in the glTF array. """ if glTF.get('meshes') is None: return -1 index = 0 for mesh in glTF['meshes']: if mesh['name'] == name: return index index += 1 return -1 def get_skin_index(glTF, name, index_offset): """ Return the skin index in the glTF array. """ if glTF.get('skins') is None: return -1 skeleton = get_node_index(glTF, name) index = 0 for skin in glTF['skins']: if skin['skeleton'] == skeleton: return index + index_offset index += 1 return -1 def get_camera_index(glTF, name): """ Return the camera index in the glTF array. """ if glTF.get('cameras') is None: return -1 index = 0 for camera in glTF['cameras']: if camera['name'] == name: return index index += 1 return -1 def get_light_index(glTF, name): """ Return the light index in the glTF array. """ if glTF.get('extensions') is None: return -1 extensions = glTF['extensions'] if extensions.get('KHR_lights_punctual') is None: return -1 khr_lights_punctual = extensions['KHR_lights_punctual'] if khr_lights_punctual.get('lights') is None: return -1 lights = khr_lights_punctual['lights'] index = 0 for light in lights: if light['name'] == name: return index index += 1 return -1 def get_node_index(glTF, name): """ Return the node index in the glTF array. """ if glTF.get('nodes') is None: return -1 index = 0 for node in glTF['nodes']: if node['name'] == name: return index index += 1 return -1 def get_scene_index(glTF, name): """ Return the scene index in the glTF array. """ if glTF.get('scenes') is None: return -1 index = 0 for scene in glTF['scenes']: if scene['name'] == name: return index index += 1 return -1 def get_texture_index(glTF, filename): """ Return the texture index in the glTF array by a given filepath. """ if glTF.get('textures') is None: return -1 image_index = get_image_index(glTF, filename) if image_index == -1: return -1 for texture_index, texture in enumerate(glTF['textures']): if image_index == texture['source']: return texture_index return -1 def get_image_index(glTF, filename): """ Return the image index in the glTF array. """ if glTF.get('images') is None: return -1 image_name = get_image_name(filename) for index, current_image in enumerate(glTF['images']): if image_name == current_image['name']: return index return -1 def get_image_name(filename): """ Return user-facing, extension-agnostic name for image. """ return os.path.splitext(filename)[0] def get_scalar(default_value, init_value = 0.0): """ Return scalar with a given default/fallback value. """ return_value = init_value if default_value is None: return return_value return_value = default_value return return_value def get_vec2(default_value, init_value = [0.0, 0.0]): """ Return vec2 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 2: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 2: return return_value return return_value def get_vec3(default_value, init_value = [0.0, 0.0, 0.0]): """ Return vec3 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 3: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 3: return return_value return return_value def get_vec4(default_value, init_value = [0.0, 0.0, 0.0, 1.0]): """ Return vec4 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 4: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 4: return return_value return return_value def get_index(elements, name): """ Return index of a glTF element by a given name. """ if elements is None or name is None: return -1 index = 0 for element in elements: if element.get('name') is None: return -1 if element['name'] == name: return index index += 1 return -1

20.091146

114

0.6

# Copyright (c) 2017 The Khronos Group 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. # # Imports # import os from ...io.com.gltf2_io_debug import * # # Globals # # # Functions # def get_material_requires_texcoords(glTF, index): """ Query function, if a material "needs" texture coordinates. This is the case, if a texture is present and used. """ if glTF.get('materials') is None: return False materials = glTF['materials'] if index < 0 or index >= len(materials): return False material = materials[index] # General if material.get('emissiveTexture') is not None: return True if material.get('normalTexture') is not None: return True if material.get('occlusionTexture') is not None: return True # Metallic roughness if material.get('baseColorTexture') is not None: return True if material.get('metallicRoughnessTexture') is not None: return True # Specular glossiness if material.get('diffuseTexture') is not None: return True if material.get('specularGlossinessTexture') is not None: return True # Unlit Material if material.get('baseColorTexture') is not None: return True if material.get('diffuseTexture') is not None: return True # Displacement if material.get('displacementTexture') is not None: return True return False def get_material_requires_normals(glTF, index): """ Query function, if a material "needs" normals. This is the case, if a texture is present and used. At point of writing, same function as for texture coordinates. """ return get_material_requires_texcoords(glTF, index) def get_material_index(glTF, name): """ Return the material index in the glTF array. """ if name is None: return -1 if glTF.get('materials') is None: return -1 index = 0 for material in glTF['materials']: if material['name'] == name: return index index += 1 return -1 def get_mesh_index(glTF, name): """ Return the mesh index in the glTF array. """ if glTF.get('meshes') is None: return -1 index = 0 for mesh in glTF['meshes']: if mesh['name'] == name: return index index += 1 return -1 def get_skin_index(glTF, name, index_offset): """ Return the skin index in the glTF array. """ if glTF.get('skins') is None: return -1 skeleton = get_node_index(glTF, name) index = 0 for skin in glTF['skins']: if skin['skeleton'] == skeleton: return index + index_offset index += 1 return -1 def get_camera_index(glTF, name): """ Return the camera index in the glTF array. """ if glTF.get('cameras') is None: return -1 index = 0 for camera in glTF['cameras']: if camera['name'] == name: return index index += 1 return -1 def get_light_index(glTF, name): """ Return the light index in the glTF array. """ if glTF.get('extensions') is None: return -1 extensions = glTF['extensions'] if extensions.get('KHR_lights_punctual') is None: return -1 khr_lights_punctual = extensions['KHR_lights_punctual'] if khr_lights_punctual.get('lights') is None: return -1 lights = khr_lights_punctual['lights'] index = 0 for light in lights: if light['name'] == name: return index index += 1 return -1 def get_node_index(glTF, name): """ Return the node index in the glTF array. """ if glTF.get('nodes') is None: return -1 index = 0 for node in glTF['nodes']: if node['name'] == name: return index index += 1 return -1 def get_scene_index(glTF, name): """ Return the scene index in the glTF array. """ if glTF.get('scenes') is None: return -1 index = 0 for scene in glTF['scenes']: if scene['name'] == name: return index index += 1 return -1 def get_texture_index(glTF, filename): """ Return the texture index in the glTF array by a given filepath. """ if glTF.get('textures') is None: return -1 image_index = get_image_index(glTF, filename) if image_index == -1: return -1 for texture_index, texture in enumerate(glTF['textures']): if image_index == texture['source']: return texture_index return -1 def get_image_index(glTF, filename): """ Return the image index in the glTF array. """ if glTF.get('images') is None: return -1 image_name = get_image_name(filename) for index, current_image in enumerate(glTF['images']): if image_name == current_image['name']: return index return -1 def get_image_name(filename): """ Return user-facing, extension-agnostic name for image. """ return os.path.splitext(filename)[0] def get_scalar(default_value, init_value = 0.0): """ Return scalar with a given default/fallback value. """ return_value = init_value if default_value is None: return return_value return_value = default_value return return_value def get_vec2(default_value, init_value = [0.0, 0.0]): """ Return vec2 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 2: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 2: return return_value return return_value def get_vec3(default_value, init_value = [0.0, 0.0, 0.0]): """ Return vec3 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 3: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 3: return return_value return return_value def get_vec4(default_value, init_value = [0.0, 0.0, 0.0, 1.0]): """ Return vec4 with a given default/fallback value. """ return_value = init_value if default_value is None or len(default_value) < 4: return return_value index = 0 for number in default_value: return_value[index] = number index += 1 if index == 4: return return_value return return_value def get_index(elements, name): """ Return index of a glTF element by a given name. """ if elements is None or name is None: return -1 index = 0 for element in elements: if element.get('name') is None: return -1 if element['name'] == name: return index index += 1 return -1

0

527d5a647225fe1451d9322370f1d08e468c34af

696

py

Python

Src/Web/config.py

23233/sproxy

b40d6593e3a60ba089484c527de1bc4afc3a2d24

[ "MIT" ]

167

2019-07-05T13:25:39.000Z

2020-04-25T11:31:19.000Z

Src/Web/config.py

23233/sproxy

b40d6593e3a60ba089484c527de1bc4afc3a2d24

[ "MIT" ]

10

2018-12-11T06:07:37.000Z

2019-07-03T11:14:36.000Z

Src/Web/config.py

23233/sproxy

b40d6593e3a60ba089484c527de1bc4afc3a2d24

[ "MIT" ]

38

2019-07-04T07:50:08.000Z

2020-04-17T21:05:46.000Z

# Create dummy secrey key so we can use sessions SECRET_KEY = '1234567890' # Flask-Security config SECURITY_URL_PREFIX = "/admin" SECURITY_PASSWORD_HASH = "pbkdf2_sha256" SECURITY_PASSWORD_SALT = "ATGUOHAELKiubahiughaerGOJAEGj" SECURITY_USER_IDENTITY_ATTRIBUTES = ["name"] # Flask-Security URLs, overridden because they don't put a / at the end SECURITY_LOGIN_URL = "/login/" SECURITY_LOGOUT_URL = "/logout/" SECURITY_REGISTER_URL = "/register/" SECURITY_POST_LOGIN_VIEW = "/admin/" SECURITY_POST_LOGOUT_VIEW = "/admin/" SECURITY_POST_REGISTER_VIEW = "/admin/" # Flask-Security features SECURITY_REGISTERABLE = True SECURITY_SEND_REGISTER_EMAIL = False SQLALCHEMY_TRACK_MODIFICATIONS = False

30.26087

71

0.804598

# Create dummy secrey key so we can use sessions SECRET_KEY = '1234567890' # Flask-Security config SECURITY_URL_PREFIX = "/admin" SECURITY_PASSWORD_HASH = "pbkdf2_sha256" SECURITY_PASSWORD_SALT = "ATGUOHAELKiubahiughaerGOJAEGj" SECURITY_USER_IDENTITY_ATTRIBUTES = ["name"] # Flask-Security URLs, overridden because they don't put a / at the end SECURITY_LOGIN_URL = "/login/" SECURITY_LOGOUT_URL = "/logout/" SECURITY_REGISTER_URL = "/register/" SECURITY_POST_LOGIN_VIEW = "/admin/" SECURITY_POST_LOGOUT_VIEW = "/admin/" SECURITY_POST_REGISTER_VIEW = "/admin/" # Flask-Security features SECURITY_REGISTERABLE = True SECURITY_SEND_REGISTER_EMAIL = False SQLALCHEMY_TRACK_MODIFICATIONS = False

0

813d0a9cf8930b06ac9932bbfc5b272617c5c4ac

3,785

py

Python

homeassistant/components/mobile_app/websocket_api.py

jeanfpoulin/home-assistant

04dbe5bc841e1a429873efbd850c35b823ef26ce

[ "Apache-2.0" ]

3

2020-05-18T10:18:16.000Z

2020-12-08T11:27:55.000Z

homeassistant/components/mobile_app/websocket_api.py

jeanfpoulin/home-assistant

04dbe5bc841e1a429873efbd850c35b823ef26ce

[ "Apache-2.0" ]

3

2021-02-08T20:54:46.000Z

2021-09-08T02:30:04.000Z

homeassistant/components/mobile_app/websocket_api.py

jeanfpoulin/home-assistant

04dbe5bc841e1a429873efbd850c35b823ef26ce

[ "Apache-2.0" ]

6

2020-04-10T06:21:11.000Z

2021-07-01T08:53:38.000Z

"""Websocket API for mobile_app.""" import voluptuous as vol from homeassistant.components.cloud import async_delete_cloudhook from homeassistant.components.websocket_api import ( ActiveConnection, async_register_command, async_response, error_message, result_message, websocket_command, ws_require_user, ) from homeassistant.components.websocket_api.const import ( ERR_INVALID_FORMAT, ERR_NOT_FOUND, ERR_UNAUTHORIZED, ) from homeassistant.const import CONF_WEBHOOK_ID from homeassistant.exceptions import HomeAssistantError from homeassistant.helpers import config_validation as cv from homeassistant.helpers.typing import HomeAssistantType from .const import ( CONF_CLOUDHOOK_URL, CONF_USER_ID, DATA_CONFIG_ENTRIES, DATA_DELETED_IDS, DATA_STORE, DOMAIN, ) from .helpers import safe_registration, savable_state def register_websocket_handlers(hass: HomeAssistantType) -> bool: """Register the websocket handlers.""" async_register_command(hass, websocket_get_user_registrations) async_register_command(hass, websocket_delete_registration) return True @ws_require_user() @async_response @websocket_command( { vol.Required("type"): "mobile_app/get_user_registrations", vol.Optional(CONF_USER_ID): cv.string, } ) async def websocket_get_user_registrations( hass: HomeAssistantType, connection: ActiveConnection, msg: dict ) -> None: """Return all registrations or just registrations for given user ID.""" user_id = msg.get(CONF_USER_ID, connection.user.id) if user_id != connection.user.id and not connection.user.is_admin: # If user ID is provided and is not current user ID and current user # isn't an admin user connection.send_error(msg["id"], ERR_UNAUTHORIZED, "Unauthorized") return user_registrations = [] for config_entry in hass.config_entries.async_entries(domain=DOMAIN): registration = config_entry.data if connection.user.is_admin or registration[CONF_USER_ID] is user_id: user_registrations.append(safe_registration(registration)) connection.send_message(result_message(msg["id"], user_registrations)) @ws_require_user() @async_response @websocket_command( { vol.Required("type"): "mobile_app/delete_registration", vol.Required(CONF_WEBHOOK_ID): cv.string, } ) async def websocket_delete_registration( hass: HomeAssistantType, connection: ActiveConnection, msg: dict ) -> None: """Delete the registration for the given webhook_id.""" user = connection.user webhook_id = msg.get(CONF_WEBHOOK_ID) if webhook_id is None: connection.send_error(msg["id"], ERR_INVALID_FORMAT, "Webhook ID not provided") return config_entry = hass.data[DOMAIN][DATA_CONFIG_ENTRIES][webhook_id] registration = config_entry.data if registration is None: connection.send_error( msg["id"], ERR_NOT_FOUND, "Webhook ID not found in storage" ) return if registration[CONF_USER_ID] != user.id and not user.is_admin: return error_message( msg["id"], ERR_UNAUTHORIZED, "User is not registration owner" ) await hass.config_entries.async_remove(config_entry.entry_id) hass.data[DOMAIN][DATA_DELETED_IDS].append(webhook_id) store = hass.data[DOMAIN][DATA_STORE] try: await store.async_save(savable_state(hass)) except HomeAssistantError: return error_message(msg["id"], "internal_error", "Error deleting registration") if CONF_CLOUDHOOK_URL in registration and "cloud" in hass.config.components: await async_delete_cloudhook(hass, webhook_id) connection.send_message(result_message(msg["id"], "ok"))

30.772358

88

0.732893

"""Websocket API for mobile_app.""" import voluptuous as vol from homeassistant.components.cloud import async_delete_cloudhook from homeassistant.components.websocket_api import ( ActiveConnection, async_register_command, async_response, error_message, result_message, websocket_command, ws_require_user, ) from homeassistant.components.websocket_api.const import ( ERR_INVALID_FORMAT, ERR_NOT_FOUND, ERR_UNAUTHORIZED, ) from homeassistant.const import CONF_WEBHOOK_ID from homeassistant.exceptions import HomeAssistantError from homeassistant.helpers import config_validation as cv from homeassistant.helpers.typing import HomeAssistantType from .const import ( CONF_CLOUDHOOK_URL, CONF_USER_ID, DATA_CONFIG_ENTRIES, DATA_DELETED_IDS, DATA_STORE, DOMAIN, ) from .helpers import safe_registration, savable_state def register_websocket_handlers(hass: HomeAssistantType) -> bool: """Register the websocket handlers.""" async_register_command(hass, websocket_get_user_registrations) async_register_command(hass, websocket_delete_registration) return True @ws_require_user() @async_response @websocket_command( { vol.Required("type"): "mobile_app/get_user_registrations", vol.Optional(CONF_USER_ID): cv.string, } ) async def websocket_get_user_registrations( hass: HomeAssistantType, connection: ActiveConnection, msg: dict ) -> None: """Return all registrations or just registrations for given user ID.""" user_id = msg.get(CONF_USER_ID, connection.user.id) if user_id != connection.user.id and not connection.user.is_admin: # If user ID is provided and is not current user ID and current user # isn't an admin user connection.send_error(msg["id"], ERR_UNAUTHORIZED, "Unauthorized") return user_registrations = [] for config_entry in hass.config_entries.async_entries(domain=DOMAIN): registration = config_entry.data if connection.user.is_admin or registration[CONF_USER_ID] is user_id: user_registrations.append(safe_registration(registration)) connection.send_message(result_message(msg["id"], user_registrations)) @ws_require_user() @async_response @websocket_command( { vol.Required("type"): "mobile_app/delete_registration", vol.Required(CONF_WEBHOOK_ID): cv.string, } ) async def websocket_delete_registration( hass: HomeAssistantType, connection: ActiveConnection, msg: dict ) -> None: """Delete the registration for the given webhook_id.""" user = connection.user webhook_id = msg.get(CONF_WEBHOOK_ID) if webhook_id is None: connection.send_error(msg["id"], ERR_INVALID_FORMAT, "Webhook ID not provided") return config_entry = hass.data[DOMAIN][DATA_CONFIG_ENTRIES][webhook_id] registration = config_entry.data if registration is None: connection.send_error( msg["id"], ERR_NOT_FOUND, "Webhook ID not found in storage" ) return if registration[CONF_USER_ID] != user.id and not user.is_admin: return error_message( msg["id"], ERR_UNAUTHORIZED, "User is not registration owner" ) await hass.config_entries.async_remove(config_entry.entry_id) hass.data[DOMAIN][DATA_DELETED_IDS].append(webhook_id) store = hass.data[DOMAIN][DATA_STORE] try: await store.async_save(savable_state(hass)) except HomeAssistantError: return error_message(msg["id"], "internal_error", "Error deleting registration") if CONF_CLOUDHOOK_URL in registration and "cloud" in hass.config.components: await async_delete_cloudhook(hass, webhook_id) connection.send_message(result_message(msg["id"], "ok"))

0

fba6df107748d61010973df91014413655aef5fb

4,000

py

Python

test_locks.py

zbentley/amqp-locks-python

7da8e941fff752bde1f1fd56caa51810fdac7577

[ "MIT" ]

null

test_locks.py

zbentley/amqp-locks-python

7da8e941fff752bde1f1fd56caa51810fdac7577

[ "MIT" ]

null

test_locks.py

zbentley/amqp-locks-python

7da8e941fff752bde1f1fd56caa51810fdac7577

[ "MIT" ]

null

#!/usr/bin/env python3 import pika import time import sys import argparse sys.path.append("lib") from rabbitlock.mutex import Mutex from rabbitlock.semaphore import Semaphore # http://www.huyng.com/posts/python-performance-analysis/ parse_and_dispatch(sys.argv[1:])

29.411765

101

0.62375

#!/usr/bin/env python3 import pika import time import sys import argparse sys.path.append("lib") from rabbitlock.mutex import Mutex from rabbitlock.semaphore import Semaphore def positive_float(value): retval = float(value) if retval < 0: raise argparse.ArgumentTypeError("%s not a positive float" % value) return retval # http://www.huyng.com/posts/python-performance-analysis/ class Timer(object): def __init__(self, verbose=True): self.verbose = verbose def __enter__(self): self.start = time.time() return self def __exit__(self, *args): self.end = time.time() self.secs = self.end - self.start self.msecs = self.secs * 1000 # millisecs if self.verbose: print('elapsed time: %f ms' % self.msecs) def get_connection_parameters(): return pika.ConnectionParameters("localhost", 5672, "/", pika.PlainCredentials("guest", "guest")) def true_mutex_operations(args): print(args) lock = Mutex("foo", get_connection_parameters()) while True: with Timer(args.sleep): acquired = lock.ensure_acquired() if acquired: print("Got lock") else: print("Lost lock") time.sleep(args.sleep) if not args.loop: break def get_held_semaphore(greedy, verbose=False): sems = [Semaphore("foo", get_connection_parameters())] with Timer(verbose): acquired = sems[0].ensure_semaphore_held() if acquired: if verbose: print("Got lock: %d" % acquired) else: return [] if greedy and acquired: success = get_held_semaphore(greedy, verbose) if success: sems.extend(success) return sems def semaphore_operations(args): lock = Semaphore("foo", get_connection_parameters()) if args.destroy: with Timer(args.verbose): lock.ensure_semaphore_destroyed() elif args.acquire: sems = [] while True: sems.extend(get_held_semaphore(args.greedy, args.verbose)) for sem in sems: with Timer(args.verbose): num = sem.ensure_semaphore_held() if num: if args.loop: print("Held: %d" % num) else: sems.remove(sem) if args.verbose: print("") if not args.loop: if not sems: print("Could not get lock") break time.sleep(args.sleep) elif args.change: if lock.adjust_semaphore(args.change): print("Adjustment success") else: print("Adjustment failure") def parse_and_dispatch(args): parser = argparse.ArgumentParser() subparsers = parser.add_subparsers() parser.add_argument("--sleep", type=positive_float, default=0.5) parser.add_argument("--verbose", action="store_true") semparser = subparsers.add_parser("semaphore") semparser.set_defaults(func=semaphore_operations) semparser.add_argument("--greedy", action="store_true") semparser.add_argument("--loop", action="store_true") semops = semparser.add_mutually_exclusive_group() semops.add_argument("--change", type=int) semops.add_argument("--acquire", action="store_true") semops.add_argument("--destroy", action="store_true") truemutexparser = subparsers.add_parser("true_mutex") truemutexparser.set_defaults(func=true_mutex_operations) truemutexparser.add_argument("--acquire", action="store_true", default=True) truemutexparser.add_argument("--loop", action="store_true") result = parser.parse_args(args) if hasattr(result, "func"): # TODO if semaphore, fail if greedy or loop are specified in anything other but acquire mode. result.func(result) else: parser.error("No lock mode specified") parse_and_dispatch(sys.argv[1:])

3,483

-1

240

5b3f69f42eae7163ac1ea43116b8a85cdb9c27b7

1,068

py

Python

django_sourcebook/sourcebook/migrations/0009_auto_20200101_2140.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

django_sourcebook/sourcebook/migrations/0009_auto_20200101_2140.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

django_sourcebook/sourcebook/migrations/0009_auto_20200101_2140.py

maxblee/django_sourcebook

f90ca62cfe43c875a485f783ca1a06be40d9bbc5

[ "MIT" ]

null

# Generated by Django 3.0.1 on 2020-01-02 02:40 import datetime from django.db import migrations, models

29.666667

78

0.495318

# Generated by Django 3.0.1 on 2020-01-02 02:40 import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("sourcebook", "0008_foiarequest_time_completed"), ] operations = [ migrations.AddField( model_name="story", name="publication_date", field=models.DateField(default=datetime.date(2020, 1, 2)), ), migrations.AlterField( model_name="source", name="source_type", field=models.CharField( choices=[ ("db", "Database administrator"), ("rp", "Person affected (anecdotal source)"), ("e", "Expert"), ("pr", "Spokesperson, PR-Rep"), ("f", "Public Records officer"), ("o", "Public/Company official"), ("i", "Company or business employee (current or former)"), ], max_length=2, ), ), ]

0

938

23

0655f1c66e00a9e31b9d920ea6a88703dd4f5793

3,688

py

Python

src/exp_num_query.py

moseslichten/TAFSSL

b236dee4936fe933293ebdd65e48a332a3aacdea

[ "Apache-2.0" ]

10

2020-09-26T07:07:52.000Z

2021-10-11T12:11:33.000Z

src/exp_num_query.py

moseslichten/TAFSSL

b236dee4936fe933293ebdd65e48a332a3aacdea

[ "Apache-2.0" ]

1

2021-04-16T14:36:28.000Z

src/exp_num_query.py

moseslichten/TAFSSL

b236dee4936fe933293ebdd65e48a332a3aacdea

[ "Apache-2.0" ]

2

2020-09-26T07:00:21.000Z

2021-08-14T14:12:50.000Z

# -------------------------------------------------------- # TAFSSL # Copyright (c) 2019 IBM Corp # Licensed under The Apache-2.0 License [see LICENSE for details] # -------------------------------------------------------- import numpy as np from utils.proto_msp import ProtoMSP import time import pickle from utils.misc import print_params from utils.misc import load_features from utils.misc import print_msg from utils.misc import avg, ci_95, parse_args from utils.misc import create_episode, calc_acc from utils.misc import get_color from utils.misc import get_features if __name__ == '__main__': get_features() n_query_exp() n_query_exp_fig()

32.637168

116

0.608731

# -------------------------------------------------------- # TAFSSL # Copyright (c) 2019 IBM Corp # Licensed under The Apache-2.0 License [see LICENSE for details] # -------------------------------------------------------- import numpy as np from utils.proto_msp import ProtoMSP import time import pickle from utils.misc import print_params from utils.misc import load_features from utils.misc import print_msg from utils.misc import avg, ci_95, parse_args from utils.misc import create_episode, calc_acc from utils.misc import get_color from utils.misc import get_features def run_episode(train_mean, cl_data_file, model, n_way=5, n_support=5, n_query=15): z_all, y = create_episode(cl_data_file, n_way, n_support, n_query) model.train_mean = train_mean model.opt.reduced_dim = 4 scores = [model.method_sub(z_all, model.sub_train_mean), model.method_sub(z_all, model.mean_and_norm_ber), model.method_project(z_all, model.mean_and_norm_ber, model.calc_ica), ] model.opt.reduced_dim = 10 scores += [ model.method_proj_and_cluster(z_all, model.mean_and_norm_ber, model.calc_ica), ] scores += [ model.method_project_and_mean_shift(z_all, model.mean_and_norm_ber, model.calc_ica), ] return calc_acc(scores, y) def run_exp(params, verbose): print_params(params) n_episodes = 10000 few_shot_params = dict(n_way=params.n_way, n_support=params.n_shot) model = ProtoMSP(opt=params) model = model.cuda() train_mean, cl_data_file = load_features(params) acc_list = [] start_time = time.perf_counter() for i in range(1, n_episodes + 1): acc = run_episode(train_mean, cl_data_file, model, n_query=params.n_query, **few_shot_params) acc_list += acc if i % verbose == 0: print_msg(i, n_episodes, start_time, acc_list, acc) res = [avg(acc_list[ind::len(acc)]) for ind in range(len(acc))] ci = [ci_95(acc_list[ind::len(acc)]) for ind in range(len(acc))] return res, ci def n_query_exp(): params = parse_args('test') for ds in ['mini', 'tiered']: params.dataset = ds exp_dict = {} for q in [2, 5, 10, 15, 30, 40, 50]: params.n_query = q res, ci = run_exp(params, verbose=1000) exp_dict[q] = (res, ci) with open(f'exp_n_queries_{ds}.pickle', 'wb') as handle: pickle.dump(exp_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) def n_query_exp_fig(): import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt for ds in ['mini', 'tiered']: with open(f'exp_n_queries_{ds}.pickle', 'rb') as handle: exp_dict = pickle.load(handle) x = np.array(list(exp_dict.keys())) values = np.array(list(exp_dict.values())) y = values[:, 0, :] ax = plt.subplot(111) exp_name = ['simple', 'sub', 'ica', 'ica & bkm', 'ica & msp'] y_lim = None for n in [*range(y.shape[1])]: # plt.plot(x, y[:, n].reshape(-1, ), label=exp_name[n]) plt.plot(x[1:], y[:, n].reshape(-1, )[1:], label=exp_name[n], color=get_color(exp_name[n]), linewidth=2) if exp_name[n] == 'simple': y_lim = np.min(y[:, n]) ax.legend(loc='lower center', fancybox=True, shadow=True, ncol=3) plt.ylim(bottom=y_lim - 3) plt.grid() fig = plt.figure(1) plt.xlabel("queries") plt.ylabel("accuracy") plt.savefig(f"num-query-exp-{ds}.png") plt.close(fig) if __name__ == '__main__': get_features() n_query_exp() n_query_exp_fig()

2,929

0

92

0276265483623e630c7767433a7cadbaf7c7e997

25

py

Python

src/scripts/__init__.py

arcosin/ANP_TrackDriver

d1705bb77959cb3653490e126a40f8086ba084bd

[ "MIT" ]

4

2019-05-22T00:17:28.000Z

2020-08-26T02:03:33.000Z

preprocess/__init__.py

ryanwongsa/open-images-2019-challenge

b49e0933451c4bf9b31a9a8faf1bd8ba3dee1cc5

[ "Apache-2.0" ]

3

2019-07-14T09:17:11.000Z

2022-01-13T01:15:10.000Z

preprocess/__init__.py

ryanwongsa/open-images-2019-challenge

b49e0933451c4bf9b31a9a8faf1bd8ba3dee1cc5

[ "Apache-2.0" ]

5

2021-03-24T01:02:06.000Z

2021-05-21T16:57:05.000Z

from .preprocess import *

25

0.8

from .preprocess import *

0

5e9233f492e89e51840f8454bc6abde3f18cbc0a

3,259

py

Python

qcommunity/optimization/obj.py

rsln-s/QCommunity

926c64efc7add34dae5fbab20359ef5b9b4aa420

[ "Apache-2.0" ]

2

2019-09-05T23:21:49.000Z

2020-03-10T13:54:32.000Z

qcommunity/optimization/obj.py

rsln-s/QCommunity

926c64efc7add34dae5fbab20359ef5b9b4aa420

[ "Apache-2.0" ]

null

qcommunity/optimization/obj.py

rsln-s/QCommunity

926c64efc7add34dae5fbab20359ef5b9b4aa420

[ "Apache-2.0" ]

1

2020-03-10T13:54:34.000Z

#!/usr/bin/env python # Returns obj_val function to be used in an optimizer # A better and updated version of qaoa_obj.py import networkx as nx import numpy as np # import matplotlib.pyplot as plt from networkx.generators.classic import barbell_graph import copy import sys import warnings import qcommunity.modularity.graphs as gm from qcommunity.utils.import_graph import generate_graph from ibmqxbackend.ansatz import IBMQXVarForm def get_obj(n_nodes, B, C=None, obj_params='ndarray', sign=1, backend='IBMQX', backend_params={'depth': 3}, return_x=False): """ :param obj_params: defines the signature of obj_val function. 'beta gamma' or 'ndarray' (added to support arbitrary number of steps and scipy.optimize.minimize.) :return: obj_val function, number of variational parameters :rtype: tuple """ if return_x: all_x = [] all_vals = [] # TODO refactor, remove code duplication if backend == 'IBMQX': var_form = IBMQXVarForm( num_qubits=n_nodes, depth=backend_params['depth']) num_parameters = var_form.num_parameters if obj_params == 'ndarray': else: raise ValueError( "obj_params '{}' not compatible with backend '{}'".format( obj_params, backend)) else: raise ValueError("Unsupported backend: {}".format(backend)) if return_x: return obj_val, num_parameters, all_x, all_vals else: return obj_val, num_parameters if __name__ == "__main__": x = np.array([2.1578616206475347, 0.1903995547630178]) obj_val, _ = get_obj_val("get_barbell_graph", 3, 3) print(obj_val(x[0], x[1])) obj_val, num_parameters = get_obj_val( "get_barbell_graph", 3, 3, obj_params='ndarray', backend='IBMQX') y = np.random.uniform(-np.pi, np.pi, num_parameters) print(obj_val(y)) obj_val, num_parameters = get_obj_val( "get_barbell_graph", 3, 3, obj_params='ndarray') z = np.random.uniform(-np.pi, np.pi, num_parameters) print(obj_val(z))

32.267327

166

0.59865

#!/usr/bin/env python # Returns obj_val function to be used in an optimizer # A better and updated version of qaoa_obj.py import networkx as nx import numpy as np # import matplotlib.pyplot as plt from networkx.generators.classic import barbell_graph import copy import sys import warnings import qcommunity.modularity.graphs as gm from qcommunity.utils.import_graph import generate_graph from ibmqxbackend.ansatz import IBMQXVarForm def get_obj(n_nodes, B, C=None, obj_params='ndarray', sign=1, backend='IBMQX', backend_params={'depth': 3}, return_x=False): """ :param obj_params: defines the signature of obj_val function. 'beta gamma' or 'ndarray' (added to support arbitrary number of steps and scipy.optimize.minimize.) :return: obj_val function, number of variational parameters :rtype: tuple """ if return_x: all_x = [] all_vals = [] # TODO refactor, remove code duplication if backend == 'IBMQX': var_form = IBMQXVarForm( num_qubits=n_nodes, depth=backend_params['depth']) num_parameters = var_form.num_parameters if obj_params == 'ndarray': def obj_val(x): resstrs = var_form.run(x) modularities = [ gm.compute_modularity(n_nodes, B, x, C=C) for x in resstrs ] y = np.mean(modularities) if return_x: all_x.append(copy.deepcopy(x)) all_vals.append({'max': max(modularities), 'mean': y}) print("Actual modularity (to be maximized): {}".format(y)) return sign * y else: raise ValueError( "obj_params '{}' not compatible with backend '{}'".format( obj_params, backend)) else: raise ValueError("Unsupported backend: {}".format(backend)) if return_x: return obj_val, num_parameters, all_x, all_vals else: return obj_val, num_parameters def get_obj_val(graph_generator_name, left, right, seed=None, obj_params='ndarray', sign=1, backend='IBMQX', backend_params={'depth': 3}, return_x=False): # Generate the graph G, _ = generate_graph(graph_generator_name, left, right, seed=seed) B = nx.modularity_matrix(G).A return get_obj( G.number_of_nodes(), B, obj_params=obj_params, sign=sign, backend=backend, backend_params=backend_params, return_x=return_x) if __name__ == "__main__": x = np.array([2.1578616206475347, 0.1903995547630178]) obj_val, _ = get_obj_val("get_barbell_graph", 3, 3) print(obj_val(x[0], x[1])) obj_val, num_parameters = get_obj_val( "get_barbell_graph", 3, 3, obj_params='ndarray', backend='IBMQX') y = np.random.uniform(-np.pi, np.pi, num_parameters) print(obj_val(y)) obj_val, num_parameters = get_obj_val( "get_barbell_graph", 3, 3, obj_params='ndarray') z = np.random.uniform(-np.pi, np.pi, num_parameters) print(obj_val(z))

1,063

0

58

5d32eec903f269edde168438b4d81ba0b9cc8d71

1,274

py

Python

tests/test_write.py

reshalfahsi/GGBColorSpace

f56994ffcd6a83762d67705116e690c7a64c9093

[ "MIT" ]

1

2020-12-06T07:38:08.000Z

tests/test_write.py

reshalfahsi/GGBColorSpace

f56994ffcd6a83762d67705116e690c7a64c9093

[ "MIT" ]

1

2021-06-19T02:20:02.000Z

tests/test_write.py

reshalfahsi/GGB

f56994ffcd6a83762d67705116e690c7a64c9093

[ "MIT" ]

1

2021-05-10T04:14:24.000Z

import cv2 from PIL import Image import numpy as np import random import pytest from ggb import GGB, CVLib from ggb.testing import ggb_test from ggb.testing import get_random_image, get_filled_image @ggb_test @ggb_test if __name__ == '__main__': pytest.main([__file__])

25.48

58

0.689168

import cv2 from PIL import Image import numpy as np import random import pytest from ggb import GGB, CVLib from ggb.testing import ggb_test from ggb.testing import get_random_image, get_filled_image @ggb_test def test_opencv_write(): w = random.randint(16, 2048) h = random.randint(16, 2048) image = get_random_image(w, h, 3, CVLib.OPENCV) ggb_image = GGB(image=image).process() assert(isinstance(ggb_image.write(), np.ndarray)) w = random.randint(16, 2048) h = random.randint(16, 2048) value = random.randint(0, 255) image = get_filled_image(w, h, 3, value, CVLib.OPENCV) ggb_image = GGB(image=image).process() assert(isinstance(ggb_image.write(), np.ndarray)) @ggb_test def test_pil_write(): w = random.randint(16, 2048) h = random.randint(16, 2048) image = get_random_image(w, h, 3, CVLib.PIL) ggb_image = GGB(image=image).process() assert(isinstance(ggb_image.write(), Image.Image)) w = random.randint(16, 2048) h = random.randint(16, 2048) value = random.randint(0, 255) image = get_filled_image(w, h, 3, value, CVLib.PIL) ggb_image = GGB(image=image).process() assert(isinstance(ggb_image.write(), Image.Image)) if __name__ == '__main__': pytest.main([__file__])

949

0

44

092bdfd9880f8cd15ba0d55680a5b605d55352b5

9,518

py

Python

tests/unit/test_marc_writer.py

BookOps-CAT/NightShift

dc106b55cee3ec60c790f71f20e0754506385d59

[ "MIT" ]

null

tests/unit/test_marc_writer.py

BookOps-CAT/NightShift

dc106b55cee3ec60c790f71f20e0754506385d59

[ "MIT" ]

17

2020-12-07T04:43:48.000Z

2022-03-30T12:44:41.000Z

tests/unit/test_marc_writer.py

BookOps-CAT/NightShift

dc106b55cee3ec60c790f71f20e0754506385d59

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Tests `marc.marc_writer.py` module """ from contextlib import nullcontext as does_not_raise import logging import os import pickle from pymarc import Field, MARCReader, Record import pytest from nightshift import __title__, __version__ from nightshift.datastore import Resource from nightshift.marc.marc_writer import BibEnhancer

35.251852

109

0.560937

# -*- coding: utf-8 -*- """ Tests `marc.marc_writer.py` module """ from contextlib import nullcontext as does_not_raise import logging import os import pickle from pymarc import Field, MARCReader, Record import pytest from nightshift import __title__, __version__ from nightshift.datastore import Resource from nightshift.marc.marc_writer import BibEnhancer class TestBibEnhancer: def test_init(self, caplog, stub_resource): with does_not_raise(): with caplog.at_level(logging.INFO): result = BibEnhancer(stub_resource) assert result.library == "NYP" assert isinstance(result.resource, Resource) assert isinstance(result.bib, Record) assert "Enhancing NYP Sierra bib # b11111111a." in caplog.text def test_missing_full_bib(self, stub_resource): stub_resource.fullBib = None with pytest.raises(TypeError): BibEnhancer(stub_resource) @pytest.mark.parametrize( "resourceId,libraryId,tag, expectation", [ (1, 1, "091", "eNYPL Book"), (2, 1, "091", "eNYPL Audio"), (3, 1, "091", "eNYPL Video"), (1, 2, "099", "eBOOK"), (2, 2, "099", "eAUDIO"), (3, 2, "099", "eVIDEO"), ], ) def test_add_call_number_supported_resource_categories( self, caplog, stub_resource, resourceId, libraryId, tag, expectation ): stub_resource.resourceCategoryId = resourceId stub_resource.libraryId = libraryId if libraryId == 1: library = "NYP" elif libraryId == 2: library = "BPL" be = BibEnhancer(stub_resource) with caplog.at_level(logging.DEBUG): be._add_call_number() assert f"Added {expectation} to {library} b11111111a." in caplog.text bib = be.bib assert str(bib[tag]) == f"={tag} \\\\$a{expectation}" @pytest.mark.parametrize("library,resourceId", [("QPL", 1), ("NYP", 4), ("BPL", 4)]) def test_add_call_number_unsupported_resources( self, caplog, stub_resource, library, resourceId ): stub_resource.resourceCategoryId = resourceId be = BibEnhancer(stub_resource) be.library = library with caplog.at_level(logging.WARN): be._add_call_number() assert "091" not in be.bib assert "099" not in be.bib assert ( f"Attempting to create a call number for unsupported resource category for {library} b11111111a." in caplog.text ) @pytest.mark.parametrize( "resourceId,suppressed,libraryId,expectation", [ pytest.param(1, False, 1, "*ov=b11111111a;b2=z;", id="nyp-ebook"), pytest.param(1, False, 2, "*ov=b11111111a;b2=x;", id="bpl-ebook"), pytest.param(1, True, 1, "*ov=b11111111a;b2=z;b3=n;", id="nyp-ebook-supp"), pytest.param(1, True, 2, "*ov=b11111111a;b2=x;b3=n;", id="bpl-ebook-supp"), pytest.param(2, False, 1, "*ov=b11111111a;b2=n;", id="nyp-eaudio"), pytest.param(2, False, 2, "*ov=b11111111a;b2=z;", id="bpl-eaudio"), pytest.param(3, True, 1, "*ov=b11111111a;b2=3;b3=n;", id="nyp-evideo-supp"), pytest.param(3, True, 2, "*ov=b11111111a;b2=v;b3=n;", id="bpl-evideo-supp"), pytest.param(4, False, 1, "*ov=b11111111a;b2=a;", id="nyp-print"), pytest.param(4, False, 2, "*ov=b11111111a;b2=a;", id="bpl-print"), ], ) def test_add_command_tag( self, caplog, resourceId, suppressed, libraryId, expectation, stub_resource ): stub_resource.resourceCategoryId = resourceId stub_resource.suppressed = suppressed stub_resource.libraryId = libraryId if libraryId == 1: library = "NYP" elif libraryId == 2: library = "BPL" be = BibEnhancer(stub_resource) with caplog.at_level(logging.DEBUG): be._add_command_tag() assert ( f"Added 949 command tag: {expectation} to {library} b11111111a." in caplog.text ) bib = be.bib assert str(bib["949"]) == f"=949 \\\\$a{expectation}" def test_add_local_tags(self, caplog, stub_resource): fields = [ Field(tag="020", indicators=[" ", " "], subfields=["a", "978123456789x"]), Field( tag="037", indicators=[" ", " "], subfields=["a", "123", "b", "Overdrive Inc."], ), Field( tag="856", indicators=["0", "4"], subfields=["u", "url_here", "2", "opac msg"], ), ] pickled_fields = pickle.dumps(fields) stub_resource.srcFieldsToKeep = pickled_fields be = BibEnhancer(stub_resource) with caplog.at_level(logging.DEBUG): be._add_local_tags() assert ( "Added following local fields ['020', '037', '856'] to NYP b11111111a." in caplog.text ) bib = be.bib assert str(bib["020"]) == "=020 \\\\$a978123456789x" assert str(bib["037"]) == "=037 \\\\$a123$bOverdrive Inc." assert str(bib["856"]) == "=856 04$uurl_here$2opac msg" def test_add_local_tags_missing_tags(self, caplog, stub_resource): be = BibEnhancer(stub_resource) with caplog.at_level(logging.DEBUG): be._add_local_tags() assert "No local tags to keep were found for NYP b11111111a." in caplog.text @pytest.mark.parametrize( "library,tag", [ ("NYP", "901"), ("BPL", "947"), ], ) def test_add_initials_tag(self, caplog, library, tag, stub_resource): be = BibEnhancer(stub_resource) be.library = library with caplog.at_level(logging.DEBUG): be._add_initials_tag() assert f"Added initials tag {tag} to {library} b11111111a." in caplog.text assert str(be.bib[tag]) == f"={tag} \\\\$a{__title__}/{__version__}" def test_add_initials_tag_invalid_library(self, stub_resource): be = BibEnhancer(stub_resource) bib_before = str(be.bib) be.library = "foo" be._add_initials_tag() assert str(be.bib) == bib_before def test_purge_tags(self, caplog, stub_resource): be = BibEnhancer(stub_resource) fields = [ Field(tag="020", indicators=[" ", " "], subfields=["a", "978123456789x"]), Field( tag="037", indicators=[" ", " "], subfields=["a", "123", "b", "Overdrive Inc."], ), Field( tag="856", indicators=["0", "4"], subfields=["u", "url_here", "2", "opac msg"], ), ] for field in fields: be.bib.add_field(field) # make sure added tags are present for field in fields: assert field.tag in be.bib with caplog.at_level(logging.DEBUG): be._purge_tags() assert ( "Removed ['020', '029', '037', '090', '856', '910', '938'] from NYP b11111111a." in caplog.text ) # test if they were removed for field in fields: assert field.tag not in be.bib def test_purge_tags_non_existent(self, stub_resource): be = BibEnhancer(stub_resource) with does_not_raise(): be._purge_tags() def test_manipulate(self, stub_resource): stub_resource.resourceCategoryId = 1 stub_resource.libraryId = 1 fields = [ Field(tag="020", indicators=[" ", " "], subfields=["a", "978123456789x"]), Field( tag="037", indicators=[" ", " "], subfields=["a", "123", "b", "Overdrive Inc."], ), Field( tag="856", indicators=["0", "4"], subfields=["u", "url_here", "2", "opac msg"], ), ] pickled_fields = pickle.dumps(fields) stub_resource.srcFieldsToKeep = pickled_fields be = BibEnhancer(stub_resource) with does_not_raise(): be.manipulate() assert str(be.bib["020"]) == "=020 \\\\$a978123456789x" assert str(be.bib["037"]) == "=037 \\\\$a123$bOverdrive Inc." assert str(be.bib["856"]) == "=856 04$uurl_here$2opac msg" assert str(be.bib["091"]) == "=091 \\\\$aeNYPL Book" assert str(be.bib["901"]) == f"=901 \\\\$a{__title__}/{__version__}" assert str(be.bib["949"]) == "=949 \\\\$a*ov=b11111111a;b2=z;" def test_save2file(self, caplog, stub_resource): be = BibEnhancer(stub_resource) with caplog.at_level(logging.DEBUG): be.save2file() assert "Saving to file NYP record b11111111a." in caplog.text assert os.path.exists("temp.mrc") with open("temp.mrc", "rb") as f: reader = MARCReader(f) bib = next(reader) assert isinstance(bib, Record) # cleanup os.remove("temp.mrc") def test_save2file_os_error(self, caplog, stub_resource, mock_os_error): be = BibEnhancer(stub_resource) with caplog.at_level(logging.ERROR): with pytest.raises(OSError): be.save2file() assert "Unable to save record to a temp file. Error" in caplog.text

7,247

1,886

23

db40eccb4e3a9805eb8362b1213257f77b448646

590

py

Python

schedule/migrations/0014_event_status.py

itmagistr/django-scheduler

14d403b581d6ec5c195f7cd4f1567f6f636d1efb

[ "BSD-3-Clause" ]

null

schedule/migrations/0014_event_status.py

itmagistr/django-scheduler

14d403b581d6ec5c195f7cd4f1567f6f636d1efb

[ "BSD-3-Clause" ]

null

schedule/migrations/0014_event_status.py

itmagistr/django-scheduler

14d403b581d6ec5c195f7cd4f1567f6f636d1efb

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-06-02 18:17 from __future__ import unicode_literals from django.db import migrations, models

28.095238

190

0.640678

# -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-06-02 18:17 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('schedule', '0013_auto_20180424_1145'), ] operations = [ migrations.AddField( model_name='event', name='status', field=models.IntegerField(choices=[(0, 'Open'), (1, 'Closed')], default=0, help_text='The Status codes behavior should be defined into application.', verbose_name='Status code'), ), ]

0

410

23

5f18738c3a2c1e5ce066a636d50938a7b409ce4a

939

py

Python

test/wien2k/w2kutils_test.py

minyez/mykit

911413120c081be2cfcaef06d62dc40b2abd2747

[ "MIT" ]

4

2019-01-02T09:17:54.000Z

2019-12-26T07:15:59.000Z

test/wien2k/w2kutils_test.py

minyez/mykit

911413120c081be2cfcaef06d62dc40b2abd2747

[ "MIT" ]

6

2019-03-06T03:16:12.000Z

2019-03-14T14:36:01.000Z

test/wien2k/w2kutils_test.py

minyez/mykit

911413120c081be2cfcaef06d62dc40b2abd2747

[ "MIT" ]

null

#!/usr/bin/env python3 # coding = utf-8 import os import unittest as ut from mykit.wien2k.utils import get_casename, find_complex_file, get_default_r0, get_default_rmt, get_z if __name__ == '__main__': ut.main()

27.617647

102

0.683706

#!/usr/bin/env python3 # coding = utf-8 import os import unittest as ut from mykit.wien2k.utils import get_casename, find_complex_file, get_default_r0, get_default_rmt, get_z class test_get_elements_info(ut.TestCase): def test_r0(self): self.assertEqual(get_default_r0('Ne1'), get_default_r0('Ne10')) def test_rmt(self): self.assertEqual(get_default_rmt('Mg2'), get_default_rmt('Mg')) def test_z(self): self.assertEqual(get_z('X'), 0.0) self.assertEqual(get_z('Pb'), 82.0) self.assertEqual(get_z('Cl'), 17.0) self.assertEqual(get_z('Cu'), 29.0) self.assertEqual(get_z('Ag'), 47.0) class test_search_file(ut.TestCase): def test_find_complex_file(self): self.assertRaises(FileNotFoundError, find_complex_file, 'fake_case', 'in') self.assertEqual(get_casename(os.path.dirname(__file__)), 'wien2k') if __name__ == '__main__': ut.main()

525

36

158

646ad04a6e28b43086a847390064d260e25ae021

849

py

Python

tests/test_nbp_api.py

woj-i/degiro-p

d6905b240fa558ba4affe835da7274272fe38dcf

[ "MIT" ]

5

2021-02-21T08:16:15.000Z

2021-05-26T14:14:10.000Z

tests/test_nbp_api.py

woj-i/degiro-pit

d6905b240fa558ba4affe835da7274272fe38dcf

[ "MIT" ]

null

tests/test_nbp_api.py

woj-i/degiro-pit

d6905b240fa558ba4affe835da7274272fe38dcf

[ "MIT" ]

null

from unittest import TestCase from degiro_pit.config import Currency from degiro_pit.nbp_api import NbpApi

28.3

61

0.664311

from unittest import TestCase from degiro_pit.config import Currency from degiro_pit.nbp_api import NbpApi class TestNbpApi(TestCase): test_date = "2021-01-22" def test_get_eur_pln(self): nbp = NbpApi() res = nbp.get_pln(TestNbpApi.test_date, Currency.EUR) self.assertEqual(4.5354, res) def test_get_usd_pln(self): nbp = NbpApi() res = nbp.get_pln(TestNbpApi.test_date, Currency.USD) self.assertEqual(3.7255, res) def test_all_currencies_are_supported(self): nbp = NbpApi() for currency in Currency: res = nbp.get_pln(TestNbpApi.test_date, currency) self.assertIsInstance(res, float) def test_get_none_for_holiday_date(self): nbp = NbpApi() res = nbp.get_pln("2020-11-11", Currency.EUR) self.assertIsNone(res)

574

143

23

6e87c4e4158b3902ea8dc3803de751b0eaa24e67

11,586

py

Python

annotate/annotation/tasks.py

dojo-modeling/dojo

8abc71790cbb8639a56c89791535b3df59c725ac

[ "MIT" ]

3

2022-01-28T01:43:30.000Z

2022-02-24T21:42:13.000Z

annotate/annotation/tasks.py

dojo-modeling/dojo

8abc71790cbb8639a56c89791535b3df59c725ac

[ "MIT" ]

8

2022-01-12T16:39:57.000Z

2022-01-31T19:05:45.000Z

annotate/annotation/tasks.py

dojo-modeling/dojo

8abc71790cbb8639a56c89791535b3df59c725ac

[ "MIT" ]

null

import copy import json import logging import os import time from .rename import rename as rename_function from django.apps import apps from django.conf import settings from django_rq import job from mixmasta import mixmasta as mix from utils.cache_helper import cache_get # Load GADM3 from gadm app if settings.CACHE_GADM: gadm3 = apps.get_app_config("gadm").gadm3() gadm2 = apps.get_app_config("gadm").gadm2() else: gadm3 = None gadm2 = None def dupe(annotations, rename_list, new_names): """annotations is a list of dictionaries, if entry["name"] is in rename_list copy over an entry for every name in new_names and rename the entry["name"] to the new name""" added = [] new_list = [] rename_count = 0 for entry in annotations: # if entry["name"] in primary_geo_renames: # RTK if entry["name"] in rename_list: # if not primary_geo_renamed: # RTK if rename_count < len(rename_list): rename_count += 1 for new_name in new_names: # Don't add again, although duplicates are removed below. if new_name in added: continue e = entry.copy() e["name"] = new_name e["display_name"] = new_name e["type"] = new_name new_list.append(e) added.append(e["name"]) else: if entry["name"] not in added: new_list.append(entry) added.append(entry["name"]) return new_list def build_mapper(uuid): """ Description ----------- Performs two functions: (1) Build and return the mixmasta mapper.json from annotations.json. (2) Return geo_select if "Geo_Select_Form" is annotated. Returns ------- ret: dictionary geo, date, and feature keys for mixmasta process. geo_select: string, default None admin_level if set during annotation: country, admin1, admin2, admin3 """ # Set default return value (None) for geo_select. geo_select = None fp = f"data/{uuid}/annotations.json" with open(fp, "r") as f: annotations = json.load(f) conversion_names = { "name": "display_name", "geo": "geo_type", "time": "date_type", "format": "time_format", "data_type": "feature_type", "unit_description": "units_description", "coord_pair_form": "is_geo_pair", "qualifycolumn": "qualifies", "string": "str", } ret = {"geo": [], "date": [], "feature": []} for orig_name in annotations: entry = {} entry["name"] = orig_name for x in annotations[orig_name].keys(): if x in ["redir_col"]: continue # Set geo_select if annotated. if str(x).lower() == "geo_select_form": geo_select = annotations[orig_name][x] # Mixmasta expects "admin0" not "country". if geo_select.lower() == "country": geo_select = "admin0" if x.lower() in conversion_names.keys(): new_col_name = conversion_names[x.lower()] else: new_col_name = x.lower() if new_col_name != "display_name": if new_col_name == "qualifies": if type(annotations[orig_name][x]) == str: annotations[orig_name][x] = [annotations[orig_name][x]] if type(annotations[orig_name][x]) == str and new_col_name not in [ "is_geo_pair", "qualifies", "dateformat", "time_format", "description", ]: entry[new_col_name] = annotations[orig_name][x].lower() else: entry[new_col_name] = annotations[orig_name][x] else: entry[new_col_name] = annotations[orig_name][x] for x in ["dateassociate", "isgeopair", "qualify"]: if x in entry.keys(): del entry[x] ret[entry["type"]].append(entry) for x in range(len(ret["date"])): if "dateformat" in ret["date"][x]: ret["date"][x]["time_format"] = ret["date"][x]["dateformat"] del ret["date"][x]["dateformat"] if ret["date"][x].get("primary_time", False): ret["date"][x]["primary_date"] = True del ret["date"][x]["primary_time"] return ret, geo_select @job("default", timeout=-1) def post_mixmasta_annotation_processing(rename, context): """change annotations to reflect mixmasta's output""" uuid = context["uuid"] with open(context["mapper_fp"], "r") as f: mixmasta_ready_annotations = json.load(f) to_rename = {} for k, x in rename.items(): for y in x: to_rename[y] = k mixmasta_ready_annotations = rename_function(mixmasta_ready_annotations, to_rename) primary_date_renames = [ x["name"] for x in mixmasta_ready_annotations["date"] if x.get("primary_geo", False) ] primary_geo_renames = [ x["name"] for x in mixmasta_ready_annotations["geo"] if x.get("primary_geo", False) ] primary_geo_rename_count = 0 # RTK mixmasta_ready_annotations["geo"] = dupe( mixmasta_ready_annotations["geo"], primary_geo_renames, ["admin1", "admin2", "admin3", "country", "lat", "lng"], ) mixmasta_ready_annotations["date"] = dupe( mixmasta_ready_annotations["date"], primary_date_renames, ["timestamp"] ) json.dump( mixmasta_ready_annotations, open(f"data/{uuid}/mixmasta_ready_annotations.json", "w"), )

33.102857

175

0.566718

import copy import json import logging import os import time from .rename import rename as rename_function from django.apps import apps from django.conf import settings from django_rq import job from mixmasta import mixmasta as mix from utils.cache_helper import cache_get # Load GADM3 from gadm app if settings.CACHE_GADM: gadm3 = apps.get_app_config("gadm").gadm3() gadm2 = apps.get_app_config("gadm").gadm2() else: gadm3 = None gadm2 = None def dupe(annotations, rename_list, new_names): """annotations is a list of dictionaries, if entry["name"] is in rename_list copy over an entry for every name in new_names and rename the entry["name"] to the new name""" added = [] new_list = [] rename_count = 0 for entry in annotations: # if entry["name"] in primary_geo_renames: # RTK if entry["name"] in rename_list: # if not primary_geo_renamed: # RTK if rename_count < len(rename_list): rename_count += 1 for new_name in new_names: # Don't add again, although duplicates are removed below. if new_name in added: continue e = entry.copy() e["name"] = new_name e["display_name"] = new_name e["type"] = new_name new_list.append(e) added.append(e["name"]) else: if entry["name"] not in added: new_list.append(entry) added.append(entry["name"]) return new_list def build_mapper(uuid): """ Description ----------- Performs two functions: (1) Build and return the mixmasta mapper.json from annotations.json. (2) Return geo_select if "Geo_Select_Form" is annotated. Returns ------- ret: dictionary geo, date, and feature keys for mixmasta process. geo_select: string, default None admin_level if set during annotation: country, admin1, admin2, admin3 """ # Set default return value (None) for geo_select. geo_select = None fp = f"data/{uuid}/annotations.json" with open(fp, "r") as f: annotations = json.load(f) conversion_names = { "name": "display_name", "geo": "geo_type", "time": "date_type", "format": "time_format", "data_type": "feature_type", "unit_description": "units_description", "coord_pair_form": "is_geo_pair", "qualifycolumn": "qualifies", "string": "str", } ret = {"geo": [], "date": [], "feature": []} for orig_name in annotations: entry = {} entry["name"] = orig_name for x in annotations[orig_name].keys(): if x in ["redir_col"]: continue # Set geo_select if annotated. if str(x).lower() == "geo_select_form": geo_select = annotations[orig_name][x] # Mixmasta expects "admin0" not "country". if geo_select.lower() == "country": geo_select = "admin0" if x.lower() in conversion_names.keys(): new_col_name = conversion_names[x.lower()] else: new_col_name = x.lower() if new_col_name != "display_name": if new_col_name == "qualifies": if type(annotations[orig_name][x]) == str: annotations[orig_name][x] = [annotations[orig_name][x]] if type(annotations[orig_name][x]) == str and new_col_name not in [ "is_geo_pair", "qualifies", "dateformat", "time_format", "description", ]: entry[new_col_name] = annotations[orig_name][x].lower() else: entry[new_col_name] = annotations[orig_name][x] else: entry[new_col_name] = annotations[orig_name][x] for x in ["dateassociate", "isgeopair", "qualify"]: if x in entry.keys(): del entry[x] ret[entry["type"]].append(entry) for x in range(len(ret["date"])): if "dateformat" in ret["date"][x]: ret["date"][x]["time_format"] = ret["date"][x]["dateformat"] del ret["date"][x]["dateformat"] if ret["date"][x].get("primary_time", False): ret["date"][x]["primary_date"] = True del ret["date"][x]["primary_time"] return ret, geo_select def valid_qualifier_target(entry): k = entry.keys() for x in ["qualify", "primary_geo", "primary_time"]: if x in k: if entry[x] == True: return False return True def is_qualifier(entry): for x in ["qualify", "qualifies", "qualifyColumn"]: if x in entry.keys(): return True return False def clear_invalid_qualifiers(uuid): fp = f"data/{uuid}/annotations.json" with open(fp, "r") as f: annotations = json.load(f) to_del = {} for x in annotations.keys(): if "qualify" in annotations[x].keys(): if annotations[x]["qualify"] == True: to_del[x] = [] if type(annotations[x]["qualifyColumn"]) == str: annotations[x]["qualifyColumn"] = [annotations[x]["qualifyColumn"]] for y in annotations[x]["qualifyColumn"]: if y in annotations.keys(): if not valid_qualifier_target(annotations[y]): to_del[x].append(y) else: to_del[x].append(y) to_drop = [] for x in to_del.keys(): for y in to_del[x]: annotations[x]["qualifyColumn"].remove(y) if annotations[x]["qualifyColumn"] == []: to_drop.append(x) for x in to_drop: if x in annotations.keys(): del annotations[x] with open(fp, "w") as f: json.dump(annotations, f) def build_meta(uuid, d, geo_select, context): fnames = [x.split(".")[0] for x in os.listdir(d)] ft = context.get('ft', 'csv') fp = context.get('uploaded_file_fp', f'data/{uuid}/raw_data.csv') meta = {} meta["ftype"] = ft logging.info(f"context is: {context}") if ft == "geotiff": with open(f"data/{uuid}/geotiff_info.json", "r") as f: tif = json.load(f) { "geotiff_Feature_Name": "feat", "geotiff_Band": "1", "geotiff_Null_Val": "-9999", "geotiff_Date": "", } if 'bands' in context: meta["ftype"] = context.get('ft', 'csv') meta["bands"] = context.get('bands', '1') meta["null_val"] = context.get('null_val', '-9999') meta["date"] = context.get('date', '') meta["date"] = context.get('date', '01/01/2001') meta["feature_name"] = context.get('Feature_name', tif.get('geotiff_Feature_Name', 'feature')) meta['band_name'] = context.get('Feature_name', tif.get('geotiff_Feature_Name', 'feature')) meta["band"] = 0 meta["null_val"] = -9999 meta["bands"] =context.get('bands', {}) meta["band_type"] = context.get('band_type', 'category') else: meta["feature_name"] = tif["geotiff_Feature_Name"] meta["band_name"] = tif["geotiff_Feature_Name"] meta["null_val"] = tif["geotiff_Null_Val"] meta["date"] = tif["geotiff_Date"] if ft == "excel": xl = json.load(open(f"data/{uuid}/excel_info.json", "r")) meta["sheet"] = xl["excel_Sheet"] # Update meta with geocode_level if set as geo_select above. # If present Mimaxta will override admin param with this value. # Meant for use with DMC model runs. if geo_select != None: meta["geocode_level"] = geo_select return meta, fp.split('/')[-1], fp @job("default", timeout=-1) def generate_mixmasta_files(context): uuid = context["uuid"] email = cache_get(uuid, "email", None) clear_invalid_qualifiers(uuid) # Build the mapper.json annotations, and get geo_select for geo_coding # admin level if set annotating lat/lng pairs. mixmasta_ready_annotations, geo_select = build_mapper(uuid) logging_preface = cache_get(uuid, "logging_preface", None) d = f"data/{uuid}" fp = "" meta = {} fn = None mixmasta_ready_annotations["meta"], fn, fp = build_meta(uuid, d, geo_select, context) logging.info(f"{logging_preface} - Began mixmasta process") # BYOM handling if context.get("mode") == "byom": # Default to admin2 if geo_select is not set or too precise. if geo_select in (None, "admin3"): admin_level = "admin2" else: admin_level = geo_select logging.info(f"{logging_preface} - set admin_level to {admin_level}") byom_annotations = copy.deepcopy(mixmasta_ready_annotations) fn = f"{d}/raw_data.csv" fp = fn byom_annotations["meta"] = {"ftype": "csv"} with open(f"data/{uuid}/byom_annotations.json", "w") as f: json.dump( byom_annotations, f, ) mapper = "byom_annotations" # BYOD handling else: # Default to admin3 if geo_select is not set. if geo_select == None: admin_level = "admin3" else: admin_level = geo_select logging.info(f"{logging_preface} - set admin_level to {admin_level}") mapper = "mixmasta_ready_annotations" # Set gadm level based on geocoding level; still using gadm2 for gadm0/1. with open(f"data/{uuid}/mixmasta_ready_annotations.json", "w") as f: json.dump( mixmasta_ready_annotations, f, ) gadm_level = gadm3 if admin_level == "admin3" else gadm2 context["gadm_level"] = gadm_level context["output_directory"] = d context["mapper_fp"] = f"data/{uuid}/{mapper}.json" context["raw_data_fp"] = fp context["admin_level"] = admin_level json.dump( mixmasta_ready_annotations, open(f"data/{uuid}/sent_to_mixmasta.json", "w"), ) def post_mixmasta_annotation_processing(rename, context): """change annotations to reflect mixmasta's output""" uuid = context["uuid"] with open(context["mapper_fp"], "r") as f: mixmasta_ready_annotations = json.load(f) to_rename = {} for k, x in rename.items(): for y in x: to_rename[y] = k mixmasta_ready_annotations = rename_function(mixmasta_ready_annotations, to_rename) primary_date_renames = [ x["name"] for x in mixmasta_ready_annotations["date"] if x.get("primary_geo", False) ] primary_geo_renames = [ x["name"] for x in mixmasta_ready_annotations["geo"] if x.get("primary_geo", False) ] primary_geo_rename_count = 0 # RTK mixmasta_ready_annotations["geo"] = dupe( mixmasta_ready_annotations["geo"], primary_geo_renames, ["admin1", "admin2", "admin3", "country", "lat", "lng"], ) mixmasta_ready_annotations["date"] = dupe( mixmasta_ready_annotations["date"], primary_date_renames, ["timestamp"] ) json.dump( mixmasta_ready_annotations, open(f"data/{uuid}/mixmasta_ready_annotations.json", "w"), )

5,599

0

114

90a6a38783bd1c23407224dbe76204ca852dc6a5

59

py

Python

_Sensation0/__init__.py

Geson-anko/JARVIS3

bc599a352401a7e135ebaabead4d8e6d8835747e

[ "MIT" ]

null

_Sensation0/__init__.py

Geson-anko/JARVIS3

bc599a352401a7e135ebaabead4d8e6d8835747e

[ "MIT" ]

null

_Sensation0/__init__.py

Geson-anko/JARVIS3

bc599a352401a7e135ebaabead4d8e6d8835747e

[ "MIT" ]

null

from .sensation import Sensation from .train import Train

19.666667

32

0.813559

from .sensation import Sensation from .train import Train

0

c590a9496079ddf0947998d4b4dfc4b5a7470b6f

17,967

py

Python

trec2014/python/cuttsum/pipeline/representation.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

6

2015-09-10T02:22:21.000Z

2021-10-01T16:36:46.000Z

trec2014/python/cuttsum/pipeline/representation.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

null

trec2014/python/cuttsum/pipeline/representation.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

2

2018-04-04T10:44:32.000Z

2021-10-01T16:37:26.000Z

import marisa_trie import os import gzip from collections import defaultdict from nltk.tokenize import RegexpTokenizer from cuttsum.srilm import Client from itertools import izip import string from ..geo import GeoQuery import numpy as np import pandas as pd from nltk.corpus import wordnet as wn import re

36.969136

79

0.554294

import marisa_trie import os import gzip from collections import defaultdict from nltk.tokenize import RegexpTokenizer from cuttsum.srilm import Client from itertools import izip import string from ..geo import GeoQuery import numpy as np import pandas as pd from nltk.corpus import wordnet as wn import re class SalienceFeatureSet(object): def __init__(self, features=None): self.character_features = False self.language_model_features = False self.frequency_features = False self.geographic_features = False self.query_features = False if features is not None: self.activate_features(features) def fs_name(self): buf = 'fs' if self.character_features: buf += '-char' if self.language_model_features: buf += '-lm' if self.frequency_features: buf += '-tf' if self.geographic_features: buf += '-geo' if self.query_features: buf += '-query' return buf def get_feature_regex(self): patts = [] if self.character_features: patts.append('BASIC_') if self.language_model_features: patts.append('LM_') if self.frequency_features: patts.append('TFIDF_') if self.geographic_features: patts.append('GEO_') if self.query_features: patts.append('QUERY_') return '|'.join(patts) def __unicode__(self): return u'SalienceFeatureSet: ' \ u'char[{}] lm[{}] freq[{}] geo[{}] query[{}]'.format( u'X' if self.character_features is True else u' ', u'X' if self.language_model_features is True else u' ', u'X' if self.frequency_features is True else u' ', u'X' if self.geographic_features is True else u' ', u'X' if self.query_features is True else u' ') def __str__(self): return unicode(self).decode(u'utf-8') def activate_features(self, features): for fset in features: if fset == u'all': self.character_features = True self.language_model_features = True self.frequency_features = True self.geographic_features = True self.query_features = True break elif fset == u'character': self.character_features = True elif fset == u'language model': self.language_model_features = True elif fset == u'frequency': self.frequency_features = True elif fset == u'geographic': self.geographic_features = True elif fset == u'query': self.query_features = True def as_list(self): features = [] if self.character_features is True: features.append(u'character') if self.language_model_features is True: features.append(u'language model') if self.frequency_features is True: features.append(u'frequency') if self.geographic_features is True: features.append(u'geographic') if self.query_features is True: features.append(u'query') return features def as_set(self): return set(self.as_list()) class TfIdfExtractor(object): def __init__(self, current_idf_path, prev_idf_paths): paths = [current_idf_path] + prev_idf_paths tries = [] for path in paths: if os.path.exists(path): with gzip.open(path, u'r') as f: trie = marisa_trie.RecordTrie("<dd") trie.read(f) tries.append(trie) else: tries.append(None) self.current_trie_ = tries[0] self.prev_tries_ = tries[1:] n_paths = len(paths) self.features = \ [u"TFIDF_FEATS: time since start", 'TFIDF_FEATS: avg tfidf, t0'] \ + [u'TFIDF_FEATS: tfidf-delta, tm-{}'.format(i) for i in xrange(1, n_paths)] def tokenize(self, strings): sentences = [] for string in strings: tokens = [token.lower() for token in string.split(' ')] sentences.append(tokens) return sentences def process_streamcorpus_strings(self, strings, secs_since_start): sentences = self.tokenize(strings) tf_counts = self.make_tf_counts(sentences) feats = [] for sentence in sentences: sent_feat = {"TFIDF_FEATS: time since start": secs_since_start} avg_tfidf_t0 = self.avg_tfidf( self.current_trie_, sentence, tf_counts) sent_feat[u'TFIDF_FEATS: avg tfidf, t0'] = avg_tfidf_t0 for i, trie in enumerate(self.prev_tries_, 1): delta_idf = avg_tfidf_t0 - self.avg_tfidf( trie, sentence, tf_counts) label = u'TFIDF_FEATS: tfidf-delta, tm-{}'.format(i) sent_feat[label] = delta_idf feats.append(sent_feat) return feats def make_tf_counts(self, sentences): tf_counts = defaultdict(int) for sentence in sentences: for token in sentence: tf_counts[token] += 1 return tf_counts def process_article(self, si, corpus): if u'article-clf' not in si.body.sentences: return list() avg_tfidfs = list() sents = si.body.sentences[u'article-clf'] tf_counts = self.make_tf_counts(sents) for sentence in sents: avg_tfidf = self.avg_tfidf(sentence, tf_counts) avg_tfidfs.append(avg_tfidf) features = [] for avg_tfidf in avg_tfidfs: features.append({'avg_tfidf_t0': avg_tfidf}) return features def avg_tfidf(self, trie, sentence, tf_counts): if trie is None: return 0 #float('nan') total_tfidf = 0 n_terms = 0 unique_words = set() for token in sentence: unique_words.add(token) n_terms = len(unique_words) if n_terms == 0: return 0 for word in unique_words: idf = trie.get(word, None) if idf is None: idf = 0 else: # trie packs single items as a list of tuple, so we need to # pull the actual data out. idf = idf[0][0] #print word, idf, tf_counts[word] * idf total_tfidf += tf_counts[word] * idf return total_tfidf / float(n_terms) class BasicFeaturesExtractor(object): def __init__(self): self.features = [ u'BASIC_FEATS: doc position', u'BASIC_FEATS: sentence length', u'BASIC_FEATS: punc ratio', u'BASIC_FEATS: lower ratio', u'BASIC_FEATS: upper ratio', u'BASIC_FEATS: all caps ratio', u'BASIC_FEATS: person ratio', u'BASIC_FEATS: location ratio', u'BASIC_FEATS: organization ratio', u'BASIC_FEATS: date ratio', u'BASIC_FEATS: time ratio', u'BASIC_FEATS: duration ratio', u'BASIC_FEATS: number ratio', u'BASIC_FEATS: ordinal ratio', u'BASIC_FEATS: percent ratio', u'BASIC_FEATS: money ratio', u'BASIC_FEATS: set ratio', u'BASIC_FEATS: misc ratio'] self.ne_features = [ u'BASIC_FEATS: person ratio', u'BASIC_FEATS: location ratio', u'BASIC_FEATS: organization ratio', u'BASIC_FEATS: date ratio', u'BASIC_FEATS: time ratio', u'BASIC_FEATS: duration ratio', u'BASIC_FEATS: number ratio', u'BASIC_FEATS: ordinal ratio', u'BASIC_FEATS: percent ratio', u'BASIC_FEATS: money ratio', u'BASIC_FEATS: set ratio', u'BASIC_FEATS: misc ratio'] def process_sentences(self, sc_strings, cnlp_strings): feats = [self.process_sentence(sc_string, cnlp_string) for sc_string, cnlp_string in izip(sc_strings, cnlp_strings)] n_sents = float(len(feats)) for i, feat in enumerate(feats): feat[u'BASIC_FEATS: doc position'] = i / n_sents return feats def process_sentence(self, sc_string, cnlp_string): feats = {} cnlp_tokens = cnlp_string.split(' ') sc_tokens = sc_string.split(' ') feats[u'BASIC_FEATS: sentence length'] = len(cnlp_tokens) sc_no_space = sc_string.replace(' ', '') sc_no_space_punc = sc_no_space.translate( string.maketrans("",""), string.punctuation) punc_ratio = 1 - float(len(sc_no_space_punc)) / float(len(sc_no_space)) feats[u'BASIC_FEATS: punc ratio'] = punc_ratio n_lower = len(re.findall(r'\b[a-z]', sc_string)) n_upper = len(re.findall(r'\b[A-Z]', sc_string)) n_all_caps = len(re.findall(r'\b[A-Z]+\b', sc_string)) n_total = float(len(re.findall(r'\b[A-Za-z]', sc_string))) if n_total == 0: n_total = 1 feats[u'BASIC_FEATS: lower ratio'] = n_lower / n_total feats[u'BASIC_FEATS: upper ratio'] = n_upper / n_total feats[u'BASIC_FEATS: all caps ratio'] = n_all_caps / n_total n_tokens = len(cnlp_tokens) ne_counts = {ne_feature: 0 for ne_feature in self.ne_features} for token in cnlp_tokens: if token.startswith('__') and token.endswith('__'): label = u'BASIC_FEATS: {} ratio'.format(token[2:-2]) ne_counts[label] = ne_counts.get(label) + 1 for token, count in ne_counts.iteritems(): #label = u'BASIC_FEATS: {} ratio'.format(token[2:-2]) feats[token] = \ float(count) / n_tokens return feats class LMProbExtractor(object): def __init__(self, domain_port, domain_order, gigaword_port, gigaword_order): self.tok_ = RegexpTokenizer(r'\w+') self.domain_lm_ = Client(domain_port, domain_order, True) self.gigaword_lm_ = Client(gigaword_port, gigaword_order, True) self.features = [u"LM_FEATS: domain avg lp", u"LM_FEATS: gigaword avg lp"] def process_corenlp_strings(self, strings): return [self.process_corenlp_string(string) for string in strings] def process_corenlp_string(self, string): dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob(string) gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob(string) return {u"LM_FEATS: domain avg lp": dmn_avg_lp, u"LM_FEATS: gigaword avg lp": gw_avg_lp} def process_article(self, si): if u'article-clf' not in si.body.sentences: return list() lm_scores = [] for sentence in si.body.sentences[u'article-clf']: bytes_string = ' '.join(token.token for token in sentence.tokens) uni_string = bytes_string.decode(u'utf-8') uni_string = uni_string.lower() uni_tokens = self.tok_.tokenize(uni_string) uni_string = u' '.join(uni_tokens) bytes_string = uni_string.encode(u'utf-8') dmn_lp, dmn_avg_lp = self.domain_lm_.sentence_log_prob( bytes_string) gw_lp, gw_avg_lp = self.gigaword_lm_.sentence_log_prob( bytes_string) lm_scores.append( {u"domain avg lp": dmn_avg_lp, u"gigaword avg lp": gw_avg_lp}) return lm_scores class QueryFeaturesExtractor(object): def __init__(self, event): self.event_query_ = event.query self.synonyms_ = [] self.hypernyms_ = [] self.hyponyms_ = [] print event.type.split(' ')[0] for synset in wn.synsets(event.type.split(' ')[0]): synonyms = \ [lemma.name().lower().replace(u'_', u' ').encode(u'utf-8') for lemma in synset.lemmas()] self.synonyms_.extend(synonyms) hypernyms = \ [lemma.name().lower().replace(u'_', u' ').encode(u'utf-8') for synset in synset.hypernyms() for lemma in synset.lemmas()] self.hypernyms_.extend(hypernyms) hyponyms = \ [lemma.name().lower().replace(u'_', u' ').encode(u'utf-8') for synset in synset.hyponyms() for lemma in synset.lemmas()] self.hyponyms_.extend(hyponyms) self.query_size_ = float(len(event.query)) self.features = [u'QUERY_FEATS: query coverage', u'QUERY_FEATS: total query matches', u'QUERY_FEATS: synonyms coverage', u'QUERY_FEATS: total synonyms matches', u'QUERY_FEATS: hypernyms coverage', u'QUERY_FEATS: total hypernyms matches', u'QUERY_FEATS: hyponyms coverage', u'QUERY_FEATS: total hyponyms matches',] def process_streamcorpus_strings(self, strings): return [self.process_streamcorpus_string(string) for string in strings] def process_streamcorpus_string(self, string): query_feats = self.search_(string, self.event_query_, 'query') syn_feats = self.search_(string, self.synonyms_ , 'synonyms') hyper_feats = self.search_(string, self.hypernyms_ , 'hypernyms') hypo_feats = self.search_(string, self.hyponyms_ , 'hyponyms') query_feats.update(syn_feats.iteritems()) query_feats.update(hyper_feats.iteritems()) query_feats.update(hypo_feats.iteritems()) return query_feats def search_(self, string, queries, feature_name): queries_covered = 0 total_covered = 0 for query in queries: hits = re.findall(query, string, re.I) if len(hits) > 0: queries_covered += 1 total_covered += len(hits) if len(queries) > 0: qt_coverage = queries_covered / float(len(queries)) else: qt_coverage = 0 return {u'QUERY_FEATS: {} coverage'.format(feature_name): qt_coverage, u'QUERY_FEATS: total {} matches'.format(feature_name): \ total_covered} class GeoFeaturesExtractor(object): def __init__(self, geo_cache_tsv_path, cluster_paths): self.gq_ = GeoQuery(geo_cache_tsv_path) self.hourly_clusters_ = self.load_cluster_paths_(cluster_paths) self.features = \ [u'GEO_FEATS: median dist to clust_tm{}'.format(t) for t in xrange(len(cluster_paths))] \ + [u'GEO_FEATS: first loc min dist to clust_tm{}'.format(t) for t in range(len(cluster_paths))] def process_geo_strings(self, strings): first_loc = None counts = {} str2ll = {} locations = [] for string in strings: if isinstance(string, float): locations.append(list()) else: slocs = list() for loc in string.split(','): ll = self.gq_.lookup_location(loc) if ll is None: continue if first_loc is None: first_loc = np.array(ll) counts[loc] = counts.get(loc, 0) + 1 str2ll[loc] = ll slocs.append(ll) locations.append(slocs) loc_counts = counts.items() loc_counts.sort(key=lambda x: x[1], reverse=True) lls = np.array([str2ll[loc] for loc, count in loc_counts]) feats = {} for t, clusters in enumerate(self.hourly_clusters_): if clusters is None or len(lls) == 0: label = u'GEO_FEATS: median dist to clust_tm{}'.format( t) feats[label] = 12451.0 #float('nan') label = \ u'GEO_FEATS: first loc min dist to clust_tm{}'.format(t) feats[label] = 12451.0 #float('nan') else: D = self.gq_.compute_distances(lls[:,None], clusters) label = u'GEO_FEATS: median dist to clust_tm{}'.format( t) med_dist = np.min(np.median(D, axis=0)) #if np.isnan(med_dist): # med_dist = 12451.0 feats[label] = med_dist d = self.gq_.compute_distances(first_loc, clusters) label = \ u'GEO_FEATS: first loc min dist to clust_tm{}'.format(t) feats[label] = \ np.min(d) feats = [feats] * len(strings) # for i, locs in enumerate(locations): # if len(locs) > 0: # feats[i][u'GEO_FEATS: contains loc str'] = 1 # else: # feats[i][u'GEO_FEATS: contains loc str'] = 0 return feats def load_cluster_paths_(self, cluster_paths): clusters = [] for path in cluster_paths: if os.path.exists(path): with gzip.open(path, u'r') as f: clusters_df = pd.io.parsers.read_csv( f, sep='\t', quoting=3, header=0) clusters.append(clusters_df.as_matrix()) else: clusters.append(None) return clusters

16,656

75

907

f0ff86078deb2aa6845135b2c376566c8a81691f

1,880

py

Python

dags/utils/scale_features.py

tiendatscorpy/ETL-pipeline

e3f800596e17e0ac2b32f52ea06a196e477c3414

[ "MIT" ]

null

dags/utils/scale_features.py

tiendatscorpy/ETL-pipeline

e3f800596e17e0ac2b32f52ea06a196e477c3414

[ "MIT" ]

null

dags/utils/scale_features.py

tiendatscorpy/ETL-pipeline

e3f800596e17e0ac2b32f52ea06a196e477c3414

[ "MIT" ]

null

import json import os, errno from typing import Dict import numpy as np import os from sklearn.preprocessing import scale, minmax_scale import logging LOGGER = logging.getLogger(__name__) def scale_features(input_folder: str, output_folder: str, op_conf: str, **kwargs): """ input_folder: folder which contains input audio files output_folder: folder to store output numpy files """ optional_params = eval(op_conf) LOGGER.info("kwargs ", optional_params) for genre in list(os.listdir(input_folder)): if os.path.isdir(f"{input_folder}/{genre}"): genre_input_folder = f"{input_folder}/{genre}/" genre_output_folder = f"{output_folder}/{genre}/" try: os.makedirs(genre_output_folder) except OSError as e: if e.errno != errno.EEXIST: raise for file_name in list(os.listdir(genre_input_folder)): input_file_abs_path = f"{genre_input_folder}/{file_name}" if os.path.isfile(f"{input_file_abs_path}") and file_name.endswith( ".npy" ): LOGGER.info( f"scale_features.task >>> INFO current file: {file_name}" ) file_name_wo_ex = file_name[:-4] # load np array y = np.load(f"{input_file_abs_path}") y_std_scaled = scale(y) np.save( f"{genre_output_folder}/{file_name_wo_ex}_standardcaler.npy", y_std_scaled, ) y_mm_scaled = minmax_scale(y) np.save( f"{genre_output_folder}/{file_name_wo_ex}_minmaxnormalizer.npy", y_mm_scaled, )

35.471698

88

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import json import os, errno from typing import Dict import numpy as np import os from sklearn.preprocessing import scale, minmax_scale import logging LOGGER = logging.getLogger(__name__) def scale_features(input_folder: str, output_folder: str, op_conf: str, **kwargs): """ input_folder: folder which contains input audio files output_folder: folder to store output numpy files """ optional_params = eval(op_conf) LOGGER.info("kwargs ", optional_params) for genre in list(os.listdir(input_folder)): if os.path.isdir(f"{input_folder}/{genre}"): genre_input_folder = f"{input_folder}/{genre}/" genre_output_folder = f"{output_folder}/{genre}/" try: os.makedirs(genre_output_folder) except OSError as e: if e.errno != errno.EEXIST: raise for file_name in list(os.listdir(genre_input_folder)): input_file_abs_path = f"{genre_input_folder}/{file_name}" if os.path.isfile(f"{input_file_abs_path}") and file_name.endswith( ".npy" ): LOGGER.info( f"scale_features.task >>> INFO current file: {file_name}" ) file_name_wo_ex = file_name[:-4] # load np array y = np.load(f"{input_file_abs_path}") y_std_scaled = scale(y) np.save( f"{genre_output_folder}/{file_name_wo_ex}_standardcaler.npy", y_std_scaled, ) y_mm_scaled = minmax_scale(y) np.save( f"{genre_output_folder}/{file_name_wo_ex}_minmaxnormalizer.npy", y_mm_scaled, )

0

e54f3fcf098da3812b7d4a0e9f85780e8a12a31b

934

py

Python

benches/bench_merge.py

crkrenn/distogram

9701233e46bd9e7528071c3c90600516d06c7f5a

[ "MIT" ]

16

2020-07-12T17:27:46.000Z

2022-03-22T23:42:24.000Z

benches/bench_merge.py

crkrenn/distogram

9701233e46bd9e7528071c3c90600516d06c7f5a

[ "MIT" ]

7

2020-06-20T01:43:34.000Z

2022-02-02T17:41:36.000Z

benches/bench_merge.py

crkrenn/distogram

9701233e46bd9e7528071c3c90600516d06c7f5a

[ "MIT" ]

3

2020-12-27T07:36:48.000Z

2021-11-04T18:56:46.000Z

import time from functools import reduce import distogram import utils if __name__ == '__main__': bench_merge()

21.227273

56

0.591006

import time from functools import reduce import distogram import utils def bench_merge(): num_samples = 10 num_points = 100_000 values_list = [ utils.create_values(mean, 0.3, num_points) for mean in range(num_samples) ] times_dict: utils.TimesDict = {num_points: dict()} for n in range(6): bin_count = 32 * (2 ** n) histograms = [ utils.create_distogram(bin_count, values) for values in values_list ] start = time.time() _ = reduce( lambda res, val: distogram.merge(res, val), histograms, distogram.Distogram(bin_count=bin_count) ) time_taken = (time.time() - start) / num_samples times_dict[num_points][bin_count] = time_taken utils.plot_times_dict( times_dict, title='merge', ) return if __name__ == '__main__': bench_merge()

791

0

23

094ba497e8b98cc0b00f380b8d7c8ff4348b9eb1

1,210

py

Python

Chapter14/r2_paths_auth/my_library/models/sample_auth_http.py

a17juanbl/exercicios

8df4bdf232207fcbc8f1308d1937ee599c1b39d4

[ "MIT" ]

125

2020-11-28T18:00:34.000Z

2022-03-07T17:53:22.000Z

Chapter14/r2_paths_auth/my_library/models/sample_auth_http.py

a17juanbl/exercicios

8df4bdf232207fcbc8f1308d1937ee599c1b39d4

[ "MIT" ]

5

2021-02-02T10:03:29.000Z

2022-03-16T07:32:28.000Z

Chapter14/r2_paths_auth/my_library/models/sample_auth_http.py

a17juanbl/exercicios

8df4bdf232207fcbc8f1308d1937ee599c1b39d4

[ "MIT" ]

182

2020-11-29T12:07:07.000Z

2022-03-22T04:27:51.000Z

# -*- coding: utf-8 -*- # /!\/!\/!\/!\/!\/!\/!\/!\ # Note that this is just a sample code # You need to add this file in __init__.py # /!\/!\/!\/!\/!\/!\/!\/!\ from odoo import exceptions, models from odoo.http import request

28.139535

72

0.623967

# -*- coding: utf-8 -*- # /!\/!\/!\/!\/!\/!\/!\/!\ # Note that this is just a sample code # You need to add this file in __init__.py # /!\/!\/!\/!\/!\/!\/!\/!\ from odoo import exceptions, models from odoo.http import request class IrHttp(models.AbstractModel): _inherit = 'ir.http' @classmethod def _auth_method_base_group_user(cls): cls._auth_method_user() if not request.env.user.has_group('base.group_user'): raise exceptions.AccessDenied() # this is for the exercise @classmethod def _auth_method_groups(cls, group_xmlids=None): cls._auth_method_user() if not any(map(request.env.user.has_group, group_xmlids or [])): raise exceptions.AccessDenied() # the controller will be like this add this in main.py @http.route('/my_module/all-books/group_user', type='http', auth='base_group_user') def all_books_mine_base_group_user(self): # your code return ... # this is for the exercise @http.route('/my_module/all-books/groups', type='http', auth='groups(base.group_no_one)') def all_books_mine_groups(self): # your code return ...

