xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

0895736ffacb80f0aa3e0f0691583c43198edc4a

343

py

Python

session_1/Test.py

idlaviV/intro-to-python

c3bd8e4f65ce6f1159f769a3da0fcc841b9d6d22

[ "MIT" ]

null

session_1/Test.py

idlaviV/intro-to-python

c3bd8e4f65ce6f1159f769a3da0fcc841b9d6d22

[ "MIT" ]

null

session_1/Test.py

idlaviV/intro-to-python

c3bd8e4f65ce6f1159f769a3da0fcc841b9d6d22

[ "MIT" ]

null

for index, entry in enumerate({"Tam", "Tim", "Tom"}): print(f"{index}: {entry}") for index, entry in enumerate({"Tam", "Tom", "Tim"}): print(f"{index}: {entry}") for index, entry in enumerate({"Tam", "Tom", "Tim"}): print(f"{index}: {entry}") for index, entry in enumerate({"Tam", "Tom", "Tim"}): print(f"{index}: {entry}")

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08af45abc5d3404eac4f79a1cd4f2df3ff58a65c

56,248

py

Python

RecoJets/JetAnalyzers/test/runCMSDAS11DijetTestAnalyzer.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

RecoJets/JetAnalyzers/test/runCMSDAS11DijetTestAnalyzer.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

RecoJets/JetAnalyzers/test/runCMSDAS11DijetTestAnalyzer.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

# PYTHON configuration file for class: CMSDAS11DijetAnalyzer.cc # Description: Example of simple EDAnalyzer for dijet mass & dijet spectrum ratio analysis # Authors: J.P. Chou, Jason St. John # Date: 01 - January - 2011 import FWCore.ParameterSet.Config as cms process = cms.Process("Ana") process.load("FWCore.MessageService.MessageLogger_cfi") ############# Set the number of events ############# process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1000) ) ############# Set the input for the analyzer ######### # Which input files to use # 1: data, 10k events # 2: MC resonance 1.2TeV # 3: MC QCD only, no resonance whichfiles = 1; ############# Format MessageLogger ################# process.MessageLogger.cerr.FwkReport.reportEvery = 10 ############# This is how CMS handles output ROOT files ################# process.TFileService = cms.Service( "TFileService", fileName = cms.string("histos.root") ) if (whichfiles==1): thefileNames = cms.untracked.vstring('file:/uscms_data/d2/kalanand/dijet-Run2010A-JetMET-Nov4ReReco-9667events.root') elif (whichfiles==2): thefileNames = cms.untracked.vstring('/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0026/6C0BC238-4247-DF11-81D7-E41F1318160C.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/FE3B276E-1C47-DF11-83B7-00215E22053A.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/FCD94068-2647-DF11-80FF-E41F131817F8.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/F843DD92-2647-DF11-9B8F-E41F13181668.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/F62E9060-1C47-DF11-B431-00215E221BC0.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/DA63FD75-1C47-DF11-A347-00215E221692.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/D802BE12-1D47-DF11-9FA1-00215E93D738.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/D65DA979-1C47-DF11-941C-00215E21DD56.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/D2647956-1C47-DF11-9C8E-00215E2216EC.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/D0A03150-1C47-DF11-ACE6-E41F1318099C.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/CC2D830B-1D47-DF11-966E-E41F13181498.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/CACB401B-1847-DF11-B9C5-E41F13181D00.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/CA85A868-2647-DF11-86BA-00215E221818.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/CA4A0A60-1C47-DF11-836E-00215E21D86A.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/CA070629-2747-DF11-819C-00215E222790.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/C0757277-1C47-DF11-90B7-00215E2212D2.root', 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'/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/5CAAC1A3-1B47-DF11-9B5B-00215E21DBBE.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/5A173FCA-2547-DF11-8F56-E41F131816A0.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/5670C812-1D47-DF11-8020-E41F13181890.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/52597D68-1E47-DF11-B7D1-E41F131816B4.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/4ECB6F7B-1E47-DF11-833D-E41F131816A0.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/4EB42E67-1C47-DF11-A1FA-00215E2205AC.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/4EB42E67-1C47-DF11-A1FA-00215E2205AC.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/4AEB1C64-1C47-DF11-BA6A-00215E221B48.root', 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'/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/289FB808-1D47-DF11-A091-00215E222808.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/28160333-1847-DF11-BAD5-00215E21D702.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/220A4374-1C47-DF11-9FA1-00215E21DF18.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/20D4E62F-1847-DF11-8D41-00215E2222A4.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/1C8DD3A3-1B47-DF11-BAF8-E41F1318168C.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/169CCA06-1D47-DF11-B2C0-00215E221EEA.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/102D9F52-1C47-DF11-B437-00215E22181E.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/0EB6DB6E-1E47-DF11-8D0B-00215E93DCFC.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/08056A58-1C47-DF11-A42D-00215E21DAAA.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0025/06E9D555-1C47-DF11-9E40-00215E2219E6.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0024/E2CAFC76-0C47-DF11-9784-00215E93E7DC.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0024/94318864-1147-DF11-B2FD-E41F131815B8.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0024/66451B78-0C47-DF11-8CB0-00215E93EE44.root', '/store/mc/Spring10/Qstar_DiJet1200/GEN-SIM-RECO/START3X_V26_S09-v1/0024/5A3665E0-1047-DF11-9537-00215E22175E.root' ) elif (whichfiles==3): thefileNames = cms.untracked.vstring() ############# Define the source file ############### process.source = cms.Source("PoolSource", fileNames = thefileNames ) if (whichfiles ==3): thefileNames.extend( [ 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'/store/mc/Fall10/QCD_Pt-15to3000_TuneD6T_Flat_7TeV-pythia6/GEN-SIM-RECO/START38_V12-v1/0022/063B0197-3AF1-DF11-9FC8-842B2B17F557.root', '/store/mc/Fall10/QCD_Pt-15to3000_TuneD6T_Flat_7TeV-pythia6/GEN-SIM-RECO/START38_V12-v1/0022/02E62CA9-91F1-DF11-812A-0002C90B743A.root' ] ) ##-------------------- Communicate with the DB ----------------------- process.load('Configuration.StandardSequences.Services_cff') process.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff') process.GlobalTag.globaltag = 'START38_V13::All' ############# Include the jet corrections ########## process.load("JetMETCorrections.Configuration.DefaultJEC_cff") # set the record's IOV. Must be defined once. Choose ANY correction service. # if whichfiles == 1: correctionsToGet = "ak7CaloL2L3Residual" else: correctionsToGet = "ak7CaloL2L3" ############# Correct Calo Jets on the fly ######### process.dijetAna = cms.EDAnalyzer("CMSDAS11DijetTestAnalyzer", jetSrc = cms.InputTag("ak7CaloJets"), vertexSrc = cms.InputTag("offlinePrimaryVertices"), jetCorrections = cms.string(correctionsToGet), innerDeltaEta = cms.double(0.7), outerDeltaEta = cms.double(1.3), JESbias = cms.double(1.0) ) ############# Path ########################### process.p = cms.Path(process.dijetAna)

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py

Python

scryptos/crypto/attack/__init__.py

scryptos/scryptoslib

bdde5b26dfbf7473b53c22408f97db44821ccbb3

[ "MIT" ]

30

2018-10-10T13:48:22.000Z

2022-03-14T07:03:57.000Z

scryptos/crypto/attack/__init__.py

scryptos/scryptoslib

bdde5b26dfbf7473b53c22408f97db44821ccbb3

[ "MIT" ]

2

2018-10-12T10:05:03.000Z

2020-05-18T22:53:15.000Z

scryptos/crypto/attack/__init__.py

scryptos/scryptoslib

bdde5b26dfbf7473b53c22408f97db44821ccbb3

[ "MIT" ]

5

2018-10-10T16:11:54.000Z

2021-04-04T13:13:53.000Z

import scryptos.crypto.attack.rsautil as rsautil import scryptos.crypto.attack.knapsackutil as knapsackutil import scryptos.crypto.attack.prngutil as prngutil

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Python

Macroeconomia/Argentina/IndicadoresEmpleoDesocupacion.py

alejivo/Macroeconomics

82091ab013774fd6d652e4f9874599b07b1ff152

[ "MIT" ]

null

Macroeconomia/Argentina/IndicadoresEmpleoDesocupacion.py

alejivo/Macroeconomics

82091ab013774fd6d652e4f9874599b07b1ff152

[ "MIT" ]

null

Macroeconomia/Argentina/IndicadoresEmpleoDesocupacion.py

alejivo/Macroeconomics

82091ab013774fd6d652e4f9874599b07b1ff152

[ "MIT" ]

null

import pandas as pd import io import requests import json class IndicadoresEmpleoDesocupacion: def __init__(self): """ Los indicadores de empleo y desocupacion se basan en gran medida en la EPC (encuesta permanente de hogares) en 31 aglomeraciones urbanas. """ pass def getTasaActividad(self, periodo = "Anual"): """ La tasa de actividad es PEA/PoblacionTotal Se considera como una tasa indicadora de la oferta laboral por parte de los trabajadores. Parameters ---------- periodo : str, optional (puede ser "Anual", "Trimestral") DESCRIPTION. The default is "Anual". Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-principales-variables-ocupacionales-eph-continua-actividad" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 1 if periodo == "Trimestral" else 0 #si no es trimestral siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp def getTasaEmpleo(self, periodo = "Anual"): """ La tasa de empleo se calcula como poblacion ocupada/poblacion total Se concidera como una tasa representativa de la demanda laboral ejercida por la empresas. Parameters ---------- periodo : str, optional (puede ser "Anual", "Trimestral") DESCRIPTION. The default is "Anual". Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-principales-variables-ocupacionales-eph-continua-empleo" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 1 if periodo == "Trimestral" else 0 #si no es trimestral siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp def getTasaDesocupacion(self, periodo = "Anual"): """ Se calcula como: poblacion desocupada/PEA Parameters ---------- periodo : str, optional (puede ser "Anual", "Trimestral") DESCRIPTION. The default is "Anual". Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-principales-variables-ocupacionales-eph-continua-desempleo" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 1 if periodo == "Trimestral" else 0 #si no es trimestral siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp def getTasaSubocupacionDemandante(self, periodo = "Anual"): """ Se calcula como poblacion desocupada demandante/PEA Parameters ---------- periodo : str, optional (puede ser "Anual", "Trimestral") DESCRIPTION. The default is "Anual". Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-principales-variables-ocupacionales-eph-continua-subocupacion-demandante" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 1 if periodo == "Trimestral" else 0 #si no es trimestral siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp def getTasaSubocupacionNoDemandante(self, periodo = "Anual"): """ Se calcula como poblacion desocupada NO demandante/PEA Parameters ---------- periodo : str, optional (puede ser "Anual", "Trimestral") DESCRIPTION. The default is "Anual". Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-principales-variables-ocupacionales-eph-continua-subocupacion-no-demandante" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 1 if periodo == "Trimestral" else 0 #si no es trimestral siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp def getIndiceSalariosBase2016(self): """ Es un indice que estima la evolucion de los salarios de la economia Base octubre 2016 Returns ------- pd.DataFrame(). """ #Obtener la url de descarga del cvs urlPackage="https://datos.gob.ar/api/3/action/package_show?id=sspm-indice-salarios-base-octubre-2016" s=requests.get(urlPackage).content objJson = json.loads(s) resultado = objJson['result']['resources'] selector = 0 #si no es trimestral o mensual siempre es anual ultimoResultado = resultado[selector] urlDescarga = ultimoResultado['url'] descripcion = ultimoResultado['description'] print("Descargando: {}".format(descripcion)) print("Archivo: {}".format(urlDescarga)) #Descargar la url con cvs y generar pandas dataframe contenidoCVS = requests.get(urlDescarga).content flujoCVS = io.StringIO(contenidoCVS.decode('utf-8')) df_temp = pd.read_csv(flujoCVS) #transform string to datetime df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore') df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date #set index df_temp.set_index('indice_tiempo', inplace=True) return df_temp

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py

Python

run_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

run_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

run_test.py

NREL/scout

acf38df7ce877cbd8c1c10f4f61fdf1d088fd947

[ "Apache-2.0", "BSD-3-Clause" ]

null

#!/usr/bin/env python3 """ Tests for running the engine """ # Import code to be tested import run # Import needed packages import unittest import numpy import copy import itertools import os class CommonTestMeasures(object): """Class of common sample measures for tests. Attributes: sample_measure (dict): Sample residential measure #1. sample_measure2 (dict): Sample residential measure #2. sample_measure3 (dict): Sample commercial measure #1. """ def __init__(self): self.sample_measure = { "name": "sample measure 1", "active": 1, "market_entry_year": None, "market_exit_year": None, "market_scaling_fractions": None, "market_scaling_fractions_source": None, "measure_type": "full service", "structure_type": ["new", "existing"], "climate_zone": ["AIA_CZ1", "AIA_CZ2"], "bldg_type": ["single family home"], "fuel_type": {"primary": ["electricity (grid)"], "secondary": None}, "fuel_switch_to": None, "end_use": {"primary": ["heating", "cooling"], "secondary": None}, "technology_type": {"primary": "supply", "secondary": None}, "technology": {"primary": ["resistance heat", "ASHP", "GSHP", "room AC"], "secondary": None}, "markets": { "Technical potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}}} self.sample_measure2 = { "name": "sample measure 2", "active": 1, "market_entry_year": None, "market_exit_year": None, "market_scaling_fractions": None, "market_scaling_fractions_source": None, "measure_type": "full service", "structure_type": ["new", "existing"], "climate_zone": ["AIA_CZ1", "AIA_CZ2"], "bldg_type": ["single family home"], "fuel_type": {"primary": ["electricity (grid)"], "secondary": ["electricity (grid)"]}, "fuel_switch_to": None, "end_use": {"primary": ["heating", "cooling"], "secondary": ["lighting"]}, "technology_type": {"primary": "supply", "secondary": "supply"}, "technology": {"primary": ["resistance heat", "ASHP", "GSHP", "room AC"], "secondary": ["general service (LED)"]}, "markets": { "Technical potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}}} self.sample_measure3 = { "name": "sample measure 3 (commercial)", "active": 1, "market_entry_year": None, "market_exit_year": None, "market_scaling_fractions": None, "market_scaling_fractions_source": None, "measure_type": "full service", "structure_type": ["new", "existing"], "climate_zone": ["AIA_CZ1", "AIA_CZ2"], "bldg_type": ["assembly"], "fuel_type": {"primary": ["electricity"], "secondary": None}, "fuel_switch_to": None, "end_use": {"primary": ["heating", "cooling"], "secondary": None}, "technology_type": {"primary": "supply", "secondary": None}, "technology": {"primary": ["resistance heat", "ASHP", "GSHP", "room AC"], "secondary": None}, "markets": { "Technical potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}}} self.sample_measure4 = { "name": "sample measure 4", "active": 1, "market_entry_year": None, "market_exit_year": None, "market_scaling_fractions": None, "market_scaling_fractions_source": None, "measure_type": "full service", "structure_type": ["new", "existing"], "climate_zone": ["AIA_CZ1", "AIA_CZ2"], "bldg_type": ["single family home"], "fuel_type": {"primary": ["electricity (grid)"], "secondary": None}, "fuel_switch_to": None, "end_use": {"primary": ["lighting"], "secondary": None}, "technology_type": {"primary": "supply", "secondary": None}, "technology": {"primary": ["general service (CFL)"], "secondary": None}, "markets": { "Technical potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}}} self.sample_measure5 = { "name": "sample measure 5 (commercial)", "active": 1, "market_entry_year": None, "market_exit_year": None, "market_scaling_fractions": None, "market_scaling_fractions_source": None, "measure_type": "full service", "structure_type": ["new", "existing"], "climate_zone": ["AIA_CZ1", "AIA_CZ2"], "bldg_type": ["assembly"], "fuel_type": {"primary": ["electricity"], "secondary": None}, "fuel_switch_to": None, "end_use": {"primary": ["lighting"], "secondary": None}, "technology_type": {"primary": "supply", "secondary": None}, "technology": {"primary": ["F32T8"], "secondary": None}, "markets": { "Technical potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": {}, "mseg_adjust": { "contributing mseg keys and values": {}, "competed choice parameters": {}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {} }}}, "mseg_out_break": {}}}} class CommonMethods(object): """Define common methods for use in all tests below.""" def dict_check(self, dict1, dict2): """Check the equality of two dicts. Args: dict1 (dict): First dictionary to be compared dict2 (dict): Second dictionary to be compared Raises: AssertionError: If dictionaries are not equal. """ # zip() and zip_longest() produce tuples for the items # identified, where in the case of a dict, the first item # in the tuple is the key and the second item is the value; # in the case where the dicts are not of identical size, # zip_longest() will use the fill value created below as a # substitute in the dict that has missing content; this # value is given as a tuple to be of comparable structure # to the normal output from zip_longest() fill_val = ('substituted entry', 5.2) # In this structure, k and k2 are the keys that correspond to # the dicts or unitary values that are found in i and i2, # respectively, at the current level of the recursive # exploration of dict1 and dict2, respectively for (k, i), (k2, i2) in itertools.zip_longest(sorted(dict1.items()), sorted(dict2.items()), fillvalue=fill_val): # Confirm that at the current location in the dict structure, # the keys are equal; this should fail if one of the dicts # is empty, is missing section(s), or has different key names self.assertEqual(k, k2) # If the recursion has not yet reached the terminal/leaf node if isinstance(i, dict): # Test that the dicts from the current keys are equal self.assertCountEqual(i, i2) # Continue to recursively traverse the dict self.dict_check(i, i2) # At the terminal/leaf node, formatted as a numpy array # (for input uncertainty test cases) elif isinstance(i, numpy.ndarray): self.assertTrue(type(i) == type(i2)) for x in range(0, len(i)): self.assertAlmostEqual(i[x], i2[x], places=2) # At the terminal/leaf node, formatted as a point value else: self.assertAlmostEqual(i, i2, places=2) class TestMeasureInit(unittest.TestCase): """Ensure that measure attributes are correctly initiated. Attributes: sample_measure (object): Residential sample measure object. attribute_dict (dict): Dict of sample measure attributes. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) cls.sample_measure = CommonTestMeasures().sample_measure measure_instance = run.Measure(handyvars, **cls.sample_measure) cls.attribute_dict = measure_instance.__dict__ def test_attributes(self): """Compare object attributes to keys from input dict.""" for key in self.sample_measure.keys(): self.assertEqual( self.attribute_dict[key], self.sample_measure[key]) class OutputBreakoutDictWalkTest(unittest.TestCase, CommonMethods): """Test operation of 'out_break_walk' function. Verify that function properly applies a climate zone/building type/end use partition to a total energy or carbon market/savings value. Attributes: a_run (object): Sample analysis engine object. ok_total (dict): Sample unpartitioned measure results data. ok_partitions (dict): Sample results partitioning fraction. ok_out (dict): Sample partitioned measure results data. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) sample_measure = CommonTestMeasures().sample_measure measure_list = [run.Measure(handyvars, **sample_measure)] cls.a_run = run.Engine(handyvars, measure_list) cls.ok_total = {"2009": 100, "2010": 100} cls.ok_partitions = { "AIA CZ1": { "Residential": { "Heating": {"2009": .10, "2010": .10}, "Cooling": {"2009": .15, "2010": .15}}, "Commercial": { "Heating": {"2009": .20, "2010": .20}, "Cooling": {"2009": .25, "2010": .25}}}, "AIA CZ2": { "Residential": { "Heating": {"2009": .30, "2010": .30}, "Cooling": {"2009": .35, "2010": .35}}, "Commercial": { "Heating": {"2009": .40, "2010": .40}, "Cooling": {"2009": .45, "2010": .45}}}} cls.ok_out = { "AIA CZ1": { "Residential": { "Heating": {"2009": 10, "2010": 10}, "Cooling": {"2009": 15, "2010": 15}}, "Commercial": { "Heating": {"2009": 20, "2010": 20}, "Cooling": {"2009": 25, "2010": 25}}}, "AIA CZ2": { "Residential": { "Heating": {"2009": 30, "2010": 30}, "Cooling": {"2009": 35, "2010": 35}}, "Commercial": { "Heating": {"2009": 40, "2010": 40}, "Cooling": {"2009": 45, "2010": 45}}}} def test_ok(self): """Test for correct function output given valid inputs.""" dict1 = self.a_run.out_break_walk( self.ok_partitions, self.ok_total) dict2 = self.ok_out self.dict_check(dict1, dict2) class PrioritizationMetricsTest(unittest.TestCase, CommonMethods): """Test the operation of the 'calc_savings_metrics' function. Verify that measure master microsegment inputs yield expected savings and financial metrics outputs. Attributes: handyvars (object): Useful variables across the class. sample_measure_res (object): Sample residential measure data. sample_measure_com (object): Sample commercial measure data. test_adopt_scheme (string): Sample consumer adoption scheme. ok_rate (float): Sample discount rate. ok_master_mseg_point (dict): Sample measure master microsegment including all point values at terminal leaf nodes. ok_master_mseg_dist1 (dict): Sample measure master microsegment including energy, carbon, and energy/carbon cost arrays. ok_master_mseg_dist2 (dict): Sample measure master microsegment including stock cost array. ok_master_mseg_dist3 (dict): Sample measure master microsegment including measure lifetime array. ok_master_mseg_dist4 (dict): Sample measure master microsegment including stock cost and measure lifetime array. ok_out_point_res (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_point' with a residential sample measure. ok_out_point_com (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_point' with a residential sample measure. ok_out_dist1 (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_dist1' with a residential sample measure. ok_out_dist2 (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_dist2' with a residential sample measure. ok_out_dist3 (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_dist3' with a residential sample measure. ok_out_dist4 (dict): Measure attribute update status, savings, and portfolio/consumer-level financial metrics that should be generated given 'ok_master_mseg_dist4' with a residential sample measure. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) # Reset aeo_years cls.handyvars.aeo_years = ["2009", "2010"] cls.sample_measure_res = CommonTestMeasures().sample_measure4 cls.sample_measure_com = CommonTestMeasures().sample_measure5 cls.test_adopt_scheme = 'Max adoption potential' cls.ok_rate = 0.07 cls.ok_master_mseg_point = { "stock": { "total": { "all": {"2009": 10, "2010": 20}, "measure": {"2009": 15, "2010": 25}}, "competed": { "all": {"2009": 5, "2010": 10}, "measure": {"2009": 5, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 0, "2010": 50}}}, "carbon": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 50, "2010": 100}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}, "competed": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}, "competed": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}, "competed": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 2}} cls.ok_master_mseg_dist1 = { "stock": { "total": { "all": {"2009": 10, "2010": 20}, "measure": {"2009": 15, "2010": 25}}, "competed": { "all": {"2009": 5, "2010": 10}, "measure": {"2009": 5, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": { "2009": numpy.array([16, 27, 31, 6, 51]), "2010": numpy.array([106, 95, 81, 11, 124])}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": { "2009": numpy.array([6, 7, 1, 16, 1]), "2010": numpy.array([36, 45, 61, 5, 54])}}}, "carbon": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": { "2009": numpy.array([50.6, 57.7, 58.1, 50, 51.1]), "2010": numpy.array( [100.6, 108.7, 105.1, 105, 106.1])}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": { "2009": numpy.array([50.6, 57.7, 58.1, 50, 51.1]), "2010": numpy.array( [100.6, 108.7, 105.1, 105, 106.1])}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}, "competed": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 35}, "efficient": { "2009": numpy.array([9.1, 8.7, 7.7, 11.2, 12.5]), "2010": numpy.array( [20.1, 18.7, 21.7, 21.2, 22.5])}}, "competed": { "baseline": {"2009": 20, "2010": 35}, "efficient": { "2009": numpy.array([9.1, 8.7, 7.7, 11.2, 12.5]), "2010": numpy.array( [20.1, 18.7, 21.7, 21.2, 22.5])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 40}, "efficient": { "2009": numpy.array( [25.1, 24.7, 23.7, 31.2, 18.5]), "2010": numpy.array( [20.1, 18.7, 21.7, 21.2, 22.5])}}, "competed": { "baseline": {"2009": 30, "2010": 40}, "efficient": { "2009": numpy.array( [25.1, 24.7, 23.7, 31.2, 18.5]), "2010": numpy.array( [20.1, 18.7, 21.7, 21.2, 22.5])}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 2}} cls.ok_master_mseg_dist2 = { "stock": { "total": { "all": {"2009": 10, "2010": 20}, "measure": {"2009": 15, "2010": 25}}, "competed": { "all": {"2009": 5, "2010": 10}, "measure": {"2009": 5, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 0, "2010": 50}}}, "carbon": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 50, "2010": 100}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 15}, "efficient": { "2009": numpy.array( [15.1, 12.7, 14.1, 14.2, 15.5]), "2010": numpy.array([20.1, 18.7, 21.7, 19.2, 20.5]) }}, "competed": { "baseline": {"2009": 10, "2010": 15}, "efficient": { "2009": numpy.array( [15.1, 12.7, 14.1, 14.2, 15.5]), "2010": numpy.array([20.1, 18.7, 21.7, 19.2, 20.5]) }}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}, "competed": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}, "competed": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 2}} cls.ok_master_mseg_dist3 = { "stock": { "total": { "all": {"2009": 10, "2010": 20}, "measure": {"2009": 15, "2010": 25}}, "competed": { "all": {"2009": 5, "2010": 10}, "measure": {"2009": 5, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 0, "2010": 50}}}, "carbon": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 50, "2010": 100}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}, "competed": { "baseline": {"2009": 10, "2010": 15}, "efficient": {"2009": 15, "2010": 25}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}, "competed": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}, "competed": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": numpy.array([0.5, 1.2, 2.1, 2.2, 4.6])}} cls.ok_master_mseg_dist4 = { "stock": { "total": { "all": {"2009": 10, "2010": 20}, "measure": {"2009": 15, "2010": 25}}, "competed": { "all": {"2009": 5, "2010": 10}, "measure": {"2009": 5, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 0, "2010": 50}}}, "carbon": { "total": { "baseline": {"2009": 200, "2010": 300}, "efficient": {"2009": 50, "2010": 100}}, "competed": { "baseline": {"2009": 100, "2010": 150}, "efficient": {"2009": 50, "2010": 100}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 15}, "efficient": { "2009": numpy.array( [15.1, 12.7, 14.1, 14.2, 15.5]), "2010": numpy.array([20.1, 18.7, 21.7, 19.2, 20.5]) }}, "competed": { "baseline": {"2009": 10, "2010": 15}, "efficient": { "2009": numpy.array( [15.1, 12.7, 14.1, 14.2, 15.5]), "2010": numpy.array([20.1, 18.7, 21.7, 19.2, 20.5]) }}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}, "competed": { "baseline": {"2009": 20, "2010": 35}, "efficient": {"2009": 10, "2010": 20}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}, "competed": { "baseline": {"2009": 30, "2010": 40}, "efficient": {"2009": 25, "2010": 25}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": numpy.array([0.5, 1.2, 2.1, 2.2, 4.6])}} cls.ok_out_point_res = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": {"2009": -5, "2010": -10}, "cost savings (annual)": {"2009": -5, "2010": -10}}, "energy": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 100, "2010": 100}, "cost savings (total)": {"2009": 10, "2010": 15}, "cost savings (annual)": {"2009": 10, "2010": 15}}, "carbon": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 50, "2010": 50}, "cost savings (total)": {"2009": 5, "2010": 15}, "cost savings (annual)": {"2009": 5, "2010": 15}}}, { "cce": {"2009": -0.01602415, "2010": -0.01111353}, "cce (w/ carbon cost benefits)": { "2009": -0.04935749, "2010": -0.08611353}, "ccc": {"2009": -1.602415e-08, "2010": -1.111353e-08}, "ccc (w/ energy cost benefits)": { "2009": -8.269082e-08, "2010": -8.611353e-08}}, { "anpv": { "stock cost": { "residential": { "2009": numpy.pmt(0.07, 2, 0.4345794), "2010": numpy.pmt(0.07, 2, 0.2009346)}, "commercial": {"2009": None, "2010": None}}, "energy cost": { "residential": { "2009": numpy.pmt(0.07, 2, 1.808018), "2010": numpy.pmt(0.07, 2, 1.356014)}, "commercial": {"2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": numpy.pmt(0.07, 2, 0.9040091), "2010": numpy.pmt(0.07, 2, 1.356014)}, "commercial": {"2009": None, "2010": None}}}, "irr (w/ energy costs)": { "2009": 3.45, "2010": 2.44}, "irr (w/ energy and carbon costs)": { "2009": 4.54, "2010": 4.09}, "payback (w/ energy costs)": { "2009": 0.25, "2010": 0.33}, "payback (w/ energy and carbon costs)": { "2009": 0.2, "2010": 0.22}}] cls.ok_out_point_com = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": {"2009": -5, "2010": -10}, "cost savings (annual)": {"2009": -5, "2010": -10}}, "energy": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 100, "2010": 100}, "cost savings (total)": {"2009": 10, "2010": 15}, "cost savings (annual)": {"2009": 10, "2010": 15}}, "carbon": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 50, "2010": 50}, "cost savings (total)": {"2009": 5, "2010": 15}, "cost savings (annual)": {"2009": 5, "2010": 15}}}, { "cce": {"2009": -0.01602415, "2010": -0.01111353}, "cce (w/ carbon cost benefits)": { "2009": -0.04935749, "2010": -0.08611353}, "ccc": {"2009": -1.602415e-08, "2010": -1.111353e-08}, "ccc (w/ energy cost benefits)": { "2009": -8.269082e-08, "2010": -8.611353e-08}}, { "anpv": { "stock cost": { "residential": {"2009": None, "2010": None}, "commercial": { "2009": { "rate 1": numpy.pmt(10.0, 2, -0.4090909), "rate 2": numpy.pmt(1.0, 2, 0), "rate 3": numpy.pmt(0.45, 2, 0.1896552), "rate 4": numpy.pmt(0.25, 2, 0.3), "rate 5": numpy.pmt(0.15, 2, 0.3695652), "rate 6": numpy.pmt(0.065, 2, 0.4389671), "rate 7": -0.25}, "2010": { "rate 1": numpy.pmt(10.0, 2, -0.4318182), "rate 2": numpy.pmt(1.0, 2, -0.125), "rate 3": numpy.pmt(0.45, 2, 0.01724138), "rate 4": numpy.pmt(0.25, 2, 0.1), "rate 5": numpy.pmt(0.15, 2, 0.1521739), "rate 6": numpy.pmt(0.065, 2, 0.2042254), "rate 7": -0.125}}}, "energy cost": { "residential": {"2009": None, "2010": None}, "commercial": { "2009": { "rate 1": numpy.pmt(10.0, 2, 0.09917355), "rate 2": numpy.pmt(1.0, 2, 0.75), "rate 3": numpy.pmt(0.45, 2, 1.165279), "rate 4": numpy.pmt(0.25, 2, 1.44), "rate 5": numpy.pmt(0.15, 2, 1.625709), "rate 6": numpy.pmt(0.065, 2, 1.820626), "rate 7": -1}, "2010": { "rate 1": numpy.pmt(10.0, 2, 0.07438017), "rate 2": numpy.pmt(1.0, 2, 0.5625), "rate 3": numpy.pmt(0.45, 2, 0.8739596), "rate 4": numpy.pmt(0.25, 2, 1.08), "rate 5": numpy.pmt(0.15, 2, 1.219282), "rate 6": numpy.pmt(0.065, 2, 1.36547), "rate 7": -0.75}}}, "carbon cost": { "residential": {"2009": None, "2010": None}, "commercial": { "2009": { "rate 1": numpy.pmt(10.0, 2, 0.04958678), "rate 2": numpy.pmt(1.0, 2, 0.375), "rate 3": numpy.pmt(0.45, 2, 0.5826397), "rate 4": numpy.pmt(0.25, 2, 0.72), "rate 5": numpy.pmt(0.15, 2, 0.8128544), "rate 6": numpy.pmt(0.065, 2, 0.9103132), "rate 7": -0.5}, "2010": { "rate 1": numpy.pmt(10.0, 2, 0.07438017), "rate 2": numpy.pmt(1.0, 2, 0.5625), "rate 3": numpy.pmt(0.45, 2, 0.8739596), "rate 4": numpy.pmt(0.25, 2, 1.08), "rate 5": numpy.pmt(0.15, 2, 1.219282), "rate 6": numpy.pmt(0.065, 2, 1.36547), "rate 7": -0.75}}}}, "irr (w/ energy costs)": { "2009": 3.45, "2010": 2.44}, "irr (w/ energy and carbon costs)": { "2009": 4.54, "2010": 4.09}, "payback (w/ energy costs)": { "2009": 0.25, "2010": 0.33}, "payback (w/ energy and carbon costs)": { "2009": 0.2, "2010": 0.22}}] cls.ok_out_dist1 = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": {"2009": -5, "2010": -10}, "cost savings (annual)": {"2009": -5, "2010": -10}}, "energy": { "savings (total)": { "2009": numpy.array([184, 173, 169, 194, 149]), "2010": numpy.array([194, 205, 219, 289, 176])}, "savings (annual)": { "2009": numpy.array([94, 93, 99, 84, 99]), "2010": numpy.array([114, 105, 89, 145, 96])}, "cost savings (total)": { "2009": numpy.array([10.9, 11.3, 12.3, 8.8, 7.5]), "2010": numpy.array([14.9, 16.3, 13.3, 13.8, 12.5])}, "cost savings (annual)": { "2009": numpy.array([10.9, 11.3, 12.3, 8.8, 7.5]), "2010": numpy.array([14.9, 16.3, 13.3, 13.8, 12.5])}}, "carbon": { "savings (total)": { "2009": numpy.array([149.4, 142.3, 141.9, 150.0, 148.9]), "2010": numpy.array([199.4, 191.3, 194.9, 195.0, 193.9])}, "savings (annual)": { "2009": numpy.array([49.4, 42.3, 41.9, 50.0, 48.9]), "2010": numpy.array([49.4, 41.3, 44.9, 45.0, 43.9])}, "cost savings (total)": { "2009": numpy.array([4.9, 5.3, 6.3, -1.2, 11.5]), "2010": numpy.array([19.9, 21.3, 18.3, 18.8, 17.5])}, "cost savings (annual)": { "2009": numpy.array([4.9, 5.3, 6.3, -1.2, 11.5]), "2010": numpy.array([19.9, 21.3, 18.3, 18.8, 17.5])}}}, { "cce": { "2009": numpy.array([ -0.01306317, -0.01389378, -0.01422262, -0.01238981, -0.01613170]), "2010": numpy.array([ -0.01145724, -0.01084246, -0.01014934, -0.007691022, -0.01262901])}, "cce (w/ carbon cost benefits)": { "2009": numpy.array([ -0.0396936, -0.04452961, -0.05150073, -0.006204243, -0.09331291]), "2010": numpy.array([ -0.1140346, -0.11474490, -0.09371098, -0.072742925, -0.11206083])}, "ccc": { "2009": numpy.array([ -1.608851e-08, -1.689124e-08, -1.693885e-08, -1.602415e-08, -1.614253e-08]), "2010": numpy.array([ -1.114697e-08, -1.161895e-08, -1.140434e-08, -1.139849e-08, -1.146315e-08])}, "ccc (w/ energy cost benefits)": { "2009": numpy.array([ -8.904701e-08, -9.630094e-08, -1.036196e-07, -7.469082e-08, -6.651191e-08]), "2010": numpy.array([ -8.587114e-08, -9.682543e-08, -7.964446e-08, -8.216772e-08, -7.592937e-08])}}, { "anpv": { "stock cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 2, 0.4345794)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 2, 0.2009346)]) }, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5) }}, "energy cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 1.97074), numpy.pmt(0.07, 2, 2.043061), numpy.pmt(0.07, 2, 2.223862), numpy.pmt(0.07, 2, 1.591056), numpy.pmt(0.07, 2, 1.356014)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 1.346974), numpy.pmt(0.07, 2, 1.473535), numpy.pmt(0.07, 2, 1.202332), numpy.pmt(0.07, 2, 1.247533), numpy.pmt(0.07, 2, 1.130011)]) }, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5) }}, "carbon cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 0.8859289), numpy.pmt(0.07, 2, 0.9582496), numpy.pmt(0.07, 2, 1.139051), numpy.pmt(0.07, 2, -0.2169622), numpy.pmt(0.07, 2, 2.079221)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 1.798978), numpy.pmt(0.07, 2, 1.925539), numpy.pmt(0.07, 2, 1.654337), numpy.pmt(0.07, 2, 1.699537), numpy.pmt(0.07, 2, 1.582016)]) }, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5) }}}, "irr (w/ energy costs)": { "2009": numpy.array([ 3.648926, 3.737086, 3.956335, 3.180956, 2.886001]), "2010": numpy.array([ 2.425032, 2.584709, 2.240438, 2.298386, 2.147181])}, "irr (w/ energy and carbon costs)": { "2009": numpy.array([ 4.713113, 4.884221, 5.309580, 2.908860, 5.394281]), "2010": numpy.array([ 4.601286, 4.897553, 4.260683, 4.367373, 4.089454])}, "payback (w/ energy costs)": { "2009": numpy.array([ 0.2392344, 0.2347418, 0.2242152, 0.2659574, 0.2857143]), "2010": numpy.array([ 0.3344482, 0.3194888, 0.3533569, 0.3472222, 0.3636364])}, "payback (w/ energy and carbon costs)": { "2009": numpy.array([ 0.1937984, 0.1879699, 0.1748252, 0.2840909, 0.1724138]), "2010": numpy.array([ 0.2008032, 0.1901141, 0.2145923, 0.2100840, 0.2222222])}}] cls.ok_out_dist2 = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": { "2009": numpy.array([-5.1, -2.7, -4.1, -4.2, -5.5]), "2010": numpy.array([-5.1, -3.7, -6.7, -4.2, -5.5])}, "cost savings (annual)": { "2009": numpy.array([-5.1, -2.7, -4.1, -4.2, -5.5]), "2010": numpy.array([-5.1, -3.7, -6.7, -4.2, -5.5])}}, "energy": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 100, "2010": 100}, "cost savings (total)": {"2009": 10, "2010": 15}, "cost savings (annual)": {"2009": 10, "2010": 15}}, "carbon": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 50, "2010": 50}, "cost savings (total)": {"2009": 5, "2010": 15}, "cost savings (annual)": {"2009": 5, "2010": 15}}}, { "cce": { "2009": numpy.array([ -0.01565543, -0.02450490, -0.01934271, -0.01897398, -0.01418052]), "2010": numpy.array([ -0.02466428, -0.02853592, -0.02023954, -0.02715319, -0.02355809])}, "cce (w/ carbon cost benefits)": { "2009": numpy.array([ -0.04898876, -0.05783823, -0.05267604, -0.05230731, -0.04751385]), "2010": numpy.array([ -0.09966428, -0.10353592, -0.09523954, -0.10215319, -0.09855809])}, "ccc": { "2009": numpy.array([ -1.565543e-08, -2.450490e-08, -1.934271e-08, -1.897398e-08, -1.418052e-08]), "2010": numpy.array([ -2.466428e-08, -2.853592e-08, -2.023954e-08, -2.715319e-08, -2.355809e-08])}, "ccc (w/ energy cost benefits)": { "2009": numpy.array([ -8.232209e-08, -9.117156e-08, -8.600937e-08, -8.564064e-08, -8.084718e-08]), "2010": numpy.array([ -9.966428e-08, -1.035359e-07, -9.523954e-08, -1.021532e-07, -9.855809e-08])}}, { "anpv": { "stock cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 0.4245794), numpy.pmt(0.07, 2, 0.6645794), numpy.pmt(0.07, 2, 0.5245794), numpy.pmt(0.07, 2, 0.5145794), numpy.pmt(0.07, 2, 0.3845794)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 0.4459346), numpy.pmt(0.07, 2, 0.5159346), numpy.pmt(0.07, 2, 0.3659346), numpy.pmt(0.07, 2, 0.4909346), numpy.pmt(0.07, 2, 0.4259346)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "energy cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "carbon cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091)]), "2010": numpy.array([ numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}}, "irr (w/ energy costs)": {"2009": numpy.array([ 3.370236, 6.877566, 4.335205, 4.218185, 3.081800]), "2010": numpy.array([ 5.345834, 7.580577, 3.931585, 6.612039, 4.915578])}, "irr (w/ energy and carbon costs)": {"2009": numpy.array([ 4.442382, 8.824726, 5.647891, 5.501689, 4.082098]), "2010": numpy.array([ 8.446248, 11.795815, 6.327488, 10.343948, 7.801544])}, "payback (w/ energy costs)": {"2009": numpy.array([ 0.255, 0.1350000, 0.2050000, 0.21, 0.2750000]), "2010": numpy.array([ 0.1700000, 0.1233333, 0.2233333, 0.1400000, 0.1833333])}, "payback (w/ energy and carbon costs)": {"2009": numpy.array([ 0.2040000, 0.10800000, 0.1640000, 0.16800000, 0.2200000]), "2010": numpy.array([ 0.1133333, 0.08222222, 0.1488889, 0.09333333, 0.1222222])}}] cls.ok_out_dist3 = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": {"2009": -5, "2010": -10}, "cost savings (annual)": {"2009": -5, "2010": -10}}, "energy": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 100, "2010": 100}, "cost savings (total)": {"2009": 10, "2010": 15}, "cost savings (annual)": {"2009": 10, "2010": 15}}, "carbon": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 50, "2010": 50}, "cost savings (total)": {"2009": 5, "2010": 15}, "cost savings (annual)": {"2009": 5, "2010": 15}}}, { "cce": { "2009": numpy.array([ 0.03566667, 0.03566667, -0.01602415, -0.01602415, -0.04694426]), "2010": numpy.array([ 0.05350000, 0.05350000, -0.01111353, -0.01111353, -0.04976366])}, "cce (w/ carbon cost benefits)": { "2009": numpy.array([ 0.002333333, 0.002333333, -0.04935749, -0.04935749, -0.0802776]), "2010": numpy.array([ -0.021500000, -0.021500000, -0.08611353, -0.08611353, -0.1247637])}, "ccc": { "2009": numpy.array([ 3.566667e-08, 3.566667e-08, -1.602415e-08, -1.602415e-08, -4.694426e-08]), "2010": numpy.array([ 5.350000e-08, 5.350000e-08, -1.111353e-08, -1.111353e-08, -4.976366e-08])}, "ccc (w/ energy cost benefits)": { "2009": numpy.array([ -3.10e-08, -3.10e-08, -8.269082e-08, -8.269082e-08, -1.136109e-07]), "2010": numpy.array([ -2.15e-08, -2.15e-08, -8.611353e-08, -8.611353e-08, -1.247637e-07])}}, { "anpv": { "stock cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, -0.5), numpy.pmt(0.07, 1, -0.5), numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 2, 0.4345794), numpy.pmt(0.07, 5, 2.887211)]), "2010": numpy.array([ numpy.pmt(0.07, 1, -0.5), numpy.pmt(0.07, 1, -0.5), numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 2, 0.2009346), numpy.pmt(0.07, 5, 2.040408)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "energy cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, 0.9345794), numpy.pmt(0.07, 1, 0.9345794), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 5, 4.100197)]), "2010": numpy.array([ numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 5, 3.075148)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "carbon cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, 0.4672897), numpy.pmt(0.07, 1, 0.4672897), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 5, 2.050099)]), "2010": numpy.array([ numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 5, 3.075148)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}}, "irr (w/ energy costs)": {"2009": numpy.array([1.00, 1.00, 3.45, 3.45, 4.00]), "2010": numpy.array([0.50, 0.50, 2.44, 2.44, 2.99])}, "irr (w/ energy and carbon costs)": {"2009": numpy.array([2.00, 2.00, 4.54, 4.54, 5.00]), "2010": numpy.array([2.00, 2.00, 4.09, 4.09, 4.50])}, "payback (w/ energy costs)": {"2009": numpy.array([0.50, 0.50, 0.25, 0.25, 0.25]), "2010": numpy.array([0.67, 0.67, 0.33, 0.33, 0.33])}, "payback (w/ energy and carbon costs)": {"2009": numpy.array([0.33, 0.33, 0.20, 0.20, 0.20]), "2010": numpy.array([0.33, 0.33, 0.22, 0.22, 0.22])}}] cls.ok_out_dist4 = [{ "savings and portfolio metrics": { "Technical potential": { "uncompeted": True, "competed": True}, "Max adoption potential": { "uncompeted": False, "competed": True}}, "consumer metrics": False}, { "stock": { "cost savings (total)": { "2009": numpy.array([-5.1, -2.7, -4.1, -4.2, -5.5]), "2010": numpy.array([-5.1, -3.7, -6.7, -4.2, -5.5])}, "cost savings (annual)": { "2009": numpy.array([-5.1, -2.7, -4.1, -4.2, -5.5]), "2010": numpy.array([-5.1, -3.7, -6.7, -4.2, -5.5])}}, "energy": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 100, "2010": 100}, "cost savings (total)": {"2009": 10, "2010": 15}, "cost savings (annual)": {"2009": 10, "2010": 15}}, "carbon": { "savings (total)": {"2009": 150, "2010": 200}, "savings (annual)": {"2009": 50, "2010": 50}, "cost savings (total)": {"2009": 5, "2010": 15}, "cost savings (annual)": {"2009": 5, "2010": 15}}}, { "cce": { "2009": numpy.array([ 0.036380, 0.019260, -0.01934271, -0.01897398, -0.04613129]), "2010": numpy.array([ 0.027285, 0.019795, -0.02023954, -0.02715319, -0.05525120])}, "cce (w/ carbon cost benefits)": { "2009": numpy.array([ 0.003046667, -0.01407333, -0.05267604, -0.05230731, -0.07946463]), "2010": numpy.array([ -0.047715000, -0.05520500, -0.09523954, -0.10215319, -0.13025120])}, "ccc": { "2009": numpy.array([ 3.6380e-08, 1.9260e-08, -1.934271e-08, -1.897398e-08, -4.613129e-08]), "2010": numpy.array([ 2.7285e-08, 1.9795e-08, -2.023954e-08, -2.715319e-08, -5.525120e-08])}, "ccc (w/ energy cost benefits)": { "2009": numpy.array([ -3.028667e-08, -4.740667e-08, -8.600937e-08, -8.564064e-08, -1.127980e-07]), "2010": numpy.array([ -4.771500e-08, -5.520500e-08, -9.523954e-08, -1.021532e-07, -1.302512e-07])}}, { "anpv": { "stock cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, -0.51), numpy.pmt(0.07, 1, -0.27), numpy.pmt(0.07, 2, 0.5245794), numpy.pmt(0.07, 2, 0.5145794), numpy.pmt(0.07, 5, 2.837211)]), "2010": numpy.array([ numpy.pmt(0.07, 1, -0.255), numpy.pmt(0.07, 1, -0.185), numpy.pmt(0.07, 2, 0.3659346), numpy.pmt(0.07, 2, 0.4909346), numpy.pmt(0.07, 5, 2.265408)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "energy cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, 0.9345794), numpy.pmt(0.07, 1, 0.9345794), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 2, 1.808018), numpy.pmt(0.07, 5, 4.100197)]), "2010": numpy.array([ numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 5, 3.075148)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}, "carbon cost": { "residential": { "2009": numpy.array([ numpy.pmt(0.07, 1, 0.4672897), numpy.pmt(0.07, 1, 0.4672897), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 2, 0.9040091), numpy.pmt(0.07, 5, 2.050099)]), "2010": numpy.array([ numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 1, 0.7009346), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 2, 1.356014), numpy.pmt(0.07, 5, 3.075148)])}, "commercial": { "2009": numpy.repeat(None, 5), "2010": numpy.repeat(None, 5)}}}, "irr (w/ energy costs)": {"2009": numpy.array([ 0.9607843, 2.703704, 4.335205, 4.218185, 3.631559]), "2010": numpy.array([ 1.9411765, 3.054054, 3.931585, 6.612039, 5.452729])}, "irr (w/ energy and carbon costs)": {"2009": numpy.array([ 1.941176, 4.555556, 5.647891, 5.501689, 4.543007]), "2010": numpy.array([ 4.882353, 7.108108, 6.327488, 10.343948, 8.181351])}, "payback (w/ energy costs)": {"2009": numpy.array([ 0.51, 0.2700000, 0.2050000, 0.21, 0.2750000]), "2010": numpy.array([ 0.34, 0.2466667, 0.2233333, 0.14, 0.1833333])}, "payback (w/ energy and carbon costs)": {"2009": numpy.array([ 0.34, 0.1800000, 0.1640000, 0.16800000, 0.2200000]), "2010": numpy.array([ 0.17, 0.1233333, 0.1488889, 0.09333333, 0.1222222])}}] cls.ok_savings_mkts_comp_schemes = ["competed", "uncompeted"] def test_metrics_ok_point_res(self): """Test output given residential measure with point value inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_point'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_res) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_point # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # For first test case, verify correct adoption/competition scenario # keys for measure markets/savings/portfolio metrics for adopt_scheme in self.handyvars.adopt_schemes: # Markets self.assertEqual(list(sorted( engine_instance.measures[0].markets[adopt_scheme].keys())), self.ok_savings_mkts_comp_schemes) # Savings self.assertEqual(list(sorted( engine_instance.measures[0].savings[adopt_scheme].keys())), self.ok_savings_mkts_comp_schemes) # Portfolio metrics self.assertEqual(list(sorted(engine_instance.measures[ 0].portfolio_metrics[adopt_scheme].keys())), self.ok_savings_mkts_comp_schemes) # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_point_res[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_point_res[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_point_res[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_point_res[3]) def test_metrics_ok_point_com(self): """Test output given commercial measure with point value inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_point'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_com) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_point # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_point_com[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_point_com[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_point_com[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_point_com[3]) def test_metrics_ok_distrib1(self): """Test output given residential measure with array inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_dist1'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_res) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_dist1 # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_dist1[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist1[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist1[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_dist1[3]) def test_metrics_ok_distrib2(self): """Test output given residential measure with array inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_dist2'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_res) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_dist2 # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_dist2[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist2[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist2[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_dist2[3]) def test_metrics_ok_distrib3(self): """Test output given residential measure with array inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_dist3'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_res) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_dist3 # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_dist3[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist3[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist3[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_dist3[3]) def test_metrics_ok_distrib4(self): """Test output given residential measure with array inputs.""" # Initialize test measure and assign it a sample 'uncompeted' # market ('ok_master_mseg_dist4'), the focus of this test suite test_meas = run.Measure(self.handyvars, **self.sample_measure_res) test_meas.markets[self.test_adopt_scheme]["uncompeted"][ "master_mseg"] = self.ok_master_mseg_dist4 # Create Engine instance using test measure, run function on it engine_instance = run.Engine(self.handyvars, [test_meas]) engine_instance.calc_savings_metrics( self.test_adopt_scheme, "uncompeted") # Verify test measure results update status self.dict_check(engine_instance.measures[ 0].update_results, self.ok_out_dist4[0]) # Verify test measure savings self.dict_check(engine_instance.measures[0].savings[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist4[1]) # Verify test measure portfolio-level financial metrics self.dict_check(engine_instance.measures[0].portfolio_metrics[ self.test_adopt_scheme]["uncompeted"], self.ok_out_dist4[2]) # Verify test measure consumer-level metrics self.dict_check(engine_instance.measures[ 0].consumer_metrics, self.ok_out_dist4[3]) class MetricUpdateTest(unittest.TestCase, CommonMethods): """Test the operation of the 'metrics_update' function. Verify that cashflow inputs generate expected prioritization metric outputs. Attributes: handyvars (object): Useful variables across the class. measure_list (list): List for Engine including one sample residential measure. ok_num_units (int): Sample number of competed units. ok_base_life (int): Sample baseline technology lifetime. ok_product_lifetime (float): Sample measure lifetime. ok_life_ratio (int): Sample measure->baseline lifetime ratio. ok_base_scost (int): Sample baseline stock cost. ok_scostsave (int): Sample baseline->measure stock cost delta. ok_esave (int): Sample measure energy savings. ok_ecostsave (int): Sample measure energy cost savings. ok_csave (int): Sample measure avoided carbon emissions. ok_ccostsave (int): Sample measure avoided carbon costs. ok_out_dicts (list): Output annuity equivalent Net Present Value dicts that should be generated given valid sample inputs. ok_out_array (list): Other financial metric values that should be generated given valid sample inputs. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) sample_measure = CommonTestMeasures().sample_measure4 cls.measure_list = [run.Measure(cls.handyvars, **sample_measure)] cls.ok_base_life = 3 cls.ok_product_lifetime = 6.2 cls.ok_life_ratio = 2 cls.ok_base_scost = 1 cls.ok_meas_sdelt = -1 cls.ok_esave = 7.5 cls.ok_ecostsave = 0.5 cls.ok_csave = 50 cls.ok_ccostsave = 1 cls.ok_out_array = [ numpy.pmt(0.07, 6, -0.1837021), numpy.pmt(0.07, 6, 2.38327), numpy.pmt(0.07, 6, 4.76654), None, None, None, 0.62, 1.59, 2, 0.67, 0.005, -0.13, 7.7e-10, -9.2e-9] def test_metric_updates(self): """Test for correct outputs given valid inputs.""" # Create an Engine instance using sample_measure list engine_instance = run.Engine(self.handyvars, self.measure_list) # Record the output for the test run of the 'metric_update' # function function_output = engine_instance.metric_update( self.measure_list[0], self.ok_base_life, int(self.ok_product_lifetime), self.ok_base_scost, self.ok_meas_sdelt, self.ok_esave, self.ok_ecostsave, self.ok_csave, self.ok_ccostsave) # Test that valid inputs yield correct anpv, irr, payback, and # cost of conserved energy/carbon outputs for ind, x in enumerate(self.ok_out_array): if x is not None: self.assertAlmostEqual(function_output[ind], x, places=2) else: self.assertEqual(function_output[ind], x) class PaybackTest(unittest.TestCase): """Test the operation of the 'payback' function. Verify cashflow input generates expected payback output. Attributes: handyvars (object): Useful variables across the class. measure_list (list): List for Engine including one sample residential measure. ok_cashflows (list): Set of sample input cash flows. ok_out (list): Outputs that should be generated for each set of sample cash flows. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) sample_measure = CommonTestMeasures().sample_measure cls.measure_list = [run.Measure(cls.handyvars, **sample_measure)] cls.ok_cashflows = [[-10, 1, 1, 1, 1, 5, 7, 8], [-10, 14, 2, 3, 4], [-10, 0, 1, 2], [10, 4, 7, 8, 10], [-100, 0, 1]] cls.ok_out = [5.14, 0.71, 6.5, 0, 999] def test_cashflow_paybacks(self): """Test for correct outputs given valid inputs.""" # Create an Engine instance using sample_measure list engine_instance = run.Engine(self.handyvars, self.measure_list) # Test that valid input cashflows yield correct output payback values for idx, cf in enumerate(self.ok_cashflows): self.assertAlmostEqual(engine_instance.payback(cf), self.ok_out[idx], places=2) class ResCompeteTest(unittest.TestCase, CommonMethods): """Test 'compete_res_primary,' and 'htcl_adj'. Verify that 'compete_res_primary' correctly calculates primary market shares and updates master microsegments for a series of competing residential measures; and that 'htcl_adj' properly accounts for heating and cooling supply-demand overlaps. Attributes: handyvars (object): Useful variables across the class. test_adopt_scheme (string): Sample consumer adoption scheme. test_htcl_adj (dict): Sample dict with supply-demand overlap data. adjust_key1 (string): First sample string for competed demand-side and supply-side market microsegment key chain being tested. adjust_key2 (string): Second sample string for competed demand-side and supply-side market microsegment key chain being tested. compete_meas1 (dict): Sample residential demand-side cooling measure 1. compete_meas1_dist (dict): Alternative version of sample residential demand-side cooling measure 1 including lists of energy/carbon and associated cost input values instead of point values. compete_meas2 (dict): Sample residential demand-side cooling measure 2. compete_meas3 (dict): Sample residential supply-side cooling measure 1. compete_meas3_dist (dict): Alternative version of sample residential supply-side cooling measure 1 including lists of stock cost input values instead of point values. compete_meas4 (dict): Sample residential supply-side cooling measure 2. compete_meas5 (dict): Sample residential supply-side cooling measure 3. measures_all (list): List of all competing/interacting sample Measure objects with point value inputs. measures_demand (list): Demand-side subset of 'measures_all'. measures_supply (list): Supply-side subset of 'measures_all'. measures_overlap1 (dict): List of supply-side Measure objects and associated contributing microsegment keys that overlap with 'measures_demand' Measure objects. measures_overlap2 (dict): List of demand-side Measure objects and associated contributing microsegment keys that overlap with 'measures_supply' Measure objects. a_run (object): Analysis engine object incorporating all 'measures_all' objects. measures_all_dist (list): List including competing/interacting sample Measure objects with array inputs. measures_demand_dist (list): Demand-side subset of 'measures_all_dist'. measures_supply_dist (list): Supply-side subset of 'measures_all_dist'. measures_overlap1_dist (dict): List of supply-side Measure objects and associated contributing microsegment keys that overlap with 'measures_demand_dist' Measure objects. measures_overlap2_dist (dict): List of demand-side Measure objects and associated contributing microsegment keys that overlap with 'measures_supply_dist' Measure objects. a_run_dist (object): Engine object incorporating all 'measures_all_dist' objects. measure_master_msegs_out (dict): Master market microsegments that should be generated for each Measure object in 'measures_all' following competition and supply-demand overlap adjustments. measure_master_msegs_out_dist (dict): Master market microsegments that should be generated for each Measure object in 'measures_all_dist' following competition and supply-demand overlap adjustments. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) cls.handyvars.aeo_years = ["2009", "2010"] cls.handyvars.retro_rate = 0 cls.test_adopt_scheme = "Max adoption potential" cls.adjust_key1 = str( ('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'demand', 'windows', 'existing')) cls.adjust_key2 = str( ('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing')) cls.test_htcl_adj = { "supply": { "['AIA_CZ1', 'single family home', 'existing']": { "total": { yr: 10 for yr in cls.handyvars.aeo_years}, "total affected": { yr: 5 for yr in cls.handyvars.aeo_years}, "affected savings": { yr: 5 for yr in cls.handyvars.aeo_years}}, }, "demand": { "['AIA_CZ1', 'single family home', 'existing']": { "total": { yr: 10 for yr in cls.handyvars.aeo_years}, "total affected": { yr: 5 for yr in cls.handyvars.aeo_years}, "affected savings": { yr: 5 for yr in cls.handyvars.aeo_years}}, }} cls.compete_meas1 = { "name": "sample compete measure r1", "climate_zone": ["AIA_CZ1"], "bldg_type": ["single family home"], "end_use": {"primary": ["cooling"], "secondary": None}, "technology": ["windows"], "technology_type": {"primary": "demand", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": 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"competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}}} cls.compete_meas1_dist = { "name": "sample compete measure r1 dist", "climate_zone": ["AIA_CZ1"], "bldg_type": ["single family home"], "end_use": {"primary": ["cooling"], "secondary": None}, "technology": ["windows"], "technology_type": {"primary": "demand", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { 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numpy.array([5, 6, 7])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": { "2009": numpy.array([20, 21, 22]), "2010": numpy.array([20, 21, 22])}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": { "2009": numpy.array([15, 16, 17]), "2010": numpy.array( [15, 16, 17])}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": { "2009": 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{"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}, "supply-demand adjustment": { "savings": { cls.adjust_key1: { "2009": 0, "2010": 0}}, "total": { cls.adjust_key1: { "2009": 100, "2010": 100}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": { "2009": numpy.array([15, 16, 17]), "2010": numpy.array([15, 16, 17])}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": { "2009": numpy.array([20, 21, 22]), "2010": numpy.array([20, 21, 22])}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": { "2009": numpy.array([15, 16, 17]), "2010": numpy.array([15, 16, 17])}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": { "2009": numpy.array([20, 21, 22]), "2010": numpy.array([20, 21, 22])}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": { "2009": numpy.array([15, 16, 17]), "2010": numpy.array( [15, 16, 17])}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": { "2009": numpy.array([20, 21, 22]), "2010": numpy.array( [20, 21, 22])}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": numpy.array( [15, 16, 17]), "2010": numpy.array( [15, 16, 17])}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array( [5, 6, 7])}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": numpy.array( [20, 21, 22]), "2010": numpy.array( [20, 21, 22])}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array( [5, 6, 7])}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": 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"2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 20, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key1: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key1: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}} }, "mseg_out_break": {}}}} cls.compete_meas3 = { "name": "sample compete measure r3", "climate_zone": ["AIA_CZ1"], "bldg_type": ["single family home"], "end_use": {"primary": ["cooling"], "secondary": None}, "technology": ["ASHP"], "technology_type": {"primary": "supply", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 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5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, 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"energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { 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"secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": { "2009": numpy.array([0, 1, 2]), "2010": numpy.array([0, 1, 2])}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": numpy.array( [5, 6, 7]), "2010": numpy.array( [5, 6, 7])}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": numpy.array( [0, 1, 2]), "2010": numpy.array( [0, 1, 2])}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}, "supply-demand adjustment": { "savings": { cls.adjust_key2: { "2009": 0, "2010": 0}}, "total": { cls.adjust_key2: { "2009": 100, "2010": 100}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 10}, "efficient": { "2009": numpy.array([5, 6, 7]), "2010": numpy.array([5, 6, 7])}}, "competed": { "baseline": {"2009": 5, "2010": 5}, "efficient": { "2009": numpy.array([0, 1, 2]), "2010": numpy.array([0, 1, 2])}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { 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"2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}, "supply-demand adjustment": { "savings": { cls.adjust_key2: { "2009": 0, "2010": 0}}, "total": { cls.adjust_key2: { "2009": 100, "2010": 100}}}}, "mseg_out_break": {}}}} cls.compete_meas4 = { "name": "sample compete measure r4", "climate_zone": ["AIA_CZ1"], "bldg_type": ["single family home"], "end_use": {"primary": ["cooling"], "secondary": None}, "technology": ["ASHP"], "technology_type": {"primary": "supply", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 20, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 20, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}}, "mseg_out_break": {}}}} cls.compete_meas5 = { "name": "sample compete measure r5", "climate_zone": ["AIA_CZ1"], "bldg_type": ["single family home"], "end_use": {"primary": ["cooling"], "secondary": None}, "technology": ["ASHP"], "technology_type": {"primary": "supply", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 30, "2010": 30}, "measure": {"2009": 30, "2010": 30}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": {"2009": 15, "2010": 15}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}, "cost": { "stock": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, str(('primary', 'AIA_CZ2', 'multi family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 30, "2010": 30}, "measure": {"2009": 30, "2010": 30}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": {"2009": 15, "2010": 15}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}, "cost": { "stock": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.adjust_key2: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, str(('primary', 'AIA_CZ2', 'multi family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.adjust_key2: { "b1": {"2009": -0.95, "2010": -0.95}, "b2": {"2009": -0.10, "2010": -0.10}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": {}, "original energy (competed and captured)": {}, "adjusted energy (total captured)": {}, "adjusted energy (competed and captured)": {}}}}, "mseg_out_break": {}}}} cls.measures_all = [run.Measure(cls.handyvars, **x) for x in [ cls.compete_meas1, copy.deepcopy(cls.compete_meas2), cls.compete_meas3, copy.deepcopy(cls.compete_meas4), copy.deepcopy(cls.compete_meas5)]] cls.measures_demand = cls.measures_all[0:2] cls.measures_supply = cls.measures_all[2:5] cls.measures_overlap1 = { "measures": cls.measures_all[2:5], "keys": [[str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))]]} cls.measures_overlap2 = { "measures": cls.measures_all[0:2], "keys": [[str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'demand', 'windows', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'demand', 'windows', 'existing'))]]} cls.a_run = run.Engine(cls.handyvars, cls.measures_all) # Set information needed to finalize point value test measure # consumer metrics consumer_metrics_final = [{ "stock cost": { "residential": { "2009": 95, "2010": 95}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -150, "2010": -150}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -150, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 120, "2010": 120}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -400, "2010": -400}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -50, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 95, "2010": 95}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -150, "2010": -150}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -150, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 120, "2010": 120}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -400, "2010": -400}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -50, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 100, "2010": 100}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -200, "2010": -200}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -100, "2010": -100}, "commercial": { "2009": None, "2010": None}}}] # Adjust/finalize point value test measure consumer metrics for ind, m in enumerate(cls.a_run.measures): m.consumer_metrics['anpv'] = consumer_metrics_final[ind] cls.measures_all_dist = [run.Measure(cls.handyvars, **x) for x in [ cls.compete_meas1_dist, copy.deepcopy(cls.compete_meas2), cls.compete_meas3_dist, copy.deepcopy(cls.compete_meas4), copy.deepcopy(cls.compete_meas5)]] cls.measures_demand_dist = cls.measures_all_dist[0:2] cls.measures_supply_dist = cls.measures_all_dist[2:5] cls.supply_demand_adjust1_dist = cls.measures_all_dist[0:2] cls.supply_demand_adjust2_dist = cls.measures_all_dist[2:5] cls.measures_overlap1_dist = { "measures": cls.measures_all_dist[2:5], "keys": [[str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'supply', 'ASHP', 'existing'))]]} cls.measures_overlap2_dist = { "measures": cls.measures_all_dist[0:2], "keys": [[str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'demand', 'windows', 'existing'))], [str(('primary', 'AIA_CZ1', 'single family home', 'electricity (grid)', 'cooling', 'demand', 'windows', 'existing'))]]} cls.a_run_dist = run.Engine(cls.handyvars, cls.measures_all_dist) # Set information needed to finalize array test measure consumer # metrics consumer_metrics_final_dist = [{ "stock cost": { "residential": { "2009": 95, "2010": 95}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": numpy.array([-150, -200, -100]), "2010": numpy.array([-150, -200, -100])}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": numpy.array([-150, -200, -100]), "2010": numpy.array([-50, -100, -10])}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 120, "2010": 120}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -400, "2010": -400}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -50, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": numpy.array([95, 100, 90]), "2010": numpy.array([95, 100, 90])}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -150, "2010": -150}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -150, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 120, "2010": 120}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -400, "2010": -400}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -50, "2010": -50}, "commercial": { "2009": None, "2010": None}}}, { "stock cost": { "residential": { "2009": 100, "2010": 100}, "commercial": { "2009": None, "2010": None}}, "energy cost": { "residential": { "2009": -200, "2010": -200}, "commercial": { "2009": None, "2010": None}}, "carbon cost": { "residential": { "2009": -100, "2010": -100}, "commercial": { "2009": None, "2010": None}}}] # Adjust/finalize point value test measure consumer metrics for ind, m in enumerate(cls.a_run_dist.measures): m.consumer_metrics['anpv'] = consumer_metrics_final_dist[ind] cls.measures_master_msegs_out = [{ "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 2.23, "2010": 2.23}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 1.11, "2010": 1.11}}}, "energy": { "total": { "baseline": {"2009": 2.227001, "2010": 2.227001}, "efficient": {"2009": 1.670251, "2010": 1.670251}}, "competed": { "baseline": {"2009": 1.113501, "2010": 1.113501}, "efficient": {"2009": 0.5567503, "2010": 0.5567503}}}, "carbon": { "total": { "baseline": {"2009": 3.340502, "2010": 3.340502}, "efficient": {"2009": 2.227001, "2010": 2.227001}}, "competed": { "baseline": {"2009": 1.670251, "2010": 1.670251}, "efficient": {"2009": 0.5567503, "2010": 0.5567503}}}, "cost": { "stock": { "total": { "baseline": {"2009": 2.227001, "2010": 2.227001}, "efficient": {"2009": 1.113501, "2010": 1.113501}}, "competed": { "baseline": {"2009": 1.113501, "2010": 1.113501}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 2.227001, "2010": 2.227001}, "efficient": {"2009": 1.670251, "2010": 1.670251}}, "competed": { "baseline": {"2009": 1.113501, "2010": 1.113501}, "efficient": {"2009": 0.5567503, "2010": 0.5567503}}}, "carbon": { "total": { "baseline": {"2009": 3.340502, "2010": 3.340502}, "efficient": {"2009": 2.227001, "2010": 2.227001}}, "competed": { "baseline": {"2009": 1.670251, "2010": 1.670251}, "efficient": {"2009": 0.5567503, "2010": 0.5567503}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 17.77, "2010": 17.77}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 8.89, "2010": 8.89}}}, "energy": { "total": { "baseline": {"2009": 27.77300, "2010": 27.77300}, "efficient": {"2009": 20.82975, "2010": 20.82975}}, "competed": { "baseline": {"2009": 13.88650, "2010": 13.88650}, "efficient": {"2009": 6.943250, "2010": 6.943250}}}, "carbon": { "total": { "baseline": {"2009": 41.65950, "2010": 41.65950}, "efficient": {"2009": 27.77300, "2010": 27.77300}}, "competed": { "baseline": {"2009": 20.82975, "2010": 20.82975}, "efficient": {"2009": 6.943250, "2010": 6.943250}}}, "cost": { "stock": { "total": { "baseline": {"2009": 17.77300, "2010": 17.77300}, "efficient": {"2009": 8.886499, "2010": 8.886499}}, "competed": { "baseline": {"2009": 8.886499, "2010": 8.886499}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 27.77300, "2010": 27.77300}, "efficient": {"2009": 20.82975, "2010": 20.82975}}, "competed": { "baseline": {"2009": 13.88650, "2010": 13.88650}, "efficient": {"2009": 6.943250, "2010": 6.943250}}}, "carbon": { "total": { "baseline": {"2009": 41.65950, "2010": 41.65950}, "efficient": {"2009": 27.77300, "2010": 27.77300}}, "competed": { "baseline": {"2009": 20.82975, "2010": 20.82975}, "efficient": {"2009": 6.943250, "2010": 6.943250}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 1.73, "2010": 1.73}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 0.87, "2010": 0.87}}}, "energy": { "total": { "baseline": {"2009": 1.73179114, "2010": 1.73179114}, "efficient": {"2009": 1.29884336, "2010": 1.29884336}}, "competed": { "baseline": {"2009": 0.865895571, "2010": 0.865895571}, "efficient": {"2009": 0.432947785, "2010": 0.432947785}}}, "carbon": { "total": { "baseline": {"2009": 2.59768671, "2010": 2.59768671}, "efficient": {"2009": 1.73179114, "2010": 1.73179114}}, "competed": { "baseline": {"2009": 1.29884336, "2010": 1.29884336}, "efficient": {"2009": 0.432947785, "2010": 0.432947785}}}, "cost": { "stock": { "total": { "baseline": { "2009": 1.73179114, "2010": 1.73179114}, "efficient": { "2009": 0.865895571, "2010": 0.865895571}}, "competed": { "baseline": {"2009": 0.865895571, "2010": 0.865895571}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 1.73179114, "2010": 1.73179114}, "efficient": { "2009": 1.29884336, "2010": 1.29884336}}, "competed": { "baseline": { "2009": 0.865895571, "2010": 0.865895571}, "efficient": { "2009": 0.432947785, "2010": 0.432947785}}}, "carbon": { "total": { "baseline": 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10.05]), "2010": numpy.array([11.11, 11.34, 10.05])}}}, "energy": { "total": { "baseline": { "2009": numpy.array([ 42.22366, 42.68455, 40.10668]), "2010": numpy.array([ 42.22366, 42.68455, 40.10668])}, "efficient": { "2009": numpy.array([ 31.66775, 32.01341, 30.08001]), "2010": numpy.array([ 31.66775, 32.01341, 30.08001])}}, "competed": { "baseline": { "2009": numpy.array([ 21.11183, 21.34227, 20.05334]), "2010": numpy.array([ 21.11183, 21.34227, 20.05334])}, "efficient": { "2009": numpy.array([ 10.55592, 10.67114, 10.02667]), "2010": numpy.array([ 10.55592, 10.67114, 10.02667])}}}, "carbon": { "total": { "baseline": { "2009": numpy.array([ 63.33550, 64.02682, 60.16002]), "2010": numpy.array([ 63.33550, 64.02682, 60.16002])}, "efficient": { "2009": numpy.array([ 42.22366, 42.68455, 40.10668]), "2010": numpy.array([ 42.22366, 42.68455, 40.10668])}}, "competed": { "baseline": { "2009": numpy.array([ 31.66775, 32.01341, 30.08001]), "2010": numpy.array([ 31.66775, 32.01341, 30.08001])}, "efficient": { "2009": numpy.array([ 10.55592, 10.67114, 10.02667]), "2010": numpy.array([ 10.55592, 10.67114, 10.02667])}}}, "cost": { "stock": { "total": { "baseline": { "2009": numpy.array([ 22.22366, 22.68455, 20.10668]), "2010": numpy.array([ 22.22366, 22.68455, 20.10668])}, "efficient": { "2009": numpy.array([ 11.11183, 11.34227, 10.05334]), "2010": numpy.array([ 11.11183, 11.34227, 10.05334])}}, "competed": { "baseline": { "2009": numpy.array([ 11.11183, 11.34227, 10.05334]), "2010": numpy.array([ 11.11183, 11.34227, 10.05334])}, "efficient": { "2009": numpy.array([0, 0, 0]), "2010": numpy.array([0, 0, 0])}}}, "energy": { "total": { "baseline": { "2009": numpy.array([ 42.22366, 42.68455, 40.10668]), "2010": numpy.array([ 42.22366, 42.68455, 40.10668])}, "efficient": { "2009": numpy.array([ 31.66775, 32.01341, 30.08001]), "2010": numpy.array([ 31.66775, 32.01341, 30.08001])}}, "competed": { "baseline": { "2009": numpy.array([ 21.11183, 21.34227, 20.05334]), "2010": numpy.array([ 21.11183, 21.34227, 20.05334])}, "efficient": { "2009": numpy.array([ 10.55592, 10.67114, 10.02667]), "2010": numpy.array([ 10.55592, 10.67114, 10.02667])}}}, "carbon": { "total": { "baseline": { "2009": numpy.array([ 63.33550, 64.02682, 60.16002]), "2010": numpy.array([ 63.33550, 64.02682, 60.16002])}, "efficient": { "2009": numpy.array([ 42.22366, 42.68455, 