440

518

23

6d5634d1807e48c38dd5968a7d8b0e5bd2f87b3d

817

py

Python

pypy/iterator_generator_coroutine/first_of_all/2_generator.py

DowsonJones/test_test

9f941b20de42090e6ec9b449953ce1dae405ef73

[ "MIT" ]

1

2020-03-30T07:13:08.000Z

pypy/iterator_generator_coroutine/first_of_all/2_generator.py

DowsonJones/test_test

9f941b20de42090e6ec9b449953ce1dae405ef73

[ "MIT" ]

null

pypy/iterator_generator_coroutine/first_of_all/2_generator.py

DowsonJones/test_test

9f941b20de42090e6ec9b449953ce1dae405ef73

[ "MIT" ]

null

F = fib(10) # 运行到这里没有任何反映 # print(next(F)) for i in F: print(i) """ yield: 1. 保存运行状态-断点, 暂停执行将生成器挂起 2. 将yield后面表达式的值, 作为返回值返回 """ # 使用yield实现协程 import time if __name__ == '__main__': main()

15.12963

42

0.49694

def fib(n): print('进入') current = 0 num1, num2 = 0, 1 while current < n: num = num1 num1, num2 = num2, num2 + num1 current += 1 yield num return 'done' F = fib(10) # 运行到这里没有任何反映 # print(next(F)) for i in F: print(i) """ yield: 1. 保存运行状态-断点, 暂停执行将生成器挂起 2. 将yield后面表达式的值, 作为返回值返回 """ # 使用yield实现协程 import time def work1(): current = 0 while True: yield f'work1 {current}th called' current += 1 def work2(): current = 0 while True: yield f'work2 {current}th called' current += 1 def main(): w1 = work1() w2 = work2() while True: print(next(w1)) print(next(w2)) time.sleep(1) if __name__ == '__main__': main()

493

0

98

5783c68228392e653ee096d49b29e66e2ac5a022

1,563

py

Python

lib/opticalflow.py

hanebarla/CrowdCounting-using-PedestrianFlow

0f7c196a8b396d92901172a334ecf0256f30ff8a

[ "MIT" ]

null

lib/opticalflow.py

hanebarla/CrowdCounting-using-PedestrianFlow

0f7c196a8b396d92901172a334ecf0256f30ff8a

[ "MIT" ]

null

lib/opticalflow.py

hanebarla/CrowdCounting-using-PedestrianFlow

0f7c196a8b396d92901172a334ecf0256f30ff8a

[ "MIT" ]

null

import os import json import cv2 import numpy as np import matplotlib.pyplot as plt import torch if __name__ == "__main__": # json file contains the test images test_json_path = './test.json' # the folder to output density map and flow maps output_folder = './plot' with open(test_json_path, 'r') as outfile: img_paths = json.load(outfile) for i in range(2): img_path = img_paths[i] img_folder = os.path.dirname(img_path) img_name = os.path.basename(img_path) index = int(img_name.split('.')[0]) prev_index = int(max(1,index-5)) prev_img_path = os.path.join(img_folder,'%03d.jpg'%(prev_index)) c_img = cv2.imread(img_path) c_img = cv2.resize(c_img, (640, 360)) c_prev_img = cv2.imread(prev_img_path) c_prev_img = cv2.resize(c_prev_img, (640, 360)) hsv = OptFlow(c_prev_img, c_img) rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) save_img = np.concatenate([c_prev_img, rgb], 0) cv2.imwrite('opticalflow/opticalflow_{}.png'.format(i), save_img)

27.910714

86

0.632118

import os import json import cv2 import numpy as np import matplotlib.pyplot as plt import torch def OptFlow(c_prev_img, c_img): prev_img = cv2.cvtColor(c_prev_img, cv2.COLOR_BGR2GRAY) img = cv2.cvtColor(c_img, cv2.COLOR_BGR2GRAY) hsv = np.zeros_like(c_img) hsv[..., 1] = 255 flow = cv2.calcOpticalFlowFarneback(prev_img, img, None, 0.5, 3, 15, 3, 5, 1.2, 0) mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1]) hsv[..., 0] = ang * 180 / np.pi / 2 hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) return hsv if __name__ == "__main__": # json file contains the test images test_json_path = './test.json' # the folder to output density map and flow maps output_folder = './plot' with open(test_json_path, 'r') as outfile: img_paths = json.load(outfile) for i in range(2): img_path = img_paths[i] img_folder = os.path.dirname(img_path) img_name = os.path.basename(img_path) index = int(img_name.split('.')[0]) prev_index = int(max(1,index-5)) prev_img_path = os.path.join(img_folder,'%03d.jpg'%(prev_index)) c_img = cv2.imread(img_path) c_img = cv2.resize(c_img, (640, 360)) c_prev_img = cv2.imread(prev_img_path) c_prev_img = cv2.resize(c_prev_img, (640, 360)) hsv = OptFlow(c_prev_img, c_img) rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) save_img = np.concatenate([c_prev_img, rgb], 0) cv2.imwrite('opticalflow/opticalflow_{}.png'.format(i), save_img)

445

0

23

d367c19a7c2a7b23b387887269c0b45058aff294

6,978

py

Python

fairseq/models/nat/old/latent/predictor_module.py

wangqi1996/cmlm

78472e7edfecd8e36eaf5ae77c674033e1498735

[ "MIT" ]

null

fairseq/models/nat/old/latent/predictor_module.py

wangqi1996/cmlm

78472e7edfecd8e36eaf5ae77c674033e1498735

[ "MIT" ]

null

fairseq/models/nat/old/latent/predictor_module.py

wangqi1996/cmlm

78472e7edfecd8e36eaf5ae77c674033e1498735

[ "MIT" ]

null

from torch import nn from fairseq.modules import TransformerEncoderLayer, TransformerDecoderLayer class Perceptron(nn.Module): """ 1. 是否激活通过是否有激活层来控制，最后一层都没有激活层 """ class LogisticModel(nn.Module): """ 两层感知机 """ def __init__(self, args, activation=None, dropout=0.1, contain_normalize=False, **unused): """ 如果Logistic是模型的最后一层，contain_normalize=True; 否则，设置为False""" super().__init__() self.layers = nn.Sequential( Perceptron(args.encoder_embed_dim, int(args.encoder_embed_dim / 2), drouput=dropout, activation=activation), Perceptron(int(args.encoder_embed_dim / 2), 1, drouput=dropout, activation=None) ) self.activation = None if contain_normalize: self.activation = nn.Sigmoid() """ TODO: AT Decoder """

32.915094

120

0.633276

from torch import nn from fairseq.modules import TransformerEncoderLayer, TransformerDecoderLayer def mean_ds(x, dim=None): return ( x.float().mean().type_as(x) if dim is None else x.float().mean(dim).type_as(x) ) class Perceptron(nn.Module): """ 1. 是否激活通过是否有激活层来控制，最后一层都没有激活层 """ def __init__(self, input_dim, output_dim, activation=None, drouput=0.1, **unused): super(Perceptron, self).__init__() self.linear = nn.Linear(input_dim, output_dim) self.activate = None if activation is not None: self.activate = activation() self.dropout = None if drouput > 0: self.dropout = nn.Dropout(p=drouput) def forward(self, x, dropout=True): # inference时关掉dropout开关 if not self.training: dropout = False out = self.linear(x) if self.activate: out = self.activate(out) if dropout: out = self.dropout(out) return out class LogisticModel(nn.Module): """ 两层感知机 """ def __init__(self, args, activation=None, dropout=0.1, contain_normalize=False, **unused): """ 如果Logistic是模型的最后一层，contain_normalize=True; 否则，设置为False""" super().__init__() self.layers = nn.Sequential( Perceptron(args.encoder_embed_dim, int(args.encoder_embed_dim / 2), drouput=dropout, activation=activation), Perceptron(int(args.encoder_embed_dim / 2), 1, drouput=dropout, activation=None) ) self.activation = None if contain_normalize: self.activation = nn.Sigmoid() def forward(self, decoder_out=None, normalize=False, **unused): out = self.layers(decoder_out) if self.activation is not None and normalize: out = self.activation(out) return out.squeeze(-1) """ TODO: AT Decoder """ class DecoderLayer(nn.Module): def __init__(self, args, pad, num_layers, contain_normalize=True, **unused): super().__init__() self.padding_idx = pad self.layers = nn.ModuleList( [TransformerDecoderLayer(args, no_encoder_attn=False) for _ in range(num_layers)] ) self.predictor = LogisticModel(args, activation=nn.Sigmoid, dropout=0.3, contain_normalize=contain_normalize, **unused) def forward(self, encoder_output, encoder_padding_mask, decoder_out, decoder_input, normalize=False, **unused): # NAT的decoder decoder_padding_mask = decoder_input.eq(self.padding_idx) batch_size, _ = encoder_padding_mask.shape b, _, _ = decoder_out.shape if b == batch_size: decoder_out = decoder_out.transpose(0, 1) b, _, _ = encoder_output.shape if b == batch_size: encoder_output = encoder_output.transpose(0, 1) input = decoder_out for layer in self.layers: input, _, _ = layer( input, encoder_output, encoder_padding_mask, incremental_state=None, self_attn_mask=None, self_attn_padding_mask=decoder_padding_mask) out = self.predictor(decoder_out=input.transpose(0, 1), normalize=normalize) # 使用logistic做了normalize的处理啦 return out class EncoderLayer(nn.Module): def __init__(self, args, num_layers=1, contain_logistic=True, contain_normalize=True, pad=0, **unused): """ contain_logistic和contain_normalize其实是绑定的""" super().__init__() self.padding_idx = pad self.layers = nn.ModuleList( [TransformerEncoderLayer(args) for _ in range(num_layers)] ) self.predictor = None if contain_logistic: self.predictor = LogisticModel(args, activation=nn.Sigmoid, dropout=0.3, contain_normalize=contain_normalize, **unused) def forward(self, input, mask=None, decoder_input=None, normalize=False): """ 1. 用于encoder: mask=encoder, decoder_input=None 2. 用于decoder: mask=None, decoder_input=decoder.input """ if mask is None: mask = decoder_input.eq(self.padding_idx) for layer in self.layers: input = layer(input, encoder_padding_mask=mask) if self.predictor is not None: out = self.predictor(input.transpose(0, 1), normalize=normalize) return out return input class LSTMLayer(nn.Module): def __init__(self, args, num_layers=1, activation=nn.Sigmoid, dropout=0.2, pad=0, contain_logistic=True): super().__init__() self.padding_idx = pad self.lstm = nn.GRU(input_size=args.encoder_embed_dim, hidden_size=300, num_layers=num_layers, bidirectional=True, dropout=dropout, batch_first=True) self.predictor = None if contain_logistic: self.predictor = nn.Sequential( Perceptron(input_dim=600, output_dim=300, activation=activation, drouput=0.2), Perceptron(input_dim=300, output_dim=1, activation=None, drouput=0.2) # 最后一层不用激活 ) self.activate = activation() def forward(self, decoder_out=None, normalize=False, decoder_input=None, h_0=None, **unused): x_length = decoder_input.ne(self.padding_idx).sum(-1) packed_x = nn.utils.rnn.pack_padded_sequence(decoder_out, x_length, batch_first=True, enforce_sorted=False) lstm_output, _ = self.lstm(packed_x, h_0) lstm_output, _ = nn.utils.rnn.pad_packed_sequence(lstm_output, padding_value=self.padding_idx, batch_first=True) if self.activate: out = self.predictor(lstm_output).squeeze(-1) else: out = lstm_output return self.activate(out) if normalize else out class EncoderDecoder(nn.Module): def __init__(self, args, encoder_layers=3, decoder_layers=3, pad=0): # 三层block super().__init__() self.encoder = EncoderLayer(args, num_layers=encoder_layers, contain_logistic=False) self.decoder = DecoderLayer(args, num_layers=decoder_layers, pad=pad) def forward(self, encoder_out, decoder_out, decoder_input, normalize=False, **unused): encoder_output = self.encoder(encoder_out.encoder_out, encoder_out.encoder_padding_mask, normalize=False) decoder_output = self.decoder(encoder_output, encoder_out.encoder_padding_mask, decoder_out, decoder_input, normalize=normalize) return decoder_output class BCELoss(nn.Module): def __init__(self): super().__init__() self.loss_class = nn.BCEWithLogitsLoss(reduction="none") def forward(self, outputs, targets): # target: 1表示需要mask # output：得分越高表示不需要mask targets = (~targets).float() return self.loss_class(outputs, targets)

4,602

1,193

432

5f0496938b0764b39076e5b64d229dd8946afff8

598

py

Python

python-experiments/lambda_example.py

MichaelCurrin/python-2016

00af4e61175fc15aebfab208c76fe9db845d2f4c

[ "MIT" ]

null

python-experiments/lambda_example.py

MichaelCurrin/python-2016

00af4e61175fc15aebfab208c76fe9db845d2f4c

[ "MIT" ]

null

python-experiments/lambda_example.py

MichaelCurrin/python-2016

00af4e61175fc15aebfab208c76fe9db845d2f4c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ flatten a 2 dimensional list into a 1 dimension list by joining column items in a row into one item as single comma-separated string This is useful for preparing data for a CSV writer function which requires a 1-dimensional list of rows with no columns Alternatively, the join functional can be moved into the CSV writer so that the function can accept 2 dimensional lists """ list = [("A", "B", "C"), ("34", "32647", "43"), ("4556", "35235", "23623")] str = map(lambda x: ",".join(x), list) print str """ #output ['A,B,C', '34,32647,43', '4556,35235,23623'] """

27.181818

119

0.680602

# -*- coding: utf-8 -*- """ flatten a 2 dimensional list into a 1 dimension list by joining column items in a row into one item as single comma-separated string This is useful for preparing data for a CSV writer function which requires a 1-dimensional list of rows with no columns Alternatively, the join functional can be moved into the CSV writer so that the function can accept 2 dimensional lists """ list = [("A", "B", "C"), ("34", "32647", "43"), ("4556", "35235", "23623")] str = map(lambda x: ",".join(x), list) print str """ #output ['A,B,C', '34,32647,43', '4556,35235,23623'] """

0

afcd895d40b418f27e030dda81dde145aeb07ff0

49,651

py

Python

src/NebulaBayes/tests/test_NB.py

ADThomas-astro/NebulaBayes

bda65809f43bc336914ce39ae59123e8be1abcbc

[ "MIT" ]

2

2018-08-13T13:50:38.000Z

2019-09-12T01:45:21.000Z

src/NebulaBayes/tests/test_NB.py

ADThomas-astro/NebulaBayes

bda65809f43bc336914ce39ae59123e8be1abcbc

[ "MIT" ]

null

src/NebulaBayes/tests/test_NB.py

ADThomas-astro/NebulaBayes

bda65809f43bc336914ce39ae59123e8be1abcbc

[ "MIT" ]

1

2018-09-17T19:10:07.000Z

from __future__ import print_function, division from collections import OrderedDict as OD import itertools import os import unittest from astropy.io import fits # FITS file I/O from astropy.table import Table # Used in converting to pandas DataFrame import numpy as np import pandas as pd import NebulaBayes from NebulaBayes import NB_Model, __version__ from NebulaBayes.NB1_Process_grids import RegularGridResampler from NebulaBayes.NB3_Bayes import NB_nd_pdf """ Test suite to test NebulaBayes. Mostly functional and regression tests, with some unit tests as well. Works with Python 2 and Python 3. To run only a particular test, type (e.g.): python3 test_NB.py Test_real_data_with_dereddening This test suite can be run in-place in NebulaBayes/tests under the NebulaBayes installation directory (but use the correct python version for the installation location). Adam D. Thomas 2017 """ clean_up = True # Delete test output files after running? # Save test outputs in NebulaBayes/tests/test_outputs THIS_FILE_DIR = os.path.dirname(os.path.realpath(__file__)) TEST_DIR = os.path.join(THIS_FILE_DIR, "test_outputs") ############################################################################### # Helper functions def build_grid(param_range_dict, line_peaks_dict, n_gridpts_list, std_frac=0.25): """ Initialise a grid - create a pandas DataFrame table. Fluxes for each emission line form a Gaussian ball around a specified point. param_range_dict: Ordered dict mapping parameter names to a tuple giving the parameter minimum and maximum line_peaks_dict: Ordered dict mapping line names to the location (as a tuple) of the peak of the line flux in the grid, in gridpoint index coordinates (from zero) std_frac: Fraction of the range in each dimension used for the std n_gridpts_list is a list of the number of gridpoints in each dimension. """ param_names = list(param_range_dict.keys()) param_val_arrs = [np.linspace(r[0], r[1], n) for r,n in zip( param_range_dict.values(), n_gridpts_list)] line_names = list(line_peaks_dict.keys()) std = np.array([(r[1] - r[0]) * std_frac for r in param_range_dict.values()]) line_peak_vals = {} for line, peak_inds in line_peaks_dict.items(): line_peak = [] for p, peak_ind, val_arr in zip(param_names, peak_inds, param_val_arrs): p_min, dp = val_arr[0], np.diff(val_arr)[0] line_peak.append(p_min + peak_ind*dp) line_peak_vals[line] = line_peak # An ND list corresponding to peak_inds flux_fns = {} for l,peak_tuple in line_peaks_dict.items(): peak = np.array(line_peak_vals[l]) # ND vector flux_fns[l] = gaussian # Make DataFrame table: columns = param_names + line_names n_gridpts = np.product(n_gridpts_list) OD_for_DF = OD([(c, np.full(n_gridpts, np.nan)) for c in columns]) DF_grid = pd.DataFrame(OD_for_DF) # Iterate over rows, filling in the table for i, p_tuple in enumerate(itertools.product(*param_val_arrs)): # Add parameter values into their columns: for p,n in zip(p_tuple, param_names): DF_grid.loc[i,n] = p # Add "model" line fluxes into their columns: for l in line_names: DF_grid.loc[i,l] = flux_fns[l](np.array(p_tuple)) return DF_grid def extract_grid_fluxes_i(DF, p_name_ind_map, line_names): """ Extract emission line fluxes from a grid (represented as a DataFrame) by inputting gridpoint indices and taking the fluxes at the nearest gridpoint """ val_arrs = {p:np.unique(DF[p].values) for p in p_name_ind_map} assert len(DF) == np.product([len(v) for v in val_arrs.values()]) where = np.full(len(DF), 1, dtype=bool) for p,ind in p_name_ind_map.items(): where &= (DF.loc[:,p] == val_arrs[p][ind]) assert np.sum(where) == 1 return [DF[line].values[where][0] for line in line_names] ############################################################################### # Helper class class Base_2D_Grid_2_Lines(unittest.TestCase): """ Base class holding setup and cleanup methods to make a 2D grid with only 2 emission lines, and using a 2D Gaussian to make the grid. There are only two lines, but one has fluxes set to all 1 and is just for normalisation. """ params = ["p1", "p2"] param_range_dict = OD( [("p1", (-5, 3)), ("p2", (1.2e6, 15e6))] ) n_gridpts_list = (11, 9) # Number of gridpoints in each dimension interpd_shape = (50, 45) lines = ["L1", "L2"] # Line names line_peaks = [8, 5] # Gridpoint indices from zero @classmethod def setUpClass(cls): """ Make grid and run NebulaBayes to obtain the result object """ line_peaks_dict = OD([(l,cls.line_peaks) for l in cls.lines]) cls.DF = build_grid(cls.param_range_dict, line_peaks_dict, cls.n_gridpts_list) cls.val_arrs = OD([(p,np.unique(cls.DF[p].values)) for p in cls.params]) cls.DF.loc[:,"L1"] = 1. # We'll normalise by this line cls.grid_file = os.path.join(TEST_DIR, cls.__name__ + "_grid.csv") cls.DF.to_csv(cls.grid_file, index=False) cls.NB_Model_1 = NB_Model(cls.grid_file, cls.params, cls.lines, interpd_grid_shape=cls.interpd_shape) @classmethod def tearDownClass(cls): """ Remove the output when tests in this class have finished """ if clean_up: os.remove(cls.grid_file) if hasattr(cls, "posterior_plot"): os.remove(cls.posterior_plot) ############################################################################### class Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines): """ Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is at the peak of the Gaussian ball of emission line fluxes, and check that treating these fluxes as observations leads to correct estimates from NebulaBayes. """ test_gridpoint = [8, 5] # From zero. [11, 9] total gridpoints in each dim @classmethod def test_parameter_estimates(self): """ Ensure the parameter estimates are as expected """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue(all(p in DF_est.index for p in self.params)) # Tolerance for distance between gridpoint we chose and the estimate: grid_sep_frac = 0.1 # Allowed fraction of distance between gridpoints for p, test_ind in zip(self.params, self.test_gridpoint): tol = np.diff(self.val_arrs[p])[0] * grid_sep_frac value = self.val_arrs[p][test_ind] # Expected parameter value est = DF_est.loc[p, "Estimate"] # NebulaBayes estimate self.assertTrue(np.isclose(est, value, atol=tol)) def test_raw_Grid_spec(self): """ Ensure the raw grid spec is as expected """ RGrid_spec = self.NB_Model_1.Raw_grids self.assertEqual(RGrid_spec.param_names, self.params) self.assertEqual(RGrid_spec.ndim, len(self.params)) self.assertEqual(RGrid_spec.shape, self.n_gridpts_list) self.assertEqual(RGrid_spec.n_gridpoints, np.product(self.n_gridpts_list)) for a1, a2 in zip(RGrid_spec.param_values_arrs, self.val_arrs.values()): self.assertTrue(np.allclose(np.asarray(a1), np.asarray(a2))) def test_interpolated_Grid_spec(self): """ Ensure the interpolated grid spec is as expected """ IGrid_spec = self.Result.Grid_spec self.assertEqual(IGrid_spec.param_names, self.params) self.assertEqual(IGrid_spec.param_display_names, self.params) self.assertEqual(IGrid_spec.shape, tuple(self.interpd_shape)) self.assertEqual(IGrid_spec.n_gridpoints, np.product(self.interpd_shape)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ super(Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines,cls).tearDownClass() if clean_up: files = [os.path.join(TEST_DIR, l + "_PDF_contributes_to_likelihood.pdf") for l in ["L1", "L2"]] for file_i in files: os.remove(file_i) ############################################################################### class Test_Obs_from_nonPeak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines): """ Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is NOT at the peak of the Gaussian ball of emission line fluxes. Note that we don't check the values in the posterior or parameter estimates - there isn't an obvious way to do this here. We also test that a numpy array prior is accepted. """ longMessage = True # Append messages to existing message test_gridpoint = [6, 4] # From zero. [11, 9] total gridpoints in each dim, # the line peak is at line_peaks = [8, 5] @classmethod def test_parameters_in_output(self): """ Check all parameters are found in output """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue(all(p in DF_est.index for p in self.params)) # Posterior is shaped like a donut. Check for a single local min? ############################################################################### # Test the NebulaBayes ND linear interpolation ############################################################################### class Test_1D_grid_and_public_attributes(unittest.TestCase): """ Test that a 1D grid works and gives expected results. We use a gaussian 1D "grid", and input a point at the peak into NB to ensure NB finds the correct point. We also test that a DataFrame grid table is accepted. """ longMessage = True # Append messages to existing message @classmethod def test_parameter_estimate(self): """ Ensure the single parameter estimate is as expected """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue("P0" in DF_est.index) lower = self.p_vals[self.test_gridpoint - 1] upper = self.p_vals[self.test_gridpoint + 1] est = DF_est.loc["P0", "Estimate"] self.assertTrue(lower < est < upper, msg="{0}, {1}, {2}".format( lower, est, upper)) def test_NB_Model_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.NB_Model_1) if not a.startswith("_")]) expected_attrs = ["Interpd_grids", "Raw_grids"] self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_NB_Result_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.Result) if not a.startswith("_")]) expected_attrs = [ "DF_obs", "Grid_spec", "Likelihood", "Plot_Config", "Plotter", "Posterior", "Prior", "deredden", "obs_flux_arrs", "obs_flux_err_arrs", "propagate_dered_errors"] self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_NB_nd_pdf_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.Result.Posterior) if not a.startswith("_")]) expected_attrs = sorted(["DF_estimates", "Grid_spec", "best_model", "marginalised_1D", "marginalised_2D", "name", "nd_pdf", "show"]) self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_best_model_dict_keys(self): """ Check that the list of best model keys is what is documented """ expected_keys = sorted(["table", "chi2", "extinction_Av_mag", "grid_location"]) key_list = sorted(list(self.Result.Posterior.best_model.keys())) self.assertEqual(key_list, expected_keys) @classmethod def tearDownClass(cls): """ Remove the output when tests in this class have finished """ if clean_up: if hasattr(cls, "posterior_plot"): os.remove(cls.posterior_plot) if hasattr(cls, "best_model_table"): os.remove(cls.best_model_table) ############################################################################### class Test_default_initialisation(unittest.TestCase): """ Test that we can initialise fully default HII and NLR NB models """ ############################################################################### class Test_real_data_with_dereddening(unittest.TestCase): """ Test some real data, from the S7 nuclear spectrum for NGC4691, a star- forming galaxy. Include a line ratio prior and dereddening in NebulaBayes. Test saving plots for all 3 Bayes Theorem PDFs. """ longMessage = True # Append messages to existing message lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] @classmethod def test_parameter_estimates(self): """ Regression check on parameter estimates. """ ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001), msg=str(ests["12 + log O/H"])) self.assertTrue(np.isclose(ests["log P/k"], 6.82636, atol=0.0001), msg=str(ests["log P/k"])) self.assertTrue(np.isclose(ests["log U"], -2.84848, atol=0.0001), msg=str(ests["log U"])) def test_estimate_bounds_checks(self): """ Ensure that the "checking columns" in the estimate table are all showing that the estimates are good. """ DF = self.Result.Posterior.DF_estimates # Parameter estimate table for p in ["12 + log O/H", "log P/k", "log U"]: for col in ["Est_in_CI68?", "Est_in_CI95?"]: self.assertTrue(DF.loc[p,col] == "Y") for col in ["Est_at_lower?", "Est_at_upper?", "P(lower)>50%?", "P(upper)>50%?"]: self.assertTrue(DF.loc[p,col] == "N") self.assertTrue(DF.loc[p,"n_local_maxima"] == 1) def test_chi2(self): """ Regression check that chi2 doesn't change """ chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(np.isclose(chi2, 2568.7, atol=0.2), msg=str(chi2)) def test_interp_order(self): """ Ensure the correct interpolation order (linear) is preserved """ self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 1) def test_all_zero_prior(self): """ We permit an all-zero prior - check that it works (a warning should be printed). """ shape = self.NB_Model_1.Interpd_grids.shape self.Result1 = self.NB_Model_1(self.obs_fluxes, self.obs_errs, self.lines, prior=np.zeros(shape)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ if clean_up: files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot, cls.estimate_table] for file_i in files: os.remove(file_i) ############################################################################### class Test_real_data_with_cubic_interpolation(unittest.TestCase): """ Very similar to the previous test class, but we use cubic interpolation instead of linear interpolation when interpolating model flux grids. We also test resetting the logging level after using the "verbosity" kwarg. """ longMessage = True # Append messages to existing message lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] @classmethod def test_parameter_estimates(self): """ Regression check on parameter estimates. Estimates for P and U are slightly different with the cubic interpolation. """ ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001), msg=str(ests["12 + log O/H"])) self.assertTrue(np.isclose(ests["log P/k"], 6.86047, atol=0.0001), msg=str(ests["log P/k"])) self.assertTrue(np.isclose(ests["log U"], -2.82828, atol=0.0001), msg=str(ests["log U"])) def test_chi2(self): """ Regression check that chi2 doesn't change """ chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(np.isclose(chi2, 2522.7, atol=0.2), msg=str(chi2)) def test_interp_order(self): """ Ensure the correct interpolation order (cubic) is preserved """ self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 3) def test_resetting_log_level(self): """ Ensure that after using the verbosity keyword, the NB_logger level is unchanged (i.e. was reset to its previous value) """ self.assertEqual(NebulaBayes.NB_logger.level, self.test_log_level) def test_dereddening_result_attributes(self): """Ensure dereddening attributes added to Result object.""" self.assertTrue(self.Result.deredden) self.assertTrue(self.Result.propagate_dered_errors) @classmethod def tearDownClass(cls): """ Remove output files when tests in this class have finished, and undo change to logging level. """ NebulaBayes.NB_logger.setLevel(cls.old_log_level) if clean_up: files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot, cls.estimate_table] for file_i in files: os.remove(file_i) ############################################################################### class Test_upper_bounds_1D(unittest.TestCase): """ Test the treatment of upper bounds. We use a 1D grid. """ longMessage = True # Append messages to existing message lines = ["line1", "line2", "line3", "line4", "line5", "line6"] obs_fluxes = [ 1.0, 8.0, 10.2, -np.inf, -np.inf, -np.inf] obs_errs = [ 0.05, 0.3, 3.1, 0.3, 0.4, 0.2] pred_fluxes = [ 1.0, 5.0, 10.2, 0.1, 0.4, 0.4] # The pred_fluxes are at the "peak" of the grid, that we'll input to NB. @classmethod def test_parameter_estimates(self): """ Regression test - check the parameter estimate is as expected. """ DF_est = self.Result.Posterior.DF_estimates # DataFrame p0_est = DF_est.loc["p0", "Estimate"] self.assertTrue(np.isclose(p0_est, self.expected_p0, atol=1)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ if clean_up: files = [os.path.join(TEST_DIR, l + "_PDF_contributes_to_likelihood.pdf") for l in cls.lines] for file_i in files: os.remove(file_i) ############################################################################### class Test_all_zero_likelihood(unittest.TestCase): """ Test forcing a log_likelihood of all -inf, so the likelihood is all zero. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 1e250, 1, 1.2e250, 1.2e250, 1e250] obs_errs = [ 0.004, 1, 0.005, 0.003, 0.002] @classmethod def test_likelihood_all_zero(self): """ Regression test - check likelihood is all zero. """ likelihood = self.Result.Likelihood.nd_pdf self.assertTrue(np.all(likelihood == 0)) def test_posterior_all_zero(self): """ Regression test - check posterior is all zero. """ posterior = self.Result.Posterior.nd_pdf self.assertTrue(np.all(posterior == 0)) ############################################################################### class Test_data_that_matches_models_poorly(unittest.TestCase): """ Test inputting fluxes and errors that are very poorly fit by the entire model grid. In this case most of the likelihood is zero, and using a reasonable-ish prior gives a posterior that is zero everywhere. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2] obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01] # Note the very small errors @classmethod def test_likelihood_mostly_zero(self): """ Regression test - check likelihood is mostly zero. """ likelihood = self.Result.Likelihood.nd_pdf self.assertTrue(np.sum(likelihood != 0) < 65) def test_posterior_all_zero(self): """ Regression test - check posterior is all zero. """ posterior = self.Result.Posterior.nd_pdf self.assertTrue(np.all(posterior == 0)) ############################################################################### class Test_NB_nd_pdf(unittest.TestCase): """ Test the methods in the NB_nd_pdf class """ @classmethod # We want to test NB_nd_pdf attributes; some of "DF_estimates", "Grid_spec", # "best_model", marginalised_1D", "marginalised_2D", "name", "nd_pdf" # "best_model" keys: "table", "chi2", "extinction_Av_mag", "grid_location" def test_best_model_table(self): """ Check that a single table value matches the desired gridpoint. This test would fail on NebulaBayes 0.9.7 and earlier """ best_coords = (self.peak_ind_y, self.peak_ind_x) normed_grids = self.NB_Model_1.Interpd_grids.grids["Hbeta_norm"] model_OIII = normed_grids["OIII5007"][best_coords] DF_best = self.NB_nd_pdf_1.best_model["table"] table_model_OIII = DF_best.loc["OIII5007", "Model"] self.assertEqual(table_model_OIII, model_OIII) def test_marginalised_1D_pdf(self): """ Check that the marginalised 1D pdfs are as expected """ m_1D = self.NB_nd_pdf_1.marginalised_1D self.assertEqual(len(m_1D), 2) # Scale the pdfs to compare despite the m_1D PDFs being normalised m_1D["log U"] /= m_1D["log U"].max() m_1D["12 + log O/H"] /= m_1D["12 + log O/H"].max() expected_x_pdf = self.marginalised_x / self.marginalised_x.max() expected_y_pdf = self.marginalised_y / self.marginalised_y.max() self.assertTrue(np.allclose(m_1D["log U"], expected_x_pdf, atol=1e-12, rtol=0)) self.assertTrue(np.allclose(m_1D["12 + log O/H"], expected_y_pdf, atol=1e-12, rtol=0)) # May have swapped x and y, but it's all symmetric anyway... def test_nd_pdf(self): """ Check that the normalised nd_pdf matches the input raw nd_pdf. We avoid doing a proper normalisation by comparing with a simple scaling. """ pdf = self.NB_nd_pdf_1.nd_pdf scaled_raw_nd_pdf = self.raw_pdf / self.raw_pdf.max() self.assertTrue(np.array_equal(pdf / pdf.max(), scaled_raw_nd_pdf)) ############################################################################### class Test_dereddening_changes_results(unittest.TestCase): """ Test that using dereddening changes all three PDFs (when obs data are used in the prior). There previously was a bug where the obs data in the line ratio priors weren't dereddened. Also test that PDFs change when errors from the Balmer decrement are propagated into the dereddened line fluxes. """ @classmethod def test_priors_differ(self): """ Check that dereddened data was used in line ratio prior, when requested. This test fails on NebulaBayes 0.9.7 """ pdf_dered1 = self.Result_dered1.Prior.nd_pdf pdf_nodered = self.Result_nodered.Prior.nd_pdf max_diff1 = np.max(np.abs(pdf_dered1 - pdf_nodered)) self.assertTrue(max_diff1 > 0.01, str(max_diff1)) # Test uncertainty propagation has an effect pdf_dered2 = self.Result_dered2.Prior.nd_pdf max_diff_u = np.max(np.abs(pdf_dered1 - pdf_dered2)) self.assertTrue(max_diff_u > 0.01, str(max_diff_u)) def test_propagate_dered_errors(self): """Check propagate_dered_errors values on Result object""" # Checks default value of False self.assertFalse(self.Result_dered1.propagate_dered_errors) self.assertTrue(self.Result_dered2.propagate_dered_errors) ############################################################################### class Test_likelihood_lines_keyword(unittest.TestCase): """ Test inputting fluxes and errors that aren't used in the likelihood, and test that these lines may be used in a prior. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2] obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01] exclude_lines = ["Halpha", "OIII5007"] @classmethod def test_non_likelihood_lines_in_best_model_table(self): """ Regression test - lines not included in likelihood calculation should still appear in the "best model" table. """ self.assertTrue(all(l in self.DF_best.index for l in self.exclude_lines)) def test_best_model_table_fields(self): """ Regression test - check fields of best model table (we test for the case of no dereddening; field names are different with dereddening). """ correct_fields = ["In_lhood?", "Obs", "Model", "Resid_Stds", "Obs_S/N"] t_fields = self.DF_best.columns.tolist() self.assertTrue(t_fields == correct_fields, t_fields) def test_In_lhood_field_in_best_model_table(self): """ Regression test - the "In_lhood?" field in the best model table should correctly identify if a line was used in the likelihood. """ correct = [("N" if l in self.exclude_lines else "Y") for l in self.lines] self.assertTrue(self.DF_best["In_lhood?"].values.tolist() == correct) def test_permuting_input_line_order(self): """ Regression test - the order of the input lines should not affect the results. There was a real bug introduced with the "likelihood_lines" feature - this test fails on NB version 0.9.6 and 0.9.7! """ n = len(self.lines) for i, ind_tuple in enumerate(itertools.permutations(range(n))): # There are 5! = 120 permutations, so only check one in five: if i % 5 != 2: continue obs_fluxes = [self.obs_fluxes[j] for j in ind_tuple] obs_errs = [self.obs_errs[j] for j in ind_tuple] lines = [self.lines[j] for j in ind_tuple] Result_i = self.NB_Model_1(obs_fluxes, obs_errs, lines, likelihood_lines=self.likelihood_lines, **self.kwargs) P_i = Result_i.Posterior estimate_Z_i = P_i.DF_estimates.loc["12 + log O/H", "Estimate"] self.assertEqual(estimate_Z_i, self.estimate_Z) ############################################################################### class Test_raising_errors(unittest.TestCase): """ Test raising errors on bad inputs """ @classmethod def test_bad_grid_parameter_with_too_few_unique_values(self): """ Test correct error is raised if there are too few unique values for a grid parameter. """ DF = pd.DataFrame({"p1": [4, 4, 4, 4], "p2": [1, 2, 3, 4], "l2": [5, 6, 7, 8]}) self.assertRaisesRE(ValueError, "3 unique values are required", NB_Model, DF, ["p1", "p2"]) ############################################################################### def interactive_plot_tests(): """ This function needs to be called manually to test the interactive plotting. from test_NB import interactive_plot_tests interactive_plot_tests() """ lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] NB_Model_1 = NB_Model("HII", grid_params=None, line_list=lines, interpd_grid_shape=[50, 70, 50], grid_error=0.35) kwargs = {"deredden": True, "propagate_dered_errors": True, "obs_wavelengths": obs_wavelengths, "prior":[("SII6716","SII6731")], "plot_configs": [{"table_on_plot": True, "legend_fontsize": 5}]*4, } Result = NB_Model_1(obs_fluxes, obs_errs, lines, **kwargs) # Test both ways to make an interactive plot Result.Plotter.interactive(Result.Posterior) Result.Prior.show(Result.Plotter) ############################################################################### # Ideas for more tests: # Check that parameter estimates are inside the CIs, and check the flags for this # Test normalising to different lines repeatedly, and checking that the # unnecessary interpolated grids are deleted. # Check coverage of the code, to see what isn't being run? if __name__ == "__main__": print("\nTesting NebulaBayes version {0} ...\n".format(__version__)) unittest.main(verbosity=2)

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from __future__ import print_function, division from collections import OrderedDict as OD import itertools import os import unittest from astropy.io import fits # FITS file I/O from astropy.table import Table # Used in converting to pandas DataFrame import numpy as np import pandas as pd import NebulaBayes from NebulaBayes import NB_Model, __version__ from NebulaBayes.NB1_Process_grids import RegularGridResampler from NebulaBayes.NB3_Bayes import NB_nd_pdf """ Test suite to test NebulaBayes. Mostly functional and regression tests, with some unit tests as well. Works with Python 2 and Python 3. To run only a particular test, type (e.g.): python3 test_NB.py Test_real_data_with_dereddening This test suite can be run in-place in NebulaBayes/tests under the NebulaBayes installation directory (but use the correct python version for the installation location). Adam D. Thomas 2017 """ clean_up = True # Delete test output files after running? # Save test outputs in NebulaBayes/tests/test_outputs THIS_FILE_DIR = os.path.dirname(os.path.realpath(__file__)) TEST_DIR = os.path.join(THIS_FILE_DIR, "test_outputs") ############################################################################### # Helper functions def build_grid(param_range_dict, line_peaks_dict, n_gridpts_list, std_frac=0.25): """ Initialise a grid - create a pandas DataFrame table. Fluxes for each emission line form a Gaussian ball around a specified point. param_range_dict: Ordered dict mapping parameter names to a tuple giving the parameter minimum and maximum line_peaks_dict: Ordered dict mapping line names to the location (as a tuple) of the peak of the line flux in the grid, in gridpoint index coordinates (from zero) std_frac: Fraction of the range in each dimension used for the std n_gridpts_list is a list of the number of gridpoints in each dimension. """ param_names = list(param_range_dict.keys()) param_val_arrs = [np.linspace(r[0], r[1], n) for r,n in zip( param_range_dict.values(), n_gridpts_list)] line_names = list(line_peaks_dict.keys()) std = np.array([(r[1] - r[0]) * std_frac for r in param_range_dict.values()]) line_peak_vals = {} for line, peak_inds in line_peaks_dict.items(): line_peak = [] for p, peak_ind, val_arr in zip(param_names, peak_inds, param_val_arrs): p_min, dp = val_arr[0], np.diff(val_arr)[0] line_peak.append(p_min + peak_ind*dp) line_peak_vals[line] = line_peak # An ND list corresponding to peak_inds flux_fns = {} for l,peak_tuple in line_peaks_dict.items(): peak = np.array(line_peak_vals[l]) # ND vector def gaussian(x): # N.B. x, peak and std are all ND vectors distance = np.sqrt(np.sum(((x - peak) / std)**2)) return np.exp(-distance / 2) flux_fns[l] = gaussian # Make DataFrame table: columns = param_names + line_names n_gridpts = np.product(n_gridpts_list) OD_for_DF = OD([(c, np.full(n_gridpts, np.nan)) for c in columns]) DF_grid = pd.DataFrame(OD_for_DF) # Iterate over rows, filling in the table for i, p_tuple in enumerate(itertools.product(*param_val_arrs)): # Add parameter values into their columns: for p,n in zip(p_tuple, param_names): DF_grid.loc[i,n] = p # Add "model" line fluxes into their columns: for l in line_names: DF_grid.loc[i,l] = flux_fns[l](np.array(p_tuple)) return DF_grid def extract_grid_fluxes_i(DF, p_name_ind_map, line_names): """ Extract emission line fluxes from a grid (represented as a DataFrame) by inputting gridpoint indices and taking the fluxes at the nearest gridpoint """ val_arrs = {p:np.unique(DF[p].values) for p in p_name_ind_map} assert len(DF) == np.product([len(v) for v in val_arrs.values()]) where = np.full(len(DF), 1, dtype=bool) for p,ind in p_name_ind_map.items(): where &= (DF.loc[:,p] == val_arrs[p][ind]) assert np.sum(where) == 1 return [DF[line].values[where][0] for line in line_names] ############################################################################### # Helper class class Base_2D_Grid_2_Lines(unittest.TestCase): """ Base class holding setup and cleanup methods to make a 2D grid with only 2 emission lines, and using a 2D Gaussian to make the grid. There are only two lines, but one has fluxes set to all 1 and is just for normalisation. """ params = ["p1", "p2"] param_range_dict = OD( [("p1", (-5, 3)), ("p2", (1.2e6, 15e6))] ) n_gridpts_list = (11, 9) # Number of gridpoints in each dimension interpd_shape = (50, 45) lines = ["L1", "L2"] # Line names line_peaks = [8, 5] # Gridpoint indices from zero @classmethod def setUpClass(cls): """ Make grid and run NebulaBayes to obtain the result object """ line_peaks_dict = OD([(l,cls.line_peaks) for l in cls.lines]) cls.DF = build_grid(cls.param_range_dict, line_peaks_dict, cls.n_gridpts_list) cls.val_arrs = OD([(p,np.unique(cls.DF[p].values)) for p in cls.params]) cls.DF.loc[:,"L1"] = 1. # We'll normalise by this line cls.grid_file = os.path.join(TEST_DIR, cls.__name__ + "_grid.csv") cls.DF.to_csv(cls.grid_file, index=False) cls.NB_Model_1 = NB_Model(cls.grid_file, cls.params, cls.lines, interpd_grid_shape=cls.interpd_shape) @classmethod def tearDownClass(cls): """ Remove the output when tests in this class have finished """ if clean_up: os.remove(cls.grid_file) if hasattr(cls, "posterior_plot"): os.remove(cls.posterior_plot) ############################################################################### class Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines): """ Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is at the peak of the Gaussian ball of emission line fluxes, and check that treating these fluxes as observations leads to correct estimates from NebulaBayes. """ test_gridpoint = [8, 5] # From zero. [11, 9] total gridpoints in each dim @classmethod def setUpClass(cls): super(Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines, cls).setUpClass() test_pt = OD(zip(cls.params, cls.test_gridpoint)) # Map params to gridpt indices obs_fluxes = extract_grid_fluxes_i(cls.DF, test_pt, ["L1", "L2"]) obs_errors = [f / 7. for f in obs_fluxes] cls.posterior_plot = os.path.join(TEST_DIR, cls.__name__ + "_posterior.pdf") kwargs = {"posterior_plot": cls.posterior_plot, "line_plot_dir": TEST_DIR, "norm_line": "L1"} cls.Result = cls.NB_Model_1(obs_fluxes, obs_errors, cls.lines, **kwargs) def test_output_deredden_flag(self): self.assertTrue(self.Result.deredden is False) def test_parameter_estimates(self): """ Ensure the parameter estimates are as expected """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue(all(p in DF_est.index for p in self.params)) # Tolerance for distance between gridpoint we chose and the estimate: grid_sep_frac = 0.1 # Allowed fraction of distance between gridpoints for p, test_ind in zip(self.params, self.test_gridpoint): tol = np.diff(self.val_arrs[p])[0] * grid_sep_frac value = self.val_arrs[p][test_ind] # Expected parameter value est = DF_est.loc[p, "Estimate"] # NebulaBayes estimate self.assertTrue(np.isclose(est, value, atol=tol)) def test_raw_Grid_spec(self): """ Ensure the raw grid spec is as expected """ RGrid_spec = self.NB_Model_1.Raw_grids self.assertEqual(RGrid_spec.param_names, self.params) self.assertEqual(RGrid_spec.ndim, len(self.params)) self.assertEqual(RGrid_spec.shape, self.n_gridpts_list) self.assertEqual(RGrid_spec.n_gridpoints, np.product(self.n_gridpts_list)) for a1, a2 in zip(RGrid_spec.param_values_arrs, self.val_arrs.values()): self.assertTrue(np.allclose(np.asarray(a1), np.asarray(a2))) def test_interpolated_Grid_spec(self): """ Ensure the interpolated grid spec is as expected """ IGrid_spec = self.Result.Grid_spec self.assertEqual(IGrid_spec.param_names, self.params) self.assertEqual(IGrid_spec.param_display_names, self.params) self.assertEqual(IGrid_spec.shape, tuple(self.interpd_shape)) self.assertEqual(IGrid_spec.n_gridpoints, np.product(self.interpd_shape)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ super(Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines,cls).tearDownClass() if clean_up: files = [os.path.join(TEST_DIR, l + "_PDF_contributes_to_likelihood.pdf") for l in ["L1", "L2"]] for file_i in files: os.remove(file_i) ############################################################################### class Test_Obs_from_nonPeak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines): """ Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is NOT at the peak of the Gaussian ball of emission line fluxes. Note that we don't check the values in the posterior or parameter estimates - there isn't an obvious way to do this here. We also test that a numpy array prior is accepted. """ longMessage = True # Append messages to existing message test_gridpoint = [6, 4] # From zero. [11, 9] total gridpoints in each dim, # the line peak is at line_peaks = [8, 5] @classmethod def setUpClass(cls): super(Test_Obs_from_nonPeak_Gridpoint_2D_Grid_2_Lines, cls).setUpClass() test_pt = OD(zip(cls.params, cls.test_gridpoint)) # Map params to gridpt indices obs_fluxes = extract_grid_fluxes_i(cls.DF, test_pt, ["L1", "L2"]) obs_errors = [f / 7. for f in obs_fluxes] cls.posterior_plot = os.path.join(TEST_DIR, cls.__name__ + "_posterior.pdf") kwargs = {"posterior_plot": cls.posterior_plot, "norm_line": "L1", "prior": np.ones(cls.NB_Model_1.Interpd_grids.shape) } cls.Result = cls.NB_Model_1(obs_fluxes, obs_errors, cls.lines, **kwargs) def test_output_deredden_flag(self): self.assertTrue(self.Result.deredden is False) def test_output_chi2_positive(self): chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(chi2 > 0, msg="chi2 is " + str(chi2)) def test_output_extinction_is_NA(self): # Since we didn't deredden Av = self.Result.Posterior.best_model["extinction_Av_mag"] self.assertTrue(Av == "NA (deredden is False)") def test_parameters_in_output(self): """ Check all parameters are found in output """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue(all(p in DF_est.index for p in self.params)) # Posterior is shaped like a donut. Check for a single local min? ############################################################################### # Test the NebulaBayes ND linear interpolation class test_linear_interpolation_1D(unittest.TestCase): def test_linear_interpolation_simple_1D(self): R1 = RegularGridResampler([[10, 20, 30]], [5]) R1_pout, R1_arr = R1(np.array([-100,-200,-300])) assert np.array_equal(R1_pout[0], np.array([ 10., 15., 20., 25., 30.])) assert np.array_equal(R1_arr, np.array([-100., -150., -200., -250., -300.])) class test_linear_interpolation_2D(unittest.TestCase): def test_linear_interpolation_simple_2D(self): R2 = RegularGridResampler([[10, 20, 30], [8e4, 9e4]], [5,2]) R2_pout, R2_arr = R2(np.array([[-100,-2000], [-300,-4000], [-500,-6000]])) assert np.array_equal(R2_pout[0], np.array([ 10., 15., 20., 25., 30.])) assert np.array_equal(R2_pout[1], np.array([8e4, 9e4])) assert np.array_equal(R2_arr, np.array([[-100., -2000.], [-200., -3000.], [-300., -4000.], [-400., -5000.], [-500., -6000.]])) class test_linear_interpolation_3D(unittest.TestCase): def test_linear_interpolation_simple_3D(self): R3 = RegularGridResampler([[-2, -1],[10, 20, 30], [8e4, 9e4]], [2, 5, 2]) R3_pout, R3_arr = R3(np.array([[[-100, -2000], [-300, -4000], [-500, -6000]], [[-100, -2000], [-300, -4000], [-500, -6000]] ])) assert np.array_equal(R3_pout[0], np.array([-2, -1])) assert np.array_equal(R3_pout[1], np.array([10., 15., 20., 25., 30.])) assert np.array_equal(R3_pout[2], np.array([8e4, 9e4])) assert np.array_equal(R3_arr, np.array([[[-100., -2000.], [-200., -3000.], [-300., -4000.], [-400., -5000.], [-500., -6000.]], [[-100., -2000.], [-200., -3000.], [-300., -4000.], [-400., -5000.], [-500., -6000.]] ])) ############################################################################### class Test_1D_grid_and_public_attributes(unittest.TestCase): """ Test that a 1D grid works and gives expected results. We use a gaussian 1D "grid", and input a point at the peak into NB to ensure NB finds the correct point. We also test that a DataFrame grid table is accepted. """ longMessage = True # Append messages to existing message @classmethod def setUpClass(cls): # Make a 1D grid: test_gridpoint = 45 # From zero cls.test_gridpoint = test_gridpoint p_vals = np.linspace(-2, 8, 100) cls.p_vals = p_vals peak1, peak2, peak3 = p_vals[8], p_vals[60], p_vals[83] std1, std2, std3 = 1.1, 1.8, 4.3 flux_0 = 3.0 * np.ones_like(p_vals) flux_1 = 13. * np.exp(-np.sqrt(((p_vals - peak1) / std1)**2) / 2) flux_2 = 13. * np.exp(-np.sqrt(((p_vals - peak2) / std2)**2) / 2) flux_3 = 21. * np.exp(-np.sqrt(((p_vals - peak3) / std3)**2) / 2) cls.lines = ["l0", "l1", "l2", "l3"] DF_grid1D = pd.DataFrame({"P0":p_vals, "l0":flux_0, "l1":flux_1, "l2":flux_2, "l3":flux_3}) obs_fluxes = [x[test_gridpoint] for x in [flux_0,flux_1,flux_2,flux_3]] obs_errors = [f / 7. for f in obs_fluxes] cls.posterior_plot = os.path.join(TEST_DIR, cls.__name__ + "_posterior.pdf") cls.best_model_table = os.path.join(TEST_DIR, cls.__name__ + "_best_model.csv") cls.NB_Model_1 = NB_Model(DF_grid1D, ["P0"], cls.lines, interp_order=1, interpd_grid_shape=[300]) # Also test cubic interpolation for the 1D case: cls.NB_Model_2 = NB_Model(DF_grid1D, ["P0"], cls.lines, interp_order=3, interpd_grid_shape=[300]) # We test the case-insensitivity of the norm_line, by writing # "L0" instead of "l0" here: kwargs = {"posterior_plot":cls.posterior_plot, "norm_line":"L0", "best_model_table":cls.best_model_table} cls.Result = cls.NB_Model_1(obs_fluxes, obs_errors, cls.lines, **kwargs) def test_output_deredden_flag(self): self.assertTrue(self.Result.deredden is False) def test_output_chi2_positive(self): chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(chi2 > 0, msg="chi2 is " + str(chi2)) def test_output_extinction_is_NA(self): # Since we didn't deredden Av = self.Result.Posterior.best_model["extinction_Av_mag"] self.assertTrue(Av == "NA (deredden is False)") def test_parameter_estimate(self): """ Ensure the single parameter estimate is as expected """ DF_est = self.Result.Posterior.DF_estimates self.assertTrue("P0" in DF_est.index) lower = self.p_vals[self.test_gridpoint - 1] upper = self.p_vals[self.test_gridpoint + 1] est = DF_est.loc["P0", "Estimate"] self.assertTrue(lower < est < upper, msg="{0}, {1}, {2}".format( lower, est, upper)) def test_NB_Model_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.NB_Model_1) if not a.startswith("_")]) expected_attrs = ["Interpd_grids", "Raw_grids"] self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_NB_Result_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.Result) if not a.startswith("_")]) expected_attrs = [ "DF_obs", "Grid_spec", "Likelihood", "Plot_Config", "Plotter", "Posterior", "Prior", "deredden", "obs_flux_arrs", "obs_flux_err_arrs", "propagate_dered_errors"] self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_NB_nd_pdf_attributes(self): """ Check that the list of public attributes is what is documented """ public_attrs = sorted([a for a in dir(self.Result.Posterior) if not a.startswith("_")]) expected_attrs = sorted(["DF_estimates", "Grid_spec", "best_model", "marginalised_1D", "marginalised_2D", "name", "nd_pdf", "show"]) self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs)) def test_best_model_dict_keys(self): """ Check that the list of best model keys is what is documented """ expected_keys = sorted(["table", "chi2", "extinction_Av_mag", "grid_location"]) key_list = sorted(list(self.Result.Posterior.best_model.keys())) self.assertEqual(key_list, expected_keys) @classmethod def tearDownClass(cls): """ Remove the output when tests in this class have finished """ if clean_up: if hasattr(cls, "posterior_plot"): os.remove(cls.posterior_plot) if hasattr(cls, "best_model_table"): os.remove(cls.best_model_table) ############################################################################### class Test_default_initialisation(unittest.TestCase): """ Test that we can initialise fully default HII and NLR NB models """ def test_default_HII_initialisation(self): NB_Model("HII") def test_default_NLR_initialisation(self): NB_Model("NLR") ############################################################################### class Test_real_data_with_dereddening(unittest.TestCase): """ Test some real data, from the S7 nuclear spectrum for NGC4691, a star- forming galaxy. Include a line ratio prior and dereddening in NebulaBayes. Test saving plots for all 3 Bayes Theorem PDFs. """ longMessage = True # Append messages to existing message lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] @classmethod def setUpClass(cls): cls.prior_plot = os.path.join(TEST_DIR, cls.__name__ + "_prior.pdf") cls.likelihood_plot = os.path.join(TEST_DIR, cls.__name__ + "_likelihood.pdf") # Test saving as a png cls.posterior_plot = os.path.join(TEST_DIR, cls.__name__ + "_posterior.png") cls.estimate_table = os.path.join(TEST_DIR, cls.__name__ + "_parameter_estimates.csv") # Test different values along each dimension in interpd_grid_shape cls.NB_Model_1 = NB_Model("HII", grid_params=None, line_list=cls.lines, interpd_grid_shape=[100, 130, 80], grid_error=0.35) kwargs = {"prior_plot": cls.prior_plot, "likelihood_plot": cls.likelihood_plot, "posterior_plot": cls.posterior_plot, "estimate_table": cls.estimate_table, "deredden": True, "propagate_dered_errors": True, "obs_wavelengths": cls.obs_wavelengths, "prior":[("SII6716","SII6731")], "plot_configs": [{"table_on_plot": True, "legend_fontsize": 5}]*4, } cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, **kwargs) def test_parameter_estimates(self): """ Regression check on parameter estimates. """ ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001), msg=str(ests["12 + log O/H"])) self.assertTrue(np.isclose(ests["log P/k"], 6.82636, atol=0.0001), msg=str(ests["log P/k"])) self.assertTrue(np.isclose(ests["log U"], -2.84848, atol=0.0001), msg=str(ests["log U"])) def test_estimate_bounds_checks(self): """ Ensure that the "checking columns" in the estimate table are all showing that the estimates are good. """ DF = self.Result.Posterior.DF_estimates # Parameter estimate table for p in ["12 + log O/H", "log P/k", "log U"]: for col in ["Est_in_CI68?", "Est_in_CI95?"]: self.assertTrue(DF.loc[p,col] == "Y") for col in ["Est_at_lower?", "Est_at_upper?", "P(lower)>50%?", "P(upper)>50%?"]: self.assertTrue(DF.loc[p,col] == "N") self.assertTrue(DF.loc[p,"n_local_maxima"] == 1) def test_chi2(self): """ Regression check that chi2 doesn't change """ chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(np.isclose(chi2, 2568.7, atol=0.2), msg=str(chi2)) def test_interp_order(self): """ Ensure the correct interpolation order (linear) is preserved """ self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 1) def test_all_zero_prior(self): """ We permit an all-zero prior - check that it works (a warning should be printed). """ shape = self.NB_Model_1.Interpd_grids.shape self.Result1 = self.NB_Model_1(self.obs_fluxes, self.obs_errs, self.lines, prior=np.zeros(shape)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ if clean_up: files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot, cls.estimate_table] for file_i in files: os.remove(file_i) ############################################################################### class Test_real_data_with_cubic_interpolation(unittest.TestCase): """ Very similar to the previous test class, but we use cubic interpolation instead of linear interpolation when interpolating model flux grids. We also test resetting the logging level after using the "verbosity" kwarg. """ longMessage = True # Append messages to existing message lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] @classmethod def setUpClass(cls): cls.prior_plot = os.path.join(TEST_DIR, cls.__name__ + "_prior.pdf") cls.likelihood_plot = os.path.join(TEST_DIR, cls.__name__ + "_likelihood.pdf") cls.posterior_plot = os.path.join(TEST_DIR, cls.__name__ + "_posterior.pdf") cls.estimate_table = os.path.join(TEST_DIR, cls.__name__ + "_parameter_estimates.csv") # Test different values along each dimension in interpd_grid_shape cls.NB_Model_1 = NB_Model("HII", line_list=cls.lines, interp_order=3, interpd_grid_shape=[100, 130, 80], grid_error=0.35) cls.old_log_level = NebulaBayes.NB_logger.level cls.test_log_level = 0 # A low number different to default assert cls.old_log_level != cls.test_log_level # For test to work NebulaBayes.NB_logger.setLevel(cls.test_log_level) kwargs = {"prior_plot": cls.prior_plot, "likelihood_plot": cls.likelihood_plot, "posterior_plot": cls.posterior_plot, "estimate_table": cls.estimate_table, "deredden": True, "propagate_dered_errors": True, "obs_wavelengths": cls.obs_wavelengths, "prior":[("SII6716","SII6731")], "plot_configs": [{"table_on_plot": True, "legend_fontsize": 5}]*4, "verbosity": "DEBUG", # Test that level change is temporary } cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, **kwargs) def test_parameter_estimates(self): """ Regression check on parameter estimates. Estimates for P and U are slightly different with the cubic interpolation. """ ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001), msg=str(ests["12 + log O/H"])) self.assertTrue(np.isclose(ests["log P/k"], 6.86047, atol=0.0001), msg=str(ests["log P/k"])) self.assertTrue(np.isclose(ests["log U"], -2.82828, atol=0.0001), msg=str(ests["log U"])) def test_chi2(self): """ Regression check that chi2 doesn't change """ chi2 = self.Result.Posterior.best_model["chi2"] self.assertTrue(np.isclose(chi2, 2522.7, atol=0.2), msg=str(chi2)) def test_interp_order(self): """ Ensure the correct interpolation order (cubic) is preserved """ self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 3) def test_resetting_log_level(self): """ Ensure that after using the verbosity keyword, the NB_logger level is unchanged (i.e. was reset to its previous value) """ self.assertEqual(NebulaBayes.NB_logger.level, self.test_log_level) def test_dereddening_result_attributes(self): """Ensure dereddening attributes added to Result object.""" self.assertTrue(self.Result.deredden) self.assertTrue(self.Result.propagate_dered_errors) @classmethod def tearDownClass(cls): """ Remove output files when tests in this class have finished, and undo change to logging level. """ NebulaBayes.NB_logger.setLevel(cls.old_log_level) if clean_up: files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot, cls.estimate_table] for file_i in files: os.remove(file_i) ############################################################################### class Test_upper_bounds_1D(unittest.TestCase): """ Test the treatment of upper bounds. We use a 1D grid. """ longMessage = True # Append messages to existing message lines = ["line1", "line2", "line3", "line4", "line5", "line6"] obs_fluxes = [ 1.0, 8.0, 10.2, -np.inf, -np.inf, -np.inf] obs_errs = [ 0.05, 0.3, 3.1, 0.3, 0.4, 0.2] pred_fluxes = [ 1.0, 5.0, 10.2, 0.1, 0.4, 0.4] # The pred_fluxes are at the "peak" of the grid, that we'll input to NB. @classmethod def setUpClass(cls): n = 100 # Length of grid best_i = 65 DF_grid1D = pd.DataFrame() DF_grid1D["p0"] = np.arange(n) - 572.3 # Linear DF_grid1D["dummy"] = np.exp(-((DF_grid1D["p0"] - DF_grid1D["p0"].values[best_i])/17.2)**2) DF_grid1D["dummy"] = DF_grid1D["dummy"].values / DF_grid1D["dummy"].max() for line, pred_flux in zip(cls.lines, cls.pred_fluxes): DF_grid1D[line] = DF_grid1D["dummy"].values * pred_flux # All of the fluxes peak at the point we'll input to NB DF_grid1D["line1"] = np.ones_like(DF_grid1D["line1"].values) cls.expected_p0 = DF_grid1D["p0"].values[best_i] # Note that we set grid_error to zero! cls.NB_Model_1 = NB_Model(DF_grid1D, grid_params=["p0"], grid_error=0, line_list=cls.lines, interpd_grid_shape=[500]) kwargs = {"deredden": False, "norm_line": "line1", "line_plot_dir": TEST_DIR} cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, **kwargs) def test_parameter_estimates(self): """ Regression test - check the parameter estimate is as expected. """ DF_est = self.Result.Posterior.DF_estimates # DataFrame p0_est = DF_est.loc["p0", "Estimate"] self.assertTrue(np.isclose(p0_est, self.expected_p0, atol=1)) @classmethod def tearDownClass(cls): """ Remove the output files when tests in this class have finished """ if clean_up: files = [os.path.join(TEST_DIR, l + "_PDF_contributes_to_likelihood.pdf") for l in cls.lines] for file_i in files: os.remove(file_i) ############################################################################### class Test_all_zero_likelihood(unittest.TestCase): """ Test forcing a log_likelihood of all -inf, so the likelihood is all zero. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 1e250, 1, 1.2e250, 1.2e250, 1e250] obs_errs = [ 0.004, 1, 0.005, 0.003, 0.002] @classmethod def setUpClass(cls): cls.NB_Model_1 = NB_Model("HII", line_list=cls.lines, grid_error=0.01, interpd_grid_shape=[30,30,30]) kwargs = {"deredden": False, "norm_line": "Hbeta", "prior":[("NII6583","Halpha")]} cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, **kwargs) def test_likelihood_all_zero(self): """ Regression test - check likelihood is all zero. """ likelihood = self.Result.Likelihood.nd_pdf self.assertTrue(np.all(likelihood == 0)) def test_posterior_all_zero(self): """ Regression test - check posterior is all zero. """ posterior = self.Result.Posterior.nd_pdf self.assertTrue(np.all(posterior == 0)) ############################################################################### class Test_data_that_matches_models_poorly(unittest.TestCase): """ Test inputting fluxes and errors that are very poorly fit by the entire model grid. In this case most of the likelihood is zero, and using a reasonable-ish prior gives a posterior that is zero everywhere. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2] obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01] # Note the very small errors @classmethod def setUpClass(cls): cls.NB_Model_1 = NB_Model("HII", line_list=cls.lines, grid_error=0.01, interpd_grid_shape=[30,30,30]) kwargs = {"deredden": False, "norm_line": "Hbeta", "prior":[("NII6583", "OIII4363")]} cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, **kwargs) def test_likelihood_mostly_zero(self): """ Regression test - check likelihood is mostly zero. """ likelihood = self.Result.Likelihood.nd_pdf self.assertTrue(np.sum(likelihood != 0) < 65) def test_posterior_all_zero(self): """ Regression test - check posterior is all zero. """ posterior = self.Result.Posterior.nd_pdf self.assertTrue(np.all(posterior == 0)) ############################################################################### class Test_NB_nd_pdf(unittest.TestCase): """ Test the methods in the NB_nd_pdf class """ @classmethod def setUpClass(cls): # Run NB in 2D to obtain an NB_Result object lines = ["Hbeta", "OIII5007", "Halpha"] fluxes = [1.0, 1.3, 2.8] errors = [0.2, 0.2, 0.2] NB_dir = os.path.dirname(os.path.realpath(NebulaBayes.__file__)) grid_table_file = os.path.join(NB_dir, "grids", "NB_HII_grid.fits.gz") BinTableHDU_0 = fits.getdata(grid_table_file, 0) DF_grid = Table(BinTableHDU_0).to_pandas() DF_grid = DF_grid[DF_grid["log P/k"] == 6.6] # Reduce to 2D grid nx, ny = 30, 30 # Square interpolated grid cls.NB_Model_1 = NB_Model(DF_grid, line_list=lines, grid_params=["12 + log O/H", "log U"], interpd_grid_shape=[ny, nx]) cls.Result_1 = cls.NB_Model_1(fluxes,errors, lines, deredden=False) # Now make a 2D pdf which is a single line of pixels along a quarter- # circle around the origin. This ensures the point defined by the peaks # of the 1D marginalised PDFs is very different from the 2D pdf peak. x, y = np.arange(nx), np.arange(ny) xx, yy = np.meshgrid(x, y) raw_pdf = np.zeros((ny, nx), dtype=float) rr2 = xx**2 + yy**2 where_arc = (rr2 > (20 - 0.5)**2) & (rr2 < (20 + 0.5)**2) raw_pdf[where_arc] = 1.0 # Value 1 along the circular arc; 0 elsewhere # Make a 2D PDF peak at the intersection of the circular arc and y = x raw_pdf[np.abs(xx - yy) < 1.5] *= 2 # print(np.unravel_index(raw_pdf.argmax(), raw_pdf.shape)) # (14, 14) cls.raw_pdf = raw_pdf val_arrs = cls.NB_Model_1.Interpd_grids.param_values_arrs dx, dy = np.diff(val_arrs[1])[0], np.diff(val_arrs[0])[0] cls.marginalised_x = np.trapz(raw_pdf, dx=dy, axis=0) cls.peak_ind_x = np.argmax(cls.marginalised_x) cls.marginalised_y = np.trapz(raw_pdf, dx=dx, axis=1) cls.peak_ind_y = np.argmax(cls.marginalised_y) # print(cls.peak_ind_y, cls.peak_ind_x) # (20, 20) as desired # plt.imshow(raw_pdf, origin="lower") # plt.scatter([14, 20], [14, 20], marker="*") # Make a new NB_nd_pdf object with the custom raw 2D PDF cls.NB_nd_pdf_1 = NB_nd_pdf(raw_pdf, cls.Result_1, cls.NB_Model_1.Interpd_grids, name="Posterior") # We want to test NB_nd_pdf attributes; some of "DF_estimates", "Grid_spec", # "best_model", marginalised_1D", "marginalised_2D", "name", "nd_pdf" # "best_model" keys: "table", "chi2", "extinction_Av_mag", "grid_location" def test_best_model_grid_location(self): grid_location = self.NB_nd_pdf_1.best_model["grid_location"] self.assertEqual(grid_location, (self.peak_ind_y, self.peak_ind_x)) def test_DF_estimates_Index_of_peak(self): DF_estimates = self.NB_nd_pdf_1.DF_estimates x_Index_of_peak = DF_estimates.loc["log U", "Index_of_peak"] self.assertEqual(x_Index_of_peak, self.peak_ind_x) y_Index_of_peak = DF_estimates.loc["12 + log O/H", "Index_of_peak"] self.assertEqual(y_Index_of_peak, self.peak_ind_y) def test_best_model_table(self): """ Check that a single table value matches the desired gridpoint. This test would fail on NebulaBayes 0.9.7 and earlier """ best_coords = (self.peak_ind_y, self.peak_ind_x) normed_grids = self.NB_Model_1.Interpd_grids.grids["Hbeta_norm"] model_OIII = normed_grids["OIII5007"][best_coords] DF_best = self.NB_nd_pdf_1.best_model["table"] table_model_OIII = DF_best.loc["OIII5007", "Model"] self.assertEqual(table_model_OIII, model_OIII) def test_marginalised_1D_pdf(self): """ Check that the marginalised 1D pdfs are as expected """ m_1D = self.NB_nd_pdf_1.marginalised_1D self.assertEqual(len(m_1D), 2) # Scale the pdfs to compare despite the m_1D PDFs being normalised m_1D["log U"] /= m_1D["log U"].max() m_1D["12 + log O/H"] /= m_1D["12 + log O/H"].max() expected_x_pdf = self.marginalised_x / self.marginalised_x.max() expected_y_pdf = self.marginalised_y / self.marginalised_y.max() self.assertTrue(np.allclose(m_1D["log U"], expected_x_pdf, atol=1e-12, rtol=0)) self.assertTrue(np.allclose(m_1D["12 + log O/H"], expected_y_pdf, atol=1e-12, rtol=0)) # May have swapped x and y, but it's all symmetric anyway... def test_nd_pdf(self): """ Check that the normalised nd_pdf matches the input raw nd_pdf. We avoid doing a proper normalisation by comparing with a simple scaling. """ pdf = self.NB_nd_pdf_1.nd_pdf scaled_raw_nd_pdf = self.raw_pdf / self.raw_pdf.max() self.assertTrue(np.array_equal(pdf / pdf.max(), scaled_raw_nd_pdf)) ############################################################################### class Test_dereddening_changes_results(unittest.TestCase): """ Test that using dereddening changes all three PDFs (when obs data are used in the prior). There previously was a bug where the obs data in the line ratio priors weren't dereddened. Also test that PDFs change when errors from the Balmer decrement are propagated into the dereddened line fluxes. """ @classmethod def setUpClass(cls): # Run NB in 2D to obtain an NB_Result object lines = ["Hbeta", "OIII5007", "Halpha"] waves = [4861., 5007., 6563.] fluxes = [1.0, 1.3, 5.1] errors = [0.2, 0.2, 0.2] NB_Model_1 = NB_Model("HII", line_list=lines, grid_error=0.35, interpd_grid_shape=(15, 15, 15)) prior = [("OIII5007", "Hbeta")] # Need obs data in prior cls.Result_dered1 = NB_Model_1(fluxes, errors, lines, prior=prior, obs_wavelengths=waves, deredden=True) cls.Result_dered2 = NB_Model_1(fluxes, errors, lines, prior=prior, obs_wavelengths=waves, deredden=True, propagate_dered_errors=True) cls.Result_nodered = NB_Model_1(fluxes, errors, lines, prior=prior, deredden=False) def test_priors_differ(self): """ Check that dereddened data was used in line ratio prior, when requested. This test fails on NebulaBayes 0.9.7 """ pdf_dered1 = self.Result_dered1.Prior.nd_pdf pdf_nodered = self.Result_nodered.Prior.nd_pdf max_diff1 = np.max(np.abs(pdf_dered1 - pdf_nodered)) self.assertTrue(max_diff1 > 0.01, str(max_diff1)) # Test uncertainty propagation has an effect pdf_dered2 = self.Result_dered2.Prior.nd_pdf max_diff_u = np.max(np.abs(pdf_dered1 - pdf_dered2)) self.assertTrue(max_diff_u > 0.01, str(max_diff_u)) def test_likelihoods_differ(self): pdf_dered1 = self.Result_dered1.Likelihood.nd_pdf pdf_nodered = self.Result_nodered.Likelihood.nd_pdf max_diff1 = np.max(np.abs(pdf_dered1 - pdf_nodered)) self.assertTrue(max_diff1 > 0.01, str(max_diff1)) pdf_dered2 = self.Result_dered2.Likelihood.nd_pdf max_diff_u = np.max(np.abs(pdf_dered1 - pdf_dered2)) self.assertTrue(max_diff_u > 0.01, str(max_diff_u)) def test_posteriors_differ(self): pdf_dered1 = self.Result_dered1.Posterior.nd_pdf pdf_nodered = self.Result_nodered.Posterior.nd_pdf max_diff1 = np.max(np.abs(pdf_dered1 - pdf_nodered)) self.assertTrue(max_diff1 > 0.01, str(max_diff1)) pdf_dered2 = self.Result_dered2.Posterior.nd_pdf max_diff_u = np.max(np.abs(pdf_dered1 - pdf_dered2)) self.assertTrue(max_diff_u > 0.01, str(max_diff_u)) def test_propagate_dered_errors(self): """Check propagate_dered_errors values on Result object""" # Checks default value of False self.assertFalse(self.Result_dered1.propagate_dered_errors) self.assertTrue(self.Result_dered2.propagate_dered_errors) ############################################################################### class Test_likelihood_lines_keyword(unittest.TestCase): """ Test inputting fluxes and errors that aren't used in the likelihood, and test that these lines may be used in a prior. """ longMessage = True # Append messages to existing message lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"] obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2] obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01] exclude_lines = ["Halpha", "OIII5007"] @classmethod def setUpClass(cls): cls.NB_Model_1 = NB_Model("HII", line_list=cls.lines, interpd_grid_shape=[30,30,30]) cls.kwargs = {"deredden": False, "norm_line": "Hbeta", "prior": [("NII6583", cls.exclude_lines[0])], "verbosity": "WARNING", } cls.likelihood_lines = [l for l in cls.lines if l not in cls.exclude_lines] cls.Result = cls.NB_Model_1(cls.obs_fluxes, cls.obs_errs, cls.lines, likelihood_lines=cls.likelihood_lines, **cls.kwargs) P = cls.Result.Posterior cls.DF_best = P.best_model["table"] cls.estimate_Z = P.DF_estimates.loc["12 + log O/H", "Estimate"] def test_non_likelihood_lines_in_best_model_table(self): """ Regression test - lines not included in likelihood calculation should still appear in the "best model" table. """ self.assertTrue(all(l in self.DF_best.index for l in self.exclude_lines)) def test_best_model_table_fields(self): """ Regression test - check fields of best model table (we test for the case of no dereddening; field names are different with dereddening). """ correct_fields = ["In_lhood?", "Obs", "Model", "Resid_Stds", "Obs_S/N"] t_fields = self.DF_best.columns.tolist() self.assertTrue(t_fields == correct_fields, t_fields) def test_In_lhood_field_in_best_model_table(self): """ Regression test - the "In_lhood?" field in the best model table should correctly identify if a line was used in the likelihood. """ correct = [("N" if l in self.exclude_lines else "Y") for l in self.lines] self.assertTrue(self.DF_best["In_lhood?"].values.tolist() == correct) def test_permuting_input_line_order(self): """ Regression test - the order of the input lines should not affect the results. There was a real bug introduced with the "likelihood_lines" feature - this test fails on NB version 0.9.6 and 0.9.7! """ n = len(self.lines) for i, ind_tuple in enumerate(itertools.permutations(range(n))): # There are 5! = 120 permutations, so only check one in five: if i % 5 != 2: continue obs_fluxes = [self.obs_fluxes[j] for j in ind_tuple] obs_errs = [self.obs_errs[j] for j in ind_tuple] lines = [self.lines[j] for j in ind_tuple] Result_i = self.NB_Model_1(obs_fluxes, obs_errs, lines, likelihood_lines=self.likelihood_lines, **self.kwargs) P_i = Result_i.Posterior estimate_Z_i = P_i.DF_estimates.loc["12 + log O/H", "Estimate"] self.assertEqual(estimate_Z_i, self.estimate_Z) ############################################################################### class Test_raising_errors(unittest.TestCase): """ Test raising errors on bad inputs """ @classmethod def setUpClass(cls): try: # Python 3 cls.assertRaisesRE = cls.assertRaisesRegex except AttributeError: # Python 2 cls.assertRaisesRE = cls.assertRaisesRegexp def test_bad_grid_parameter_with_too_few_unique_values(self): """ Test correct error is raised if there are too few unique values for a grid parameter. """ DF = pd.DataFrame({"p1": [4, 4, 4, 4], "p2": [1, 2, 3, 4], "l2": [5, 6, 7, 8]}) self.assertRaisesRE(ValueError, "3 unique values are required", NB_Model, DF, ["p1", "p2"]) ############################################################################### def interactive_plot_tests(): """ This function needs to be called manually to test the interactive plotting. from test_NB import interactive_plot_tests interactive_plot_tests() """ lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007", "NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"] obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942, 0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482] obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012, 0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099] obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8, 5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8] NB_Model_1 = NB_Model("HII", grid_params=None, line_list=lines, interpd_grid_shape=[50, 70, 50], grid_error=0.35) kwargs = {"deredden": True, "propagate_dered_errors": True, "obs_wavelengths": obs_wavelengths, "prior":[("SII6716","SII6731")], "plot_configs": [{"table_on_plot": True, "legend_fontsize": 5}]*4, } Result = NB_Model_1(obs_fluxes, obs_errs, lines, **kwargs) # Test both ways to make an interactive plot Result.Plotter.interactive(Result.Posterior) Result.Prior.show(Result.Plotter) ############################################################################### # Ideas for more tests: # Check that parameter estimates are inside the CIs, and check the flags for this # Test normalising to different lines repeatedly, and checking that the # unnecessary interpolated grids are deleted. # Check coverage of the code, to see what isn't being run? if __name__ == "__main__": print("\nTesting NebulaBayes version {0} ...\n".format(__version__)) unittest.main(verbosity=2)

17,028

99

838

7f5c849a1922a15d5a237861a7d432656ec07858

224

py

Python

spider/spider/distance/rfd/__init__.py

Rosna/P4ML-UI

edf0dd830588f03b197e4d6532830a5aedd88424

[ "Apache-2.0" ]

1

2021-11-05T17:42:47.000Z

spider/spider/distance/rfd/__init__.py

Rosna/P4ML-UI

edf0dd830588f03b197e4d6532830a5aedd88424

[ "Apache-2.0" ]

null

spider/spider/distance/rfd/__init__.py

Rosna/P4ML-UI

edf0dd830588f03b197e4d6532830a5aedd88424

[ "Apache-2.0" ]

2

2019-02-21T18:29:51.000Z

2019-09-02T21:21:26.000Z

""" spider.distance.metricl.rfd sub-package __init.py__ @author: david johnson Primitive that learns and applies random-forest-based distance metric. defines the module index """ from .rfd import RFD

17.230769

74

0.709821

""" spider.distance.metricl.rfd sub-package __init.py__ @author: david johnson Primitive that learns and applies random-forest-based distance metric. defines the module index """ from .rfd import RFD

0

e6cd940742010007d0946f002755532450bf69fa

4,842

py

Python

prission.py

mandypepe/pythontets

5bf1d84e5c7c769b27c5cda221ab08cc6625daa4

[ "Apache-2.0" ]

null

prission.py

mandypepe/pythontets

5bf1d84e5c7c769b27c5cda221ab08cc6625daa4

[ "Apache-2.0" ]

null

prission.py

mandypepe/pythontets

5bf1d84e5c7c769b27c5cda221ab08cc6625daa4

[ "Apache-2.0" ]

null

import pygame, sys from pygame.locals import * # Aclaraciones # Requiere "pygame" para las graficas # # Se grafican las figuras para una mejor comprencion pero como son coordenadas tan pequenas no se muestran bien # (aumentando las proporciones pude verse mejo) # pero la orden del problema no lo permite. # # La solucion trate de buscarla matematicamente # 1- para saber si es ciudadano o prisionero : # comprobamos si el punto esta fuera o dentro de un poligono o en uno de sus vertices # # definiendo colores NEGRO = (0, 0, 0) ROJO = (255, 0, 0) CAFE = (90, 50, 15) BLANCO = (255, 255, 255) AZUL = (0, 0, 255) # Abriendo Fichero infile = open('texto.txt', 'r') for line in infile: lista = line pygame.init() # Asignando dimenciones a la ventana dimensiones = (500, 500) pantalla = pygame.display.set_mode(dimensiones) # asignando nombre de la ventana pantalla.fill(BLANCO) # rellenando ventana terminar = False reloj = pygame.time.Clock() while not terminar: for Evento in pygame.event.get(): if Evento.type == pygame.QUIT: terminar = True # limpiando lista y declarando variables lista = lista.replace(" ", ",").replace("|", ",") lista_limpa = lista.split(",") lista_x = [] lista_y = [] longitud = len(lista_limpa) poligono = [] #separando las cordenadas X,Y y conformando el Poligono i = 0 while i < longitud - 2: cordenada_x = int(lista_limpa[i]) if (cordenada_x >= 0 and cordenada_x <= 10): temp_x = int(lista_limpa[i]) # aca se puede aumentar las proporciones lista_x.append(temp_x) j = i + 1 cordenada_y = int(lista_limpa[j]) if (cordenada_y >= 0 and cordenada_y <= 10): temp_y = int(lista_limpa[j]) # aca se puede aumentar las proporciones lista_y.append(temp_y) poligono.append((temp_x, temp_y)) i = i + 2 # Preparando las cordenadas para dibujar P (puntos de rectas ) D para las diagonales # o rectas de cierre de la figura i = 0 while i < len(lista_x): px = int(lista_x[i]) py = int(lista_y[i]) pxx = int(lista_x[i + 1]) pyy = int(lista_y[i + 1]) if i == 0: dx = int(lista_x[i]) dy = int(lista_y[i]) dxx = int(lista_x[i + 3]) dyy = int(lista_y[i + 3]) if i == 2: dx = int(lista_x[i - 1]) dy = int(lista_y[i - 1]) dxx = int(lista_x[i]) dyy = int(lista_y[i]) #dibujando la figura pygame.draw.line(pantalla, ROJO, [px, py], [pxx, pyy], 2) pygame.draw.aaline(pantalla, ROJO, [dx, dy], [dxx, dyy], True) i = i + 2 #campturando los puntos del usuario punto_x = int(lista_limpa[len(lista_limpa) - 2]) punto_y = int(lista_limpa[len(lista_limpa) - 1]) #aplicando restricciones if punto_y >= 3 and punto_y <= 12 and punto_x >= 3 and punto_x <= 12: punto_x = punto_x punto_y = punto_y pygame.draw.circle(pantalla, CAFE, [punto_x, punto_y], 1) #dibujando el . #metodo para definir si el punto esta dentro o fuera del poligono if punto_en_poligono(punto_x, punto_y, poligono) == 2: pygame.display.set_caption("Prisionero Estas en unode los vertice") elif punto_en_poligono(punto_x, punto_y, poligono) == 1: pygame.display.set_caption("Prisionero") else: pygame.display.set_caption("Ciudadano") pygame.display.flip() reloj.tick(20) # Cerramos el fichero. infile.close() pygame.quit()

37.828125

117

0.522305

import pygame, sys from pygame.locals import * # Aclaraciones # Requiere "pygame" para las graficas # # Se grafican las figuras para una mejor comprencion pero como son coordenadas tan pequenas no se muestran bien # (aumentando las proporciones pude verse mejo) # pero la orden del problema no lo permite. # # La solucion trate de buscarla matematicamente # 1- para saber si es ciudadano o prisionero : # comprobamos si el punto esta fuera o dentro de un poligono o en uno de sus vertices # # definiendo colores NEGRO = (0, 0, 0) ROJO = (255, 0, 0) CAFE = (90, 50, 15) BLANCO = (255, 255, 255) AZUL = (0, 0, 255) # Abriendo Fichero infile = open('texto.txt', 'r') for line in infile: lista = line pygame.init() # Asignando dimenciones a la ventana dimensiones = (500, 500) pantalla = pygame.display.set_mode(dimensiones) # asignando nombre de la ventana pantalla.fill(BLANCO) # rellenando ventana terminar = False reloj = pygame.time.Clock() while not terminar: for Evento in pygame.event.get(): if Evento.type == pygame.QUIT: terminar = True # limpiando lista y declarando variables lista = lista.replace(" ", ",").replace("|", ",") lista_limpa = lista.split(",") lista_x = [] lista_y = [] longitud = len(lista_limpa) poligono = [] #separando las cordenadas X,Y y conformando el Poligono i = 0 while i < longitud - 2: cordenada_x = int(lista_limpa[i]) if (cordenada_x >= 0 and cordenada_x <= 10): temp_x = int(lista_limpa[i]) # aca se puede aumentar las proporciones lista_x.append(temp_x) j = i + 1 cordenada_y = int(lista_limpa[j]) if (cordenada_y >= 0 and cordenada_y <= 10): temp_y = int(lista_limpa[j]) # aca se puede aumentar las proporciones lista_y.append(temp_y) poligono.append((temp_x, temp_y)) i = i + 2 # Preparando las cordenadas para dibujar P (puntos de rectas ) D para las diagonales # o rectas de cierre de la figura i = 0 while i < len(lista_x): px = int(lista_x[i]) py = int(lista_y[i]) pxx = int(lista_x[i + 1]) pyy = int(lista_y[i + 1]) if i == 0: dx = int(lista_x[i]) dy = int(lista_y[i]) dxx = int(lista_x[i + 3]) dyy = int(lista_y[i + 3]) if i == 2: dx = int(lista_x[i - 1]) dy = int(lista_y[i - 1]) dxx = int(lista_x[i]) dyy = int(lista_y[i]) #dibujando la figura pygame.draw.line(pantalla, ROJO, [px, py], [pxx, pyy], 2) pygame.draw.aaline(pantalla, ROJO, [dx, dy], [dxx, dyy], True) i = i + 2 #campturando los puntos del usuario punto_x = int(lista_limpa[len(lista_limpa) - 2]) punto_y = int(lista_limpa[len(lista_limpa) - 1]) #aplicando restricciones if punto_y >= 3 and punto_y <= 12 and punto_x >= 3 and punto_x <= 12: punto_x = punto_x punto_y = punto_y pygame.draw.circle(pantalla, CAFE, [punto_x, punto_y], 1) #dibujando el . #metodo para definir si el punto esta dentro o fuera del poligono def punto_en_poligono(x, y, poligono): i = 0 j = len(poligono) - 1 salida = 0 for i in range(len(poligono)): # condicion para saber si el punto esta en uno de los vertices de la figura if poligono[i][0] == x and poligono[i][1] == y: salida = 2 break if (poligono[i][1] < y and poligono[j][1] >= y) or (poligono[j][1] < y and poligono[i][1] >= y): if poligono[i][0] + (y - poligono[i][1]) / (poligono[j][1] - poligono[i][1]) * ( poligono[j][0] - poligono[i][0]) < x: salida = 1 j = i return salida if punto_en_poligono(punto_x, punto_y, poligono) == 2: pygame.display.set_caption("Prisionero Estas en unode los vertice") elif punto_en_poligono(punto_x, punto_y, poligono) == 1: pygame.display.set_caption("Prisionero") else: pygame.display.set_caption("Ciudadano") pygame.display.flip() reloj.tick(20) # Cerramos el fichero. infile.close() pygame.quit()

777

0

35

1dc936213106f9d125c70a922c89b50eee90b068

4,148

py

Python

scripts/mg-compare-heatmap.py

natalie-robinson/MG-RAST-Tools

ed28ffaeb17a156a1d249e4104b042a9ba9cb8d5

[ "BSD-2-Clause" ]

21

2015-01-18T01:43:12.000Z

2021-09-09T03:26:59.000Z

scripts/mg-compare-heatmap.py

natalie-robinson/MG-RAST-Tools

ed28ffaeb17a156a1d249e4104b042a9ba9cb8d5

[ "BSD-2-Clause" ]

26

2015-01-22T21:23:09.000Z

2021-11-13T17:55:37.000Z

scripts/mg-compare-heatmap.py

natalie-robinson/MG-RAST-Tools

ed28ffaeb17a156a1d249e4104b042a9ba9cb8d5

[ "BSD-2-Clause" ]

22

2015-01-18T01:44:36.000Z

2021-09-18T09:29:06.000Z

#!/usr/bin/env python import os import sys import json from argparse import ArgumentParser from mglib import obj_from_url, tab_to_matrix, AUTH_LIST, API_URL, biom_to_matrix, VERSION prehelp = """ NAME mg-compare-heatmap VERSION %s SYNOPSIS mg-compare-heatmap [ --help, --input <input file or stdin>, --output <output file or stdout>, --format <cv: 'text' or 'biom'>, --cluster <cv: ward, single, complete, mcquitty, median, centroid>, --distance <cv: bray-curtis, euclidean, maximum, manhattan, canberra, minkowski, difference>, --name <boolean>, --normalize <boolean> ] DESCRIPTION Retrieve Dendogram Heatmap from abundance profiles for multiple metagenomes. """ posthelp = """ Input Tab-delimited table of abundance profiles, metagenomes in columns and annotation in rows. OR BIOM format of abundance profiles. Output JSON struct containing ordered distances for metagenomes and annotations, along with dendogram data. EXAMPLES mg-compare-taxa --ids "mgm4441679.3,mgm4441680.3,mgm4441681.3,mgm4441682.3" --level class --source RefSeq --format text | mg-compare-heatmap --input - --format text --cluster median --distance manhattan SEE ALSO - AUTHORS %s """ if __name__ == "__main__": sys.exit(main(sys.argv))

41.48

334

0.662729

#!/usr/bin/env python import os import sys import json from argparse import ArgumentParser from mglib import obj_from_url, tab_to_matrix, AUTH_LIST, API_URL, biom_to_matrix, VERSION prehelp = """ NAME mg-compare-heatmap VERSION %s SYNOPSIS mg-compare-heatmap [ --help, --input <input file or stdin>, --output <output file or stdout>, --format <cv: 'text' or 'biom'>, --cluster <cv: ward, single, complete, mcquitty, median, centroid>, --distance <cv: bray-curtis, euclidean, maximum, manhattan, canberra, minkowski, difference>, --name <boolean>, --normalize <boolean> ] DESCRIPTION Retrieve Dendogram Heatmap from abundance profiles for multiple metagenomes. """ posthelp = """ Input Tab-delimited table of abundance profiles, metagenomes in columns and annotation in rows. OR BIOM format of abundance profiles. Output JSON struct containing ordered distances for metagenomes and annotations, along with dendogram data. EXAMPLES mg-compare-taxa --ids "mgm4441679.3,mgm4441680.3,mgm4441681.3,mgm4441682.3" --level class --source RefSeq --format text | mg-compare-heatmap --input - --format text --cluster median --distance manhattan SEE ALSO - AUTHORS %s """ def main(args): ArgumentParser.format_description = lambda self, formatter: self.description ArgumentParser.format_epilog = lambda self, formatter: self.epilog parser = ArgumentParser(usage='', description=prehelp%VERSION, epilog=posthelp%AUTH_LIST) parser.add_argument("--url", dest="url", default=API_URL, help="communities API url") parser.add_argument("--input", dest="input", default='-', help="input: filename or stdin (-), default is stdin") parser.add_argument("--output", dest="output", default='-', help="output: filename or stdout (-), default is stdout") parser.add_argument("--format", dest="format", default='biom', help="input format: 'text' for tabbed table, 'biom' for BIOM format, default is biom") parser.add_argument("--cluster", dest="cluster", default='ward', help="cluster function, one of: ward, single, complete, mcquitty, median, centroid, default is ward") parser.add_argument("--distance", dest="distance", default='bray-curtis', help="distance function, one of: bray-curtis, euclidean, maximum, manhattan, canberra, minkowski, difference, default is bray-curtis") parser.add_argument("--name", dest="name", type=int, default=0, help="label columns by name, default is by id: 1=true, 0=false") parser.add_argument("--normalize", dest="normalize", type=int, default=0, help="normalize the input data, default is off: 1=true, 0=false") # get inputs opts = parser.parse_args() if (opts.input != '-') and (not os.path.isfile(opts.input)): sys.stderr.write("ERROR: input data missing\n") return 1 if opts.format not in ['text', 'biom']: sys.stderr.write("ERROR: invalid input format\n") return 1 # parse inputs rows = [] cols = [] data = [] try: indata = sys.stdin.read() if opts.input == '-' else open(opts.input, 'r').read() if opts.format == 'biom': try: biom = json.loads(indata) rows, cols, data = biom_to_matrix(biom, col_name=opts.name) except: sys.stderr.write("ERROR: input BIOM data not correct format\n") return 1 else: rows, cols, data = tab_to_matrix(indata) except: sys.stderr.write("ERROR: unable to load input data\n") return 1 # retrieve data raw = '0' if opts.normalize else '1' post = {"raw": raw, "cluster": opts.cluster, "distance": opts.distance, "columns": cols, "rows": rows, "data": data} hmap = obj_from_url(opts.url+'/compute/heatmap', data=json.dumps(post, separators=(',',':'))) # output data if (not opts.output) or (opts.output == '-'): out_hdl = sys.stdout else: out_hdl = open(opts.output, 'w') out_hdl.write(json.dumps(hmap, separators=(', ',': '), indent=4)+"\n") out_hdl.close() return 0 if __name__ == "__main__": sys.exit(main(sys.argv))

2,855

0

23

3bf02f939d9fa44b09bbbe9fe168ef56750390f0

5,733

py

Python

Youtube Bot Video Generator/videoscript.py

wasimakh2/Automatic-Youtube-Reddit-Text-To-Speech-Video-Generator-and-Uploader

8f6e1c7fcbd93f7da8f437cb4bf2d9c67c2932c2

[ "MIT" ]

1

2020-08-24T07:02:32.000Z

Youtube Bot Video Generator/videoscript.py

wasimakh2/Automatic-Youtube-Reddit-Text-To-Speech-Video-Generator-and-Uploader

8f6e1c7fcbd93f7da8f437cb4bf2d9c67c2932c2

[ "MIT" ]

null

Youtube Bot Video Generator/videoscript.py

wasimakh2/Automatic-Youtube-Reddit-Text-To-Speech-Video-Generator-and-Uploader

8f6e1c7fcbd93f7da8f437cb4bf2d9c67c2932c2

[ "MIT" ]

1

2021-01-25T17:09:35.000Z

import settings import cv2 from VideoTypes import imageframe, standardredditformat import generatemovie import generatorclient import datetime import os import shutil import videouploader import random import pickle from time import sleep videoscripts = []

41.846715

117

0.631083

import settings import cv2 from VideoTypes import imageframe, standardredditformat import generatemovie import generatorclient import datetime import os import shutil import videouploader import random import pickle from time import sleep videoscripts = [] class VideoScriptEngine(): def __init__(self, scriptno=None, scripttitle=None, author=None, ups=None, final_script=None, videotype=None, video_settings=None, music_type=None, thumbnail=None, characters_amount=None, youtube_title=None, youtube_description=None, youtube_tags=None): self.scriptno = scriptno self.final_script = final_script self.scripttitle = scripttitle self.author = author self.ups = ups self.videotype = videotype self.video_settings = video_settings self.music_type = music_type self.thumbnail = thumbnail self.characters_amount = characters_amount self.youtube_title = youtube_title self.youtube_description = youtube_description self.youtube_tags = youtube_tags standard_path = settings.finishedvideosdirectory + "/vid%s/" % self.scriptno self.vid_path = standard_path + "vid%s.mp4" % self.scriptno self.vid_description = standard_path + "description.txt" self.vid_thumbnail = standard_path + "thumbnail.png" self.vid_tags = standard_path + "youtubetags.txt" self.vid_title = standard_path + "youtubetitle.txt" self.isRendered = False videoscripts.append(self) self.save() def renderVideo(self): if not self.isRendered: print("Started Rendering Script %s" % self.scriptno) imageframe.deleteAllFilesInPath(settings.tempPath) try: video_format = standardredditformat.StandardReddit("shit", self.video_settings, self.music_type) formatted_script = imageframe.parseScript(self.final_script) newMovie = generatemovie.Movie(video_format, formatted_script, (self.author, self.scripttitle, self.ups), self.scriptno) export_location = newMovie.renderVideo() try: cv2.imwrite(export_location + "/thumbnail.png", cv2.cvtColor(self.thumbnail, cv2.COLOR_RGB2BGR)) except Exception: pass writeTextToFile(export_location + "/description.txt", self.youtube_description) writeTextToFile(export_location + "/youtubetitle.txt", self.youtube_title) writeTextToFile(export_location + "/youtubetags.txt", self.youtube_tags) except Exception as e: print(e) print("Sorry, a error occured rendering this video. Skipping it") self.isRendered = True self.save() if settings.exportOffline: generatorclient.updateUploadDetails(self.scriptno, None, None) deleteRawSave(self.scriptno) videoscripts.remove(self) print("Video Successfully exported offline") else: print("VID GEN script %s already rendered" % self.scriptno) def save(self): path_name = settings.rawvideosaves + "\\rawvideo%s.save" % self.scriptno with open(path_name, 'wb') as pickle_file: pickle.dump(self, pickle_file) print("VID GEN Saved vid %s to %s" % (self.scriptno, path_name)) def uploadVideo(self): description = (loadTextFile(self.vid_description)).encode("utf8") title = (loadTextFile(self.vid_title)).encode("utf8") tags = loadTextFile(self.vid_tags) # title, description, tags, thumbnailpath, filepath time_to_upload = calculateUploadTime() print("Uploading video %s, sceduled release %s" % (self.scriptno, time_to_upload)) success = videouploader.upload(title, description, tags, self.vid_thumbnail, self.vid_path, time_to_upload.replace(" ", "T") + ".0Z") if success: print("Successfully Uploaded video %s" % self.scriptno) now = datetime.datetime.now() time_uploaded = now.strftime('%Y-%m-%d %H:%M:%S') generatorclient.updateUploadDetails(self.scriptno, time_uploaded, time_to_upload) print("Done Uploading Video %s" % self.scriptno) videoscripts.remove(self) deleteRenderedVideoFolder(self.scriptno) else: return False def loadTextFile(file): f = open(file, "r", encoding="utf8") content = f.read() f.close() return content def writeTextToFile(location, text): with open(location, "w", encoding="utf8") as text_file: text_file.write(text) def deleteRawSave(scriptno): try: path = settings.rawvideosaves + "/rawvideo%s.save" % scriptno os.remove(path) print("Removed raw save %s at %s" % (scriptno, path)) except FileNotFoundError: print("Couldn't find save and delete for %s" % scriptno) def deleteRenderedVideoFolder(scriptno): shutil.rmtree(settings.finishedvideosdirectory + "/vid%s" % scriptno) deleteRawSave(scriptno) def calculateUploadTime(): now = datetime.datetime.now() random_hour = random.randint(16, 18) suggested = now.replace(hour=random_hour) if suggested < now: suggested=now.replace(hour=now.hour + 2) suggested = suggested.replace(minute=0, second=0, microsecond=0) return suggested.strftime('%Y-%m-%d %H:%M:%S')

5,184

5

266

c7cec01c629502ed181bf15521238a3423e46732

258

py

Python

start.py

ddpaimon/treedbclient

ff03343556090c0398ee1130a91b386e9e2f4cec

[ "MIT" ]

null

start.py

ddpaimon/treedbclient

ff03343556090c0398ee1130a91b386e9e2f4cec

[ "MIT" ]

null

start.py

ddpaimon/treedbclient

ff03343556090c0398ee1130a91b386e9e2f4cec

[ "MIT" ]

null

from tkinter import * from main_window import MainWindow if __name__ == "__main__": root = Tk() root.columnconfigure(0, weight=1) root.columnconfigure(2, weight=1) root.rowconfigure(0, weight=1) m = MainWindow(root) root.mainloop()

21.5

37

0.686047

from tkinter import * from main_window import MainWindow if __name__ == "__main__": root = Tk() root.columnconfigure(0, weight=1) root.columnconfigure(2, weight=1) root.rowconfigure(0, weight=1) m = MainWindow(root) root.mainloop()

0

a927846d4e9a598f9eff23e33973c320f1038c9d

28,220

py

Python

sim_db/sim_db_lib.py

task123/sim_db

892934693fc166640f6cbf7af905a2c0d48ea99b

[ "MIT" ]

null

sim_db/sim_db_lib.py

task123/sim_db

892934693fc166640f6cbf7af905a2c0d48ea99b

[ "MIT" ]

null

sim_db/sim_db_lib.py

task123/sim_db

892934693fc166640f6cbf7af905a2c0d48ea99b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Read and write parameters, results and metadata to the 'sim_db' database.""" # Copyright (C) 2017-2019 Håkon Austlid Taskén <hakon.tasken@gmail.com> # Licenced under the MIT License. import sim_db.src_command_line_tool.commands.helpers as helpers import sqlite3 import argparse import subprocess import time import hashlib import threading import os import sys class SimDB: """To interact with the **sim_db** database. For an actuall simulation it should be initialised at the very start of the simulation (with 'store_metadata' set to True) and closed with :func:`~SimDB.close` at the very end of the simulation. This must be done to add the corrrect metadata. For multithreading/multiprocessing each thread/process MUST have its own connection (instance of this class) and MUST provide it with its rank. """ def __init__(self, store_metadata=True, db_id=None, rank=None, only_write_on_rank=0): """Connect to the **sim_db** database. :param store_metadata: If False, no metadata is added to the database. Typically used when postprocessing (visualizing) data from a simulation. :type store_metadata: bool :param db_id: ID number of the simulation parameters in the **sim_db** database. If it is 'None', then it is read from the argument passed to the program after option '--id'. :type db_id: int :param rank: Number identifing the calling process and/or thread. (Typically the MPI or OpenMP rank.) If provided, only the 'rank' matching 'only_write_on_rank' will write to the database to avoid too much concurrent writing to the database. Single process and threaded programs may ignore this, while multithreading/multiprocessing programs need to provide it. :type rank: int :param only_write_on_rank: Number identifing the only process/thread that will write to the database. Only used if 'rank' is provided. :type only_write_on_rank: int """ self.rank = rank self.only_write_on_rank = only_write_on_rank self.start_time = time.time() self.store_metadata = store_metadata self.id, self.path_proj_root = self.__read_from_command_line_arguments( db_id) self.db = helpers.connect_sim_db() self.db_cursor = self.db.cursor() self.column_names = [] self.column_types = [] if (self.store_metadata and (self.rank == None or self.rank == self.only_write_on_rank)): self.write('status', 'running') self.write('time_started', self.__get_date_and_time_as_string()) if (self.store_metadata and self.__is_a_git_project() and (self.rank == None or self.rank == self.only_write_on_rank)): proc = subprocess.Popen( [ 'cd "{0}"; git rev-parse HEAD'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="git_hash", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( [ 'cd "{0}"; git log -n 1 --format=%B HEAD'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="commit_message", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( [ 'cd "{0}"; git diff HEAD --stat'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="git_diff_stat", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( ['cd "{0}"; git diff HEAD'.format(self.path_proj_root)], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() out = out.decode('ascii', 'replace') if len(out) > 3000: warning = "WARNING: Diff limited to first 3000 characters.\n" out = warning + '\n' + out[0:3000] + '\n\n' + warning self.write(column="git_diff", value=out) def read(self, column, check_type_is=''): """Read parameter in 'column' from the database. Return None if parameter is empty. :param column: Name of the column the parameter is read from. :type column: str :param check_type_is: Throws ValueError if type does not match 'check_type_is'.The valid types the strings 'int', 'float', 'bool', 'string' and 'int/float/bool/string array' or the types int, float, bool, str and list. :raises ColumnError: If column do not exists. :raises ValueError: If return type does not match 'check_type_is'. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ if column not in self.column_names: self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) if column not in self.column_names: raise ColumnError("Column, {0}, is NOT a column in the " "database.".format(column)) self.db_cursor.execute("SELECT {0} FROM runs WHERE id={1}".format( column, self.id)) value = self.db_cursor.fetchone() if value != None: value = value[0] value = self.__check_type(check_type_is, column, self.column_names, self.column_types, value) return value def write(self, column, value, type_of_value='', only_if_empty=False): """Write value to 'column' in the database. If 'column' does not exists, a new is added. If value is None and type_of_value is not set, the entry under 'column' is set to empty. For multithreaded and multiprocess programs only a single will process/thread write to the database to avoid too much concurrent writing to the database. This is as long as the 'rank' was passed to SimDB under initialisation. :param column: Name of the column the parameter is read from. :type column: str :param value: New value of the specified entry in the database. :param type_of_value: Needed if column does note exists or if value is empty list. The valid types the strings 'int', 'float', 'bool', 'string' and 'int/float/bool/string array' or the types int, float, bool and str. :type type_of_value: str or type :param only_if_empty: If True, it will only write to the database if the simulation's entry under 'column' is empty. :type only_if_empty: bool :raises ValueError: If column exists, but type does not match, or empty list is passed without type_of_value given. """ # For multithreaded/multiprocess programs only a single process/thread # does any writing. if self.rank != None and self.rank != self.only_write_on_rank: return self.__add_column_if_not_exists_and_check_type(column, type_of_value, value) value_string = self.__convert_to_value_string(value, type_of_value) value_string = self.__escape_quote_with_two_quotes(value_string) type_dict = dict(zip(self.column_names, self.column_types)) # 'and type(value != None) != bool' allow numpy arrays to be check # without importing numpy and thereby relying on it being availble. if (type_dict[column] == 'TEXT' and (type(value != None) != bool or value != None)): value_string = "'{0}'".format(value_string) if only_if_empty and self.is_empty(column): self.db_cursor.execute("UPDATE runs SET \"{0}\" = {1} WHERE \"id\" " "= {2} AND {0} IS NULL".format(column, value_string, self.id)) self.db.commit() else: self.db_cursor.execute( "UPDATE runs SET \"{0}\" = {1} WHERE id = {2}".format( column, value_string, self.id)) self.db.commit() def unique_results_dir(self, path_directory): """Get path to subdirectory in 'path_directory' unique to simulation. The subdirectory will be named 'date_time_name_id' and is intended to store results in. If 'results_dir' in the database is empty, a new and unique directory is created and the path stored in 'results_dir'. Otherwise the path in 'results_dir' is just returned. :param path_directory: Path to directory of which to make a subdirectory. If 'path_directory' starts with 'root/', that part will be replaced by the full path of the root directory of the project. :type path_directory: str :returns: Full path to new subdirectory. :rtype: str """ results_dir = self.read("results_dir") if results_dir == None: if self.rank == None or self.rank == self.only_write_on_rank: if (len(path_directory) >= 5 and path_directory[0:5] == 'root/'): path_directory = os.path.join(self.path_proj_root, path_directory[5:]) results_dir = os.path.join(path_directory, self.__get_date_and_time_as_string()) results_dir += '_' + str(self.read('name')) + '_' + str(self.id) results_dir = os.path.abspath(os.path.realpath(results_dir)) os.mkdir(results_dir) self.write(column="results_dir", value=results_dir, only_if_empty=False) else: while results_dir == None: results_dir = self.read("results_dir") return results_dir def column_exists(self, column): """Return True if column is a column in the database. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ if column in self.column_names: return True else: self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) if column in self.column_names: return True else: return False def is_empty(self, column): """Return True if entry in the database under 'column' is empty. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ value = self.read(column) if value == None: return True else: return False def set_empty(self, column): """Set entry under 'column' in the database to empty.""" self.write(column, None) def get_id(self): """Return 'ID' of the connected simulation.""" return self.id def get_path_proj_root(self): """Return the path to the root directory of the project. The project's root directory is assumed to be where the '.sim_db/' directory is located. """ return self.path_proj_root def update_sha1_executables(self, paths_executables): """Update the 'sha1_executable' column in the database. Sets the entry to the sha1 of all the executables. The order will affect the value. :param paths_executables: List of full paths to executables. :type paths_executables: [str] :raises sqlite3.OperationalError: Waited more than 5 seconds to write to the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ sha1 = hashlib.sha1() for executable in executables: with open(executable, 'r') as executable_file: sha1.update(executable_file.read()) self.write('sha1_executables', sha1) def delete_from_database(self): """Delete simulation from database. :raises sqlite3.OperationalError: Waited more than 5 seconds to write to the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ self.db_cursor.execute("DELETE FROM runs WHERE id = {0}".format( self.id)) self.db.commit() self.store_metadata = False def close(self): """Closes connection to **sim_db** database and add metadata.""" if (self.store_metadata and (self.rank == None or self.rank == self.only_write_on_rank)): used_time = time.time() - self.start_time used_walltime = "{0}h {1}m {2}s".format( int(used_time / 3600), int(used_time / 60), used_time % 60) self.write('used_walltime', used_walltime) self.write('status', 'finished') self.db_cursor.close() self.db.close() def __get_date_and_time_as_string(self): """Return data and time as 'Year-Month-Date_Hours-Minutes-Seconds'.""" return time.strftime("%Y-%b-%d_%H-%M-%S") def add_empty_sim(store_metadata=False): """Add an empty entry into the database and SimDB connected to it. :param store_metadata: If False, no metadata is added to the database. Typically used when postprocessing (visualizing) data from a simulation. :type store_metadata: bool """ db = helpers.connect_sim_db() db_cursor = db.cursor() default_db_columns = "" for key in helpers.default_db_columns: default_db_columns += key + " " + str( helpers.default_db_columns[key]) + ", " default_db_columns = default_db_columns[:-2] db_cursor.execute("CREATE TABLE IF NOT EXISTS runs ({0});".format( default_db_columns)) db_cursor.execute("INSERT INTO runs DEFAULT VALUES") db_id = db_cursor.lastrowid db.commit() db_cursor.close() db.close() return SimDB(db_id=db_id, store_metadata=store_metadata)

43.684211

80

0.545216

# -*- coding: utf-8 -*- """Read and write parameters, results and metadata to the 'sim_db' database.""" # Copyright (C) 2017-2019 Håkon Austlid Taskén <hakon.tasken@gmail.com> # Licenced under the MIT License. import sim_db.src_command_line_tool.commands.helpers as helpers import sqlite3 import argparse import subprocess import time import hashlib import threading import os import sys class SimDB: """To interact with the **sim_db** database. For an actuall simulation it should be initialised at the very start of the simulation (with 'store_metadata' set to True) and closed with :func:`~SimDB.close` at the very end of the simulation. This must be done to add the corrrect metadata. For multithreading/multiprocessing each thread/process MUST have its own connection (instance of this class) and MUST provide it with its rank. """ def __init__(self, store_metadata=True, db_id=None, rank=None, only_write_on_rank=0): """Connect to the **sim_db** database. :param store_metadata: If False, no metadata is added to the database. Typically used when postprocessing (visualizing) data from a simulation. :type store_metadata: bool :param db_id: ID number of the simulation parameters in the **sim_db** database. If it is 'None', then it is read from the argument passed to the program after option '--id'. :type db_id: int :param rank: Number identifing the calling process and/or thread. (Typically the MPI or OpenMP rank.) If provided, only the 'rank' matching 'only_write_on_rank' will write to the database to avoid too much concurrent writing to the database. Single process and threaded programs may ignore this, while multithreading/multiprocessing programs need to provide it. :type rank: int :param only_write_on_rank: Number identifing the only process/thread that will write to the database. Only used if 'rank' is provided. :type only_write_on_rank: int """ self.rank = rank self.only_write_on_rank = only_write_on_rank self.start_time = time.time() self.store_metadata = store_metadata self.id, self.path_proj_root = self.__read_from_command_line_arguments( db_id) self.db = helpers.connect_sim_db() self.db_cursor = self.db.cursor() self.column_names = [] self.column_types = [] if (self.store_metadata and (self.rank == None or self.rank == self.only_write_on_rank)): self.write('status', 'running') self.write('time_started', self.__get_date_and_time_as_string()) if (self.store_metadata and self.__is_a_git_project() and (self.rank == None or self.rank == self.only_write_on_rank)): proc = subprocess.Popen( [ 'cd "{0}"; git rev-parse HEAD'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="git_hash", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( [ 'cd "{0}"; git log -n 1 --format=%B HEAD'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="commit_message", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( [ 'cd "{0}"; git diff HEAD --stat'.format( self.path_proj_root) ], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() self.write( column="git_diff_stat", value=out.decode('ascii', 'replace')) proc = subprocess.Popen( ['cd "{0}"; git diff HEAD'.format(self.path_proj_root)], stdout=subprocess.PIPE, stderr=open(os.devnull, 'w'), shell=True) (out, err) = proc.communicate() out = out.decode('ascii', 'replace') if len(out) > 3000: warning = "WARNING: Diff limited to first 3000 characters.\n" out = warning + '\n' + out[0:3000] + '\n\n' + warning self.write(column="git_diff", value=out) def read(self, column, check_type_is=''): """Read parameter in 'column' from the database. Return None if parameter is empty. :param column: Name of the column the parameter is read from. :type column: str :param check_type_is: Throws ValueError if type does not match 'check_type_is'.The valid types the strings 'int', 'float', 'bool', 'string' and 'int/float/bool/string array' or the types int, float, bool, str and list. :raises ColumnError: If column do not exists. :raises ValueError: If return type does not match 'check_type_is'. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ if column not in self.column_names: self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) if column not in self.column_names: raise ColumnError("Column, {0}, is NOT a column in the " "database.".format(column)) self.db_cursor.execute("SELECT {0} FROM runs WHERE id={1}".format( column, self.id)) value = self.db_cursor.fetchone() if value != None: value = value[0] value = self.__check_type(check_type_is, column, self.column_names, self.column_types, value) return value def write(self, column, value, type_of_value='', only_if_empty=False): """Write value to 'column' in the database. If 'column' does not exists, a new is added. If value is None and type_of_value is not set, the entry under 'column' is set to empty. For multithreaded and multiprocess programs only a single will process/thread write to the database to avoid too much concurrent writing to the database. This is as long as the 'rank' was passed to SimDB under initialisation. :param column: Name of the column the parameter is read from. :type column: str :param value: New value of the specified entry in the database. :param type_of_value: Needed if column does note exists or if value is empty list. The valid types the strings 'int', 'float', 'bool', 'string' and 'int/float/bool/string array' or the types int, float, bool and str. :type type_of_value: str or type :param only_if_empty: If True, it will only write to the database if the simulation's entry under 'column' is empty. :type only_if_empty: bool :raises ValueError: If column exists, but type does not match, or empty list is passed without type_of_value given. """ # For multithreaded/multiprocess programs only a single process/thread # does any writing. if self.rank != None and self.rank != self.only_write_on_rank: return self.__add_column_if_not_exists_and_check_type(column, type_of_value, value) value_string = self.__convert_to_value_string(value, type_of_value) value_string = self.__escape_quote_with_two_quotes(value_string) type_dict = dict(zip(self.column_names, self.column_types)) # 'and type(value != None) != bool' allow numpy arrays to be check # without importing numpy and thereby relying on it being availble. if (type_dict[column] == 'TEXT' and (type(value != None) != bool or value != None)): value_string = "'{0}'".format(value_string) if only_if_empty and self.is_empty(column): self.db_cursor.execute("UPDATE runs SET \"{0}\" = {1} WHERE \"id\" " "= {2} AND {0} IS NULL".format(column, value_string, self.id)) self.db.commit() else: self.db_cursor.execute( "UPDATE runs SET \"{0}\" = {1} WHERE id = {2}".format( column, value_string, self.id)) self.db.commit() def unique_results_dir(self, path_directory): """Get path to subdirectory in 'path_directory' unique to simulation. The subdirectory will be named 'date_time_name_id' and is intended to store results in. If 'results_dir' in the database is empty, a new and unique directory is created and the path stored in 'results_dir'. Otherwise the path in 'results_dir' is just returned. :param path_directory: Path to directory of which to make a subdirectory. If 'path_directory' starts with 'root/', that part will be replaced by the full path of the root directory of the project. :type path_directory: str :returns: Full path to new subdirectory. :rtype: str """ results_dir = self.read("results_dir") if results_dir == None: if self.rank == None or self.rank == self.only_write_on_rank: if (len(path_directory) >= 5 and path_directory[0:5] == 'root/'): path_directory = os.path.join(self.path_proj_root, path_directory[5:]) results_dir = os.path.join(path_directory, self.__get_date_and_time_as_string()) results_dir += '_' + str(self.read('name')) + '_' + str(self.id) results_dir = os.path.abspath(os.path.realpath(results_dir)) os.mkdir(results_dir) self.write(column="results_dir", value=results_dir, only_if_empty=False) else: while results_dir == None: results_dir = self.read("results_dir") return results_dir def column_exists(self, column): """Return True if column is a column in the database. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ if column in self.column_names: return True else: self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) if column in self.column_names: return True else: return False def is_empty(self, column): """Return True if entry in the database under 'column' is empty. :raises sqlite3.OperationalError: Waited more than 5 seconds to read from the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ value = self.read(column) if value == None: return True else: return False def set_empty(self, column): """Set entry under 'column' in the database to empty.""" self.write(column, None) def get_id(self): """Return 'ID' of the connected simulation.""" return self.id def get_path_proj_root(self): """Return the path to the root directory of the project. The project's root directory is assumed to be where the '.sim_db/' directory is located. """ return self.path_proj_root def update_sha1_executables(self, paths_executables): """Update the 'sha1_executable' column in the database. Sets the entry to the sha1 of all the executables. The order will affect the value. :param paths_executables: List of full paths to executables. :type paths_executables: [str] :raises sqlite3.OperationalError: Waited more than 5 seconds to write to the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ sha1 = hashlib.sha1() for executable in executables: with open(executable, 'r') as executable_file: sha1.update(executable_file.read()) self.write('sha1_executables', sha1) def delete_from_database(self): """Delete simulation from database. :raises sqlite3.OperationalError: Waited more than 5 seconds to write to the database, because other threads/processes are busy writing to it. Way too much concurrent writing is done and it indicates an design error in the user program. """ self.db_cursor.execute("DELETE FROM runs WHERE id = {0}".format( self.id)) self.db.commit() self.store_metadata = False def close(self): """Closes connection to **sim_db** database and add metadata.""" if (self.store_metadata and (self.rank == None or self.rank == self.only_write_on_rank)): used_time = time.time() - self.start_time used_walltime = "{0}h {1}m {2}s".format( int(used_time / 3600), int(used_time / 60), used_time % 60) self.write('used_walltime', used_walltime) self.write('status', 'finished') self.db_cursor.close() self.db.close() def __read_from_command_line_arguments(self, db_id): path_proj_root = None if db_id == None: parser = argparse.ArgumentParser() parser.add_argument( '--id', '-i', type=int, default=None, required=True, help=("<Required> ID of parameters in the database used " "to run the simulation.")) parser.add_argument( '--path_proj_root', '-p', type=str, default=None, help="Path to the root directory of the project.") args, unknowns = parser.parse_known_args() db_id = args.id if args.path_proj_root != None: path_proj_root = os.path.abspath(args.path_proj_root) if (path_proj_root == None): path_proj_root = os.path.dirname(helpers.get_dot_sim_db_dir_path()) else: if path_proj_root[-1] == '/': path_proj_root = path_proj_root[:-1] if (db_id == None): ValueError("'db_id' is NOT provided to SimDB(db_id=None). If not " "passed as function parameters, then '--id ID' must be " "passed to program as command line arguments.") return (db_id, path_proj_root) def __check_type(self, check_type_is, column, column_names, column_types, value=None): type_dict = dict(zip(column_names, column_types)) type_of_value = type_dict[column] if ((check_type_is == 'int' or check_type_is == int) and type_of_value == 'INTEGER'): correct_type = True if value != None: try: int(value) except: correct_type = False elif ((check_type_is == 'float' or check_type_is == float) and type_of_value == 'REAL'): correct_type = True if value != None: try: float(value) except: correct_type = False # 'type(value != None) != bool' allow numpy arrays to be check # without importing numpy and thereby relying on it being availble. elif (type_of_value == 'TEXT' and (type(value != None) != bool or value != None)): if (type(value) == str or (sys.version_info[0] < 3 and type(value) == unicode)): value, correct_type = self.__convert_text_to_correct_type( value, check_type_is) elif type(value) == bool: correct_type = True elif check_type_is == 'bool' or check_type_is == bool: try: value = bool(value) correct_type = True except ValueError: correct_type = False else: try: value = list(value) correct_type = True except TypeError: correct_type = False # 'and type(value != None) == bool' allow numpy arrays to be check # without importing numpy and thereby relying on it being availble. elif (type_of_value == 'TEXT' and (type(value == None) == bool and value == None) and (check_type_is == 'string' or check_type_is == str or check_type_is == 'bool' or check_type_is == bool or check_type_is == list or check_type_is == 'int array' or check_type_is == 'float array' or check_type_is == 'bool array' or check_type_is == 'string array')): correct_type = True else: correct_type = False if not correct_type and check_type_is != '': raise ValueError("The type is NOT {0}.".format(check_type_is)) return value def __convert_text_to_correct_type(self, value, check_type_is): correct_type = False value_split = value.split('[') if value == "True": value = True if (check_type_is == 'bool' or check_type_is == bool): correct_type = True elif value == "False": value = False if (check_type_is == 'bool' or check_type_is == bool): correct_type = True elif len(value_split) == 1: if (check_type_is == 'string' or check_type_is == str): correct_type = True else: value = [] if value_split[0].strip() == 'int': if (check_type_is == 'int array' or check_type_is == list): correct_type = True for element in value_split[1].split(']')[0].split(','): value.append(int(element)) elif value_split[0].strip() == 'float': if (check_type_is == 'float array' or check_type_is == list): correct_type = True for element in value_split[1].split(']')[0].split(','): value.append(float(element)) elif value_split[0].strip() == 'string': if (check_type_is == 'string array' or check_type_is == list): correct_type = True for i, element in enumerate( value_split[1].split(']')[0].split(',')): if i > 0 and len(element) > 0 and element[0] == ' ': element = element[1:] value.append(str(element)) elif value_split[0].strip() == 'bool': if (check_type_is == 'bool array' or check_type_is == list): correct_type = True for i, element in enumerate( value_split[1].split(']')[0].split(',')): if i > 0 and len(element) > 0 and element[0] == ' ': element = element[1:] if element == 'True': element = True elif element == 'False': element = False else: correct_type = False value.append(element) else: if (check_type_is == 'string' or check_type_is == str): correct_type = True else: correct_type = False return value, correct_type def __convert_to_value_string(self, value, type_of_value): if sys.version_info[0] < 3 and type(value) == unicode: return value.encode('ascii', 'replace') elif (type(value) == int or type(value) == float or type(value) == str or type_of_value == 'int' or type_of_value == int or type_of_value == 'float' or type_of_value == float or type_of_value == 'string' or type_of_value == str): return str(value) elif type(value) == bool: if value: return "True" else: return "False" # 'type(value == None) == bool' allow numpy arrays to be check # without importing numpy and thereby relying on it being availble. elif (type(value == None) == bool and value == None): return "NULL" else: try: value = list(value) except TypeError: raise ValueError("'value' have invalid type.") if len(value) > 0: if type_of_value == 'int array' or type(value[0]) == int: value_string = "int[" elif type_of_value == 'float array' or type(value[0]) == float: value_string = "float[" elif type_of_value == 'string array' or type(value[0]) == str: value_string = "string[" elif type_of_value == 'bool array' or type(value[0]) == bool: value_string = "bool[" for element in value: if type(value[0]) == bool: if element: element = "True" else: element = "False" value_string += str(element) + ", " value_string = value_string[:-2] + "]" return value_string else: if type_of_value == 'int array': value_string = "int[]" elif type_of_value == 'float array': value_string = "float[]" elif type_of_value == 'string array': value_string = "string[]" elif type_of_value == 'bool array': value_string = "bool[]" else: raise ValueError( "The type_of_value must be set to 'int array', " "'float array', 'string array' or 'bool array' " "when a empty list is passed to SimDB.write().") return value_string def __add_column(self, column, type_of_value): if type_of_value == '': print("ERROR: Column {0} does not exists in database and " "'type_of_value' must be provided for it to be added." .format(column)) exit(1) if self.rank == None or self.rank == self.only_write_on_rank: if type_of_value == 'int' or type_of_value == int: self.db_cursor.execute( "ALTER TABLE runs ADD COLUMN {0} INTEGER".format( column)) elif type_of_value == 'float' or type_of_value == float: self.db_cursor.execute( "ALTER TABLE runs ADD COLUMN {0} REAL".format( column)) else: self.db_cursor.execute( "ALTER TABLE runs ADD COLUMN {0} TEXT".format( column)) self.db.commit() def __add_column_if_not_exists_and_check_type(self, column, type_of_value, value): if column in self.column_names: self.__check_type(type_of_value, column, self.column_names, self.column_types, value=value) else: self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) if column in self.column_names: self.__check_type(type_of_value, column, self.column_names, self.column_types, value=value) else: if (type_of_value == 'int' or type_of_value == int): column_type = 'INTEGER' elif (type_of_value == 'float' or type_of_value == float): column_type = 'REAL' else: column_type = 'TEXT' self.__check_type( type_of_value, column, [column], [column_type], value=value) self.__add_column(column, type_of_value) self.column_names, self.column_types = ( helpers.get_db_column_names_and_types(self.db_cursor)) def __is_a_git_project(self): directory = self.path_proj_root prev_dir = "" while directory != prev_dir: if os.path.exists(os.path.join(directory, ".git")): return True prev_dir = directory directory = os.path.dirname(directory) return False def __escape_quote_with_two_quotes(self, string): escaped_string = "" for letter in string: if letter == "'": escaped_string += "''" else: escaped_string += letter return escaped_string def __get_date_and_time_as_string(self): """Return data and time as 'Year-Month-Date_Hours-Minutes-Seconds'.""" return time.strftime("%Y-%b-%d_%H-%M-%S") class ColumnError(Exception): pass def add_empty_sim(store_metadata=False): """Add an empty entry into the database and SimDB connected to it. :param store_metadata: If False, no metadata is added to the database. Typically used when postprocessing (visualizing) data from a simulation. :type store_metadata: bool """ db = helpers.connect_sim_db() db_cursor = db.cursor() default_db_columns = "" for key in helpers.default_db_columns: default_db_columns += key + " " + str( helpers.default_db_columns[key]) + ", " default_db_columns = default_db_columns[:-2] db_cursor.execute("CREATE TABLE IF NOT EXISTS runs ({0});".format( default_db_columns)) db_cursor.execute("INSERT INTO runs DEFAULT VALUES") db_id = db_cursor.lastrowid db.commit() db_cursor.close() db.close() return SimDB(db_id=db_id, store_metadata=store_metadata)

12,088

17

239

36ac6b6d184184fbc0b05f1ff60528053b819d9d

14,351

py

Python

SBW_GAN_TF/generator.py

huangleiBuaa/StochasticityBW

11db7ed0238f0c7cd5f6e336a087fc1d0427b1e6

[ "BSD-2-Clause" ]

8

2020-03-23T15:46:13.000Z

2022-03-25T03:11:17.000Z

SBW_GAN_TF/generator.py

huangleiBuaa/StochasticityBW

11db7ed0238f0c7cd5f6e336a087fc1d0427b1e6

[ "BSD-2-Clause" ]

null

SBW_GAN_TF/generator.py

huangleiBuaa/StochasticityBW

11db7ed0238f0c7cd5f6e336a087fc1d0427b1e6

[ "BSD-2-Clause" ]

1

2022-03-25T03:11:20.000Z

from tensorflow.python.keras.models import Input, Model from tensorflow.python.keras.layers import Dense, Reshape, Activation, Conv2D, Conv2DTranspose from tensorflow.python.keras.layers import BatchNormalization, Add, Embedding, Concatenate import numpy as np import tensorflow as tf from tensorflow.python.keras import backend as K from gan.utils import glorot_init, resblock, dcblock, get_m_group from gan.layers.coloring import ConditionalConv11, ConditionalCenterScale, CenterScale, FactorizedConv11 from gan.layers.normalization import DecorelationNormalization from gan.layers.misc import Split from layers.spectral_normalized_layers import SNConv2D, SNConditionalConv11, SNDense, SNEmbeding, SNFactorizedConv11 from functools import partial

54.774809

181

0.548464

from tensorflow.python.keras.models import Input, Model from tensorflow.python.keras.layers import Dense, Reshape, Activation, Conv2D, Conv2DTranspose from tensorflow.python.keras.layers import BatchNormalization, Add, Embedding, Concatenate import numpy as np import tensorflow as tf from tensorflow.python.keras import backend as K from gan.utils import glorot_init, resblock, dcblock, get_m_group from gan.layers.coloring import ConditionalConv11, ConditionalCenterScale, CenterScale, FactorizedConv11 from gan.layers.normalization import DecorelationNormalization from gan.layers.misc import Split from layers.spectral_normalized_layers import SNConv2D, SNConditionalConv11, SNDense, SNEmbeding, SNFactorizedConv11 from functools import partial def create_norm(norm, coloring, decomposition='zca', iter_num=5, whitten_m=0, coloring_m=0, instance_norm=0, device='cpu', cls=None, number_of_classes=None, filters_emb=10, uncoditional_conv_layer=Conv2D, conditional_conv_layer=ConditionalConv11, factor_conv_layer=FactorizedConv11): assert norm in ['n', 'b', 'd', 'dr'] assert coloring in ['ucs', 'ccs', 'uccs', 'uconv', 'fconv', 'ufconv', 'cconv', 'ucconv', 'ccsuconv', 'n'] if norm == 'n': norm_layer = lambda axis, name: (lambda inp: inp) elif norm == 'b': norm_layer = lambda axis, name: BatchNormalization(axis=axis, center=False, scale=False, name=name) elif norm == 'd': norm_layer = lambda axis, name: DecorelationNormalization(name=name, m_per_group=whitten_m, decomposition=decomposition, iter_num=iter_num, instance_norm=instance_norm, device=device) elif norm == 'dr': norm_layer = lambda axis, name: DecorelationNormalization(name=name, m_per_group=whitten_m, decomposition=decomposition, iter_num=iter_num, instance_norm=instance_norm, renorm=True) if coloring == 'ccs': after_norm_layer = lambda axis, name: lambda x: ConditionalCenterScale(number_of_classes=number_of_classes, axis=axis, name=name)([x, cls]) elif coloring == 'ucs': after_norm_layer = lambda axis, name: lambda x: CenterScale(axis=axis, name=name)(x) elif coloring == 'uccs': def after_norm_layer(axis, name): def f(x): c = ConditionalCenterScale(number_of_classes=number_of_classes, axis=axis, name=name + '_c')([x, cls]) u = CenterScale(axis=axis, name=name + '_u')(x) out = Add(name=name + '_a')([c, u]) return out return f elif coloring == 'cconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) if coloring_group > 1: splits = Split(coloring_group, axis)(x) outs = [] for i, split in enumerate(splits): split_out = conditional_conv_layer(filters=m, number_of_classes=number_of_classes, name=name+str(i))([split, cls]) outs.append(split_out) out = tf.keras.layers.Concatenate(axis)(outs) else: out = conditional_conv_layer(filters=K.int_shape(x)[axis], number_of_classes=number_of_classes, name=name)([x, cls]) return out return f elif coloring == 'fconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) if coloring_group > 1: splits = Split(coloring_group, axis)(x) outs = [] for i, split in enumerate(splits): split_out = factor_conv_layer(filters=m, number_of_classes=number_of_classes, name=name + '_c'+str(i), filters_emb=filters_emb, use_bias=False)([split, cls]) outs.append(split_out) out = tf.keras.layers.Concatenate(axis)(outs) else: out = factor_conv_layer(filters=K.int_shape(x)[axis], number_of_classes=number_of_classes, name=name + '_c', filters_emb=filters_emb, use_bias=False)([x, cls]) return out return f elif coloring == 'uconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) if coloring_group > 1: splits = Split(coloring_group, axis)(x) outs = [] for i, split in enumerate(splits): split_out = uncoditional_conv_layer(filters=m, kernel_size=(1, 1), name=name+str(i))(split) outs.append(split_out) out = tf.keras.layers.Concatenate(axis)(outs) else: out = uncoditional_conv_layer(filters=K.int_shape(x)[axis], kernel_size=(1, 1), name=name)(x) return out return f elif coloring == 'ucconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) if coloring_group > 1: splits = Split(coloring_group, axis)(x) cs = [] us = [] for i, split in enumerate(splits): split_c = conditional_conv_layer(filters=m, number_of_classes=number_of_classes, name=name + '_c'+str(i))([split, cls]) split_u = uncoditional_conv_layer(kernel_size=(1, 1), filters=K.int_shape(x)[axis], name=name + '_u'+str(i))(split) cs.append(split_c) us.append(split_u) c = tf.keras.layers.Concatenate(axis)(cs) u = tf.keras.layers.Concatenate(axis)(us) else: c = conditional_conv_layer(filters=K.int_shape(x)[axis], number_of_classes=number_of_classes, name=name + '_c')([x, cls]) u = uncoditional_conv_layer(kernel_size=(1, 1), filters=K.int_shape(x)[axis], name=name + '_u')(x) out = Add(name=name + '_a')([c, u]) return out return f elif coloring == 'ccsuconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) c = ConditionalCenterScale(number_of_classes=number_of_classes, axis=axis, name=name + '_c')([x, cls]) if coloring_group > 1: splits = Split(coloring_group, axis)(x) us = [] for i, split in enumerate(splits): split_u = uncoditional_conv_layer(kernel_size=(1, 1), filters=m, name=name + '_u'+str(i))(split) us.append(split_u) u = tf.keras.layers.Concatenate(axis)(us) else: u = uncoditional_conv_layer(kernel_size=(1, 1), filters=K.int_shape(x)[axis], name=name + '_u')(x) out = Add(name=name + '_a')([c, u]) return out return f elif coloring == 'ufconv': def after_norm_layer(axis, name): def f(x): coloring_group, m = get_m_group(x, coloring_m, axis) if coloring_group > 1: splits = Split(coloring_group, axis)(x) cs = [] us = [] for i, split in enumerate(splits): split_c = factor_conv_layer(number_of_classes=number_of_classes, name=name + '_c'+str(i), filters=m, filters_emb=filters_emb, use_bias=False)([split, cls]) split_u = uncoditional_conv_layer(kernel_size=(1, 1), filters=m, name=name + '_u'+str(i))(split) cs.append(split_c) us.append(split_u) c = tf.keras.layers.Concatenate(axis)(cs) u = tf.keras.layers.Concatenate(axis)(us) else: c = factor_conv_layer(number_of_classes=number_of_classes, name=name + '_c', filters=K.int_shape(x)[axis], filters_emb=filters_emb, use_bias=False)([x, cls]) u = uncoditional_conv_layer(kernel_size=(1, 1), filters=K.int_shape(x)[axis], name=name + '_u')(x) out = Add(name=name + '_a')([c, u]) return out return f elif coloring == 'n': after_norm_layer = lambda axis, name: lambda x: x def result_norm(axis, name): def stack(inp): out = inp out = norm_layer(axis=axis, name=name + '_npart')(out) out = after_norm_layer(axis=axis, name=name + '_repart')(out) return out return stack return result_norm def make_generator(input_noise_shape=(128,), output_channels=3, input_cls_shape=(1, ), block_sizes=(128, 128, 128), resamples=("UP", "UP", "UP"), first_block_shape=(4, 4, 128), number_of_classes=10, concat_cls=False, block_norm='u', block_coloring='cs', filters_emb=10, last_norm='u', last_coloring='cs', decomposition='cholesky', whitten_m=0, coloring_m=0, iter_num=5, instance_norm=0, device='cpu', gan_type=None, arch='res', spectral=False, before_conv=0, fully_diff_spectral=False, spectral_iterations=1, conv_singular=True,): assert arch in ['res', 'dcgan'] inp = Input(input_noise_shape, name='GInputImage') cls = Input(input_cls_shape, dtype='int32', name='GLabel') if spectral: conv_layer = partial(SNConv2D, conv_singular=conv_singular, fully_diff_spectral=fully_diff_spectral, spectral_iterations=spectral_iterations) cond_conv_layer = partial(SNConditionalConv11, fully_diff_spectral=fully_diff_spectral, spectral_iterations=spectral_iterations) dense_layer = partial(SNDense, fully_diff_spectral=fully_diff_spectral, spectral_iterations=spectral_iterations) emb_layer = partial(SNEmbeding, fully_diff_spectral=fully_diff_spectral, spectral_iterations=spectral_iterations) factor_conv_layer = partial(SNFactorizedConv11, fully_diff_spectral=fully_diff_spectral, spectral_iterations=spectral_iterations) else: conv_layer = Conv2D cond_conv_layer = ConditionalConv11 dense_layer = Dense emb_layer = Embedding factor_conv_layer = FactorizedConv11 if concat_cls: y = emb_layer(input_dim=number_of_classes, output_dim=first_block_shape[-1])(cls) y = Reshape((first_block_shape[-1], ))(y) y = Concatenate(axis=-1)([y, inp]) else: y = inp y = dense_layer(units=np.prod(first_block_shape), kernel_initializer=glorot_init)(y) y = Reshape(first_block_shape)(y) block_norm_layer = create_norm(block_norm, block_coloring, decomposition=decomposition, whitten_m=whitten_m, coloring_m=coloring_m, iter_num=iter_num, instance_norm=instance_norm, device=device, cls=cls, number_of_classes=number_of_classes, filters_emb=filters_emb, uncoditional_conv_layer=conv_layer, conditional_conv_layer=cond_conv_layer, factor_conv_layer=factor_conv_layer) last_norm_layer = create_norm(last_norm, last_coloring, decomposition=decomposition, whitten_m=whitten_m, coloring_m=coloring_m, iter_num=iter_num, instance_norm=instance_norm, device=device, cls=cls, number_of_classes=number_of_classes, filters_emb=filters_emb, uncoditional_conv_layer=conv_layer, conditional_conv_layer=cond_conv_layer, factor_conv_layer=factor_conv_layer) i = 0 for block_size, resample in zip(block_sizes, resamples): if arch == 'res': y = resblock(y, kernel_size=(3, 3), resample=resample, nfilters=block_size, name='Generator.' + str(i), norm=block_norm_layer, is_first=False, conv_layer=conv_layer) else: # TODO: SN DECONV y = dcblock(y, kernel_size=(4, 4), resample=resample, nfilters=block_size, name='Generator.' + str(i), norm=block_norm_layer, is_first=False, conv_layer=Conv2DTranspose, before_conv=before_conv) i += 1 y = last_norm_layer(axis=-1, name='Generator.BN.Final')(y) y = Activation('relu')(y) output = conv_layer(filters=output_channels, kernel_size=(3, 3), name='Generator.Final', kernel_initializer=glorot_init, use_bias=True, padding='same')(y) output = Activation('tanh')(output) if gan_type is None: return Model(inputs=[inp], outputs=output) else: return Model(inputs=[inp, cls], outputs=output)

13,551

0

46

154902bea517a92105fa6ca2dc639b1aa8ad881c

1,591

py

Python

fossor/checks/loadavg.py

NeolithEra/fossor

d8dbdc40f2f16da601c317dfa74b83e3932c9bb3

[ "BSD-2-Clause" ]

165

2017-12-14T18:44:25.000Z

2020-12-09T01:48:57.000Z

fossor/checks/loadavg.py

NeolithEra/fossor

d8dbdc40f2f16da601c317dfa74b83e3932c9bb3

[ "BSD-2-Clause" ]

12

2017-12-14T23:42:45.000Z

2020-05-29T15:11:02.000Z

fossor/checks/loadavg.py

NeolithEra/fossor

d8dbdc40f2f16da601c317dfa74b83e3932c9bb3

[ "BSD-2-Clause" ]

32

2017-12-14T17:51:57.000Z

2020-06-12T13:11:47.000Z

# Copyright 2017 LinkedIn Corporation. All rights reserved. Licensed under the BSD-2 Clause license. # See LICENSE in the project root for license information. import os from fossor.checks.check import Check class LoadAvg(Check): '''this Check will compare the current load average summaries against the count of CPU cores in play, and will alert the user if there are more processes waiting''' if __name__ == '__main__': l = LoadAvg() print(l.run({}))

43

122

0.614079

# Copyright 2017 LinkedIn Corporation. All rights reserved. Licensed under the BSD-2 Clause license. # See LICENSE in the project root for license information. import os from fossor.checks.check import Check class LoadAvg(Check): '''this Check will compare the current load average summaries against the count of CPU cores in play, and will alert the user if there are more processes waiting''' def run(self, variables): ON_LINUX = os.path.isdir('/proc') if ON_LINUX: with open('/proc/loadavg') as f: contents = f.read().strip() load_summaries = [float(i) for i in contents.split()[:3]] with open('/proc/cpuinfo') as f: cpu_count = len([c for c in f.read().splitlines() if c.startswith('processor')]) else: uptime, err, return_code = self.shell_call('uptime') contents = uptime.strip() load_summaries = [float(i.replace(',', '')) for i in contents.split()[-3:]] cpu, err, return_code = self.shell_call('sysctl -n hw.ncpu') cpu_count = int(cpu.strip()) # Alert the user if any of the 1, 5, or 15-minute load averages is greater than the processor count to handle them if any(c / cpu_count > 1 for c in load_summaries): return 'Load average shows processes queued beyond CPU count!\nCPU Count: {0}\nLoad averages: {1}'.format( cpu_count, ' '.join(str(x) for x in load_summaries) ) if __name__ == '__main__': l = LoadAvg() print(l.run({}))