40.10668]), "2010": numpy.array([ 42.22366, 42.68455, 40.10668])}}, "competed": { "baseline": { "2009": numpy.array([ 31.66775, 32.01341, 30.08001]), "2010": numpy.array([ 31.66775, 32.01341, 30.08001])}, "efficient": { "2009": numpy.array([ 10.55592, 10.67114, 10.02667]), "2010": numpy.array([ 10.55592, 10.67114, 10.02667])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}] def test_compete_res(self): """Test outcomes given valid sample measures w/ point value inputs.""" # Run the measure competition routine on sample demand-side measures self.a_run.compete_res_primary( self.measures_demand, self.adjust_key1, self.test_adopt_scheme) # Remove any market overlaps across the supply and demand sides of # heating and cooling self.a_run.htcl_adj( self.measures_demand, self.test_adopt_scheme, self.test_htcl_adj) # Run the measure competition routine on sample supply-side measures self.a_run.compete_res_primary( self.measures_supply, self.adjust_key2, self.test_adopt_scheme) # Remove any market overlaps across the supply and demand sides of # heating and cooling self.a_run.htcl_adj( self.measures_supply, self.test_adopt_scheme, self.test_htcl_adj) # Check updated competed master microsegments for each sample measure # following competition/supply-demand overlap adjustments for ind, d in enumerate(self.a_run.measures): self.dict_check( self.measures_master_msegs_out[ind], self.a_run.measures[ind].markets[self.test_adopt_scheme][ "competed"]["master_mseg"]) def test_compete_res_dist(self): """Test outcomes given valid sample measures w/ some array inputs.""" # Run the measure competition routine on sample demand-side measures self.a_run_dist.compete_res_primary( self.measures_demand_dist, self.adjust_key1, self.test_adopt_scheme) # Remove any market overlaps across the supply and demand sides of # heating and cooling self.a_run_dist.htcl_adj( self.measures_demand_dist, self.test_adopt_scheme, self.test_htcl_adj) # Run the measure competition routine on sample supply-side measures self.a_run_dist.compete_res_primary( self.measures_supply_dist, self.adjust_key2, self.test_adopt_scheme) # Remove any market overlaps across the supply and demand sides of # heating and cooling self.a_run_dist.htcl_adj( self.measures_supply_dist, self.test_adopt_scheme, self.test_htcl_adj) # Check updated competed master microsegments for each sample measure # following competition/supply-demand overlap adjustments for ind, d in enumerate(self.a_run_dist.measures): self.dict_check( self.measures_master_msegs_out_dist[ind], self.a_run_dist.measures[ind].markets[self.test_adopt_scheme][ "competed"]["master_mseg"]) class ComCompeteTest(unittest.TestCase, CommonMethods): """Test 'compete_com_primary' and 'secondary_adj' functions. Verify that 'compete_com_primary' correctly calculates primary market shares and updates master microsegments for a series of competing commercial measures; and that 'secondary_adj' correctly adjusts any secondary markets associated with these primary market microsegments. Attributes: handyvars (object): Useful variables across the class. test_adopt_scheme (string): Sample consumer adoption scheme. overlap_key (string): First sample string for competed primary market microsegment key chain being tested. overlap_key_scnd (string): Second sample string for secondary market microsegment key chain being tested. secnd_adj_key (string): Key used to link primary and secondary market microsegments (by climate, building type, structure type). compete_meas1 (dict): Sample commercial supply-side lighting measure 1. compete_meas2 (dict): Sample commercial supply-side lighting measure 2. compete_meas3 (dict): Sample commercial supply-side lighting measure 3. compete_meas_dist (dict): Alternative version of sample commercial supply-side lighting measure 1 including lists stock cost input values instead of point values. measures_all (list): List of all competing measures with point value inputs. measures_secondary (list): Subset of 'measures_all' with secondary microsegments to adjust. a_run (object): Analysis engine object incorporating all 'measures_primary' objects. measures_all_dist (list): List of competing measures including some measures with array inputs. measures_secondary_dist (list): Subset of 'measures_all_dist' with secondary microsegments to adjust. a_run_dist (object): Analysis engine object incorporating all 'measures_primary_dist' objects. measures_overlap (dict): List of supply-side Measure objects and associated contributing microsegment keys that overlap with 'measures_demand' Measure objects. measure_master_msegs_out (dict): Master market microsegments that should be generated for each Measure object in 'measures_all' following competition and supply-demand overlap adjustments. measure_master_msegs_out_dist (dict): Master market microsegments that should be generated for each Measure object in 'measures_all_dist' following competition and supply-demand overlap adjustments. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) cls.handyvars.retro_rate = 0 cls.handyvars.aeo_years = ["2009", "2010"] cls.test_adopt_scheme = "Max adoption potential" cls.overlap_key = str( ('primary', 'AIA_CZ1', 'assembly', 'electricity (grid)', 'lighting', 'reflector (LED)', 'existing')) cls.overlap_key_scnd = str( ('secondary', 'AIA_CZ1', 'assembly', 'electricity (grid)', 'cooling', 'demand', 'lighting gain', 'existing')) cls.secnd_adj_key = str(('AIA_CZ1', 'assembly', 'existing')) cls.compete_meas1 = { "name": "sample compete measure c1", "climate_zone": ["AIA_CZ1"], "bldg_type": ["assembly"], "end_use": { "primary": ["lighting"], "secondary": None}, "technology": ["reflector (LED)"], "technology_type": { "primary": "supply", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 20, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 20, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 30, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 10, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 40, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 10, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}}, "mseg_out_break": {}}}} cls.compete_meas2 = { "name": "sample compete measure c2", "climate_zone": ["AIA_CZ1"], "bldg_type": ["assembly"], "end_use": { "primary": ["lighting"], "secondary": ["heating", "secondary heating", "cooling"]}, "technology": ["reflector (LED)"], "technology_type": { "primary": "supply", "secondary": "demand"}, "market_entry_year": 2010, "market_exit_year": None, "yrs_on_mkt": ["2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 0, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 40, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 60, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 40, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 60, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 0, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 0, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, cls.overlap_key_scnd: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 10}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}, cls.overlap_key_scnd: { "rate distribution": {}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 0, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 40, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 10}}}, "carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 60, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 10}}}, "cost": { "stock": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 10}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 40, "2010": 30}}, "competed": { "baseline": {"2009": 20, "2010": 20}, "efficient": 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"efficient": { "2009": 0, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, cls.overlap_key_scnd: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 10}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 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"carbon": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 60, "2010": 40}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 10}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 0, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 0, "2010": 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distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}, cls.overlap_key_scnd: { "rate distribution": {}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}, "supply-demand adjustment": { "savings": {}, "total": {}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 0, "2010": 20}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}}, "energy": { "total": { "baseline": {"2009": 40, "2010": 40}, "efficient": {"2009": 40, "2010": 30}}, "competed": { "baseline": {"2009": 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values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 0, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 0, "2010": numpy.array( [5, 6, 7])}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": numpy.array( [0, 1, 2])}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, cls.overlap_key_scnd: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 0, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 20, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 30, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 15, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": numpy.array( [5, 6, 7])}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 5, "2010": numpy.array([ 0, 1, 2])}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 20, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 10, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 30, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 15, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}, cls.overlap_key_scnd: { "rate distribution": {}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}, "supply-demand adjustment": { "savings": {}, "total": {}}}, "mseg_out_break": {}}}} cls.compete_meas3 = { "name": "sample compete measure c3", "climate_zone": ["AIA_CZ1"], "bldg_type": ["assembly"], "end_use": { "primary": ["lighting"], "secondary": None}, "technology": ["reflector (LED)"], "technology_type": { "primary": "supply", "secondary": None}, "market_entry_year": 2009, "market_exit_year": None, "yrs_on_mkt": ["2009", "2010"], "markets": { "Technical potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 30, "2010": 30}, "measure": {"2009": 30, "2010": 30}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": {"2009": 15, "2010": 15}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}, "cost": { "stock": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, 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"efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}}, "mseg_out_break": {}}, "Max adoption potential": { "master_mseg": { "stock": { "total": { "all": {"2009": 30, "2010": 30}, "measure": {"2009": 30, "2010": 30}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": {"2009": 15, "2010": 15}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}, "cost": { "stock": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 60, "2010": 60}, "efficient": {"2009": 45, "2010": 45}}, "competed": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 15, "2010": 15}}}, "carbon": { "total": { "baseline": {"2009": 90, "2010": 90}, "efficient": {"2009": 60, "2010": 60}}, "competed": { "baseline": {"2009": 45, "2010": 45}, "efficient": {"2009": 15, "2010": 15}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, "mseg_adjust": { "contributing mseg keys and values": { cls.overlap_key: { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}}, str(('primary', 'AIA_CZ2', 'single family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, str(('primary', 'AIA_CZ2', 'multi family home', 'electricity (grid)', 'lighting', 'reflector (LED)')): { "stock": { "total": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 10, "2010": 10}}, "competed": { "all": {"2009": 5, "2010": 5}, "measure": {"2009": 5, "2010": 5}}}, "energy": { "total": { "baseline": {"2009": 20, "2010": 20}, "efficient": {"2009": 15, "2010": 15}}, "competed": { "baseline": {"2009": 10, "2010": 10}, "efficient": {"2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": {"2009": 30, "2010": 30}, "efficient": {"2009": 20, "2010": 20}}, "competed": { "baseline": {"2009": 15, "2010": 15}, "efficient": {"2009": 5, "2010": 5}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}, "competed": { "baseline": { "2009": 5, "2010": 5}, "efficient": { "2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": { "2009": 20, "2010": 20}, "efficient": { "2009": 15, "2010": 15}}, "competed": { "baseline": { "2009": 10, "2010": 10}, "efficient": { "2009": 5, "2010": 5}}}, "carbon": { "total": { "baseline": { "2009": 30, "2010": 30}, "efficient": { "2009": 20, "2010": 20}}, "competed": { "baseline": { "2009": 15, "2010": 15}, "efficient": { "2009": 5, "2010": 5}}}}, "lifetime": { "baseline": {"2009": 1, "2010": 1}, "measure": 1}, "sub-market scaling": 1}, "competed choice parameters": { cls.overlap_key: { "rate distribution": { "2009": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4], "2010": [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.4]}}}, "secondary mseg adjustments": { "market share": { "original energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "original energy (competed and captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (total captured)": { cls.secnd_adj_key: {"2009": 0, "2010": 0}}, "adjusted energy (competed and captured)": { cls.secnd_adj_key: { "2009": 0, "2010": 0}}}}}, "mseg_out_break": {}}}} cls.measures_all = [run.Measure( cls.handyvars, **x) for x in [ copy.deepcopy(cls.compete_meas1), cls.compete_meas2, copy.deepcopy(cls.compete_meas3)]] cls.measures_secondary = [cls.measures_all[1]] # Instantiate engine object based on above measures cls.a_run = run.Engine(cls.handyvars, cls.measures_all) # Set information needed to finalize array test measure consumer # metrics consumer_metrics = [{ "stock cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": 100, "rate 2": 110, "rate 3": 120, "rate 4": 130, "rate 5": 140, "rate 6": 150, "rate 7": 160}, "2010": { "rate 1": 100, "rate 2": 110, "rate 3": 120, "rate 4": 130, "rate 5": 140, "rate 6": 150, "rate 7": 160}}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -350, "rate 2": -60, "rate 3": -70, "rate 4": -380, "rate 5": -390, "rate 6": -150, "rate 7": -400}, "2010": { "rate 1": -350, "rate 2": -60, "rate 3": -70, "rate 4": -380, "rate 5": -390, "rate 6": -150, "rate 7": -400}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -40, "rate 2": -50, "rate 3": -55, "rate 4": -60, "rate 5": -65, "rate 6": -70, "rate 7": -75}, "2010": { "rate 1": -40, "rate 2": -50, "rate 3": -55, "rate 4": -60, "rate 5": -65, "rate 6": -70, "rate 7": -75}}}}, { "stock cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": 85, "rate 2": 90, "rate 3": 95, "rate 4": 100, "rate 5": 105, "rate 6": 110, "rate 7": 115}, "2010": { "rate 1": 85, "rate 2": 90, "rate 3": 95, "rate 4": 100, "rate 5": 105, "rate 6": 110, "rate 7": 115}}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -435, "rate 2": -440, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -370}, "2010": { "rate 1": -435, "rate 2": -440, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -370}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -135, "rate 2": -140, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -170}, "2010": { "rate 1": -135, "rate 2": -140, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -170}}}}, { "stock cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": 50, "rate 2": 60, "rate 3": 70, "rate 4": 80, "rate 5": 90, "rate 6": 100, "rate 7": 110}, "2010": { "rate 1": 50, "rate 2": 60, "rate 3": 70, "rate 4": 80, "rate 5": 90, "rate 6": 100, "rate 7": 110}}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -190, "rate 2": -195, "rate 3": -190, "rate 4": -205, "rate 5": -180, "rate 6": -230, "rate 7": -200}, "2010": { "rate 1": -190, "rate 2": -195, "rate 3": -190, "rate 4": -205, "rate 5": -180, "rate 6": -230, "rate 7": -200}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -90, "rate 2": -95, "rate 3": -100, "rate 4": -105, "rate 5": -110, "rate 6": -115, "rate 7": -120}, "2010": { "rate 1": -90, "rate 2": -95, "rate 3": -100, "rate 4": -105, "rate 5": -110, "rate 6": -115, "rate 7": -120}}}}] # Adjust/finalize point value test measure consumer metrics for ind, m in enumerate(cls.a_run.measures): m.consumer_metrics['anpv'] = consumer_metrics[ind] cls.measures_all_dist = [run.Measure( cls.handyvars, **x) for x in [ copy.deepcopy(cls.compete_meas1), cls.compete_meas2_dist, copy.deepcopy(cls.compete_meas3)]] cls.measures_secondary_dist = [cls.measures_all_dist[1]] cls.a_run_dist = run.Engine(cls.handyvars, cls.measures_all_dist) # Set information needed to finalize array test measure consumer # metrics consumer_metrics_dist = [{ "stock cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": 100, "rate 2": 110, "rate 3": 120, "rate 4": 130, "rate 5": 140, "rate 6": 150, "rate 7": 160}, "2010": { "rate 1": 100, "rate 2": 110, "rate 3": 120, "rate 4": 130, "rate 5": 140, "rate 6": 150, "rate 7": 160}}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -350, "rate 2": -60, "rate 3": -70, "rate 4": -380, "rate 5": -390, "rate 6": -150, "rate 7": -400}, "2010": { "rate 1": -350, "rate 2": -60, "rate 3": -70, "rate 4": -380, "rate 5": -390, "rate 6": -150, "rate 7": -400}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -40, "rate 2": -50, "rate 3": -55, "rate 4": -60, "rate 5": -65, "rate 6": -70, "rate 7": -75}, "2010": { "rate 1": -40, "rate 2": -50, "rate 3": -55, "rate 4": -60, "rate 5": -65, "rate 6": -70, "rate 7": -75}}}}, { "stock cost": { "residential": { "2009": None, "2010": None }, "commercial": { "2009": None, "2010": numpy.array([ { "rate 1": 85, "rate 2": 90, "rate 3": 95, "rate 4": 100, "rate 5": 105, "rate 6": 110, "rate 7": 115}, { "rate 1": 205, "rate 2": 100, "rate 3": 105, "rate 4": 110, "rate 5": 115, "rate 6": 120, "rate 7": 125}, { "rate 1": 105, "rate 2": 110, "rate 3": 115, "rate 4": 120, "rate 5": 125, "rate 6": 10, "rate 7": 135}])}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -435, "rate 2": -440, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -370}, "2010": { "rate 1": -435, "rate 2": -440, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -370}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -135, "rate 2": -140, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -170}, "2010": { "rate 1": -135, "rate 2": -140, "rate 3": -145, "rate 4": -150, "rate 5": -155, "rate 6": -160, "rate 7": -170}}}}, { "stock cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": 50, "rate 2": 60, "rate 3": 70, "rate 4": 80, "rate 5": 90, "rate 6": 100, "rate 7": 110}, "2010": { "rate 1": 50, "rate 2": 60, "rate 3": 70, "rate 4": 80, "rate 5": 90, "rate 6": 100, "rate 7": 110}}}, "energy cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -190, "rate 2": -195, "rate 3": -190, "rate 4": -205, "rate 5": -180, "rate 6": -230, "rate 7": -200}, "2010": { "rate 1": -190, "rate 2": -195, "rate 3": -190, "rate 4": -205, "rate 5": -180, "rate 6": -230, "rate 7": -200}}}, "carbon cost": { "residential": { "2009": None, "2010": None}, "commercial": { "2009": { "rate 1": -90, "rate 2": -95, "rate 3": -100, "rate 4": -105, "rate 5": -110, "rate 6": -115, "rate 7": -120}, "2010": { "rate 1": -90, "rate 2": -95, "rate 3": -100, "rate 4": -105, "rate 5": -110, "rate 6": -115, "rate 7": -120}}}}] # Adjust/finalize point value test measure consumer metrics for ind, m in enumerate(cls.a_run_dist.measures): m.consumer_metrics['anpv'] = consumer_metrics_dist[ind] cls.measures_master_msegs_out = [{ "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 17, "2010": 12}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 8.5, "2010": 6}}}, "energy": { "total": { "baseline": {"2009": 34, "2010": 24}, "efficient": {"2009": 25.5, "2010": 18}}, "competed": { "baseline": {"2009": 17, "2010": 12}, "efficient": {"2009": 8.5, "2010": 6}}}, "carbon": { "total": { "baseline": {"2009": 51, "2010": 36}, "efficient": {"2009": 34, "2010": 24}}, "competed": { "baseline": {"2009": 25.5, "2010": 18}, "efficient": {"2009": 8.5, "2010": 6}}}, "cost": { "stock": { "total": { "baseline": {"2009": 17, "2010": 12}, "efficient": {"2009": 8.5, "2010": 6}}, "competed": { "baseline": {"2009": 8.5, "2010": 6}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 34, "2010": 24}, "efficient": {"2009": 25.5, "2010": 18}}, "competed": { "baseline": {"2009": 17, "2010": 12}, "efficient": {"2009": 8.5, "2010": 6}}}, "carbon": { "total": { "baseline": {"2009": 51, "2010": 36}, "efficient": {"2009": 34, "2010": 24}}, "competed": { "baseline": {"2009": 25.5, "2010": 18}, "efficient": {"2009": 8.5, "2010": 6}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": {"2009": 0, "2010": 16}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": {"2009": 0, "2010": 8}}}, "energy": { "total": { "baseline": {"2009": 0, "2010": 24}, "efficient": {"2009": 0, "2010": 18}}, "competed": { "baseline": {"2009": 0, "2010": 12}, "efficient": {"2009": 0, "2010": 6}}}, "carbon": { "total": { "baseline": {"2009": 0, "2010": 36}, "efficient": {"2009": 0, "2010": 24}}, "competed": { "baseline": {"2009": 0, "2010": 18}, "efficient": {"2009": 0, "2010": 6}}}, "cost": { "stock": { "total": { "baseline": {"2009": 10, "2010": 16}, "efficient": {"2009": 20, "2010": 8}}, "competed": { "baseline": {"2009": 5, "2010": 8}, "efficient": {"2009": 10, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 0, "2010": 24}, "efficient": {"2009": 0, "2010": 18}}, "competed": { "baseline": {"2009": 0, "2010": 12}, "efficient": {"2009": 0, "2010": 6}}}, "carbon": { "total": { "baseline": {"2009": 0, "2010": 36}, "efficient": {"2009": 0, "2010": 24}}, "competed": { "baseline": {"2009": 0, "2010": 18}, "efficient": {"2009": 0, "2010": 6}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": {"2009": 30, "2010": 30}, "measure": {"2009": 23, "2010": 22}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": {"2009": 11.5, "2010": 11}}}, "energy": { "total": { "baseline": {"2009": 46, "2010": 44}, "efficient": {"2009": 34.5, "2010": 33}}, "competed": { "baseline": {"2009": 23, "2010": 22}, "efficient": {"2009": 11.5, "2010": 11}}}, "carbon": { "total": { "baseline": {"2009": 69, "2010": 66}, "efficient": {"2009": 46, "2010": 44}}, "competed": { "baseline": {"2009": 34.5, "2010": 33}, "efficient": {"2009": 11.5, "2010": 11}}}, "cost": { "stock": { "total": { "baseline": {"2009": 23, "2010": 22}, "efficient": {"2009": 11.5, "2010": 11}}, "competed": { "baseline": {"2009": 11.5, "2010": 11}, "efficient": {"2009": 0, "2010": 0}}}, "energy": { "total": { "baseline": {"2009": 46, "2010": 44}, "efficient": {"2009": 34.5, "2010": 33}}, "competed": { "baseline": {"2009": 23, "2010": 22}, "efficient": {"2009": 11.5, "2010": 11}}}, "carbon": { "total": { "baseline": {"2009": 69, "2010": 66}, "efficient": {"2009": 46, "2010": 44}}, "competed": { "baseline": {"2009": 34.5, "2010": 33}, "efficient": {"2009": 11.5, "2010": 11}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}] cls.measures_master_msegs_out_dist = [{ "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": { "2009": 17, "2010": numpy.array([12, 13, 16])}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}}}, "energy": { "total": { "baseline": { "2009": 34, "2010": numpy.array([24, 26, 32])}, "efficient": { "2009": 25.5, "2010": numpy.array([18, 19.5, 24])}}, "competed": { "baseline": { "2009": 17, "2010": numpy.array([12, 13, 16])}, "efficient": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}}}, "carbon": { "total": { "baseline": { "2009": 51, "2010": numpy.array([36, 39, 48])}, "efficient": { "2009": 34, "2010": numpy.array([24, 26, 32])}}, "competed": { "baseline": { "2009": 25.5, "2010": numpy.array([18.0, 19.5, 24.0])}, "efficient": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}}}, "cost": { "stock": { "total": { "baseline": { "2009": 17, "2010": numpy.array([12, 13, 16])}, "efficient": { "2009": 8.5, "2010": numpy.array([6, 6.5, 8])}}, "competed": { "baseline": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}, "efficient": { "2009": 0, "2010": numpy.array([0, 0, 0])}}}, "energy": { "total": { "baseline": { "2009": 34, "2010": numpy.array([24, 26, 32])}, "efficient": { "2009": 25.5, "2010": numpy.array([18, 19.5, 24])}}, "competed": { "baseline": { "2009": 17, "2010": numpy.array([12, 13, 16])}, "efficient": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}}}, "carbon": { "total": { "baseline": { "2009": 51, "2010": numpy.array([36, 39, 48])}, "efficient": { "2009": 34, "2010": numpy.array([24, 26, 32])}}, "competed": { "baseline": { "2009": 25.5, "2010": numpy.array([18.0, 19.5, 24.0])}, "efficient": { "2009": 8.5, "2010": numpy.array([6.0, 6.5, 8.0])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": {"2009": 20, "2010": 20}, "measure": { "2009": 0, "2010": numpy.array([16, 15, 13])}}, "competed": { "all": {"2009": 10, "2010": 10}, "measure": { "2009": 0, "2010": numpy.array([8.0, 7.5, 6.5])}}}, "energy": { "total": { "baseline": { "2009": 0, "2010": numpy.array([24, 20, 12])}, "efficient": { "2009": 0, "2010": numpy.array([18, 15, 9])}}, "competed": { "baseline": { "2009": 0, "2010": numpy.array([12, 10, 6])}, "efficient": { "2009": 0, "2010": numpy.array([6, 5, 3])}}}, "carbon": { "total": { "baseline": { "2009": 0, "2010": numpy.array([36, 30, 18])}, "efficient": { "2009": 0, "2010": numpy.array([24, 20, 12])}}, "competed": { "baseline": { "2009": 0, "2010": numpy.array([18, 15, 9])}, "efficient": { "2009": 0, "2010": numpy.array([6, 5, 3])}}}, "cost": { "stock": { "total": { "baseline": { "2009": 10, "2010": numpy.array([16, 15, 13])}, "efficient": { "2009": 20, "2010": numpy.array([8, 9, 9.1])}}, "competed": { "baseline": { "2009": 5, "2010": numpy.array([8.0, 7.5, 6.5])}, "efficient": { "2009": 10, "2010": numpy.array([0, 1.5, 2.6])}}}, "energy": { "total": { "baseline": { "2009": 0, "2010": numpy.array([24, 20, 12])}, "efficient": { "2009": 0, "2010": numpy.array([18, 15, 9])}}, "competed": { "baseline": { "2009": 0, "2010": numpy.array([12, 10, 6])}, "efficient": { "2009": 0, "2010": numpy.array([6, 5, 3])}}}, "carbon": { "total": { "baseline": { "2009": 0, "2010": numpy.array([36, 30, 18])}, "efficient": { "2009": 0, "2010": numpy.array([24, 20, 12])}}, "competed": { "baseline": { "2009": 0, "2010": numpy.array([18, 15, 9])}, "efficient": { "2009": 0, "2010": numpy.array([6, 5, 3])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}, { "stock": { "total": { "all": { "2009": 30, "2010": 30}, "measure": { "2009": 23, "2010": numpy.array([22, 22, 21])}}, "competed": { "all": {"2009": 15, "2010": 15}, "measure": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}}}, "energy": { "total": { "baseline": { "2009": 46, "2010": numpy.array([44, 44, 42])}, "efficient": { "2009": 34.5, "2010": numpy.array([33, 33, 31.5])}}, "competed": { "baseline": { "2009": 23, "2010": numpy.array([22, 22, 21])}, "efficient": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}}}, "carbon": { "total": { "baseline": { "2009": 69, "2010": numpy.array([66, 66, 63])}, "efficient": { "2009": 46, "2010": numpy.array([44, 44, 42])}}, "competed": { "baseline": { "2009": 34.5, "2010": numpy.array([33.0, 33.0, 31.5])}, "efficient": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}}}, "cost": { "stock": { "total": { "baseline": { "2009": 23, "2010": numpy.array([22, 22, 21])}, "efficient": { "2009": 11.5, "2010": numpy.array([11, 11, 10.5])}}, "competed": { "baseline": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}, "efficient": { "2009": 0, "2010": numpy.array([0, 0, 0])}}}, "energy": { "total": { "baseline": { "2009": 46, "2010": numpy.array([44, 44, 42])}, "efficient": { "2009": 34.5, "2010": numpy.array([33, 33, 31.5])}}, "competed": { "baseline": { "2009": 23, "2010": numpy.array([22, 22, 21])}, "efficient": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}}}, "carbon": { "total": { "baseline": { "2009": 69, "2010": numpy.array([66, 66, 63])}, "efficient": { "2009": 46, "2010": numpy.array([44, 44, 42])}}, "competed": { "baseline": { "2009": 34.5, "2010": numpy.array([33.0, 33.0, 31.5])}, "efficient": { "2009": 11.5, "2010": numpy.array([11.0, 11.0, 10.5])}}}}, "lifetime": {"baseline": {"2009": 1, "2010": 1}, "measure": 1}}] def test_compete_com(self): """Test outcomes given sample measures w/ point value inputs.""" # Run measure competition routine on sample measures self.a_run.compete_com_primary( self.measures_all, self.overlap_key, self.test_adopt_scheme) # Run secondary microsegment adjustments on sample measure self.a_run.secondary_adj( self.measures_secondary, self.overlap_key_scnd, self.secnd_adj_key, self.test_adopt_scheme) # Check updated competed master microsegments for each sample measure # following competition/secondary microsegment adjustments for ind, d in enumerate(self.a_run.measures): self.dict_check( self.measures_master_msegs_out[ind], self.a_run.measures[ind].markets[self.test_adopt_scheme][ "competed"]["master_mseg"]) def test_compete_com_dist(self): """Test outcomes given valid sample measures w/ some array inputs.""" # Run measure competition routine on sample measures self.a_run_dist.compete_com_primary( self.measures_all_dist, self.overlap_key, self.test_adopt_scheme) # Run secondary microsegment adjustments on sample measure self.a_run_dist.secondary_adj( self.measures_secondary_dist, self.overlap_key_scnd, self.secnd_adj_key, self.test_adopt_scheme) # Check updated competed master microsegments for each sample measure # following competition/secondary microsegment adjustments for ind, d in enumerate(self.a_run_dist.measures): self.dict_check( self.measures_master_msegs_out_dist[ind], self.a_run_dist.measures[ind].markets[self.test_adopt_scheme][ "competed"]["master_mseg"]) class NumpyConversionTest(unittest.TestCase, CommonMethods): """Test the operation of the 'convert_to_numpy' function. Verify that the function converts terminal/leaf node lists in a dict to numpy arrays. Attributes: handyvars (object): Useful variables across the class. sample_measure (object): Sample measure data with lists to convert. """ @classmethod def setUpClass(cls): """Define objects/variables for use across all class functions.""" base_dir = os.getcwd() cls.handyvars = run.UsefulVars(base_dir, run.UsefulInputFiles()) cls.sample_measure = { "market_entry_year": None, "market_exit_year": None, "markets": { "Technical potential": { "key 1": { "nested key 1": [1, 2, 3, 4, 5], "nested key 2": 5}, "key 2": 10.8}, "Max adoption potential": { "key 1": { "nested key 1": [0.5, 0.2, 0.3, 0.4, 0.5], "nested key 2": 2}, "key 2": 5.8}}} def test_numpy_convert(self): """Test for correct function output given valid input.""" # Instantiate measure measure_instance = run.Measure(self.handyvars, **self.sample_measure) # Test for correct data types in measure markets attribute for adopt_scheme in self.handyvars.adopt_schemes: for comp_scheme in ["uncompeted", "competed"]: tested_data = \ measure_instance.markets[adopt_scheme][comp_scheme] self.assertTrue( all([isinstance(x, y) for x, y in zip([ tested_data["key 1"]["nested key 1"], tested_data["key 1"]["nested key 2"], tested_data["key 2"]], [numpy.ndarray, int, float])])) # Offer external code execution (include all lines below this point in all # test files) def main(): """Trigger default behavior of running all test fixtures in the file.""" unittest.main() if __name__ == "__main__": main()

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eb1310dd2d1a2dc568fc369bcec39edb4b9db158

117

py

Python

code/ch08-outbound-text-messages/db/__all_models.py

rcastleman/twilio-and-sendgrid-python-course

b1aa8e02725cf9580edb4b0310537fac33a685b0

[ "MIT" ]

29

2021-01-08T13:46:35.000Z

2022-03-30T04:31:29.000Z

code/ch08-outbound-text-messages/db/__all_models.py

rcastleman/twilio-and-sendgrid-python-course

b1aa8e02725cf9580edb4b0310537fac33a685b0

[ "MIT" ]

2

2021-08-29T15:11:23.000Z

2022-03-05T00:01:46.000Z

code/ch08-outbound-text-messages/db/__all_models.py

rcastleman/twilio-and-sendgrid-python-course

b1aa8e02725cf9580edb4b0310537fac33a685b0

[ "MIT" ]

30

2021-07-02T00:14:58.000Z

2022-03-06T00:47:53.000Z

# noinspection PyUnresolvedReferences from db import order # noinspection PyUnresolvedReferences from db import user

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9

eb38cd6c2026cdc2c91f9b5998a41a05edd9faba

14,175

py

Python

src/models/operation.py

takedarts/DenseResNet

d5f9c143ed3c484436a2a5bac366c3795e5d47ec

[ "MIT" ]

null

src/models/operation.py

takedarts/DenseResNet

d5f9c143ed3c484436a2a5bac366c3795e5d47ec

[ "MIT" ]

null

src/models/operation.py

takedarts/DenseResNet

d5f9c143ed3c484436a2a5bac366c3795e5d47ec

[ "MIT" ]

null

from .modules import DropBlock, SEModule, SKConv2d, BlurPool2d, SplitAttentionModule import torch.nn as nn import collections class BasicOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=3, padding=1, stride=stride, groups=groups, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', nn.Conv2d( channels, out_channels, kernel_size=3, padding=1, stride=1, groups=1, bias=False)), ('norm2', normalization(out_channels)), ('drop2', None if not dropblock else DropBlock()), ] if m[1] is not None)) class BottleneckOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck * groups) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=stride, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', nn.Conv2d( channels, channels, kernel_size=3, padding=1, stride=1, groups=groups, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class SelectedKernelOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, radix, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck * groups) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=stride, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', SKConv2d( channels, channels, kernel_size=3, padding=1, stride=1, radix=radix, groups=groups)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class PreActBasicOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('norm1', normalization(in_channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv1', nn.Conv2d( in_channels, channels, kernel_size=3, padding=1, stride=stride, groups=groups, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv2', nn.Conv2d( channels, out_channels, kernel_size=3, padding=1, stride=1, groups=1, bias=False)), ] if m[1] is not None)) class SingleActBasicOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('norm1', normalization(in_channels)), ('conv1', nn.Conv2d( in_channels, channels, kernel_size=3, padding=1, stride=stride, groups=groups, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv2', nn.Conv2d( channels, out_channels, kernel_size=3, padding=1, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class SingleActBottleneckOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck * groups) super().__init__(collections.OrderedDict(m for m in [ ('norm1', normalization(in_channels)), ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv2', nn.Conv2d( channels, channels, kernel_size=3, padding=1, stride=stride, groups=groups, bias=False)), ('norm3', normalization(channels)), ('drop3', None if not dropblock else DropBlock()), ('act3', activation(inplace=True)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm4', normalization(out_channels)), ('drop4', None if not dropblock else DropBlock()), ] if m[1] is not None)) class TweakedBottleneckOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', nn.Conv2d( channels, channels, kernel_size=3, padding=1, stride=1, groups=groups, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('pool', None if stride == 1 else BlurPool2d(channels, stride=stride)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class TweakedSlectedKernelOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, radix, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', SKConv2d( channels, channels, kernel_size=3, padding=1, stride=1, radix=radix, groups=groups)), ('drop2', None if not dropblock else DropBlock()), ('pool', None if stride == 1 else BlurPool2d(channels, stride=stride)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class MobileNetOperation(nn.Sequential): def __init__(self, in_channels, out_channels, kernel, stride, expansion, normalization, activation, dropblock, seoperation, sereduction, sesigmoid, **kwargs): channels = int(in_channels * expansion) modules = [] if in_channels != channels: modules.extend([ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ]) modules.extend([ ('conv2', nn.Conv2d( channels, channels, kernel_size=kernel, padding=kernel // 2, stride=stride, groups=channels, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('semodule', None if not seoperation else SEModule( channels, reduction=sereduction, activation=nn.ReLU, sigmoid=sesigmoid)), ('act2', activation(inplace=True)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ]) super().__init__(collections.OrderedDict(m for m in modules if m[1] is not None)) class SplitAttentionOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, radix, groups, bottleneck, normalization, activation, dropblock, **kwargs): channels = round(out_channels / bottleneck) super().__init__(collections.OrderedDict(m for m in [ ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm1', normalization(channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv2', nn.Conv2d( channels, channels * radix, kernel_size=3, padding=1, stride=1, groups=groups * radix, bias=False)), ('norm2', normalization(channels * radix)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('attention', SplitAttentionModule( channels, radix=radix, groups=groups, normalization=normalization, activation=activation)), ('downsample', None if stride == 1 else nn.AvgPool2d( kernel_size=3, stride=stride, padding=1)), ('conv3', nn.Conv2d( channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm3', normalization(out_channels)), ('drop3', None if not dropblock else DropBlock()), ] if m[1] is not None)) class DenseNetOperation(nn.Sequential): def __init__(self, in_channels, out_channels, stride, growth, expansion, normalization, activation, dropblock, **kwargs): if stride != 1: super().__init__(collections.OrderedDict(m for m in [ ('norm1', normalization(in_channels)), ('act1', activation(inplace=True)), ('conv1', nn.Conv2d( in_channels, out_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('pool1', nn.AvgPool2d(kernel_size=2, stride=stride)), ] if m[1] is not None)) else: channels = growth * expansion super().__init__(collections.OrderedDict(m for m in [ ('norm1', normalization(in_channels)), ('drop1', None if not dropblock else DropBlock()), ('act1', activation(inplace=True)), ('conv1', nn.Conv2d( in_channels, channels, kernel_size=1, padding=0, stride=1, groups=1, bias=False)), ('norm2', normalization(channels)), ('drop2', None if not dropblock else DropBlock()), ('act2', activation(inplace=True)), ('conv2', nn.Conv2d( channels, growth, kernel_size=3, padding=1, stride=1, bias=False)), ] if m[1] is not None))

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0.03487

0.067416

0.868526

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0.859357

0.843472

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0

1

0

1

0

null

0

7

de607818154794bc7b5d3bac1008f150395fc1f4

8,991

py

Python

code/route_betweenness.py

tlarock/shipping

9d8e608e98824281126248d726783eda8ddf80f5

[ "MIT" ]

null

code/route_betweenness.py

tlarock/shipping

9d8e608e98824281126248d726783eda8ddf80f5

[ "MIT" ]

null

code/route_betweenness.py

tlarock/shipping

9d8e608e98824281126248d726783eda8ddf80f5

[ "MIT" ]

null

import numpy as np import networkx as nx def route_node_betweenness_from_paths(G, filtered_paths): ''' Computes route betweenness for nodes starting from set of paths filtered_paths. Uses G only to get the number of nodes; could be done by iterating over pairs of nodes in filtered_paths or given as input parameter. Uses dense numpy arrays for computations. ''' ## Zero array of dimensions len(G.nodes()) by len(filtered_paths) node_to_idx = {node:idx for idx, node in enumerate(G.nodes())} pair_to_idx = {pair:idx for idx, pair in enumerate(filtered_paths.keys())} numerator = np.zeros((len(G.nodes()), len(filtered_paths))) denominator = [] for pair in filtered_paths: denominator.append(len(filtered_paths[pair])) for path in filtered_paths[pair]: for node in path: numerator[node_to_idx[node], pair_to_idx[pair]] += 1 denominator = np.array(denominator) normalized_counts = numerator / denominator total_betweenness = normalized_counts.sum(axis=1) route_betweenness = {node:total_betweenness[idx] for node, idx in node_to_idx.items()} return route_betweenness def route_node_betweenness_from_file(filename): ''' Computes route betweenness for nodes by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True node_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: nodes_to_norm = set() for path in filtered_paths[prev_pair]: for node in path: node_to_pair_dict.setdefault(node, dict()) node_to_pair_dict[node].setdefault(prev_pair, 0) node_to_pair_dict[node][prev_pair] += 1 nodes_to_norm.add(node) ## Normalize for node in nodes_to_norm: node_to_pair_dict[node][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: nodes_to_norm = set() for node in path: node_to_pair_dict.setdefault(node, dict()) node_to_pair_dict[node].setdefault(prev_pair, 0) node_to_pair_dict[node][prev_pair] += 1 nodes_to_norm.add(node) ## Normalize for node in nodes_to_norm: node_to_pair_dict[node][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each node route_betweenness = {node:sum(node_to_pair_dict[node].values()) for node in node_to_pair_dict} return route_betweenness def route_edge_betweenness_from_paths(G, filtered_paths): ''' Computes route betweenness for edges starting from set of paths filtered_paths. Uses G only to get the number of nodes; could be done by iterating over pairs of nodes in filtered_paths or given as input parameter. Uses dense numpy arrays for computations. ''' ## Zero array of dimensions len(G.edges()) by len(filtered_paths) edge_to_idx = {edge:idx for idx, edge in enumerate(G.edges())} pair_to_idx = {pair:idx for idx, pair in enumerate(filtered_paths.keys())} numerator = np.zeros((len(G.edges()), len(filtered_paths))) denominator = [] for pair in filtered_paths: denominator.append(len(filtered_paths[pair])) for path in filtered_paths[pair]: for i in range(1, len(path)): numerator[edge_to_idx[(path[i-1], path[i])], pair_to_idx[pair]] += 1 denominator = np.array(denominator) normalized_counts = numerator / denominator total_betweenness = normalized_counts.sum(axis=1) route_betweenness = {edge:total_betweenness[idx] for edge, idx in edge_to_idx.items()} return route_betweenness def route_edge_betweenness_from_file(filename): ''' Computes route betweenness for edges by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True edge_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: edges_to_norm = set() for path in filtered_paths[prev_pair]: for i in range(1, len(path)): edge = path[i-1], path[i] edge_to_pair_dict.setdefault(edge, dict()) edge_to_pair_dict[edge].setdefault(prev_pair, 0) edge_to_pair_dict[edge][prev_pair] += 1 edges_to_norm.add(edge) ## Normalize for edge in edges_to_norm: edge_to_pair_dict[edge][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: edges_to_norm = set() for i in range(1, len(path)): edge = path[i-1], path[i] edge_to_pair_dict.setdefault(edge, dict()) edge_to_pair_dict[edge].setdefault(prev_pair, 0) edge_to_pair_dict[edge][prev_pair] += 1 edges_to_norm.add(edge) ## Normalize for edge in edges_to_norm: edge_to_pair_dict[edge][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each edge route_betweenness = {edge:sum(edge_to_pair_dict[edge].values()) for edge in edge_to_pair_dict} return route_betweenness def route_path_betweenness_from_file(filename, k): ''' Computes route betweenness for paths by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True path_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: paths_to_norm = set() for path in filtered_paths[prev_pair]: for i in range(0, len(path)-k): kpath = tuple(path[i:i+k+1]) path_to_pair_dict.setdefault(kpath, dict()) path_to_pair_dict[kpath].setdefault(prev_pair, 0) path_to_pair_dict[kpath][prev_pair] += 1 paths_to_norm.add(kpath) ## Normalize for path in paths_to_norm: path_to_pair_dict[path][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: paths_to_norm = set() for i in range(0, len(path)-k): kpath = tuple(path[i:i+k+1]) path_to_pair_dict.setdefault(kpath, dict()) path_to_pair_dict[kpath].setdefault(prev_pair, 0) path_to_pair_dict[kpath][prev_pair] += 1 paths_to_norm.add(kpath) ## Normalize for path in paths_to_norm: path_to_pair_dict[path][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each path route_betweenness = {path:sum(path_to_pair_dict[path].values()) for path in path_to_pair_dict} return route_betweenness

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null

0

7

de742dc5458ebc73e271222d4830c67daf1fed8c

2,513

py

Python

datawarehouse/edw_migrations/versions/336bfa51f077_facttablesaddactiveflag.py

bcgov/foi-reporting

25856ce87b668df964ddd16ac7459fae4aa6a7c5

[ "Apache-2.0" ]

null

datawarehouse/edw_migrations/versions/336bfa51f077_facttablesaddactiveflag.py

bcgov/foi-reporting

25856ce87b668df964ddd16ac7459fae4aa6a7c5

[ "Apache-2.0" ]

3

2022-01-05T18:01:41.000Z

2022-02-08T21:51:32.000Z

datawarehouse/edw_migrations/versions/336bfa51f077_facttablesaddactiveflag.py

bcgov/foi-reporting

25856ce87b668df964ddd16ac7459fae4aa6a7c5

[ "Apache-2.0" ]

null

"""facttablesaddactiveflag Revision ID: 336bfa51f077 Revises: 32a5c13c6efd Create Date: 2022-02-28 19:58:46.104675 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '336bfa51f077' down_revision = '32a5c13c6efd' branch_labels = None depends_on = None def upgrade(): op.add_column('factRequestDetails', sa.Column('secondaryusers', sa.VARCHAR(length=3000))) op.add_column('factRequestDetails', sa.Column('noofdocdelivered', sa.Integer())) op.add_column('factRequestDetails', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestPaymentTransaction', sa.Column('paymentreceiveddate', sa.DateTime())) op.add_column('factRequestPaymentTransaction', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestDocumentReviewLog', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestRedactionLayers', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestExtensions', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestDocumentsDetails', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestInvoices', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestRequesters', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestForDocuments', sa.Column('activeflag', sa.CHAR(length=1))) def downgrade(): op.add_column('factRequestDetails', sa.Column('secondaryusers', sa.VARCHAR(length=3000))) op.add_column('factRequestDetails', sa.Column('noofdocdelivered', sa.Integer())) op.add_column('factRequestDetails', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestPaymentTransaction', sa.Column('paymentreceiveddate', sa.DateTime())) op.add_column('factRequestPaymentTransaction', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestDocumentReviewLog', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestRedactionLayers', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestExtensions', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestDocumentsDetails', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestInvoices', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestRequesters', sa.Column('activeflag', sa.CHAR(length=1))) op.add_column('factRequestForDocuments', sa.Column('activeflag', sa.CHAR(length=1)))

53.468085

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0.750099

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2,513

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null

0

1

0

1

0

null

0

8

def8bcce7771078ec9255e8159c19a588668cf9a

235

py

Python

hiddenfigures/__init__.py

millcityrunner/carball

c94cb8caf2f00e616409d2c22b73475e6268fffa

[ "Apache-2.0" ]

null

hiddenfigures/__init__.py

millcityrunner/carball

c94cb8caf2f00e616409d2c22b73475e6268fffa

[ "Apache-2.0" ]

null

hiddenfigures/__init__.py

millcityrunner/carball

c94cb8caf2f00e616409d2c22b73475e6268fffa

[ "Apache-2.0" ]

null

try: from hiddenfigures.decompile_replays import decompile_replay from hiddenfigures.decompile_replays import analyze_replay_file except ModuleNotFoundError as e: print("Not importing functions due to missing packages:", e)

47

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0.276596

0.351064

0.414894

0

0.13617

235

5

68

47

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0.20339

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null

0

1

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1

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1

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7

defa4821fe10db31dd23c7e76e31ba30c228307c

9,155

py

Python

Features/hadoop.py

Archishman-Ghosh/AI_Assistant

5510ae1852c7bbe41e29123b20fea6ac6aff8e2f

[ "MIT" ]

1

2021-02-01T05:08:06.000Z

Features/hadoop.py

Archishman-Ghosh/AI_Assistant

5510ae1852c7bbe41e29123b20fea6ac6aff8e2f

[ "MIT" ]

null

Features/hadoop.py

Archishman-Ghosh/AI_Assistant

5510ae1852c7bbe41e29123b20fea6ac6aff8e2f

[ "MIT" ]

2

2020-12-06T06:02:37.000Z

2021-02-01T05:07:51.000Z

import os import subprocess as sp def namenode_configuration(namenode_ip, namenode_directory, namenode_password, host): """ this function configures the namenode on local host and remote system if host = 0 --> local configure if host = 1 --> remote configure :param namenode_ip: string ip of namenode :param namenode_directory: string path to namenode directory :param namenode_password: string namenode password :param host: integer value (0 or 1) :return: """ if host == 0: os.system("sudo mkdir /{}".format(namenode_directory)) f1 = open("/etc/hadoop/hdfs-site.xml", "w") f1.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?> \n\n\n\n\n\n<configuration>\n\n<property>\n<name>dfs.name.dir</name>\n<value>/{}</value>\n</property>\n\n</configuration>'.format( namenode_directory)) f1.close() f2 = open("/etc/hadoop/core-site.xml", "w") f2.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>\n\n\n\n\n<configuration>\n\n<property>\n<name>fs.default.name</name>\n<value>hdfs://{}:9001</value>\n</property>\n\n</configuration>'.format( namenode_ip)) f2.close() os.system("sudo echo Y | hadoop namenode -format") else: os.system('sshpass -p "{}" sudo ssh root@{} "sudo mkdir /root/{}"'.format(namenode_password, namenode_ip, namenode_directory)) os.system("sudo touch /root/hdfs-site.xml") f1 = open("/root/hdfs-site.xml", "w") f1.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?> \n\n\n\n\n\n<configuration>\n\n<property>\n<name>dfs.name.dir</name>\n<value>/{}</value>\n</property>\n\n</configuration>'.format( namenode_directory)) f1.close() os.system("sudo touch /root/core-site.xml") f2 = open("/root/core-site.xml", "w") f2.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>\n\n\n\n\n<configuration>\n\n<property>\n<name>fs.default.name</name>\n<value>hdfs://{}:9001</value>\n</property>\n\n</configuration>'.format( namenode_ip)) f2.close() os.system('sshpass -p "{}" sudo scp /root/hdfs-site.xml {}:/etc/hadoop'.format(namenode_password, namenode_ip)) os.system('sshpass -p "{}" sudo scp /root/core-site.xml {}:/etc/hadoop'.format(namenode_password, namenode_ip)) os.system( "sshpass -p '{}' sudo ssh root@{} sudo hadoop namenode -format -y".format(namenode_password, namenode_ip)) def datanode_configuration(namenode_ip, datanode_ip, datanode_directory, datanode_password, host): """ this function configures the datanode on local host and remote system if host = 0 --> local configure if host = 1 --> remote configure :param namenode_ip: string datatype namenode ip :param datanode_ip: string datatype datanode ip :param datanode_directory: string datatype datanode directory path :param datanode_password: string password of datanode system :param host: integer value (0 or 1) :return: """ if host == 0: os.system("sudo mkdir /{}".format(datanode_directory)) f1 = open("/etc/hadoop/hdfs-site.xml", "w") f1.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?> \n\n\n\n\n\n<configuration>\n\n<property>\n<name>dfs.data.dir</name>\n<value>/{}</value>\n</property>\n\n</configuration>'.format( datanode_directory)) f1.close() f2 = open("/etc/hadoop/core-site.xml", "w") f2.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>\n\n\n\n\n<configuration>\n\n<property>\n<name>fs.default.name</name>\n<value>hdfs://{}:9001</value>\n</property>\n\n</configuration>'.format( namenode_ip)) f2.close() else: os.system('sshpass -p "{}" sudo ssh root@{} "sudo mkdir /{}"'.format(datanode_password, datanode_ip, datanode_directory)) os.system("sudo touch /root/hdfs-site.xml") f1 = open("/root/hdfs-site.xml", "w") f1.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?> \n\n\n\n\n\n<configuration>\n\n<property>\n<name>dfs.data.dir</name>\n<value>/{}</value>\n</property>\n\n</configuration>'.format( datanode_directory)) f1.close() os.system("sudo touch /root/core-site.xml") f2 = open("/root/core-site.xml", "w") f2.write( '<?xml version="1.0"?>\n<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>\n\n\n\n\n<configuration>\n\n<property>\n<name>fs.default.name</name>\n<value>hdfs://{}:9001</value>\n</property>\n\n</configuration>'.format( namenode_ip)) f2.close() os.system('sshpass -p "{}" sudo scp hdfs-site.xml {}:/etc/hadoop'.format(datanode_password, datanode_ip)) os.system('sshpass -p "{}" sudo scp core-site.xml {}:/etc/hadoop'.format(datanode_password, datanode_ip)) def namenode_start(namenode_ip, namenode_password, host): """ this function starts the namenode if host = 0 --> local start if host = 1 --> remote start :param namenode_ip: string datatype namenode ip :param namenode_password: string datatype namenode password :param host: integer value (0 or 1) :return: output tuple variable """ if host == 0: output = sp.getstatusoutput("sudo hadoop-daemon.sh start namenode") return output else: output = sp.getstatusoutput( 'sshpass -p "{}" sudo ssh root@{} "sudo hadoop-daemon.sh start namenode"'.format(namenode_password, namenode_ip)) return output def datanode_start(datanode_ip, datanode_password, host): """ this function starts the datanode :param datanode_ip: string datatype datanode ip :param datanode_password: string datatype datanode ip :param host: integer value (0 or 1) :return: output tuple datatype variable """ if host == 0: output = sp.getstatusoutput("sudo hadoop-daemon.sh start datanode") return output else: output = sp.getstatusoutput( 'sshpass -p "{}" sudo ssh root@{} "sudo hadoop-daemon.sh start datanode"'.format(datanode_password, datanode_ip)) return output def namenode_stop(namenode_ip, namenode_password, host): """ this function stops the name node daemon. if host = 0 --> local name node stop if host = 1 --> remote name node stop :param namenode_ip: string datatype namenode ip :param namenode_password: string datatype name node ip :param host: integer value (0 or 1) :return: output tuple datatype variable """ if host == 0: output = sp.getstatusoutput("sudo hadoop-daemon.sh stop namenode") return output else: output = sp.getstatusoutput( 'sshpass -p "{}" sudo ssh root@{} "sudo hadoop-daemon.sh stop namenode"'.format(namenode_password, namenode_ip)) return output def datanode_stop(datanode_ip, datanode_password, host): """ this function stops the data node daemon. if host = 0 --> local data node stop if host = 1 --> remote data node stop :param datanode_ip: string datatype data node ip :param datanode_password: string datatype data node password :param host: integer value (0 or 1) :return: output tuple datatype variable """ if host == 0: output = sp.getstatusoutput("sudo hadoop-daemon.sh stop datanode") return output else: output = sp.getstatusoutput( 'sshpass -p "{}" sudo ssh root@{} "sudo hadoop-daemon.sh stop datanode"'.format(datanode_password, datanode_ip)) return output def check_report(): """ this function shows the report of hadoop cluster :return: output tuple variable containing cluster report. """ output = sp.getstatusoutput("sudo hadoop dfsadmin -report") return output