1,091

0

26

db7b405dd77b26c6f3e2362687a6b0991b3bd6de

5,048

py

Python

tests/app/clients/test_firetext.py

LouisStAmour/notifications-api

16734595e70113d85fb10689017b2c30bab61fb3

[ "MIT" ]

10

2020-05-04T14:11:06.000Z

2022-02-22T19:06:36.000Z

tests/app/clients/test_firetext.py

GouvQC/notification-api

e865b8b92a9a45c7cee006f427dcf77d71b09d6d

[ "MIT" ]

554

2020-05-07T21:56:24.000Z

2022-03-31T23:04:51.000Z

tests/app/clients/test_firetext.py

LouisStAmour/notifications-api

16734595e70113d85fb10689017b2c30bab61fb3

[ "MIT" ]

4

2020-08-27T16:43:29.000Z

2021-02-17T22:17:27.000Z

from requests import HTTPError from urllib.parse import parse_qs from requests.exceptions import ConnectTimeout, ReadTimeout import pytest import requests_mock from app.clients.sms.firetext import get_firetext_responses, SmsClientResponseException, FiretextClientResponseException

36.057143

120

0.724643

from requests import HTTPError from urllib.parse import parse_qs from requests.exceptions import ConnectTimeout, ReadTimeout import pytest import requests_mock from app.clients.sms.firetext import get_firetext_responses, SmsClientResponseException, FiretextClientResponseException def test_should_return_correct_details_for_delivery(): get_firetext_responses('0') == 'delivered' def test_should_return_correct_details_for_bounced(): get_firetext_responses('1') == 'permanent-failure' def test_should_return_correct_details_for_complaint(): get_firetext_responses('2') == 'pending' def test_should_be_none_if_unrecognised_status_code(): with pytest.raises(KeyError) as e: get_firetext_responses('99') assert '99' in str(e.value) def test_send_sms_successful_returns_firetext_response(mocker, mock_firetext_client): to = content = reference = 'foo' response_dict = { 'data': [], 'description': 'SMS successfully queued', 'code': 0, 'responseData': 1 } with requests_mock.Mocker() as request_mock: request_mock.post('https://example.com/firetext', json=response_dict, status_code=200) response = mock_firetext_client.send_sms(to, content, reference) response_json = response.json() assert response.status_code == 200 assert response_json['code'] == 0 assert response_json['description'] == 'SMS successfully queued' def test_send_sms_calls_firetext_correctly(mocker, mock_firetext_client): to = '+447234567890' content = 'my message' reference = 'my reference' response_dict = { 'code': 0, } with requests_mock.Mocker() as request_mock: request_mock.post('https://example.com/firetext', json=response_dict, status_code=200) mock_firetext_client.send_sms(to, content, reference) assert request_mock.call_count == 1 assert request_mock.request_history[0].url == 'https://example.com/firetext' assert request_mock.request_history[0].method == 'POST' request_args = parse_qs(request_mock.request_history[0].text) assert request_args['apiKey'][0] == 'foo' assert request_args['from'][0] == 'bar' assert request_args['to'][0] == '447234567890' assert request_args['message'][0] == content assert request_args['reference'][0] == reference def test_send_sms_raises_if_firetext_rejects(mocker, mock_firetext_client): to = content = reference = 'foo' response_dict = { 'data': [], 'description': 'Some kind of error', 'code': 1, 'responseData': '' } with pytest.raises(SmsClientResponseException) as exc, requests_mock.Mocker() as request_mock: request_mock.post('https://example.com/firetext', json=response_dict, status_code=200) mock_firetext_client.send_sms(to, content, reference) assert exc.value.status_code == 200 assert '"description": "Some kind of error"' in exc.value.text assert '"code": 1' in exc.value.text def test_send_sms_raises_if_firetext_rejects_with_unexpected_data(mocker, mock_firetext_client): to = content = reference = 'foo' response_dict = {"something": "gone bad"} with pytest.raises(SmsClientResponseException) as exc, requests_mock.Mocker() as request_mock: request_mock.post('https://example.com/firetext', json=response_dict, status_code=400) mock_firetext_client.send_sms(to, content, reference) assert exc.value.status_code == 400 assert exc.value.text == '{"something": "gone bad"}' assert type(exc.value.exception) == HTTPError def test_send_sms_override_configured_shortcode_with_sender(mocker, mock_firetext_client): to = '+447234567890' content = 'my message' reference = 'my reference' response_dict = { 'code': 0, } sender = 'fromservice' with requests_mock.Mocker() as request_mock: request_mock.post('https://example.com/firetext', json=response_dict, status_code=200) mock_firetext_client.send_sms(to, content, reference, sender=sender) request_args = parse_qs(request_mock.request_history[0].text) assert request_args['from'][0] == 'fromservice' def test_send_sms_raises_if_firetext_rejects_with_connect_timeout(rmock, mock_firetext_client): to = content = reference = 'foo' with pytest.raises(FiretextClientResponseException) as exc: rmock.register_uri('POST', 'https://example.com/firetext', exc=ConnectTimeout) mock_firetext_client.send_sms(to, content, reference) assert exc.value.status_code == 504 assert exc.value.text == 'Gateway Time-out' def test_send_sms_raises_if_firetext_rejects_with_read_timeout(rmock, mock_firetext_client): to = content = reference = 'foo' with pytest.raises(FiretextClientResponseException) as exc: rmock.register_uri('POST', 'https://example.com/firetext', exc=ReadTimeout) mock_firetext_client.send_sms(to, content, reference) assert exc.value.status_code == 504 assert exc.value.text == 'Gateway Time-out'

4,501

0

253

f4b37dcb02cb91970f72659ca8869f6a3bbb5643

1,532

py

Python

day26_lists_NATO_alphabet/main.py

frnkvsk/python100days

70d607ca58a526f0d66544ed65405b2425718108

[ "Unlicense" ]

null

day26_lists_NATO_alphabet/main.py

frnkvsk/python100days

70d607ca58a526f0d66544ed65405b2425718108

[ "Unlicense" ]

null

day26_lists_NATO_alphabet/main.py

frnkvsk/python100days

70d607ca58a526f0d66544ed65405b2425718108

[ "Unlicense" ]

null

import random # names = ['Alex', 'Beth', 'Carol', 'Dave', 'Kim', 'Sam', 'Heather', 'Hank'] # students_scores = {student:random.randint(1, 100) for student in names} # passed_students = {student:score for (student, score) in students_scores.items() if score > 59} # print(students_scores) # print(passed_students) # sentence = "What is the Airspeed Velocity of an Unladden Swallow?" # result = {word:len(word) for word in sentence.split(' ')} # print(result) # weather_c = { # 'Monday': 12, # 'Tuesday': 14, # 'Wednesday': 15, # 'Thursday': 14, # 'Friday': 21, # 'Saturday': 22, # 'Sunday': 24 # } # weather_f = {day: temp * 9 / 5 + 32 for (day, temp) in weather_c.items()} # print(weather_f) # import pandas # # student_dict = { # 'student': ['Mary', 'Andy', 'Peter'], # 'score': [56, 76, 98] # } # student_data_frame = pandas.DataFrame(student_dict) # print(student_data_frame) # for (index, row) in student_data_frame.iterrows(): # if row.student == 'Mary': # print(row.score) import pandas letter_dict = {r.letter:r.code for (i, r) in pandas.read_csv('nato_phonetic_alphabet.csv').iterrows()} generate_phonetic()

30.039216

103

0.622063

import random # names = ['Alex', 'Beth', 'Carol', 'Dave', 'Kim', 'Sam', 'Heather', 'Hank'] # students_scores = {student:random.randint(1, 100) for student in names} # passed_students = {student:score for (student, score) in students_scores.items() if score > 59} # print(students_scores) # print(passed_students) # sentence = "What is the Airspeed Velocity of an Unladden Swallow?" # result = {word:len(word) for word in sentence.split(' ')} # print(result) # weather_c = { # 'Monday': 12, # 'Tuesday': 14, # 'Wednesday': 15, # 'Thursday': 14, # 'Friday': 21, # 'Saturday': 22, # 'Sunday': 24 # } # weather_f = {day: temp * 9 / 5 + 32 for (day, temp) in weather_c.items()} # print(weather_f) # import pandas # # student_dict = { # 'student': ['Mary', 'Andy', 'Peter'], # 'score': [56, 76, 98] # } # student_data_frame = pandas.DataFrame(student_dict) # print(student_data_frame) # for (index, row) in student_data_frame.iterrows(): # if row.student == 'Mary': # print(row.score) import pandas letter_dict = {r.letter:r.code for (i, r) in pandas.read_csv('nato_phonetic_alphabet.csv').iterrows()} def generate_phonetic(): in_word = input('Enter a word ').upper() try: phonetic_list = [letter_dict[letter] for letter in list(in_word.strip())] except KeyError: print(f"Sorry, only letters in the alphabet please.") generate_phonetic() else: print(phonetic_list) generate_phonetic()

297

0

25

41ee8bdcdc4e5cfd537d50225bfde851709ef2eb

30,500

py

Python

pyeccodes/defs/grib2/localConcepts/kwbc/shortName_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

7

2020-04-14T09:41:17.000Z

2021-08-06T09:38:19.000Z

pyeccodes/defs/grib2/localConcepts/kwbc/shortName_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

null

pyeccodes/defs/grib2/localConcepts/kwbc/shortName_def.py

ecmwf/pyeccodes

dce2c72d3adcc0cb801731366be53327ce13a00b

[ "Apache-2.0" ]

3

2020-04-30T12:44:48.000Z

2020-12-15T08:40:26.000Z

import pyeccodes.accessors as _

36.396181

85

0.600557

import pyeccodes.accessors as _ def load(h): def wrapped(h): discipline = h.get_l('discipline') parameterCategory = h.get_l('parameterCategory') parameterNumber = h.get_l('parameterNumber') if discipline == 0 and parameterCategory == 1 and parameterNumber == 8: return 'tp' if discipline == 0 and parameterCategory == 6 and parameterNumber == 1: return 'tcc' if discipline == 0 and parameterCategory == 7 and parameterNumber == 7: return 'cin' if discipline == 0 and parameterCategory == 2 and parameterNumber == 224: return 'VRATE' if discipline == 0 and parameterCategory == 19 and parameterNumber == 234: return 'ICSEV' if discipline == 0 and parameterCategory == 6 and parameterNumber == 201: return 'SUNSD' if discipline == 255 and parameterCategory == 255 and parameterNumber == 255: return 'imgd' if discipline == 10 and parameterCategory == 4 and parameterNumber == 197: return 'ohc' if discipline == 10 and parameterCategory == 4 and parameterNumber == 196: return 'intfd' if discipline == 10 and parameterCategory == 4 and parameterNumber == 195: return 'dbss' if discipline == 10 and parameterCategory == 4 and parameterNumber == 194: return 'bkeng' if discipline == 10 and parameterCategory == 4 and parameterNumber == 193: return 'salin' if discipline == 10 and parameterCategory == 4 and parameterNumber == 192: return 'wtmpc' if discipline == 10 and parameterCategory == 3 and parameterNumber == 202: return 'sltfl' if discipline == 10 and parameterCategory == 3 and parameterNumber == 201: return 'keng' if discipline == 10 and parameterCategory == 3 and parameterNumber == 200: return 'ssst' if discipline == 10 and parameterCategory == 3 and parameterNumber == 199: return 'sstt' if discipline == 10 and parameterCategory == 3 and parameterNumber == 198: return 'ashfl' if discipline == 10 and parameterCategory == 3 and parameterNumber == 197: return 'aohflx' if discipline == 10 and parameterCategory == 3 and parameterNumber == 196: return 'p2omlt' if discipline == 10 and parameterCategory == 3 and parameterNumber == 195: return 'sshg' if discipline == 10 and parameterCategory == 3 and parameterNumber == 194: return 'elevhtml' if discipline == 10 and parameterCategory == 3 and parameterNumber == 193: return 'etsrg' if discipline == 10 and parameterCategory == 3 and parameterNumber == 192: return 'surge' if discipline == 10 and parameterCategory == 1 and parameterNumber == 195: return 'vbaro' if discipline == 10 and parameterCategory == 1 and parameterNumber == 194: return 'ubaro' if discipline == 10 and parameterCategory == 1 and parameterNumber == 193: return 'omlv' if discipline == 10 and parameterCategory == 1 and parameterNumber == 192: return 'omlu' if discipline == 10 and parameterCategory == 0 and parameterNumber == 192: return 'wstp' if discipline == 3 and parameterCategory == 1 and parameterNumber == 193: return 'vsct' if discipline == 3 and parameterCategory == 1 and parameterNumber == 192: return 'usct' if discipline == 2 and parameterCategory == 3 and parameterNumber == 203: return 'fldcp' if discipline == 2 and parameterCategory == 3 and parameterNumber == 202: return 'radt' if discipline == 2 and parameterCategory == 3 and parameterNumber == 201: return 'avsft' if discipline == 2 and parameterCategory == 3 and parameterNumber == 200: return 'baret' if discipline == 2 and parameterCategory == 3 and parameterNumber == 199: return 'lspa' if discipline == 2 and parameterCategory == 3 and parameterNumber == 198: return 'evbs' if discipline == 2 and parameterCategory == 3 and parameterNumber == 194: return 'sltyp' if discipline == 2 and parameterCategory == 0 and parameterNumber == 230: return 'trans' if discipline == 2 and parameterCategory == 0 and parameterNumber == 229: return 'evcw' if discipline == 2 and parameterCategory == 0 and parameterNumber == 228: return 'acond' if discipline == 2 and parameterCategory == 0 and parameterNumber == 227: return 'wcvflx' if discipline == 2 and parameterCategory == 0 and parameterNumber == 226: return 'wcuflx' if discipline == 2 and parameterCategory == 0 and parameterNumber == 225: return 'wvvflx' if discipline == 2 and parameterCategory == 0 and parameterNumber == 224: return 'wvuflx' if discipline == 2 and parameterCategory == 0 and parameterNumber == 223: return 'wcconv' if discipline == 2 and parameterCategory == 0 and parameterNumber == 222: return 'wvconv' if discipline == 2 and parameterCategory == 0 and parameterNumber == 221: return 'wcinc' if discipline == 2 and parameterCategory == 0 and parameterNumber == 220: return 'wvinc' if discipline == 2 and parameterCategory == 0 and parameterNumber == 219: return 'amixl' if discipline == 2 and parameterCategory == 0 and parameterNumber == 218: return 'landn' if discipline == 2 and parameterCategory == 0 and parameterNumber == 217: return 'ndvi' if discipline == 2 and parameterCategory == 0 and parameterNumber == 216: return 'sfcrh' if discipline == 2 and parameterCategory == 0 and parameterNumber == 215: return 'qrec' if discipline == 2 and parameterCategory == 0 and parameterNumber == 214: return 'gwrec' if discipline == 2 and parameterCategory == 0 and parameterNumber == 213: return 'ewatr' if discipline == 2 and parameterCategory == 0 and parameterNumber == 212: return 'lsoil' if discipline == 2 and parameterCategory == 0 and parameterNumber == 211: return 'sstor' if discipline == 2 and parameterCategory == 0 and parameterNumber == 210: return 'vegt' if discipline == 2 and parameterCategory == 0 and parameterNumber == 209: return 'akms' if discipline == 2 and parameterCategory == 0 and parameterNumber == 208: return 'akhs' if discipline == 2 and parameterCategory == 0 and parameterNumber == 207: return 'icwat' if discipline == 2 and parameterCategory == 0 and parameterNumber == 206: return 'rdrip' if discipline == 2 and parameterCategory == 0 and parameterNumber == 201: return 'wilt' if discipline == 2 and parameterCategory == 0 and parameterNumber == 198: return 'vgtyp' if discipline == 1 and parameterCategory == 1 and parameterNumber == 195: return 'cwr' if discipline == 1 and parameterCategory == 1 and parameterNumber == 194: return 'ppffg' if discipline == 1 and parameterCategory == 1 and parameterNumber == 192: return 'cpozp' if discipline == 0 and parameterCategory == 191 and parameterNumber == 197: return 'elonn' if discipline == 0 and parameterCategory == 191 and parameterNumber == 196: return 'nlatn' if discipline == 0 and parameterCategory == 191 and parameterNumber == 195: return 'mlyno' if discipline == 0 and parameterCategory == 191 and parameterNumber == 193: return 'elon' if discipline == 0 and parameterCategory == 191 and parameterNumber == 192: return 'nlat' if discipline == 0 and parameterCategory == 19 and parameterNumber == 232: return 'vaftd' if discipline == 0 and parameterCategory == 19 and parameterNumber == 219: return 'tpfi' if discipline == 0 and parameterCategory == 19 and parameterNumber == 218: return 'epsr' if discipline == 0 and parameterCategory == 19 and parameterNumber == 217: return 'sipd' if discipline == 0 and parameterCategory == 19 and parameterNumber == 216: return 'prsigsvr' if discipline == 0 and parameterCategory == 19 and parameterNumber == 215: return 'prsvr' if discipline == 0 and parameterCategory == 19 and parameterNumber == 214: return 'nwsalb' if discipline == 0 and parameterCategory == 19 and parameterNumber == 213: return 'nbsalb' if discipline == 0 and parameterCategory == 19 and parameterNumber == 212: return 'swsalb' if discipline == 0 and parameterCategory == 19 and parameterNumber == 211: return 'sbsalb' if discipline == 0 and parameterCategory == 19 and parameterNumber == 210: return 'havni' if discipline == 0 and parameterCategory == 19 and parameterNumber == 209: return 'lavni' if discipline == 0 and parameterCategory == 19 and parameterNumber == 208: return 'ciflt' if discipline == 0 and parameterCategory == 19 and parameterNumber == 207: return 'civis' if discipline == 0 and parameterCategory == 19 and parameterNumber == 206: return 'cicel' if discipline == 0 and parameterCategory == 19 and parameterNumber == 205: return 'flght' if discipline == 0 and parameterCategory == 19 and parameterNumber == 204: return 'mixly' if discipline == 0 and parameterCategory == 19 and parameterNumber == 203: return 'tstmc' if discipline == 0 and parameterCategory == 19 and parameterNumber == 202: return 'swindpro' if discipline == 0 and parameterCategory == 19 and parameterNumber == 201: return 'shailpro' if discipline == 0 and parameterCategory == 19 and parameterNumber == 200: return 'storprob' if discipline == 0 and parameterCategory == 19 and parameterNumber == 199: return 'windprob' if discipline == 0 and parameterCategory == 19 and parameterNumber == 198: return 'hailprob' if discipline == 0 and parameterCategory == 19 and parameterNumber == 197: return 'torprob' if discipline == 0 and parameterCategory == 19 and parameterNumber == 196: return 'hrcono' if discipline == 0 and parameterCategory == 19 and parameterNumber == 195: return 'mrcono' if discipline == 0 and parameterCategory == 19 and parameterNumber == 194: return 'srcono' if discipline == 0 and parameterCategory == 17 and parameterNumber == 192: return 'ltng' if discipline == 0 and parameterCategory == 16 and parameterNumber == 196: return 'refc' if discipline == 0 and parameterCategory == 16 and parameterNumber == 195: return 'refd' if discipline == 0 and parameterCategory == 16 and parameterNumber == 194: return 'refzc' if discipline == 0 and parameterCategory == 16 and parameterNumber == 193: return 'refzi' if discipline == 0 and parameterCategory == 16 and parameterNumber == 192: return 'refzr' if discipline == 0 and parameterCategory == 14 and parameterNumber == 199: return 'pozo' if discipline == 0 and parameterCategory == 14 and parameterNumber == 198: return 'pozt' if discipline == 0 and parameterCategory == 14 and parameterNumber == 197: return 'toz' if discipline == 0 and parameterCategory == 14 and parameterNumber == 196: return 'poz' if discipline == 0 and parameterCategory == 14 and parameterNumber == 195: return 'vdfoz' if discipline == 0 and parameterCategory == 14 and parameterNumber == 194: return 'ozcat' if discipline == 0 and parameterCategory == 14 and parameterNumber == 193: return 'ozcon' if discipline == 0 and parameterCategory == 13 and parameterNumber == 195: return 'lipmf' if discipline == 0 and parameterCategory == 13 and parameterNumber == 194: return 'lpmtf' if discipline == 0 and parameterCategory == 13 and parameterNumber == 193: return 'pmtf' if discipline == 0 and parameterCategory == 13 and parameterNumber == 192: return 'pmtc' if discipline == 0 and parameterCategory == 7 and parameterNumber == 198: return 'lai' if discipline == 0 and parameterCategory == 7 and parameterNumber == 197: return 'uphl' if discipline == 0 and parameterCategory == 7 and parameterNumber == 195: return 'cwdi' if discipline == 0 and parameterCategory == 7 and parameterNumber == 194: return 'ri' if discipline == 0 and parameterCategory == 6 and parameterNumber == 200: return 'mflux' if discipline == 0 and parameterCategory == 5 and parameterNumber == 197: return 'cfnlf' if discipline == 0 and parameterCategory == 5 and parameterNumber == 196: return 'csdlf' if discipline == 0 and parameterCategory == 5 and parameterNumber == 195: return 'csulf' if discipline == 0 and parameterCategory == 5 and parameterNumber == 194: return 'lwhr' if discipline == 0 and parameterCategory == 4 and parameterNumber == 205: return 'utrf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 204: return 'dtrf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 203: return 'nddsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 202: return 'nbdsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 201: return 'vddsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 200: return 'vbdsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 199: return 'cfnsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 198: return 'csusf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 197: return 'swhr' if discipline == 0 and parameterCategory == 4 and parameterNumber == 196: return 'csdsf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 195: return 'cduvb' if discipline == 0 and parameterCategory == 4 and parameterNumber == 194: return 'duvb' if discipline == 0 and parameterCategory == 3 and parameterNumber == 212: return 'presn' if discipline == 0 and parameterCategory == 3 and parameterNumber == 211: return 'hgtn' if discipline == 0 and parameterCategory == 3 and parameterNumber == 210: return 'lmh' if discipline == 0 and parameterCategory == 3 and parameterNumber == 209: return 'cnvdemf' if discipline == 0 and parameterCategory == 3 and parameterNumber == 208: return 'cnvdmf' if discipline == 0 and parameterCategory == 3 and parameterNumber == 207: return 'cnvumf' if discipline == 0 and parameterCategory == 3 and parameterNumber == 206: return 'nlgsp' if discipline == 0 and parameterCategory == 3 and parameterNumber == 205: return 'layth' if discipline == 0 and parameterCategory == 3 and parameterNumber == 204: return 'hgty' if discipline == 0 and parameterCategory == 3 and parameterNumber == 203: return 'hgtx' if discipline == 0 and parameterCategory == 3 and parameterNumber == 202: return 'lpsy' if discipline == 0 and parameterCategory == 3 and parameterNumber == 201: return 'lpsx' if discipline == 0 and parameterCategory == 3 and parameterNumber == 200: return 'plpl' if discipline == 0 and parameterCategory == 3 and parameterNumber == 199: return 'tslsa' if discipline == 0 and parameterCategory == 3 and parameterNumber == 198: return 'mslma' if discipline == 0 and parameterCategory == 3 and parameterNumber == 192: return 'mslet' if discipline == 0 and parameterCategory == 2 and parameterNumber == 219: return 'pvmww' if discipline == 0 and parameterCategory == 2 and parameterNumber == 218: return 'lmv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 217: return 'cngwdv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 216: return 'cngwdu' if discipline == 0 and parameterCategory == 2 and parameterNumber == 215: return 'omgalf' if discipline == 0 and parameterCategory == 2 and parameterNumber == 214: return 'wtend' if discipline == 0 and parameterCategory == 2 and parameterNumber == 213: return 'cnvv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 212: return 'cnvu' if discipline == 0 and parameterCategory == 2 and parameterNumber == 211: return 'gwdv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 210: return 'gwdu' if discipline == 0 and parameterCategory == 2 and parameterNumber == 209: return 'vdfva' if discipline == 0 and parameterCategory == 2 and parameterNumber == 208: return 'vdfua' if discipline == 0 and parameterCategory == 2 and parameterNumber == 207: return 'covtm' if discipline == 0 and parameterCategory == 2 and parameterNumber == 206: return 'covtz' if discipline == 0 and parameterCategory == 2 and parameterNumber == 205: return 'covmz' if discipline == 0 and parameterCategory == 2 and parameterNumber == 204: return 'vedh' if discipline == 0 and parameterCategory == 2 and parameterNumber == 203: return 'lopp' if discipline == 0 and parameterCategory == 2 and parameterNumber == 202: return 'lapp' if discipline == 0 and parameterCategory == 2 and parameterNumber == 201: return 'lovv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 200: return 'lavv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 199: return 'louv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 198: return 'lauv' if discipline == 0 and parameterCategory == 1 and parameterNumber == 225: return 'frzr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 224: return 'acpcpn' if discipline == 0 and parameterCategory == 1 and parameterNumber == 223: return 'apcpn' if discipline == 0 and parameterCategory == 1 and parameterNumber == 222: return 'snowt' if discipline == 0 and parameterCategory == 1 and parameterNumber == 221: return 'arain' if discipline == 0 and parameterCategory == 1 and parameterNumber == 220: return 'qmin' if discipline == 0 and parameterCategory == 1 and parameterNumber == 219: return 'qmax' if discipline == 0 and parameterCategory == 1 and parameterNumber == 218: return 'qz0' if discipline == 0 and parameterCategory == 1 and parameterNumber == 217: return 'lrgmr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 216: return 'condp' if discipline == 0 and parameterCategory == 1 and parameterNumber == 215: return 'vdfmr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 214: return 'shamr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 213: return 'cnvmr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 212: return 'sbsno' if discipline == 0 and parameterCategory == 1 and parameterNumber == 211: return 'emnp' if discipline == 0 and parameterCategory == 1 and parameterNumber == 210: return 'tcolm' if discipline == 0 and parameterCategory == 1 and parameterNumber == 209: return 'tclsw' if discipline == 0 and parameterCategory == 1 and parameterNumber == 208: return 'snot' if discipline == 0 and parameterCategory == 1 and parameterNumber == 207: return 'ncip' if discipline == 0 and parameterCategory == 1 and parameterNumber == 206: return 'tipd' if discipline == 0 and parameterCategory == 1 and parameterNumber == 198: return 'minrh' if discipline == 0 and parameterCategory == 0 and parameterNumber == 204: return 'tchp' if discipline == 0 and parameterCategory == 0 and parameterNumber == 203: return 'thz0' if discipline == 0 and parameterCategory == 0 and parameterNumber == 202: return 'vdfhr' if discipline == 0 and parameterCategory == 0 and parameterNumber == 201: return 'shahr' if discipline == 0 and parameterCategory == 0 and parameterNumber == 200: return 'tsd1d' if discipline == 0 and parameterCategory == 0 and parameterNumber == 199: return 'ttphy' if discipline == 0 and parameterCategory == 0 and parameterNumber == 198: return 'ttdia' if discipline == 0 and parameterCategory == 0 and parameterNumber == 197: return 'thflx' if discipline == 0 and parameterCategory == 0 and parameterNumber == 196: return 'cnvhr' if discipline == 0 and parameterCategory == 0 and parameterNumber == 195: return 'lrghr' if discipline == 0 and parameterCategory == 0 and parameterNumber == 194: return 'rev' if discipline == 0 and parameterCategory == 0 and parameterNumber == 193: return 'ttrad' if discipline == 2 and parameterCategory == 3 and parameterNumber == 197: return 'poros' if discipline == 2 and parameterCategory == 3 and parameterNumber == 196: return 'smdry' if discipline == 2 and parameterCategory == 3 and parameterNumber == 195: return 'smref' if discipline == 2 and parameterCategory == 3 and parameterNumber == 193: return 'rlyrs' if discipline == 2 and parameterCategory == 3 and parameterNumber == 192: return 'soill' if discipline == 2 and parameterCategory == 0 and parameterNumber == 204: return 'rcq' if discipline == 2 and parameterCategory == 0 and parameterNumber == 205: return 'rcsol' if discipline == 2 and parameterCategory == 0 and parameterNumber == 203: return 'rct' if discipline == 2 and parameterCategory == 0 and parameterNumber == 202: return 'rcs' if discipline == 2 and parameterCategory == 0 and parameterNumber == 200: return 'rsmin' if discipline == 2 and parameterCategory == 0 and parameterNumber == 199: return 'ccond' if discipline == 2 and parameterCategory == 0 and parameterNumber == 197: return 'bmixl' if discipline == 2 and parameterCategory == 0 and parameterNumber == 196: return 'cnwat' if discipline == 2 and parameterCategory == 0 and parameterNumber == 195: return 'sfexc' if discipline == 2 and parameterCategory == 0 and parameterNumber == 194: return 'mstav' if discipline == 2 and parameterCategory == 0 and parameterNumber == 193: return 'gflux' if discipline == 2 and parameterCategory == 0 and parameterNumber == 192: return 'soilw' if discipline == 1 and parameterCategory == 0 and parameterNumber == 193: return 'ssrun' if discipline == 1 and parameterCategory == 0 and parameterNumber == 192: return 'bgrun' if discipline == 0 and parameterCategory == 191 and parameterNumber == 194: return 'tsec' if discipline == 0 and parameterCategory == 19 and parameterNumber == 193: return 'snfalb' if discipline == 0 and parameterCategory == 19 and parameterNumber == 192: return 'mxsalb' if discipline == 0 and parameterCategory == 14 and parameterNumber == 192: return 'o3mr' if discipline == 0 and parameterCategory == 7 and parameterNumber == 193: return '4lftx' if discipline == 0 and parameterCategory == 7 and parameterNumber == 192: return 'lftx' if discipline == 0 and parameterCategory == 6 and parameterNumber == 199: return 'fice' if discipline == 0 and parameterCategory == 6 and parameterNumber == 198: return 'tcolc' if discipline == 0 and parameterCategory == 6 and parameterNumber == 197: return 'tcoli' if discipline == 0 and parameterCategory == 6 and parameterNumber == 196: return 'tcolw' if discipline == 0 and parameterCategory == 6 and parameterNumber == 194: return 'cuefi' if discipline == 0 and parameterCategory == 6 and parameterNumber == 193: return 'cwork' if discipline == 0 and parameterCategory == 6 and parameterNumber == 192: return 'cdlyr' if discipline == 0 and parameterCategory == 5 and parameterNumber == 193: return 'ulwrf' if discipline == 0 and parameterCategory == 5 and parameterNumber == 192: return 'dlwrf' if discipline == 0 and parameterCategory == 7 and parameterNumber == 196: return 'uvi' if discipline == 0 and parameterCategory == 4 and parameterNumber == 193: return 'uswrf' if discipline == 0 and parameterCategory == 4 and parameterNumber == 192: return 'dswrf' if discipline == 0 and parameterCategory == 3 and parameterNumber == 197: return '5wava' if discipline == 0 and parameterCategory == 3 and parameterNumber == 196: return 'hpbl' if discipline == 0 and parameterCategory == 3 and parameterNumber == 195: return 'v-gwd' if discipline == 0 and parameterCategory == 3 and parameterNumber == 194: return 'u-gwd' if discipline == 0 and parameterCategory == 3 and parameterNumber == 193: return '5wavh' if discipline == 0 and parameterCategory == 2 and parameterNumber == 197: return 'fricv' if discipline == 0 and parameterCategory == 2 and parameterNumber == 196: return 'cd' if discipline == 0 and parameterCategory == 2 and parameterNumber == 195: return 'vstm' if discipline == 0 and parameterCategory == 2 and parameterNumber == 194: return 'ustm' if discipline == 0 and parameterCategory == 2 and parameterNumber == 193: return 'mflx' if discipline == 0 and parameterCategory == 2 and parameterNumber == 192: return 'vwsh' if discipline == 0 and parameterCategory == 1 and parameterNumber == 11: return 'sdwe' if discipline == 0 and parameterCategory == 1 and parameterNumber == 205: return 'tcols' if discipline == 0 and parameterCategory == 1 and parameterNumber == 204: return 'tcolr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 203: return 'rime' if discipline == 0 and parameterCategory == 1 and parameterNumber == 202: return 'frain' if discipline == 0 and parameterCategory == 1 and parameterNumber == 201: return 'snowc' if discipline == 0 and parameterCategory == 1 and parameterNumber == 200: return 'pevpr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 199: return 'pevap' if discipline == 1 and parameterCategory == 1 and parameterNumber == 193: return 'cpofp' if discipline == 0 and parameterCategory == 1 and parameterNumber == 196: return 'cprat' if discipline == 0 and parameterCategory == 1 and parameterNumber == 195: return 'csnow' if discipline == 0 and parameterCategory == 1 and parameterNumber == 194: return 'cicep' if discipline == 0 and parameterCategory == 1 and parameterNumber == 193: return 'cfrzr' if discipline == 0 and parameterCategory == 1 and parameterNumber == 192: return 'crain' if discipline == 0 and parameterCategory == 1 and parameterNumber == 197: return 'mconv' if discipline == 0 and parameterCategory == 6 and parameterNumber == 195: return 'tcond' if discipline == 0 and parameterCategory == 0 and parameterNumber == 192: return 'snohf' if discipline == 0 and parameterCategory == 7 and parameterNumber == 6: return 'cape' return wrapped

30,444

0

23

f855524a5a17dbd4474da10c04f38088efe0a849

4,279

py

Python

gladiator/prepare/command.py

hellcat17/gladiator

4f1ea65112dab04f584b148d23a150f28a94c434

[ "Apache-2.0" ]

null

gladiator/prepare/command.py

hellcat17/gladiator

4f1ea65112dab04f584b148d23a150f28a94c434

[ "Apache-2.0" ]

null

gladiator/prepare/command.py

hellcat17/gladiator

4f1ea65112dab04f584b148d23a150f28a94c434

[ "Apache-2.0" ]

null

"""Prepare OpenGL commands for use in templates.""" from enum import auto, Enum from typing import Iterable, Mapping, Optional, Union import attr from gladiator.parse.command import Command, Type from gladiator.prepare.enum import PreparedEnum from gladiator.prepare.style import transform_symbol from gladiator.optional import OptionalValue from gladiator.options import Options from gladiator.resources import read_resource_file @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) _TYPE_TRANSLATIONS = dict( t.split(",") for t in read_resource_file("data/type_translations").split("\n") if t ) # TODO: take options such as casing, style and namespace # TODO: generate special wrappers for generators and deleters def prepare_commands( commands: Iterable[Command], prepared_enums: Mapping[str, PreparedEnum], options: Options, ): """Prepare the given commands for use as references and in templates. The given enums are used to construct type references. Yields tuples mapping the original command name to the prepared command. """ for command in commands: yield command.name, PreparedCommand( original=command, type_=CommandType.DEFAULT, implementation=_make_default_implementation(command, prepared_enums), name=transform_symbol( command.name, options.function_case, options.omit_prefix ), )

28.526667

87

0.714419

"""Prepare OpenGL commands for use in templates.""" from enum import auto, Enum from typing import Iterable, Mapping, Optional, Union import attr from gladiator.parse.command import Command, Type from gladiator.prepare.enum import PreparedEnum from gladiator.prepare.style import transform_symbol from gladiator.optional import OptionalValue from gladiator.options import Options from gladiator.resources import read_resource_file class CommandType(Enum): DEFAULT = auto() GENERATOR = auto() DELETER = auto() class ConversionType(Enum): CAST = auto() METHOD_CALL = auto() @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class TypeReference: low_level: str high_level: Optional[PreparedEnum] front_modifiers: Optional[str] back_modifiers: Optional[str] @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class PreparedParameter: type_: TypeReference name: str @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class CastAction: param: str to: TypeReference @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class MethodCallAction: param: str method: str args: Iterable[str] @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class PreparedConversion: type_: ConversionType action: Union[CastAction, MethodCallAction] @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class PreparedImplementation: return_type: TypeReference params: Iterable[PreparedParameter] retval_conversion: PreparedConversion param_conversions: Iterable[PreparedConversion] retval_temporary = "retval" @attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True) class PreparedCommand: original: Command name: str type_: CommandType implementation: PreparedImplementation _TYPE_TRANSLATIONS = dict( t.split(",") for t in read_resource_file("data/type_translations").split("\n") if t ) def _translate_low_level_type(low_level: str): return _TYPE_TRANSLATIONS.get(low_level, low_level) def _make_type_reference(target: Type, prepared_enums: Mapping[str, PreparedEnum]): return TypeReference( low_level=_translate_low_level_type(target.low_level), front_modifiers=target.front_modifiers, back_modifiers=target.back_modifiers, high_level=OptionalValue(target.high_level) .map(prepared_enums.get) .value_or_none, ) def _make_default_implementation( command: Command, prepared_enums: Mapping[str, PreparedEnum] ): retval_typeref = _make_type_reference(command.return_type, prepared_enums) params_with_refs = [ (param, _make_type_reference(param.type_, prepared_enums)) for param in command.params ] return PreparedImplementation( return_type=retval_typeref, params=[ PreparedParameter(type_=ref, name=param.name) for param, ref in params_with_refs ], retval_conversion=PreparedConversion( type_=ConversionType.CAST, action=CastAction( param=PreparedImplementation.retval_temporary, to=retval_typeref ), ), param_conversions=[ PreparedConversion( type_=ConversionType.CAST, action=CastAction(param=param.name, to=ref) ) for param, ref in params_with_refs ], ) # TODO: take options such as casing, style and namespace # TODO: generate special wrappers for generators and deleters def prepare_commands( commands: Iterable[Command], prepared_enums: Mapping[str, PreparedEnum], options: Options, ): """Prepare the given commands for use as references and in templates. The given enums are used to construct type references. Yields tuples mapping the original command name to the prepared command. """ for command in commands: yield command.name, PreparedCommand( original=command, type_=CommandType.DEFAULT, implementation=_make_default_implementation(command, prepared_enums), name=transform_symbol( command.name, options.function_case, options.omit_prefix ), )

1,387

761

269

c7393ff14c969a96e0698f982393eac0fd77f625

351

py

Python

notebooks/bqm_functions.py

ozlemsalehi/qa-edu

4b74a1d06820df36b710919c31a82a71b4e85d84

[ "MIT" ]

1

2021-10-10T03:35:03.000Z

notebooks/bqm_functions.py

ozlemsalehi/qa-edu

4b74a1d06820df36b710919c31a82a71b4e85d84

[ "MIT" ]

null

notebooks/bqm_functions.py

ozlemsalehi/qa-edu

4b74a1d06820df36b710919c31a82a71b4e85d84

[ "MIT" ]

null

""" This file contains all the functions used in the notebooks under the Binary Quadratic Model section. Prepared by Akash Narayanan B """ from dimod import BinaryQuadraticModel # Task 3 linear = {'x1': 3, 'x2': -1, 'x3': 10, 'x4': 7} quadratic = {('x1', 'x2'): 2, ('x1', 'x3'): -5, ('x2', 'x3'): 3, ('x3', 'x4'): 11} offset = 8 vartype = 'BINARY'

25.071429

82

0.621083

""" This file contains all the functions used in the notebooks under the Binary Quadratic Model section. Prepared by Akash Narayanan B """ from dimod import BinaryQuadraticModel # Task 3 linear = {'x1': 3, 'x2': -1, 'x3': 10, 'x4': 7} quadratic = {('x1', 'x2'): 2, ('x1', 'x3'): -5, ('x2', 'x3'): 3, ('x3', 'x4'): 11} offset = 8 vartype = 'BINARY'

0

21724846cd453a1ea089631982f272aa7f74cddf

754

py

Python

project/delibere/migrations/0036_auto_20170922_1319.py

guglielmo/mosic2-db-delibere

a7c92adf42ad53023af47776c32baa2ee525f5e9

[ "BSD-3-Clause" ]

null

project/delibere/migrations/0036_auto_20170922_1319.py

guglielmo/mosic2-db-delibere

a7c92adf42ad53023af47776c32baa2ee525f5e9

[ "BSD-3-Clause" ]

null

project/delibere/migrations/0036_auto_20170922_1319.py

guglielmo/mosic2-db-delibere

a7c92adf42ad53023af47776c32baa2ee525f5e9

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.8 on 2017-09-22 13:19 from __future__ import unicode_literals from django.db import migrations, models

27.925926

124

0.619363

# -*- coding: utf-8 -*- # Generated by Django 1.10.8 on 2017-09-22 13:19 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('delibere', '0035_auto_20170621_1621'), ] operations = [ migrations.AlterField( model_name='delibera', name='codice', field=models.CharField(default=None, help_text='Codice identificativo anno/seduta.', max_length=8, unique=True), preserve_default=False, ), migrations.AlterField( model_name='documento', name='nome', field=models.CharField(blank=True, max_length=32, null=True, unique=True), ), ]

0

575

23

b4791417864a20c51bb22fe093517c0cb17c5969

2,605

py

Python

classification_ScanObjectNN/ScanObjectNN.py

ma-xu/pointMLP-pytorch

205669826d269972e2616b13198dd52f41c9992e

[ "Apache-2.0" ]

95

2022-02-15T10:09:31.000Z

2022-03-30T04:29:12.000Z

classification_ScanObjectNN/ScanObjectNN.py

ma-xu/pointMLP-pytorch

205669826d269972e2616b13198dd52f41c9992e

[ "Apache-2.0" ]

17

2022-02-16T06:41:09.000Z

2022-03-31T13:00:09.000Z

classification_ScanObjectNN/ScanObjectNN.py

13952522076/pointMLP-pytorch

205669826d269972e2616b13198dd52f41c9992e

[ "Apache-2.0" ]

13

2022-02-16T10:06:11.000Z

2022-03-31T12:55:44.000Z

""" ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip """ import os import sys import glob import h5py import numpy as np from torch.utils.data import Dataset os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" if __name__ == '__main__': train = ScanObjectNN(1024) test = ScanObjectNN(1024, 'test') for data, label in train: print(data.shape) print(label)

32.160494

108

0.66142

""" ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip """ import os import sys import glob import h5py import numpy as np from torch.utils.data import Dataset os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" def download(): BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'h5_files')): # note that this link only contains the hardest perturbed variant (PB_T50_RS). # for full versions, consider the following link. www = 'https://web.northeastern.edu/smilelab/xuma/datasets/h5_files.zip' # www = 'http://103.24.77.34/scanobjectnn/h5_files.zip' zipfile = os.path.basename(www) os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def load_scanobjectnn_data(partition): download() BASE_DIR = os.path.dirname(os.path.abspath(__file__)) all_data = [] all_label = [] h5_name = BASE_DIR + '/data/h5_files/main_split/' + partition + '_objectdataset_augmentedrot_scale75.h5' f = h5py.File(h5_name, mode="r") data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2. / 3., high=3. / 2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud class ScanObjectNN(Dataset): def __init__(self, num_points, partition='training'): self.data, self.label = load_scanobjectnn_data(partition) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'training': pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) return pointcloud, label def __len__(self): return self.data.shape[0] if __name__ == '__main__': train = ScanObjectNN(1024) test = ScanObjectNN(1024, 'test') for data, label in train: print(data.shape) print(label)

2,021

7

172

a4a4356ef17d15a32870aceafc1ab8a92b4c9303

71

py

Python

sgi/recursos_humanos/__init__.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

null

sgi/recursos_humanos/__init__.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

8

2019-12-07T13:13:34.000Z

2021-09-02T03:07:25.000Z

sgi/recursos_humanos/__init__.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

null

default_app_config = "sgi.recursos_humanos.apps.RecursosHumanosConfig"

35.5

70

0.873239

default_app_config = "sgi.recursos_humanos.apps.RecursosHumanosConfig"

0

9903afe6a1227ea53c957090155382ddf95e9914

13,824

py

Python

gntp/__init__.py

pcjacobse/sabnzbd

494e72a9963a1810e69f4e0f69df7c9dfb9256b0

[ "0BSD", "PSF-2.0", "BSD-2-Clause", "MIT", "BSD-3-Clause" ]

null

gntp/__init__.py

pcjacobse/sabnzbd

494e72a9963a1810e69f4e0f69df7c9dfb9256b0

[ "0BSD", "PSF-2.0", "BSD-2-Clause", "MIT", "BSD-3-Clause" ]

null

gntp/__init__.py

pcjacobse/sabnzbd

494e72a9963a1810e69f4e0f69df7c9dfb9256b0

[ "0BSD", "PSF-2.0", "BSD-2-Clause", "MIT", "BSD-3-Clause" ]

null

import re import hashlib import time import StringIO __version__ = '0.8' #GNTP/<version> <messagetype> <encryptionAlgorithmID>[:<ivValue>][ <keyHashAlgorithmID>:<keyHash>.<salt>] GNTP_INFO_LINE = re.compile( 'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)' + ' (?P<encryptionAlgorithmID>[A-Z0-9]+(:(?P<ivValue>[A-F0-9]+))?) ?' + '((?P<keyHashAlgorithmID>[A-Z0-9]+):(?P<keyHash>[A-F0-9]+).(?P<salt>[A-F0-9]+))?\r\n', re.IGNORECASE ) GNTP_INFO_LINE_SHORT = re.compile( 'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)', re.IGNORECASE ) GNTP_HEADER = re.compile('([\w-]+):(.+)') GNTP_EOL = '\r\n' class _GNTPBuffer(StringIO.StringIO): """GNTP Buffer class""" def writefmt(self, message="", *args): """Shortcut function for writing GNTP Headers""" self.write((message % args).encode('utf8', 'replace')) self.write(GNTP_EOL) class _GNTPBase(object): """Base initilization :param string messagetype: GNTP Message type :param string version: GNTP Protocol version :param string encription: Encryption protocol """ def _parse_info(self, data): """Parse the first line of a GNTP message to get security and other info values :param string data: GNTP Message :return dict: Parsed GNTP Info line """ match = GNTP_INFO_LINE.match(data) if not match: raise ParseError('ERROR_PARSING_INFO_LINE') info = match.groupdict() if info['encryptionAlgorithmID'] == 'NONE': info['encryptionAlgorithmID'] = None return info def set_password(self, password, encryptAlgo='MD5'): """Set a password for a GNTP Message :param string password: Null to clear password :param string encryptAlgo: Supports MD5, SHA1, SHA256, SHA512 """ hash = { 'MD5': hashlib.md5, 'SHA1': hashlib.sha1, 'SHA256': hashlib.sha256, 'SHA512': hashlib.sha512, } self.password = password self.encryptAlgo = encryptAlgo.upper() if not password: self.info['encryptionAlgorithmID'] = None self.info['keyHashAlgorithm'] = None return if not self.encryptAlgo in hash.keys(): raise UnsupportedError('INVALID HASH "%s"' % self.encryptAlgo) hashfunction = hash.get(self.encryptAlgo) password = password.encode('utf8') seed = time.ctime() salt = hashfunction(seed).hexdigest() saltHash = hashfunction(seed).digest() keyBasis = password + saltHash key = hashfunction(keyBasis).digest() keyHash = hashfunction(key).hexdigest() self.info['keyHashAlgorithmID'] = self.encryptAlgo self.info['keyHash'] = keyHash.upper() self.info['salt'] = salt.upper() def _decode_hex(self, value): """Helper function to decode hex string to `proper` hex string :param string value: Human readable hex string :return string: Hex string """ result = '' for i in range(0, len(value), 2): tmp = int(value[i:i + 2], 16) result += chr(tmp) return result def _validate_password(self, password): """Validate GNTP Message against stored password""" self.password = password if password == None: raise AuthError('Missing password') keyHash = self.info.get('keyHash', None) if keyHash is None and self.password is None: return True if keyHash is None: raise AuthError('Invalid keyHash') if self.password is None: raise AuthError('Missing password') password = self.password.encode('utf8') saltHash = self._decode_hex(self.info['salt']) keyBasis = password + saltHash key = hashlib.md5(keyBasis).digest() keyHash = hashlib.md5(key).hexdigest() if not keyHash.upper() == self.info['keyHash'].upper(): raise AuthError('Invalid Hash') return True def validate(self): """Verify required headers""" for header in self._requiredHeaders: if not self.headers.get(header, False): raise ParseError('Missing Notification Header: ' + header) def _format_info(self): """Generate info line for GNTP Message :return string: """ info = u'GNTP/%s %s' % ( self.info.get('version'), self.info.get('messagetype'), ) if self.info.get('encryptionAlgorithmID', None): info += ' %s:%s' % ( self.info.get('encryptionAlgorithmID'), self.info.get('ivValue'), ) else: info += ' NONE' if self.info.get('keyHashAlgorithmID', None): info += ' %s:%s.%s' % ( self.info.get('keyHashAlgorithmID'), self.info.get('keyHash'), self.info.get('salt') ) return info def _parse_dict(self, data): """Helper function to parse blocks of GNTP headers into a dictionary :param string data: :return dict: """ dict = {} for line in data.split('\r\n'): match = GNTP_HEADER.match(line) if not match: continue key = unicode(match.group(1).strip(), 'utf8', 'replace') val = unicode(match.group(2).strip(), 'utf8', 'replace') dict[key] = val return dict def add_resource(self, data): """Add binary resource :param string data: Binary Data """ identifier = hashlib.md5(data).hexdigest() self.resources[identifier] = data return 'x-growl-resource://%s' % identifier def decode(self, data, password=None): """Decode GNTP Message :param string data: """ self.password = password self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self.headers = self._parse_dict(parts[0]) def encode(self): """Encode a generic GNTP Message :return string: GNTP Message ready to be sent """ buffer = _GNTPBuffer() buffer.writefmt(self._format_info()) #Headers for k, v in self.headers.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Resources for resource, data in self.resources.iteritems(): buffer.writefmt('Identifier: %s', resource) buffer.writefmt('Length: %d', len(data)) buffer.writefmt() buffer.write(data) buffer.writefmt() buffer.writefmt() return buffer.getvalue() class GNTPRegister(_GNTPBase): """Represents a GNTP Registration Command :param string data: (Optional) See decode() :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Application-Name', 'Notifications-Count' ] _requiredNotificationHeaders = ['Notification-Name'] def validate(self): '''Validate required headers and validate notification headers''' for header in self._requiredHeaders: if not self.headers.get(header, False): raise ParseError('Missing Registration Header: ' + header) for notice in self.notifications: for header in self._requiredNotificationHeaders: if not notice.get(header, False): raise ParseError('Missing Notification Header: ' + header) def decode(self, data, password): """Decode existing GNTP Registration message :param string data: Message to decode """ self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self._validate_password(password) self.headers = self._parse_dict(parts[0]) for i, part in enumerate(parts): if i == 0: continue # Skip Header if part.strip() == '': continue notice = self._parse_dict(part) if notice.get('Notification-Name', False): self.notifications.append(notice) elif notice.get('Identifier', False): notice['Data'] = self._decode_binary(part, notice) #open('register.png','wblol').write(notice['Data']) self.resources[notice.get('Identifier')] = notice def add_notification(self, name, enabled=True): """Add new Notification to Registration message :param string name: Notification Name :param boolean enabled: Enable this notification by default """ notice = {} notice['Notification-Name'] = u'%s' % name notice['Notification-Enabled'] = u'%s' % enabled self.notifications.append(notice) self.add_header('Notifications-Count', len(self.notifications)) def encode(self): """Encode a GNTP Registration Message :return string: Encoded GNTP Registration message """ buffer = _GNTPBuffer() buffer.writefmt(self._format_info()) #Headers for k, v in self.headers.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Notifications if len(self.notifications) > 0: for notice in self.notifications: for k, v in notice.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Resources for resource, data in self.resources.iteritems(): buffer.writefmt('Identifier: %s', resource) buffer.writefmt('Length: %d', len(data)) buffer.writefmt() buffer.write(data) buffer.writefmt() buffer.writefmt() return buffer.getvalue() class GNTPNotice(_GNTPBase): """Represents a GNTP Notification Command :param string data: (Optional) See decode() :param string app: (Optional) Set Application-Name :param string name: (Optional) Set Notification-Name :param string title: (Optional) Set Notification Title :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Application-Name', 'Notification-Name', 'Notification-Title' ] def decode(self, data, password): """Decode existing GNTP Notification message :param string data: Message to decode. """ self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self._validate_password(password) self.headers = self._parse_dict(parts[0]) for i, part in enumerate(parts): if i == 0: continue # Skip Header if part.strip() == '': continue notice = self._parse_dict(part) if notice.get('Identifier', False): notice['Data'] = self._decode_binary(part, notice) #open('notice.png','wblol').write(notice['Data']) self.resources[notice.get('Identifier')] = notice class GNTPSubscribe(_GNTPBase): """Represents a GNTP Subscribe Command :param string data: (Optional) See decode() :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Subscriber-ID', 'Subscriber-Name', ] class GNTPOK(_GNTPBase): """Represents a GNTP OK Response :param string data: (Optional) See _GNTPResponse.decode() :param string action: (Optional) Set type of action the OK Response is for """ _requiredHeaders = ['Response-Action'] class GNTPError(_GNTPBase): """Represents a GNTP Error response :param string data: (Optional) See _GNTPResponse.decode() :param string errorcode: (Optional) Error code :param string errordesc: (Optional) Error Description """ _requiredHeaders = ['Error-Code', 'Error-Description'] def parse_gntp(data, password=None): """Attempt to parse a message as a GNTP message :param string data: Message to be parsed :param string password: Optional password to be used to verify the message """ match = GNTP_INFO_LINE_SHORT.match(data) if not match: raise ParseError('INVALID_GNTP_INFO') info = match.groupdict() if info['messagetype'] == 'REGISTER': return GNTPRegister(data, password=password) elif info['messagetype'] == 'NOTIFY': return GNTPNotice(data, password=password) elif info['messagetype'] == 'SUBSCRIBE': return GNTPSubscribe(data, password=password) elif info['messagetype'] == '-OK': return GNTPOK(data) elif info['messagetype'] == '-ERROR': return GNTPError(data) raise ParseError('INVALID_GNTP_MESSAGE')

27.105882

105

0.697844

import re import hashlib import time import StringIO __version__ = '0.8' #GNTP/<version> <messagetype> <encryptionAlgorithmID>[:<ivValue>][ <keyHashAlgorithmID>:<keyHash>.<salt>] GNTP_INFO_LINE = re.compile( 'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)' + ' (?P<encryptionAlgorithmID>[A-Z0-9]+(:(?P<ivValue>[A-F0-9]+))?) ?' + '((?P<keyHashAlgorithmID>[A-Z0-9]+):(?P<keyHash>[A-F0-9]+).(?P<salt>[A-F0-9]+))?\r\n', re.IGNORECASE ) GNTP_INFO_LINE_SHORT = re.compile( 'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)', re.IGNORECASE ) GNTP_HEADER = re.compile('([\w-]+):(.+)') GNTP_EOL = '\r\n' class BaseError(Exception): def gntp_error(self): error = GNTPError(self.errorcode, self.errordesc) return error.encode() class ParseError(BaseError): errorcode = 500 errordesc = 'Error parsing the message' class AuthError(BaseError): errorcode = 400 errordesc = 'Error with authorization' class UnsupportedError(BaseError): errorcode = 500 errordesc = 'Currently unsupported by gntp.py' class _GNTPBuffer(StringIO.StringIO): """GNTP Buffer class""" def writefmt(self, message="", *args): """Shortcut function for writing GNTP Headers""" self.write((message % args).encode('utf8', 'replace')) self.write(GNTP_EOL) class _GNTPBase(object): """Base initilization :param string messagetype: GNTP Message type :param string version: GNTP Protocol version :param string encription: Encryption protocol """ def __init__(self, messagetype=None, version='1.0', encryption=None): self.info = { 'version': version, 'messagetype': messagetype, 'encryptionAlgorithmID': encryption } self.headers = {} self.resources = {} def __str__(self): return self.encode() def _parse_info(self, data): """Parse the first line of a GNTP message to get security and other info values :param string data: GNTP Message :return dict: Parsed GNTP Info line """ match = GNTP_INFO_LINE.match(data) if not match: raise ParseError('ERROR_PARSING_INFO_LINE') info = match.groupdict() if info['encryptionAlgorithmID'] == 'NONE': info['encryptionAlgorithmID'] = None return info def set_password(self, password, encryptAlgo='MD5'): """Set a password for a GNTP Message :param string password: Null to clear password :param string encryptAlgo: Supports MD5, SHA1, SHA256, SHA512 """ hash = { 'MD5': hashlib.md5, 'SHA1': hashlib.sha1, 'SHA256': hashlib.sha256, 'SHA512': hashlib.sha512, } self.password = password self.encryptAlgo = encryptAlgo.upper() if not password: self.info['encryptionAlgorithmID'] = None self.info['keyHashAlgorithm'] = None return if not self.encryptAlgo in hash.keys(): raise UnsupportedError('INVALID HASH "%s"' % self.encryptAlgo) hashfunction = hash.get(self.encryptAlgo) password = password.encode('utf8') seed = time.ctime() salt = hashfunction(seed).hexdigest() saltHash = hashfunction(seed).digest() keyBasis = password + saltHash key = hashfunction(keyBasis).digest() keyHash = hashfunction(key).hexdigest() self.info['keyHashAlgorithmID'] = self.encryptAlgo self.info['keyHash'] = keyHash.upper() self.info['salt'] = salt.upper() def _decode_hex(self, value): """Helper function to decode hex string to `proper` hex string :param string value: Human readable hex string :return string: Hex string """ result = '' for i in range(0, len(value), 2): tmp = int(value[i:i + 2], 16) result += chr(tmp) return result def _decode_binary(self, rawIdentifier, identifier): rawIdentifier += '\r\n\r\n' dataLength = int(identifier['Length']) pointerStart = self.raw.find(rawIdentifier) + len(rawIdentifier) pointerEnd = pointerStart + dataLength data = self.raw[pointerStart:pointerEnd] if not len(data) == dataLength: raise ParseError('INVALID_DATA_LENGTH Expected: %s Recieved %s' % (dataLength, len(data))) return data def _validate_password(self, password): """Validate GNTP Message against stored password""" self.password = password if password == None: raise AuthError('Missing password') keyHash = self.info.get('keyHash', None) if keyHash is None and self.password is None: return True if keyHash is None: raise AuthError('Invalid keyHash') if self.password is None: raise AuthError('Missing password') password = self.password.encode('utf8') saltHash = self._decode_hex(self.info['salt']) keyBasis = password + saltHash key = hashlib.md5(keyBasis).digest() keyHash = hashlib.md5(key).hexdigest() if not keyHash.upper() == self.info['keyHash'].upper(): raise AuthError('Invalid Hash') return True def validate(self): """Verify required headers""" for header in self._requiredHeaders: if not self.headers.get(header, False): raise ParseError('Missing Notification Header: ' + header) def _format_info(self): """Generate info line for GNTP Message :return string: """ info = u'GNTP/%s %s' % ( self.info.get('version'), self.info.get('messagetype'), ) if self.info.get('encryptionAlgorithmID', None): info += ' %s:%s' % ( self.info.get('encryptionAlgorithmID'), self.info.get('ivValue'), ) else: info += ' NONE' if self.info.get('keyHashAlgorithmID', None): info += ' %s:%s.%s' % ( self.info.get('keyHashAlgorithmID'), self.info.get('keyHash'), self.info.get('salt') ) return info def _parse_dict(self, data): """Helper function to parse blocks of GNTP headers into a dictionary :param string data: :return dict: """ dict = {} for line in data.split('\r\n'): match = GNTP_HEADER.match(line) if not match: continue key = unicode(match.group(1).strip(), 'utf8', 'replace') val = unicode(match.group(2).strip(), 'utf8', 'replace') dict[key] = val return dict def add_header(self, key, value): if isinstance(value, unicode): self.headers[key] = value else: self.headers[key] = unicode('%s' % value, 'utf8', 'replace') def add_resource(self, data): """Add binary resource :param string data: Binary Data """ identifier = hashlib.md5(data).hexdigest() self.resources[identifier] = data return 'x-growl-resource://%s' % identifier def decode(self, data, password=None): """Decode GNTP Message :param string data: """ self.password = password self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self.headers = self._parse_dict(parts[0]) def encode(self): """Encode a generic GNTP Message :return string: GNTP Message ready to be sent """ buffer = _GNTPBuffer() buffer.writefmt(self._format_info()) #Headers for k, v in self.headers.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Resources for resource, data in self.resources.iteritems(): buffer.writefmt('Identifier: %s', resource) buffer.writefmt('Length: %d', len(data)) buffer.writefmt() buffer.write(data) buffer.writefmt() buffer.writefmt() return buffer.getvalue() class GNTPRegister(_GNTPBase): """Represents a GNTP Registration Command :param string data: (Optional) See decode() :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Application-Name', 'Notifications-Count' ] _requiredNotificationHeaders = ['Notification-Name'] def __init__(self, data=None, password=None): _GNTPBase.__init__(self, 'REGISTER') self.notifications = [] if data: self.decode(data, password) else: self.set_password(password) self.add_header('Application-Name', 'pygntp') self.add_header('Notifications-Count', 0) def validate(self): '''Validate required headers and validate notification headers''' for header in self._requiredHeaders: if not self.headers.get(header, False): raise ParseError('Missing Registration Header: ' + header) for notice in self.notifications: for header in self._requiredNotificationHeaders: if not notice.get(header, False): raise ParseError('Missing Notification Header: ' + header) def decode(self, data, password): """Decode existing GNTP Registration message :param string data: Message to decode """ self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self._validate_password(password) self.headers = self._parse_dict(parts[0]) for i, part in enumerate(parts): if i == 0: continue # Skip Header if part.strip() == '': continue notice = self._parse_dict(part) if notice.get('Notification-Name', False): self.notifications.append(notice) elif notice.get('Identifier', False): notice['Data'] = self._decode_binary(part, notice) #open('register.png','wblol').write(notice['Data']) self.resources[notice.get('Identifier')] = notice def add_notification(self, name, enabled=True): """Add new Notification to Registration message :param string name: Notification Name :param boolean enabled: Enable this notification by default """ notice = {} notice['Notification-Name'] = u'%s' % name notice['Notification-Enabled'] = u'%s' % enabled self.notifications.append(notice) self.add_header('Notifications-Count', len(self.notifications)) def encode(self): """Encode a GNTP Registration Message :return string: Encoded GNTP Registration message """ buffer = _GNTPBuffer() buffer.writefmt(self._format_info()) #Headers for k, v in self.headers.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Notifications if len(self.notifications) > 0: for notice in self.notifications: for k, v in notice.iteritems(): buffer.writefmt('%s: %s', k, v) buffer.writefmt() #Resources for resource, data in self.resources.iteritems(): buffer.writefmt('Identifier: %s', resource) buffer.writefmt('Length: %d', len(data)) buffer.writefmt() buffer.write(data) buffer.writefmt() buffer.writefmt() return buffer.getvalue() class GNTPNotice(_GNTPBase): """Represents a GNTP Notification Command :param string data: (Optional) See decode() :param string app: (Optional) Set Application-Name :param string name: (Optional) Set Notification-Name :param string title: (Optional) Set Notification Title :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Application-Name', 'Notification-Name', 'Notification-Title' ] def __init__(self, data=None, app=None, name=None, title=None, password=None): _GNTPBase.__init__(self, 'NOTIFY') if data: self.decode(data, password) else: self.set_password(password) if app: self.add_header('Application-Name', app) if name: self.add_header('Notification-Name', name) if title: self.add_header('Notification-Title', title) def decode(self, data, password): """Decode existing GNTP Notification message :param string data: Message to decode. """ self.raw = data parts = self.raw.split('\r\n\r\n') self.info = self._parse_info(data) self._validate_password(password) self.headers = self._parse_dict(parts[0]) for i, part in enumerate(parts): if i == 0: continue # Skip Header if part.strip() == '': continue notice = self._parse_dict(part) if notice.get('Identifier', False): notice['Data'] = self._decode_binary(part, notice) #open('notice.png','wblol').write(notice['Data']) self.resources[notice.get('Identifier')] = notice class GNTPSubscribe(_GNTPBase): """Represents a GNTP Subscribe Command :param string data: (Optional) See decode() :param string password: (Optional) Password to use while encoding/decoding messages """ _requiredHeaders = [ 'Subscriber-ID', 'Subscriber-Name', ] def __init__(self, data=None, password=None): _GNTPBase.__init__(self, 'SUBSCRIBE') if data: self.decode(data, password) else: self.set_password(password) class GNTPOK(_GNTPBase): """Represents a GNTP OK Response :param string data: (Optional) See _GNTPResponse.decode() :param string action: (Optional) Set type of action the OK Response is for """ _requiredHeaders = ['Response-Action'] def __init__(self, data=None, action=None): _GNTPBase.__init__(self, '-OK') if data: self.decode(data) if action: self.add_header('Response-Action', action) class GNTPError(_GNTPBase): """Represents a GNTP Error response :param string data: (Optional) See _GNTPResponse.decode() :param string errorcode: (Optional) Error code :param string errordesc: (Optional) Error Description """ _requiredHeaders = ['Error-Code', 'Error-Description'] def __init__(self, data=None, errorcode=None, errordesc=None): _GNTPBase.__init__(self, '-ERROR') if data: self.decode(data) if errorcode: self.add_header('Error-Code', errorcode) self.add_header('Error-Description', errordesc) def error(self): return (self.headers.get('Error-Code', None), self.headers.get('Error-Description', None)) def parse_gntp(data, password=None): """Attempt to parse a message as a GNTP message :param string data: Message to be parsed :param string password: Optional password to be used to verify the message """ match = GNTP_INFO_LINE_SHORT.match(data) if not match: raise ParseError('INVALID_GNTP_INFO') info = match.groupdict() if info['messagetype'] == 'REGISTER': return GNTPRegister(data, password=password) elif info['messagetype'] == 'NOTIFY': return GNTPNotice(data, password=password) elif info['messagetype'] == 'SUBSCRIBE': return GNTPSubscribe(data, password=password) elif info['messagetype'] == '-OK': return GNTPOK(data) elif info['messagetype'] == '-ERROR': return GNTPError(data) raise ParseError('INVALID_GNTP_MESSAGE')

2,062

212

354

838d7c175e3f9e7de18126370cf750f1cc4ae14d

124

py

Python

wsm/__init__.py

Rayologist/windows-sshd-manager

4f78a0cdaa12fe3c2a785aca31066c3be886878b

[ "Apache-2.0" ]

9

2022-02-09T09:09:43.000Z

2022-02-09T09:10:06.000Z

wsm/__init__.py

Rayologist/windows-sshd-manager

4f78a0cdaa12fe3c2a785aca31066c3be886878b

[ "Apache-2.0" ]

null

wsm/__init__.py

Rayologist/windows-sshd-manager

4f78a0cdaa12fe3c2a785aca31066c3be886878b

[ "Apache-2.0" ]

null

from .backend import * from .commands import * check_config() if not DB_PATH.is_file(): init_db() init_firewall()

13.777778

25

0.693548

from .backend import * from .commands import * check_config() if not DB_PATH.is_file(): init_db() init_firewall()

0

7c28df5688401e05150d6b2da6280bdaa82a847b

3,088

py

Python

drive/snippets/drive-v3/team_drive_snippets/recover_team_drives.py

himanshupr2627/python-samples

4a04e3aee1068dc1f1402e9e9c90044ff101a6c8

[ "Apache-2.0" ]

479

2018-03-16T16:45:11.000Z

2020-10-13T11:32:02.000Z

drive/snippets/drive-v3/team_drive_snippets/recover_team_drives.py

himanshupr2627/python-samples

4a04e3aee1068dc1f1402e9e9c90044ff101a6c8

[ "Apache-2.0" ]

159

2018-03-28T20:03:56.000Z

2020-10-13T06:00:08.000Z

drive/snippets/drive-v3/team_drive_snippets/recover_team_drives.py

himanshupr2627/python-samples

4a04e3aee1068dc1f1402e9e9c90044ff101a6c8

[ "Apache-2.0" ]

493

2018-03-21T01:07:21.000Z

2020-10-14T10:31:00.000Z

"""Copyright 2022 Google LLC 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. """ # [START drive_recover_team_drives] from __future__ import print_function import google.auth from googleapiclient.discovery import build from googleapiclient.errors import HttpError def recover_team_drives(real_user): """Finds all Team Drives without an organizer and add one Args: real_user:User ID for the new organizer. Returns: team drives_object. Load pre-authorized user credentials from the environment. TODO(developer) - See https://developers.google.com/identity for guides on implementing OAuth2 for the application. """ creds, _ = google.auth.default() try: # call drive api client service = build('drive', 'v3', credentials=creds) # pylint: disable=maybe-no-member team_drives = [] page_token = None new_organizer_permission = {'type': 'user', 'role': 'organizer', 'value': 'user@example.com'} new_organizer_permission['emailAddress'] = real_user while True: response = service.teamdrives().list(q='organizerCount = 0', fields='nextPageToken, ' 'teamDrives(id, ' 'name)', useDomainAdminAccess=True, pageToken=page_token ).execute() for team_drive in response.get('teamDrives', []): print('Found Team Drive without organizer: {team_drive.get(' '"title")},{team_drive.get("id")}') permission = service.permissions().create( fileId=team_drive.get('id'), body=new_organizer_permission, useDomainAdminAccess=True, supportsTeamDrives=True, fields='id').execute() print(F'Added organizer permission:{permission.get("id")}') team_drives.extend(response.get('teamDrives', [])) page_token = response.get('nextPageToken', None) if page_token is None: break except HttpError as error: print(F'An error occurred: {error}') team_drives = None print(team_drives) if __name__ == '__main__': recover_team_drives(real_user='gduser1@workspacesamples.dev') # [END drive_recover_team_drives]

36.329412

77

0.591645

"""Copyright 2022 Google LLC 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. """ # [START drive_recover_team_drives] from __future__ import print_function import google.auth from googleapiclient.discovery import build from googleapiclient.errors import HttpError def recover_team_drives(real_user): """Finds all Team Drives without an organizer and add one Args: real_user:User ID for the new organizer. Returns: team drives_object. Load pre-authorized user credentials from the environment. TODO(developer) - See https://developers.google.com/identity for guides on implementing OAuth2 for the application. """ creds, _ = google.auth.default() try: # call drive api client service = build('drive', 'v3', credentials=creds) # pylint: disable=maybe-no-member team_drives = [] page_token = None new_organizer_permission = {'type': 'user', 'role': 'organizer', 'value': 'user@example.com'} new_organizer_permission['emailAddress'] = real_user while True: response = service.teamdrives().list(q='organizerCount = 0', fields='nextPageToken, ' 'teamDrives(id, ' 'name)', useDomainAdminAccess=True, pageToken=page_token ).execute() for team_drive in response.get('teamDrives', []): print('Found Team Drive without organizer: {team_drive.get(' '"title")},{team_drive.get("id")}') permission = service.permissions().create( fileId=team_drive.get('id'), body=new_organizer_permission, useDomainAdminAccess=True, supportsTeamDrives=True, fields='id').execute() print(F'Added organizer permission:{permission.get("id")}') team_drives.extend(response.get('teamDrives', [])) page_token = response.get('nextPageToken', None) if page_token is None: break except HttpError as error: print(F'An error occurred: {error}') team_drives = None print(team_drives) if __name__ == '__main__': recover_team_drives(real_user='gduser1@workspacesamples.dev') # [END drive_recover_team_drives]

0

5686d915b3397f6683c4fad42732286540a3a962

3,136

py

Python

src/data_preparation/prepare_loc.py

tobiasraabe/locus-of-control

fe249bf8c85d163527d82e0c018e86bd37eff345

[ "BSD-3-Clause" ]

null

src/data_preparation/prepare_loc.py

tobiasraabe/locus-of-control

fe249bf8c85d163527d82e0c018e86bd37eff345

[ "BSD-3-Clause" ]

56

2019-01-27T14:39:33.000Z

2020-06-22T20:42:32.000Z

src/data_preparation/prepare_loc.py

tobiasraabe/locus-of-control

fe249bf8c85d163527d82e0c018e86bd37eff345

[ "BSD-3-Clause" ]

1

2018-03-01T09:19:00.000Z

import json import numpy as np import pandas as pd from sklearn.externals import joblib from bld.project_paths import project_paths_join as ppj # This list is ordered according to the item table in our paper. PERCEIVED_CONTROL = [ "LOC_LIFES_COURSE", "LOC_ACHIEVED_DESERVE", "LOC_LUCK", "LOC_OTHERS", "LOC_DOUBT", "LOC_POSSIBILITIES", "LOC_LITTLE_CONTROL", ] LOC_VALUES = { "[1] Trifft ueberhaupt nicht zu": 1, "[2] [2/10]": 2, "[3] [3/10]": 3, "[4] [4/10]": 4, "[5] [5/10]": 5, "[6] [6/10]": 6, "[7] Trifft voll zu": 7, } def invert_items(df): """This function inverts the scale of some items of LoC so that for all items higher numbers reflect greater feelings of control.""" inverted_items = [ "LOC_ACHIEVED_DESERVE", "LOC_LUCK", "LOC_OTHERS", "LOC_DOUBT", "LOC_POSSIBILITIES", "LOC_ABILITIES", "LOC_LITTLE_CONTROL", ] for item in inverted_items: df[item].replace( to_replace=[1, 2, 3, 4, 5, 6, 7], value=[7, 6, 5, 4, 3, 2, 1], inplace=True ) return df def create_index(df): """This function creates and index which is the average over all LoC items.""" df["LOC_INDEX"] = df[PERCEIVED_CONTROL].mean(axis="columns") return df if __name__ == "__main__": # Load dataset df = pd.read_pickle(ppj("OUT_DATA", "loc_raw.pkl")) # Clean the data df = clean_variables(df) # Invert items so that higher numbers indicate greater feelings of control df = invert_items(df) # Calculate Cronbach's alpha for the whole scale data = df[[i for i in df if "LOC" in i]].values.T cronbachs_alpha_ten = calculate_cronbachs_alpha(data) # Restrict to seven item scale proposed by Specht et al (2013) df = df[["ID", "YEAR"] + PERCEIVED_CONTROL] # Create an index as the average of LoC items df = create_index(df) # Calculate Cronbach's Alpha for seven item scale. First, reshape the data # to n (items) * p (observations) data = df[PERCEIVED_CONTROL].values.T cronbachs_alpha_seven = calculate_cronbachs_alpha(data) # Create container container = {} container["data"] = df # Save numbers to json with open(ppj("OUT_TABLES", "cronbachs_alphas.json"), "w") as file: file.write( json.dumps( {"ca_seven": cronbachs_alpha_seven, "ca_ten": cronbachs_alpha_ten} ) ) # Save data for PCA and FA joblib.dump(container, ppj("OUT_DATA", "loc_container.pkl"))

29.037037

87

0.642219

import json import numpy as np import pandas as pd from sklearn.externals import joblib from bld.project_paths import project_paths_join as ppj # This list is ordered according to the item table in our paper. PERCEIVED_CONTROL = [ "LOC_LIFES_COURSE", "LOC_ACHIEVED_DESERVE", "LOC_LUCK", "LOC_OTHERS", "LOC_DOUBT", "LOC_POSSIBILITIES", "LOC_LITTLE_CONTROL", ] LOC_VALUES = { "[1] Trifft ueberhaupt nicht zu": 1, "[2] [2/10]": 2, "[3] [3/10]": 3, "[4] [4/10]": 4, "[5] [5/10]": 5, "[6] [6/10]": 6, "[7] Trifft voll zu": 7, } def calculate_cronbachs_alpha(itemscores): itemscores = np.asarray(itemscores) itemvars = itemscores.var(axis=1, ddof=1) tscores = itemscores.sum(axis=0) nitems = len(itemscores) return nitems / (nitems - 1.0) * (1 - itemvars.sum() / tscores.var(ddof=1)) def clean_variables(df): # Replace values and cast to integers for variable in df.select_dtypes("category"): df[variable].cat.rename_categories(LOC_VALUES, inplace=True) df[variable] = pd.to_numeric(df[variable], errors="raise", downcast="integer") return df def invert_items(df): """This function inverts the scale of some items of LoC so that for all items higher numbers reflect greater feelings of control.""" inverted_items = [ "LOC_ACHIEVED_DESERVE", "LOC_LUCK", "LOC_OTHERS", "LOC_DOUBT", "LOC_POSSIBILITIES", "LOC_ABILITIES", "LOC_LITTLE_CONTROL", ] for item in inverted_items: df[item].replace( to_replace=[1, 2, 3, 4, 5, 6, 7], value=[7, 6, 5, 4, 3, 2, 1], inplace=True ) return df def create_index(df): """This function creates and index which is the average over all LoC items.""" df["LOC_INDEX"] = df[PERCEIVED_CONTROL].mean(axis="columns") return df if __name__ == "__main__": # Load dataset df = pd.read_pickle(ppj("OUT_DATA", "loc_raw.pkl")) # Clean the data df = clean_variables(df) # Invert items so that higher numbers indicate greater feelings of control df = invert_items(df) # Calculate Cronbach's alpha for the whole scale data = df[[i for i in df if "LOC" in i]].values.T cronbachs_alpha_ten = calculate_cronbachs_alpha(data) # Restrict to seven item scale proposed by Specht et al (2013) df = df[["ID", "YEAR"] + PERCEIVED_CONTROL] # Create an index as the average of LoC items df = create_index(df) # Calculate Cronbach's Alpha for seven item scale. First, reshape the data # to n (items) * p (observations) data = df[PERCEIVED_CONTROL].values.T cronbachs_alpha_seven = calculate_cronbachs_alpha(data) # Create container container = {} container["data"] = df # Save numbers to json with open(ppj("OUT_TABLES", "cronbachs_alphas.json"), "w") as file: file.write( json.dumps( {"ca_seven": cronbachs_alpha_seven, "ca_ten": cronbachs_alpha_ten} ) ) # Save data for PCA and FA joblib.dump(container, ppj("OUT_DATA", "loc_container.pkl"))

519

0

46

e99f8216a81c1cdba2df8dcfdf8089339385f03b

5,265

py

Python

misctools/archit.py

H4CKY54CK/misctools

e6f1f944046f07b808d19bb4e4c8fae6264eb428

[ "MIT" ]

3

2020-08-23T21:18:09.000Z

2021-12-08T15:48:38.000Z

misctools/archit.py

H4CKY54CK/misctools

e6f1f944046f07b808d19bb4e4c8fae6264eb428

[ "MIT" ]

2

2020-04-14T09:18:54.000Z

2020-07-13T06:09:22.000Z

misctools/archit.py

H4CKY54CK/misctools

e6f1f944046f07b808d19bb4e4c8fae6264eb428

[ "MIT" ]

null

import os import sys import time import shutil import argparse import traceback import subprocess from . import __version__

47.863636

225

0.605128

import os import sys import time import shutil import argparse import traceback import subprocess from . import __version__ def unarchit(args=None): source = os.path.abspath(args.source) base, suffix = os.path.splitext(source) if suffix.lower() in ['.gz', '.bz2', '.xz']: base, second = os.path.splitext(base) suffix = second + '.' + suffix.strip('.2') output = str(source).split(suffix)[0] if not args.output else args.output shutil.unpack_archive(source, output) if not args.quiet: print('Finished extracting.') if args.delete: os.remove(source) def umain(argv=None): argv = (argv or sys.argv)[1:] parser = argparse.ArgumentParser() parser.add_argument('source', help='zip file to extract') parser.add_argument('output', default=None, nargs='?', help='output destination directory') parser.add_argument('-D', '--delete', dest='delete', action='store_true', help="delete zip file after extraction") parser.add_argument('-q', '--quiet', action='store_true') parser.set_defaults(func=unarchit) args = parser.parse_args(argv) args.func(args) def archit(args, options=None): args.output = args.output or args.source base_out = os.path.basename(args.output) errors = False if not args.quiet: print(f"\n\033[38;2;30;144;255m \033[4mMisctools v{__version__}\033[24m\033[38;2;123;104;238m\n") try: for item in args.format: try: ext = f"tar.{item.split('tar')[0]}" if item != 'tar' and 'tar' in item else item ext = 'tar.bz2' if item == 'bztar' else ext if not args.quiet: print(f" Creating archive {base_out}.{ext}...") shutil.make_archive(args.output, item, args.source) if not args.quiet: print(f" Archive `{base_out}.{ext}` created...\n") except Exception as e: with open('archiving_errors.log', 'a') as f: f.write(traceback.format_exc()+'\n') errors = True finally: if args.install: ext = f"tar.{args.sys.split('tar')[0]}" if args.sys != 'tar' and 'tar' in args.sys else args.sys if not args.quiet: print(f" \033[38;2;255;165;0mPreparing to install `{base_out}.{ext}` via pip...\033[0m\n") pkg = f"{args.output}.{ext}" subprocess.check_call([sys.executable, '-m', 'pip', 'install', pkg, *options]) # os.system(f"{sys.executable} -m pip install {' '.join(options)} {args.output}.{ext}") if not args.quiet: print(f"\n \033[38;2;255;165;0mFinished installing `{base_out}.{ext}`...\033[0m\n") if not args.quiet: print(f" \033[38;2;255;165;0mCleaning up...\033[0m\n") outpath = os.path.abspath(f'{args.output}.{ext}') os.remove(outpath) if errors: if not args.quiet: print(" \033[38;2;50;252;50mFinished with errors. See `archiving_errors.log` for a more detailed report.\033[0m") else: if not args.quiet: print(" \033[38;2;50;252;50mFinished.\033[0m") def main(argv=None): argv = (argv or sys.argv)[1:] dformats = {"zip": "zip", "next": "xztar", "tar": "tar", "bz": "bztar", "bztar": "bztar", "bz2": "bztar", "gz": "gztar", "gztar": "gztar", "xz": "xztar", "xztar": "xztar", "all": ["bztar", "gztar", "zip", "tar", "xztar"]} parser = argparse.ArgumentParser() parser.add_argument('source', type=str, help='package name in current directory you wish to compress') parser.add_argument('output', type=str, nargs='?', help='optionally specify an output name for the compressed file') parser.add_argument('-f', '--format', dest='format', type=str, choices=dformats, nargs='+', help="archive compression format(s) desired") # parser.add_argument('-z', '--zip', dest='zip', action='store_true', help='choose this archive format') # parser.add_argument('-t', '--tar', dest='tar', action='store_true', help='choose this archive format') # parser.add_argument('-g', '--gztar', dest='gztar', action='store_true', help='choose this archive format') # parser.add_argument('-b', '--bztar', dest='bztar', action='store_true', help='choose this archive format') # parser.add_argument('-x', '--xztar', dest='xztar', action='store_true', help='choose this archive format') parser.add_argument('-i', '--install', dest='install', action='store_true', help='after creating the archive, install it via pip') parser.add_argument('-d', '--debug', dest='debug', action='store_true') parser.add_argument('-q', '--quiet', dest='quiet', action='store_true', help='keep quiet') parser.set_defaults(func=archit) args, options = parser.parse_known_args(argv) args.sys = None if sys.platform == 'win32': args.sys = 'zip' else: args.sys = 'gztar' if not args.format: args.format = [args.sys] elif 'all' in args.format: args.format = dformats['all'] else: args.format = [dformats[item] for item in args.format] args.format.append(args.sys) args.func(args, options)

5,049

0

91

62566eb6cd30c2061bec5af80d2812e59322c27e

860

py

Python

open_bus_pipelines/operators/api_bash_operator.py

OriHoch/open-bus-pipelines

ad8a5bcb2a84ebbae373c6ecc94fbcd666b81cde

[ "MIT" ]

2

2021-11-14T00:06:18.000Z

2022-02-15T13:50:37.000Z

open_bus_pipelines/operators/api_bash_operator.py

OriHoch/open-bus-pipelines

ad8a5bcb2a84ebbae373c6ecc94fbcd666b81cde

[ "MIT" ]

null

open_bus_pipelines/operators/api_bash_operator.py

OriHoch/open-bus-pipelines

ad8a5bcb2a84ebbae373c6ecc94fbcd666b81cde

[ "MIT" ]

1

2021-11-23T07:53:22.000Z

import os import json import shlex from .cli_bash_operator import CliBashOperator from ..config import OPEN_BUS_PIPELINES_ROOTDIR

34.4

131

0.70814

import os import json import shlex from .cli_bash_operator import CliBashOperator from ..config import OPEN_BUS_PIPELINES_ROOTDIR class ApiBashOperator(CliBashOperator): def __init__(self, config, **kwargs): super(ApiBashOperator, self).__init__(self._get_cli_bash_operator_cmd(config), **kwargs) def _get_cli_bash_operator_cmd(self, config): return ' '.join([ 'python', '-u', os.path.join(OPEN_BUS_PIPELINES_ROOTDIR, 'open_bus_pipelines', 'operators', '_api_bash_operator_script.py'), shlex.quote(json.dumps(config)), '__airflow_dag_run_conf__' ]) def execute(self, context): self.bash_command = self.bash_command.replace('__airflow_dag_run_conf__', shlex.quote(json.dumps(context['dag_run'].conf))) return super(ApiBashOperator, self).execute(context)

606

18

104

75723ed8882576506d24cd1e306148f4fe9a583e

341

py

Python

main.py

kadin008/ArticleSpider

51c9c79d7aec2d23333cd96b5e0e83c3ee677374

[ "Apache-2.0" ]

null

main.py

kadin008/ArticleSpider

51c9c79d7aec2d23333cd96b5e0e83c3ee677374

[ "Apache-2.0" ]

null

main.py

kadin008/ArticleSpider

51c9c79d7aec2d23333cd96b5e0e83c3ee677374

[ "Apache-2.0" ]

null

#!/usr/bin/env python # _*_ coding: utf-8 _*_ __author__: 'Patrick Wang' __date__: '2019/2/28 14:52' from scrapy.cmdline import execute import sys import os sys.path.append(os.path.dirname(os.path.abspath(__file__))) # execute(["scrapy", "crawl", "jobbole"]) execute(["scrapy", "crawl", "zhihu"]) # execute(["scrapy", "crawl", "lagou"])

21.3125

59

0.686217

#!/usr/bin/env python # _*_ coding: utf-8 _*_ __author__: 'Patrick Wang' __date__: '2019/2/28 14:52' from scrapy.cmdline import execute import sys import os sys.path.append(os.path.dirname(os.path.abspath(__file__))) # execute(["scrapy", "crawl", "jobbole"]) execute(["scrapy", "crawl", "zhihu"]) # execute(["scrapy", "crawl", "lagou"])

0

7cd2c0657c5d91ac0480863e7350e58fe1fcdc05

4,517

py

Python

prestodb/exceptions.py

highker/presto-python-client

4f5be9c81950cbddd4579a9e55dfce6db54fb8b2

[ "Apache-2.0" ]

null

prestodb/exceptions.py

highker/presto-python-client

4f5be9c81950cbddd4579a9e55dfce6db54fb8b2

[ "Apache-2.0" ]

null

prestodb/exceptions.py

highker/presto-python-client

4f5be9c81950cbddd4579a9e55dfce6db54fb8b2

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This module defines exceptions for Presto operations. It follows the structure defined in pep-0249. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import random import time import prestodb.logging logger = prestodb.logging.get_logger(__name__)

25.811429

81

0.615231

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This module defines exceptions for Presto operations. It follows the structure defined in pep-0249. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import random import time import prestodb.logging logger = prestodb.logging.get_logger(__name__) class HttpError(Exception): pass class Http503Error(HttpError): pass class PrestoError(Exception): pass class DatabaseError(Exception): pass class PrestoQueryError(Exception): def __init__(self, error, query_id=None): self._error = error self._query_id = query_id @property def error_code(self): return self._error.get('errorCode', None) @property def error_name(self): return self._error.get('errorName', None) @property def error_type(self): return self._error.get('errorType', None) @property def error_exception(self): return self.failure_info.get('type', None) if self.failure_info else None @property def failure_info(self): return self._error.get('failureInfo', None) @property def message(self): return self._error.get( 'message', 'Presto did no return an error message', ) @property def error_location(self): location = self._error['errorLocation'] return (location['lineNumber'], location['columnNumber']) @property def query_id(self): return self._query_id def __repr__(self): return '{}(type={}, name={}, message="{}", query_id={})'.format( self.__class__.__name__, self.error_type, self.error_name, self.message, self.query_id, ) def __str__(self): return repr(self) class PrestoExternalError(PrestoQueryError): pass class PrestoInternalError(PrestoQueryError): pass class PrestoUserError(PrestoQueryError): pass def retry_with(handle_retry, exceptions, conditions, max_attempts): def wrapper(func): @functools.wraps(func) def decorated(*args, **kwargs): error = None result = None for attempt in range(1, max_attempts + 1): try: result = func(*args, **kwargs) if any(guard(result) for guard in conditions): handle_retry.retry(func, args, kwargs, None, attempt) continue return result except Exception as err: error = err if any(isinstance(err, exc) for exc in exceptions): handle_retry.retry(func, args, kwargs, err, attempt) continue break logger.info('failed after {} attempts'.format(attempt)) if error is not None: raise error return result return decorated return wrapper class DelayExponential(object): def __init__( self, base=0.1, # 100ms exponent=2, jitter=True, max_delay=2 * 3600, # 2 hours ): self._base = base self._exponent = exponent self._jitter = jitter self._max_delay = max_delay def __call__(self, attempt): delay = float(self._base) * (self._exponent ** attempt) if self._jitter: delay *= random.random() delay = min(float(self._max_delay), delay) return delay class RetryWithExponentialBackoff(object): def __init__( self, base=0.1, # 100ms exponent=2, jitter=True, max_delay=2 * 3600 # 2 hours ): self._get_delay = DelayExponential( base, exponent, jitter, max_delay) def retry(self, func, args, kwargs, err, attempt): delay = self._get_delay(attempt) time.sleep(delay)

2,654

613

359

b186eb72fe48e7225c8c7512994e3ef73f7156df

2,699

py

Python

prody/tests/apps/test_prody_pca.py

grandevelia/ProDy

7c725640a94c16543423c0756388998cb86a97ae

[ "MIT" ]

210

2015-01-26T08:17:56.000Z

2022-03-30T01:40:34.000Z

prody/tests/apps/test_prody_pca.py

grandevelia/ProDy

7c725640a94c16543423c0756388998cb86a97ae

[ "MIT" ]

555

2015-01-05T21:51:54.000Z

2022-03-31T16:51:41.000Z

prody/tests/apps/test_prody_pca.py

grandevelia/ProDy

7c725640a94c16543423c0756388998cb86a97ae

[ "MIT" ]

99

2015-02-09T18:00:39.000Z

2022-03-07T12:52:51.000Z

from os import remove import shlex from os.path import isfile, join, split, splitext from prody.tests import TestCase, skipIf, skipUnless from numpy.testing import * try: import numpy.testing.decorators as dec except ImportError: from numpy.testing import dec from prody.tests.datafiles import TEMPDIR, pathDatafile from prody.apps import prody_parser from prody.tests import MATPLOTLIB, NOPRODYCMD, WINDOWS

31.022989

74

0.586514

from os import remove import shlex from os.path import isfile, join, split, splitext from prody.tests import TestCase, skipIf, skipUnless from numpy.testing import * try: import numpy.testing.decorators as dec except ImportError: from numpy.testing import dec from prody.tests.datafiles import TEMPDIR, pathDatafile from prody.apps import prody_parser from prody.tests import MATPLOTLIB, NOPRODYCMD, WINDOWS class TestPCACommand(TestCase): def setUp(self): self.command = ('pca --pdb {pdb} ' '-e -r -o {outdir} -v -z -t all -j ' '-f %8g -d , -x .dat ' '-R -Q -J 1,2 ' '-F png -D 120 -W 5 -H 4 ').format(outdir=TEMPDIR, pdb=pathDatafile('multi_model_truncated')) self.suffixes = [ '_pca_cc.png', '_pca.pca.npz', '_pca_covariance.dat', '_pca_cross-correlations.dat', '_pca_proj_1_2.png', '_pca_evalues.dat', '_pca_proj.dat', '_pca_evectors.dat', '_pca_sf.png', '_pca_extended_all.nmd', '_pca.nmd', ] self.tearDown() @dec.slow @skipIf(NOPRODYCMD, 'prody command not found') @skipUnless(MATPLOTLIB, 'matplotlib not found') @skipIf(WINDOWS, 'command tests are not run on Windows') def testPCACommandDCD(self): dcd = pathDatafile('dcd') command = self.command + dcd prefix = splitext(split(dcd)[1])[0] namespace = prody_parser.parse_args(shlex.split(command)) namespace.func(namespace) for suffix in self.suffixes: fn = join(TEMPDIR, prefix + suffix) self.assertTrue(isfile(fn), msg=fn+' not found') @dec.slow @skipIf(NOPRODYCMD, 'prody command not found') @skipUnless(MATPLOTLIB, 'matplotlib not found') @skipIf(WINDOWS, 'command tests are not run on Windows') def testPCACommandPDB(self): dcd = pathDatafile('multi_model_truncated') command = self.command + dcd prefix = splitext(split(dcd)[1])[0] namespace = prody_parser.parse_args(shlex.split(command)) namespace.func(namespace) for suffix in self.suffixes: fn = join(TEMPDIR, prefix + suffix) self.assertTrue(isfile(fn), msg=fn+' not found') def tearDown(self): for dcd in [pathDatafile('multi_model_truncated'), pathDatafile('dcd')]: prefix = splitext(split(dcd)[1])[0] for suffix in self.suffixes: fn = join(TEMPDIR, prefix + suffix) if isfile(fn): remove(fn)

1,781

475

23

7725ec364d6620e651e05ce578e7d17391e0e6ff

1,414

py

Python

talks/events/factories.py

alan-turing-institute/talks.ox

5e172b7bb7296fcfc2d5c1b5978ec98a6643d90a

[ "Apache-2.0" ]

5

2015-09-03T11:46:07.000Z

2022-01-12T10:15:50.000Z

talks/events/factories.py

alan-turing-institute/talks.ox

5e172b7bb7296fcfc2d5c1b5978ec98a6643d90a

[ "Apache-2.0" ]

306

2015-01-05T10:16:56.000Z

2021-06-10T08:00:31.000Z

talks/events/factories.py

alan-turing-institute/talks.ox

5e172b7bb7296fcfc2d5c1b5978ec98a6643d90a

[ "Apache-2.0" ]

5

2016-04-21T10:40:20.000Z

2021-01-05T09:15:23.000Z

from __future__ import absolute_import from datetime import datetime import factory from . import models from talks.users.models import Collection, CollectionItem, CollectedDepartment

23.966102

78

0.73338

from __future__ import absolute_import from datetime import datetime import factory from . import models from talks.users.models import Collection, CollectionItem, CollectedDepartment class EventGroupFactory(factory.django.DjangoModelFactory): class Meta: model = models.EventGroup class EventCollectionFactory(factory.django.DjangoModelFactory): class Meta: model = Collection class EventFactory(factory.django.DjangoModelFactory): start = datetime(2015, 10, 23, 12, 18) end = datetime(2015, 10, 30, 20, 25) class Meta: model = models.Event class PersonFactory(factory.django.DjangoModelFactory): class Meta: model = models.Person class PersonEventFactory(factory.django.DjangoModelFactory): class Meta: model = models.PersonEvent class TopicItemFactory(factory.django.DjangoModelFactory): item = factory.SubFactory(EventFactory) @factory.sequence def uri(n): return "http://example.com/%s" % n class Meta: model = models.TopicItem class TopicItemFactory_noSubFactory(factory.django.DjangoModelFactory): class Meta: model = models.TopicItem class CollectedDepartmentFactory(factory.django.DjangoModelFactory): class Meta: model = CollectedDepartment class CollectionItemFactory(factory.django.DjangoModelFactory): class Meta: model = CollectionItem

33

974

215

7873ab8bfeb4ee79e6303fa1dcf5e53421732720

126

py

Python

edm_web1/app/track/apps.py

zhouli121018/nodejsgm

0ccbc8acf61badc812f684dd39253d55c99f08eb

[ "MIT" ]

null

edm_web1/app/track/apps.py

zhouli121018/nodejsgm

0ccbc8acf61badc812f684dd39253d55c99f08eb

[ "MIT" ]

18

2020-06-05T18:17:40.000Z

2022-03-11T23:25:21.000Z

edm_web1/app/track/apps.py

zhouli121018/nodejsgm

0ccbc8acf61badc812f684dd39253d55c99f08eb

[ "MIT" ]

null

from __future__ import unicode_literals from django.apps import AppConfig

15.75

39

0.785714

from __future__ import unicode_literals from django.apps import AppConfig class TrackConfig(AppConfig): name = 'track'

0

27

23

2c4404e3104ac6f6cfb652d20db04410a3d2b584

2,585

py

Python

setup.py

ufzq/stagger

e88123e465f0f720f334b7f0960af52ffffd069d

[ "BSD-2-Clause" ]

4

2015-01-25T18:58:10.000Z

2016-09-21T04:10:31.000Z

setup.py

ufzq/stagger

e88123e465f0f720f334b7f0960af52ffffd069d

[ "BSD-2-Clause" ]

1

2016-01-12T18:56:56.000Z

2016-01-13T15:01:57.000Z

setup.py

ufzq/stagger

e88123e465f0f720f334b7f0960af52ffffd069d

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # # setup.py # From the stagger project: http://code.google.com/p/stagger/ # # Copyright (c) 2009-2011 Karoly Lorentey <karoly@lorentey.hu> # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # - Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # - Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materials provided with the # distribution. # # 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. import distribute_setup distribute_setup.use_setuptools() from setuptools import setup; setup( name="stagger", version="0.4.2", url="http://code.google.com/p/stagger", author="Karoly Lorentey", author_email="karoly@lorentey.hu", packages=["stagger"], entry_points = { 'console_scripts': ['stagger = stagger.commandline:main'] }, test_suite = "test.alltests.suite", license="BSD", description="ID3v1/ID3v2 tag manipulation package in pure Python 3", long_description=""" The ID3v2 tag format is notorious for its useless specification documents and its quirky, mutually incompatible part-implementations. Stagger is to provide a robust tagging package that is able to handle all the various badly formatted tags out there and allow you to convert them to a consensus format. """, classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python :: 3", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Topic :: Multimedia :: Sound/Audio" ], )

38.58209

72

0.733075

#!/usr/bin/env python3 # # setup.py # From the stagger project: http://code.google.com/p/stagger/ # # Copyright (c) 2009-2011 Karoly Lorentey <karoly@lorentey.hu> # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # - Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # - Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materials provided with the # distribution. # # 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. import distribute_setup distribute_setup.use_setuptools() from setuptools import setup; setup( name="stagger", version="0.4.2", url="http://code.google.com/p/stagger", author="Karoly Lorentey", author_email="karoly@lorentey.hu", packages=["stagger"], entry_points = { 'console_scripts': ['stagger = stagger.commandline:main'] }, test_suite = "test.alltests.suite", license="BSD", description="ID3v1/ID3v2 tag manipulation package in pure Python 3", long_description=""" The ID3v2 tag format is notorious for its useless specification documents and its quirky, mutually incompatible part-implementations. Stagger is to provide a robust tagging package that is able to handle all the various badly formatted tags out there and allow you to convert them to a consensus format. """, classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python :: 3", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Topic :: Multimedia :: Sound/Audio" ], )

0

04eff52184b988d1cc793b12b3a99112a4eafd58

1,243

py

Python

python/multiprocessing/main.py

marvinklimke/pi

5ecd16175c39f9e7a70e6e7ceb26155d4948b86f

[ "MIT" ]

null

python/multiprocessing/main.py

marvinklimke/pi

5ecd16175c39f9e7a70e6e7ceb26155d4948b86f

[ "MIT" ]

null

python/multiprocessing/main.py

marvinklimke/pi

5ecd16175c39f9e7a70e6e7ceb26155d4948b86f

[ "MIT" ]

null

#!/usr/bin/env python3 """Calculate the value of pi using multiprocessing in Python""" from datetime import datetime from multiprocessing import Pool import os from sys import argv if __name__ == "__main__": main()

24.86

94

0.608206

#!/usr/bin/env python3 """Calculate the value of pi using multiprocessing in Python""" from datetime import datetime from multiprocessing import Pool import os from sys import argv def compute(args): begin, end, num_steps = args subtotal = 0.0 for i in range(begin, end): x = (i + 0.5) / num_steps subtotal += 4.0 / (1.0 + x**2) subtotal /= num_steps return subtotal def main(): num_processes = os.cpu_count() num_steps = 1000000 if len(argv) > 2: num_processes = int(argv[2]) if len(argv) > 1: num_steps = int(argv[1]) print("Calculating pi using {} processes in {} steps...".format(num_processes, num_steps)) start = datetime.now() args = [] for i in range(num_processes): args.append((int(i * num_steps / num_processes), int((i + 1) * num_steps / num_processes), num_steps)) with Pool(num_processes) as pool: subtotals = pool.map(compute, args) total = sum(subtotals) finish = datetime.now() duration = finish - start print("==> pi = {}".format(total)) print("Calculation took {} seconds.".format(duration.total_seconds())) if __name__ == "__main__": main()

973

0

46

d8dc135da44f05d9d3ca90831a0906ba17d225d9

3,533

py

Python

tensorflow_datasets/text/squad_question_generation/squad_question_generation.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

null

tensorflow_datasets/text/squad_question_generation/squad_question_generation.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

null

tensorflow_datasets/text/squad_question_generation/squad_question_generation.py

vanshhhhh/datasets

aee32f95273ca3bfe83e09fb9b00ba4bf23597a5

[ "Apache-2.0" ]

1

2021-11-22T18:07:30.000Z

# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """squad_question_generation dataset.""" import os import tensorflow as tf import tensorflow_datasets.public_api as tfds from tensorflow_datasets.question_answering import qa_utils _CITATION = """\ @article{zhou2017neural, title={Neural Question Generation from Text: A Preliminary Study}, author={Zhou, Qingyu and Yang, Nan and Wei, Furu and Tan, Chuanqi and Bao, Hangbo and Zhou, Ming}, journal={arXiv preprint arXiv:1704.01792}, year={2017} } @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } """ _DESCRIPTION = """\ Question generation using squad dataset and data split described in 'Neural Question Generation from Text: A Preliminary Study'. """ _URLS = { "train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json", "dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json", "mapping": "https://res.qyzhou.me/qas_id_in_squad.zip", } _HOMEPAGE_URL = "https://github.com/magic282/NQG" class SquadQuestionGeneration(tfds.core.GeneratorBasedBuilder): """DatasetBuilder for squad_question_generation dataset.""" VERSION = tfds.core.Version("1.0.0") RELEASE_NOTES = { "1.0.0": "Initial build.", } def _info(self): """Returns the dataset metadata.""" features_dict = qa_utils.SQUADLIKE_FEATURES return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=features_dict, supervised_keys=("context", "question"), homepage=_HOMEPAGE_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_paths = dl_manager.download_and_extract(_URLS) mapping_dir = os.path.join(dl_paths["mapping"], "qas_id_in_squad") return { tfds.Split.TRAIN: self._generate_examples(dl_paths["train"], os.path.join(mapping_dir, "train.txt.id")), tfds.Split.VALIDATION: self._generate_examples( dl_paths["dev"], os.path.join(mapping_dir, "dev.txt.shuffle.dev.id")), tfds.Split.TEST: self._generate_examples( dl_paths["dev"], os.path.join(mapping_dir, "dev.txt.shuffle.test.id")), } def _generate_examples(self, data_path: str, mapping_path: str): """Yields examples.""" with tf.io.gfile.GFile(mapping_path, "r") as f: ids = set(f.read().splitlines()) for k, ex in qa_utils.generate_squadlike_examples(data_path): if k in ids: yield k, ex

33.647619

100

0.669403

# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """squad_question_generation dataset.""" import os import tensorflow as tf import tensorflow_datasets.public_api as tfds from tensorflow_datasets.question_answering import qa_utils _CITATION = """\ @article{zhou2017neural, title={Neural Question Generation from Text: A Preliminary Study}, author={Zhou, Qingyu and Yang, Nan and Wei, Furu and Tan, Chuanqi and Bao, Hangbo and Zhou, Ming}, journal={arXiv preprint arXiv:1704.01792}, year={2017} } @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } """ _DESCRIPTION = """\ Question generation using squad dataset and data split described in 'Neural Question Generation from Text: A Preliminary Study'. """ _URLS = { "train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json", "dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json", "mapping": "https://res.qyzhou.me/qas_id_in_squad.zip", } _HOMEPAGE_URL = "https://github.com/magic282/NQG" class SquadQuestionGeneration(tfds.core.GeneratorBasedBuilder): """DatasetBuilder for squad_question_generation dataset.""" VERSION = tfds.core.Version("1.0.0") RELEASE_NOTES = { "1.0.0": "Initial build.", } def _info(self): """Returns the dataset metadata.""" features_dict = qa_utils.SQUADLIKE_FEATURES return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=features_dict, supervised_keys=("context", "question"), homepage=_HOMEPAGE_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_paths = dl_manager.download_and_extract(_URLS) mapping_dir = os.path.join(dl_paths["mapping"], "qas_id_in_squad") return { tfds.Split.TRAIN: self._generate_examples(dl_paths["train"], os.path.join(mapping_dir, "train.txt.id")), tfds.Split.VALIDATION: self._generate_examples( dl_paths["dev"], os.path.join(mapping_dir, "dev.txt.shuffle.dev.id")), tfds.Split.TEST: self._generate_examples( dl_paths["dev"], os.path.join(mapping_dir, "dev.txt.shuffle.test.id")), } def _generate_examples(self, data_path: str, mapping_path: str): """Yields examples.""" with tf.io.gfile.GFile(mapping_path, "r") as f: ids = set(f.read().splitlines()) for k, ex in qa_utils.generate_squadlike_examples(data_path): if k in ids: yield k, ex

0

0bd306ed8f7194f27a682ebda70b085b53ee2b23

159

py

Python

json-load-transform-save/ujsonproc.py

rolando/python-benchmarks

9ef4dc320983ef7ca3ff689b33f3b0c633388061

[ "MIT" ]

1

2016-06-23T11:29:01.000Z

json-load-transform-save/ujsonproc.py

rolando/python-benchmarks

9ef4dc320983ef7ca3ff689b33f3b0c633388061

[ "MIT" ]

null

json-load-transform-save/ujsonproc.py

rolando/python-benchmarks

9ef4dc320983ef7ca3ff689b33f3b0c633388061

[ "MIT" ]

null

import sys from ujson import loads, dumps for line in sys.stdin: obj = loads(line) sys.stdout.write(dumps(obj['actor'])) sys.stdout.write('\n')

15.9

41

0.660377

import sys from ujson import loads, dumps for line in sys.stdin: obj = loads(line) sys.stdout.write(dumps(obj['actor'])) sys.stdout.write('\n')

0

6de6758fa523ec1489447da0b79cbce0ee433ec0

2,120

py

Python

code/insert_sensordata_from_azure_queue.py

jurjanbrust/wsl2_mysql_grafana

df5e236897738f47d2f84d0cbff35e0f7ec6d364

[ "MIT" ]

null

code/insert_sensordata_from_azure_queue.py

jurjanbrust/wsl2_mysql_grafana

df5e236897738f47d2f84d0cbff35e0f7ec6d364

[ "MIT" ]

null

code/insert_sensordata_from_azure_queue.py

jurjanbrust/wsl2_mysql_grafana

df5e236897738f47d2f84d0cbff35e0f7ec6d364

[ "MIT" ]

null

from azure.storage.queue import ( QueueClient, TextBase64EncodePolicy, TextBase64DecodePolicy ) import os, uuid, time, json import mysql.connector from datetime import datetime connect_str = "DefaultEndpointsProtocol=https;AccountName=replace;AccountKey=replacewithyours;EndpointSuffix=core.windows.net" queue_name = "name of queue" mySql_dbName = "sensordata" mySql_tableName = "temperature" queue_client = QueueClient.from_connection_string(conn_str=connect_str, queue_name=queue_name, message_decode_policy=TextBase64DecodePolicy()) messages = queue_client.receive_messages(messages_per_page=5) db = mysql.connector.connect( host="db", user="root", passwd="example", database=mySql_dbName ) cursor = db.cursor() for message in messages: processMessage() queue_client.delete_message(message.id, message.pop_receipt) time.sleep(0.1) print("All Done")

36.551724

180

0.734906

from azure.storage.queue import ( QueueClient, TextBase64EncodePolicy, TextBase64DecodePolicy ) import os, uuid, time, json import mysql.connector from datetime import datetime connect_str = "DefaultEndpointsProtocol=https;AccountName=replace;AccountKey=replacewithyours;EndpointSuffix=core.windows.net" queue_name = "name of queue" mySql_dbName = "sensordata" mySql_tableName = "temperature" queue_client = QueueClient.from_connection_string(conn_str=connect_str, queue_name=queue_name, message_decode_policy=TextBase64DecodePolicy()) messages = queue_client.receive_messages(messages_per_page=5) db = mysql.connector.connect( host="db", user="root", passwd="example", database=mySql_dbName ) cursor = db.cursor() def processMessage(): message_json = json.loads(message.content) payload_raw = message_json["payload_raw"] payload_bytes = bytes(payload_raw, 'ascii') sensor_counter = payload_bytes[0] + 256 * payload_bytes[1] sensor_temperature = payload_bytes[2] + (payload_bytes[3] / 100) sensor_time = message_json["metadata"]["time"][0: 19] sensor_latitude = message_json["metadata"]["latitude"] sensor_longitude = message_json["metadata"]["longitude"] sensor_rssi = message_json["metadata"]["gateways"][0]["rssi"] sensor_dev_id = message_json["dev_id"] sensor_app_id = message_json["app_id"] sensor_hardware_serial = message_json["hardware_serial"] print("counter: " + str(sensor_counter) + " temp: " + str(sensor_temperature)) sql = "INSERT INTO " + mySql_tableName + " (counter, temperature, time, latitude, longitude, rssi, dev_id, app_id, hardware_serial) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)" val = (sensor_counter, sensor_temperature, sensor_time, sensor_latitude, sensor_longitude, sensor_rssi, sensor_dev_id, sensor_app_id, sensor_hardware_serial) try: cursor.execute(sql, val) db.commit() except Exception as ex: print(ex) for message in messages: processMessage() queue_client.delete_message(message.id, message.pop_receipt) time.sleep(0.1) print("All Done")

1,193

0

23

65d1ca74db776761cd8564119bbc577e67c51c88

200

py

Python

app/__init__.py

twitbox/candidate-sentiment-api

c06141963756a4c6251fbd53f3ddced422bd10b4

[ "Unlicense", "MIT" ]

1

2017-09-12T12:18:13.000Z

app/__init__.py

twitbox/candidate-sentiment-api

c06141963756a4c6251fbd53f3ddced422bd10b4

[ "Unlicense", "MIT" ]

null

app/__init__.py

twitbox/candidate-sentiment-api

c06141963756a4c6251fbd53f3ddced422bd10b4

[ "Unlicense", "MIT" ]

null

from .controllers.twitter import search

20

39

0.675

from .controllers.twitter import search def router(app): @app.route('/') def index(): return 'OK' @app.route('/twitter/<hashtag>') def search_twitter(hashtag): return search(hashtag)

138

0

23

79d19cfb9e26ce72a74fab548e234650eae67dbb

57

py

Python

python/testData/highlighting/continueInFinallyBlock.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/highlighting/continueInFinallyBlock.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2022-02-19T09:45:05.000Z

2022-02-27T20:32:55.000Z

python/testData/highlighting/continueInFinallyBlock.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

while True: try: print("a") finally: continue

11.4

14

0.578947

while True: try: print("a") finally: continue

0

9fce0a533c0967a19b5da21b4115ccd074cab091

582

py

Python

studies/airfoil_optimizer/gradient_check.py

peterdsharpe/DawnDesignTool

7c8b87db33e151cf50e07da982f51494ef84d80a

[ "MIT" ]

3

2021-09-28T16:05:51.000Z

2021-12-29T01:08:24.000Z

studies/airfoil_optimizer/gradient_check.py

peterdsharpe/DawnDesignTool

7c8b87db33e151cf50e07da982f51494ef84d80a

[ "MIT" ]

1

2021-12-08T17:35:41.000Z

studies/airfoil_optimizer/gradient_check.py

peterdsharpe/DawnDesignTool

7c8b87db33e151cf50e07da982f51494ef84d80a

[ "MIT" ]

null

import copy if __name__ == '__main__': epss = np.logspace(-10, -1, 30) baseline_objective = augmented_objective(x0) xis = [] for eps in epss: xi = copy.copy(x0) xi[4] += eps xis.append(xi) objs = [augmented_objective(xi) for xi in xis] # pool = mp.Pool(mp.cpu_count()) # objs = pool.map(augmented_objective, xis) # pool.close() objs = np.array(objs) derivs = (objs - baseline_objective) / epss fig, ax = plt.subplots(1, 1, figsize=(6.4, 4.8), dpi=200) plt.loglog(epss, np.abs(derivs), ".-") plt.show()

25.304348

61

0.585911

import copy if __name__ == '__main__': epss = np.logspace(-10, -1, 30) baseline_objective = augmented_objective(x0) xis = [] for eps in epss: xi = copy.copy(x0) xi[4] += eps xis.append(xi) objs = [augmented_objective(xi) for xi in xis] # pool = mp.Pool(mp.cpu_count()) # objs = pool.map(augmented_objective, xis) # pool.close() objs = np.array(objs) derivs = (objs - baseline_objective) / epss fig, ax = plt.subplots(1, 1, figsize=(6.4, 4.8), dpi=200) plt.loglog(epss, np.abs(derivs), ".-") plt.show()

0

984cd9e9856e57b6b0cd4778d04824c76290e492

2,190

py

Python

awx/main/models/fact.py

gitEdouble/awx

5885654405ccaf465f08df4db998a6dafebd9b4d

[ "Apache-2.0" ]

2

2018-11-12T18:52:24.000Z

2020-05-22T18:41:21.000Z

awx/main/models/fact.py

gitEdouble/awx

5885654405ccaf465f08df4db998a6dafebd9b4d

[ "Apache-2.0" ]

4

2020-04-29T23:03:16.000Z

2022-03-01T23:56:09.000Z

awx/main/models/fact.py

gitEdouble/awx

5885654405ccaf465f08df4db998a6dafebd9b4d

[ "Apache-2.0" ]

9

2019-05-11T00:03:30.000Z

2021-07-07T16:09:17.000Z

# Copyright (c) 2016 Ansible, Inc. # All Rights Reserved. import logging from django.db import models from django.utils.translation import ugettext_lazy as _ from awx.main.fields import JSONBField __all__ = ('Fact',) logger = logging.getLogger('awx.main.models.fact') class Fact(models.Model): """A model representing a fact returned from Ansible. Facts are stored as JSON dictionaries. """ host = models.ForeignKey( 'Host', related_name='facts', db_index=True, on_delete=models.CASCADE, help_text=_('Host for the facts that the fact scan captured.'), ) timestamp = models.DateTimeField( default=None, editable=False, help_text=_('Date and time of the corresponding fact scan gathering time.') ) module = models.CharField(max_length=128) facts = JSONBField(blank=True, default={}, help_text=_('Arbitrary JSON structure of module facts captured at timestamp for a single host.')) @staticmethod @staticmethod @staticmethod

30.84507

144

0.629224

# Copyright (c) 2016 Ansible, Inc. # All Rights Reserved. import logging from django.db import models from django.utils.translation import ugettext_lazy as _ from awx.main.fields import JSONBField __all__ = ('Fact',) logger = logging.getLogger('awx.main.models.fact') class Fact(models.Model): """A model representing a fact returned from Ansible. Facts are stored as JSON dictionaries. """ host = models.ForeignKey( 'Host', related_name='facts', db_index=True, on_delete=models.CASCADE, help_text=_('Host for the facts that the fact scan captured.'), ) timestamp = models.DateTimeField( default=None, editable=False, help_text=_('Date and time of the corresponding fact scan gathering time.') ) module = models.CharField(max_length=128) facts = JSONBField(blank=True, default={}, help_text=_('Arbitrary JSON structure of module facts captured at timestamp for a single host.')) class Meta: app_label = 'main' index_together = [ ["timestamp", "module", "host"], ] @staticmethod def get_host_fact(host_id, module, timestamp): qs = Fact.objects.filter(host__id=host_id, module=module, timestamp__lte=timestamp).order_by('-timestamp') if qs: return qs[0] else: return None @staticmethod def get_timeline(host_id, module=None, ts_from=None, ts_to=None): kwargs = { 'host__id': host_id, } if module: kwargs['module'] = module if ts_from and ts_to and ts_from == ts_to: kwargs['timestamp'] = ts_from else: if ts_from: kwargs['timestamp__gt'] = ts_from if ts_to: kwargs['timestamp__lte'] = ts_to return Fact.objects.filter(**kwargs).order_by('-timestamp').only('timestamp', 'module').order_by('-timestamp', 'module') @staticmethod def add_fact(host_id, module, timestamp, facts): fact_obj = Fact.objects.create(host_id=host_id, module=module, timestamp=timestamp, facts=facts) fact_obj.save() return fact_obj

942

99

105

92d5e207f5dc0651947ac8ccca6c9f69f1ca937e

293

py

Python

securex/SecureX-3/scripts/gunicorn.conf.py

CiscoDevNet/security-api-workshop--resources

ee7dbdb7909ee9689c4b061d74cdd6dbb4a8cd20

[ "Apache-2.0" ]

4

2020-10-13T16:08:11.000Z

2021-11-28T11:44:45.000Z

securex/SecureX-3/scripts/gunicorn.conf.py

CiscoDevNet/security-api-workshop--resources

ee7dbdb7909ee9689c4b061d74cdd6dbb4a8cd20

[ "Apache-2.0" ]

1

2021-02-16T18:07:24.000Z

securex/SecureX-3/scripts/gunicorn.conf.py

CiscoDevNet/security-api-workshop--resources

ee7dbdb7909ee9689c4b061d74cdd6dbb4a8cd20

[ "Apache-2.0" ]

8

2020-10-16T15:30:28.000Z

2021-03-25T15:28:49.000Z

# import multiprocessing pidfile = 'flask_app.pid' workers = 2 # workers = multiprocessing.cpu_count() * 2 + 1 bind = '0.0.0.0:80' accesslog = './logs/access.log' errorlog = './logs/error.log' #certfile = './certs/local.cer' #keyfile = './certs/local.key' # user = 'ubuntu' # group = 'ubuntu'

24.416667

47

0.665529

# import multiprocessing pidfile = 'flask_app.pid' workers = 2 # workers = multiprocessing.cpu_count() * 2 + 1 bind = '0.0.0.0:80' accesslog = './logs/access.log' errorlog = './logs/error.log' #certfile = './certs/local.cer' #keyfile = './certs/local.key' # user = 'ubuntu' # group = 'ubuntu'

0

ca9a3657148f07444639079c4bf6b45a6d685933

1,164

py

Python

src/discussion/management/commands/remove_low_quality_content.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

18

2021-05-20T13:20:16.000Z

2022-02-11T02:40:18.000Z

src/discussion/management/commands/remove_low_quality_content.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

109

2021-05-21T20:14:23.000Z

2022-03-31T20:56:10.000Z

src/discussion/management/commands/remove_low_quality_content.py

ResearchHub/ResearchHub-Backend-Open

d36dca33afae2d442690694bb2ab17180d84bcd3

[ "MIT" ]

4

2021-05-17T13:47:53.000Z

2022-02-12T10:48:21.000Z

from django.core.management.base import BaseCommand from django.contrib.contenttypes.models import ContentType from django.db.models.functions import Length from user.models import User from discussion.models import Thread import uuid from utils.siftscience import decisions_api, events_api

34.235294

119

0.620275

from django.core.management.base import BaseCommand from django.contrib.contenttypes.models import ContentType from django.db.models.functions import Length from user.models import User from discussion.models import Thread import uuid from utils.siftscience import decisions_api, events_api class Command(BaseCommand): def handle(self, *args, **options): low_threads = Thread.objects.annotate(text_len=Length('plain_text')).filter(text_len__lte=25, is_removed=False) thread_count = low_threads.count() for i, thread in enumerate(low_threads): print('{} / {}'.format(i, thread_count)) thread.is_removed = True content_id = f'{type(thread).__name__}_{thread.id}' if not thread.created_by: continue try: decisions_api.apply_bad_content_decision(thread.created_by, content_id) events_api.track_flag_content( thread.created_by, content_id, 1, ) except Exception as e: print(e) pass thread.save()

815

6

50

72c693fbfd1853a2f9b44a4dafbf326095a24308

23,027

py

Python

pyzoo/zoo/automl/search/ray_tune_search_engine.py

ankitdobhal/analytics-zoo

b8374bcd6c73bba49fe0b0ab075528cdd94cf2af

[ "Apache-2.0" ]

null

pyzoo/zoo/automl/search/ray_tune_search_engine.py

ankitdobhal/analytics-zoo

b8374bcd6c73bba49fe0b0ab075528cdd94cf2af

[ "Apache-2.0" ]

null

pyzoo/zoo/automl/search/ray_tune_search_engine.py

ankitdobhal/analytics-zoo

b8374bcd6c73bba49fe0b0ab075528cdd94cf2af

[ "Apache-2.0" ]

null

# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import ray from ray import tune from copy import deepcopy import os from zoo.automl.search.abstract import * from zoo.automl.common.util import * from zoo.automl.common.metrics import Evaluator from zoo.automl.impute.impute import * from ray.tune import Trainable import ray.tune.track from zoo.automl.logger import TensorboardXLogger from zoo.automl.model.model_builder import ModelBuilder from zoo.automl.feature.identity_transformer import IdentityTransformer from zoo.automl.search.tune_utils import (create_searcher, create_scheduler) SEARCH_ALG_ALLOWED = ("variant_generator", "skopt", "bayesopt") class RayTuneSearchEngine(SearchEngine): """ Tune driver """ def __init__(self, logs_dir="", resources_per_trial=None, name="", remote_dir=None, ): """ Constructor :param resources_per_trial: resources for each trial """ self.pipeline = None self.train_func = None self.trainable_class = None self.resources_per_trail = resources_per_trial self.trials = None self.remote_dir = remote_dir self.name = name self.logs_dir = os.path.abspath(os.path.expanduser(logs_dir)) def compile(self, data, model_create_func, recipe, search_space=None, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, feature_transformers=None, mc=False, metric="mse"): """ Do necessary preparations for the engine :param input_df: :param search_space: :param num_samples: :param stop: :param search_algorithm: :param search_algorithm_params: :param fixed_params: :param feature_transformers: :param model: :param validation_df: :param metric: :return: """ # data mode detection assert isinstance(data, dict), 'ERROR: Argument \'data\' should be a dictionary.' data_mode = None # data_mode can only be 'dataframe' or 'ndarray' data_schema = set(data.keys()) if set(["df"]).issubset(data_schema): data_mode = 'dataframe' if set(["x", "y"]).issubset(data_schema): data_mode = 'ndarray' assert data_mode in ['dataframe', 'ndarray'],\ 'ERROR: Argument \'data\' should fit either \ dataframe schema (include \'df\' in keys) or\ ndarray (include \'x\' and \'y\' in keys) schema.' # data extract if data_mode == 'dataframe': input_df = data['df'] feature_cols = data.get("feature_cols", None) target_col = data.get("target_col", None) validation_df = data.get("val_df", None) else: if data["x"].ndim == 1: data["x"] = data["x"].reshape(-1, 1) if data["y"].ndim == 1: data["y"] = data["y"].reshape(-1, 1) if "val_x" in data.keys() and data["val_x"].ndim == 1: data["val_x"] = data["val_x"].reshape(-1, 1) if "val_y" in data.keys() and data["val_y"].ndim == 1: data["val_y"] = data["val_y"].reshape(-1, 1) input_data = {"x": data["x"], "y": data["y"]} if 'val_x' in data.keys(): validation_data = {"x": data["val_x"], "y": data["val_y"]} else: validation_data = None # prepare parameters for search engine runtime_params = recipe.runtime_params() self.num_samples = runtime_params['num_samples'] stop = dict(runtime_params) del stop['num_samples'] self.stop_criteria = stop if search_space is None: search_space = recipe.search_space(all_available_features=None) self._search_alg = RayTuneSearchEngine._set_search_alg(search_alg, search_alg_params, recipe, search_space) self._scheduler = RayTuneSearchEngine._set_scheduler(scheduler, scheduler_params) self.search_space = self._prepare_tune_config(search_space) if feature_transformers is None and data_mode == 'dataframe': feature_transformers = IdentityTransformer(feature_cols, target_col) if data_mode == 'dataframe': self.train_func = self._prepare_train_func(input_data=input_df, model_create_func=model_create_func, feature_transformers=feature_transformers, validation_data=validation_df, metric=metric, mc=mc, remote_dir=self.remote_dir, numpy_format=False ) else: self.train_func = self._prepare_train_func(input_data=input_data, model_create_func=model_create_func, feature_transformers=None, validation_data=validation_data, metric=metric, mc=mc, remote_dir=self.remote_dir, numpy_format=True ) # self.trainable_class = self._prepare_trainable_class(input_df, # feature_transformers, # # model, # future_seq_len, # validation_df, # metric_op, # self.remote_dir) @staticmethod @staticmethod def run(self): """ Run trials :return: trials result """ analysis = tune.run( self.train_func, local_dir=self.logs_dir, name=self.name, stop=self.stop_criteria, config=self.search_space, search_alg=self._search_alg, num_samples=self.num_samples, scheduler=self._scheduler, resources_per_trial=self.resources_per_trail, verbose=1, reuse_actors=True ) self.trials = analysis.trials # Visualization code for ray (leaderboard) # catch the ImportError Since it has been processed in TensorboardXLogger tf_config, tf_metric = self._log_adapt(analysis) self.logger = TensorboardXLogger(os.path.join(self.logs_dir, self.name+"_leaderboard")) self.logger.run(tf_config, tf_metric) self.logger.close() return analysis @staticmethod def _get_best_trial(trial_list, metric): """Retrieve the best trial.""" return max(trial_list, key=lambda trial: trial.last_result.get(metric, 0)) @staticmethod @staticmethod def _get_best_result(trial_list, metric): """Retrieve the last result from the best trial.""" return {metric: RayTuneSearchEngine._get_best_trial(trial_list, metric).last_result[metric]} @staticmethod @staticmethod def _prepare_train_func(input_data, model_create_func, feature_transformers, metric, validation_data=None, mc=False, remote_dir=None, numpy_format=False, ): """ Prepare the train function for ray tune :param input_df: input dataframe :param feature_transformers: feature transformers :param model: model or model selector :param validation_df: validation dataframe :param metric: the rewarding metric :return: the train function """ numpy_format_id = ray.put(numpy_format) input_data_id = ray.put(input_data) ft_id = ray.put(feature_transformers) # model_id = ray.put(model) # validation data processing df_not_empty = isinstance(validation_data, dict) or\ (isinstance(validation_data, pd.DataFrame) and not validation_data.empty) df_list_not_empty = isinstance(validation_data, dict) or\ (isinstance(validation_data, list) and validation_data and not all([d.empty for d in validation_data])) if validation_data is not None and (df_not_empty or df_list_not_empty): validation_data_id = ray.put(validation_data) is_val_valid = True else: is_val_valid = False return train_func @staticmethod def _prepare_trainable_class(input_df, feature_transformers, future_seq_len, metric, validation_df=None, mc=False, remote_dir=None ): """ Prepare the train function for ray tune :param input_df: input dataframe :param feature_transformers: feature transformers :param model: model or model selector :param validation_df: validation dataframe :param metric: the rewarding metric :return: the train function """ input_df_id = ray.put(input_df) ft_id = ray.put(feature_transformers) # model_id = ray.put(model) df_not_empty = isinstance(validation_df, pd.DataFrame) and not validation_df.empty df_list_not_empty = isinstance(validation_df, list) and validation_df \ and not all([d.empty for d in validation_df]) if validation_df is not None and (df_not_empty or df_list_not_empty): validation_df_id = ray.put(validation_df) is_val_df_valid = True else: is_val_df_valid = False return TrainableClass

43.365348

100

0.537065

# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import ray from ray import tune from copy import deepcopy import os from zoo.automl.search.abstract import * from zoo.automl.common.util import * from zoo.automl.common.metrics import Evaluator from zoo.automl.impute.impute import * from ray.tune import Trainable import ray.tune.track from zoo.automl.logger import TensorboardXLogger from zoo.automl.model.model_builder import ModelBuilder from zoo.automl.feature.identity_transformer import IdentityTransformer from zoo.automl.search.tune_utils import (create_searcher, create_scheduler) SEARCH_ALG_ALLOWED = ("variant_generator", "skopt", "bayesopt") class RayTuneSearchEngine(SearchEngine): """ Tune driver """ def __init__(self, logs_dir="", resources_per_trial=None, name="", remote_dir=None, ): """ Constructor :param resources_per_trial: resources for each trial """ self.pipeline = None self.train_func = None self.trainable_class = None self.resources_per_trail = resources_per_trial self.trials = None self.remote_dir = remote_dir self.name = name self.logs_dir = os.path.abspath(os.path.expanduser(logs_dir)) def compile(self, data, model_create_func, recipe, search_space=None, search_alg=None, search_alg_params=None, scheduler=None, scheduler_params=None, feature_transformers=None, mc=False, metric="mse"): """ Do necessary preparations for the engine :param input_df: :param search_space: :param num_samples: :param stop: :param search_algorithm: :param search_algorithm_params: :param fixed_params: :param feature_transformers: :param model: :param validation_df: :param metric: :return: """ # data mode detection assert isinstance(data, dict), 'ERROR: Argument \'data\' should be a dictionary.' data_mode = None # data_mode can only be 'dataframe' or 'ndarray' data_schema = set(data.keys()) if set(["df"]).issubset(data_schema): data_mode = 'dataframe' if set(["x", "y"]).issubset(data_schema): data_mode = 'ndarray' assert data_mode in ['dataframe', 'ndarray'],\ 'ERROR: Argument \'data\' should fit either \ dataframe schema (include \'df\' in keys) or\ ndarray (include \'x\' and \'y\' in keys) schema.' # data extract if data_mode == 'dataframe': input_df = data['df'] feature_cols = data.get("feature_cols", None) target_col = data.get("target_col", None) validation_df = data.get("val_df", None) else: if data["x"].ndim == 1: data["x"] = data["x"].reshape(-1, 1) if data["y"].ndim == 1: data["y"] = data["y"].reshape(-1, 1) if "val_x" in data.keys() and data["val_x"].ndim == 1: data["val_x"] = data["val_x"].reshape(-1, 1) if "val_y" in data.keys() and data["val_y"].ndim == 1: data["val_y"] = data["val_y"].reshape(-1, 1) input_data = {"x": data["x"], "y": data["y"]} if 'val_x' in data.keys(): validation_data = {"x": data["val_x"], "y": data["val_y"]} else: validation_data = None # prepare parameters for search engine runtime_params = recipe.runtime_params() self.num_samples = runtime_params['num_samples'] stop = dict(runtime_params) del stop['num_samples'] self.stop_criteria = stop if search_space is None: search_space = recipe.search_space(all_available_features=None) self._search_alg = RayTuneSearchEngine._set_search_alg(search_alg, search_alg_params, recipe, search_space) self._scheduler = RayTuneSearchEngine._set_scheduler(scheduler, scheduler_params) self.search_space = self._prepare_tune_config(search_space) if feature_transformers is None and data_mode == 'dataframe': feature_transformers = IdentityTransformer(feature_cols, target_col) if data_mode == 'dataframe': self.train_func = self._prepare_train_func(input_data=input_df, model_create_func=model_create_func, feature_transformers=feature_transformers, validation_data=validation_df, metric=metric, mc=mc, remote_dir=self.remote_dir, numpy_format=False ) else: self.train_func = self._prepare_train_func(input_data=input_data, model_create_func=model_create_func, feature_transformers=None, validation_data=validation_data, metric=metric, mc=mc, remote_dir=self.remote_dir, numpy_format=True ) # self.trainable_class = self._prepare_trainable_class(input_df, # feature_transformers, # # model, # future_seq_len, # validation_df, # metric_op, # self.remote_dir) @staticmethod def _set_search_alg(search_alg, search_alg_params, recipe, search_space): if search_alg: if not isinstance(search_alg, str): raise ValueError(f"search_alg should be of type str." f" Got {search_alg.__class__.__name__}") search_alg = search_alg.lower() if search_alg_params is None: search_alg_params = dict() if search_alg not in SEARCH_ALG_ALLOWED: raise ValueError(f"search_alg must be one of {SEARCH_ALG_ALLOWED}. " f"Got: {search_alg}") if search_alg == "skopt": from skopt import Optimizer opt_params = recipe.opt_params() optimizer = Optimizer(opt_params) search_alg_params.update(dict( optimizer=optimizer, parameter_names=list(search_space.keys()), )) elif search_alg == "bayesopt": search_alg_params.update({"space": recipe.manual_search_space()}) search_alg_params.update(dict( metric="reward_metric", mode="max", )) search_alg = create_searcher(search_alg, **search_alg_params) return search_alg @staticmethod def _set_scheduler(scheduler, scheduler_params): if scheduler: if not isinstance(scheduler, str): raise ValueError(f"Scheduler should be of type str. " f"Got {scheduler.__class__.__name__}") if scheduler_params is None: scheduler_params = dict() scheduler_params.update(dict( time_attr="training_iteration", metric="reward_metric", mode="max", )) scheduler = create_scheduler(scheduler, **scheduler_params) return scheduler def run(self): """ Run trials :return: trials result """ analysis = tune.run( self.train_func, local_dir=self.logs_dir, name=self.name, stop=self.stop_criteria, config=self.search_space, search_alg=self._search_alg, num_samples=self.num_samples, scheduler=self._scheduler, resources_per_trial=self.resources_per_trail, verbose=1, reuse_actors=True ) self.trials = analysis.trials # Visualization code for ray (leaderboard) # catch the ImportError Since it has been processed in TensorboardXLogger tf_config, tf_metric = self._log_adapt(analysis) self.logger = TensorboardXLogger(os.path.join(self.logs_dir, self.name+"_leaderboard")) self.logger.run(tf_config, tf_metric) self.logger.close() return analysis def get_best_trials(self, k=1): sorted_trials = RayTuneSearchEngine._get_sorted_trials(self.trials, metric="reward_metric") best_trials = sorted_trials[:k] return [self._make_trial_output(t) for t in best_trials] def _make_trial_output(self, trial): return TrialOutput(config=trial.config, model_path=os.path.join(trial.logdir, trial.last_result["checkpoint"])) @staticmethod def _get_best_trial(trial_list, metric): """Retrieve the best trial.""" return max(trial_list, key=lambda trial: trial.last_result.get(metric, 0)) @staticmethod def _get_sorted_trials(trial_list, metric): return sorted( trial_list, key=lambda trial: trial.last_result.get(metric, 0), reverse=True) @staticmethod def _get_best_result(trial_list, metric): """Retrieve the last result from the best trial.""" return {metric: RayTuneSearchEngine._get_best_trial(trial_list, metric).last_result[metric]} def test_run(self): def mock_reporter(**kwargs): assert "reward_metric" in kwargs, "Did not report proper metric" assert "checkpoint" in kwargs, "Accidentally removed `checkpoint`?" raise GoodError("This works.") try: self.train_func({'out_units': 1, 'selected_features': ["MONTH(datetime)", "WEEKDAY(datetime)"]}, mock_reporter) # self.train_func(self.search_space, mock_reporter) except TypeError as e: print("Forgot to modify function signature?") raise e except GoodError: print("Works!") return 1 raise Exception("Didn't call reporter...") @staticmethod def _is_validation_df_valid(validation_df): df_not_empty = isinstance(validation_df, pd.DataFrame) and not validation_df.empty df_list_not_empty = isinstance(validation_df, list) and validation_df \ and not all([d.empty for d in validation_df]) return validation_df is not None and not (df_not_empty or df_list_not_empty) @staticmethod def _prepare_train_func(input_data, model_create_func, feature_transformers, metric, validation_data=None, mc=False, remote_dir=None, numpy_format=False, ): """ Prepare the train function for ray tune :param input_df: input dataframe :param feature_transformers: feature transformers :param model: model or model selector :param validation_df: validation dataframe :param metric: the rewarding metric :return: the train function """ numpy_format_id = ray.put(numpy_format) input_data_id = ray.put(input_data) ft_id = ray.put(feature_transformers) # model_id = ray.put(model) # validation data processing df_not_empty = isinstance(validation_data, dict) or\ (isinstance(validation_data, pd.DataFrame) and not validation_data.empty) df_list_not_empty = isinstance(validation_data, dict) or\ (isinstance(validation_data, list) and validation_data and not all([d.empty for d in validation_data])) if validation_data is not None and (df_not_empty or df_list_not_empty): validation_data_id = ray.put(validation_data) is_val_valid = True else: is_val_valid = False def train_func(config): numpy_format = ray.get(numpy_format_id) if isinstance(model_create_func, ModelBuilder): trial_model = model_create_func.build(config) else: trial_model = model_create_func() if not numpy_format: global_ft = ray.get(ft_id) trial_ft = deepcopy(global_ft) imputer = None if "imputation" in config: if config["imputation"] == "LastFillImpute": imputer = LastFillImpute() elif config["imputation"] == "FillZeroImpute": imputer = FillZeroImpute() # handling input global_input_df = ray.get(input_data_id) trial_input_df = deepcopy(global_input_df) if imputer: trial_input_df = imputer.impute(trial_input_df) config = convert_bayes_configs(config).copy() # print("config is ", config) (x_train, y_train) = trial_ft.fit_transform(trial_input_df, **config) # trial_ft.fit(trial_input_df, **config) # handling validation data validation_data = None if is_val_valid: global_validation_df = ray.get(validation_data_id) trial_validation_df = deepcopy(global_validation_df) validation_data = trial_ft.transform(trial_validation_df) else: train_data = ray.get(input_data_id) x_train, y_train = (train_data["x"], train_data["y"]) validation_data = None if is_val_valid: validation_data = ray.get(validation_data_id) validation_data = (validation_data["x"], validation_data["y"]) trial_ft = None # no need to call build since it is called the first time fit_eval is called. # callbacks = [TuneCallback(tune_reporter)] # fit model best_reward_m = None # print("config:", config) for i in range(1, 101): result = trial_model.fit_eval(x_train, y_train, validation_data=validation_data, mc=mc, metric=metric, # verbose=1, **config) reward_m = result if Evaluator.get_metric_mode(metric) == "max" else -result ckpt_name = "best.ckpt" if best_reward_m is None or reward_m > best_reward_m: best_reward_m = reward_m save_zip(ckpt_name, trial_ft, trial_model, config) if remote_dir is not None: upload_ppl_hdfs(remote_dir, ckpt_name) tune.track.log(training_iteration=i, reward_metric=reward_m, checkpoint="best.ckpt") return train_func @staticmethod def _prepare_trainable_class(input_df, feature_transformers, future_seq_len, metric, validation_df=None, mc=False, remote_dir=None ): """ Prepare the train function for ray tune :param input_df: input dataframe :param feature_transformers: feature transformers :param model: model or model selector :param validation_df: validation dataframe :param metric: the rewarding metric :return: the train function """ input_df_id = ray.put(input_df) ft_id = ray.put(feature_transformers) # model_id = ray.put(model) df_not_empty = isinstance(validation_df, pd.DataFrame) and not validation_df.empty df_list_not_empty = isinstance(validation_df, list) and validation_df \ and not all([d.empty for d in validation_df]) if validation_df is not None and (df_not_empty or df_list_not_empty): validation_df_id = ray.put(validation_df) is_val_df_valid = True else: is_val_df_valid = False class TrainableClass(Trainable): def _setup(self, config): # print("config in set up is", config) global_ft = ray.get(ft_id) # global_model = ray.get(model_id) self.trial_ft = deepcopy(global_ft) self.trial_model = TimeSequenceModel(check_optional_config=False, future_seq_len=future_seq_len) # handling input global_input_df = ray.get(input_df_id) trial_input_df = deepcopy(global_input_df) self.config = convert_bayes_configs(config).copy() (self.x_train, self.y_train) = self.trial_ft.fit_transform(trial_input_df, **self.config) # trial_ft.fit(trial_input_df, **config) # handling validation data self.validation_data = None if is_val_df_valid: global_validation_df = ray.get(validation_df_id) trial_validation_df = deepcopy(global_validation_df) self.validation_data = self.trial_ft.transform(trial_validation_df) # no need to call build since it is called the first time fit_eval is called. # callbacks = [TuneCallback(tune_reporter)] # fit model self.best_reward_m = -999 self.reward_m = -999 self.ckpt_name = "pipeline.ckpt" self.metric_op = 1 if metric_mode is "max" else -1 def _train(self): # print("self.config in train is ", self.config) result = self.trial_model.fit_eval(self.x_train, self.y_train, validation_data=self.validation_data, # verbose=1, **self.config) self.reward_m = result if Evaluator.get_metric_mode(metric) == "max" else -result # if metric == "mean_squared_error": # self.reward_m = (-1) * result # # print("running iteration: ",i) # elif metric == "r_square": # self.reward_m = result # else: # raise ValueError("metric can only be \"mean_squared_error\" or \"r_square\"") return {"reward_metric": self.reward_m, "checkpoint": self.ckpt_name} def _save(self, checkpoint_dir): # print("checkpoint dir is ", checkpoint_dir) ckpt_name = self.ckpt_name # save in the working dir (without "checkpoint_{}".format(training_iteration)) path = os.path.join(checkpoint_dir, "..", ckpt_name) # path = os.path.join(checkpoint_dir, ckpt_name) # print("checkpoint save path is ", checkpoint_dir) if self.reward_m > self.best_reward_m: self.best_reward_m = self.reward_m print("****this reward is", self.reward_m) print("*********saving checkpoint") save_zip(ckpt_name, self.trial_ft, self.trial_model, self.config) if remote_dir is not None: upload_ppl_hdfs(remote_dir, ckpt_name) return path def _restore(self, checkpoint_path): # print("checkpoint path in restore is ", checkpoint_path) if remote_dir is not None: restore_hdfs(checkpoint_path, remote_dir, self.trial_ft, self.trial_model) else: restore_zip(checkpoint_path, self.trial_ft, self.trial_model) return TrainableClass def _prepare_tune_config(self, space): tune_config = {} for k, v in space.items(): if isinstance(v, RandomSample): tune_config[k] = tune.sample_from(v.func) elif isinstance(v, GridSearch): tune_config[k] = tune.grid_search(v.values) else: tune_config[k] = v return tune_config def _log_adapt(self, analysis): # config config = analysis.get_all_configs() # metric metric_raw = analysis.fetch_trial_dataframes() metric = {} for key, value in metric_raw.items(): metric[key] = dict(zip(list(value.columns), list(map(list, value.values.T)))) config[key]["address"] = key return config, metric

11,125

11

441

48c5f6e971f56e8aa3465449742bc2aac20738cb

2,409

py

Python

mmseg/datasets/waymo_depth_datset.py

anhvth/Swin-Transformer-Semantic-Segmentation

31f69fe98de90cbf51bc9910f6fb777b0561bd4e

[ "Apache-2.0" ]

null

mmseg/datasets/waymo_depth_datset.py

anhvth/Swin-Transformer-Semantic-Segmentation

31f69fe98de90cbf51bc9910f6fb777b0561bd4e

[ "Apache-2.0" ]

null

mmseg/datasets/waymo_depth_datset.py

anhvth/Swin-Transformer-Semantic-Segmentation

31f69fe98de90cbf51bc9910f6fb777b0561bd4e

[ "Apache-2.0" ]

null

from .custom import * @DATASETS.register_module(force=True)

43.017857

81

0.579909

from .custom import * @DATASETS.register_module(force=True) class WaymoDepthDataset(CustomDataset): def __init__(self, *args, depth_dir=None, depth_map_suffix='.npy',**kwargs): self.depth_dir = depth_dir self.depth_map_suffix = depth_map_suffix super(WaymoDepthDataset, self).__init__(*args,**kwargs) def pre_pipeline(self, results): super().pre_pipeline(results) results['depth_prefix'] = self.depth_dir results['flip'] = False results['flip_direction'] = None def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix, split): """Load annotation from directory. Args: img_dir (str): Path to image directory img_suffix (str): Suffix of images. ann_dir (str|None): Path to annotation directory. seg_map_suffix (str|None): Suffix of segmentation maps. split (str|None): Split txt file. If split is specified, only file with suffix in the splits will be loaded. Otherwise, all images in img_dir/ann_dir will be loaded. Default: None Returns: list[dict]: All image info of dataset. """ depth_dir = self.depth_dir depth_map_suffix = self.depth_map_suffix img_infos = [] if split is not None: with open(split) as f: for line in f: img_name = line.strip() img_info = dict(filename=img_name + img_suffix) if ann_dir is not None: seg_map = img_name + seg_map_suffix img_info['ann'] = dict(seg_map=seg_map) img_infos.append(img_info) else: for img in mmcv.scandir(img_dir, img_suffix, recursive=True): img_info = dict(filename=img) if ann_dir is not None: seg_map = img.replace(img_suffix, seg_map_suffix) img_info['ann'] = dict(seg_map=seg_map) if depth_dir is not None: depth_map = img.replace(img_suffix, depth_map_suffix) img_info['ann'] = dict(depth_map=depth_map) img_infos.append(img_info) print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger()) return img_infos

375

1,952

22

996a8a1128b5be33b3b9ae21ba65e8dc91142069

1,167

py

Python

index.py

samuel14luis/Proyecto-en-Python-criptograf-a-II

2c1812299f46aa7fb8f289d78ca9004fe334b618

[ "MIT" ]

null

index.py

samuel14luis/Proyecto-en-Python-criptograf-a-II

2c1812299f46aa7fb8f289d78ca9004fe334b618

[ "MIT" ]

null

index.py

samuel14luis/Proyecto-en-Python-criptograf-a-II

2c1812299f46aa7fb8f289d78ca9004fe334b618

[ "MIT" ]

null

import os from utilidades.consola import * from CriptografiaModerna.menuCM import menuCM from CriptografiaClasica.menuCC import menuCC #DEFINICIÓN DE VARIABLES #DEFINICIÓN DE FUNCIONES limpiarPantalla() iniciarMenu() despedida() input('') limpiarPantalla()

22.442308

81

0.521851

import os from utilidades.consola import * from CriptografiaModerna.menuCM import menuCM from CriptografiaClasica.menuCC import menuCC #DEFINICIÓN DE VARIABLES #DEFINICIÓN DE FUNCIONES def iniciarMenu(): cont = 0 eleccion = 'seguir' output_config = 5 menu = [ '[1] Criptografía Clásica', '[2] Criptografía Moderna', '[0] Salir' ] print ('Bienvenido') while eleccion == 'seguir': mostrarMenu(menu) op = input('Elija una opción> ') limpiarPantalla() if op == '1': aux = menuCC(output_config) eleccion = aux[0] output_config = aux[1] cont = 0 elif op == '2': aux = menuCM(output_config) eleccion = aux[0] output_config = aux[1] cont = 0 elif op == '0': eleccion = salir() else: cont += 1 mostrarError('Debe ingresar una opción válida. {' + str(cont) + '}') eleccion = 'seguir' limpiarPantalla() iniciarMenu() despedida() input('') limpiarPantalla()

874

0

23

3e8649d6ffb3892810d5e3b3d26d82ac9734c0ff

3,284

py

Python

index_cli/main.py

lishnih/index_cli

57f23d5df5168bcc73e23e0eeabbb8317014585b

[ "MIT" ]

null

index_cli/main.py

lishnih/index_cli

57f23d5df5168bcc73e23e0eeabbb8317014585b

[ "MIT" ]

null

index_cli/main.py

lishnih/index_cli

57f23d5df5168bcc73e23e0eeabbb8317014585b

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 # Stan 2012-03-12 from __future__ import (division, absolute_import, print_function, unicode_literals) import sys import os import logging from importlib import import_module from .core.types23 import * from .core.db import getDbUri, openDbUri from .core.recorder import Recorder from . import __version__ as index_version from .base import proceed from .base.parse import parse_files from .base.models import Base, Error

30.981132

80

0.678136

#!/usr/bin/env python # coding=utf-8 # Stan 2012-03-12 from __future__ import (division, absolute_import, print_function, unicode_literals) import sys import os import logging from importlib import import_module from .core.types23 import * from .core.db import getDbUri, openDbUri from .core.recorder import Recorder from . import __version__ as index_version from .base import proceed from .base.parse import parse_files from .base.models import Base, Error def main(files=None, profile=None, options={}, recorder=None, **kargs): files = files or options.get('files') profile = profile or options.get('profile', 'default') parser = options.get('parser') or \ '.file_parsers.{0}'.format(profile) if profile else None # Инициализируем recorder и устанавливаем уровень debug_level recorder = recorder or Recorder() recorder.logging_class = Error recorder.set_debug_level(options.get('debug_level', 1)) # Устанавливаем уровень logging и выводим параметры logging_level = options.get('logging_level', 'WARNING') logging.basicConfig(level=logging.getLevelName(logging_level)) logging.debug((index_version, logging_level, logging.root.level, \ recorder.get_debug_level())) # Определяем dburi dburi = getDbUri(options) if not dburi: recorder.warning("Database not specified!") return -11 # Устанавливаем соединение с БД engine, session = openDbUri(dburi) Base.metadata.create_all(session.bind) # Определяем сессию и таблицу для сообщений системы recorder.session = session if not files: recorder.warning("Files not specified!") return -1 # Сканирование файлов # (создание записей директорий, файлов и элементов файлов) files = os.path.expanduser(files) try: source_type = proceed(files, options, recorder) except Exception as e: recorder.exception("Exception during scanning!", target=str(files)) raise if not parser: recorder.debug("Parser not specified, exiting!") return -2 # Загружаем парсер package = options.get('package', __package__) recorder.debug(('Loading of parser', package, parser)) try: mod = import_module(parser, package) except Exception as e: recorder.exception("Unable to load the parser, exiting!", target="{0}{1}".format(package, parser)) raise entry = options.get('entry') opening = options.get('opening') closing = options.get('closing') recorder.debug("Parser loaded with entry '{0}' and auxilaries '{1}'/'{2}'".\ format(entry, opening, closing), target="{0}.{1}".format(package, parser)) recorder.func = getattr(mod, entry) if entry else None opening_func = getattr(mod, opening) if opening else None closing_func = getattr(mod, closing) if closing else None er = 0 try: if opening_func: opening_func(options, recorder) if recorder.func: er = parse_files(files, source_type, options, recorder) if closing_func: closing_func(options, recorder) except Exception as e: recorder.exception("Exception during parsing!", target=str(files)) return er

3,004

0

23

a786d47f5e861416affdf90f0ab36292e84ae8ee

4,447

py

Python

mri_works/NodeEditor/modules/File_IO/Save_image_nii_gz.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

2

2020-08-20T21:00:53.000Z

2021-08-16T15:28:51.000Z

mri_works/NodeEditor/modules/File_IO/Save_image_nii_gz.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

3

2020-09-24T06:50:43.000Z

2020-12-15T11:02:04.000Z

mri_works/NodeEditor/modules/File_IO/Save_image_nii_gz.py

montigno/mri_works

8ec6ff1500aa34d3540e44e4b0148023cf821f61

[ "CECILL-B" ]

1

2020-08-20T21:00:59.000Z

############################################################################## ############################################################################## ############################################################################## ##############################################################################