50.302198

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0.608411

1,166

9,155

4.716124

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0.023277

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0.753046

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0.248826

9,155

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0

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0.067308

false

0.163462

0.019231

0

0.173077

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null

0

1

0

1

0

null

0

1

0

8

72111f717911ce0301dac0771d58a2d77bf255f1

1,923

py

Python

Paper_Specific_Versions/2019_DTI/Code/10-ADNI_classification_feature_selection.py

adamwild/AD-ML

e4ac0b7d312ab482b9b52bb3f5c6745cc06431e9

[ "MIT" ]

null

Paper_Specific_Versions/2019_DTI/Code/10-ADNI_classification_feature_selection.py

adamwild/AD-ML

e4ac0b7d312ab482b9b52bb3f5c6745cc06431e9

[ "MIT" ]

null

Paper_Specific_Versions/2019_DTI/Code/10-ADNI_classification_feature_selection.py

adamwild/AD-ML

e4ac0b7d312ab482b9b52bb3f5c6745cc06431e9

[ "MIT" ]

null

from clinica_ml_dwi.mlworkflow_dwi_utils import run_voxel_based_classification # ######################## # ### Original classification # ######################## caps_directory= 'PATH/TO/CAPS' output_dir = 'PATH/TO/CLASSIFICATION_OUTPUT' diagnoses_tsv = 'PATH/TO/DIAGONISIS_TSV' subjects_visits_tsv = 'PATH/TO/DIAGONISIS_TSV' n_threads = 72 n_iterations = 250 test_size = 0.2 grid_search_folds = 10 tissue_type=['GM_WM'] task='AD_vs_CN_VB' # ######################## # ### ANOVA feature selection # ######################## ## Nested ANOVA feature_selection_method='ANOVA' run_voxel_based_classification(caps_directory, diagnoses_tsv, subjects_visits_tsv, output_dir, task, n_threads, n_iterations, test_size, grid_search_folds, tissue_type=tissue_type, feature_selection_nested=True, feature_selection_method=feature_selection_method) ## Nested SVM-RFE feature_selection_method='RFE' run_voxel_based_classification(caps_directory, diagnoses_tsv, subjects_visits_tsv, output_dir, task, n_threads, n_iterations, test_size, grid_search_folds, tissue_type=tissue_type, feature_selection_nested=True, feature_selection_method=feature_selection_method) ## Non_nested ANOVA feature_selection_method='ANOVA' run_voxel_based_classification(caps_directory, diagnoses_tsv, subjects_visits_tsv, output_dir, task, n_threads, n_iterations, test_size, grid_search_folds, tissue_type=tissue_type, feature_selection_non_nested=True, feature_selection_method=feature_selection_method) ## Non_nested SVM-RFE feature_selection_method='RFE' run_voxel_based_classification(caps_directory, diagnoses_tsv, subjects_visits_tsv, output_dir, task, n_threads, n_iterations, test_size, grid_search_folds, tissue_type=tissue_type, feature_selection_non_nested=True, feature_selection_method=feature_selection_method)

48.075

203

0.75455

242

1,923

5.495868

0.223141

0.204511

0.198496

0.101504

0.798496

0.765414

0

0.00536

0.126885

1,923

40

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48.075

0.786778

0.059802

0

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0

0.069231

0.043195

0

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false

0

0.043478

0

0.043478

0

null

1

0

1

0

null

0

7

722e8cdcdb23d289453fecd05b928fe11b76cb3c

679

py

Python

boto3_type_annotations/boto3_type_annotations/ecs/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

119

2018-12-01T18:20:57.000Z

2022-02-02T10:31:29.000Z

boto3_type_annotations/boto3_type_annotations/ecs/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

15

2018-11-16T00:16:44.000Z

2021-11-13T03:44:18.000Z

boto3_type_annotations/boto3_type_annotations/ecs/waiter.py

cowboygneox/boto3_type_annotations

450dce1de4e066b939de7eac2ec560ed1a7ddaa2

[ "MIT" ]

11

2019-05-06T05:26:51.000Z

2021-09-28T15:27:59.000Z

from typing import Dict from typing import List from botocore.waiter import Waiter class ServicesInactive(Waiter): def wait(self, services: List, cluster: str = None, include: List = None, WaiterConfig: Dict = None): pass class ServicesStable(Waiter): def wait(self, services: List, cluster: str = None, include: List = None, WaiterConfig: Dict = None): pass class TasksRunning(Waiter): def wait(self, tasks: List, cluster: str = None, include: List = None, WaiterConfig: Dict = None): pass class TasksStopped(Waiter): def wait(self, tasks: List, cluster: str = None, include: List = None, WaiterConfig: Dict = None): pass

28.291667

105

0.686303

85

679

5.482353

0.270588

0.077253

0.111588

0.145923

0.723176

0

0.212077

679

23

106

29.521739

0.871028

0

0.533333

0

1

0.266667

false

0.266667

0.2

0

0.733333

0

null

0

1

0

null

0

1

0

1

0

1

0

8

72447827cb919ab2b32326c0bcb9ab5ec7998e7a

165

py

Python

test_utils.py

alan125999/alien-invasion

12c6b0bff7907863f6efe824ab9df537cf016a25

[ "MIT" ]

null

test_utils.py

alan125999/alien-invasion

12c6b0bff7907863f6efe824ab9df537cf016a25

[ "MIT" ]

null

test_utils.py

alan125999/alien-invasion

12c6b0bff7907863f6efe824ab9df537cf016a25

[ "MIT" ]

null

from utils import hide_mouse_cursor import pygame # Test Utils def test_hide_mouse_cursor(): hide_mouse_cursor() assert pygame.mouse.get_visible() == False

20.625

46

0.775758

24

165

5

0.541667

0.225

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a0cf825ea2b599801da33a9be93b6bec2d322cb5

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py

Python

e2efold_rt/e2efold/evaluation.py

Lucmon/TopologyDetect

79607f3ce39a1ee6ded41b2500629065cf1cfe51

[ "Apache-2.0" ]

64

2020-02-18T00:49:05.000Z

2022-03-25T01:52:29.000Z

e2efold_rt/e2efold/evaluation.py

Lucmon/TopologyDetect

79607f3ce39a1ee6ded41b2500629065cf1cfe51

[ "Apache-2.0" ]

11

2020-02-28T11:45:40.000Z

2022-03-24T05:41:17.000Z

e2efold_rt/e2efold/evaluation.py

Lucmon/TopologyDetect

79607f3ce39a1ee6ded41b2500629065cf1cfe51

[ "Apache-2.0" ]

17

2020-02-19T03:10:25.000Z

2021-12-23T07:57:17.000Z

import torch from e2efold.common.utils import * from e2efold.postprocess import postprocess import _pickle as pickle # randomly select one sample from the test set and perform the evaluation def model_eval(val_generator, contact_net, lag_pp_net, device): contact_net.eval() lag_pp_net.eval() contacts, seq_embeddings, matrix_reps, seq_lens = next(iter(val_generator)) contacts_batch = torch.Tensor(contacts.float()).to(device) seq_embedding_batch = torch.Tensor(seq_embeddings.float()).to(device) matrix_reps_batch = torch.unsqueeze( torch.Tensor(matrix_reps.float()).to(device), -1) # padding the states for supervised training with all 0s state_pad = torch.zeros(contacts.shape).to(device) PE_batch = get_pe(seq_lens, contacts.shape[-1]).float().to(device) with torch.no_grad(): pred_contacts = contact_net(PE_batch, seq_embedding_batch, state_pad) a_pred_list = lag_pp_net(pred_contacts, seq_embedding_batch) final_pred = (a_pred_list[-1].cpu()>0.5).float() result_tuple_list = list(map(lambda i: evaluate_exact(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) exact_p,exact_r,exact_f1 = zip(*result_tuple_list) print('Average testing precision: ', np.average(exact_p)) print('Average testing recall score: ', np.average(exact_r)) print('Average testing f1 score: ', np.average(exact_f1)) result_tuple_list_shift = list(map(lambda i: evaluate_shifted(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) shift_p,shift_r,shift_f1 = zip(*result_tuple_list_shift) print('Average testing precision allow shift: ', np.average(shift_p)) print('Average testing recall score allow shift: ', np.average(shift_r)) print('Average testing f1 score allow shift: ', np.average(shift_f1)) def model_eval_all_test(test_generator, contact_net, lag_pp_net, device): contact_net.eval() lag_pp_net.eval() result_no_train = list() result_no_train_shift = list() result_pp = list() result_pp_shift = list() f1_no_train = list() f1_pp = list() seq_lens_list = list() batch_n = 0 for contacts, seq_embeddings, matrix_reps, seq_lens in test_generator: if batch_n %10==0: print('Batch number: ', batch_n) batch_n += 1 contacts_batch = torch.Tensor(contacts.float()).to(device) seq_embedding_batch = torch.Tensor(seq_embeddings.float()).to(device) matrix_reps_batch = torch.unsqueeze( torch.Tensor(matrix_reps.float()).to(device), -1) state_pad = torch.zeros(contacts.shape).to(device) PE_batch = get_pe(seq_lens, contacts.shape[-1]).float().to(device) with torch.no_grad(): pred_contacts = contact_net(PE_batch, seq_embedding_batch, state_pad) a_pred_list = lag_pp_net(pred_contacts, seq_embedding_batch) # only post-processing without learning u_no_train = postprocess(pred_contacts, seq_embedding_batch, 0.01, 0.1, 50, 1.0, True) map_no_train = (u_no_train > 0.5).float() result_no_train_tmp = list(map(lambda i: evaluate_exact(map_no_train.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_no_train += result_no_train_tmp result_no_train_tmp_shift = list(map(lambda i: evaluate_shifted(map_no_train.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_no_train_shift += result_no_train_tmp_shift f1_no_train_tmp = list(map(lambda i: F1_low_tri(map_no_train.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) f1_no_train += f1_no_train_tmp # the learning pp result final_pred = (a_pred_list[-1].cpu()>0.5).float() result_tmp = list(map(lambda i: evaluate_exact(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_pp += result_tmp result_tmp_shift = list(map(lambda i: evaluate_shifted(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_pp_shift += result_tmp_shift f1_tmp = list(map(lambda i: F1_low_tri(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) f1_pp += f1_tmp seq_lens_list += list(seq_lens) nt_exact_p,nt_exact_r,nt_exact_f1 = zip(*result_no_train) nt_shift_p,nt_shift_r,nt_shift_f1 = zip(*result_no_train_shift) pp_exact_p,pp_exact_r,pp_exact_f1 = zip(*result_pp) pp_shift_p,pp_shift_r,pp_shift_f1 = zip(*result_pp_shift) print('Average testing F1 score with learning post-processing: ', np.average(pp_exact_f1)) print('Average testing F1 score with zero parameter pp: ', np.average(nt_exact_f1)) print('Average testing F1 score with learning post-processing allow shift: ', np.average(pp_shift_f1)) print('Average testing F1 score with zero parameter pp allow shift: ', np.average(nt_shift_f1)) print('Average testing precision with learning post-processing: ', np.average(pp_exact_p)) print('Average testing precision with zero parameter pp: ', np.average(nt_exact_p)) print('Average testing precision with learning post-processing allow shift: ', np.average(pp_shift_p)) print('Average testing precision with zero parameter pp allow shift: ', np.average(nt_shift_p)) print('Average testing recall with learning post-processing: ', np.average(pp_exact_r)) print('Average testing recall with zero parameter pp : ', np.average(nt_exact_r)) print('Average testing recall with learning post-processing allow shift: ', np.average(pp_shift_r)) print('Average testing recall with zero parameter pp allow shift: ', np.average(nt_shift_r)) result_dict = dict() result_dict['exact_p'] = pp_exact_p result_dict['exact_r'] = pp_exact_r result_dict['exact_f1'] = pp_exact_f1 result_dict['shift_p'] = pp_shift_p result_dict['shift_r'] = pp_shift_r result_dict['shift_f1'] = pp_shift_f1 result_dict['seq_lens'] = seq_lens_list result_dict['exact_weighted_f1'] = np.sum(np.array(pp_exact_f1)*np.array(seq_lens_list)/np.sum(seq_lens_list)) result_dict['shift_weighted_f1'] = np.sum(np.array(pp_shift_f1)*np.array(seq_lens_list)/np.sum(seq_lens_list)) # with open('../results/rnastralign_short_e2e_evaluation_dict.pickle', 'wb') as f: # pickle.dump(result_dict, f) def all_test_only_e2e(test_generator, contact_net, lag_pp_net, device, test_data): contact_net.eval() lag_pp_net.eval() result_no_train = list() result_no_train_shift = list() result_pp = list() result_pp_shift = list() f1_no_train = list() f1_pp = list() seq_lens_list = list() batch_n = 0 for contacts, seq_embeddings, matrix_reps, seq_lens in test_generator: if batch_n %10==0: print('Batch number: ', batch_n) batch_n += 1 contacts_batch = torch.Tensor(contacts.float()).to(device) seq_embedding_batch = torch.Tensor(seq_embeddings.float()).to(device) matrix_reps_batch = torch.unsqueeze( torch.Tensor(matrix_reps.float()).to(device), -1) state_pad = torch.zeros(contacts.shape).to(device) PE_batch = get_pe(seq_lens, contacts.shape[-1]).float().to(device) with torch.no_grad(): pred_contacts = contact_net(PE_batch, seq_embedding_batch, state_pad) a_pred_list = lag_pp_net(pred_contacts, seq_embedding_batch) # the learning pp result final_pred = (a_pred_list[-1].cpu()>0.5).float() result_tmp = list(map(lambda i: evaluate_exact(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_pp += result_tmp result_tmp_shift = list(map(lambda i: evaluate_shifted(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) result_pp_shift += result_tmp_shift f1_tmp = list(map(lambda i: F1_low_tri(final_pred.cpu()[i], contacts_batch.cpu()[i]), range(contacts_batch.shape[0]))) f1_pp += f1_tmp seq_lens_list += list(seq_lens) pp_exact_p,pp_exact_r,pp_exact_f1 = zip(*result_pp) pp_shift_p,pp_shift_r,pp_shift_f1 = zip(*result_pp_shift) # the following is inserted for arxiv ii pp_exact_p = np.nan_to_num(np.array(pp_exact_p)) pp_exact_r = np.nan_to_num(np.array(pp_exact_r)) pp_exact_f1 = np.nan_to_num(np.array(pp_exact_f1)) pp_shift_p = np.nan_to_num(np.array(pp_shift_p)) pp_shift_r = np.nan_to_num(np.array(pp_shift_r)) pp_shift_f1 = np.nan_to_num(np.array(pp_shift_f1)) print('Average testing F1 score with learning post-processing: ', np.average(pp_exact_f1)) print('Average testing F1 score with learning post-processing allow shift: ', np.average(pp_shift_f1)) print('Average testing precision with learning post-processing: ', np.average(pp_exact_p)) print('Average testing precision with learning post-processing allow shift: ', np.average(pp_shift_p)) print('Average testing recall with learning post-processing: ', np.average(pp_exact_r)) print('Average testing recall with learning post-processing allow shift: ', np.average(pp_shift_r)) result_dict = dict() result_dict['exact_p'] = pp_exact_p result_dict['exact_r'] = pp_exact_r result_dict['exact_f1'] = pp_exact_f1 result_dict['shift_p'] = pp_shift_p result_dict['shift_r'] = pp_shift_r result_dict['shift_f1'] = pp_shift_f1 result_dict['seq_lens'] = seq_lens_list result_dict['exact_weighted_f1'] = np.sum(np.array(pp_exact_f1)*np.array(seq_lens_list)/np.sum(seq_lens_list)) result_dict['shift_weighted_f1'] = np.sum(np.array(pp_shift_f1)*np.array(seq_lens_list)/np.sum(seq_lens_list)) result_dict['name'] = [a.name for a in test_data.data] # with open('../results/rnastralign_short_e2e_evaluation_dict.pickle', 'wb') as f: # pickle.dump(result_dict, f) # with open('../results/archiveii_short_e2e_evaluation_dict.pickle', 'wb') as f: # pickle.dump(result_dict, f)

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null

0

7

a0d41ab5f8ea8f87635db8b48a9556cc76f6c29d

211

py

Python

nmigen/back/pysim.py

psumesh/nmigen

7d611b8fc1d9e58853ff268ec38ff8f4131a9774

[ "BSD-2-Clause" ]

528

2020-01-28T18:21:00.000Z

2021-12-09T06:27:51.000Z

nmigen/back/pysim.py

DX-MON/nmigen

a6a13dd612ee1c9215719c70a5aa410a8775ffdb

[ "BSD-2-Clause" ]

360

2020-01-28T18:34:30.000Z

2021-12-10T08:03:32.000Z

nmigen/back/pysim.py

DX-MON/nmigen

a6a13dd612ee1c9215719c70a5aa410a8775ffdb

[ "BSD-2-Clause" ]

100

2020-02-06T21:55:46.000Z

2021-11-25T19:20:44.000Z

from amaranth.back.pysim import * from amaranth.back.pysim import __all__ import warnings warnings.warn("instead of nmigen.back.pysim, use amaranth.back.pysim", DeprecationWarning, stacklevel=2)

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a0dfb37409cb18601c6a0c5c43a29a2c29c66d5d

92

py

Python

orquestador/nyc_ccci_etl/utils/get_current_ip.py

gemathus/dpa-2020

b5d7a350b058e77a9b358fa2758632fa2265a9cb

[ "IJG" ]

1

2020-04-01T01:12:16.000Z

orquestador/nyc_ccci_etl/utils/get_current_ip.py

gemathus/dpa-2020

b5d7a350b058e77a9b358fa2758632fa2265a9cb

[ "IJG" ]

3

2021-06-02T02:01:07.000Z

2022-03-12T00:33:16.000Z

orquestador/nyc_ccci_etl/utils/get_current_ip.py

dpa-2020-equipo-5/dpa-2020

b5d7a350b058e77a9b358fa2758632fa2265a9cb

[ "IJG" ]

3

2020-05-11T01:15:14.000Z

2021-03-12T02:34:23.000Z

from requests import get def get_current_ip(): return get('https://api.ipify.org').text

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1

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7

9d024c6e289a42c53e63b7aef0dd65b3149fc5d3

2,184

py

Python

2017/Day1-1.py

josephgruber/AoC

8b103ab9f4ac396b7a0ccf04667443a43bef704d

[ "MIT" ]

null

2017/Day1-1.py

josephgruber/AoC

8b103ab9f4ac396b7a0ccf04667443a43bef704d

[ "MIT" ]

null

2017/Day1-1.py

josephgruber/AoC

8b103ab9f4ac396b7a0ccf04667443a43bef704d

[ "MIT" ]

null

inputData = '3294199471327195994824832197564859876682638188889768298894243832665654681412886862234525991553276578641265589959178414218389329361496673991614673626344552179413995562266818138372393213966143124914469397692587251112663217862879233226763533911128893354536353213847122251463857894159819828724827969576432191847787772732881266875469721189331882228146576832921314638221317393256471998598117289632684663355273845983933845721713497811766995367795857965222183668765517454263354111134841334631345111596131682726196574763165187889337599583345634413436165539744188866156771585647718555182529936669683581662398618765391487164715724849894563314426959348119286955144439452731762666568741612153254469131724137699832984728937865956711925592628456617133695259554548719328229938621332325125972547181236812263887375866231118312954369432937359357266467383318326239572877314765121844831126178173988799765218913178825966268816476559792947359956859989228917136267178571776316345292573489873792149646548747995389669692188457724414468727192819919448275922166321158141365237545222633688372891451842434458527698774342111482498999383831492577615154591278719656798277377363284379468757998373193231795767644654155432692988651312845433511879457921638934877557575241394363721667237778962455961493559848522582413748218971212486373232795878362964873855994697149692824917183375545192119453587398199912564474614219929345185468661129966379693813498542474732198176496694746111576925715493967296487258237854152382365579876894391815759815373319159213475555251488754279888245492373595471189191353244684697662848376529881512529221627313527441221459672786923145165989611223372241149929436247374818467481641931872972582295425936998535194423916544367799522276914445231582272368388831834437562752119325286474352863554693373718848649568451797751926315617575295381964426843625282819524747119726872193569785611959896776143539915299968276374712996485367853494734376257511273443736433464496287219615697341973131715166768916149828396454638596713572963686159214116763' sumTotal = 0 for index, digit in enumerate(inputData): if inputData[index] == inputData[index-1]: sumTotal += int(inputData[index]) print(sumTotal)

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c20ba944e75a62eaef6d1ad88caf651ad40f606c

175

py

Python

sitepackages/csp/tests/__init__.py

bitcpf/djangoage

f116860cbfa799eb6c47306a72d742b63c970dce

[ "Apache-2.0" ]

null

sitepackages/csp/tests/__init__.py

bitcpf/djangoage

f116860cbfa799eb6c47306a72d742b63c970dce

[ "Apache-2.0" ]

null

sitepackages/csp/tests/__init__.py

bitcpf/djangoage

f116860cbfa799eb6c47306a72d742b63c970dce

[ "Apache-2.0" ]

null

from csp.tests.test_decorators import DecoratorTests # noqa from csp.tests.test_middleware import MiddlewareTests # noqa from csp.tests.test_utils import UtilsTests # noqa

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0

1

0

1

0

7

dfce7efed020ef4a65b5c2537d48d2788deac6bd

203

py

Python

Application/cdpapp/admin.py

Adi1222/Customer-Data-Protection

3182c4d8faeb233f55da7be61f6488606e7941b8

[ "MIT" ]

1

2021-12-03T11:24:57.000Z

PropelRapp/admin.py

Adi1222/PropelR

453196cb8ad7c251b650c6bd147a8be5ee8eed50

[ "MIT" ]

1

2021-05-01T12:28:38.000Z

PropelRapp/admin.py

Adi1222/PropelR

453196cb8ad7c251b650c6bd147a8be5ee8eed50

[ "MIT" ]

null

from django.contrib import admin '''from .models import Cluster, Camera, Customer # Register your models here. admin.site.register(Customer) admin.site.register(Camera) admin.site.register(Customer) '''

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7

dfe3228a53ef857adbdd6f1ee7d54cae6bdbd815

22,363

py

Python

fixtures/fifa.py

Abhishek741119/fifa_simulator

a9218f272c871863aafdf879c875dda4758725f5

[ "MIT" ]