35.015748

115

0.506634

class Save_NiiGz: def __init__(self, image=[[0.0]], filepath='path', **options): import nibabel as nib import numpy as np import tkinter as tk from tkinter import filedialog root = tk.Tk() root.withdraw() if type(image).__name__ == 'list': image = np.array(image) if filepath == 'path': self.fileSaved = filedialog.asksaveasfile(**options).name else: if '.nii.gz' in filepath: self.fileSaved = filepath elif '.nii' in filepath: self.fileSaved = filepath + '.gz' else: self.fileSaved = filepath + '.nii.gz' img = nib.Nifti1Image(image, np.eye(4)) nib.save(img, self.fileSaved) def pathFile(self: 'path'): return self.fileSaved ############################################################################## class Save_NiiGz_header: def __init__(self, image=[[0.0]], filepath='path', new_header='', **options): import nibabel as nib import tkinter as tk from tkinter import filedialog root = tk.Tk() root.withdraw() if type(image).__name__ == 'list': image = np.array(image) if filepath == 'path': self.fileSaved = filedialog.asksaveasfile(**options).name else: if filepath.endswith('.nii.gz'): self.fileSaved = filepath elif filepath.endswith('.nii'): self.fileSaved = filepath + '.gz' else: self.fileSaved = filepath + '.nii.gz' img = nib.Nifti1Image(image, None, header=new_header) nib.save(img, self.fileSaved) def pathFile(self: 'path'): return self.fileSaved ############################################################################## class Save_Nifti: def __init__(self, image=[[0.0]], filepath='path', out_type="enumerate(('nii','nii.gz'))", **options): import nibabel as nib import numpy as np import tkinter as tk from tkinter import filedialog root = tk.Tk() root.withdraw() if type(image).__name__ == 'list': image = np.array(image) if filepath == 'path': self.fileSaved = filedialog.asksaveasfile(**options).name else: if filepath.endswith('.nii') or filepath.endswith('.nii.gz'): self.fileSaved = filepath[0:filepath.index('.nii')] + '.' + out_type else: self.fileSaved = filepath + '.' + out_type img = nib.Nifti1Image(image, np.eye(4)) nib.save(img, self.fileSaved) def pathFile(self: 'path'): return self.fileSaved ############################################################################## class Save_Nifti_header: def __init__(self, image=[[0.0]], filepath='path', new_header='', out_type="enumerate(('nii','nii.gz'))", **options): import nibabel as nib import tkinter as tk from tkinter import filedialog root = tk.Tk() root.withdraw() if type(image).__name__ == 'list': image = np.array(image) if filepath == 'path': self.fileSaved = filedialog.asksaveasfile(**options).name else: if filepath.endswith('.nii') or filepath.endswith('.nii.gz'): self.fileSaved = filepath[0:filepath.index('.nii')] + '.' + out_type else: self.fileSaved = filepath + '.' + out_type img = nib.Nifti1Image(image, None, header=new_header) nib.save(img, self.fileSaved) def pathFile(self: 'path'): return self.fileSaved ############################################################################## class Create_gzip: def __init__(self, file_in='path', delete_file=False): import gzip import shutil import os self.out_gz = '' with open(file_in, 'rb') as f_in: with gzip.open(file_in+'.gz', 'wb') as f_out: shutil.copyfileobj(f_in, f_out) self.out_gz = file_in+'.gz' if delete_file: try: os.remove(file_in) except OSError as e: print("Error : %s : %s" % (input_file, e.strerror)) def out_file(self:'path'): return self.out_gz

3,721

-5

383

bc5269769868f479d59e7a9477cb82de75d75760

19,744

py

Python

dataloader.py

gregersn/Super-SloMo

11f3b562ca44c068b847da33e09e4087adc1c032

[ "MIT" ]

null

dataloader.py

gregersn/Super-SloMo

11f3b562ca44c068b847da33e09e4087adc1c032

[ "MIT" ]

null

dataloader.py

gregersn/Super-SloMo

11f3b562ca44c068b847da33e09e4087adc1c032

[ "MIT" ]

null

import torch.utils.data as data from PIL import Image import os import os.path import random def _make_dataset(dir): """ Creates a 2D list of all the frames in N clips containing M frames each. 2D List Structure: [[frame00, frame01,...frameM] <-- clip0 [frame00, frame01,...frameM] <-- clip0 : [frame00, frame01,...frameM]] <-- clipN Parameters ---------- dir : string root directory containing clips. Returns ------- list 2D list described above. """ framesPath = [] # Find and loop over all the clips in root `dir`. for index, folder in enumerate(os.listdir(dir)): clipsFolderPath = os.path.join(dir, folder) # Skip items which are not folders. if not (os.path.isdir(clipsFolderPath)): continue framesPath.append([]) # Find and loop over all the frames inside the clip. for image in sorted(os.listdir(clipsFolderPath)): # Add path to list. framesPath[index].append(os.path.join(clipsFolderPath, image)) return framesPath def _make_video_dataset(dir): """ Creates a 1D list of all the frames. 1D List Structure: [frame0, frame1,...frameN] Parameters ---------- dir : string root directory containing frames. Returns ------- list 1D list described above. """ framesPath = [] # Find and loop over all the frames in root `dir`. for image in sorted(os.listdir(dir)): # Add path to list. framesPath.append(os.path.join(dir, image)) return framesPath def _pil_loader(path, cropArea=None, resizeDim=None, frameFlip=0): """ Opens image at `path` using pil and applies data augmentation. Parameters ---------- path : string path of the image. cropArea : tuple, optional coordinates for cropping image. Default: None resizeDim : tuple, optional dimensions for resizing image. Default: None frameFlip : int, optional Non zero to flip image horizontally. Default: 0 Returns ------- list 2D list described above. """ # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: img = Image.open(f) # Resize image if specified. resized_img = img.resize(resizeDim, Image.ANTIALIAS) if (resizeDim != None) else img # Crop image if crop area specified. cropped_img = img.crop(cropArea) if (cropArea != None) else resized_img # Flip image horizontally if specified. flipped_img = cropped_img.transpose(Image.FLIP_LEFT_RIGHT) if frameFlip else cropped_img return flipped_img.convert('RGB') class SuperSloMo(data.Dataset): """ A dataloader for loading N samples arranged in this way: |-- clip0 |-- frame00 |-- frame01 : |-- frame11 |-- frame12 |-- clip1 |-- frame00 |-- frame01 : |-- frame11 |-- frame12 : : |-- clipN |-- frame00 |-- frame01 : |-- frame11 |-- frame12 ... Attributes ---------- framesPath : list List of frames' path in the dataset. Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None, dim=(640, 360), randomCropSize=(352, 352), train=True): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. dim : tuple, optional Dimensions of images in dataset. Default: (640, 360) randomCropSize : tuple, optional Dimensions of random crop to be applied. Default: (352, 352) train : boolean, optional Specifies if the dataset is for training or testing/validation. `True` returns samples with data augmentation like random flipping, random cropping, etc. while `False` returns the samples without randomization. Default: True """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_dataset(root) # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n")) self.randomCropSize = randomCropSize self.cropX0 = dim[0] - randomCropSize[0] self.cropY0 = dim[1] - randomCropSize[1] self.root = root self.transform = transform self.train = train self.framesPath = framesPath def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1 - and a random frame chosen from the 7 intermediate frames available between I0 and I1 along with it's relative index. Parameters ---------- index : int Index Returns ------- tuple (sample, returnIndex) where sample is [I0, intermediate_frame, I1] and returnIndex is the position of `random_intermediate_frame`. e.g.- `returnIndex` of frame next to I0 would be 0 and frame before I1 would be 6. """ sample = [] if (self.train): ### Data Augmentation ### # To select random 9 frames from 12 frames in a clip firstFrame = random.randint(0, 3) # Apply random crop on the 9 input frames cropX = random.randint(0, self.cropX0) cropY = random.randint(0, self.cropY0) cropArea = (cropX, cropY, cropX + self.randomCropSize[0], cropY + self.randomCropSize[1]) # Random reverse frame #frameRange = range(firstFrame, firstFrame + 9) if (random.randint(0, 1)) else range(firstFrame + 8, firstFrame - 1, -1) IFrameIndex = random.randint(firstFrame + 1, firstFrame + 7) if (random.randint(0, 1)): frameRange = [firstFrame, IFrameIndex, firstFrame + 8] returnIndex = IFrameIndex - firstFrame - 1 else: frameRange = [firstFrame + 8, IFrameIndex, firstFrame] returnIndex = firstFrame - IFrameIndex + 7 # Random flip frame randomFrameFlip = random.randint(0, 1) else: # Fixed settings to return same samples every epoch. # For validation/test sets. firstFrame = 0 cropArea = (0, 0, self.randomCropSize[0], self.randomCropSize[1]) IFrameIndex = ((index) % 7 + 1) returnIndex = IFrameIndex - 1 frameRange = [0, IFrameIndex, 8] randomFrameFlip = 0 # Loop over for all frames corresponding to the `index`. for frameIndex in frameRange: # Open image using pil and augment the image. image = _pil_loader(self.framesPath[index][frameIndex], cropArea=cropArea, frameFlip=randomFrameFlip) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample, returnIndex def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ return len(self.framesPath) def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class UCI101Test(data.Dataset): """ A dataloader for loading N samples arranged in this way: |-- clip0 |-- frame00 |-- frame01 |-- frame02 |-- clip1 |-- frame00 |-- frame01 |-- frame02 : : |-- clipN |-- frame00 |-- frame01 |-- frame02 ... Attributes ---------- framesPath : list List of frames' path in the dataset. Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_dataset(root) # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n")) self.root = root self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1 - and a intermediate frame between I0 and I1. Parameters ---------- index : int Index Returns ------- tuple (sample, returnIndex) where sample is [I0, intermediate_frame, I1] and returnIndex is the position of `intermediate_frame`. The returnIndex is always 3 and is being returned to maintain compatibility with the `SuperSloMo` dataloader where 3 corresponds to the middle frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in self.framesPath[index]: # Open image using pil. image = _pil_loader(framePath) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample, 3 def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ return len(self.framesPath) def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class Video(data.Dataset): """ A dataloader for loading all video frames in a folder: |-- frame0 |-- frame1 : : |-- frameN ... Attributes ---------- framesPath : list List of frames' path in the dataset. origDim : tuple original dimensions of the video. dim : tuple resized dimensions of the video (for CNN). Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_video_dataset(root) # Get dimensions of frames frame = _pil_loader(framesPath[0]) self.origDim = frame.size self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32 # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in: " + root + "\n")) self.root = root self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1. Parameters ---------- index : int Index Returns ------- list sample is [I0, I1] where I0 is the frame with index `index` and I1 is the next frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in [self.framesPath[index], self.framesPath[index + 1]]: # Open image using pil. image = _pil_loader(framePath, resizeDim=self.dim) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ # Using `-1` so that dataloader accesses only upto # frames [N-1, N] and not [N, N+1] which because frame # N+1 doesn't exist. return len(self.framesPath) - 1 def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class Images(data.Dataset): """ A dataloader for loading all video frames in a folder: |-- frame0 |-- frame1 : : |-- frameN ... Attributes ---------- framesPath : list List of frames' path in the dataset. origDim : tuple original dimensions of the video. dim : tuple resized dimensions of the video (for CNN). Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, frame0, frame1, transform=None): """ Parameters ---------- frame0 : string Input image 1 frame1: string Input image 2 transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = [frame0, frame1] # Get dimensions of frames frame = _pil_loader(frame0) self.origDim = frame.size self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32 # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in: " + root + "\n")) self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1. Parameters ---------- index : int Index Returns ------- list sample is [I0, I1] where I0 is the frame with index `index` and I1 is the next frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in [self.framesPath[index], self.framesPath[index + 1]]: # Open image using pil. image = _pil_loader(framePath, resizeDim=self.dim) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ # Using `-1` so that dataloader accesses only upto # frames [N-1, N] and not [N, N+1] which because frame # N+1 doesn't exist. return len(self.framesPath) - 1 def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str

29.293769

132

0.538341

import torch.utils.data as data from PIL import Image import os import os.path import random def _make_dataset(dir): """ Creates a 2D list of all the frames in N clips containing M frames each. 2D List Structure: [[frame00, frame01,...frameM] <-- clip0 [frame00, frame01,...frameM] <-- clip0 : [frame00, frame01,...frameM]] <-- clipN Parameters ---------- dir : string root directory containing clips. Returns ------- list 2D list described above. """ framesPath = [] # Find and loop over all the clips in root `dir`. for index, folder in enumerate(os.listdir(dir)): clipsFolderPath = os.path.join(dir, folder) # Skip items which are not folders. if not (os.path.isdir(clipsFolderPath)): continue framesPath.append([]) # Find and loop over all the frames inside the clip. for image in sorted(os.listdir(clipsFolderPath)): # Add path to list. framesPath[index].append(os.path.join(clipsFolderPath, image)) return framesPath def _make_video_dataset(dir): """ Creates a 1D list of all the frames. 1D List Structure: [frame0, frame1,...frameN] Parameters ---------- dir : string root directory containing frames. Returns ------- list 1D list described above. """ framesPath = [] # Find and loop over all the frames in root `dir`. for image in sorted(os.listdir(dir)): # Add path to list. framesPath.append(os.path.join(dir, image)) return framesPath def _pil_loader(path, cropArea=None, resizeDim=None, frameFlip=0): """ Opens image at `path` using pil and applies data augmentation. Parameters ---------- path : string path of the image. cropArea : tuple, optional coordinates for cropping image. Default: None resizeDim : tuple, optional dimensions for resizing image. Default: None frameFlip : int, optional Non zero to flip image horizontally. Default: 0 Returns ------- list 2D list described above. """ # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: img = Image.open(f) # Resize image if specified. resized_img = img.resize(resizeDim, Image.ANTIALIAS) if (resizeDim != None) else img # Crop image if crop area specified. cropped_img = img.crop(cropArea) if (cropArea != None) else resized_img # Flip image horizontally if specified. flipped_img = cropped_img.transpose(Image.FLIP_LEFT_RIGHT) if frameFlip else cropped_img return flipped_img.convert('RGB') class SuperSloMo(data.Dataset): """ A dataloader for loading N samples arranged in this way: |-- clip0 |-- frame00 |-- frame01 : |-- frame11 |-- frame12 |-- clip1 |-- frame00 |-- frame01 : |-- frame11 |-- frame12 : : |-- clipN |-- frame00 |-- frame01 : |-- frame11 |-- frame12 ... Attributes ---------- framesPath : list List of frames' path in the dataset. Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None, dim=(640, 360), randomCropSize=(352, 352), train=True): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. dim : tuple, optional Dimensions of images in dataset. Default: (640, 360) randomCropSize : tuple, optional Dimensions of random crop to be applied. Default: (352, 352) train : boolean, optional Specifies if the dataset is for training or testing/validation. `True` returns samples with data augmentation like random flipping, random cropping, etc. while `False` returns the samples without randomization. Default: True """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_dataset(root) # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n")) self.randomCropSize = randomCropSize self.cropX0 = dim[0] - randomCropSize[0] self.cropY0 = dim[1] - randomCropSize[1] self.root = root self.transform = transform self.train = train self.framesPath = framesPath def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1 - and a random frame chosen from the 7 intermediate frames available between I0 and I1 along with it's relative index. Parameters ---------- index : int Index Returns ------- tuple (sample, returnIndex) where sample is [I0, intermediate_frame, I1] and returnIndex is the position of `random_intermediate_frame`. e.g.- `returnIndex` of frame next to I0 would be 0 and frame before I1 would be 6. """ sample = [] if (self.train): ### Data Augmentation ### # To select random 9 frames from 12 frames in a clip firstFrame = random.randint(0, 3) # Apply random crop on the 9 input frames cropX = random.randint(0, self.cropX0) cropY = random.randint(0, self.cropY0) cropArea = (cropX, cropY, cropX + self.randomCropSize[0], cropY + self.randomCropSize[1]) # Random reverse frame #frameRange = range(firstFrame, firstFrame + 9) if (random.randint(0, 1)) else range(firstFrame + 8, firstFrame - 1, -1) IFrameIndex = random.randint(firstFrame + 1, firstFrame + 7) if (random.randint(0, 1)): frameRange = [firstFrame, IFrameIndex, firstFrame + 8] returnIndex = IFrameIndex - firstFrame - 1 else: frameRange = [firstFrame + 8, IFrameIndex, firstFrame] returnIndex = firstFrame - IFrameIndex + 7 # Random flip frame randomFrameFlip = random.randint(0, 1) else: # Fixed settings to return same samples every epoch. # For validation/test sets. firstFrame = 0 cropArea = (0, 0, self.randomCropSize[0], self.randomCropSize[1]) IFrameIndex = ((index) % 7 + 1) returnIndex = IFrameIndex - 1 frameRange = [0, IFrameIndex, 8] randomFrameFlip = 0 # Loop over for all frames corresponding to the `index`. for frameIndex in frameRange: # Open image using pil and augment the image. image = _pil_loader(self.framesPath[index][frameIndex], cropArea=cropArea, frameFlip=randomFrameFlip) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample, returnIndex def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ return len(self.framesPath) def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class UCI101Test(data.Dataset): """ A dataloader for loading N samples arranged in this way: |-- clip0 |-- frame00 |-- frame01 |-- frame02 |-- clip1 |-- frame00 |-- frame01 |-- frame02 : : |-- clipN |-- frame00 |-- frame01 |-- frame02 ... Attributes ---------- framesPath : list List of frames' path in the dataset. Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_dataset(root) # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n")) self.root = root self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1 - and a intermediate frame between I0 and I1. Parameters ---------- index : int Index Returns ------- tuple (sample, returnIndex) where sample is [I0, intermediate_frame, I1] and returnIndex is the position of `intermediate_frame`. The returnIndex is always 3 and is being returned to maintain compatibility with the `SuperSloMo` dataloader where 3 corresponds to the middle frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in self.framesPath[index]: # Open image using pil. image = _pil_loader(framePath) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample, 3 def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ return len(self.framesPath) def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class Video(data.Dataset): """ A dataloader for loading all video frames in a folder: |-- frame0 |-- frame1 : : |-- frameN ... Attributes ---------- framesPath : list List of frames' path in the dataset. origDim : tuple original dimensions of the video. dim : tuple resized dimensions of the video (for CNN). Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, root, transform=None): """ Parameters ---------- root : string Root directory path. transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = _make_video_dataset(root) # Get dimensions of frames frame = _pil_loader(framesPath[0]) self.origDim = frame.size self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32 # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in: " + root + "\n")) self.root = root self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1. Parameters ---------- index : int Index Returns ------- list sample is [I0, I1] where I0 is the frame with index `index` and I1 is the next frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in [self.framesPath[index], self.framesPath[index + 1]]: # Open image using pil. image = _pil_loader(framePath, resizeDim=self.dim) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ # Using `-1` so that dataloader accesses only upto # frames [N-1, N] and not [N, N+1] which because frame # N+1 doesn't exist. return len(self.framesPath) - 1 def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str class Images(data.Dataset): """ A dataloader for loading all video frames in a folder: |-- frame0 |-- frame1 : : |-- frameN ... Attributes ---------- framesPath : list List of frames' path in the dataset. origDim : tuple original dimensions of the video. dim : tuple resized dimensions of the video (for CNN). Methods ------- __getitem__(index) Returns the sample corresponding to `index` from dataset. __len__() Returns the size of dataset. Invoked as len(datasetObj). __repr__() Returns printable representation of the dataset object. """ def __init__(self, frame0, frame1, transform=None): """ Parameters ---------- frame0 : string Input image 1 frame1: string Input image 2 transform : callable, optional A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. """ # Populate the list with image paths for all the # frame in `root`. framesPath = [frame0, frame1] # Get dimensions of frames frame = _pil_loader(frame0) self.origDim = frame.size self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32 # Raise error if no images found in root. if len(framesPath) == 0: raise(RuntimeError("Found 0 files in: " + root + "\n")) self.framesPath = framesPath self.transform = transform def __getitem__(self, index): """ Returns the sample corresponding to `index` from dataset. The sample consists of two reference frames - I0 and I1. Parameters ---------- index : int Index Returns ------- list sample is [I0, I1] where I0 is the frame with index `index` and I1 is the next frame. """ sample = [] # Loop over for all frames corresponding to the `index`. for framePath in [self.framesPath[index], self.framesPath[index + 1]]: # Open image using pil. image = _pil_loader(framePath, resizeDim=self.dim) # Apply transformation if specified. if self.transform is not None: image = self.transform(image) sample.append(image) return sample def get_single(self, index): image = _pil_loader(self.framePath[index]) if self.transform is not None: image = self.transform(image) return image def __len__(self): """ Returns the size of dataset. Invoked as len(datasetObj). Returns ------- int number of samples. """ # Using `-1` so that dataloader accesses only upto # frames [N-1, N] and not [N, N+1] which because frame # N+1 doesn't exist. return len(self.framesPath) - 1 def __repr__(self): """ Returns printable representation of the dataset object. Returns ------- string info. """ fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str

160

0

31

40f10ba9cba8e68dff7b7eb5342f4e0f3d77da91

215,751

py

Python

boto3_type_annotations_with_docs/boto3_type_annotations/greengrass/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

119

2018-12-01T18:20:57.000Z

2022-02-02T10:31:29.000Z

boto3_type_annotations_with_docs/boto3_type_annotations/greengrass/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

15

2018-11-16T00:16:44.000Z

2021-11-13T03:44:18.000Z

boto3_type_annotations_with_docs/boto3_type_annotations/greengrass/client.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

11

2019-05-06T05:26:51.000Z

2021-09-28T15:27:59.000Z

from typing import Optional from botocore.client import BaseClient from typing import Dict from botocore.paginate import Paginator from botocore.waiter import Waiter from typing import Union from typing import List

49.850046

525

0.544276

from typing import Optional from botocore.client import BaseClient from typing import Dict from botocore.paginate import Paginator from botocore.waiter import Waiter from typing import Union from typing import List class Client(BaseClient): def associate_role_to_group(self, GroupId: str, RoleArn: str = None) -> Dict: """ Associates a role with a group. Your Greengrass core will use the role to access AWS cloud services. The role's permissions should allow Greengrass core Lambda functions to perform actions against the cloud. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/AssociateRoleToGroup>`_ **Request Syntax** :: response = client.associate_role_to_group( GroupId='string', RoleArn='string' ) **Response Syntax** :: { 'AssociatedAt': 'string' } **Response Structure** - *(dict) --* success - **AssociatedAt** *(string) --* The time, in milliseconds since the epoch, when the role ARN was associated with the group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type RoleArn: string :param RoleArn: The ARN of the role you wish to associate with this group. :rtype: dict :returns: """ pass def associate_service_role_to_account(self, RoleArn: str = None) -> Dict: """ Associates a role with your account. AWS IoT Greengrass will use the role to access your Lambda functions and AWS IoT resources. This is necessary for deployments to succeed. The role must have at least minimum permissions in the policy ''AWSGreengrassResourceAccessRolePolicy''. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/AssociateServiceRoleToAccount>`_ **Request Syntax** :: response = client.associate_service_role_to_account( RoleArn='string' ) **Response Syntax** :: { 'AssociatedAt': 'string' } **Response Structure** - *(dict) --* success - **AssociatedAt** *(string) --* The time when the service role was associated with the account. :type RoleArn: string :param RoleArn: The ARN of the service role you wish to associate with your account. :rtype: dict :returns: """ pass def can_paginate(self, operation_name: str = None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name as the method name on the client. For example, if the method name is ``create_foo``, and you\'d normally invoke the operation as ``client.create_foo(**kwargs)``, if the ``create_foo`` operation can be paginated, you can use the call ``client.get_paginator(\"create_foo\")``. :return: ``True`` if the operation can be paginated, ``False`` otherwise. """ pass def create_connector_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a connector definition. You may provide the initial version of the connector definition now or use ''CreateConnectorDefinitionVersion'' at a later time. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateConnectorDefinition>`_ **Request Syntax** :: response = client.create_connector_definition( AmznClientToken='string', InitialVersion={ 'Connectors': [ { 'ConnectorArn': 'string', 'Id': 'string', 'Parameters': { 'string': 'string' } }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the connector definition. - **Connectors** *(list) --* A list of references to connectors in this version, with their corresponding configuration settings. - *(dict) --* Information about a connector. Connectors run on the Greengrass core and contain built-in integration with local infrastructure, device protocols, AWS, and other cloud services. - **ConnectorArn** *(string) --* The ARN of the connector. - **Id** *(string) --* A descriptive or arbitrary ID for the connector. This value must be unique within the connector definition version. Max length is 128 characters with pattern [a-zA-Z0-9:_-]+. - **Parameters** *(dict) --* The parameters or configuration that the connector uses. - *(string) --* - *(string) --* :type Name: string :param Name: The name of the connector definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_connector_definition_version(self, ConnectorDefinitionId: str, AmznClientToken: str = None, Connectors: List = None) -> Dict: """ Creates a version of a connector definition which has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateConnectorDefinitionVersion>`_ **Request Syntax** :: response = client.create_connector_definition_version( AmznClientToken='string', ConnectorDefinitionId='string', Connectors=[ { 'ConnectorArn': 'string', 'Id': 'string', 'Parameters': { 'string': 'string' } }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :type Connectors: list :param Connectors: A list of references to connectors in this version, with their corresponding configuration settings. - *(dict) --* Information about a connector. Connectors run on the Greengrass core and contain built-in integration with local infrastructure, device protocols, AWS, and other cloud services. - **ConnectorArn** *(string) --* The ARN of the connector. - **Id** *(string) --* A descriptive or arbitrary ID for the connector. This value must be unique within the connector definition version. Max length is 128 characters with pattern [a-zA-Z0-9:_-]+. - **Parameters** *(dict) --* The parameters or configuration that the connector uses. - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_core_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a core definition. You may provide the initial version of the core definition now or use ''CreateCoreDefinitionVersion'' at a later time. Greengrass groups must each contain exactly one Greengrass core. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateCoreDefinition>`_ **Request Syntax** :: response = client.create_core_definition( AmznClientToken='string', InitialVersion={ 'Cores': [ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the core definition. - **Cores** *(list) --* A list of cores in the core definition version. - *(dict) --* Information about a core. - **CertificateArn** *(string) --* The ARN of the certificate associated with the core. - **Id** *(string) --* A descriptive or arbitrary ID for the core. This value must be unique within the core definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **SyncShadow** *(boolean) --* If true, the core\'s local shadow is automatically synced with the cloud. - **ThingArn** *(string) --* The ARN of the thing which is the core. :type Name: string :param Name: The name of the core definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_core_definition_version(self, CoreDefinitionId: str, AmznClientToken: str = None, Cores: List = None) -> Dict: """ Creates a version of a core definition that has already been defined. Greengrass groups must each contain exactly one Greengrass core. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateCoreDefinitionVersion>`_ **Request Syntax** :: response = client.create_core_definition_version( AmznClientToken='string', CoreDefinitionId='string', Cores=[ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :type Cores: list :param Cores: A list of cores in the core definition version. - *(dict) --* Information about a core. - **CertificateArn** *(string) --* The ARN of the certificate associated with the core. - **Id** *(string) --* A descriptive or arbitrary ID for the core. This value must be unique within the core definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **SyncShadow** *(boolean) --* If true, the core\'s local shadow is automatically synced with the cloud. - **ThingArn** *(string) --* The ARN of the thing which is the core. :rtype: dict :returns: """ pass def create_deployment(self, GroupId: str, AmznClientToken: str = None, DeploymentId: str = None, DeploymentType: str = None, GroupVersionId: str = None) -> Dict: """ Creates a deployment. ''CreateDeployment'' requests are idempotent with respect to the ''X-Amzn-Client-Token'' token and the request parameters. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateDeployment>`_ **Request Syntax** :: response = client.create_deployment( AmznClientToken='string', DeploymentId='string', DeploymentType='NewDeployment'|'Redeployment'|'ResetDeployment'|'ForceResetDeployment', GroupId='string', GroupVersionId='string' ) **Response Syntax** :: { 'DeploymentArn': 'string', 'DeploymentId': 'string' } **Response Structure** - *(dict) --* Success. The group was deployed. - **DeploymentArn** *(string) --* The ARN of the deployment. - **DeploymentId** *(string) --* The ID of the deployment. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type DeploymentId: string :param DeploymentId: The ID of the deployment if you wish to redeploy a previous deployment. :type DeploymentType: string :param DeploymentType: The type of deployment. When used for \'\'CreateDeployment\'\', only \'\'NewDeployment\'\' and \'\'Redeployment\'\' are valid. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type GroupVersionId: string :param GroupVersionId: The ID of the group version to be deployed. :rtype: dict :returns: """ pass def create_device_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a device definition. You may provide the initial version of the device definition now or use ''CreateDeviceDefinitionVersion'' at a later time. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateDeviceDefinition>`_ **Request Syntax** :: response = client.create_device_definition( AmznClientToken='string', InitialVersion={ 'Devices': [ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the device definition. - **Devices** *(list) --* A list of devices in the definition version. - *(dict) --* Information about a device. - **CertificateArn** *(string) --* The ARN of the certificate associated with the device. - **Id** *(string) --* A descriptive or arbitrary ID for the device. This value must be unique within the device definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **SyncShadow** *(boolean) --* If true, the device\'s local shadow will be automatically synced with the cloud. - **ThingArn** *(string) --* The thing ARN of the device. :type Name: string :param Name: The name of the device definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_device_definition_version(self, DeviceDefinitionId: str, AmznClientToken: str = None, Devices: List = None) -> Dict: """ Creates a version of a device definition that has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateDeviceDefinitionVersion>`_ **Request Syntax** :: response = client.create_device_definition_version( AmznClientToken='string', DeviceDefinitionId='string', Devices=[ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :type Devices: list :param Devices: A list of devices in the definition version. - *(dict) --* Information about a device. - **CertificateArn** *(string) --* The ARN of the certificate associated with the device. - **Id** *(string) --* A descriptive or arbitrary ID for the device. This value must be unique within the device definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **SyncShadow** *(boolean) --* If true, the device\'s local shadow will be automatically synced with the cloud. - **ThingArn** *(string) --* The thing ARN of the device. :rtype: dict :returns: """ pass def create_function_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a Lambda function definition which contains a list of Lambda functions and their configurations to be used in a group. You can create an initial version of the definition by providing a list of Lambda functions and their configurations now, or use ''CreateFunctionDefinitionVersion'' later. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateFunctionDefinition>`_ **Request Syntax** :: response = client.create_function_definition( AmznClientToken='string', InitialVersion={ 'DefaultConfig': { 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } } }, 'Functions': [ { 'FunctionArn': 'string', 'FunctionConfiguration': { 'EncodingType': 'binary'|'json', 'Environment': { 'AccessSysfs': True|False, 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } }, 'ResourceAccessPolicies': [ { 'Permission': 'ro'|'rw', 'ResourceId': 'string' }, ], 'Variables': { 'string': 'string' } }, 'ExecArgs': 'string', 'Executable': 'string', 'MemorySize': 123, 'Pinned': True|False, 'Timeout': 123 }, 'Id': 'string' }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the function definition. - **DefaultConfig** *(dict) --* The default configuration that applies to all Lambda functions in this function definition version. Individual Lambda functions can override these settings. - **Execution** *(dict) --* Configuration information that specifies how a Lambda function runs. - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set \'\'IsolationMode\'\' to \'\'NoContainer\'\' and update config.json in \'\'greengrass-root/config\'\' to set \'\'allowFunctionsToRunAsRoot\'\' to \'\'yes\'\'. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. - **Functions** *(list) --* A list of Lambda functions in this function definition version. - *(dict) --* Information about a Lambda function. - **FunctionArn** *(string) --* The ARN of the Lambda function. - **FunctionConfiguration** *(dict) --* The configuration of the Lambda function. - **EncodingType** *(string) --* The expected encoding type of the input payload for the function. The default is \'\'json\'\'. - **Environment** *(dict) --* The environment configuration of the function. - **AccessSysfs** *(boolean) --* If true, the Lambda function is allowed to access the host\'s /sys folder. Use this when the Lambda function needs to read device information from /sys. This setting applies only when you run the Lambda function in a Greengrass container. - **Execution** *(dict) --* Configuration related to executing the Lambda function - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set \'\'IsolationMode\'\' to \'\'NoContainer\'\' and update config.json in \'\'greengrass-root/config\'\' to set \'\'allowFunctionsToRunAsRoot\'\' to \'\'yes\'\'. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. - **ResourceAccessPolicies** *(list) --* A list of the resources, with their permissions, to which the Lambda function will be granted access. A Lambda function can have at most 10 resources. ResourceAccessPolicies apply only when you run the Lambda function in a Greengrass container. - *(dict) --* A policy used by the function to access a resource. - **Permission** *(string) --* The permissions that the Lambda function has to the resource. Can be one of \'\'rw\'\' (read/write) or \'\'ro\'\' (read-only). - **ResourceId** *(string) --* The ID of the resource. (This ID is assigned to the resource when you create the resource definiton.) - **Variables** *(dict) --* Environment variables for the Lambda function\'s configuration. - *(string) --* - *(string) --* - **ExecArgs** *(string) --* The execution arguments. - **Executable** *(string) --* The name of the function executable. - **MemorySize** *(integer) --* The memory size, in KB, which the function requires. This setting is not applicable and should be cleared when you run the Lambda function without containerization. - **Pinned** *(boolean) --* True if the function is pinned. Pinned means the function is long-lived and starts when the core starts. - **Timeout** *(integer) --* The allowed function execution time, after which Lambda should terminate the function. This timeout still applies to pinned Lambda functions for each request. - **Id** *(string) --* A descriptive or arbitrary ID for the function. This value must be unique within the function definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. :type Name: string :param Name: The name of the function definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_function_definition_version(self, FunctionDefinitionId: str, AmznClientToken: str = None, DefaultConfig: Dict = None, Functions: List = None) -> Dict: """ Creates a version of a Lambda function definition that has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateFunctionDefinitionVersion>`_ **Request Syntax** :: response = client.create_function_definition_version( AmznClientToken='string', DefaultConfig={ 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } } }, FunctionDefinitionId='string', Functions=[ { 'FunctionArn': 'string', 'FunctionConfiguration': { 'EncodingType': 'binary'|'json', 'Environment': { 'AccessSysfs': True|False, 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } }, 'ResourceAccessPolicies': [ { 'Permission': 'ro'|'rw', 'ResourceId': 'string' }, ], 'Variables': { 'string': 'string' } }, 'ExecArgs': 'string', 'Executable': 'string', 'MemorySize': 123, 'Pinned': True|False, 'Timeout': 123 }, 'Id': 'string' }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type DefaultConfig: dict :param DefaultConfig: The default configuration that applies to all Lambda functions in this function definition version. Individual Lambda functions can override these settings. - **Execution** *(dict) --* Configuration information that specifies how a Lambda function runs. - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set \'\'IsolationMode\'\' to \'\'NoContainer\'\' and update config.json in \'\'greengrass-root/config\'\' to set \'\'allowFunctionsToRunAsRoot\'\' to \'\'yes\'\'. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :type Functions: list :param Functions: A list of Lambda functions in this function definition version. - *(dict) --* Information about a Lambda function. - **FunctionArn** *(string) --* The ARN of the Lambda function. - **FunctionConfiguration** *(dict) --* The configuration of the Lambda function. - **EncodingType** *(string) --* The expected encoding type of the input payload for the function. The default is \'\'json\'\'. - **Environment** *(dict) --* The environment configuration of the function. - **AccessSysfs** *(boolean) --* If true, the Lambda function is allowed to access the host\'s /sys folder. Use this when the Lambda function needs to read device information from /sys. This setting applies only when you run the Lambda function in a Greengrass container. - **Execution** *(dict) --* Configuration related to executing the Lambda function - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set \'\'IsolationMode\'\' to \'\'NoContainer\'\' and update config.json in \'\'greengrass-root/config\'\' to set \'\'allowFunctionsToRunAsRoot\'\' to \'\'yes\'\'. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. - **ResourceAccessPolicies** *(list) --* A list of the resources, with their permissions, to which the Lambda function will be granted access. A Lambda function can have at most 10 resources. ResourceAccessPolicies apply only when you run the Lambda function in a Greengrass container. - *(dict) --* A policy used by the function to access a resource. - **Permission** *(string) --* The permissions that the Lambda function has to the resource. Can be one of \'\'rw\'\' (read/write) or \'\'ro\'\' (read-only). - **ResourceId** *(string) --* The ID of the resource. (This ID is assigned to the resource when you create the resource definiton.) - **Variables** *(dict) --* Environment variables for the Lambda function\'s configuration. - *(string) --* - *(string) --* - **ExecArgs** *(string) --* The execution arguments. - **Executable** *(string) --* The name of the function executable. - **MemorySize** *(integer) --* The memory size, in KB, which the function requires. This setting is not applicable and should be cleared when you run the Lambda function without containerization. - **Pinned** *(boolean) --* True if the function is pinned. Pinned means the function is long-lived and starts when the core starts. - **Timeout** *(integer) --* The allowed function execution time, after which Lambda should terminate the function. This timeout still applies to pinned Lambda functions for each request. - **Id** *(string) --* A descriptive or arbitrary ID for the function. This value must be unique within the function definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. :rtype: dict :returns: """ pass def create_group(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a group. You may provide the initial version of the group or use ''CreateGroupVersion'' at a later time. Tip: You can use the ''gg_group_setup'' package (https://github.com/awslabs/aws-greengrass-group-setup) as a library or command-line application to create and deploy Greengrass groups. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateGroup>`_ **Request Syntax** :: response = client.create_group( AmznClientToken='string', InitialVersion={ 'ConnectorDefinitionVersionArn': 'string', 'CoreDefinitionVersionArn': 'string', 'DeviceDefinitionVersionArn': 'string', 'FunctionDefinitionVersionArn': 'string', 'LoggerDefinitionVersionArn': 'string', 'ResourceDefinitionVersionArn': 'string', 'SubscriptionDefinitionVersionArn': 'string' }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* Success. The group was created. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the group. - **ConnectorDefinitionVersionArn** *(string) --* The ARN of the connector definition version for this group. - **CoreDefinitionVersionArn** *(string) --* The ARN of the core definition version for this group. - **DeviceDefinitionVersionArn** *(string) --* The ARN of the device definition version for this group. - **FunctionDefinitionVersionArn** *(string) --* The ARN of the function definition version for this group. - **LoggerDefinitionVersionArn** *(string) --* The ARN of the logger definition version for this group. - **ResourceDefinitionVersionArn** *(string) --* The ARN of the resource definition version for this group. - **SubscriptionDefinitionVersionArn** *(string) --* The ARN of the subscription definition version for this group. :type Name: string :param Name: The name of the group. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_group_certificate_authority(self, GroupId: str, AmznClientToken: str = None) -> Dict: """ Creates a CA for the group. If a CA already exists, it will rotate the existing CA. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateGroupCertificateAuthority>`_ **Request Syntax** :: response = client.create_group_certificate_authority( AmznClientToken='string', GroupId='string' ) **Response Syntax** :: { 'GroupCertificateAuthorityArn': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the new active CA ARN. - **GroupCertificateAuthorityArn** *(string) --* The ARN of the group certificate authority. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def create_group_version(self, GroupId: str, AmznClientToken: str = None, ConnectorDefinitionVersionArn: str = None, CoreDefinitionVersionArn: str = None, DeviceDefinitionVersionArn: str = None, FunctionDefinitionVersionArn: str = None, LoggerDefinitionVersionArn: str = None, ResourceDefinitionVersionArn: str = None, SubscriptionDefinitionVersionArn: str = None) -> Dict: """ Creates a version of a group which has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateGroupVersion>`_ **Request Syntax** :: response = client.create_group_version( AmznClientToken='string', ConnectorDefinitionVersionArn='string', CoreDefinitionVersionArn='string', DeviceDefinitionVersionArn='string', FunctionDefinitionVersionArn='string', GroupId='string', LoggerDefinitionVersionArn='string', ResourceDefinitionVersionArn='string', SubscriptionDefinitionVersionArn='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* Success. The response contains information about the group version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type ConnectorDefinitionVersionArn: string :param ConnectorDefinitionVersionArn: The ARN of the connector definition version for this group. :type CoreDefinitionVersionArn: string :param CoreDefinitionVersionArn: The ARN of the core definition version for this group. :type DeviceDefinitionVersionArn: string :param DeviceDefinitionVersionArn: The ARN of the device definition version for this group. :type FunctionDefinitionVersionArn: string :param FunctionDefinitionVersionArn: The ARN of the function definition version for this group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type LoggerDefinitionVersionArn: string :param LoggerDefinitionVersionArn: The ARN of the logger definition version for this group. :type ResourceDefinitionVersionArn: string :param ResourceDefinitionVersionArn: The ARN of the resource definition version for this group. :type SubscriptionDefinitionVersionArn: string :param SubscriptionDefinitionVersionArn: The ARN of the subscription definition version for this group. :rtype: dict :returns: """ pass def create_logger_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a logger definition. You may provide the initial version of the logger definition now or use ''CreateLoggerDefinitionVersion'' at a later time. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateLoggerDefinition>`_ **Request Syntax** :: response = client.create_logger_definition( AmznClientToken='string', InitialVersion={ 'Loggers': [ { 'Component': 'GreengrassSystem'|'Lambda', 'Id': 'string', 'Level': 'DEBUG'|'INFO'|'WARN'|'ERROR'|'FATAL', 'Space': 123, 'Type': 'FileSystem'|'AWSCloudWatch' }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the logger definition. - **Loggers** *(list) --* A list of loggers. - *(dict) --* Information about a logger - **Component** *(string) --* The component that will be subject to logging. - **Id** *(string) --* A descriptive or arbitrary ID for the logger. This value must be unique within the logger definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **Level** *(string) --* The level of the logs. - **Space** *(integer) --* The amount of file space, in KB, to use if the local file system is used for logging purposes. - **Type** *(string) --* The type of log output which will be used. :type Name: string :param Name: The name of the logger definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_logger_definition_version(self, LoggerDefinitionId: str, AmznClientToken: str = None, Loggers: List = None) -> Dict: """ Creates a version of a logger definition that has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateLoggerDefinitionVersion>`_ **Request Syntax** :: response = client.create_logger_definition_version( AmznClientToken='string', LoggerDefinitionId='string', Loggers=[ { 'Component': 'GreengrassSystem'|'Lambda', 'Id': 'string', 'Level': 'DEBUG'|'INFO'|'WARN'|'ERROR'|'FATAL', 'Space': 123, 'Type': 'FileSystem'|'AWSCloudWatch' }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :type Loggers: list :param Loggers: A list of loggers. - *(dict) --* Information about a logger - **Component** *(string) --* The component that will be subject to logging. - **Id** *(string) --* A descriptive or arbitrary ID for the logger. This value must be unique within the logger definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **Level** *(string) --* The level of the logs. - **Space** *(integer) --* The amount of file space, in KB, to use if the local file system is used for logging purposes. - **Type** *(string) --* The type of log output which will be used. :rtype: dict :returns: """ pass def create_resource_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a resource definition which contains a list of resources to be used in a group. You can create an initial version of the definition by providing a list of resources now, or use ''CreateResourceDefinitionVersion'' later. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateResourceDefinition>`_ **Request Syntax** :: response = client.create_resource_definition( AmznClientToken='string', InitialVersion={ 'Resources': [ { 'Id': 'string', 'Name': 'string', 'ResourceDataContainer': { 'LocalDeviceResourceData': { 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'LocalVolumeResourceData': { 'DestinationPath': 'string', 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'S3MachineLearningModelResourceData': { 'DestinationPath': 'string', 'S3Uri': 'string' }, 'SageMakerMachineLearningModelResourceData': { 'DestinationPath': 'string', 'SageMakerJobArn': 'string' }, 'SecretsManagerSecretResourceData': { 'ARN': 'string', 'AdditionalStagingLabelsToDownload': [ 'string', ] } } }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the resource definition. - **Resources** *(list) --* A list of resources. - *(dict) --* Information about a resource. - **Id** *(string) --* The resource ID, used to refer to a resource in the Lambda function configuration. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. This must be unique within a Greengrass group. - **Name** *(string) --* The descriptive resource name, which is displayed on the AWS IoT Greengrass console. Max length 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. This must be unique within a Greengrass group. - **ResourceDataContainer** *(dict) --* A container of data for all resource types. - **LocalDeviceResourceData** *(dict) --* Attributes that define the local device resource. - **GroupOwnerSetting** *(dict) --* Group/owner related settings for local resources. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the device resource. The source path for a device resource can refer only to a character device or block device under \'\'/dev\'\'. - **LocalVolumeResourceData** *(dict) --* Attributes that define the local volume resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **GroupOwnerSetting** *(dict) --* Allows you to configure additional group privileges for the Lambda process. This field is optional. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the volume resource on the host. The source path for a volume resource type cannot start with \'\'/sys\'\'. - **S3MachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon S3 machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **S3Uri** *(string) --* The URI of the source model in an S3 bucket. The model package must be in tar.gz or .zip format. - **SageMakerMachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon SageMaker machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **SageMakerJobArn** *(string) --* The ARN of the Amazon SageMaker training job that represents the source model. - **SecretsManagerSecretResourceData** *(dict) --* Attributes that define a secret resource, which references a secret from AWS Secrets Manager. - **ARN** *(string) --* The ARN of the Secrets Manager secret to make available on the core. The value of the secret\'s latest version (represented by the \'\'AWSCURRENT\'\' staging label) is included by default. - **AdditionalStagingLabelsToDownload** *(list) --* Optional. The staging labels whose values you want to make available on the core, in addition to \'\'AWSCURRENT\'\'. - *(string) --* :type Name: string :param Name: The name of the resource definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_resource_definition_version(self, ResourceDefinitionId: str, AmznClientToken: str = None, Resources: List = None) -> Dict: """ Creates a version of a resource definition that has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateResourceDefinitionVersion>`_ **Request Syntax** :: response = client.create_resource_definition_version( AmznClientToken='string', ResourceDefinitionId='string', Resources=[ { 'Id': 'string', 'Name': 'string', 'ResourceDataContainer': { 'LocalDeviceResourceData': { 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'LocalVolumeResourceData': { 'DestinationPath': 'string', 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'S3MachineLearningModelResourceData': { 'DestinationPath': 'string', 'S3Uri': 'string' }, 'SageMakerMachineLearningModelResourceData': { 'DestinationPath': 'string', 'SageMakerJobArn': 'string' }, 'SecretsManagerSecretResourceData': { 'ARN': 'string', 'AdditionalStagingLabelsToDownload': [ 'string', ] } } }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :type Resources: list :param Resources: A list of resources. - *(dict) --* Information about a resource. - **Id** *(string) --* The resource ID, used to refer to a resource in the Lambda function configuration. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. This must be unique within a Greengrass group. - **Name** *(string) --* The descriptive resource name, which is displayed on the AWS IoT Greengrass console. Max length 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. This must be unique within a Greengrass group. - **ResourceDataContainer** *(dict) --* A container of data for all resource types. - **LocalDeviceResourceData** *(dict) --* Attributes that define the local device resource. - **GroupOwnerSetting** *(dict) --* Group/owner related settings for local resources. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the device resource. The source path for a device resource can refer only to a character device or block device under \'\'/dev\'\'. - **LocalVolumeResourceData** *(dict) --* Attributes that define the local volume resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **GroupOwnerSetting** *(dict) --* Allows you to configure additional group privileges for the Lambda process. This field is optional. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the volume resource on the host. The source path for a volume resource type cannot start with \'\'/sys\'\'. - **S3MachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon S3 machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **S3Uri** *(string) --* The URI of the source model in an S3 bucket. The model package must be in tar.gz or .zip format. - **SageMakerMachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon SageMaker machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **SageMakerJobArn** *(string) --* The ARN of the Amazon SageMaker training job that represents the source model. - **SecretsManagerSecretResourceData** *(dict) --* Attributes that define a secret resource, which references a secret from AWS Secrets Manager. - **ARN** *(string) --* The ARN of the Secrets Manager secret to make available on the core. The value of the secret\'s latest version (represented by the \'\'AWSCURRENT\'\' staging label) is included by default. - **AdditionalStagingLabelsToDownload** *(list) --* Optional. The staging labels whose values you want to make available on the core, in addition to \'\'AWSCURRENT\'\'. - *(string) --* :rtype: dict :returns: """ pass def create_software_update_job(self, AmznClientToken: str = None, S3UrlSignerRole: str = None, SoftwareToUpdate: str = None, UpdateAgentLogLevel: str = None, UpdateTargets: List = None, UpdateTargetsArchitecture: str = None, UpdateTargetsOperatingSystem: str = None) -> Dict: """ Creates a software update for a core or group of cores (specified as an IoT thing group.) Use this to update the OTA Agent as well as the Greengrass core software. It makes use of the IoT Jobs feature which provides additional commands to manage a Greengrass core software update job. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateSoftwareUpdateJob>`_ **Request Syntax** :: response = client.create_software_update_job( AmznClientToken='string', S3UrlSignerRole='string', SoftwareToUpdate='core'|'ota_agent', UpdateAgentLogLevel='NONE'|'TRACE'|'DEBUG'|'VERBOSE'|'INFO'|'WARN'|'ERROR'|'FATAL', UpdateTargets=[ 'string', ], UpdateTargetsArchitecture='armv7l'|'x86_64'|'aarch64', UpdateTargetsOperatingSystem='ubuntu'|'raspbian'|'amazon_linux' ) **Response Syntax** :: { 'IotJobArn': 'string', 'IotJobId': 'string' } **Response Structure** - *(dict) --* success - **IotJobArn** *(string) --* The IoT Job ARN corresponding to this update. - **IotJobId** *(string) --* The IoT Job Id corresponding to this update. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type S3UrlSignerRole: string :param S3UrlSignerRole: The IAM Role that Greengrass will use to create pre-signed URLs pointing towards the update artifact. :type SoftwareToUpdate: string :param SoftwareToUpdate: The piece of software on the Greengrass core that will be updated. :type UpdateAgentLogLevel: string :param UpdateAgentLogLevel: The minimum level of log statements that should be logged by the OTA Agent during an update. :type UpdateTargets: list :param UpdateTargets: The ARNs of the targets (IoT things or IoT thing groups) that this update will be applied to. - *(string) --* :type UpdateTargetsArchitecture: string :param UpdateTargetsArchitecture: The architecture of the cores which are the targets of an update. :type UpdateTargetsOperatingSystem: string :param UpdateTargetsOperatingSystem: The operating system of the cores which are the targets of an update. :rtype: dict :returns: """ pass def create_subscription_definition(self, AmznClientToken: str = None, InitialVersion: Dict = None, Name: str = None, tags: Dict = None) -> Dict: """ Creates a subscription definition. You may provide the initial version of the subscription definition now or use ''CreateSubscriptionDefinitionVersion'' at a later time. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateSubscriptionDefinition>`_ **Request Syntax** :: response = client.create_subscription_definition( AmznClientToken='string', InitialVersion={ 'Subscriptions': [ { 'Id': 'string', 'Source': 'string', 'Subject': 'string', 'Target': 'string' }, ] }, Name='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type InitialVersion: dict :param InitialVersion: Information about the initial version of the subscription definition. - **Subscriptions** *(list) --* A list of subscriptions. - *(dict) --* Information about a subscription. - **Id** *(string) --* A descriptive or arbitrary ID for the subscription. This value must be unique within the subscription definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **Source** *(string) --* The source of the subscription. Can be a thing ARN, a Lambda function ARN, a connector ARN, \'cloud\' (which represents the AWS IoT cloud), or \'GGShadowService\'. - **Subject** *(string) --* The MQTT topic used to route the message. - **Target** *(string) --* Where the message is sent to. Can be a thing ARN, a Lambda function ARN, a connector ARN, \'cloud\' (which represents the AWS IoT cloud), or \'GGShadowService\'. :type Name: string :param Name: The name of the subscription definition. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def create_subscription_definition_version(self, SubscriptionDefinitionId: str, AmznClientToken: str = None, Subscriptions: List = None) -> Dict: """ Creates a version of a subscription definition which has already been defined. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/CreateSubscriptionDefinitionVersion>`_ **Request Syntax** :: response = client.create_subscription_definition_version( AmznClientToken='string', SubscriptionDefinitionId='string', Subscriptions=[ { 'Id': 'string', 'Source': 'string', 'Subject': 'string', 'Target': 'string' }, ] ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :type Subscriptions: list :param Subscriptions: A list of subscriptions. - *(dict) --* Information about a subscription. - **Id** *(string) --* A descriptive or arbitrary ID for the subscription. This value must be unique within the subscription definition version. Max length is 128 characters with pattern \'\'[a-zA-Z0-9:_-]+\'\'. - **Source** *(string) --* The source of the subscription. Can be a thing ARN, a Lambda function ARN, a connector ARN, \'cloud\' (which represents the AWS IoT cloud), or \'GGShadowService\'. - **Subject** *(string) --* The MQTT topic used to route the message. - **Target** *(string) --* Where the message is sent to. Can be a thing ARN, a Lambda function ARN, a connector ARN, \'cloud\' (which represents the AWS IoT cloud), or \'GGShadowService\'. :rtype: dict :returns: """ pass def delete_connector_definition(self, ConnectorDefinitionId: str) -> Dict: """ Deletes a connector definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteConnectorDefinition>`_ **Request Syntax** :: response = client.delete_connector_definition( ConnectorDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :rtype: dict :returns: """ pass def delete_core_definition(self, CoreDefinitionId: str) -> Dict: """ Deletes a core definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteCoreDefinition>`_ **Request Syntax** :: response = client.delete_core_definition( CoreDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :rtype: dict :returns: """ pass def delete_device_definition(self, DeviceDefinitionId: str) -> Dict: """ Deletes a device definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteDeviceDefinition>`_ **Request Syntax** :: response = client.delete_device_definition( DeviceDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :rtype: dict :returns: """ pass def delete_function_definition(self, FunctionDefinitionId: str) -> Dict: """ Deletes a Lambda function definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteFunctionDefinition>`_ **Request Syntax** :: response = client.delete_function_definition( FunctionDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :rtype: dict :returns: """ pass def delete_group(self, GroupId: str) -> Dict: """ Deletes a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteGroup>`_ **Request Syntax** :: response = client.delete_group( GroupId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def delete_logger_definition(self, LoggerDefinitionId: str) -> Dict: """ Deletes a logger definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteLoggerDefinition>`_ **Request Syntax** :: response = client.delete_logger_definition( LoggerDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :rtype: dict :returns: """ pass def delete_resource_definition(self, ResourceDefinitionId: str) -> Dict: """ Deletes a resource definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteResourceDefinition>`_ **Request Syntax** :: response = client.delete_resource_definition( ResourceDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :rtype: dict :returns: """ pass def delete_subscription_definition(self, SubscriptionDefinitionId: str) -> Dict: """ Deletes a subscription definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DeleteSubscriptionDefinition>`_ **Request Syntax** :: response = client.delete_subscription_definition( SubscriptionDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :rtype: dict :returns: """ pass def disassociate_role_from_group(self, GroupId: str) -> Dict: """ Disassociates the role from a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DisassociateRoleFromGroup>`_ **Request Syntax** :: response = client.disassociate_role_from_group( GroupId='string' ) **Response Syntax** :: { 'DisassociatedAt': 'string' } **Response Structure** - *(dict) --* success - **DisassociatedAt** *(string) --* The time, in milliseconds since the epoch, when the role was disassociated from the group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def disassociate_service_role_from_account(self) -> Dict: """ Disassociates the service role from your account. Without a service role, deployments will not work. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/DisassociateServiceRoleFromAccount>`_ **Request Syntax** :: response = client.disassociate_service_role_from_account() **Response Syntax** :: { 'DisassociatedAt': 'string' } **Response Structure** - *(dict) --* success - **DisassociatedAt** *(string) --* The time when the service role was disassociated from the account. :rtype: dict :returns: """ pass def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to ``ClientMethod``. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid for. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By default, the http method is whatever is used in the method\'s model. :returns: The presigned url """ pass def get_associated_role(self, GroupId: str) -> Dict: """ Retrieves the role associated with a particular group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetAssociatedRole>`_ **Request Syntax** :: response = client.get_associated_role( GroupId='string' ) **Response Syntax** :: { 'AssociatedAt': 'string', 'RoleArn': 'string' } **Response Structure** - *(dict) --* success - **AssociatedAt** *(string) --* The time when the role was associated with the group. - **RoleArn** *(string) --* The ARN of the role that is associated with the group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def get_bulk_deployment_status(self, BulkDeploymentId: str) -> Dict: """ Returns the status of a bulk deployment. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetBulkDeploymentStatus>`_ **Request Syntax** :: response = client.get_bulk_deployment_status( BulkDeploymentId='string' ) **Response Syntax** :: { 'BulkDeploymentMetrics': { 'InvalidInputRecords': 123, 'RecordsProcessed': 123, 'RetryAttempts': 123 }, 'BulkDeploymentStatus': 'Initializing'|'Running'|'Completed'|'Stopping'|'Stopped'|'Failed', 'CreatedAt': 'string', 'ErrorDetails': [ { 'DetailedErrorCode': 'string', 'DetailedErrorMessage': 'string' }, ], 'ErrorMessage': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* Success. The response body contains the status of the bulk deployment. - **BulkDeploymentMetrics** *(dict) --* Relevant metrics on input records processed during bulk deployment. - **InvalidInputRecords** *(integer) --* The total number of records that returned a non-retryable error. For example, this can occur if a group record from the input file uses an invalid format or specifies a nonexistent group version, or if the execution role doesn't grant permission to deploy a group or group version. - **RecordsProcessed** *(integer) --* The total number of group records from the input file that have been processed so far, or attempted. - **RetryAttempts** *(integer) --* The total number of deployment attempts that returned a retryable error. For example, a retry is triggered if the attempt to deploy a group returns a throttling error. ''StartBulkDeployment'' retries a group deployment up to five times. - **BulkDeploymentStatus** *(string) --* The status of the bulk deployment. - **CreatedAt** *(string) --* The time, in ISO format, when the deployment was created. - **ErrorDetails** *(list) --* Error details - *(dict) --* Details about the error. - **DetailedErrorCode** *(string) --* A detailed error code. - **DetailedErrorMessage** *(string) --* A detailed error message. - **ErrorMessage** *(string) --* Error message - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type BulkDeploymentId: string :param BulkDeploymentId: **[REQUIRED]** The ID of the bulk deployment. :rtype: dict :returns: """ pass def get_connectivity_info(self, ThingName: str) -> Dict: """ Retrieves the connectivity information for a core. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetConnectivityInfo>`_ **Request Syntax** :: response = client.get_connectivity_info( ThingName='string' ) **Response Syntax** :: { 'ConnectivityInfo': [ { 'HostAddress': 'string', 'Id': 'string', 'Metadata': 'string', 'PortNumber': 123 }, ], 'Message': 'string' } **Response Structure** - *(dict) --* success - **ConnectivityInfo** *(list) --* Connectivity info list. - *(dict) --* Information about a Greengrass core's connectivity. - **HostAddress** *(string) --* The endpoint for the Greengrass core. Can be an IP address or DNS. - **Id** *(string) --* The ID of the connectivity information. - **Metadata** *(string) --* Metadata for this endpoint. - **PortNumber** *(integer) --* The port of the Greengrass core. Usually 8883. - **Message** *(string) --* A message about the connectivity info request. :type ThingName: string :param ThingName: **[REQUIRED]** The thing name. :rtype: dict :returns: """ pass def get_connector_definition(self, ConnectorDefinitionId: str) -> Dict: """ Retrieves information about a connector definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetConnectorDefinition>`_ **Request Syntax** :: response = client.get_connector_definition( ConnectorDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :rtype: dict :returns: """ pass def get_connector_definition_version(self, ConnectorDefinitionId: str, ConnectorDefinitionVersionId: str, NextToken: str = None) -> Dict: """ Retrieves information about a connector definition version, including the connectors that the version contains. Connectors are prebuilt modules that interact with local infrastructure, device protocols, AWS, and other cloud services. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetConnectorDefinitionVersion>`_ **Request Syntax** :: response = client.get_connector_definition_version( ConnectorDefinitionId='string', ConnectorDefinitionVersionId='string', NextToken='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Connectors': [ { 'ConnectorArn': 'string', 'Id': 'string', 'Parameters': { 'string': 'string' } }, ] }, 'Id': 'string', 'NextToken': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the connector definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the connector definition version was created. - **Definition** *(dict) --* Information about the connector definition version. - **Connectors** *(list) --* A list of references to connectors in this version, with their corresponding configuration settings. - *(dict) --* Information about a connector. Connectors run on the Greengrass core and contain built-in integration with local infrastructure, device protocols, AWS, and other cloud services. - **ConnectorArn** *(string) --* The ARN of the connector. - **Id** *(string) --* A descriptive or arbitrary ID for the connector. This value must be unique within the connector definition version. Max length is 128 characters with pattern [a-zA-Z0-9:_-]+. - **Parameters** *(dict) --* The parameters or configuration that the connector uses. - *(string) --* - *(string) --* - **Id** *(string) --* The ID of the connector definition version. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Version** *(string) --* The version of the connector definition version. :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :type ConnectorDefinitionVersionId: string :param ConnectorDefinitionVersionId: **[REQUIRED]** The ID of the connector definition version. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def get_core_definition(self, CoreDefinitionId: str) -> Dict: """ Retrieves information about a core definition version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetCoreDefinition>`_ **Request Syntax** :: response = client.get_core_definition( CoreDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :rtype: dict :returns: """ pass def get_core_definition_version(self, CoreDefinitionId: str, CoreDefinitionVersionId: str) -> Dict: """ Retrieves information about a core definition version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetCoreDefinitionVersion>`_ **Request Syntax** :: response = client.get_core_definition_version( CoreDefinitionId='string', CoreDefinitionVersionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Cores': [ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] }, 'Id': 'string', 'NextToken': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the core definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the core definition version was created. - **Definition** *(dict) --* Information about the core definition version. - **Cores** *(list) --* A list of cores in the core definition version. - *(dict) --* Information about a core. - **CertificateArn** *(string) --* The ARN of the certificate associated with the core. - **Id** *(string) --* A descriptive or arbitrary ID for the core. This value must be unique within the core definition version. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. - **SyncShadow** *(boolean) --* If true, the core's local shadow is automatically synced with the cloud. - **ThingArn** *(string) --* The ARN of the thing which is the core. - **Id** *(string) --* The ID of the core definition version. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Version** *(string) --* The version of the core definition version. :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :type CoreDefinitionVersionId: string :param CoreDefinitionVersionId: **[REQUIRED]** The ID of the core definition version. :rtype: dict :returns: """ pass def get_deployment_status(self, DeploymentId: str, GroupId: str) -> Dict: """ Returns the status of a deployment. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetDeploymentStatus>`_ **Request Syntax** :: response = client.get_deployment_status( DeploymentId='string', GroupId='string' ) **Response Syntax** :: { 'DeploymentStatus': 'string', 'DeploymentType': 'NewDeployment'|'Redeployment'|'ResetDeployment'|'ForceResetDeployment', 'ErrorDetails': [ { 'DetailedErrorCode': 'string', 'DetailedErrorMessage': 'string' }, ], 'ErrorMessage': 'string', 'UpdatedAt': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the status of the deployment for the group. - **DeploymentStatus** *(string) --* The status of the deployment: ''InProgress'', ''Building'', ''Success'', or ''Failure''. - **DeploymentType** *(string) --* The type of the deployment. - **ErrorDetails** *(list) --* Error details - *(dict) --* Details about the error. - **DetailedErrorCode** *(string) --* A detailed error code. - **DetailedErrorMessage** *(string) --* A detailed error message. - **ErrorMessage** *(string) --* Error message - **UpdatedAt** *(string) --* The time, in milliseconds since the epoch, when the deployment status was updated. :type DeploymentId: string :param DeploymentId: **[REQUIRED]** The ID of the deployment. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def get_device_definition(self, DeviceDefinitionId: str) -> Dict: """ Retrieves information about a device definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetDeviceDefinition>`_ **Request Syntax** :: response = client.get_device_definition( DeviceDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :rtype: dict :returns: """ pass def get_device_definition_version(self, DeviceDefinitionId: str, DeviceDefinitionVersionId: str, NextToken: str = None) -> Dict: """ Retrieves information about a device definition version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetDeviceDefinitionVersion>`_ **Request Syntax** :: response = client.get_device_definition_version( DeviceDefinitionId='string', DeviceDefinitionVersionId='string', NextToken='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Devices': [ { 'CertificateArn': 'string', 'Id': 'string', 'SyncShadow': True|False, 'ThingArn': 'string' }, ] }, 'Id': 'string', 'NextToken': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the device definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the device definition version was created. - **Definition** *(dict) --* Information about the device definition version. - **Devices** *(list) --* A list of devices in the definition version. - *(dict) --* Information about a device. - **CertificateArn** *(string) --* The ARN of the certificate associated with the device. - **Id** *(string) --* A descriptive or arbitrary ID for the device. This value must be unique within the device definition version. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. - **SyncShadow** *(boolean) --* If true, the device's local shadow will be automatically synced with the cloud. - **ThingArn** *(string) --* The thing ARN of the device. - **Id** *(string) --* The ID of the device definition version. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Version** *(string) --* The version of the device definition version. :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :type DeviceDefinitionVersionId: string :param DeviceDefinitionVersionId: **[REQUIRED]** The ID of the device definition version. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def get_function_definition(self, FunctionDefinitionId: str) -> Dict: """ Retrieves information about a Lambda function definition, including its creation time and latest version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetFunctionDefinition>`_ **Request Syntax** :: response = client.get_function_definition( FunctionDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :rtype: dict :returns: """ pass def get_function_definition_version(self, FunctionDefinitionId: str, FunctionDefinitionVersionId: str, NextToken: str = None) -> Dict: """ Retrieves information about a Lambda function definition version, including which Lambda functions are included in the version and their configurations. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetFunctionDefinitionVersion>`_ **Request Syntax** :: response = client.get_function_definition_version( FunctionDefinitionId='string', FunctionDefinitionVersionId='string', NextToken='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'DefaultConfig': { 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } } }, 'Functions': [ { 'FunctionArn': 'string', 'FunctionConfiguration': { 'EncodingType': 'binary'|'json', 'Environment': { 'AccessSysfs': True|False, 'Execution': { 'IsolationMode': 'GreengrassContainer'|'NoContainer', 'RunAs': { 'Gid': 123, 'Uid': 123 } }, 'ResourceAccessPolicies': [ { 'Permission': 'ro'|'rw', 'ResourceId': 'string' }, ], 'Variables': { 'string': 'string' } }, 'ExecArgs': 'string', 'Executable': 'string', 'MemorySize': 123, 'Pinned': True|False, 'Timeout': 123 }, 'Id': 'string' }, ] }, 'Id': 'string', 'NextToken': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the function definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the function definition version was created. - **Definition** *(dict) --* Information on the definition. - **DefaultConfig** *(dict) --* The default configuration that applies to all Lambda functions in this function definition version. Individual Lambda functions can override these settings. - **Execution** *(dict) --* Configuration information that specifies how a Lambda function runs. - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set ''IsolationMode'' to ''NoContainer'' and update config.json in ''greengrass-root/config'' to set ''allowFunctionsToRunAsRoot'' to ''yes''. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. - **Functions** *(list) --* A list of Lambda functions in this function definition version. - *(dict) --* Information about a Lambda function. - **FunctionArn** *(string) --* The ARN of the Lambda function. - **FunctionConfiguration** *(dict) --* The configuration of the Lambda function. - **EncodingType** *(string) --* The expected encoding type of the input payload for the function. The default is ''json''. - **Environment** *(dict) --* The environment configuration of the function. - **AccessSysfs** *(boolean) --* If true, the Lambda function is allowed to access the host's /sys folder. Use this when the Lambda function needs to read device information from /sys. This setting applies only when you run the Lambda function in a Greengrass container. - **Execution** *(dict) --* Configuration related to executing the Lambda function - **IsolationMode** *(string) --* Specifies whether the Lambda function runs in a Greengrass container (default) or without containerization. Unless your scenario requires that you run without containerization, we recommend that you run in a Greengrass container. Omit this value to run the Lambda function with the default containerization for the group. - **RunAs** *(dict) --* Specifies the user and group whose permissions are used when running the Lambda function. You can specify one or both values to override the default values. We recommend that you avoid running as root unless absolutely necessary to minimize the risk of unintended changes or malicious attacks. To run as root, you must set ''IsolationMode'' to ''NoContainer'' and update config.json in ''greengrass-root/config'' to set ''allowFunctionsToRunAsRoot'' to ''yes''. - **Gid** *(integer) --* The group ID whose permissions are used to run a Lambda function. - **Uid** *(integer) --* The user ID whose permissions are used to run a Lambda function. - **ResourceAccessPolicies** *(list) --* A list of the resources, with their permissions, to which the Lambda function will be granted access. A Lambda function can have at most 10 resources. ResourceAccessPolicies apply only when you run the Lambda function in a Greengrass container. - *(dict) --* A policy used by the function to access a resource. - **Permission** *(string) --* The permissions that the Lambda function has to the resource. Can be one of ''rw'' (read/write) or ''ro'' (read-only). - **ResourceId** *(string) --* The ID of the resource. (This ID is assigned to the resource when you create the resource definiton.) - **Variables** *(dict) --* Environment variables for the Lambda function's configuration. - *(string) --* - *(string) --* - **ExecArgs** *(string) --* The execution arguments. - **Executable** *(string) --* The name of the function executable. - **MemorySize** *(integer) --* The memory size, in KB, which the function requires. This setting is not applicable and should be cleared when you run the Lambda function without containerization. - **Pinned** *(boolean) --* True if the function is pinned. Pinned means the function is long-lived and starts when the core starts. - **Timeout** *(integer) --* The allowed function execution time, after which Lambda should terminate the function. This timeout still applies to pinned Lambda functions for each request. - **Id** *(string) --* A descriptive or arbitrary ID for the function. This value must be unique within the function definition version. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. - **Id** *(string) --* The ID of the function definition version. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Version** *(string) --* The version of the function definition version. :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :type FunctionDefinitionVersionId: string :param FunctionDefinitionVersionId: **[REQUIRED]** The ID of the function definition version. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def get_group(self, GroupId: str) -> Dict: """ Retrieves information about a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetGroup>`_ **Request Syntax** :: response = client.get_group( GroupId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def get_group_certificate_authority(self, CertificateAuthorityId: str, GroupId: str) -> Dict: """ Retreives the CA associated with a group. Returns the public key of the CA. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetGroupCertificateAuthority>`_ **Request Syntax** :: response = client.get_group_certificate_authority( CertificateAuthorityId='string', GroupId='string' ) **Response Syntax** :: { 'GroupCertificateAuthorityArn': 'string', 'GroupCertificateAuthorityId': 'string', 'PemEncodedCertificate': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the PKI Configuration. - **GroupCertificateAuthorityArn** *(string) --* The ARN of the certificate authority for the group. - **GroupCertificateAuthorityId** *(string) --* The ID of the certificate authority for the group. - **PemEncodedCertificate** *(string) --* The PEM encoded certificate for the group. :type CertificateAuthorityId: string :param CertificateAuthorityId: **[REQUIRED]** The ID of the certificate authority. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def get_group_certificate_configuration(self, GroupId: str) -> Dict: """ Retrieves the current configuration for the CA used by the group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetGroupCertificateConfiguration>`_ **Request Syntax** :: response = client.get_group_certificate_configuration( GroupId='string' ) **Response Syntax** :: { 'CertificateAuthorityExpiryInMilliseconds': 'string', 'CertificateExpiryInMilliseconds': 'string', 'GroupId': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the PKI Configuration. - **CertificateAuthorityExpiryInMilliseconds** *(string) --* The amount of time remaining before the certificate authority expires, in milliseconds. - **CertificateExpiryInMilliseconds** *(string) --* The amount of time remaining before the certificate expires, in milliseconds. - **GroupId** *(string) --* The ID of the group certificate configuration. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def get_group_version(self, GroupId: str, GroupVersionId: str) -> Dict: """ Retrieves information about a group version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetGroupVersion>`_ **Request Syntax** :: response = client.get_group_version( GroupId='string', GroupVersionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'ConnectorDefinitionVersionArn': 'string', 'CoreDefinitionVersionArn': 'string', 'DeviceDefinitionVersionArn': 'string', 'FunctionDefinitionVersionArn': 'string', 'LoggerDefinitionVersionArn': 'string', 'ResourceDefinitionVersionArn': 'string', 'SubscriptionDefinitionVersionArn': 'string' }, 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the group version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the group version was created. - **Definition** *(dict) --* Information about the group version definition. - **ConnectorDefinitionVersionArn** *(string) --* The ARN of the connector definition version for this group. - **CoreDefinitionVersionArn** *(string) --* The ARN of the core definition version for this group. - **DeviceDefinitionVersionArn** *(string) --* The ARN of the device definition version for this group. - **FunctionDefinitionVersionArn** *(string) --* The ARN of the function definition version for this group. - **LoggerDefinitionVersionArn** *(string) --* The ARN of the logger definition version for this group. - **ResourceDefinitionVersionArn** *(string) --* The ARN of the resource definition version for this group. - **SubscriptionDefinitionVersionArn** *(string) --* The ARN of the subscription definition version for this group. - **Id** *(string) --* The ID of the group version. - **Version** *(string) --* The unique ID for the version of the group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type GroupVersionId: string :param GroupVersionId: **[REQUIRED]** The ID of the group version. :rtype: dict :returns: """ pass def get_logger_definition(self, LoggerDefinitionId: str) -> Dict: """ Retrieves information about a logger definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetLoggerDefinition>`_ **Request Syntax** :: response = client.get_logger_definition( LoggerDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :rtype: dict :returns: """ pass def get_logger_definition_version(self, LoggerDefinitionId: str, LoggerDefinitionVersionId: str, NextToken: str = None) -> Dict: """ Retrieves information about a logger definition version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetLoggerDefinitionVersion>`_ **Request Syntax** :: response = client.get_logger_definition_version( LoggerDefinitionId='string', LoggerDefinitionVersionId='string', NextToken='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Loggers': [ { 'Component': 'GreengrassSystem'|'Lambda', 'Id': 'string', 'Level': 'DEBUG'|'INFO'|'WARN'|'ERROR'|'FATAL', 'Space': 123, 'Type': 'FileSystem'|'AWSCloudWatch' }, ] }, 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the logger definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the logger definition version was created. - **Definition** *(dict) --* Information about the logger definition version. - **Loggers** *(list) --* A list of loggers. - *(dict) --* Information about a logger - **Component** *(string) --* The component that will be subject to logging. - **Id** *(string) --* A descriptive or arbitrary ID for the logger. This value must be unique within the logger definition version. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. - **Level** *(string) --* The level of the logs. - **Space** *(integer) --* The amount of file space, in KB, to use if the local file system is used for logging purposes. - **Type** *(string) --* The type of log output which will be used. - **Id** *(string) --* The ID of the logger definition version. - **Version** *(string) --* The version of the logger definition version. :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :type LoggerDefinitionVersionId: string :param LoggerDefinitionVersionId: **[REQUIRED]** The ID of the logger definition version. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def get_paginator(self, operation_name: str = None) -> Paginator: """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name as the method name on the client. For example, if the method name is ``create_foo``, and you\'d normally invoke the operation as ``client.create_foo(**kwargs)``, if the ``create_foo`` operation can be paginated, you can use the call ``client.get_paginator(\"create_foo\")``. :raise OperationNotPageableError: Raised if the operation is not pageable. You can use the ``client.can_paginate`` method to check if an operation is pageable. :rtype: L{botocore.paginate.Paginator} :return: A paginator object. """ pass def get_resource_definition(self, ResourceDefinitionId: str) -> Dict: """ Retrieves information about a resource definition, including its creation time and latest version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetResourceDefinition>`_ **Request Syntax** :: response = client.get_resource_definition( ResourceDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* success - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :rtype: dict :returns: """ pass def get_resource_definition_version(self, ResourceDefinitionId: str, ResourceDefinitionVersionId: str) -> Dict: """ Retrieves information about a resource definition version, including which resources are included in the version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetResourceDefinitionVersion>`_ **Request Syntax** :: response = client.get_resource_definition_version( ResourceDefinitionId='string', ResourceDefinitionVersionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Resources': [ { 'Id': 'string', 'Name': 'string', 'ResourceDataContainer': { 'LocalDeviceResourceData': { 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'LocalVolumeResourceData': { 'DestinationPath': 'string', 'GroupOwnerSetting': { 'AutoAddGroupOwner': True|False, 'GroupOwner': 'string' }, 'SourcePath': 'string' }, 'S3MachineLearningModelResourceData': { 'DestinationPath': 'string', 'S3Uri': 'string' }, 'SageMakerMachineLearningModelResourceData': { 'DestinationPath': 'string', 'SageMakerJobArn': 'string' }, 'SecretsManagerSecretResourceData': { 'ARN': 'string', 'AdditionalStagingLabelsToDownload': [ 'string', ] } } }, ] }, 'Id': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Arn** *(string) --* Arn of the resource definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the resource definition version was created. - **Definition** *(dict) --* Information about the definition. - **Resources** *(list) --* A list of resources. - *(dict) --* Information about a resource. - **Id** *(string) --* The resource ID, used to refer to a resource in the Lambda function configuration. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. This must be unique within a Greengrass group. - **Name** *(string) --* The descriptive resource name, which is displayed on the AWS IoT Greengrass console. Max length 128 characters with pattern ''[a-zA-Z0-9:_-]+''. This must be unique within a Greengrass group. - **ResourceDataContainer** *(dict) --* A container of data for all resource types. - **LocalDeviceResourceData** *(dict) --* Attributes that define the local device resource. - **GroupOwnerSetting** *(dict) --* Group/owner related settings for local resources. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the device resource. The source path for a device resource can refer only to a character device or block device under ''/dev''. - **LocalVolumeResourceData** *(dict) --* Attributes that define the local volume resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **GroupOwnerSetting** *(dict) --* Allows you to configure additional group privileges for the Lambda process. This field is optional. - **AutoAddGroupOwner** *(boolean) --* If true, AWS IoT Greengrass automatically adds the specified Linux OS group owner of the resource to the Lambda process privileges. Thus the Lambda process will have the file access permissions of the added Linux group. - **GroupOwner** *(string) --* The name of the Linux OS group whose privileges will be added to the Lambda process. This field is optional. - **SourcePath** *(string) --* The local absolute path of the volume resource on the host. The source path for a volume resource type cannot start with ''/sys''. - **S3MachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon S3 machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **S3Uri** *(string) --* The URI of the source model in an S3 bucket. The model package must be in tar.gz or .zip format. - **SageMakerMachineLearningModelResourceData** *(dict) --* Attributes that define an Amazon SageMaker machine learning resource. - **DestinationPath** *(string) --* The absolute local path of the resource inside the Lambda environment. - **SageMakerJobArn** *(string) --* The ARN of the Amazon SageMaker training job that represents the source model. - **SecretsManagerSecretResourceData** *(dict) --* Attributes that define a secret resource, which references a secret from AWS Secrets Manager. - **ARN** *(string) --* The ARN of the Secrets Manager secret to make available on the core. The value of the secret's latest version (represented by the ''AWSCURRENT'' staging label) is included by default. - **AdditionalStagingLabelsToDownload** *(list) --* Optional. The staging labels whose values you want to make available on the core, in addition to ''AWSCURRENT''. - *(string) --* - **Id** *(string) --* The ID of the resource definition version. - **Version** *(string) --* The version of the resource definition version. :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :type ResourceDefinitionVersionId: string :param ResourceDefinitionVersionId: **[REQUIRED]** The ID of the resource definition version. :rtype: dict :returns: """ pass def get_service_role_for_account(self) -> Dict: """ Retrieves the service role that is attached to your account. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetServiceRoleForAccount>`_ **Request Syntax** :: response = client.get_service_role_for_account() **Response Syntax** :: { 'AssociatedAt': 'string', 'RoleArn': 'string' } **Response Structure** - *(dict) --* success - **AssociatedAt** *(string) --* The time when the service role was associated with the account. - **RoleArn** *(string) --* The ARN of the role which is associated with the account. :rtype: dict :returns: """ pass def get_subscription_definition(self, SubscriptionDefinitionId: str) -> Dict: """ Retrieves information about a subscription definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetSubscriptionDefinition>`_ **Request Syntax** :: response = client.get_subscription_definition( SubscriptionDefinitionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :rtype: dict :returns: """ pass def get_subscription_definition_version(self, SubscriptionDefinitionId: str, SubscriptionDefinitionVersionId: str, NextToken: str = None) -> Dict: """ Retrieves information about a subscription definition version. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/GetSubscriptionDefinitionVersion>`_ **Request Syntax** :: response = client.get_subscription_definition_version( NextToken='string', SubscriptionDefinitionId='string', SubscriptionDefinitionVersionId='string' ) **Response Syntax** :: { 'Arn': 'string', 'CreationTimestamp': 'string', 'Definition': { 'Subscriptions': [ { 'Id': 'string', 'Source': 'string', 'Subject': 'string', 'Target': 'string' }, ] }, 'Id': 'string', 'NextToken': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* - **Arn** *(string) --* The ARN of the subscription definition version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the subscription definition version was created. - **Definition** *(dict) --* Information about the subscription definition version. - **Subscriptions** *(list) --* A list of subscriptions. - *(dict) --* Information about a subscription. - **Id** *(string) --* A descriptive or arbitrary ID for the subscription. This value must be unique within the subscription definition version. Max length is 128 characters with pattern ''[a-zA-Z0-9:_-]+''. - **Source** *(string) --* The source of the subscription. Can be a thing ARN, a Lambda function ARN, a connector ARN, 'cloud' (which represents the AWS IoT cloud), or 'GGShadowService'. - **Subject** *(string) --* The MQTT topic used to route the message. - **Target** *(string) --* Where the message is sent to. Can be a thing ARN, a Lambda function ARN, a connector ARN, 'cloud' (which represents the AWS IoT cloud), or 'GGShadowService'. - **Id** *(string) --* The ID of the subscription definition version. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Version** *(string) --* The version of the subscription definition version. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :type SubscriptionDefinitionVersionId: string :param SubscriptionDefinitionVersionId: **[REQUIRED]** The ID of the subscription definition version. :rtype: dict :returns: """ pass def get_waiter(self, waiter_name: str = None) -> Waiter: """ Returns an object that can wait for some condition. :type waiter_name: str :param waiter_name: The name of the waiter to get. See the waiters section of the service docs for a list of available waiters. :returns: The specified waiter object. :rtype: botocore.waiter.Waiter """ pass def list_bulk_deployment_detailed_reports(self, BulkDeploymentId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Gets a paginated list of the deployments that have been started in a bulk deployment operation, and their current deployment status. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListBulkDeploymentDetailedReports>`_ **Request Syntax** :: response = client.list_bulk_deployment_detailed_reports( BulkDeploymentId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Deployments': [ { 'CreatedAt': 'string', 'DeploymentArn': 'string', 'DeploymentId': 'string', 'DeploymentStatus': 'string', 'DeploymentType': 'NewDeployment'|'Redeployment'|'ResetDeployment'|'ForceResetDeployment', 'ErrorDetails': [ { 'DetailedErrorCode': 'string', 'DetailedErrorMessage': 'string' }, ], 'ErrorMessage': 'string', 'GroupArn': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the list of deployments for the given group. - **Deployments** *(list) --* A list of the individual group deployments in the bulk deployment operation. - *(dict) --* Information about an individual group deployment in a bulk deployment operation. - **CreatedAt** *(string) --* The time, in ISO format, when the deployment was created. - **DeploymentArn** *(string) --* The ARN of the group deployment. - **DeploymentId** *(string) --* The ID of the group deployment. - **DeploymentStatus** *(string) --* The current status of the group deployment: ''InProgress'', ''Building'', ''Success'', or ''Failure''. - **DeploymentType** *(string) --* The type of the deployment. - **ErrorDetails** *(list) --* Details about the error. - *(dict) --* Details about the error. - **DetailedErrorCode** *(string) --* A detailed error code. - **DetailedErrorMessage** *(string) --* A detailed error message. - **ErrorMessage** *(string) --* The error message for a failed deployment - **GroupArn** *(string) --* The ARN of the Greengrass group. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type BulkDeploymentId: string :param BulkDeploymentId: **[REQUIRED]** The ID of the bulk deployment. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_bulk_deployments(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Returns a list of bulk deployments. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListBulkDeployments>`_ **Request Syntax** :: response = client.list_bulk_deployments( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'BulkDeployments': [ { 'BulkDeploymentArn': 'string', 'BulkDeploymentId': 'string', 'CreatedAt': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the list of bulk deployments. - **BulkDeployments** *(list) --* A list of bulk deployments. - *(dict) --* Information about a bulk deployment. You cannot start a new bulk deployment while another one is still running or in a non-terminal state. - **BulkDeploymentArn** *(string) --* The ARN of the bulk deployment. - **BulkDeploymentId** *(string) --* The ID of the bulk deployment. - **CreatedAt** *(string) --* The time, in ISO format, when the deployment was created. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_connector_definition_versions(self, ConnectorDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a connector definition, which are containers for connectors. Connectors run on the Greengrass core and contain built-in integration with local infrastructure, device protocols, AWS, and other cloud services. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListConnectorDefinitionVersions>`_ **Request Syntax** :: response = client.list_connector_definition_versions( ConnectorDefinitionId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_connector_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of connector definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListConnectorDefinitions>`_ **Request Syntax** :: response = client.list_connector_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_core_definition_versions(self, CoreDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a core definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListCoreDefinitionVersions>`_ **Request Syntax** :: response = client.list_core_definition_versions( CoreDefinitionId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_core_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of core definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListCoreDefinitions>`_ **Request Syntax** :: response = client.list_core_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_deployments(self, GroupId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Returns a history of deployments for the group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListDeployments>`_ **Request Syntax** :: response = client.list_deployments( GroupId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Deployments': [ { 'CreatedAt': 'string', 'DeploymentArn': 'string', 'DeploymentId': 'string', 'DeploymentType': 'NewDeployment'|'Redeployment'|'ResetDeployment'|'ForceResetDeployment', 'GroupArn': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the list of deployments for the given group. - **Deployments** *(list) --* A list of deployments for the requested groups. - *(dict) --* Information about a deployment. - **CreatedAt** *(string) --* The time, in milliseconds since the epoch, when the deployment was created. - **DeploymentArn** *(string) --* The ARN of the deployment. - **DeploymentId** *(string) --* The ID of the deployment. - **DeploymentType** *(string) --* The type of the deployment. - **GroupArn** *(string) --* The ARN of the group for this deployment. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_device_definition_versions(self, DeviceDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a device definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListDeviceDefinitionVersions>`_ **Request Syntax** :: response = client.list_device_definition_versions( DeviceDefinitionId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_device_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of device definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListDeviceDefinitions>`_ **Request Syntax** :: response = client.list_device_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_function_definition_versions(self, FunctionDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a Lambda function definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListFunctionDefinitionVersions>`_ **Request Syntax** :: response = client.list_function_definition_versions( FunctionDefinitionId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* success - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_function_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of Lambda function definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListFunctionDefinitions>`_ **Request Syntax** :: response = client.list_function_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* Success. The response contains the IDs of all the Greengrass Lambda function definitions in this account. - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_group_certificate_authorities(self, GroupId: str) -> Dict: """ Retrieves the current CAs for a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListGroupCertificateAuthorities>`_ **Request Syntax** :: response = client.list_group_certificate_authorities( GroupId='string' ) **Response Syntax** :: { 'GroupCertificateAuthorities': [ { 'GroupCertificateAuthorityArn': 'string', 'GroupCertificateAuthorityId': 'string' }, ] } **Response Structure** - *(dict) --* Success. The response body contains the PKI Configuration. - **GroupCertificateAuthorities** *(list) --* A list of certificate authorities associated with the group. - *(dict) --* Information about a certificate authority for a group. - **GroupCertificateAuthorityArn** *(string) --* The ARN of the certificate authority for the group. - **GroupCertificateAuthorityId** *(string) --* The ID of the certificate authority for the group. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def list_group_versions(self, GroupId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListGroupVersions>`_ **Request Syntax** :: response = client.list_group_versions( GroupId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* Success. The response contains the list of versions and metadata for the given group. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_groups(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of groups. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListGroups>`_ **Request Syntax** :: response = client.list_groups( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Groups': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string' }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Groups** *(list) --* Information about a group. - *(dict) --* Information about a group. - **Arn** *(string) --* The ARN of the group. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the group was created. - **Id** *(string) --* The ID of the group. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the group was last updated. - **LatestVersion** *(string) --* The latest version of the group. - **LatestVersionArn** *(string) --* The ARN of the latest version of the group. - **Name** *(string) --* The name of the group. - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_logger_definition_versions(self, LoggerDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a logger definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListLoggerDefinitionVersions>`_ **Request Syntax** :: response = client.list_logger_definition_versions( LoggerDefinitionId='string', MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_logger_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of logger definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListLoggerDefinitions>`_ **Request Syntax** :: response = client.list_logger_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_resource_definition_versions(self, ResourceDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a resource definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListResourceDefinitionVersions>`_ **Request Syntax** :: response = client.list_resource_definition_versions( MaxResults='string', NextToken='string', ResourceDefinitionId='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* success - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :rtype: dict :returns: """ pass def list_resource_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of resource definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListResourceDefinitions>`_ **Request Syntax** :: response = client.list_resource_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* The IDs of all the Greengrass resource definitions in this account. - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_subscription_definition_versions(self, SubscriptionDefinitionId: str, MaxResults: str = None, NextToken: str = None) -> Dict: """ Lists the versions of a subscription definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListSubscriptionDefinitionVersions>`_ **Request Syntax** :: response = client.list_subscription_definition_versions( MaxResults='string', NextToken='string', SubscriptionDefinitionId='string' ) **Response Syntax** :: { 'NextToken': 'string', 'Versions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'Version': 'string' }, ] } **Response Structure** - *(dict) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. - **Versions** *(list) --* Information about a version. - *(dict) --* Information about a version. - **Arn** *(string) --* The ARN of the version. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the version was created. - **Id** *(string) --* The ID of the version. - **Version** *(string) --* The unique ID of the version. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :rtype: dict :returns: """ pass def list_subscription_definitions(self, MaxResults: str = None, NextToken: str = None) -> Dict: """ Retrieves a list of subscription definitions. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListSubscriptionDefinitions>`_ **Request Syntax** :: response = client.list_subscription_definitions( MaxResults='string', NextToken='string' ) **Response Syntax** :: { 'Definitions': [ { 'Arn': 'string', 'CreationTimestamp': 'string', 'Id': 'string', 'LastUpdatedTimestamp': 'string', 'LatestVersion': 'string', 'LatestVersionArn': 'string', 'Name': 'string', 'Tags': { 'string': 'string' } }, ], 'NextToken': 'string' } **Response Structure** - *(dict) --* - **Definitions** *(list) --* Information about a definition. - *(dict) --* Information about a definition. - **Arn** *(string) --* The ARN of the definition. - **CreationTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was created. - **Id** *(string) --* The ID of the definition. - **LastUpdatedTimestamp** *(string) --* The time, in milliseconds since the epoch, when the definition was last updated. - **LatestVersion** *(string) --* The latest version of the definition. - **LatestVersionArn** *(string) --* The ARN of the latest version of the definition. - **Name** *(string) --* The name of the definition. - **Tags** *(dict) --* The tags for the definition. - *(string) --* - *(string) --* - **NextToken** *(string) --* The token for the next set of results, or ''null'' if there are no additional results. :type MaxResults: string :param MaxResults: The maximum number of results to be returned per request. :type NextToken: string :param NextToken: The token for the next set of results, or \'\'null\'\' if there are no additional results. :rtype: dict :returns: """ pass def list_tags_for_resource(self, ResourceArn: str) -> Dict: """ Retrieves the tags for a resource. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ListTagsForResource>`_ **Request Syntax** :: response = client.list_tags_for_resource( ResourceArn='string' ) **Response Syntax** :: { 'tags': { 'string': 'string' } } **Response Structure** - *(dict) --* - **tags** *(dict) --* A map of the key-value pairs for the resource tag. - *(string) --* - *(string) --* :type ResourceArn: string :param ResourceArn: **[REQUIRED]** The Amazon Resource Name (ARN) of the resource. :rtype: dict :returns: """ pass def reset_deployments(self, GroupId: str, AmznClientToken: str = None, Force: bool = None) -> Dict: """ Resets a group's deployments. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/ResetDeployments>`_ **Request Syntax** :: response = client.reset_deployments( AmznClientToken='string', Force=True|False, GroupId='string' ) **Response Syntax** :: { 'DeploymentArn': 'string', 'DeploymentId': 'string' } **Response Structure** - *(dict) --* Success. The group's deployments were reset. - **DeploymentArn** *(string) --* The ARN of the deployment. - **DeploymentId** *(string) --* The ID of the deployment. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type Force: boolean :param Force: If true, performs a best-effort only core reset. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def start_bulk_deployment(self, AmznClientToken: str = None, ExecutionRoleArn: str = None, InputFileUri: str = None, tags: Dict = None) -> Dict: """ Deploys multiple groups in one operation. This action starts the bulk deployment of a specified set of group versions. Each group version deployment will be triggered with an adaptive rate that has a fixed upper limit. We recommend that you include an ''X-Amzn-Client-Token'' token in every ''StartBulkDeployment'' request. These requests are idempotent with respect to the token and the request parameters. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/StartBulkDeployment>`_ **Request Syntax** :: response = client.start_bulk_deployment( AmznClientToken='string', ExecutionRoleArn='string', InputFileUri='string', tags={ 'string': 'string' } ) **Response Syntax** :: { 'BulkDeploymentArn': 'string', 'BulkDeploymentId': 'string' } **Response Structure** - *(dict) --* success - **BulkDeploymentArn** *(string) --* The ARN of the bulk deployment. - **BulkDeploymentId** *(string) --* The ID of the bulk deployment. :type AmznClientToken: string :param AmznClientToken: A client token used to correlate requests and responses. :type ExecutionRoleArn: string :param ExecutionRoleArn: The ARN of the execution role to associate with the bulk deployment operation. This IAM role must allow the \'\'greengrass:CreateDeployment\'\' action for all group versions that are listed in the input file. This IAM role must have access to the S3 bucket containing the input file. :type InputFileUri: string :param InputFileUri: The URI of the input file contained in the S3 bucket. The execution role must have \'\'getObject\'\' permissions on this bucket to access the input file. The input file is a JSON-serialized, line delimited file with UTF-8 encoding that provides a list of group and version IDs and the deployment type. This file must be less than 100 MB. Currently, AWS IoT Greengrass supports only \'\'NewDeployment\'\' deployment types. :type tags: dict :param tags: Tag(s) to add to the new resource - *(string) --* - *(string) --* :rtype: dict :returns: """ pass def stop_bulk_deployment(self, BulkDeploymentId: str) -> Dict: """ Stops the execution of a bulk deployment. This action returns a status of ''Stopping'' until the deployment is stopped. You cannot start a new bulk deployment while a previous deployment is in the ''Stopping'' state. This action doesn't rollback completed deployments or cancel pending deployments. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/StopBulkDeployment>`_ **Request Syntax** :: response = client.stop_bulk_deployment( BulkDeploymentId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* Success. The bulk deployment is being stopped. :type BulkDeploymentId: string :param BulkDeploymentId: **[REQUIRED]** The ID of the bulk deployment. :rtype: dict :returns: """ pass def tag_resource(self, ResourceArn: str, tags: Dict): """ Add tags to a resource. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/TagResource>`_ **Request Syntax** :: response = client.tag_resource( ResourceArn='string', tags={ 'string': 'string' } ) :type ResourceArn: string :param ResourceArn: **[REQUIRED]** The Amazon Resource Name (ARN) of the resource. :type tags: dict :param tags: **[REQUIRED]** A map of the key-value pairs for the resource tag. - *(string) --* - *(string) --* :returns: None """ pass def untag_resource(self, ResourceArn: str, TagKeys: List): """ Remove tags with specified keys from a resource. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UntagResource>`_ **Request Syntax** :: response = client.untag_resource( ResourceArn='string', TagKeys=[ 'string', ] ) :type ResourceArn: string :param ResourceArn: **[REQUIRED]** The Amazon Resource Name (ARN) of the resource. :type TagKeys: list :param TagKeys: **[REQUIRED]** A list of the keys to remove from the resource tags. - *(string) --* :returns: None """ pass def update_connectivity_info(self, ThingName: str, ConnectivityInfo: List = None) -> Dict: """ Updates the connectivity information for the core. Any devices that belong to the group which has this core will receive this information in order to find the location of the core and connect to it. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateConnectivityInfo>`_ **Request Syntax** :: response = client.update_connectivity_info( ConnectivityInfo=[ { 'HostAddress': 'string', 'Id': 'string', 'Metadata': 'string', 'PortNumber': 123 }, ], ThingName='string' ) **Response Syntax** :: { 'Message': 'string', 'Version': 'string' } **Response Structure** - *(dict) --* success - **Message** *(string) --* A message about the connectivity info update request. - **Version** *(string) --* The new version of the connectivity info. :type ConnectivityInfo: list :param ConnectivityInfo: A list of connectivity info. - *(dict) --* Information about a Greengrass core\'s connectivity. - **HostAddress** *(string) --* The endpoint for the Greengrass core. Can be an IP address or DNS. - **Id** *(string) --* The ID of the connectivity information. - **Metadata** *(string) --* Metadata for this endpoint. - **PortNumber** *(integer) --* The port of the Greengrass core. Usually 8883. :type ThingName: string :param ThingName: **[REQUIRED]** The thing name. :rtype: dict :returns: """ pass def update_connector_definition(self, ConnectorDefinitionId: str, Name: str = None) -> Dict: """ Updates a connector definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateConnectorDefinition>`_ **Request Syntax** :: response = client.update_connector_definition( ConnectorDefinitionId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type ConnectorDefinitionId: string :param ConnectorDefinitionId: **[REQUIRED]** The ID of the connector definition. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_core_definition(self, CoreDefinitionId: str, Name: str = None) -> Dict: """ Updates a core definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateCoreDefinition>`_ **Request Syntax** :: response = client.update_core_definition( CoreDefinitionId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type CoreDefinitionId: string :param CoreDefinitionId: **[REQUIRED]** The ID of the core definition. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_device_definition(self, DeviceDefinitionId: str, Name: str = None) -> Dict: """ Updates a device definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateDeviceDefinition>`_ **Request Syntax** :: response = client.update_device_definition( DeviceDefinitionId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type DeviceDefinitionId: string :param DeviceDefinitionId: **[REQUIRED]** The ID of the device definition. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_function_definition(self, FunctionDefinitionId: str, Name: str = None) -> Dict: """ Updates a Lambda function definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateFunctionDefinition>`_ **Request Syntax** :: response = client.update_function_definition( FunctionDefinitionId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type FunctionDefinitionId: string :param FunctionDefinitionId: **[REQUIRED]** The ID of the Lambda function definition. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_group(self, GroupId: str, Name: str = None) -> Dict: """ Updates a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateGroup>`_ **Request Syntax** :: response = client.update_group( GroupId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_group_certificate_configuration(self, GroupId: str, CertificateExpiryInMilliseconds: str = None) -> Dict: """ Updates the Certificate expiry time for a group. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateGroupCertificateConfiguration>`_ **Request Syntax** :: response = client.update_group_certificate_configuration( CertificateExpiryInMilliseconds='string', GroupId='string' ) **Response Syntax** :: { 'CertificateAuthorityExpiryInMilliseconds': 'string', 'CertificateExpiryInMilliseconds': 'string', 'GroupId': 'string' } **Response Structure** - *(dict) --* Success. The response body contains the PKI Configuration. - **CertificateAuthorityExpiryInMilliseconds** *(string) --* The amount of time remaining before the certificate authority expires, in milliseconds. - **CertificateExpiryInMilliseconds** *(string) --* The amount of time remaining before the certificate expires, in milliseconds. - **GroupId** *(string) --* The ID of the group certificate configuration. :type CertificateExpiryInMilliseconds: string :param CertificateExpiryInMilliseconds: The amount of time remaining before the certificate expires, in milliseconds. :type GroupId: string :param GroupId: **[REQUIRED]** The ID of the Greengrass group. :rtype: dict :returns: """ pass def update_logger_definition(self, LoggerDefinitionId: str, Name: str = None) -> Dict: """ Updates a logger definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateLoggerDefinition>`_ **Request Syntax** :: response = client.update_logger_definition( LoggerDefinitionId='string', Name='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type LoggerDefinitionId: string :param LoggerDefinitionId: **[REQUIRED]** The ID of the logger definition. :type Name: string :param Name: The name of the definition. :rtype: dict :returns: """ pass def update_resource_definition(self, ResourceDefinitionId: str, Name: str = None) -> Dict: """ Updates a resource definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateResourceDefinition>`_ **Request Syntax** :: response = client.update_resource_definition( Name='string', ResourceDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type Name: string :param Name: The name of the definition. :type ResourceDefinitionId: string :param ResourceDefinitionId: **[REQUIRED]** The ID of the resource definition. :rtype: dict :returns: """ pass def update_subscription_definition(self, SubscriptionDefinitionId: str, Name: str = None) -> Dict: """ Updates a subscription definition. See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/greengrass-2017-06-07/UpdateSubscriptionDefinition>`_ **Request Syntax** :: response = client.update_subscription_definition( Name='string', SubscriptionDefinitionId='string' ) **Response Syntax** :: {} **Response Structure** - *(dict) --* success :type Name: string :param Name: The name of the definition. :type SubscriptionDefinitionId: string :param SubscriptionDefinitionId: **[REQUIRED]** The ID of the subscription definition. :rtype: dict :returns: """ pass

0

215,512

23

d4d0c054bd8c6ab251cce470a6372d0eed7bc869

38,962

py

Python

app/ariadicom.py

MePyDo/pygqa

61cde42ee815968fdd029cc5056ede3badea3d91

[ "MIT" ]

3

2021-02-25T13:19:52.000Z

2021-03-03T03:46:46.000Z

app/ariadicom.py

MedPhyDO/pygqa

580b2c6028d2299790a38262b795b8409cbfcc37

[ "MIT" ]

null

app/ariadicom.py

MedPhyDO/pygqa

580b2c6028d2299790a38262b795b8409cbfcc37

[ "MIT" ]

null

# -*- coding: utf-8 -*- __author__ = "R. Bauer" __copyright__ = "MedPhyDO - Machbarkeitsstudien des Instituts für Medizinische Strahlenphysik und Strahlenschutz am Klinikum Dortmund im Rahmen von Bachelor und Masterarbeiten an der TU-Dortmund / FH-Dortmund" __credits__ = ["R.Bauer", "K.Loot"] __license__ = "MIT" __version__ = "0.1.2" __status__ = "Prototype" from dotmap import DotMap import os.path as osp from pathlib import Path import pandas as pd import numpy as np import json from datetime import date from isp.dicom import ispDicom from isp.config import dict_merge from app.config import infoFields from app.aria import ariaClass #from app.dicom import dicomClass from app.results import ispResults from app.qa.mlc import checkMlc from app.qa.field import checkField from app.qa.wl import checkWL from app.qa.vmat import checkVMAT import logging logger = logging.getLogger( "MQTT" ) class ariaDicomClass( ariaClass, ispDicom ): '''Zentrale Klasse Attributes ---------- config : Dot konfigurations Daten variables : Metadaten aus config.variables infoFields: Infofelder aus config dicomfiles: dict geladene Dicom dateien pd_results: pd testergebnisse als Pandas tabelle resultfile Datei mit Ergebnissen als panda File lastSQL: str die letzte durchgeführte sql Abfrage ''' def __init__( self, database=None, server="VMSDBD", config=None ): """Klasse sowie ariaClass und dicomClass initialisieren """ # Klassen defaults setzen und übergaben self.config = config self.variables = self.config.variables self.infoFields = infoFields self.dicomfiles: dict = {} self.pd_results = None self.resultfile = None self.lastSQL = "" # ariaClass initialisieren ariaClass.__init__( self, database ) # dicomClass initialisieren. Der Erfolg kann über dicomClass.initialized abgefragt werden ispDicom.__init__( self, server, self.config ) # Datei mit Ergebnissen als pandas laden self.resultfile = osp.join( self.config.get("resultsPath", ".."), self.config.get("database.gqa.name", "gqa.json") ) self.pd_results = ispResults( self.config, self.resultfile ) def initResultsPath(self, AcquisitionYear=None ): '''Den Ablegeort zu den PDF Dateien bestimmen in variables.path befindet sich jetzt der resultsPath ggf. mit angehängten AcquisitionYear Parameters ---------- AcquisitionYear : TYPE, optional DESCRIPTION. The default is None. Returns ------- dirname : str der aktuelle PDF Pfad (auch in self.variables["path"] ) ''' paths = [ ] # ist der Pfad relativ angegeben ab base path verwenden if self.config["resultsPath"][0] == ".": paths.append( self.config["BASE_DIR"] ) paths.append( self.config["resultsPath"] ) else: paths.append( self.config["resultsPath"] ) # zusätzlich noch das AcquisitionYear anfügen if AcquisitionYear: paths.append( str(AcquisitionYear) ) # den Pfad in variables["path"] ablegen dirname = osp.abspath( osp.join( *paths ) ) self.variables["path"] = dirname return dirname def getAllGQA(self, pids=None, testTags:list=None, year:int=None, month:int=None, day:int=None, withInfo=True, withResult=False ): '''Holt für die angegebenen PatientenIds aus allen Courses die Felder mit Angaben in [Radiation].[Comment] und wertet sie entsprechend aus Parameters ---------- pids : list, optional DESCRIPTION. The default is None. testTags : list, optional DESCRIPTION. The default is None. year : int, optional DESCRIPTION. The default is None. month : int, optional DESCRIPTION. The default is None. day : int, optional DESCRIPTION. The default is None. withInfo : TYPE, optional DESCRIPTION. The default is True. withResult : TYPE, optional DESCRIPTION. The default is False. Returns ------- gqa : dict Aufbau:: units: dict <unit>: dict <infoType>: dict ready: dict all: int <energy> : int gqa: dict fields: int energyFields: int counts: dict all: int <energy> : int pdf: dict, items: dict <energy>: dict <SliceUID>: {info} -> dies wird bei run in ein DataFrame umgewandelt series: [], ''' if not pids: return {} if type(pids) == str: pids = pids.split(",") if not type(pids) == list: pids = [pids] if not pids or len(pids) == 0: return {} # filter zusammenstellen where = "LEN([Radiation].[Comment]) > 0 " subSql = [] for pid in pids: subSql.append( "[Patient].[PatientId]='{}'".format( pid.strip() ) ) if len( subSql ) > 0: where += " AND (" + " OR ".join( subSql ) + ")" images, sql = self.getImages( addWhere=where, AcquisitionYear=year, AcquisitionMonth=month, AcquisitionDay=day, testTags=testTags ) self.lastSQL = sql # Pfad für die PDF Dateien self.initResultsPath( year ) return self.prepareGQA( images, year=year, withInfo=withInfo, withResult=withResult ) def prepareGQA(self, imagedatas=[], year:int=0, withInfo=True, withResult=False, withDicomData:bool=False ): """Auswertung für GQA vorbereiten zusätzlich noch Ergebnisse aus der Datenbank einfügen Benötig config.GQA und config.units - units: ["Linac-1", "Linac-2"], - gqa : dict <testId>: dict <unit>: dict fields: int energyFields: int Parameters ---------- imagedatas : list, optional Auflistungen von Bildinformationen aus der Aria Datenbank. The default is []. year : int, optional DESCRIPTION. The default is 0. withInfo : TYPE, optional alle ImageInfos mit hinzufügen. The default is True. withResult : boolean, optional Testergebnisse mit ausgeben. The default is False. withDicomData : boolean, optional Info pro gerät in dicomfiles ablegen. The default is False. Returns ------- gqa : dict # alles aus config.gqa dabei die Unites mit Daten füllen <testname> info: inaktiv tip anleitung options: TODO: tolerance: <energy> <unit-n> fields: int energyFields: int energy: list """ # dicom gerät , name , infos self.dicomfiles = {} units = self.config.units # Dateien im Pfad pdfFiles = [] if osp.exists( self.variables["path"] ): p = Path( self.variables["path"] ) pdfFiles = [i.name for i in p.glob( '*.pdf' )] # files = os.listdir( self.variables["path"] ) data = { "GQA" : self.config.get("GQA").toDict(), "units" : units, "testTags" : {}, "testIds": {} } # nur das gesuchte Jahr, ohne index df_results = self.pd_results.gqa[ self.pd_results.gqa['year'] == year ].reset_index() result_fields = [ "acceptance", "group" ] if withResult: result_fields.append("data") # neuen index setzen # Das geht nur bei daten in df_results if len(df_results.index) > 0: df_results.set_index( df_results.apply(lambda x: f"{x['year']}|{x['unit']}|{x['test']}|{x['energy']}|{x['month']}", axis=1), inplace=True ) data["results"] = df_results[ result_fields ].to_dict( orient="split" ) else: data["results"] = { "columns":result_fields, "data":[], "index":[] } # tags und gqa ids bestimmen for testid, item in self.config.GQA.items(): if "tag" in item: data["testTags"][ item["tag"] ] = testid data["testIds"][ testid ] = item["tag"] tagNotFound = {} inactiv = [] testNotFound = [] for imagedata in imagedatas: # bereitetet die Datenbank Informationen auf info = self.getImageInfos( imagedata ) unit = info["unit"] energy = info["energy"] # # zusätzlich die Daten in self.dicomfiles ablegen # if withDicomData: if not unit in self.dicomfiles: self.dicomfiles[ unit ] = {} # zusätzlich in dicomfiles ablegen self.dicomfiles[ unit ][ info["id"] ] = info # Felder zuordnen, eine Aufnahme kann für mehrere tests verwendet werden # tag für die Datenbank, testid für das PDF for testTag in info["testTags"]: # nur wenn es auch einen test gibt if not testTag in data["testTags"]: tagNotFound[ testTag ] = testTag continue testId = data["testTags"][testTag] # ist der test in gqa nicht erlaubt überspringen # inaktive kann auch einen Text enthalten der beschreibt warum # FIXME: inaktive t = "GQA.{}.info.inaktiv".format( testId ) if not self.config.get(t, False) == False: inactiv.append( self.config.get(t) ) continue # gibt es in GQA passend zum Test dem Gerät und der Energie einen Eintrag t = "GQA.{}.{}.energyFields.{}".format( testId, unit, energy ) energyFields = self.config.get(t, False) if energyFields == False: testNotFound.append( t ) continue # Art des tests MT|JT tagArt = testId[0:2] if tagArt == "JT": dateFlag = "0" else: dateFlag = str( info["AcquisitionMonth"] ) # test_unit = data["GQA"][testId][unit] if not dateFlag in test_unit: test_unit[ dateFlag ] = {} if not energy in test_unit[ dateFlag ]: test_unit[ dateFlag ][energy] = { "counts": 0, "ready": False, "pdfName" : "", "pdf": False, "acceptance" : {} } # Anzahl der Felder für das Datumsflag der jeweiligen Energie erhöhen (counts) test_unit[ dateFlag ][ energy ][ "counts" ] += 1 # auf mid Anzahl prüfen if test_unit[ dateFlag ][ energy ][ "counts" ] >= energyFields: test_unit[ dateFlag ][ energy ][ "ready" ] = True # PDF Dateiname zusammenstellen pdfName = self.config.render_template( self.config["templates"][ "PDF-" + tagArt + "-filename"], { "AcquisitionYear": info["AcquisitionYear"], "AcquisitionMonth": info["AcquisitionMonth"], "unit": unit, "energy": energy, "testId": testId } ) if pdfName in pdfFiles: test_unit[ dateFlag ][ energy ][ "pdfName" ] = pdfName test_unit[ dateFlag ][ energy ][ "pdf" ] = True # nicht gefundene Tags data["inactiv"] = inactiv data["tagNotFound"] = tagNotFound data["testNotFound"] = testNotFound return data # ---------------------- einfache Ausgaben def getTagging(self, art:str="full", pid:list=[], output_format="json" ): """alle Tags in Comment Feldern als html Tabelle zurückgeben Parameters ---------- art : str, optional Art der Tagging Tabellen (). The default is "full". * full * sum * test * tags pid : list, optional Angabe von PatientsIds für die Tags bestimmt werden sollen. The default is []. output_format: str Format der Ausgabe [ json, html ] Returns ------- str|dict html Tags code oder dict. """ style = """ <style> .gqa-tagging { } .gqa-tagging table { color: #333; font-family: Helvetica, Arial, sans-serif; min-width: 100px; border-collapse: collapse; border-spacing: 0; font-size: 10px; } .gqa-tagging table td, .gqa-tagging table th { border: 1px solid gray; text-align: center; vertical-align: middle; } .gqa-tagging table th { font-weight: bold; } .gqa-tagging table thead th, .gqa-tagging table tbody th { background-color: #F7F7F7; } .gqa-tagging table td { background-color: white; } .gqa-tagging table th, .gqa-tagging table td, .gqa-tagging table caption { padding: 2px 2px 2px 2px; } </style> """ split = True if art == "tags": # bei tags conmment nicht splitten split = False tags = self.getTags( pid, split ) if output_format == "json": return tags if not tags or len(tags) == 0: return "getTagging: keine Daten gefunden" html = '<div class="gqa-tagging flex-1">' html += '<h1 class="m-0 p-1 text-white bg-secondary">Art: ' + art + '</h2>' # Pandas erzeugen df = pd.DataFrame( tags ) if art == "full": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'PlanSetupId', 'Energy', 'DoseRate', 'RadiationId'], columns='PatientId', values= "nummer", fill_value=0 ) elif art == "sum": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'PlanSetupId','Energy', 'DoseRate'], columns=['PatientId'], values= 'nummer', aggfunc=[np.sum], fill_value=0 ) elif art == "test": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'Energy', 'DoseRate'], columns=[ 'PlanSetupId', 'PatientId'], values= 'nummer', aggfunc=[np.sum], fill_value=0 ) elif art == "tags": table = pd.pivot_table( df, index=['Comment'], columns=['PatientId'], values= 'nummer', fill_value=0 #aggfunc=[np.sum] ) # tags zurückgeben als einfache Tabelle #table = df[ ["Comment"] ].groupby( "Comment" ).first().reset_index() # table.fillna('', inplace=True) html += (table.style .applymap( highlight_fifty ) .set_table_attributes('class="gqa-tagging-table"') #.float_format() .render() ) html += '</div>' return style + html def getMatrix( self, output_format="json", params:dict={} ): """Gibt eine Liste alle Testbeschreibungen (config) mit Anleitungen Parameters ---------- output_format: str Format der Ausgabe [ json, html ] params: dict Aufrufparameter mit year und month Returns ------- str|dict html matrix code oder dict. """ # jahr und Monat bei 0 mit dem aktuellen belegen today = date.today() if params["year"] == 0: params["year"] = today.year if params["month"] == 0: params["month"] = today.month # pdf wird zum laden der Texte verwendet from isp.mpdf import PdfGenerator as ispPdf pdf = ispPdf() html_jt = "" html_mt = "" html_nn = "" data_dict = {} for key, content in self.config.GQA.items(): data = { "key" : key, "tip" : "", "need" : "", "anleitung" : "", "chips" : "" } chips = [] # units und energy for unit_key, unit in self.config.units.items(): if unit in content: for energy in content[ unit ].energy: chips.append( { "class": "badge badge-pill badge-info mr-1", "content": "{} - {}".format( unit_key, energy ) } ) # info bestimmen info = content.info data["tip"] = info.get("tip", "") need = info.get("need", "") if type(need) == str and need != "": chips.append( { "class": "badge badge-pill badge-success", "content": 'benötigt: ' + need } ) # Anleitung anleitung_filename = info.get("anleitung", "") data["anleitung"] = '<p class="badge badge-pill badge-primary">Anleitung fehlt!</p>' if anleitung_filename != "": anleitung = pdf.textFile(anleitung_filename, render = False) if anleitung: data["anleitung"] = anleitung # Toleranz tolerance = content.info.get("tolerance", False) if tolerance: data["anleitung"] += "<h6>Toleranz</h6>" # ggf formel erstellen for e, item in tolerance.items(): self.prepare_tolerance(key, e) pass # toleranz einfügen data["anleitung"] += '<pre class="toleranz bg-light text-monospace ">' + json.dumps( tolerance, indent=2 ) + '</pre>' # ist der test als inaktiv Hinweis ausgeben inaktiv = content.info.get('inaktiv', False) if inaktiv != False: chips.append( { "class": "inaktiv", "content": 'Inaktiv: ' + inaktiv } ) # gibt es optional Angaben optional = content.info.get('optional', []) if len(optional) > 0: for item in optional: chips.append( { "class": "badge badge-pill badge-primary", "content": 'Optional wenn: ' + item + ' OK' } ) # TODO todo = content.info.get("TODO", False) if todo and len(todo) > 0: data["anleitung"] += "TODO" data["anleitung"] += '<pre class="p-1 bg-warning">' for t in todo: data["anleitung"] += "* " + t + "\n" data["anleitung"] += '</pre>' # markierungen zusammenstellen for chip in chips: data["chips"] += '<div class="{class}">{content}</div>'.format(**chip) data_dict[ key ] = content.toDict() data_dict[ key ][ "anleitung" ] = anleitung card = """ <div class="card m-3" > <div class="card-header"> <span class="font-weight-bolder">{key}</span> <span class="pl-3">{tip}</span> <div class="float-right">{chips}</div> </div> <div class="card-body p-1"> {anleitung} </div> </div> """.format( **data ) if key[0:2] == "JT": html_jt += card elif key[0:2] == "MT": html_mt += card else: html_nn += card if output_format == "json": return data_dict style = """ <style> /* Anpassung pdf text */ .gqa_matrix h2 { font-size: 1.1667em; font-weight: bold; line-height: 1.286em; margin-top: 0.5em; margin-bottom: 0.5em; } .gqa_matrix .card-body p::first-of-type { background-color: #FFFFFFAA; } </style> """ html = ''' <div class="gqa_matrix"> <h1 class="m-0 p-1 text-white bg-secondary" >Angaben für: {month}/{year}</h1> <content class="p-1 d-flex flex-row" > <div class="w-50">{jt}</div> <div class="w-50">{mt}</div> <div class="">{nn}</div> </content> </div> '''.format( jt=html_jt, mt=html_mt, nn=html_nn, **params ) return style + html def prepare_tolerance(self, testid:str="", energy=None): """Prüft ob es in conig eine tolerance Angabe für die testid und die Energie gibt Stellt wenn f nicht angegeben wurde eine Formel in f zusammen Gibt es eine GQA.<testid>.info.tolerance.default Angabe, so wird diese als Grundlage für alle Energien verwendet Zweig in config:: GQA.<testid>.info.tolerance.<energy> { name: { f: formel mit {value} value: wert range: [min, max] operator: [ eq, ne, lt, gt, le, ge] } } Parameters ---------- testid : str, optional id des zu verarbeitenden tolerance Bereichs energy : str, optional Augabe der Energie für die Info. The default is None. Ohne Angabe wird nur der Parameter info zurückgegeben Returns ------- info : dict Parameter info mit zusätzlichen Angaben für die Energie. Beispiel:: "default": { "warning" : { "f":"abs({value}) > 1.0", "unit": "%" }, "error" : { "f":"abs({value}) > 2.0", "unit": "%" }, "check" : { "field": "diff", "query":"ME == 100" } }, "MU_20": { "warning" : { "f":"abs({value}) > 1.0", "unit": "%" }, "error" : { "f":"abs({value}) > 2.5", "unit": "%" }, "check" : { "field": "diff", "query":"ME == 20" } }, """ info = self.config.get( ["GQA", testid, "info" ] ) default = info.tolerance.get( "default", False ) tolerance = info.tolerance.get( energy, False ) if not tolerance and not default: return DotMap() if not default: default = DotMap() if tolerance: tolerance = dict_merge( default, tolerance) else: tolerance = default #print("prepare_tolerance tolerance", tolerance ) import functools # alle Angaben durchgehen for name in tolerance: if not isinstance( tolerance.get(name), dict ): continue for artName, art in tolerance.get(name).items(): # überspringen wenn art = soll oder f schon vorhanden if artName == "soll" or art.get("f", None): continue # gibt es keine formel dann erstellen # wurde ein wert angegeben _value = art.get("value", None) _range = art.get("range", None) if _value: #zuerst den operator festlegen operator = art.get("operator", "gt") # [ eq, ne, lt, gt, le, ge] operator = functools.reduce(lambda a, b: a.replace(*b) , [('eq','=='),('ne','!='),('lt', '<'),( 'gt', '>'),( 'le','<='),( 'ge', '>=')] #iterable of pairs: (oldval, newval) , operator #The string from which to replace values ) tolerance[name][artName]["f"] = "abs({}) {} {}".format( "{value}", operator, _value ) # wurde ein Bereich angegeben elif art.get("range", None) and len(_range) >= 2: tolerance[name][artName]["f"] = "{} <= {} >= {}".format( _range[0], "{value}", _range[1] ) return tolerance # ---------------------- Test durchführung

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# -*- coding: utf-8 -*- __author__ = "R. Bauer" __copyright__ = "MedPhyDO - Machbarkeitsstudien des Instituts für Medizinische Strahlenphysik und Strahlenschutz am Klinikum Dortmund im Rahmen von Bachelor und Masterarbeiten an der TU-Dortmund / FH-Dortmund" __credits__ = ["R.Bauer", "K.Loot"] __license__ = "MIT" __version__ = "0.1.2" __status__ = "Prototype" from dotmap import DotMap import os.path as osp from pathlib import Path import pandas as pd import numpy as np import json from datetime import date from isp.dicom import ispDicom from isp.config import dict_merge from app.config import infoFields from app.aria import ariaClass #from app.dicom import dicomClass from app.results import ispResults from app.qa.mlc import checkMlc from app.qa.field import checkField from app.qa.wl import checkWL from app.qa.vmat import checkVMAT import logging logger = logging.getLogger( "MQTT" ) class ariaDicomClass( ariaClass, ispDicom ): '''Zentrale Klasse Attributes ---------- config : Dot konfigurations Daten variables : Metadaten aus config.variables infoFields: Infofelder aus config dicomfiles: dict geladene Dicom dateien pd_results: pd testergebnisse als Pandas tabelle resultfile Datei mit Ergebnissen als panda File lastSQL: str die letzte durchgeführte sql Abfrage ''' def __init__( self, database=None, server="VMSDBD", config=None ): """Klasse sowie ariaClass und dicomClass initialisieren """ # Klassen defaults setzen und übergaben self.config = config self.variables = self.config.variables self.infoFields = infoFields self.dicomfiles: dict = {} self.pd_results = None self.resultfile = None self.lastSQL = "" # ariaClass initialisieren ariaClass.__init__( self, database ) # dicomClass initialisieren. Der Erfolg kann über dicomClass.initialized abgefragt werden ispDicom.__init__( self, server, self.config ) # Datei mit Ergebnissen als pandas laden self.resultfile = osp.join( self.config.get("resultsPath", ".."), self.config.get("database.gqa.name", "gqa.json") ) self.pd_results = ispResults( self.config, self.resultfile ) def initResultsPath(self, AcquisitionYear=None ): '''Den Ablegeort zu den PDF Dateien bestimmen in variables.path befindet sich jetzt der resultsPath ggf. mit angehängten AcquisitionYear Parameters ---------- AcquisitionYear : TYPE, optional DESCRIPTION. The default is None. Returns ------- dirname : str der aktuelle PDF Pfad (auch in self.variables["path"] ) ''' paths = [ ] # ist der Pfad relativ angegeben ab base path verwenden if self.config["resultsPath"][0] == ".": paths.append( self.config["BASE_DIR"] ) paths.append( self.config["resultsPath"] ) else: paths.append( self.config["resultsPath"] ) # zusätzlich noch das AcquisitionYear anfügen if AcquisitionYear: paths.append( str(AcquisitionYear) ) # den Pfad in variables["path"] ablegen dirname = osp.abspath( osp.join( *paths ) ) self.variables["path"] = dirname return dirname def getAllGQA(self, pids=None, testTags:list=None, year:int=None, month:int=None, day:int=None, withInfo=True, withResult=False ): '''Holt für die angegebenen PatientenIds aus allen Courses die Felder mit Angaben in [Radiation].[Comment] und wertet sie entsprechend aus Parameters ---------- pids : list, optional DESCRIPTION. The default is None. testTags : list, optional DESCRIPTION. The default is None. year : int, optional DESCRIPTION. The default is None. month : int, optional DESCRIPTION. The default is None. day : int, optional DESCRIPTION. The default is None. withInfo : TYPE, optional DESCRIPTION. The default is True. withResult : TYPE, optional DESCRIPTION. The default is False. Returns ------- gqa : dict Aufbau:: units: dict <unit>: dict <infoType>: dict ready: dict all: int <energy> : int gqa: dict fields: int energyFields: int counts: dict all: int <energy> : int pdf: dict, items: dict <energy>: dict <SliceUID>: {info} -> dies wird bei run in ein DataFrame umgewandelt series: [], ''' if not pids: return {} if type(pids) == str: pids = pids.split(",") if not type(pids) == list: pids = [pids] if not pids or len(pids) == 0: return {} # filter zusammenstellen where = "LEN([Radiation].[Comment]) > 0 " subSql = [] for pid in pids: subSql.append( "[Patient].[PatientId]='{}'".format( pid.strip() ) ) if len( subSql ) > 0: where += " AND (" + " OR ".join( subSql ) + ")" images, sql = self.getImages( addWhere=where, AcquisitionYear=year, AcquisitionMonth=month, AcquisitionDay=day, testTags=testTags ) self.lastSQL = sql # Pfad für die PDF Dateien self.initResultsPath( year ) return self.prepareGQA( images, year=year, withInfo=withInfo, withResult=withResult ) def prepareGQA(self, imagedatas=[], year:int=0, withInfo=True, withResult=False, withDicomData:bool=False ): """Auswertung für GQA vorbereiten zusätzlich noch Ergebnisse aus der Datenbank einfügen Benötig config.GQA und config.units - units: ["Linac-1", "Linac-2"], - gqa : dict <testId>: dict <unit>: dict fields: int energyFields: int Parameters ---------- imagedatas : list, optional Auflistungen von Bildinformationen aus der Aria Datenbank. The default is []. year : int, optional DESCRIPTION. The default is 0. withInfo : TYPE, optional alle ImageInfos mit hinzufügen. The default is True. withResult : boolean, optional Testergebnisse mit ausgeben. The default is False. withDicomData : boolean, optional Info pro gerät in dicomfiles ablegen. The default is False. Returns ------- gqa : dict # alles aus config.gqa dabei die Unites mit Daten füllen <testname> info: inaktiv tip anleitung options: TODO: tolerance: <energy> <unit-n> fields: int energyFields: int energy: list """ # dicom gerät , name , infos self.dicomfiles = {} units = self.config.units # Dateien im Pfad pdfFiles = [] if osp.exists( self.variables["path"] ): p = Path( self.variables["path"] ) pdfFiles = [i.name for i in p.glob( '*.pdf' )] # files = os.listdir( self.variables["path"] ) data = { "GQA" : self.config.get("GQA").toDict(), "units" : units, "testTags" : {}, "testIds": {} } # nur das gesuchte Jahr, ohne index df_results = self.pd_results.gqa[ self.pd_results.gqa['year'] == year ].reset_index() result_fields = [ "acceptance", "group" ] if withResult: result_fields.append("data") # neuen index setzen # Das geht nur bei daten in df_results if len(df_results.index) > 0: df_results.set_index( df_results.apply(lambda x: f"{x['year']}|{x['unit']}|{x['test']}|{x['energy']}|{x['month']}", axis=1), inplace=True ) data["results"] = df_results[ result_fields ].to_dict( orient="split" ) else: data["results"] = { "columns":result_fields, "data":[], "index":[] } # tags und gqa ids bestimmen for testid, item in self.config.GQA.items(): if "tag" in item: data["testTags"][ item["tag"] ] = testid data["testIds"][ testid ] = item["tag"] tagNotFound = {} inactiv = [] testNotFound = [] for imagedata in imagedatas: # bereitetet die Datenbank Informationen auf info = self.getImageInfos( imagedata ) unit = info["unit"] energy = info["energy"] # # zusätzlich die Daten in self.dicomfiles ablegen # if withDicomData: if not unit in self.dicomfiles: self.dicomfiles[ unit ] = {} # zusätzlich in dicomfiles ablegen self.dicomfiles[ unit ][ info["id"] ] = info # Felder zuordnen, eine Aufnahme kann für mehrere tests verwendet werden # tag für die Datenbank, testid für das PDF for testTag in info["testTags"]: # nur wenn es auch einen test gibt if not testTag in data["testTags"]: tagNotFound[ testTag ] = testTag continue testId = data["testTags"][testTag] # ist der test in gqa nicht erlaubt überspringen # inaktive kann auch einen Text enthalten der beschreibt warum # FIXME: inaktive t = "GQA.{}.info.inaktiv".format( testId ) if not self.config.get(t, False) == False: inactiv.append( self.config.get(t) ) continue # gibt es in GQA passend zum Test dem Gerät und der Energie einen Eintrag t = "GQA.{}.{}.energyFields.{}".format( testId, unit, energy ) energyFields = self.config.get(t, False) if energyFields == False: testNotFound.append( t ) continue # Art des tests MT|JT tagArt = testId[0:2] if tagArt == "JT": dateFlag = "0" else: dateFlag = str( info["AcquisitionMonth"] ) # test_unit = data["GQA"][testId][unit] if not dateFlag in test_unit: test_unit[ dateFlag ] = {} if not energy in test_unit[ dateFlag ]: test_unit[ dateFlag ][energy] = { "counts": 0, "ready": False, "pdfName" : "", "pdf": False, "acceptance" : {} } # Anzahl der Felder für das Datumsflag der jeweiligen Energie erhöhen (counts) test_unit[ dateFlag ][ energy ][ "counts" ] += 1 # auf mid Anzahl prüfen if test_unit[ dateFlag ][ energy ][ "counts" ] >= energyFields: test_unit[ dateFlag ][ energy ][ "ready" ] = True # PDF Dateiname zusammenstellen pdfName = self.config.render_template( self.config["templates"][ "PDF-" + tagArt + "-filename"], { "AcquisitionYear": info["AcquisitionYear"], "AcquisitionMonth": info["AcquisitionMonth"], "unit": unit, "energy": energy, "testId": testId } ) if pdfName in pdfFiles: test_unit[ dateFlag ][ energy ][ "pdfName" ] = pdfName test_unit[ dateFlag ][ energy ][ "pdf" ] = True # nicht gefundene Tags data["inactiv"] = inactiv data["tagNotFound"] = tagNotFound data["testNotFound"] = testNotFound return data # ---------------------- einfache Ausgaben def getTagging(self, art:str="full", pid:list=[], output_format="json" ): """alle Tags in Comment Feldern als html Tabelle zurückgeben Parameters ---------- art : str, optional Art der Tagging Tabellen (). The default is "full". * full * sum * test * tags pid : list, optional Angabe von PatientsIds für die Tags bestimmt werden sollen. The default is []. output_format: str Format der Ausgabe [ json, html ] Returns ------- str|dict html Tags code oder dict. """ style = """ <style> .gqa-tagging { } .gqa-tagging table { color: #333; font-family: Helvetica, Arial, sans-serif; min-width: 100px; border-collapse: collapse; border-spacing: 0; font-size: 10px; } .gqa-tagging table td, .gqa-tagging table th { border: 1px solid gray; text-align: center; vertical-align: middle; } .gqa-tagging table th { font-weight: bold; } .gqa-tagging table thead th, .gqa-tagging table tbody th { background-color: #F7F7F7; } .gqa-tagging table td { background-color: white; } .gqa-tagging table th, .gqa-tagging table td, .gqa-tagging table caption { padding: 2px 2px 2px 2px; } </style> """ split = True if art == "tags": # bei tags conmment nicht splitten split = False tags = self.getTags( pid, split ) if output_format == "json": return tags if not tags or len(tags) == 0: return "getTagging: keine Daten gefunden" html = '<div class="gqa-tagging flex-1">' html += '<h1 class="m-0 p-1 text-white bg-secondary">Art: ' + art + '</h2>' # Pandas erzeugen df = pd.DataFrame( tags ) if art == "full": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'PlanSetupId', 'Energy', 'DoseRate', 'RadiationId'], columns='PatientId', values= "nummer", fill_value=0 ) elif art == "sum": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'PlanSetupId','Energy', 'DoseRate'], columns=['PatientId'], values= 'nummer', aggfunc=[np.sum], fill_value=0 ) elif art == "test": table = pd.pivot_table( df, index=['Comment', 'CourseId', 'Energy', 'DoseRate'], columns=[ 'PlanSetupId', 'PatientId'], values= 'nummer', aggfunc=[np.sum], fill_value=0 ) elif art == "tags": table = pd.pivot_table( df, index=['Comment'], columns=['PatientId'], values= 'nummer', fill_value=0 #aggfunc=[np.sum] ) # tags zurückgeben als einfache Tabelle #table = df[ ["Comment"] ].groupby( "Comment" ).first().reset_index() # table.fillna('', inplace=True) def highlight_fifty( val ): color = 'black' if val > 0 else 'white' #print(val , color) return 'color: %s' % color html += (table.style .applymap( highlight_fifty ) .set_table_attributes('class="gqa-tagging-table"') #.float_format() .render() ) html += '</div>' return style + html def getMatrix( self, output_format="json", params:dict={} ): """Gibt eine Liste alle Testbeschreibungen (config) mit Anleitungen Parameters ---------- output_format: str Format der Ausgabe [ json, html ] params: dict Aufrufparameter mit year und month Returns ------- str|dict html matrix code oder dict. """ # jahr und Monat bei 0 mit dem aktuellen belegen today = date.today() if params["year"] == 0: params["year"] = today.year if params["month"] == 0: params["month"] = today.month # pdf wird zum laden der Texte verwendet from isp.mpdf import PdfGenerator as ispPdf pdf = ispPdf() html_jt = "" html_mt = "" html_nn = "" data_dict = {} for key, content in self.config.GQA.items(): data = { "key" : key, "tip" : "", "need" : "", "anleitung" : "", "chips" : "" } chips = [] # units und energy for unit_key, unit in self.config.units.items(): if unit in content: for energy in content[ unit ].energy: chips.append( { "class": "badge badge-pill badge-info mr-1", "content": "{} - {}".format( unit_key, energy ) } ) # info bestimmen info = content.info data["tip"] = info.get("tip", "") need = info.get("need", "") if type(need) == str and need != "": chips.append( { "class": "badge badge-pill badge-success", "content": 'benötigt: ' + need } ) # Anleitung anleitung_filename = info.get("anleitung", "") data["anleitung"] = '<p class="badge badge-pill badge-primary">Anleitung fehlt!</p>' if anleitung_filename != "": anleitung = pdf.textFile(anleitung_filename, render = False) if anleitung: data["anleitung"] = anleitung # Toleranz tolerance = content.info.get("tolerance", False) if tolerance: data["anleitung"] += "<h6>Toleranz</h6>" # ggf formel erstellen for e, item in tolerance.items(): self.prepare_tolerance(key, e) pass # toleranz einfügen data["anleitung"] += '<pre class="toleranz bg-light text-monospace ">' + json.dumps( tolerance, indent=2 ) + '</pre>' # ist der test als inaktiv Hinweis ausgeben inaktiv = content.info.get('inaktiv', False) if inaktiv != False: chips.append( { "class": "inaktiv", "content": 'Inaktiv: ' + inaktiv } ) # gibt es optional Angaben optional = content.info.get('optional', []) if len(optional) > 0: for item in optional: chips.append( { "class": "badge badge-pill badge-primary", "content": 'Optional wenn: ' + item + ' OK' } ) # TODO todo = content.info.get("TODO", False) if todo and len(todo) > 0: data["anleitung"] += "TODO" data["anleitung"] += '<pre class="p-1 bg-warning">' for t in todo: data["anleitung"] += "* " + t + "\n" data["anleitung"] += '</pre>' # markierungen zusammenstellen for chip in chips: data["chips"] += '<div class="{class}">{content}</div>'.format(**chip) data_dict[ key ] = content.toDict() data_dict[ key ][ "anleitung" ] = anleitung card = """ <div class="card m-3" > <div class="card-header"> <span class="font-weight-bolder">{key}</span> <span class="pl-3">{tip}</span> <div class="float-right">{chips}</div> </div> <div class="card-body p-1"> {anleitung} </div> </div> """.format( **data ) if key[0:2] == "JT": html_jt += card elif key[0:2] == "MT": html_mt += card else: html_nn += card if output_format == "json": return data_dict style = """ <style> /* Anpassung pdf text */ .gqa_matrix h2 { font-size: 1.1667em; font-weight: bold; line-height: 1.286em; margin-top: 0.5em; margin-bottom: 0.5em; } .gqa_matrix .card-body p::first-of-type { background-color: #FFFFFFAA; } </style> """ html = ''' <div class="gqa_matrix"> <h1 class="m-0 p-1 text-white bg-secondary" >Angaben für: {month}/{year}</h1> <content class="p-1 d-flex flex-row" > <div class="w-50">{jt}</div> <div class="w-50">{mt}</div> <div class="">{nn}</div> </content> </div> '''.format( jt=html_jt, mt=html_mt, nn=html_nn, **params ) return style + html def prepare_tolerance(self, testid:str="", energy=None): """Prüft ob es in conig eine tolerance Angabe für die testid und die Energie gibt Stellt wenn f nicht angegeben wurde eine Formel in f zusammen Gibt es eine GQA.<testid>.info.tolerance.default Angabe, so wird diese als Grundlage für alle Energien verwendet Zweig in config:: GQA.<testid>.info.tolerance.<energy> { name: { f: formel mit {value} value: wert range: [min, max] operator: [ eq, ne, lt, gt, le, ge] } } Parameters ---------- testid : str, optional id des zu verarbeitenden tolerance Bereichs energy : str, optional Augabe der Energie für die Info. The default is None. Ohne Angabe wird nur der Parameter info zurückgegeben Returns ------- info : dict Parameter info mit zusätzlichen Angaben für die Energie. Beispiel:: "default": { "warning" : { "f":"abs({value}) > 1.0", "unit": "%" }, "error" : { "f":"abs({value}) > 2.0", "unit": "%" }, "check" : { "field": "diff", "query":"ME == 100" } }, "MU_20": { "warning" : { "f":"abs({value}) > 1.0", "unit": "%" }, "error" : { "f":"abs({value}) > 2.5", "unit": "%" }, "check" : { "field": "diff", "query":"ME == 20" } }, """ info = self.config.get( ["GQA", testid, "info" ] ) default = info.tolerance.get( "default", False ) tolerance = info.tolerance.get( energy, False ) if not tolerance and not default: return DotMap() if not default: default = DotMap() if tolerance: tolerance = dict_merge( default, tolerance) else: tolerance = default #print("prepare_tolerance tolerance", tolerance ) import functools # alle Angaben durchgehen for name in tolerance: if not isinstance( tolerance.get(name), dict ): continue for artName, art in tolerance.get(name).items(): # überspringen wenn art = soll oder f schon vorhanden if artName == "soll" or art.get("f", None): continue # gibt es keine formel dann erstellen # wurde ein wert angegeben _value = art.get("value", None) _range = art.get("range", None) if _value: #zuerst den operator festlegen operator = art.get("operator", "gt") # [ eq, ne, lt, gt, le, ge] operator = functools.reduce(lambda a, b: a.replace(*b) , [('eq','=='),('ne','!='),('lt', '<'),( 'gt', '>'),( 'le','<='),( 'ge', '>=')] #iterable of pairs: (oldval, newval) , operator #The string from which to replace values ) tolerance[name][artName]["f"] = "abs({}) {} {}".format( "{value}", operator, _value ) # wurde ein Bereich angegeben elif art.get("range", None) and len(_range) >= 2: tolerance[name][artName]["f"] = "{} <= {} >= {}".format( _range[0], "{value}", _range[1] ) return tolerance # ---------------------- Test durchführung def runTests(self, pid=None, year:int=None, month:int=None, day:int=None, testId:str=None, addWhere:str="", reloadDicom:bool=False, unittest:bool=False ): # die results der jeweiligen pdf Datei test_results = {} # units und test bereitstellen # units = self.config.units unit = self.config.get( ["units", pid], False) test = self.config.get( ["GQA", testId], False) if test == False or unit == False: return test_results # tags und gqa ids bestimmen tags = {} for key, item in self.config.GQA.items(): if "tag" in item: tags[ item["tag"] ] = key testTag = test.tag #testId = tags[ testTag ] #unit = units[ pid ] # getTestData sucht in der datenbank nach dem tag des tests data = self.getTestData( PatientId=pid, AcquisitionYear=year, AcquisitionMonth=month, AcquisitionDay=day, testTags=[ testTag ] ) #print("runTests", testTag, data ) energyFields = self.config.get( ["GQA", testId, unit, 'energyFields'], {} ) for energy, info in data.items(): # nur die Energien die angegeben wurden if not energy in energyFields: continue # payload erweitert in doTestType variables und wird für MQTT verwendet payload = { "testId" : testId, "testTag" : testTag, "AcquisitionYear" : year, "AcquisitionMonth" : month, "unit" : unit, "energy" : energy, "reloadDicom" : reloadDicom, "unittest": unittest } # den test durchführen pdfFilename, results = self.doTestType( testId = testId, data = info, payload = payload ) # results in pandas ablegen if len(results) > 0: self.pd_results.upsert( results["result"] ) # nur ablegen wenn ein pdf da ist if not pdfFilename == "": # results pro dateiname merken test_results[ pdfFilename ] = results # Pandas Ergebnisse speichern self.pd_results.write() # pdf Dateien zurückgeben return test_results def doTestType(self, testId:str, data=None, payload:dict={} ): # ohne Daten nichts machen if len(data) == 0: # Anzeigen des problems? return False # metadaten als kopie bereitstellen und um payload erweitern # damit gerät und energie für das pdf vorhanden ist variables=self.variables.copy() # variables um payload erweitern variables.update( payload ) # variables um info Bereich des test erweitern #variables.update( self.prepare_tolerance( variables['testId'], variables['energy'] ) ) # metadaten um die test Variante erweitern variables["variante"] = payload["testTag"] #variables["testTag"] = payload["testTag"] #variables["testId"] = testId # variables um configdaten des Tests erweitern diese werden in der test Klasse als metadata verwendet # TODO: AcquisitionYear und AcquisitionMonth als year und month in current ablegen variables["testConfig"] = self.config.get( ["GQA", testId ], DotMap() ); current = self.config.get( ["GQA", testId, "current" ], DotMap() ) variables["testConfig"]["current"] = dict_merge( current, DotMap({ "testTag": variables["variante"], "testId": variables["testId"], "unit": variables["unit"], "energy": variables["energy"], "year": variables["AcquisitionYear"], "month": variables["AcquisitionMonth"], "fields": self.config.get( ["GQA", testId, variables["unit"], "energyFields", variables["energy"] ], current.get( "fields" ,0) ), # "tolerance": self.config.get( ["GQA", testId, "info", "tolerance", variables["energy"] ], current.get( "tolerance", {} ) ) "tolerance": self.prepare_tolerance( variables['testId'], variables['energy'] ) }) ) variables["testConfig"]["AcquisitionYear"] = variables["AcquisitionYear"] variables["testConfig"]["AcquisitionMonth"] = variables["AcquisitionMonth"] # print("doTestType", variables.testConfig.current.toDict() ) # die benötigten Daten vom server oder aus dem DICOM dir holen # in self.dicomfiles liegen dann pro gerät die Infos als dict # in self.data liegen dann pro SOPInstanceUID die eingelesenen DICOM daten if not "index" in payload: payload["index"] = 0 if "AcquisitionYear" in payload: AcquisitionYear = payload["AcquisitionYear"] else: AcquisitionYear = "" # Pfad für die Ergebnisse vorbereiten self.initResultsPath( AcquisitionYear ) imageCount = len( data ) # Dicom Daten einlesen i = 0 read_count = 0 dicomData = {} df = pd.DataFrame( data.values() ) ''' ['id', 'PatientId', 'RadiationId', 'RadiationSer', 'CourseId', 'PlanSetupId', 'SliceRTType', 'ImageId', 'SeriesId', 'SeriesNumber', 'CreationDate', 'studyID', 'filepath', 'filename', 'Kennung', 'SOPClassUID', 'acquisition', 'AcquisitionYear', 'AcquisitionMonth', 'day', 'Tag', 'unit', 'energy', 'doserate', 'MetersetExposure', 'ME', 'Technique', 'gantry', 'GantryRtnExt', 'GantryRtnDirection', 'StopAngle', 'collimator', 'CollMode', 'table', 'SID', 'MLCPlanType', 'IndexParameterType', 'X1', 'X2', 'Y1', 'Y2', 'SliceUID', 'SeriesUID', 'StudyUID', 'FrameOfReferenceUID', 'gating', 'testTags', 'subTags', 'varianten', 'AcquisitionDateTime', 'dicom', 'check_variante', 'check_subtag'], ''' #print("doTestType", variante, df[ [ 'energy', "AcquisitionYear", "AcquisitionMonth", "varianten"] ] ) # progress starten if hasattr( logger, "progressStart"): logger.progressStart( testId, payload ) # Dicomdaten mit retrieve() holen entweder lokal oder vom server for SOPInstanceUID in data: i += 1 # 40% für die dicom daten 40% für die Auswertung 20 % für das pdf if hasattr( logger, "progress"): logger.progress( testId, 40 / imageCount * i ) # Dicomdaten holen, diese werden in self.dicomData ergänzt um AcquisitionYear abgelegt result, signals = self.retrieve( { "PatientID" : data[SOPInstanceUID]["PatientId"], "SOPInstanceUID" : SOPInstanceUID, "override" : variables["reloadDicom"], "subPath" : str(AcquisitionYear) }) for dcm in result: data[ dcm.SOPInstanceUID ]["dicom"] = dcm # FIXME: in allen testModulen zugriff auf dicom daten über data und nicht mehr über dicomData # getFullData() sollte dann nicht mehr benötigt werden dicomData[ dcm.SOPInstanceUID ] = dcm read_count += 1 # dicom Verbindung falls sie geöffnet wurde schließen self.closeAE() if i > read_count: # pragma: no cover # das einlesen der Dicomdaten war nicht möglich if hasattr( logger, "progress"): logger.progress( testId, 100 ) logger.warning( "doTestType: dicom retrieve Fehler: {} - {} - {} - {}".format( SOPInstanceUID, data[SOPInstanceUID]["testTags"], data[SOPInstanceUID]["PatientId"], data[SOPInstanceUID]["ImageId"] ) ) return "", { "result":"", "content":"" } # ab hier immer 40% if hasattr( logger, "progress"): logger.progress( testId, 40 ) # DataFrame erzeugen df = pd.DataFrame( data.values() ) # die aktuelle variante und den subtag aus varianten in check_variante und check_subtag erzeugen df["check_variante"] = variables["testTag"] df["check_subtag"] = df["varianten"].apply(lambda x: x.get( variables["testTag"] )) logger.debug( "doTestTag: {}".format( variables["testTag"] ) ) # # variables aufbereiten # # Art des tests MT|JT infoTypeArt = testId[0:2] # Dateiname aus config templates variables["filename"] = self.config.get( ["templates", "PDF-{}-filename".format(infoTypeArt)], "noname.pdf" ) # wenn nicht angegeben Titel und Betreff aus config templates for t in ["Titel", "Betreff"]: if variables.get(t, "") == "": variables[t] = self.config.get( ["templates", "PDF-{}-{}".format(infoTypeArt, t)], "" ) pdfData = { "pdf_filepath" : "" } result = [] if testId=="JT-4_2_2_1-A": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_4_2_2_1_A( df ) elif testId=="JT-4_2_2_1-B": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_4_2_2_1_B( df ) elif testId=="JT-4_2_2_1-C": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_4_2_2_1_C( df ) elif testId=="JT-LeafSpeed": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_LeafSpeed( df ) elif testId=="JT-10_3_1": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_10_3_1( df ) elif testId=="JT-7_2": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_7_2( df ) elif testId=="JT-7_3": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_7_3( df ) elif testId=="JT-7_4": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_7_4( df ) elif testId=="JT-7_5": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_7_5( df ) elif testId=="JT-9_1_2": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_9_1_2( df ) elif testId=="JT-10_3": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doJT_10_3( df ) elif testId=="MT-4_1_2": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_4_1_2( df ) elif testId=="MT-WL": check = checkWL( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_WL( df ) elif testId=="MT-8_02-1-2": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_8_02_1_2( df ) elif testId=="MT-8_02-3": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_8_02_3( df ) elif testId=="MT-8_02-4": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_8_02_4( df ) elif testId=="MT-8_02-5": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_8_02_5( df ) elif testId=="MT-LeafSpeed": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_LeafSpeed( df ) elif testId=="MT-VMAT-0_1": check = checkField( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_0_1( df ) elif testId=="MT-VMAT-0_2": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_0_2( df ) elif testId=="MT-VMAT-1_1": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_1_1( df ) elif testId=="MT-VMAT-1_2": check = checkMlc( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_1_2( df ) elif testId=="MT-VMAT-2": check = checkVMAT( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_2( df ) elif testId=="MT-VMAT-3": check = checkVMAT( self.config, variables, dicomData=dicomData ) pdfData, result = check.doMT_VMAT_3( df ) # ab hier ist progress immer 100% if hasattr( logger, "progress"): logger.progress( testId, 100 ) # progress beenden if hasattr( logger, "progress"): logger.progressReady( testId ) #print("doTestType", testId, result ) return pdfData["pdf_filepath"], { "result":result, "pdfData": pdfData }

13,532

0

83

3f1054735f9d61a24ad850ea7ddd8b84f561b898

1,451

py

Python

evoware/scripts/resetfile.py

graik/evowarepy

4ed2fbebb808b6be75479790ee23b19cb50b3039

[ "Apache-2.0" ]

2

2019-03-08T08:36:24.000Z

2019-03-11T11:59:40.000Z

evoware/scripts/resetfile.py

graik/evowarepy

4ed2fbebb808b6be75479790ee23b19cb50b3039

[ "Apache-2.0" ]

1

2015-03-15T13:03:44.000Z

evoware/scripts/resetfile.py

graik/evowarepy

4ed2fbebb808b6be75479790ee23b19cb50b3039

[ "Apache-2.0" ]

null

#!/usr/bin/env python ## evoware/py -- python modules for Evoware scripting ## Copyright 2014 - 2019 Raik Gruenberg ## ## 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. """Reset a (worklist) file to empty; or create an empty file.""" import sys, os import evoware.fileutil as F import evoware.dialogs as D ########################### # MAIN ########################### if __name__ == '__main__': f = '' try: if len(sys.argv) < 2: _use() f = F.absfile(sys.argv[1]) h = open(f, 'w') h.close() except Exception as why: D.lastException('Error resetting file %r' % f)

27.377358

77

0.610613

#!/usr/bin/env python ## evoware/py -- python modules for Evoware scripting ## Copyright 2014 - 2019 Raik Gruenberg ## ## 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. """Reset a (worklist) file to empty; or create an empty file.""" import sys, os import evoware.fileutil as F import evoware.dialogs as D def _use(): print(""" resetfile.py -- reset file to empty (0 Byte length) Syntax: resetfile.py <file> If <file> exists, it will be overridden by an empty file. If <file> does not exist, it will be created. """) sys.exit(0) ########################### # MAIN ########################### if __name__ == '__main__': f = '' try: if len(sys.argv) < 2: _use() f = F.absfile(sys.argv[1]) h = open(f, 'w') h.close() except Exception as why: D.lastException('Error resetting file %r' % f)

231

0

23

56442242124bf91fdda8f794a68f6a9f91ccba36

1,240

py

Python

LESO/test.py

thesethtruth/WB-thesis-model

f786d15fa446e8c089431853a82aa4d577e13f20

[ "MIT" ]

null

LESO/test.py

thesethtruth/WB-thesis-model

f786d15fa446e8c089431853a82aa4d577e13f20

[ "MIT" ]

null

LESO/test.py

thesethtruth/WB-thesis-model

f786d15fa446e8c089431853a82aa4d577e13f20

[ "MIT" ]

null

# test.py # a list of attributes a component should have for sure basic = [ 'merit_tag', 'styling', 'dof', 'lower', 'upper', 'lifetime', 'capex', 'opex', 'variable_cost', 'variable_income', ] power_control = [ 'positive', 'negative', ]

23.396226

76

0.552419

# test.py # a list of attributes a component should have for sure basic = [ 'merit_tag', 'styling', 'dof', 'lower', 'upper', 'lifetime', 'capex', 'opex', 'variable_cost', 'variable_income', ] power_control = [ 'positive', 'negative', ] def attribute_test(component): # checking basic attributes attrs = list() for attr in basic: if not hasattr(component, attr): attrs.append(attr) # specifically for dispatchables if hasattr(component, 'power_control'): for attr in power_control: if not hasattr(component, attr): attrs.append(attr) # financial variable check if component.capex == 0: if component.lifetime is not None: raise ValueError( f'Component {component.__str__()} cannot have CAPEX = 0'+ 'whilest lifetime is not none. Either set lifetime to None'+ 'or supply CAPEX != 0.' ) # raise error if attrs: raise NotImplementedError( f'This component ({component.__str__()}) lacks'+ f'these required attributes: {attrs}' ) pass