4

2020-11-26T08:54:22.000Z

2020-11-28T04:28:08.000Z

fixtures/fifa.py

Abhishek741119/fifa_simulator

a9218f272c871863aafdf879c875dda4758725f5

[ "MIT" ]

null

fixtures/fifa.py

Abhishek741119/fifa_simulator

a9218f272c871863aafdf879c875dda4758725f5

[ "MIT" ]

null

from random import choice as rc import pandas as pd from itertools import combinations def group_game(df,group,i,j,l,subs): print(" GROUP-",subs) data=list(group["NAME"]) fix=list(combinations(data,2)) rc1=[1,2,0,1,0,0,0] rc2=[1,0,1,0,1,0,0] rc3=[0,1,0,1,0,0,0] rc4=[0,0,1,0,0,0,0] arr=[i,j] for slot in arr: slice=[fix[slot]] for p,q in slice: print("************************") print("{0} - {1}".format(p,q)) sp=0 sq=0 if group.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif group.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif group.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if group.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif group.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif group.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) print("*************************\n") df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp else: df.loc[p,"POINTS"]+=1 df.loc[q,"POINTS"]+=1 df.loc[q,"DRAW"]+=1 df.loc[p,"DRAW"]+=1 chart=df.loc[data[0]:data[3]].sort_values(["WON","POINTS","GD","RATE"],ascending=False) order=list(chart["NAME"]) for t in range(4): l[t]=order[t] print("****************************************************") print(chart[["GAMES","WON","LOST","DRAW","GD","POINTS"]]) print("****************************************************") def roundof16_game(df,s,l): rc1=[1,2,0,0,1,0,1,0,0] rc2=[1,0,1,0,1,0,0,0,0] rc3=[0,0,1,0,0,1,0,0,0] rc4=[0,0,0,1,0,0,0,0,0] for p,q in s: print("{0} - {1}".format(p,q)) sp=0 sq=0 if df.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) print(p,"ADVANCED TO QUARTER-FINAL") df.loc[q,"LEVEL"]=2 elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) print(q,"ADVANCED TO QUARTER-FINAL") df.loc[p,"LEVEL"]=2 else: while sp==sq: if df.loc[p,"RATE"]==4: for k in range(3): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(3): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(3): sp+=rc(rc3) else: for k in range(3): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(3): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(3): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(3): sq+=rc(rc3) else: for k in range(3): sq+=rc(rc4) print("({0},{1})".format(sp,sq)) if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) print(p,"ADVANCED TO QUARTER-FINAL") df.loc[q,"LEVEL"]=2 else: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) print(q,"ADVANCED TO QUARTER-FINAL") df.loc[p,"LEVEL"]=2 print("*************************") print("*************************\n") def quarter_game(df,s,l): rc1=[1,2,0,0,1,0,1,0,0] rc2=[1,0,1,0,1,0,0,0,0] rc3=[0,0,1,0,0,1,0,0,0] rc4=[0,0,0,1,0,0,0,0,0] for p,q in s: print("{0} - {1}".format(p,q)) sp=0 sq=0 if df.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) print(p,"ADVANCED TO SEMI-FINAL") df.loc[q,"LEVEL"]=3 elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) print(q,"ADVANCED TO SEMI-FINAL") df.loc[p,"LEVEL"]=3 else: while sp==sq: if df.loc[p,"RATE"]==4: for k in range(3): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(3): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(3): sp+=rc(rc3) else: for k in range(3): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(3): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(3): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(3): sq+=rc(rc3) else: for k in range(3): sq+=rc(rc4) print("({0},{1})".format(sp,sq)) if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) print(p,"ADVANCED TO SEMI-FINAL") df.loc[q,"LEVEL"]=3 else: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) print(q,"ADVANCED TO SEMI-FINAL") df.loc[p,"LEVEL"]=3 print("*************************") print("*************************") print("*************************\n") def semis_game(df,s,l,m): rc1=[1,2,0,0,1,0,1,0,0] rc2=[1,0,1,0,1,0,0,0,0] rc3=[0,0,1,0,0,1,0,0,0] rc4=[0,0,0,1,0,0,0,0,0] for p,q in s: print("{0} - {1}".format(p,q)) sp=0 sq=0 if df.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) m.append(q) print(p,"ADVANCED TO FINAL") elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) m.append(p) print(q,"ADVANCED TO FINAL") else: while sp==sq: if df.loc[p,"RATE"]==4: for k in range(3): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(3): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(3): sp+=rc(rc3) else: for k in range(3): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(3): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(3): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(3): sq+=rc(rc3) else: for k in range(3): sq+=rc(rc4) print("({0},{1})".format(sp,sq)) if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq l.append(p) m.append(q) print(p,"ADVANCED TO FINAL") else: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp l.append(q) m.append(p) print(q,"ADVANCED TO FINAL") print("*************************") print("*************************") print("*************************") print("*************************\n") def thirdplace_playoff(df,s): rc1=[1,2,0,0,1,0,1,0,0] rc2=[1,0,1,0,1,0,0,0,0] rc3=[0,0,1,0,0,1,0,0,0] rc4=[0,0,0,1,0,0,0,0,0] for p,q in s: print("{0} - {1}".format(p,q)) sp=0 sq=0 if df.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq print(p,"ATTAINED THIRD POSITION") df.loc[q,"LEVEL"]=4 df.loc[p,"LEVEL"]=5 elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp print(q,"ATTAINED THIRD POSITION") df.loc[p,"LEVEL"]=4 df.loc[q,"LEVEL"]=5 else: while sp==sq: if df.loc[p,"RATE"]==4: for k in range(3): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(3): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(3): sp+=rc(rc3) else: for k in range(3): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(3): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(3): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(3): sq+=rc(rc3) else: for k in range(3): sq+=rc(rc4) print("({0},{1})".format(sp,sq)) if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq print(p,"ATTAINED THIRD POSITION") df.loc[q,"LEVEL"]=4 df.loc[p,"LEVEL"]=5 else: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp print(q,"ATTAINED THIRD POSITION") df.loc[p,"LEVEL"]=4 df.loc[q,"LEVEL"]=5 print("*************************") print("*************************\n") def final_game(df,s): rc1=[1,2,0,0,1,0,1,0,0] rc2=[1,0,1,0,1,0,0,0,0] rc3=[0,0,1,0,0,1,0,0,0] rc4=[0,0,0,1,0,0,0,0,0] for p,q in s: print(" {0} - {1}".format(p,q)) sp=0 sq=0 if df.loc[p,"RATE"]==4: for k in range(5): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(5): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(5): sp+=rc(rc3) else: for k in range(5): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(5): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(5): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(5): sq+=rc(rc3) else: for k in range(5): sq+=rc(rc4) print(" {0}-{1}".format(sp,sq)) df.loc[p,"GAMES"]+=1 df.loc[q,"GAMES"]+=1 if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq df.loc[q,"LEVEL"]=6 df.loc[p,"LEVEL"]=7 print("============================================================================================================================================================================") print("============================================================================================================================================================================") print("***** CHAMPIONS:::-----",p," *****") print("===========================================================================================================================================================================\n") elif sp<sq: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp df.loc[p,"LEVEL"]=6 df.loc[q,"LEVEL"]=7 print("============================================================================================================================================================================") print("============================================================================================================================================================================") print("***** CHAMPIONS:::-----",q," *****") print("===========================================================================================================================================================================\n") else: while sp==sq: if df.loc[p,"RATE"]==4: for k in range(3): sp+=rc(rc1) elif df.loc[p,"RATE"]==3: for k in range(3): sp+=rc(rc2) elif df.loc[p,"RATE"]==2: for k in range(3): sp+=rc(rc3) else: for k in range(3): sp+=rc(rc4) if df.loc[q,"RATE"]==4: for k in range(3): sq+=rc(rc1) elif df.loc[q,"RATE"]==3: for k in range(3): sq+=rc(rc2) elif df.loc[q,"RATE"]==2: for k in range(3): sq+=rc(rc3) else: for k in range(3): sq+=rc(rc4) print("({0},{1})".format(sp,sq)) if sp>sq: df.loc[p,"POINTS"]+=3 df.loc[p,"WON"]+=1 df.loc[q,"LOST"]+=1 df.loc[p,"GD"]+=sp-sq df.loc[q,"GD"]-=sp-sq df.loc[q,"LEVEL"]=6 df.loc[p,"LEVEL"]=7 print("============================================================================================================================================================================") print("============================================================================================================================================================================") print("***** CHAMPIONS:::-----",p," *****") print("============================================================================================================================================================================") else: df.loc[q,"POINTS"]+=3 df.loc[q,"WON"]+=1 df.loc[p,"LOST"]+=1 df.loc[q,"GD"]+=sq-sp df.loc[p,"GD"]-=sq-sp df.loc[p,"LEVEL"]=6 df.loc[q,"LEVEL"]=7 print("============================================================================================================================================================================") print("============================================================================================================================================================================") print("***** CHAMPIONS:::-----",q," *****") print("============================================================================================================================================================================")

37.90339

198

0.289585

2,673

22,363

2.420127

0.031425

0.163085

0.097388

0.149637

0.925027

0.911578

0.910496

0.90895

0

0.050682

0.442383

22,363

589

199

37.967742

0.468083

0

0.938879

0

0.202535

0.115

0

1

0.010187

false

0

0.005093

0

0.01528

0.112054

0

null

0

1

0

1

0

1

0

null

0

8

a085a8c892833af886ec0b719817b0bdf7ce7ba7

95

py

Python

src/sage/dev/all.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

5

2015-01-04T07:15:06.000Z

2022-03-04T15:15:18.000Z

src/sage/dev/all.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

null

src/sage/dev/all.py

switzel/sage

7eb8510dacf61b691664cd8f1d2e75e5d473e5a0

[ "BSL-1.0" ]

10

2016-09-28T13:12:40.000Z

2022-02-12T09:28:34.000Z

from sage.misc.lazy_import import lazy_import lazy_import('sage.dev.sagedev_instance', 'dev')

23.75

47

0.810526

15

95

4.866667

0.533333

0.410959

0.438356

0

0.073684

95

3

48

31.666667

0.829545

0

0.294737

0.263158

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

2683e660c991f8978004c5e53caf98b7869c8112

18

py

Python

sample_60181885.py

jun0433/sample_60181885

85d8187ef429ffd4a00edec2a48111ef25f714a6

[ "MIT" ]

null

sample_60181885.py

jun0433/sample_60181885

85d8187ef429ffd4a00edec2a48111ef25f714a6

[ "MIT" ]

null

sample_60181885.py

jun0433/sample_60181885

85d8187ef429ffd4a00edec2a48111ef25f714a6

[ "MIT" ]

null

print("60181885")

9

17

0.722222

2

18

6.5

1

0

0.470588

0.055556

18

1

18

0.294118

0

0.444444

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

cd0441d91cce6b586a782045ab3e69aaef34b440

18,816

py

Python

sdk/python/pulumi_aws_native/xray/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/xray/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/xray/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GroupInsightsConfiguration', 'SamplingRule', 'SamplingRuleRecord', 'SamplingRuleUpdate', 'TagsItemProperties', ] @pulumi.output_type class GroupInsightsConfiguration(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "insightsEnabled": suggest = "insights_enabled" elif key == "notificationsEnabled": suggest = "notifications_enabled" if suggest: pulumi.log.warn(f"Key '{key}' not found in GroupInsightsConfiguration. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: GroupInsightsConfiguration.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: GroupInsightsConfiguration.__key_warning(key) return super().get(key, default) def __init__(__self__, *, insights_enabled: Optional[bool] = None, notifications_enabled: Optional[bool] = None): """ :param bool insights_enabled: Set the InsightsEnabled value to true to enable insights or false to disable insights. :param bool notifications_enabled: Set the NotificationsEnabled value to true to enable insights notifications. Notifications can only be enabled on a group with InsightsEnabled set to true. """ if insights_enabled is not None: pulumi.set(__self__, "insights_enabled", insights_enabled) if notifications_enabled is not None: pulumi.set(__self__, "notifications_enabled", notifications_enabled) @property @pulumi.getter(name="insightsEnabled") def insights_enabled(self) -> Optional[bool]: """ Set the InsightsEnabled value to true to enable insights or false to disable insights. """ return pulumi.get(self, "insights_enabled") @property @pulumi.getter(name="notificationsEnabled") def notifications_enabled(self) -> Optional[bool]: """ Set the NotificationsEnabled value to true to enable insights notifications. Notifications can only be enabled on a group with InsightsEnabled set to true. """ return pulumi.get(self, "notifications_enabled") @pulumi.output_type class SamplingRule(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "fixedRate": suggest = "fixed_rate" elif key == "hTTPMethod": suggest = "h_ttp_method" elif key == "reservoirSize": suggest = "reservoir_size" elif key == "resourceARN": suggest = "resource_arn" elif key == "ruleARN": suggest = "rule_arn" elif key == "ruleName": suggest = "rule_name" elif key == "serviceName": suggest = "service_name" elif key == "serviceType": suggest = "service_type" elif key == "uRLPath": suggest = "u_rl_path" if suggest: pulumi.log.warn(f"Key '{key}' not found in SamplingRule. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SamplingRule.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SamplingRule.__key_warning(key) return super().get(key, default) def __init__(__self__, *, attributes: Optional[Any] = None, fixed_rate: Optional[float] = None, h_ttp_method: Optional[str] = None, host: Optional[str] = None, priority: Optional[int] = None, reservoir_size: Optional[int] = None, resource_arn: Optional[str] = None, rule_arn: Optional[str] = None, rule_name: Optional[str] = None, service_name: Optional[str] = None, service_type: Optional[str] = None, u_rl_path: Optional[str] = None, version: Optional[int] = None): """ :param Any attributes: Matches attributes derived from the request. :param float fixed_rate: The percentage of matching requests to instrument, after the reservoir is exhausted. :param str h_ttp_method: Matches the HTTP method from a request URL. :param str host: Matches the hostname from a request URL. :param int priority: The priority of the sampling rule. :param int reservoir_size: A fixed number of matching requests to instrument per second, prior to applying the fixed rate. The reservoir is not used directly by services, but applies to all services using the rule collectively. :param str resource_arn: Matches the ARN of the AWS resource on which the service runs. :param str service_name: Matches the name that the service uses to identify itself in segments. :param str service_type: Matches the origin that the service uses to identify its type in segments. :param str u_rl_path: Matches the path from a request URL. :param int version: The version of the sampling rule format (1) """ if attributes is not None: pulumi.set(__self__, "attributes", attributes) if fixed_rate is not None: pulumi.set(__self__, "fixed_rate", fixed_rate) if h_ttp_method is not None: pulumi.set(__self__, "h_ttp_method", h_ttp_method) if host is not None: pulumi.set(__self__, "host", host) if priority is not None: pulumi.set(__self__, "priority", priority) if reservoir_size is not None: pulumi.set(__self__, "reservoir_size", reservoir_size) if resource_arn is not None: pulumi.set(__self__, "resource_arn", resource_arn) if rule_arn is not None: pulumi.set(__self__, "rule_arn", rule_arn) if rule_name is not None: pulumi.set(__self__, "rule_name", rule_name) if service_name is not None: pulumi.set(__self__, "service_name", service_name) if service_type is not None: pulumi.set(__self__, "service_type", service_type) if u_rl_path is not None: pulumi.set(__self__, "u_rl_path", u_rl_path) if version is not None: pulumi.set(__self__, "version", version) @property @pulumi.getter def attributes(self) -> Optional[Any]: """ Matches attributes derived from the request. """ return pulumi.get(self, "attributes") @property @pulumi.getter(name="fixedRate") def fixed_rate(self) -> Optional[float]: """ The percentage of matching requests to instrument, after the reservoir is exhausted. """ return pulumi.get(self, "fixed_rate") @property @pulumi.getter(name="hTTPMethod") def h_ttp_method(self) -> Optional[str]: """ Matches the HTTP method from a request URL. """ return pulumi.get(self, "h_ttp_method") @property @pulumi.getter def host(self) -> Optional[str]: """ Matches the hostname from a request URL. """ return pulumi.get(self, "host") @property @pulumi.getter def priority(self) -> Optional[int]: """ The priority of the sampling rule. """ return pulumi.get(self, "priority") @property @pulumi.getter(name="reservoirSize") def reservoir_size(self) -> Optional[int]: """ A fixed number of matching requests to instrument per second, prior to applying the fixed rate. The reservoir is not used directly by services, but applies to all services using the rule collectively. """ return pulumi.get(self, "reservoir_size") @property @pulumi.getter(name="resourceARN") def resource_arn(self) -> Optional[str]: """ Matches the ARN of the AWS resource on which the service runs. """ return pulumi.get(self, "resource_arn") @property @pulumi.getter(name="ruleARN") def rule_arn(self) -> Optional[str]: return pulumi.get(self, "rule_arn") @property @pulumi.getter(name="ruleName") def rule_name(self) -> Optional[str]: return pulumi.get(self, "rule_name") @property @pulumi.getter(name="serviceName") def service_name(self) -> Optional[str]: """ Matches the name that the service uses to identify itself in segments. """ return pulumi.get(self, "service_name") @property @pulumi.getter(name="serviceType") def service_type(self) -> Optional[str]: """ Matches the origin that the service uses to identify its type in segments. """ return pulumi.get(self, "service_type") @property @pulumi.getter(name="uRLPath") def u_rl_path(self) -> Optional[str]: """ Matches the path from a request URL. """ return pulumi.get(self, "u_rl_path") @property @pulumi.getter def version(self) -> Optional[int]: """ The version of the sampling rule format (1) """ return pulumi.get(self, "version") @pulumi.output_type class SamplingRuleRecord(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "createdAt": suggest = "created_at" elif key == "modifiedAt": suggest = "modified_at" elif key == "samplingRule": suggest = "sampling_rule" if suggest: pulumi.log.warn(f"Key '{key}' not found in SamplingRuleRecord. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SamplingRuleRecord.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SamplingRuleRecord.__key_warning(key) return super().get(key, default) def __init__(__self__, *, created_at: Optional[str] = None, modified_at: Optional[str] = None, sampling_rule: Optional['outputs.SamplingRule'] = None): """ :param str created_at: When the rule was created, in Unix time seconds. :param str modified_at: When the rule was modified, in Unix time seconds. """ if created_at is not None: pulumi.set(__self__, "created_at", created_at) if modified_at is not None: pulumi.set(__self__, "modified_at", modified_at) if sampling_rule is not None: pulumi.set(__self__, "sampling_rule", sampling_rule) @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: """ When the rule was created, in Unix time seconds. """ return pulumi.get(self, "created_at") @property @pulumi.getter(name="modifiedAt") def modified_at(self) -> Optional[str]: """ When the rule was modified, in Unix time seconds. """ return pulumi.get(self, "modified_at") @property @pulumi.getter(name="samplingRule") def sampling_rule(self) -> Optional['outputs.SamplingRule']: return pulumi.get(self, "sampling_rule") @pulumi.output_type class SamplingRuleUpdate(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "fixedRate": suggest = "fixed_rate" elif key == "hTTPMethod": suggest = "h_ttp_method" elif key == "reservoirSize": suggest = "reservoir_size" elif key == "resourceARN": suggest = "resource_arn" elif key == "ruleARN": suggest = "rule_arn" elif key == "ruleName": suggest = "rule_name" elif key == "serviceName": suggest = "service_name" elif key == "serviceType": suggest = "service_type" elif key == "uRLPath": suggest = "u_rl_path" if suggest: pulumi.log.warn(f"Key '{key}' not found in SamplingRuleUpdate. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SamplingRuleUpdate.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SamplingRuleUpdate.__key_warning(key) return super().get(key, default) def __init__(__self__, *, attributes: Optional[Any] = None, fixed_rate: Optional[float] = None, h_ttp_method: Optional[str] = None, host: Optional[str] = None, priority: Optional[int] = None, reservoir_size: Optional[int] = None, resource_arn: Optional[str] = None, rule_arn: Optional[str] = None, rule_name: Optional[str] = None, service_name: Optional[str] = None, service_type: Optional[str] = None, u_rl_path: Optional[str] = None): """ :param Any attributes: Matches attributes derived from the request. :param float fixed_rate: The percentage of matching requests to instrument, after the reservoir is exhausted. :param str h_ttp_method: Matches the HTTP method from a request URL. :param str host: Matches the hostname from a request URL. :param int priority: The priority of the sampling rule. :param int reservoir_size: A fixed number of matching requests to instrument per second, prior to applying the fixed rate. The reservoir is not used directly by services, but applies to all services using the rule collectively. :param str resource_arn: Matches the ARN of the AWS resource on which the service runs. :param str service_name: Matches the name that the service uses to identify itself in segments. :param str service_type: Matches the origin that the service uses to identify its type in segments. :param str u_rl_path: Matches the path from a request URL. """ if attributes is not None: pulumi.set(__self__, "attributes", attributes) if fixed_rate is not None: pulumi.set(__self__, "fixed_rate", fixed_rate) if h_ttp_method is not None: pulumi.set(__self__, "h_ttp_method", h_ttp_method) if host is not None: pulumi.set(__self__, "host", host) if priority is not None: pulumi.set(__self__, "priority", priority) if reservoir_size is not None: pulumi.set(__self__, "reservoir_size", reservoir_size) if resource_arn is not None: pulumi.set(__self__, "resource_arn", resource_arn) if rule_arn is not None: pulumi.set(__self__, "rule_arn", rule_arn) if rule_name is not None: pulumi.set(__self__, "rule_name", rule_name) if service_name is not None: pulumi.set(__self__, "service_name", service_name) if service_type is not None: pulumi.set(__self__, "service_type", service_type) if u_rl_path is not None: pulumi.set(__self__, "u_rl_path", u_rl_path) @property @pulumi.getter def attributes(self) -> Optional[Any]: """ Matches attributes derived from the request. """ return pulumi.get(self, "attributes") @property @pulumi.getter(name="fixedRate") def fixed_rate(self) -> Optional[float]: """ The percentage of matching requests to instrument, after the reservoir is exhausted. """ return pulumi.get(self, "fixed_rate") @property @pulumi.getter(name="hTTPMethod") def h_ttp_method(self) -> Optional[str]: """ Matches the HTTP method from a request URL. """ return pulumi.get(self, "h_ttp_method") @property @pulumi.getter def host(self) -> Optional[str]: """ Matches the hostname from a request URL. """ return pulumi.get(self, "host") @property @pulumi.getter def priority(self) -> Optional[int]: """ The priority of the sampling rule. """ return pulumi.get(self, "priority") @property @pulumi.getter(name="reservoirSize") def reservoir_size(self) -> Optional[int]: """ A fixed number of matching requests to instrument per second, prior to applying the fixed rate. The reservoir is not used directly by services, but applies to all services using the rule collectively. """ return pulumi.get(self, "reservoir_size") @property @pulumi.getter(name="resourceARN") def resource_arn(self) -> Optional[str]: """ Matches the ARN of the AWS resource on which the service runs. """ return pulumi.get(self, "resource_arn") @property @pulumi.getter(name="ruleARN") def rule_arn(self) -> Optional[str]: return pulumi.get(self, "rule_arn") @property @pulumi.getter(name="ruleName") def rule_name(self) -> Optional[str]: return pulumi.get(self, "rule_name") @property @pulumi.getter(name="serviceName") def service_name(self) -> Optional[str]: """ Matches the name that the service uses to identify itself in segments. """ return pulumi.get(self, "service_name") @property @pulumi.getter(name="serviceType") def service_type(self) -> Optional[str]: """ Matches the origin that the service uses to identify its type in segments. """ return pulumi.get(self, "service_type") @property @pulumi.getter(name="uRLPath") def u_rl_path(self) -> Optional[str]: """ Matches the path from a request URL. """ return pulumi.get(self, "u_rl_path") @pulumi.output_type class TagsItemProperties(dict): def __init__(__self__, *, key: str, value: str): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> str: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> str: return pulumi.get(self, "value")

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null

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8

cd5f236ac6ba3563921c41b0487c9111fcce775b

3,306

py

Python

tests/test_ntree.py

awoods/ocfl-py

ef4ff9d6b9a950088ff5373c4f1dfeec339f034d

[ "MIT" ]

14

2018-09-10T20:08:04.000Z

2022-03-29T18:10:43.000Z

tests/test_ntree.py

awoods/ocfl-py

ef4ff9d6b9a950088ff5373c4f1dfeec339f034d

[ "MIT" ]

73

2019-02-13T20:35:09.000Z

2022-03-24T15:21:34.000Z

tests/test_ntree.py

awoods/ocfl-py

ef4ff9d6b9a950088ff5373c4f1dfeec339f034d

[ "MIT" ]

3

2019-02-13T18:39:50.000Z

2021-05-04T15:39:04.000Z

"""Digest tests.""" import unittest from ocfl.ntree import Ntree class TestAll(unittest.TestCase): """TestAll class to run tests.""" def test01_encode(self): """Test encode.""" nt = Ntree() self.assertEqual(nt.encode(''), '') self.assertEqual(nt.encode('a'), 'a') self.assertEqual(nt.encode('a/b:?'), 'a=b+^3f') def test02_decode(self): """Test decode.""" nt = Ntree() self.assertEqual(nt.decode(''), '') self.assertEqual(nt.decode('a'), 'a') self.assertEqual(nt.decode('a=b+^3f'), 'a/b:?') def test03_identifier_to_path(self): """Test path creation.""" nt = Ntree(n=2, encapsulate=False) self.assertEqual(nt.identifier_to_path(''), '') self.assertEqual(nt.identifier_to_path('a'), 'a') self.assertEqual(nt.identifier_to_path('ab'), 'ab') self.assertEqual(nt.identifier_to_path('abc'), 'ab/c') self.assertEqual(nt.identifier_to_path('abcde'), 'ab/cd/e') nt = Ntree(n=3, encapsulate=False) self.assertEqual(nt.identifier_to_path('abcdefg'), 'abc/def/g') self.assertEqual(nt.identifier_to_path('abcdefgh'), 'abc/def/gh') self.assertEqual(nt.identifier_to_path('abcdefghi'), 'abc/def/ghi') nt = Ntree(n=2) self.assertEqual(nt.identifier_to_path(''), '') self.assertEqual(nt.identifier_to_path('a'), 'a/a') self.assertEqual(nt.identifier_to_path('ab'), 'ab/ab') self.assertEqual(nt.identifier_to_path('abc'), 'ab/c/abc') self.assertEqual(nt.identifier_to_path('abcde'), 'ab/cd/e/abcde') nt = Ntree(n=3) self.assertEqual(nt.identifier_to_path('abcdefg'), 'abc/def/g/abcdefg') self.assertEqual(nt.identifier_to_path('abcdefgh'), 'abc/def/gh/abcdefgh') self.assertEqual(nt.identifier_to_path('abcdefghi'), 'abc/def/ghi/abcdefghi') def test03_path_to_identifier(self): """Test path interpretation.""" nt = Ntree(n=2, encapsulate=False) self.assertEqual(nt.path_to_identifier(''), '') self.assertEqual(nt.path_to_identifier('a'), 'a') self.assertEqual(nt.path_to_identifier('ab'), 'ab') self.assertEqual(nt.path_to_identifier('ab/c'), 'abc') self.assertEqual(nt.path_to_identifier('ab/cd/e'), 'abcde') nt = Ntree(n=3, encapsulate=False) self.assertEqual(nt.path_to_identifier('abc/def/g'), 'abcdefg') self.assertEqual(nt.path_to_identifier('abc/def/gh'), 'abcdefgh') self.assertEqual(nt.path_to_identifier('abc/def/ghi'), 'abcdefghi') nt = Ntree(n=2) self.assertEqual(nt.path_to_identifier(''), '') self.assertEqual(nt.path_to_identifier('a/a'), 'a') self.assertEqual(nt.path_to_identifier('ab/ab'), 'ab') self.assertEqual(nt.path_to_identifier('ab/c/abc'), 'abc') self.assertEqual(nt.path_to_identifier('ab/cd/e/abcde'), 'abcde') nt = Ntree(n=3) self.assertEqual(nt.path_to_identifier('abc/def/g/abcdefg'), 'abcdefg') self.assertEqual(nt.path_to_identifier('abc/def/gh/abcdefgh'), 'abcdefgh') self.assertEqual(nt.path_to_identifier('abc/def/ghi/abcdefghi'), 'abcdefghi') # Bad ones self.assertRaises(Exception, nt.path_to_identifier, 'abc/def/g/a-diff-g')