930

0

23

1b6dec9d5c8deee6cd01b4c6307a5be4fdde7f1a

13,138

py

Python

MATLAB_path_generation/network_som.py

AlexRookie/neurocluster

685e4c2930e7af68b09a5ae8ed7008936d6e49d4

[ "BSD-2-Clause" ]

null

MATLAB_path_generation/network_som.py

AlexRookie/neurocluster

685e4c2930e7af68b09a5ae8ed7008936d6e49d4

[ "BSD-2-Clause" ]

null

MATLAB_path_generation/network_som.py

AlexRookie/neurocluster

685e4c2930e7af68b09a5ae8ed7008936d6e49d4

[ "BSD-2-Clause" ]

null

import tensorflow as tf #from tensorflow import keras #from tensorflow.keras import backend as K import numpy as np #import matplotlib.pyplot as plt from time import sleep #=======================================================================================# class SOMLayer(tf.keras.layers.Layer): """ Self-Organizing Map layer class with rectangular topology # Example ``` model.add(SOMLayer(map_size=(10,10))) ``` # Arguments map_size: Tuple representing the size of the rectangular map. Number of prototypes is map_size[0]*map_size[1]. prototypes: Numpy array with shape `(n_prototypes, latent_dim)` witch represents the initial cluster centers # Input shape 2D tensor with shape: `(n_samples, latent_dim)` # Output shape 2D tensor with shape: `(n_samples, n_prototypes)` """ def call(self, inputs, **kwargs): """ Calculate pairwise squared euclidean distances between inputs and prototype vectors Arguments: inputs: the variable containing data, Tensor with shape `(n_samples, latent_dim)` Return: d: distances between inputs and prototypes, Tensor with shape `(n_samples, n_prototypes)` """ # Note: (tf.expand_dims(inputs, axis=1) - self.prototypes) has shape (n_samples, n_prototypes, latent_dim) d = tf.reduce_sum(tf.square(tf.expand_dims(inputs, axis=1) - self.prototypes), axis=2) return d

40.674923

160

0.590349

import tensorflow as tf #from tensorflow import keras #from tensorflow.keras import backend as K import numpy as np #import matplotlib.pyplot as plt from time import sleep class Network: def __init__(self): self.model = [] self.map_size = None self.units = None self.classes = None self.batch_size = None self.epochs = None self.l_rate = None def define_model(self, map_size=(10,10), units=(1,10), classes=0): self.map_size = np.asarray(map_size).astype(int) self.units = np.asarray(units).astype(int) self.classes = np.asarray(classes).astype(int) inputs = tf.keras.layers.Input(shape=(self.units[0], self.units[1]), name='input') #X_train.shape[-1] flatten = tf.keras.layers.Flatten(name='flatten')(inputs) som_layer = SOMLayer(map_size=self.map_size, name='SOM')(flatten) #self.model = tf.keras.models.Model(inputs=inputs, outputs=som_layer) outputs = tf.keras.layers.Dense(units=self.classes, activation='softmax', name='classifier')(som_layer) self.model = tf.keras.models.Model(inputs=inputs, outputs=[som_layer, outputs]) return self.model def prepare_data(self, X, y=None, training_percentage=70, batch=32, randomize=True): X = np.asarray(X) y = np.asarray(y) self.batch_size = int(batch) # One hot encode y = tf.keras.utils.to_categorical(y) if randomize: m = X.shape[1] n = X.shape[2] X = X.reshape(X.shape[0], m*n) perm = np.random.permutation(X.shape[0]) X = X[perm, :] y = y[perm, :] X = X.reshape(X.shape[0], m, n) # Normalize #x = np.copy(X) #for i in range(x.shape[0]): # x[i, 0, :] = (x[i, 0, :] - np.min(x[i, 0, :])) / (np.max(x[i, 0, :]) - np.min(x[i, 0, :])) # x[i, 1, :] = (x[i, 1, :] - np.min(x[i, 1, :])) / (np.max(x[i, 1, :]) - np.min(x[i, 1, :])) # Shift x = np.copy(X) for i in range(x.shape[0]): x[i, 0, :] = x[i, 0, :] - x[i, 0, 0] x[i, 1, :] = x[i, 1, :] - x[i, 1, 0] num_of_samples = x.shape[0] train = int(training_percentage*num_of_samples/100) valid = num_of_samples-train if train < self.batch_size or valid < self.batch_size: self.batch_size = 1 else: # Samples must be multiplier of batch train = int(train/self.batch_size) * self.batch_size valid = num_of_samples-train valid = int(valid/self.batch_size) * self.batch_size x_train = x[0:train, :, :] x_valid = x[train:train+valid, :, :] y_train = y[0:train, :] y_valid = y[train:train+valid, :] self.x_train = np.array(x_train) self.x_valid = np.array(x_valid) self.y_train = np.array(y_train) self.y_valid = np.array(y_valid) return self.x_train, self.x_valid, self.y_train, self.y_valid def kmeans_loss(self, y_pred, distances): """ Calculate k-means reconstruction loss # Arguments y_pred: cluster assignments, numpy.array with shape `(n_samples,)` distances: pairwise squared euclidean distances between inputs and prototype vectors, numpy.array with shape `(n_samples, n_prototypes)` # Return k-means reconstruction loss """ return np.mean([distances[i, y_pred[i]] for i in range(len(y_pred))]) def map_dist(self, y_pred, map_size): """ Calculate pairwise Manhattan distances between cluster assignments and map prototypes (rectangular grid topology) # Arguments y_pred: cluster assignments, numpy.array with shape `(n_samples,)` # Return pairwise distance matrix (map_dist[i,k] is the distance on the map between assigned cell of data point i and cell k) """ n_prototypes = map_size[0]*map_size[1] labels = np.arange(n_prototypes) tmp = np.expand_dims(y_pred, axis=1) d_row = np.abs(tmp-labels)//map_size[1] d_col = np.abs(tmp%map_size[1]-labels%map_size[1]) return d_row + d_col def neighborhood_function(self, d, T, neighborhood='gaussian'): """ SOM neighborhood function (gaussian neighborhood) # Arguments d: distance on the map T: temperature parameter """ if neighborhood == 'gaussian': return np.exp(-(d**2)/(T**2)) elif neighborhood == 'window': return (d <= T).astype(np.float32) def som_fit(self, x_train, y_train=None, decay='exponential'): """ Training procedure # Arguments X_train: training set y_train: (optional) training labels X_val: (optional) validation set y_val: (optional) validation labels epochs: number of training epochs batch_size: training batch size decay: type of temperature decay ('exponential' or 'linear') """ x_train = np.asarray(x_train) y_train = np.asarray(y_train) Tmax = 10 # initial temperature parameter Tmin = 0.1 # final temperature parameter som_epochs = self.epochs # Number of epochs where SOM neighborhood is decreased eval_interval = 10 # Evaluate metrics on training/validation batch every eval_interval epochs # Set and compute some initial values index = 0 T = Tmax for it in range(self.epochs): # Get training and validation batches #x_batch = np.expand_dims(x_train[index], axis=0) #y_batch = np.expand_dims(y_train[index], axis=0) if (index + 1) * self.batch_size >= x_train.shape[0]: x_batch = x_train[index * self.batch_size::] if y_train is not None: y_batch = y_train[index * self.batch_size::] index = 0 else: x_batch = x_train[index * self.batch_size:(index + 1) * self.batch_size] if y_train is not None: y_batch = y_train[index * self.batch_size:(index + 1) * self.batch_size] index += 1 # Compute cluster assignments for batches d, _ = self.model.predict(x_batch) y_pred = d.argmin(axis=1) # Update temperature parameter if it < som_epochs: if decay == 'exponential': T = Tmax*(Tmin/Tmax)**(it/(som_epochs-1)) elif decay == 'linear': T = Tmax - (Tmax-Tmin)*(it/(som_epochs-1)) # Compute topographic weights batches w_batch = self.neighborhood_function(self.map_dist(y_pred, self.map_size), T, neighborhood='gaussian') # Train on batch loss = self.model.train_on_batch(x_batch, [w_batch, y_batch]) # loss: ['loss', 'SOM_loss', 'classifier_loss', 'SOM_accuracy', 'classifier_accuracy'] if it % eval_interval == 0: # Evaluate losses and metrics Lsom = loss[1] Lkm = self.kmeans_loss(y_pred, d) Ltop = loss[1] - self.kmeans_loss(y_pred, d) #quantization_err = quantization_error(d) #topographic_err = topographic_error(d, map_size) print('iteration {} - T={}'.format(it, T)) print('[Train] - Lsom={:f} (Lkm={:f}/Ltop={:f})'.format(Lsom, Lkm, Ltop)) #print('[Train] - Quantization err={:f} / Topographic err={:f}'.format(quantization_err, topographic_err)) sleep(0.2) return self.model def som_loss(self, weights, distances): """ Calculate SOM reconstruction loss # Arguments weights: weights for the weighted sum, Tensor with shape `(n_samples, n_prototypes)` distances: pairwise squared euclidean distances between inputs and prototype vectors, Tensor with shape `(n_samples, n_prototypes)` # Return SOM reconstruction loss """ return tf.reduce_mean(tf.reduce_sum(weights*distances, axis=1)) def rmse(self, y_true, y_pred): # Root mean squared error (rmse) for regression return tf.keras.backend.sqrt(tf.keras.backend.mean(tf.keras.backend.square(y_pred - y_true))) def train_model(self, x_train, y_train=None, epochs=50, learn_rate=0.01): x_train = np.asarray(x_train) y_train = np.asarray(y_train).astype(np.int8) self.epochs = int(epochs) self.l_rate = learn_rate # Compile model #self.model.compile(optimizer='adam', loss=self.som_loss) #, metrics=[self.rmse]) self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=self.l_rate), loss='categorical_crossentropy', metrics=['accuracy']) #keras.optimizers.Adam(1e-3) # Train model self.model = self.som_fit(x_train, y_train) return self.model def predict(self, inputs): inputs = np.asarray(inputs) if len(inputs.shape)==1: inputs = np.expand_dims(inputs, axis=0) # Predict som_pred, pred = self.model(inputs) # == model.predict([inputs]) #pred = np.argmin(pred) #y_classes = pred.argmax(axis=-1) return som_pred, pred def save(self, file): self.model.save_weights(file+'_weights.h5') self.model.save(file+'.h5') def load_model(self, file): self.model.load(file+'.h5') return self.model def load_weights(self, file): self.model.load_weights(file+'_weights.h5') return self.model #def plot(self): # # Plot # som_weights = self.model.get_layer(name='SOM').get_weights()[0] # # fig1, axes = plt.subplots(nrows=self.map_size[0], ncols=self.map_size[1], figsize=(10, 10)) # for k in range(self.map_size[0] * self.map_size[1]): # axes[k // self.map_size[1]][k % self.map_size[1]].imshow(som_weights[k].reshape(2, self.units), cmap='gray') # axes[k // self.map_size[1]][k % self.map_size[1]].axis('off') # plt.subplots_adjust(hspace=0.05, wspace=0.05) # # plt.draw() # non-blocking plot # plt.pause(0.1) #=======================================================================================# class SOMLayer(tf.keras.layers.Layer): """ Self-Organizing Map layer class with rectangular topology # Example ``` model.add(SOMLayer(map_size=(10,10))) ``` # Arguments map_size: Tuple representing the size of the rectangular map. Number of prototypes is map_size[0]*map_size[1]. prototypes: Numpy array with shape `(n_prototypes, latent_dim)` witch represents the initial cluster centers # Input shape 2D tensor with shape: `(n_samples, latent_dim)` # Output shape 2D tensor with shape: `(n_samples, n_prototypes)` """ def __init__(self, map_size, prototypes=None, **kwargs): if 'input_shape' not in kwargs and 'latent_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('latent_dim'),) super(SOMLayer, self).__init__(**kwargs) self.map_size = map_size self.n_prototypes = map_size[0]*map_size[1] self.initial_prototypes = prototypes self.input_spec = tf.keras.layers.InputSpec(ndim=2) self.prototypes = None self.built = False def build(self, input_shape): assert(len(input_shape) == 2) input_dim = input_shape[1] self.input_spec = tf.keras.layers.InputSpec(dtype=tf.float32, shape=(None, input_dim)) self.prototypes = self.add_weight(shape=(self.n_prototypes, input_dim), initializer='glorot_uniform', name='prototypes') if self.initial_prototypes is not None: self.set_weights(self.initial_prototypes) del self.initial_prototypes self.built = True def call(self, inputs, **kwargs): """ Calculate pairwise squared euclidean distances between inputs and prototype vectors Arguments: inputs: the variable containing data, Tensor with shape `(n_samples, latent_dim)` Return: d: distances between inputs and prototypes, Tensor with shape `(n_samples, n_prototypes)` """ # Note: (tf.expand_dims(inputs, axis=1) - self.prototypes) has shape (n_samples, n_prototypes, latent_dim) d = tf.reduce_sum(tf.square(tf.expand_dims(inputs, axis=1) - self.prototypes), axis=2) return d def compute_output_shape(self, input_shape): assert(input_shape and len(input_shape) == 2) return input_shape[0], self.n_prototypes def get_config(self): config = {'map_size': self.map_size} base_config = super(SOMLayer, self).get_config() return dict(list(base_config.items()) + list(config.items()))

5,413

6,120

131

b5e67a019e3fa2d2d081a184ac33d2894fe68755

345

py

Python

034_Aumentos_multiplos.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

034_Aumentos_multiplos.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

034_Aumentos_multiplos.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

# 034_Aumentos_multiplos.py print() salario = float(input("Salário atual: R$")) print() # if (salario <= 1250): # salario *= 1.15 # -> salario = salario * 1.15 # else: # salario *= 1.10 # -> salario = salario * 1.10 salario = salario * 1.15 if salario<= 1250 else salario * 1.10 print(f"Seu novo salário será: {salario:.2f}") print()

23

62

0.628986

# 034_Aumentos_multiplos.py print() salario = float(input("Salário atual: R$")) print() # if (salario <= 1250): # salario *= 1.15 # -> salario = salario * 1.15 # else: # salario *= 1.10 # -> salario = salario * 1.10 salario = salario * 1.15 if salario<= 1250 else salario * 1.10 print(f"Seu novo salário será: {salario:.2f}") print()

0

c9e87550b730c5f553198eebd8acac9773639a86

898

py

Python

tests/ctrl_c_test.py

twosigma/uberjob

e8afcadf8ca0cca8583b4d24ca13d000ccbf52c6

[ "Apache-2.0" ]

11

2020-09-27T13:15:29.000Z

2022-03-08T17:13:07.000Z

tests/ctrl_c_test.py

twosigma/uberjob

e8afcadf8ca0cca8583b4d24ca13d000ccbf52c6

[ "Apache-2.0" ]

8

2020-11-13T21:52:11.000Z

2022-02-05T20:59:51.000Z

tests/ctrl_c_test.py

twosigma/uberjob

e8afcadf8ca0cca8583b4d24ca13d000ccbf52c6

[ "Apache-2.0" ]

2

2021-05-18T01:22:50.000Z

2021-06-16T17:43:19.000Z

# # Copyright 2020 Two Sigma Open Source, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """This program should exit within a second or two if CTRL+C is pressed (SIGINT).""" import time import uberjob if __name__ == "__main__": main()

29.933333

84

0.727171

# # Copyright 2020 Two Sigma Open Source, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """This program should exit within a second or two if CTRL+C is pressed (SIGINT).""" import time import uberjob def main(): plan = uberjob.Plan() items = [plan.call(time.sleep, 1) for _ in range(1000)] uberjob.run(plan, output=items, max_workers=4) if __name__ == "__main__": main()

127

0

23

fa103c52f70424ba4b8057f67aac8aa5fac1d2df

782

py

Python

contests_atcoder/abc175/abc175_e_np.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

null

contests_atcoder/abc175/abc175_e_np.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

1

2021-01-02T06:36:51.000Z

contests_atcoder/abc175/abc175_e_np.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

null

from collections import defaultdict import numpy as np r, c, k = map(int, input().split()) items = np.zeros((r, c), np.int32) for _ in range(k): y, x, v = map(int, input().split()) items[y - 1, x - 1] = v dp = np.zeros((r, c, 4), np.int64) for y in range(r): for x in range(c): dp[y, x, 0] = max(dp[y - 1, x]) if y != 0 else 0 dp[y, x, 1] = max(dp[y - 1, x]) + items[y, x] if y != 0 else 0 dp[y, x, 0] = max(dp[y, x, 0], dp[y, x - 1, 0]) if x != 0 else dp[y, x, 0] if x != 0: for k in range(3): dp[y, x, k + 1] = max(dp[y, x - 1, k] + items[y, x], dp[y, x - 1, k + 1], dp[y, x, k + 1]) else: dp[y, x, 1] = dp[y, x, 0] + items[y, x] #print(*dp, sep="\n") print(max(dp[-1, -1]))

30.076923

106

0.446292

from collections import defaultdict import numpy as np r, c, k = map(int, input().split()) items = np.zeros((r, c), np.int32) for _ in range(k): y, x, v = map(int, input().split()) items[y - 1, x - 1] = v dp = np.zeros((r, c, 4), np.int64) for y in range(r): for x in range(c): dp[y, x, 0] = max(dp[y - 1, x]) if y != 0 else 0 dp[y, x, 1] = max(dp[y - 1, x]) + items[y, x] if y != 0 else 0 dp[y, x, 0] = max(dp[y, x, 0], dp[y, x - 1, 0]) if x != 0 else dp[y, x, 0] if x != 0: for k in range(3): dp[y, x, k + 1] = max(dp[y, x - 1, k] + items[y, x], dp[y, x - 1, k + 1], dp[y, x, k + 1]) else: dp[y, x, 1] = dp[y, x, 0] + items[y, x] #print(*dp, sep="\n") print(max(dp[-1, -1]))

0

ed08999c58b13ae8b6317ac1085c46f6e2599ce6

1,536

py

Python

examples/direct_shape_optimization/hesaffBaum.py

rdguez-mariano/affnet

a3f0bb32d9001d1daf024f38d29867f37816ea78

[ "MIT" ]

211

2017-11-21T11:42:49.000Z

2022-03-17T12:42:54.000Z

examples/direct_shape_optimization/hesaffBaum.py

rdguez-mariano/affnet

a3f0bb32d9001d1daf024f38d29867f37816ea78

[ "MIT" ]

31

2017-12-31T18:47:49.000Z

2020-05-26T14:43:53.000Z

examples/direct_shape_optimization/hesaffBaum.py

rdguez-mariano/affnet

a3f0bb32d9001d1daf024f38d29867f37816ea78

[ "MIT" ]

47

2017-12-04T01:03:23.000Z

2022-03-18T11:50:28.000Z

#!/usr/bin/python2 -utt # -*- coding: utf-8 -*- import torch import torch.nn as nn import numpy as np import sys import os import time from PIL import Image from torch.autograd import Variable import torch.backends.cudnn as cudnn import torch.optim as optim from tqdm import tqdm import math import torch.nn.functional as F from copy import deepcopy from SparseImgRepresenter import ScaleSpaceAffinePatchExtractor from LAF import denormalizeLAFs, LAFs2ellT, abc2A from Utils import line_prepender from architectures import AffNetFast from HandCraftedModules import AffineShapeEstimator USE_CUDA = False try: input_img_fname = sys.argv[1] output_fname = sys.argv[2] nfeats = int(sys.argv[3]) except: print "Wrong input format. Try python hesaffBaum.py imgs/cat.png cat.txt 2000" sys.exit(1) img = Image.open(input_img_fname).convert('RGB') img = np.mean(np.array(img), axis = 2) var_image = torch.autograd.Variable(torch.from_numpy(img.astype(np.float32)), volatile = True) var_image_reshape = var_image.view(1, 1, var_image.size(0),var_image.size(1)) HA = ScaleSpaceAffinePatchExtractor( mrSize = 5.192, num_features = nfeats, border = 5, num_Baum_iters = 16, AffNet = AffineShapeEstimator(patch_size=19)) if USE_CUDA: HA = HA.cuda() var_image_reshape = var_image_reshape.cuda() LAFs, resp = HA(var_image_reshape) ells = LAFs2ellT(LAFs.cpu()).cpu().numpy() np.savetxt(output_fname, ells, delimiter=' ', fmt='%10.10f') line_prepender(output_fname, str(len(ells))) line_prepender(output_fname, '1.0')

30.117647

154

0.763672

#!/usr/bin/python2 -utt # -*- coding: utf-8 -*- import torch import torch.nn as nn import numpy as np import sys import os import time from PIL import Image from torch.autograd import Variable import torch.backends.cudnn as cudnn import torch.optim as optim from tqdm import tqdm import math import torch.nn.functional as F from copy import deepcopy from SparseImgRepresenter import ScaleSpaceAffinePatchExtractor from LAF import denormalizeLAFs, LAFs2ellT, abc2A from Utils import line_prepender from architectures import AffNetFast from HandCraftedModules import AffineShapeEstimator USE_CUDA = False try: input_img_fname = sys.argv[1] output_fname = sys.argv[2] nfeats = int(sys.argv[3]) except: print "Wrong input format. Try python hesaffBaum.py imgs/cat.png cat.txt 2000" sys.exit(1) img = Image.open(input_img_fname).convert('RGB') img = np.mean(np.array(img), axis = 2) var_image = torch.autograd.Variable(torch.from_numpy(img.astype(np.float32)), volatile = True) var_image_reshape = var_image.view(1, 1, var_image.size(0),var_image.size(1)) HA = ScaleSpaceAffinePatchExtractor( mrSize = 5.192, num_features = nfeats, border = 5, num_Baum_iters = 16, AffNet = AffineShapeEstimator(patch_size=19)) if USE_CUDA: HA = HA.cuda() var_image_reshape = var_image_reshape.cuda() LAFs, resp = HA(var_image_reshape) ells = LAFs2ellT(LAFs.cpu()).cpu().numpy() np.savetxt(output_fname, ells, delimiter=' ', fmt='%10.10f') line_prepender(output_fname, str(len(ells))) line_prepender(output_fname, '1.0')

0

d816df9d1e5c02c753f0fea53b0d3108eb531074

765

py

Python

packages/pyre/schemata/EnvVar.py

rtburns-jpl/pyre

ffc4fc1b2936e355f709d084eb4055954960b3a2

[ "BSD-3-Clause" ]

null

packages/pyre/schemata/EnvVar.py

rtburns-jpl/pyre

ffc4fc1b2936e355f709d084eb4055954960b3a2

[ "BSD-3-Clause" ]

1

2021-06-10T23:42:13.000Z

packages/pyre/schemata/EnvVar.py

jlmaurer/pyre

6af38a83621d7d6228d147b4bb94f97fbb10f6e2

[ "BSD-3-Clause" ]

2

2020-08-31T18:07:52.000Z

2021-12-10T08:54:39.000Z

# -*- coding: utf-8 -*- # # michael a.g. aïvázis # orthologue # (c) 1998-2020 all rights reserved # # externals import os # superclass from .String import String # declaration class EnvVar(String): """ A type declarator for strings whose default values are associated with an environment variable """ # constants typename = 'envvar' # the name of my type # meta-methods # end of file

20.131579

98

0.639216

# -*- coding: utf-8 -*- # # michael a.g. aïvázis # orthologue # (c) 1998-2020 all rights reserved # # externals import os # superclass from .String import String # declaration class EnvVar(String): """ A type declarator for strings whose default values are associated with an environment variable """ # constants typename = 'envvar' # the name of my type # meta-methods def __init__(self, variable, **kwds): # compute the default value by attempting to read the value from the environment default = os.environ.get(variable, str()) # chain up super().__init__(default=default, **kwds) # save the variable name self.envvar = variable # all done return # end of file

322

0

26

75b598f5df23bfbeb73e495c28f7ea6456fc6405

1,367

py

Python

src/tap/stream/load.py

lmmx/tap

d60ce0ec9b4b0545b5ed2b3dfeea956457999b8d

[ "MIT" ]

null

src/tap/stream/load.py

lmmx/tap

d60ce0ec9b4b0545b5ed2b3dfeea956457999b8d

[ "MIT" ]

19

2021-02-15T23:18:35.000Z

2021-11-04T11:32:40.000Z

src/tap/stream/load.py

lmmx/tap

d60ce0ec9b4b0545b5ed2b3dfeea956457999b8d

[ "MIT" ]

null

from ..share.cal import parse_abs_from_rel_date from .streams import Stream __all__ = ["load_stream"] def load_stream( programme="Today", station="r4", broadcaster="bbc", ymd=None, ymd_ago=None, **stream_opts, ): """ Create a `Stream` for a specific episode of a radio programme from the named arguments and pass `stream_opts` through. `ymd` and `ymd_ago` are options to specify either an absolute or relative date as `(year, month, day)` tuple of 3 integers in both cases. `ymd` defaults to today's date and `ymd_ago` defaults to `(0,0,0)`. `stream_opts` include: - `transcribe=False` to determine whether the `Stream.transcribe` method is called upon initialisation - `reload=False` to control whether to reload the stream from disk - `min_s=5.`/`max_s=50.` to control the min./max. audio segment length. If `reload` is True, do not pull/preprocess/transcribe: the transcripts are expected to already exist on disk, so just load them from there and recreate the `Stream`. """ if broadcaster != "bbc": raise NotImplementedError("Only currently supporting BBC stations") date = parse_abs_from_rel_date(ymd=ymd, ymd_ago=ymd_ago) ymd = (date.year, date.month, date.day) stream = Stream(programme, station, broadcaster, ymd, **stream_opts) return stream

35.973684

88

0.697147

from ..share.cal import parse_abs_from_rel_date from .streams import Stream __all__ = ["load_stream"] def load_stream( programme="Today", station="r4", broadcaster="bbc", ymd=None, ymd_ago=None, **stream_opts, ): """ Create a `Stream` for a specific episode of a radio programme from the named arguments and pass `stream_opts` through. `ymd` and `ymd_ago` are options to specify either an absolute or relative date as `(year, month, day)` tuple of 3 integers in both cases. `ymd` defaults to today's date and `ymd_ago` defaults to `(0,0,0)`. `stream_opts` include: - `transcribe=False` to determine whether the `Stream.transcribe` method is called upon initialisation - `reload=False` to control whether to reload the stream from disk - `min_s=5.`/`max_s=50.` to control the min./max. audio segment length. If `reload` is True, do not pull/preprocess/transcribe: the transcripts are expected to already exist on disk, so just load them from there and recreate the `Stream`. """ if broadcaster != "bbc": raise NotImplementedError("Only currently supporting BBC stations") date = parse_abs_from_rel_date(ymd=ymd, ymd_ago=ymd_ago) ymd = (date.year, date.month, date.day) stream = Stream(programme, station, broadcaster, ymd, **stream_opts) return stream

0

1c350cff265b3d8016a43c446d64999c2d32b3e3

6,479

py

Python

tests/test_packages/test_skills/test_generic_seller/test_dialogues.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

[ "Apache-2.0" ]

28

2021-10-31T18:54:14.000Z

2022-03-17T13:10:43.000Z

tests/test_packages/test_skills/test_generic_seller/test_dialogues.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

[ "Apache-2.0" ]

66

2021-10-31T11:55:48.000Z

2022-03-31T06:26:23.000Z

tests/test_packages/test_skills/test_generic_seller/test_dialogues.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2022 Valory AG # Copyright 2018-2021 Fetch.AI Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ------------------------------------------------------------------------------ """This module contains the tests of the dialogue classes of the generic seller skill.""" from pathlib import Path from typing import cast import pytest from aea.exceptions import AEAEnforceError from aea.helpers.transaction.base import Terms from aea.protocols.dialogue.base import DialogueLabel from aea.test_tools.test_skill import BaseSkillTestCase, COUNTERPARTY_AGENT_ADDRESS from packages.fetchai.protocols.default.message import DefaultMessage from packages.fetchai.protocols.fipa.message import FipaMessage from packages.fetchai.protocols.ledger_api.message import LedgerApiMessage from packages.fetchai.protocols.oef_search.message import OefSearchMessage from packages.fetchai.skills.generic_seller.dialogues import ( DefaultDialogue, DefaultDialogues, FipaDialogue, FipaDialogues, LedgerApiDialogue, LedgerApiDialogues, OefSearchDialogue, OefSearchDialogues, ) from tests.conftest import ROOT_DIR class TestDialogues(BaseSkillTestCase): """Test dialogue classes of generic seller.""" path_to_skill = Path(ROOT_DIR, "packages", "fetchai", "skills", "generic_seller") @classmethod def setup(cls): """Setup the test class.""" super().setup() cls.default_dialogues = cast( DefaultDialogues, cls._skill.skill_context.default_dialogues ) cls.fipa_dialogues = cast( FipaDialogues, cls._skill.skill_context.fipa_dialogues ) cls.ledger_api_dialogues = cast( LedgerApiDialogues, cls._skill.skill_context.ledger_api_dialogues ) cls.oef_search_dialogues = cast( OefSearchDialogues, cls._skill.skill_context.oef_search_dialogues ) def test_default_dialogues(self): """Test the DefaultDialogues class.""" _, dialogue = self.default_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=DefaultMessage.Performative.BYTES, content=b"some_content", ) assert dialogue.role == DefaultDialogue.Role.AGENT assert dialogue.self_address == self.skill.skill_context.agent_address def test_fipa_dialogue(self): """Test the FipaDialogue class.""" fipa_dialogue = FipaDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=DefaultDialogue.Role.AGENT, ) # terms with pytest.raises(AEAEnforceError, match="Terms not set!"): assert fipa_dialogue.terms terms = Terms( "some_ledger_id", self.skill.skill_context.agent_address, "counterprty", {"currency_id": 50}, {"good_id": -10}, "some_nonce", ) fipa_dialogue.terms = terms with pytest.raises(AEAEnforceError, match="Terms already set!"): fipa_dialogue.terms = terms assert fipa_dialogue.terms == terms def test_fipa_dialogues(self): """Test the FipaDialogues class.""" _, dialogue = self.fipa_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=FipaMessage.Performative.CFP, query="some_query", ) assert dialogue.role == FipaDialogue.Role.SELLER assert dialogue.self_address == self.skill.skill_context.agent_address def test_ledger_api_dialogue(self): """Test the LedgerApiDialogue class.""" ledger_api_dialogue = LedgerApiDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=LedgerApiDialogue.Role.AGENT, ) # associated_fipa_dialogue with pytest.raises(AEAEnforceError, match="FipaDialogue not set!"): assert ledger_api_dialogue.associated_fipa_dialogue fipa_dialogue = FipaDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=FipaDialogue.Role.BUYER, ) ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue with pytest.raises(AEAEnforceError, match="FipaDialogue already set!"): ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue assert ledger_api_dialogue.associated_fipa_dialogue == fipa_dialogue def test_ledger_api_dialogues(self): """Test the LedgerApiDialogues class.""" _, dialogue = self.ledger_api_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=LedgerApiMessage.Performative.GET_BALANCE, ledger_id="some_ledger_id", address="some_address", ) assert dialogue.role == LedgerApiDialogue.Role.AGENT assert dialogue.self_address == str(self.skill.skill_context.skill_id) def test_oef_search_dialogues(self): """Test the OefSearchDialogues class.""" _, dialogue = self.oef_search_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=OefSearchMessage.Performative.SEARCH_SERVICES, query="some_query", ) assert dialogue.role == OefSearchDialogue.Role.AGENT assert dialogue.self_address == str(self.skill.skill_context.skill_id)

38.337278

89

0.656428

# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2022 Valory AG # Copyright 2018-2021 Fetch.AI Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ------------------------------------------------------------------------------ """This module contains the tests of the dialogue classes of the generic seller skill.""" from pathlib import Path from typing import cast import pytest from aea.exceptions import AEAEnforceError from aea.helpers.transaction.base import Terms from aea.protocols.dialogue.base import DialogueLabel from aea.test_tools.test_skill import BaseSkillTestCase, COUNTERPARTY_AGENT_ADDRESS from packages.fetchai.protocols.default.message import DefaultMessage from packages.fetchai.protocols.fipa.message import FipaMessage from packages.fetchai.protocols.ledger_api.message import LedgerApiMessage from packages.fetchai.protocols.oef_search.message import OefSearchMessage from packages.fetchai.skills.generic_seller.dialogues import ( DefaultDialogue, DefaultDialogues, FipaDialogue, FipaDialogues, LedgerApiDialogue, LedgerApiDialogues, OefSearchDialogue, OefSearchDialogues, ) from tests.conftest import ROOT_DIR class TestDialogues(BaseSkillTestCase): """Test dialogue classes of generic seller.""" path_to_skill = Path(ROOT_DIR, "packages", "fetchai", "skills", "generic_seller") @classmethod def setup(cls): """Setup the test class.""" super().setup() cls.default_dialogues = cast( DefaultDialogues, cls._skill.skill_context.default_dialogues ) cls.fipa_dialogues = cast( FipaDialogues, cls._skill.skill_context.fipa_dialogues ) cls.ledger_api_dialogues = cast( LedgerApiDialogues, cls._skill.skill_context.ledger_api_dialogues ) cls.oef_search_dialogues = cast( OefSearchDialogues, cls._skill.skill_context.oef_search_dialogues ) def test_default_dialogues(self): """Test the DefaultDialogues class.""" _, dialogue = self.default_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=DefaultMessage.Performative.BYTES, content=b"some_content", ) assert dialogue.role == DefaultDialogue.Role.AGENT assert dialogue.self_address == self.skill.skill_context.agent_address def test_fipa_dialogue(self): """Test the FipaDialogue class.""" fipa_dialogue = FipaDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=DefaultDialogue.Role.AGENT, ) # terms with pytest.raises(AEAEnforceError, match="Terms not set!"): assert fipa_dialogue.terms terms = Terms( "some_ledger_id", self.skill.skill_context.agent_address, "counterprty", {"currency_id": 50}, {"good_id": -10}, "some_nonce", ) fipa_dialogue.terms = terms with pytest.raises(AEAEnforceError, match="Terms already set!"): fipa_dialogue.terms = terms assert fipa_dialogue.terms == terms def test_fipa_dialogues(self): """Test the FipaDialogues class.""" _, dialogue = self.fipa_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=FipaMessage.Performative.CFP, query="some_query", ) assert dialogue.role == FipaDialogue.Role.SELLER assert dialogue.self_address == self.skill.skill_context.agent_address def test_ledger_api_dialogue(self): """Test the LedgerApiDialogue class.""" ledger_api_dialogue = LedgerApiDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=LedgerApiDialogue.Role.AGENT, ) # associated_fipa_dialogue with pytest.raises(AEAEnforceError, match="FipaDialogue not set!"): assert ledger_api_dialogue.associated_fipa_dialogue fipa_dialogue = FipaDialogue( DialogueLabel( ("", ""), COUNTERPARTY_AGENT_ADDRESS, self.skill.skill_context.agent_address, ), self.skill.skill_context.agent_address, role=FipaDialogue.Role.BUYER, ) ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue with pytest.raises(AEAEnforceError, match="FipaDialogue already set!"): ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue assert ledger_api_dialogue.associated_fipa_dialogue == fipa_dialogue def test_ledger_api_dialogues(self): """Test the LedgerApiDialogues class.""" _, dialogue = self.ledger_api_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=LedgerApiMessage.Performative.GET_BALANCE, ledger_id="some_ledger_id", address="some_address", ) assert dialogue.role == LedgerApiDialogue.Role.AGENT assert dialogue.self_address == str(self.skill.skill_context.skill_id) def test_oef_search_dialogues(self): """Test the OefSearchDialogues class.""" _, dialogue = self.oef_search_dialogues.create( counterparty=COUNTERPARTY_AGENT_ADDRESS, performative=OefSearchMessage.Performative.SEARCH_SERVICES, query="some_query", ) assert dialogue.role == OefSearchDialogue.Role.AGENT assert dialogue.self_address == str(self.skill.skill_context.skill_id)

0

dc48bed3180a4802fc80473edc5b04249f1db382

1,295

py

Python

data/visualizer/viz.py

PACE-challenge/Cluster-Editing-PACE-2021-instances-

67f5df368c400f15fa2527ea1eb48ac1bd6a2d7a

[ "CC0-1.0" ]

1

2021-08-23T14:47:55.000Z

data/visualizer/viz.py

PACE-challenge/Cluster-Editing-PACE-2021-instances-

67f5df368c400f15fa2527ea1eb48ac1bd6a2d7a

[ "CC0-1.0" ]

null

data/visualizer/viz.py

PACE-challenge/Cluster-Editing-PACE-2021-instances-

67f5df368c400f15fa2527ea1eb48ac1bd6a2d7a

[ "CC0-1.0" ]

1

2022-02-28T20:42:10.000Z

import numpy as np import matplotlib.pyplot as plt import sys S = read_instance(sys.argv[1]) plt.hist(S.flatten(), bins=np.linspace(0, 1, 200)) plt.title("Histogram of similarity values") plt.xlabel("Similarity") plt.ylabel("Frequency") plt.savefig(sys.argv[1]+"_viz2.pdf", dpi=400) plt.close() n = len(S) x = np.arange(n) S[(x,x)] = 0.5 S = S - 0.5 m = np.quantile(np.abs(S), 0.99) S = S / m / 2 S = S + 0.5 S[(x,x)] = 1 #print(S) plt.imshow(S, vmin=0, vmax=1, cmap='RdBu_r') plt.colorbar() plt.title("Similarity matrix") plt.savefig(sys.argv[1]+"_viz1.pdf", dpi=400) plt.close()

23.981481

59

0.559073

import numpy as np import matplotlib.pyplot as plt import sys def read_instance(filename): if filename.endswith(".csv"): return np.loadtxt(filename, delimiter=', ') assert(filename.endswith(".gr")) # adjacency matrix is the similaity matrix in this case S = None f = open(filename) p_read = False for line in f: words = line.split() if len(words) == 0 or words[0] == 'c': continue if words[0] == 'p': if p_read: continue p_read = True n = int(words[2]) S = np.zeros((n,n)) else: assert(len(words) == 2) i, j = int(words[0])-1, int(words[1])-1 S[i,j] = 1 S[j,i] = 1 return S S = read_instance(sys.argv[1]) plt.hist(S.flatten(), bins=np.linspace(0, 1, 200)) plt.title("Histogram of similarity values") plt.xlabel("Similarity") plt.ylabel("Frequency") plt.savefig(sys.argv[1]+"_viz2.pdf", dpi=400) plt.close() n = len(S) x = np.arange(n) S[(x,x)] = 0.5 S = S - 0.5 m = np.quantile(np.abs(S), 0.99) S = S / m / 2 S = S + 0.5 S[(x,x)] = 1 #print(S) plt.imshow(S, vmin=0, vmax=1, cmap='RdBu_r') plt.colorbar() plt.title("Similarity matrix") plt.savefig(sys.argv[1]+"_viz1.pdf", dpi=400) plt.close()

686

0

23

193ffbb9d91b75344c0042d5ef6cd59b2ea7f4af

4,850

py

Python

test/base/tool_shed_util.py

vimalkumarvelayudhan/galaxy

ea89dd8f149778b6c2f0f3f4a34c8b21f7033af7

[ "CC-BY-3.0" ]

null

test/base/tool_shed_util.py

vimalkumarvelayudhan/galaxy

ea89dd8f149778b6c2f0f3f4a34c8b21f7033af7

[ "CC-BY-3.0" ]

1

2015-02-21T18:48:19.000Z

2015-02-27T15:50:32.000Z

test/base/tool_shed_util.py

vimalkumarvelayudhan/galaxy

ea89dd8f149778b6c2f0f3f4a34c8b21f7033af7

[ "CC-BY-3.0" ]

3

2015-02-22T13:34:16.000Z

2020-10-01T01:28:04.000Z

import logging import os import sys cwd = os.getcwd() if cwd not in sys.path: sys.path.append( cwd ) new_path = [ os.path.join( cwd, "lib" ) ] if new_path not in sys.path: new_path.extend( sys.path ) sys.path = new_path from galaxy.util import parse_xml log = logging.getLogger(__name__) # Set a 10 minute timeout for repository installation. repository_installation_timeout = 600 def get_installed_repository_info( elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ): """ Return the GALAXY_TEST_FILE_DIR, the containing repository name and the change set revision for the tool elem. This only happens when testing tools installed from the tool shed. """ tool_config_path = elem.get( 'file' ) installed_tool_path_items = tool_config_path.split( '/repos/' ) sans_shed = installed_tool_path_items[ 1 ] path_items = sans_shed.split( '/' ) repository_owner = path_items[ 0 ] repository_name = path_items[ 1 ] changeset_revision = path_items[ 2 ] if repository_name != last_tested_repository_name or changeset_revision != last_tested_changeset_revision: # Locate the test-data directory. installed_tool_path = os.path.join( installed_tool_path_items[ 0 ], 'repos', repository_owner, repository_name, changeset_revision ) for root, dirs, files in os.walk( os.path.join(tool_path, installed_tool_path )): if '.' in dirs: dirs.remove( '.hg' ) if 'test-data' in dirs: return os.path.join( root, 'test-data' ), repository_name, changeset_revision return None, repository_name, changeset_revision return last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision def parse_tool_panel_config( config, shed_tools_dict ): """ Parse a shed-related tool panel config to generate the shed_tools_dict. This only happens when testing tools installed from the tool shed. """ last_galaxy_test_file_dir = None last_tested_repository_name = None last_tested_changeset_revision = None tool_path = None has_test_data = False tree = parse_xml( config ) root = tree.getroot() tool_path = root.get('tool_path') for elem in root: if elem.tag == 'tool': galaxy_test_file_dir, \ last_tested_repository_name, \ last_tested_changeset_revision = get_installed_repository_info( elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ) if galaxy_test_file_dir: if not has_test_data: has_test_data = True if galaxy_test_file_dir != last_galaxy_test_file_dir: if not os.path.isabs( galaxy_test_file_dir ): galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir ) guid = elem.get( 'guid' ) shed_tools_dict[ guid ] = galaxy_test_file_dir last_galaxy_test_file_dir = galaxy_test_file_dir elif elem.tag == 'section': for section_elem in elem: if section_elem.tag == 'tool': galaxy_test_file_dir, \ last_tested_repository_name, \ last_tested_changeset_revision = get_installed_repository_info( section_elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ) if galaxy_test_file_dir: if not has_test_data: has_test_data = True if galaxy_test_file_dir != last_galaxy_test_file_dir: if not os.path.isabs( galaxy_test_file_dir ): galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir ) guid = section_elem.get( 'guid' ) shed_tools_dict[ guid ] = galaxy_test_file_dir last_galaxy_test_file_dir = galaxy_test_file_dir return has_test_data, shed_tools_dict

50

142

0.573402

import logging import os import sys cwd = os.getcwd() if cwd not in sys.path: sys.path.append( cwd ) new_path = [ os.path.join( cwd, "lib" ) ] if new_path not in sys.path: new_path.extend( sys.path ) sys.path = new_path from galaxy.util import parse_xml log = logging.getLogger(__name__) # Set a 10 minute timeout for repository installation. repository_installation_timeout = 600 def get_installed_repository_info( elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ): """ Return the GALAXY_TEST_FILE_DIR, the containing repository name and the change set revision for the tool elem. This only happens when testing tools installed from the tool shed. """ tool_config_path = elem.get( 'file' ) installed_tool_path_items = tool_config_path.split( '/repos/' ) sans_shed = installed_tool_path_items[ 1 ] path_items = sans_shed.split( '/' ) repository_owner = path_items[ 0 ] repository_name = path_items[ 1 ] changeset_revision = path_items[ 2 ] if repository_name != last_tested_repository_name or changeset_revision != last_tested_changeset_revision: # Locate the test-data directory. installed_tool_path = os.path.join( installed_tool_path_items[ 0 ], 'repos', repository_owner, repository_name, changeset_revision ) for root, dirs, files in os.walk( os.path.join(tool_path, installed_tool_path )): if '.' in dirs: dirs.remove( '.hg' ) if 'test-data' in dirs: return os.path.join( root, 'test-data' ), repository_name, changeset_revision return None, repository_name, changeset_revision return last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision def parse_tool_panel_config( config, shed_tools_dict ): """ Parse a shed-related tool panel config to generate the shed_tools_dict. This only happens when testing tools installed from the tool shed. """ last_galaxy_test_file_dir = None last_tested_repository_name = None last_tested_changeset_revision = None tool_path = None has_test_data = False tree = parse_xml( config ) root = tree.getroot() tool_path = root.get('tool_path') for elem in root: if elem.tag == 'tool': galaxy_test_file_dir, \ last_tested_repository_name, \ last_tested_changeset_revision = get_installed_repository_info( elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ) if galaxy_test_file_dir: if not has_test_data: has_test_data = True if galaxy_test_file_dir != last_galaxy_test_file_dir: if not os.path.isabs( galaxy_test_file_dir ): galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir ) guid = elem.get( 'guid' ) shed_tools_dict[ guid ] = galaxy_test_file_dir last_galaxy_test_file_dir = galaxy_test_file_dir elif elem.tag == 'section': for section_elem in elem: if section_elem.tag == 'tool': galaxy_test_file_dir, \ last_tested_repository_name, \ last_tested_changeset_revision = get_installed_repository_info( section_elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ) if galaxy_test_file_dir: if not has_test_data: has_test_data = True if galaxy_test_file_dir != last_galaxy_test_file_dir: if not os.path.isabs( galaxy_test_file_dir ): galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir ) guid = section_elem.get( 'guid' ) shed_tools_dict[ guid ] = galaxy_test_file_dir last_galaxy_test_file_dir = galaxy_test_file_dir return has_test_data, shed_tools_dict

0

de4918caee09ec92475c1349ee971e4eb711dfbe

273

py

Python

tudo/ex011.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

1

2021-07-08T00:35:57.000Z

tudo/ex011.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

null

tudo/ex011.py

Ramon-Erik/Exercicios-Python

158a7f1846dd3d486aa0517fa337d46d73aab649

[ "MIT" ]

null

largura = float(input('Qual a largura da parede em metros? ')) altura = float(input('Qual a altura da parede em metros? ')) area = largura * altura print(f'A área dessa parede é: {area}m². ') tinta = area / 2 print(f'Será usado {tinta}L de tinta para cada metro quadrado.')

45.5

64

0.70696

largura = float(input('Qual a largura da parede em metros? ')) altura = float(input('Qual a altura da parede em metros? ')) area = largura * altura print(f'A área dessa parede é: {area}m². ') tinta = area / 2 print(f'Será usado {tinta}L de tinta para cada metro quadrado.')

0

cf39502934ab9e24950e06af4ca528a1d9074261

535

py

Python

app/user/models/profile.py

NicolefAvella/ApiMovie

4860b312f62dee73de6015c3029e75a6045f79a1

[ "MIT" ]

null

app/user/models/profile.py

NicolefAvella/ApiMovie

4860b312f62dee73de6015c3029e75a6045f79a1

[ "MIT" ]

null

app/user/models/profile.py

NicolefAvella/ApiMovie

4860b312f62dee73de6015c3029e75a6045f79a1

[ "MIT" ]

null

from django.db import models class Profile(models.Model): """Profile model.""" user = models.OneToOneField('user.User', on_delete=models.CASCADE) picture = models.ImageField( 'profile picture', upload_to='user/pictures/', blank=True, null=True ) biography = models.TextField(max_length=500, blank=True) movies_create = models.PositiveIntegerField(default=0) movies_recomment = models.PositiveIntegerField(default=0)

24.318182

70

0.674766

from django.db import models class Profile(models.Model): """Profile model.""" user = models.OneToOneField('user.User', on_delete=models.CASCADE) picture = models.ImageField( 'profile picture', upload_to='user/pictures/', blank=True, null=True ) biography = models.TextField(max_length=500, blank=True) movies_create = models.PositiveIntegerField(default=0) movies_recomment = models.PositiveIntegerField(default=0) def __str__(self): return str(self.user)

27

0

27

adc436be93d84dc86cd6f82501d3b84b9e1f875f

628

py

Python

ABC159D/resolve.py

staguchi0703/prob_boot_camp_medium

d6f31b3e50230877efb2ebfef40f90ef6468bfc7

[ "MIT" ]

null

ABC159D/resolve.py

staguchi0703/prob_boot_camp_medium

d6f31b3e50230877efb2ebfef40f90ef6468bfc7

[ "MIT" ]

null

ABC159D/resolve.py

staguchi0703/prob_boot_camp_medium

d6f31b3e50230877efb2ebfef40f90ef6468bfc7

[ "MIT" ]

null

def resolve(): ''' code here 求めるものは　k番目のボールを除いた N−1個のボールから、書かれている整数が等しいような異なる2つのボールを選び出す方法言い換えて　①同じ数から2個選ぶ組み合わせの和　②k番目のボールを除いた N−1個のボールから、K番目のボールと同じ数を選ぶ数 ※選ぶボールとペアになっていた個数を数え上げて引く　①-② ''' import collections N = int(input()) A_list = [int(item) for item in input().split()] origin_dict = collections.Counter(A_list) twopair_in_N = 0 for i in origin_dict.values(): twopair_in_N += i*(i-1)//2 for j in range(N): deff = origin_dict[A_list[j]] -1 print(twopair_in_N - deff) if __name__ == "__main__": resolve()

19.030303

59

0.613057

def resolve(): ''' code here 求めるものは　k番目のボールを除いた N−1個のボールから、書かれている整数が等しいような異なる2つのボールを選び出す方法言い換えて　①同じ数から2個選ぶ組み合わせの和　②k番目のボールを除いた N−1個のボールから、K番目のボールと同じ数を選ぶ数 ※選ぶボールとペアになっていた個数を数え上げて引く　①-② ''' import collections N = int(input()) A_list = [int(item) for item in input().split()] origin_dict = collections.Counter(A_list) twopair_in_N = 0 for i in origin_dict.values(): twopair_in_N += i*(i-1)//2 for j in range(N): deff = origin_dict[A_list[j]] -1 print(twopair_in_N - deff) if __name__ == "__main__": resolve()

0

61c62aec87289839c0bbe7e36af25de69071130f

6,699

py

Python

pysyncrosim/environment.py

syncrosim/pysyncrosim

20c0005674b7dbcef19c233dfa7db95e0d9d451e

[ "MIT" ]

null

pysyncrosim/environment.py

syncrosim/pysyncrosim

20c0005674b7dbcef19c233dfa7db95e0d9d451e

[ "MIT" ]

6

2021-12-27T16:42:19.000Z

2022-01-13T21:10:28.000Z

pysyncrosim/environment.py

syncrosim/pysyncrosim

20c0005674b7dbcef19c233dfa7db95e0d9d451e

[ "MIT" ]

null

import os import pandas as pd def runtime_input_folder(scenario, datasheet_name): """ Creates a SyncroSim Datasheet input folder. Parameters ---------- scenario : Scenario Scenario class instance. datasheet_name : String Name of SyncroSim Datasheet. Returns ------- String Path to input folder. """ _validate_environment() parent_folder = _environment.input_directory.item() return _create_scenario_folder(scenario, parent_folder, datasheet_name) def runtime_output_folder(scenario, datasheet_name): """ Creates a SyncroSim Datasheet output folder. Parameters ---------- scenario : Scenario Scenario class instance. datasheet_name : String Name of SyncroSim Datasheet. Returns ------- String Path to ouput folder. """ _validate_environment() parent_folder = _environment.output_directory.item() return _create_scenario_folder(scenario, parent_folder, datasheet_name) def runtime_temp_folder(folder_name): """ Creates a SyncroSim Datasheet temporary folder. Parameters ---------- folder_name : String Name of temporary folder. Returns ------- String Path to temporary folder. """ _validate_environment() return _create_temp_folder(folder_name) def progress_bar(report_type="step", iteration=None, timestep=None, total_steps=None, message=None): """ Begins, steps, ends, and reports progress for a SyncroSim simulation. Parameters ---------- report_type : String, optional Directive to "begin", "end", "report", "message", or "step" the simulation. The default is "step". iteration : Int, optional Number of iterations. The default is None. timestep : Int, optional Number of timesteps. The default is None. total_steps : Int, optional Number of total steps in the simulation. The default is None. message : String, optional A message to print to the progress bar status. The default is None. Raises ------ TypeError If iteration, timestep, or total_steps are not Integers. ValueError If report_type is not "begin", "end", "step", "report", or "message". Returns ------- None. """ _validate_environment() # Begin progress bar tracking if report_type == "begin": try: assert total_steps % 1 == 0 total_steps = int(total_steps) print("ssim-task-start=%d\r\n" % total_steps, flush=True) except AssertionError or TypeError: raise TypeError("total_steps must be an Integer") # End progress bar tracking elif report_type == "end": print("ssim-task-end=True\r\n", flush=True) # Step progress bar elif report_type == "step": print("ssim-task-step=1\r\n", flush=True) # Report iteration and timestep elif report_type == "report": try: assert iteration % 1 == 0 assert timestep % 1 == 0 print( f"ssim-task-status=Simulating -> Iteration is {iteration}" + " - Timestep is {timestep}\r\n", flush=True) except AssertionError or TypeError: raise TypeError("iteration and timestep must be Integers") # Print arbitrary message elif report_type == "message": print( "ssim-task-status=" + str(message) + "\r\n", flush=True) else: raise ValueError("Invalid report_type") def update_run_log(*message, sep=""): """ Begins, steps, ends, and reports progress for a SyncroSim simulation. Parameters ---------- *message : String Message to write to the run log. Can be provided as multiple arguments that will be concatenated together using sep. sep : String, optional String to use if concatenating multiple message arguments. The default is an empty String. Raises ------ ValueError If no message is provided. Returns ------- None. """ _validate_environment() # Check that a message is provided if len(message) == 0: raise ValueError("Please include a message to send to the run log.") # Initialize the message final_message = "ssim-task-log=" + str(message[0]) # Concatenate additional message pieces if len(message) > 1: for m in message[1:]: final_message = final_message + str(sep) + str(m) # Finalize message final_message = final_message + "\r\n" print(final_message, flush=True)

29.253275

78

0.609494

import os import pandas as pd def runtime_input_folder(scenario, datasheet_name): """ Creates a SyncroSim Datasheet input folder. Parameters ---------- scenario : Scenario Scenario class instance. datasheet_name : String Name of SyncroSim Datasheet. Returns ------- String Path to input folder. """ _validate_environment() parent_folder = _environment.input_directory.item() return _create_scenario_folder(scenario, parent_folder, datasheet_name) def runtime_output_folder(scenario, datasheet_name): """ Creates a SyncroSim Datasheet output folder. Parameters ---------- scenario : Scenario Scenario class instance. datasheet_name : String Name of SyncroSim Datasheet. Returns ------- String Path to ouput folder. """ _validate_environment() parent_folder = _environment.output_directory.item() return _create_scenario_folder(scenario, parent_folder, datasheet_name) def runtime_temp_folder(folder_name): """ Creates a SyncroSim Datasheet temporary folder. Parameters ---------- folder_name : String Name of temporary folder. Returns ------- String Path to temporary folder. """ _validate_environment() return _create_temp_folder(folder_name) def progress_bar(report_type="step", iteration=None, timestep=None, total_steps=None, message=None): """ Begins, steps, ends, and reports progress for a SyncroSim simulation. Parameters ---------- report_type : String, optional Directive to "begin", "end", "report", "message", or "step" the simulation. The default is "step". iteration : Int, optional Number of iterations. The default is None. timestep : Int, optional Number of timesteps. The default is None. total_steps : Int, optional Number of total steps in the simulation. The default is None. message : String, optional A message to print to the progress bar status. The default is None. Raises ------ TypeError If iteration, timestep, or total_steps are not Integers. ValueError If report_type is not "begin", "end", "step", "report", or "message". Returns ------- None. """ _validate_environment() # Begin progress bar tracking if report_type == "begin": try: assert total_steps % 1 == 0 total_steps = int(total_steps) print("ssim-task-start=%d\r\n" % total_steps, flush=True) except AssertionError or TypeError: raise TypeError("total_steps must be an Integer") # End progress bar tracking elif report_type == "end": print("ssim-task-end=True\r\n", flush=True) # Step progress bar elif report_type == "step": print("ssim-task-step=1\r\n", flush=True) # Report iteration and timestep elif report_type == "report": try: assert iteration % 1 == 0 assert timestep % 1 == 0 print( f"ssim-task-status=Simulating -> Iteration is {iteration}" + " - Timestep is {timestep}\r\n", flush=True) except AssertionError or TypeError: raise TypeError("iteration and timestep must be Integers") # Print arbitrary message elif report_type == "message": print( "ssim-task-status=" + str(message) + "\r\n", flush=True) else: raise ValueError("Invalid report_type") def update_run_log(*message, sep=""): """ Begins, steps, ends, and reports progress for a SyncroSim simulation. Parameters ---------- *message : String Message to write to the run log. Can be provided as multiple arguments that will be concatenated together using sep. sep : String, optional String to use if concatenating multiple message arguments. The default is an empty String. Raises ------ ValueError If no message is provided. Returns ------- None. """ _validate_environment() # Check that a message is provided if len(message) == 0: raise ValueError("Please include a message to send to the run log.") # Initialize the message final_message = "ssim-task-log=" + str(message[0]) # Concatenate additional message pieces if len(message) > 1: for m in message[1:]: final_message = final_message + str(sep) + str(m) # Finalize message final_message = final_message + "\r\n" print(final_message, flush=True) def _environment(): env_df = pd.DataFrame( {"package_directory": [os.getenv("SSIM_PACKAGE_DIRECTORY")], "program_directory": [os.getenv("SSIM_PROGRAM_DIRECTORY")], "library_filepath": [os.getenv("SSIM_LIBRARY_FILEPATH")], "project_id": [int(os.getenv("SSIM_PROJECT_ID", default=-1))], "scenario_id": [int(os.getenv("SSIM_SCENARIO_ID", default=-1))], "input_directory": [os.getenv("SSIM_INPUT_DIRECTORY")], "output_directory": [os.getenv("SSIM_OUTPUT_DIRECTORY")], "temp_directory": [os.getenv("SSIM_TEMP_DIRECTORY")], "transfer_directory": [os.getenv("SSIM_TRANSFER_DIRECTORY")], "before_iteration": [ int(os.getenv("SSIM_STOCHASTIC_TIME_BEFORE_ITERATION", default=-1))], "after_iteration": [ int(os.getenv("SSIM_STOCHASTIC_TIME_AFTER_ITERATION", default=-1))], "before_timestep": [ int(os.getenv("SSIM_STOCHASTIC_TIME_BEFORE_TIMESTEP", default=-1))], "after_timestep": [ int(os.getenv("SSIM_STOCHASTIC_TIME_AFTER_TIMESTEP", default=-1))]}) return env_df def _validate_environment(): e = _environment() if e.program_directory.item() is None: raise RuntimeError("This function requires a SyncroSim environment") def _create_scenario_folder(scenario, parent_folder, datasheet_name): sidpart = "Scenario-" + str(scenario.sid) # p = re.sub("\\", "/", parent_folder) f = os.path.join(parent_folder, sidpart, datasheet_name) if not os.path.isdir(f): os.mkdir(f) return f def _create_temp_folder(folder_name): t = _environment().temp_directory.item() # p = re.sub("\\\\", "/", t) f = os.path.join(t, folder_name) if not os.path.isdir(f): os.mkdir(f) return f

1,853

0

104

5d04a1726570f54dbd238fae0bb455d7ae1425df

12,970

py

Python

tools/extStringCppParser.py

Mu-L/palanteer

ead425a0cf05dfe6ce178c0a72f19bfc4ff743b1

[ "MIT" ]

null

tools/extStringCppParser.py

Mu-L/palanteer

ead425a0cf05dfe6ce178c0a72f19bfc4ff743b1

[ "MIT" ]

null

tools/extStringCppParser.py

Mu-L/palanteer

ead425a0cf05dfe6ce178c0a72f19bfc4ff743b1

[ "MIT" ]

null

#! /usr/bin/env python3 # The MIT License (MIT) # # Copyright(c) 2021, Damien Feneyrou <dfeneyrou@gmail.com> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files(the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions : # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Palanteer: Generate an external strings lookup from C++ code # and/or from a binary (for the automatic instrumentation case) # # What it does for source code filenames: # - loop on all the provided C/C++ files # - identify Palanteer calls and corresponding parameters + file basenames # - compute and display the tuple <key> <string> on stdout # # What it does for a provided binary: # - calls "nm" on the Linux elf binary # - collect all symbols from the text section # - format and display the tuples <key> <string> on stdout (2 per function: filename and functio import sys if sys.version_info.major < 3: print("ERROR: This tool requires python3 (not python2)", file=sys.stderr) sys.exit(1) import os import os.path import re import subprocess # Constants # ========= # Regexp to detect if a word which starts with pl[g] (so that it looks like a command) followed with a parenthesis MATCH_DETECT = re.compile(".*?(^|[^a-zA-Z\d])pl(g?)([a-zA-Z]*)\s*\((.*)") # Regexp to extract the symbols from the text section, and also the weak ones MATCH_INFO_LINE = re.compile("^([0-9a-z]+)\s+[TW]\s(.*?)\s(\S+):(\d+)$", re.IGNORECASE) # Commands whose parameters shall be processed. Associated values are: 0=convert only strings 1=convert all parameters PL_COMMANDS_TYPE = { "Assert": 1, "Begin": 0, "End": 0, "Data": 0, "Text": 0, "DeclareThread": 0, "LockNotify": 0, "LockNotifyDyn": 0, "LockWait": 0, "LockWaitDyn": 0, "LockState": 0, "LockStateDyn": 0, "LockScopeState": 0, "LockScopeStateDyn": 0, "MakeString": 0, "Marker": 0, "MarkerDyn": 0, "MemPush": 0, "RegisterCli": 1, "Scope": 0, "Text": 0, "Var": 1, } # Helpers # ======= # Main entry # ========== # Bootstrap if __name__ == "__main__": main(sys.argv) # Unit test # ========= # ./extStringCppParser.py extStringCppParser.py shall give "good" followed by a sequential numbers, and no "BAD" """ plBegin aa("BAD0"); plBegin("good01"); plBegin ("good02"); plBegin("good03", "good04"); plgBegin(BAD1, "good05"); plBegin("good06", "good07") "BAD2"; plBegin("good08", // "BAD3" "good09"); // "BAD4" plVar(good10, good11); plgVar (BAD5, good12, good13); plgVar (BAD6, good14, // BAD7 good15); plAssert(good16); plgAssert(BAD8, good17, good18); plAssert(good19(a,b()), good20("content("), good21); not at start of the line plMakeString("good22"),plMakeString ("good23" ) , plMakeString ( "good24") plBegin("good25 <<< last one"); // Easy one at the end so it is easy to detect non sequential "goods" """

34.221636

131

0.537471

#! /usr/bin/env python3 # The MIT License (MIT) # # Copyright(c) 2021, Damien Feneyrou <dfeneyrou@gmail.com> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files(the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions : # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Palanteer: Generate an external strings lookup from C++ code # and/or from a binary (for the automatic instrumentation case) # # What it does for source code filenames: # - loop on all the provided C/C++ files # - identify Palanteer calls and corresponding parameters + file basenames # - compute and display the tuple <key> <string> on stdout # # What it does for a provided binary: # - calls "nm" on the Linux elf binary # - collect all symbols from the text section # - format and display the tuples <key> <string> on stdout (2 per function: filename and functio import sys if sys.version_info.major < 3: print("ERROR: This tool requires python3 (not python2)", file=sys.stderr) sys.exit(1) import os import os.path import re import subprocess # Constants # ========= # Regexp to detect if a word which starts with pl[g] (so that it looks like a command) followed with a parenthesis MATCH_DETECT = re.compile(".*?(^|[^a-zA-Z\d])pl(g?)([a-zA-Z]*)\s*\((.*)") # Regexp to extract the symbols from the text section, and also the weak ones MATCH_INFO_LINE = re.compile("^([0-9a-z]+)\s+[TW]\s(.*?)\s(\S+):(\d+)$", re.IGNORECASE) # Commands whose parameters shall be processed. Associated values are: 0=convert only strings 1=convert all parameters PL_COMMANDS_TYPE = { "Assert": 1, "Begin": 0, "End": 0, "Data": 0, "Text": 0, "DeclareThread": 0, "LockNotify": 0, "LockNotifyDyn": 0, "LockWait": 0, "LockWaitDyn": 0, "LockState": 0, "LockStateDyn": 0, "LockScopeState": 0, "LockScopeStateDyn": 0, "MakeString": 0, "Marker": 0, "MarkerDyn": 0, "MemPush": 0, "RegisterCli": 1, "Scope": 0, "Text": 0, "Var": 1, } # Helpers # ======= def computeHash(s, isHash64bits, hashSalt): if isHash64bits: h = 14695981039346656037 + hashSalt for c in s: h = ((h ^ ord(c)) * 1099511628211) & 0xFFFFFFFFFFFFFFFF if h == 0: h = 1 # Special case for our application (0 is reserved internally) return h else: h = 2166136261 + hashSalt for c in s: h = ((h ^ ord(c)) * 16777619) & 0xFFFFFFFF if h == 0: h = 1 # Special case for our application (0 is reserved internally) return h def addString( s, hashToStringlkup, collisions, isHash64bits, hashSalt, h=None, doOverride=False ): if h == None: h = computeHash(s, isHash64bits, hashSalt) if h in hashToStringlkup: # String already in the lookup? if s == hashToStringlkup[h]: return # Collision! if not doOverride: if h in collisions: collisions[h].append(s) else: collisions[h] = [hashToStringlkup[h], s] return # Add the new entry to the lookup hashToStringlkup[h] = s # Main entry # ========== def main(argv): # Command line parameters parsing # =============================== hashSalt = 0 isHash64bits = True doPrintUsage = False exeName = None fileNames = [] i = 1 while i < len(argv): if argv[i] == "--hash32": isHash64bits = False elif argv[i] == "--salt" and i + 1 < len(argv): i = i + 1 hashSalt = int(argv[i]) elif argv[i] == "--exe" and i + 1 < len(argv): i = i + 1 exeName = argv[i] elif argv[i][0] == "-": doPrintUsage = True # Unknown option print("Unknown option '%s'" % argv[i], file=sys.stderr) else: fileNames.append(argv[i]) i = i + 1 if not fileNames and not exeName: doPrintUsage = True if doPrintUsage: print( """This tool is a part of Palanteer and useful for the 'external string' feature. It parses C++ files and dumps on stdout a hashed string lookup for the Palanteer calls. Syntax: %s [options] [<source filenames>*] Options: --hash32 : generate 32 bits hash (to use with PL_SHORT_STRING_HASH=1) --salt <value>: hash salt value (shall match PL_HASH_SALT, default value is 0) --exe <name> : ELF executable from which to extract the function names and addresses Useful only when using PL_IMPL_AUTO_INSTRUMENT (Linux only) Note 1: The 'hand-made' code parsing is simple but should be enough for most need. It may fail in some corner cases (C macro masking values etc...). Note 2: If Palanteer commands are encapsulated inside custom macros in your code, the list of command at the top of this file shall probably be modified.""" % argv[0], file=sys.stderr, ) sys.exit(1) # Process the executables # ======================= hashToStringlkup, collisions = {}, {} if exeName and sys.platform == "linux": if not os.path.exists(exeName): printf("Input executable '%s' does not exist" % exeName, file=sys.stderr) sys.exit(1) # Collect the information from 'nm' # As the base virtual address is substracted from the logged function address, no need to care about ASLR nmProcess = subprocess.run( ["nm", "--line-numbers", "--demangle", exeName], universal_newlines=True, capture_output=True, ) for l in nmProcess.stdout.split("\n"): # Filter on symbols from the text section only m = MATCH_INFO_LINE.match(l) if not m: continue # The hash value in this automatic instrumentation case is the base address of the function hashValue = int(m.group(1), 16) + hashSalt # Add the function name addString( m.group(2), hashToStringlkup, collisions, isHash64bits, hashSalt, hashValue, ) # Add the function filename, augmented with the line number # The override of any former value is due to the potential emission of dual constructor/destructor symbols by GCC/Clang # (see https://stackoverflow.com/questions/6921295/dual-emission-of-constructor-symbols for details) addString( "%s:%s" % (m.group(3), m.group(4)), hashToStringlkup, collisions, isHash64bits, hashSalt, hashValue + 1, doOverride=True, ) # Process source files # ==================== addString( "", hashToStringlkup, collisions, isHash64bits, hashSalt ) # Add the empty string which are used internally for f in fileNames: # Insert the file basename, as it is what the palanteer client is using basename = os.path.basename(f) addString(basename, hashToStringlkup, collisions, isHash64bits, hashSalt) # Load the file lines = [] with open(f, "r") as fHandle: lines = fHandle.readlines() # Loop on lines lineNbr, lineQty = 0, len(lines) while lineNbr < lineQty: # Detect a Palanteer command l = lines[lineNbr] while l: m = MATCH_DETECT.match(l) if not m: l, lineNbr = None, lineNbr + 1 continue cmdType = PL_COMMANDS_TYPE.get(m.group(3), None) if cmdType == None: l = m.group(4) continue isGroup = not not m.group(2) # Parse the parameters params, currentParam, isInQuote, parenthLevel, prevC = ( [], [], False, 0, "", ) paramLine = m.group(4) while parenthLevel >= 0: for i, c in enumerate(paramLine): if c == '"': isInQuote = not isInQuote if isInQuote: currentParam.append(c) continue if c == "/" and prevC == "/": currentParam = currentParam[:-1] # Remove first '/' break elif c == "(": parenthLevel += 1 currentParam.append(c) elif c == ")": parenthLevel -= 1 if parenthLevel < 0: # Palanteer command terminaison params.append("".join(currentParam).strip()) l = paramLine[i:] break else: currentParam.append(c) elif c == "," and parenthLevel == 0: params.append("".join(currentParam).strip()) currentParam = [] else: currentParam.append(c) prevC = c # If parsing is not complete, process next line if parenthLevel >= 0: lineNbr += 1 if lineNbr < lineQty: paramLine = lines[lineNbr] else: break # Update the lookup if isGroup: params = params[1:] # Remove group parameter for p in params: if not p: continue if p[0] == '"' and p[-1] == '"': addString( p[1:-1], hashToStringlkup, collisions, isHash64bits, hashSalt, ) # Commands of type 1 stringifies all parameters. Also with quote (for assertions) if cmdType == 1: addString( p, hashToStringlkup, collisions, isHash64bits, hashSalt ) # Output sortedK = sorted(hashToStringlkup.keys(), key=lambda x: hashToStringlkup[x].lower()) for k in sortedK: print("@@%016X@@%s" % (k, hashToStringlkup[k])) # Error if collisions: sortedK = sorted(collisions.keys()) for k in sortedK: cList = collisions[k] print( "COLLISION %016X %s" % (k, " ".join(["[%s]" % s for s in collisions[k]])), file=sys.stderr, ) # Exit status sys.exit(1 if collisions else 0) # Bootstrap if __name__ == "__main__": main(sys.argv) # Unit test # ========= # ./extStringCppParser.py extStringCppParser.py shall give "good" followed by a sequential numbers, and no "BAD" """ plBegin aa("BAD0"); plBegin("good01"); plBegin ("good02"); plBegin("good03", "good04"); plgBegin(BAD1, "good05"); plBegin("good06", "good07") "BAD2"; plBegin("good08", // "BAD3" "good09"); // "BAD4" plVar(good10, good11); plgVar (BAD5, good12, good13); plgVar (BAD6, good14, // BAD7 good15); plAssert(good16); plgAssert(BAD8, good17, good18); plAssert(good19(a,b()), good20("content("), good21); not at start of the line plMakeString("good22"),plMakeString ("good23" ) , plMakeString ( "good24") plBegin("good25 <<< last one"); // Easy one at the end so it is easy to detect non sequential "goods" """

9,097

0

69

e55bf99f4d0d8b617d13272dc1499461cf5c6975

632

py

Python

okie/tasks/migrations/0004_auto_20210528_1622.py

immortal-zeus/Django-task_dis

10e99997893d4aabc2305969894e49f2873c17b7

[ "BSD-3-Clause" ]

1

2021-06-22T06:31:56.000Z

okie/tasks/migrations/0004_auto_20210528_1622.py

immortal-zeus/Django-task_dis

10e99997893d4aabc2305969894e49f2873c17b7

[ "BSD-3-Clause" ]

null

okie/tasks/migrations/0004_auto_20210528_1622.py

immortal-zeus/Django-task_dis

10e99997893d4aabc2305969894e49f2873c17b7

[ "BSD-3-Clause" ]

null

# Generated by Django 3.2.3 on 2021-05-28 10:52 from django.db import migrations, models

26.333333

97

0.602848

# Generated by Django 3.2.3 on 2021-05-28 10:52 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tasks', '0003_rename_user_tuser'), ] operations = [ migrations.AlterField( model_name='tasks', name='task_description', field=models.CharField(blank=True, default=None, max_length=2000, null=True), ), migrations.AlterField( model_name='tasks', name='task_pic', field=models.ImageField(blank=True, default=None, null=True, upload_to='taskimage/'), ), ]

0

518

23

1be6a2ef1e1199d71dec9476b340096db69dcdad

174

py

Python

codes/1/104.py

BigShuang/python-introductory-exercises

9b8d391ce5fcbd12a654aba1c62a746ddb52a42d

[ "MIT" ]

null

codes/1/104.py

BigShuang/python-introductory-exercises

9b8d391ce5fcbd12a654aba1c62a746ddb52a42d

[ "MIT" ]

null

codes/1/104.py

BigShuang/python-introductory-exercises

9b8d391ce5fcbd12a654aba1c62a746ddb52a42d

[ "MIT" ]

null

s = input() m, n = s.split(" ") m = int(m) n = int(n) for ri in range(m): for ci in range(n): v = ( ri + 1 ) * ( ci + 1 ) print(v, end=" ") print()

14.5

35

0.408046

s = input() m, n = s.split(" ") m = int(m) n = int(n) for ri in range(m): for ci in range(n): v = ( ri + 1 ) * ( ci + 1 ) print(v, end=" ") print()

0

89e422283e506acfd5a5dd572513aa47e2b556aa

1,312

py

Python

Code/Python/PossiblePlate.py

sgino209/LPR

75da71796484f2b72e6f9626dc83fa7ec5103ba9

[ "Apache-2.0" ]

null

Code/Python/PossiblePlate.py

sgino209/LPR

75da71796484f2b72e6f9626dc83fa7ec5103ba9

[ "Apache-2.0" ]

null

Code/Python/PossiblePlate.py

sgino209/LPR

75da71796484f2b72e6f9626dc83fa7ec5103ba9

[ "Apache-2.0" ]

1

2021-01-27T12:28:14.000Z

# !/usr/bin/python # _ _ ____ _ _ ____ _ _ _ # | | (_) ___ ___ _ __ ___ ___ | _ \| | __ _| |_ ___ | _ \ ___ ___ ___ __ _ _ __ (_) |_(_) ___ _ __ # | | | |/ __/ _ \ '_ \/ __|/ _ \ | |_) | |/ _` | __/ _ \ | |_) / _ \/ __/ _ \ / _` | '_ \| | __| |/ _ \| '_ \ # | |___| | (__ __/ | | \__ \ __/ | __/| | (_| | |_ __/ | _ < __/ (__ (_) | (_| | | | | | |_| | (_) | | | | # |_____|_|\___\___|_| |_|___/\___| |_| |_|\__,_|\__\___| |_| \_\___|\___\___/ \__, |_| |_|_|\__|_|\___/|_| |_| # |___/ # (c) Shahar Gino, July-2017, sgino209@gmail.com # --------------------------------------------------------------------------------------------------------------- class PossiblePlate: """ Class for representing a (possible) license-plate object """ # -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. def __init__(self): """ Constructor """ self.imgPlate = None self.imgGrayscale = None self.imgThresh = None self.rrLocationOfPlateInScene = None self.rrLocationOfPlateInSceneGbl = None self.strChars = "" self.rectFind = False

52.48

113

0.348323

# !/usr/bin/python # _ _ ____ _ _ ____ _ _ _ # | | (_) ___ ___ _ __ ___ ___ | _ \| | __ _| |_ ___ | _ \ ___ ___ ___ __ _ _ __ (_) |_(_) ___ _ __ # | | | |/ __/ _ \ '_ \/ __|/ _ \ | |_) | |/ _` | __/ _ \ | |_) / _ \/ __/ _ \ / _` | '_ \| | __| |/ _ \| '_ \ # | |___| | (__ __/ | | \__ \ __/ | __/| | (_| | |_ __/ | _ < __/ (__ (_) | (_| | | | | | |_| | (_) | | | | # |_____|_|\___\___|_| |_|___/\___| |_| |_|\__,_|\__\___| |_| \_\___|\___\___/ \__, |_| |_|_|\__|_|\___/|_| |_| # |___/ # (c) Shahar Gino, July-2017, sgino209@gmail.com # --------------------------------------------------------------------------------------------------------------- class PossiblePlate: """ Class for representing a (possible) license-plate object """ # -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. def __init__(self): """ Constructor """ self.imgPlate = None self.imgGrayscale = None self.imgThresh = None self.rrLocationOfPlateInScene = None self.rrLocationOfPlateInSceneGbl = None self.strChars = "" self.rectFind = False

0