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0.836624

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3,306

69

86

47.913043

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1

0

1

0

null

0

1

0

9

cd84b7a7724f16025ed88a33ed95eeab88e7f3db

116,140

py

Python

dingtalk/python/alibabacloud_dingtalk/exclusive_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

15

2020-08-27T04:10:26.000Z

2022-03-07T06:25:42.000Z

dingtalk/python/alibabacloud_dingtalk/exclusive_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

1

2020-09-27T01:30:46.000Z

2021-12-29T09:15:34.000Z

dingtalk/python/alibabacloud_dingtalk/exclusive_1_0/client.py

aliyun/dingtalk-sdk

ab4f856b8cfe94f6b69f10a0730a2e5a7d4901c5

[ "Apache-2.0" ]

5

2020-08-27T04:07:44.000Z

2021-12-03T02:55:20.000Z

# -*- coding: utf-8 -*- # This file is auto-generated, don't edit it. Thanks. from Tea.core import TeaCore from alibabacloud_tea_openapi.client import Client as OpenApiClient from alibabacloud_tea_openapi import models as open_api_models from alibabacloud_tea_util.client import Client as UtilClient from alibabacloud_dingtalk.exclusive_1_0 import models as dingtalkexclusive__1__0_models from alibabacloud_tea_util import models as util_models from alibabacloud_openapi_util.client import Client as OpenApiUtilClient class Client(OpenApiClient): """ *\ """ def __init__( self, config: open_api_models.Config, ): super().__init__(config) self._endpoint_rule = '' if UtilClient.empty(self._endpoint): self._endpoint = 'api.dingtalk.com' def get_conference_detail( self, conference_id: str, ) -> dingtalkexclusive__1__0_models.GetConferenceDetailResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetConferenceDetailHeaders() return self.get_conference_detail_with_options(conference_id, headers, runtime) async def get_conference_detail_async( self, conference_id: str, ) -> dingtalkexclusive__1__0_models.GetConferenceDetailResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetConferenceDetailHeaders() return await self.get_conference_detail_with_options_async(conference_id, headers, runtime) def get_conference_detail_with_options( self, conference_id: str, headers: dingtalkexclusive__1__0_models.GetConferenceDetailHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetConferenceDetailResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetConferenceDetailResponse(), self.do_roarequest('GetConferenceDetail', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/conferences/{conference_id}', 'json', req, runtime) ) async def get_conference_detail_with_options_async( self, conference_id: str, headers: dingtalkexclusive__1__0_models.GetConferenceDetailHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetConferenceDetailResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetConferenceDetailResponse(), await self.do_roarequest_async('GetConferenceDetail', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/conferences/{conference_id}', 'json', req, runtime) ) def get_user_app_version_summary( self, data_id: str, request: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetUserAppVersionSummaryHeaders() return self.get_user_app_version_summary_with_options(data_id, request, headers, runtime) async def get_user_app_version_summary_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetUserAppVersionSummaryHeaders() return await self.get_user_app_version_summary_with_options_async(data_id, request, headers, runtime) def get_user_app_version_summary_with_options( self, data_id: str, request: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryRequest, headers: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse(), self.do_roarequest('GetUserAppVersionSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/appVersion/org/{data_id}', 'json', req, runtime) ) async def get_user_app_version_summary_with_options_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryRequest, headers: dingtalkexclusive__1__0_models.GetUserAppVersionSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetUserAppVersionSummaryResponse(), await self.do_roarequest_async('GetUserAppVersionSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/appVersion/org/{data_id}', 'json', req, runtime) ) def delete_comment( self, publisher_id: str, comment_id: str, ) -> dingtalkexclusive__1__0_models.DeleteCommentResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.DeleteCommentHeaders() return self.delete_comment_with_options(publisher_id, comment_id, headers, runtime) async def delete_comment_async( self, publisher_id: str, comment_id: str, ) -> dingtalkexclusive__1__0_models.DeleteCommentResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.DeleteCommentHeaders() return await self.delete_comment_with_options_async(publisher_id, comment_id, headers, runtime) def delete_comment_with_options( self, publisher_id: str, comment_id: str, headers: dingtalkexclusive__1__0_models.DeleteCommentHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.DeleteCommentResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.DeleteCommentResponse(), self.do_roarequest('DeleteComment', 'exclusive_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/exclusive/publishers/{publisher_id}/comments/{comment_id}', 'boolean', req, runtime) ) async def delete_comment_with_options_async( self, publisher_id: str, comment_id: str, headers: dingtalkexclusive__1__0_models.DeleteCommentHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.DeleteCommentResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.DeleteCommentResponse(), await self.do_roarequest_async('DeleteComment', 'exclusive_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/exclusive/publishers/{publisher_id}/comments/{comment_id}', 'boolean', req, runtime) ) def list_mini_app_history_version( self, request: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionRequest, ) -> dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionHeaders() return self.list_mini_app_history_version_with_options(request, headers, runtime) async def list_mini_app_history_version_async( self, request: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionRequest, ) -> dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionHeaders() return await self.list_mini_app_history_version_with_options_async(request, headers, runtime) def list_mini_app_history_version_with_options( self, request: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionRequest, headers: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.mini_app_id): query['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse(), self.do_roarequest('ListMiniAppHistoryVersion', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/miniApps/versions/historyLists', 'json', req, runtime) ) async def list_mini_app_history_version_with_options_async( self, request: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionRequest, headers: dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.mini_app_id): query['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.ListMiniAppHistoryVersionResponse(), await self.do_roarequest_async('ListMiniAppHistoryVersion', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/miniApps/versions/historyLists', 'json', req, runtime) ) def get_doc_created_dept_summary( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryHeaders() return self.get_doc_created_dept_summary_with_options(data_id, request, headers, runtime) async def get_doc_created_dept_summary_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryHeaders() return await self.get_doc_created_dept_summary_with_options_async(data_id, request, headers, runtime) def get_doc_created_dept_summary_with_options( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse(), self.do_roarequest('GetDocCreatedDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/doc/dept/{data_id}', 'json', req, runtime) ) async def get_doc_created_dept_summary_with_options_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDocCreatedDeptSummaryResponse(), await self.do_roarequest_async('GetDocCreatedDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/doc/dept/{data_id}', 'json', req, runtime) ) def create_trusted_device( self, request: dingtalkexclusive__1__0_models.CreateTrustedDeviceRequest, ) -> dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.CreateTrustedDeviceHeaders() return self.create_trusted_device_with_options(request, headers, runtime) async def create_trusted_device_async( self, request: dingtalkexclusive__1__0_models.CreateTrustedDeviceRequest, ) -> dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.CreateTrustedDeviceHeaders() return await self.create_trusted_device_with_options_async(request, headers, runtime) def create_trusted_device_with_options( self, request: dingtalkexclusive__1__0_models.CreateTrustedDeviceRequest, headers: dingtalkexclusive__1__0_models.CreateTrustedDeviceHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.user_id): body['userId'] = request.user_id if not UtilClient.is_unset(request.platform): body['platform'] = request.platform if not UtilClient.is_unset(request.mac_address): body['macAddress'] = request.mac_address if not UtilClient.is_unset(request.status): body['status'] = request.status real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse(), self.do_roarequest('CreateTrustedDevice', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/trustedDevices', 'json', req, runtime) ) async def create_trusted_device_with_options_async( self, request: dingtalkexclusive__1__0_models.CreateTrustedDeviceRequest, headers: dingtalkexclusive__1__0_models.CreateTrustedDeviceHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.user_id): body['userId'] = request.user_id if not UtilClient.is_unset(request.platform): body['platform'] = request.platform if not UtilClient.is_unset(request.mac_address): body['macAddress'] = request.mac_address if not UtilClient.is_unset(request.status): body['status'] = request.status real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.CreateTrustedDeviceResponse(), await self.do_roarequest_async('CreateTrustedDevice', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/trustedDevices', 'json', req, runtime) ) def get_partner_type_by_parent_id( self, parent_id: str, ) -> dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdHeaders() return self.get_partner_type_by_parent_id_with_options(parent_id, headers, runtime) async def get_partner_type_by_parent_id_async( self, parent_id: str, ) -> dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdHeaders() return await self.get_partner_type_by_parent_id_with_options_async(parent_id, headers, runtime) def get_partner_type_by_parent_id_with_options( self, parent_id: str, headers: dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse(), self.do_roarequest('GetPartnerTypeByParentId', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/partnerLabels/{parent_id}', 'json', req, runtime) ) async def get_partner_type_by_parent_id_with_options_async( self, parent_id: str, headers: dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetPartnerTypeByParentIdResponse(), await self.do_roarequest_async('GetPartnerTypeByParentId', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/partnerLabels/{parent_id}', 'json', req, runtime) ) def set_dept_partner_type_and_num( self, request: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumRequest, ) -> dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumHeaders() return self.set_dept_partner_type_and_num_with_options(request, headers, runtime) async def set_dept_partner_type_and_num_async( self, request: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumRequest, ) -> dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumHeaders() return await self.set_dept_partner_type_and_num_with_options_async(request, headers, runtime) def set_dept_partner_type_and_num_with_options( self, request: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumRequest, headers: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.dept_id): body['deptId'] = request.dept_id if not UtilClient.is_unset(request.partner_num): body['partnerNum'] = request.partner_num if not UtilClient.is_unset(request.label_ids): body['labelIds'] = request.label_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse(), self.do_roarequest('SetDeptPartnerTypeAndNum', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/partnerDepartments', 'none', req, runtime) ) async def set_dept_partner_type_and_num_with_options_async( self, request: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumRequest, headers: dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.dept_id): body['deptId'] = request.dept_id if not UtilClient.is_unset(request.partner_num): body['partnerNum'] = request.partner_num if not UtilClient.is_unset(request.label_ids): body['labelIds'] = request.label_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SetDeptPartnerTypeAndNumResponse(), await self.do_roarequest_async('SetDeptPartnerTypeAndNum', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/partnerDepartments', 'none', req, runtime) ) def get_active_user_summary( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetActiveUserSummaryHeaders() return self.get_active_user_summary_with_options(data_id, headers, runtime) async def get_active_user_summary_async( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetActiveUserSummaryHeaders() return await self.get_active_user_summary_with_options_async(data_id, headers, runtime) def get_active_user_summary_with_options( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetActiveUserSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse(), self.do_roarequest('GetActiveUserSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/dau/org/{data_id}', 'json', req, runtime) ) async def get_active_user_summary_with_options_async( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetActiveUserSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetActiveUserSummaryResponse(), await self.do_roarequest_async('GetActiveUserSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/dau/org/{data_id}', 'json', req, runtime) ) def get_oa_operator_log_list( self, request: dingtalkexclusive__1__0_models.GetOaOperatorLogListRequest, ) -> dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetOaOperatorLogListHeaders() return self.get_oa_operator_log_list_with_options(request, headers, runtime) async def get_oa_operator_log_list_async( self, request: dingtalkexclusive__1__0_models.GetOaOperatorLogListRequest, ) -> dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetOaOperatorLogListHeaders() return await self.get_oa_operator_log_list_with_options_async(request, headers, runtime) def get_oa_operator_log_list_with_options( self, request: dingtalkexclusive__1__0_models.GetOaOperatorLogListRequest, headers: dingtalkexclusive__1__0_models.GetOaOperatorLogListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.op_user_id): body['opUserId'] = request.op_user_id if not UtilClient.is_unset(request.start_time): body['startTime'] = request.start_time if not UtilClient.is_unset(request.end_time): body['endTime'] = request.end_time if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.category_list): body['categoryList'] = request.category_list real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse(), self.do_roarequest('GetOaOperatorLogList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/oaOperatorLogs/query', 'json', req, runtime) ) async def get_oa_operator_log_list_with_options_async( self, request: dingtalkexclusive__1__0_models.GetOaOperatorLogListRequest, headers: dingtalkexclusive__1__0_models.GetOaOperatorLogListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.op_user_id): body['opUserId'] = request.op_user_id if not UtilClient.is_unset(request.start_time): body['startTime'] = request.start_time if not UtilClient.is_unset(request.end_time): body['endTime'] = request.end_time if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.category_list): body['categoryList'] = request.category_list real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetOaOperatorLogListResponse(), await self.do_roarequest_async('GetOaOperatorLogList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/oaOperatorLogs/query', 'json', req, runtime) ) def rollback_mini_app_version( self, request: dingtalkexclusive__1__0_models.RollbackMiniAppVersionRequest, ) -> dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.RollbackMiniAppVersionHeaders() return self.rollback_mini_app_version_with_options(request, headers, runtime) async def rollback_mini_app_version_async( self, request: dingtalkexclusive__1__0_models.RollbackMiniAppVersionRequest, ) -> dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.RollbackMiniAppVersionHeaders() return await self.rollback_mini_app_version_with_options_async(request, headers, runtime) def rollback_mini_app_version_with_options( self, request: dingtalkexclusive__1__0_models.RollbackMiniAppVersionRequest, headers: dingtalkexclusive__1__0_models.RollbackMiniAppVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.rollback_version): body['rollbackVersion'] = request.rollback_version if not UtilClient.is_unset(request.target_version): body['targetVersion'] = request.target_version if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse(), self.do_roarequest('RollbackMiniAppVersion', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/rollback', 'json', req, runtime) ) async def rollback_mini_app_version_with_options_async( self, request: dingtalkexclusive__1__0_models.RollbackMiniAppVersionRequest, headers: dingtalkexclusive__1__0_models.RollbackMiniAppVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.rollback_version): body['rollbackVersion'] = request.rollback_version if not UtilClient.is_unset(request.target_version): body['targetVersion'] = request.target_version if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.RollbackMiniAppVersionResponse(), await self.do_roarequest_async('RollbackMiniAppVersion', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/rollback', 'json', req, runtime) ) def publish_file_change_notice( self, request: dingtalkexclusive__1__0_models.PublishFileChangeNoticeRequest, ) -> dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.PublishFileChangeNoticeHeaders() return self.publish_file_change_notice_with_options(request, headers, runtime) async def publish_file_change_notice_async( self, request: dingtalkexclusive__1__0_models.PublishFileChangeNoticeRequest, ) -> dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.PublishFileChangeNoticeHeaders() return await self.publish_file_change_notice_with_options_async(request, headers, runtime) def publish_file_change_notice_with_options( self, request: dingtalkexclusive__1__0_models.PublishFileChangeNoticeRequest, headers: dingtalkexclusive__1__0_models.PublishFileChangeNoticeHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.file_id): body['fileId'] = request.file_id if not UtilClient.is_unset(request.space_id): body['spaceId'] = request.space_id if not UtilClient.is_unset(request.operator_union_id): body['operatorUnionId'] = request.operator_union_id if not UtilClient.is_unset(request.operate_type): body['operateType'] = request.operate_type real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse(), self.do_roarequest('PublishFileChangeNotice', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/comments/send', 'none', req, runtime) ) async def publish_file_change_notice_with_options_async( self, request: dingtalkexclusive__1__0_models.PublishFileChangeNoticeRequest, headers: dingtalkexclusive__1__0_models.PublishFileChangeNoticeHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.file_id): body['fileId'] = request.file_id if not UtilClient.is_unset(request.space_id): body['spaceId'] = request.space_id if not UtilClient.is_unset(request.operator_union_id): body['operatorUnionId'] = request.operator_union_id if not UtilClient.is_unset(request.operate_type): body['operateType'] = request.operate_type real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.PublishFileChangeNoticeResponse(), await self.do_roarequest_async('PublishFileChangeNotice', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/comments/send', 'none', req, runtime) ) def get_general_form_created_dept_summary( self, data_id: str, request: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryHeaders() return self.get_general_form_created_dept_summary_with_options(data_id, request, headers, runtime) async def get_general_form_created_dept_summary_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryHeaders() return await self.get_general_form_created_dept_summary_with_options_async(data_id, request, headers, runtime) def get_general_form_created_dept_summary_with_options( self, data_id: str, request: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse(), self.do_roarequest('GetGeneralFormCreatedDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/generalForm/dept/{data_id}', 'json', req, runtime) ) async def get_general_form_created_dept_summary_with_options_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGeneralFormCreatedDeptSummaryResponse(), await self.do_roarequest_async('GetGeneralFormCreatedDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/generalForm/dept/{data_id}', 'json', req, runtime) ) def get_calender_summary( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetCalenderSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetCalenderSummaryHeaders() return self.get_calender_summary_with_options(data_id, headers, runtime) async def get_calender_summary_async( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetCalenderSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetCalenderSummaryHeaders() return await self.get_calender_summary_with_options_async(data_id, headers, runtime) def get_calender_summary_with_options( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetCalenderSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetCalenderSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetCalenderSummaryResponse(), self.do_roarequest('GetCalenderSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/calendar/org/{data_id}', 'json', req, runtime) ) async def get_calender_summary_with_options_async( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetCalenderSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetCalenderSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetCalenderSummaryResponse(), await self.do_roarequest_async('GetCalenderSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/calendar/org/{data_id}', 'json', req, runtime) ) def get_all_labelable_depts(self) -> dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetAllLabelableDeptsHeaders() return self.get_all_labelable_depts_with_options(headers, runtime) async def get_all_labelable_depts_async(self) -> dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetAllLabelableDeptsHeaders() return await self.get_all_labelable_depts_with_options_async(headers, runtime) def get_all_labelable_depts_with_options( self, headers: dingtalkexclusive__1__0_models.GetAllLabelableDeptsHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse(), self.do_roarequest('GetAllLabelableDepts', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/partnerDepartments', 'json', req, runtime) ) async def get_all_labelable_depts_with_options_async( self, headers: dingtalkexclusive__1__0_models.GetAllLabelableDeptsHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetAllLabelableDeptsResponse(), await self.do_roarequest_async('GetAllLabelableDepts', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/partnerDepartments', 'json', req, runtime) ) def get_publisher_summary( self, data_id: str, request: dingtalkexclusive__1__0_models.GetPublisherSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetPublisherSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetPublisherSummaryHeaders() return self.get_publisher_summary_with_options(data_id, request, headers, runtime) async def get_publisher_summary_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetPublisherSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetPublisherSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetPublisherSummaryHeaders() return await self.get_publisher_summary_with_options_async(data_id, request, headers, runtime) def get_publisher_summary_with_options( self, data_id: str, request: dingtalkexclusive__1__0_models.GetPublisherSummaryRequest, headers: dingtalkexclusive__1__0_models.GetPublisherSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetPublisherSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetPublisherSummaryResponse(), self.do_roarequest('GetPublisherSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/publisher/{data_id}', 'json', req, runtime) ) async def get_publisher_summary_with_options_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetPublisherSummaryRequest, headers: dingtalkexclusive__1__0_models.GetPublisherSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetPublisherSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetPublisherSummaryResponse(), await self.do_roarequest_async('GetPublisherSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/publisher/{data_id}', 'json', req, runtime) ) def update_mini_app_version_status( self, request: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusRequest, ) -> dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusHeaders() return self.update_mini_app_version_status_with_options(request, headers, runtime) async def update_mini_app_version_status_async( self, request: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusRequest, ) -> dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusHeaders() return await self.update_mini_app_version_status_with_options_async(request, headers, runtime) def update_mini_app_version_status_with_options( self, request: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusRequest, headers: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.version_type): body['versionType'] = request.version_type if not UtilClient.is_unset(request.version): body['version'] = request.version if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse(), self.do_roarequest('UpdateMiniAppVersionStatus', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/versionStatus', 'json', req, runtime) ) async def update_mini_app_version_status_with_options_async( self, request: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusRequest, headers: dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.version_type): body['versionType'] = request.version_type if not UtilClient.is_unset(request.version): body['version'] = request.version if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdateMiniAppVersionStatusResponse(), await self.do_roarequest_async('UpdateMiniAppVersionStatus', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/versionStatus', 'json', req, runtime) ) def update_role_visibility( self, request: dingtalkexclusive__1__0_models.UpdateRoleVisibilityRequest, ) -> dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdateRoleVisibilityHeaders() return self.update_role_visibility_with_options(request, headers, runtime) async def update_role_visibility_async( self, request: dingtalkexclusive__1__0_models.UpdateRoleVisibilityRequest, ) -> dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdateRoleVisibilityHeaders() return await self.update_role_visibility_with_options_async(request, headers, runtime) def update_role_visibility_with_options( self, request: dingtalkexclusive__1__0_models.UpdateRoleVisibilityRequest, headers: dingtalkexclusive__1__0_models.UpdateRoleVisibilityHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.label_id): body['labelId'] = request.label_id if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse(), self.do_roarequest('UpdateRoleVisibility', 'exclusive_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/exclusive/partnerDepartments/visibilityRoles', 'boolean', req, runtime) ) async def update_role_visibility_with_options_async( self, request: dingtalkexclusive__1__0_models.UpdateRoleVisibilityRequest, headers: dingtalkexclusive__1__0_models.UpdateRoleVisibilityHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.label_id): body['labelId'] = request.label_id if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdateRoleVisibilityResponse(), await self.do_roarequest_async('UpdateRoleVisibility', 'exclusive_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/exclusive/partnerDepartments/visibilityRoles', 'boolean', req, runtime) ) def get_general_form_created_summary( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryHeaders() return self.get_general_form_created_summary_with_options(data_id, headers, runtime) async def get_general_form_created_summary_async( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryHeaders() return await self.get_general_form_created_summary_with_options_async(data_id, headers, runtime) def get_general_form_created_summary_with_options( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse(), self.do_roarequest('GetGeneralFormCreatedSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/generalForm/org/{data_id}', 'json', req, runtime) ) async def get_general_form_created_summary_with_options_async( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGeneralFormCreatedSummaryResponse(), await self.do_roarequest_async('GetGeneralFormCreatedSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/generalForm/org/{data_id}', 'json', req, runtime) ) def get_doc_created_summary( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDocCreatedSummaryHeaders() return self.get_doc_created_summary_with_options(data_id, headers, runtime) async def get_doc_created_summary_async( self, data_id: str, ) -> dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDocCreatedSummaryHeaders() return await self.get_doc_created_summary_with_options_async(data_id, headers, runtime) def get_doc_created_summary_with_options( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetDocCreatedSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse(), self.do_roarequest('GetDocCreatedSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/doc/org/{data_id}', 'json', req, runtime) ) async def get_doc_created_summary_with_options_async( self, data_id: str, headers: dingtalkexclusive__1__0_models.GetDocCreatedSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse: real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDocCreatedSummaryResponse(), await self.do_roarequest_async('GetDocCreatedSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/doc/org/{data_id}', 'json', req, runtime) ) def send_app_ding( self, request: dingtalkexclusive__1__0_models.SendAppDingRequest, ) -> dingtalkexclusive__1__0_models.SendAppDingResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SendAppDingHeaders() return self.send_app_ding_with_options(request, headers, runtime) async def send_app_ding_async( self, request: dingtalkexclusive__1__0_models.SendAppDingRequest, ) -> dingtalkexclusive__1__0_models.SendAppDingResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SendAppDingHeaders() return await self.send_app_ding_with_options_async(request, headers, runtime) def send_app_ding_with_options( self, request: dingtalkexclusive__1__0_models.SendAppDingRequest, headers: dingtalkexclusive__1__0_models.SendAppDingHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SendAppDingResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.userids): body['userids'] = request.userids if not UtilClient.is_unset(request.content): body['content'] = request.content real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SendAppDingResponse(), self.do_roarequest('SendAppDing', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/appDings/send', 'none', req, runtime) ) async def send_app_ding_with_options_async( self, request: dingtalkexclusive__1__0_models.SendAppDingRequest, headers: dingtalkexclusive__1__0_models.SendAppDingHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SendAppDingResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.userids): body['userids'] = request.userids if not UtilClient.is_unset(request.content): body['content'] = request.content real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SendAppDingResponse(), await self.do_roarequest_async('SendAppDing', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/appDings/send', 'none', req, runtime) ) def update_partner_visibility( self, request: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityRequest, ) -> dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdatePartnerVisibilityHeaders() return self.update_partner_visibility_with_options(request, headers, runtime) async def update_partner_visibility_async( self, request: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityRequest, ) -> dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.UpdatePartnerVisibilityHeaders() return await self.update_partner_visibility_with_options_async(request, headers, runtime) def update_partner_visibility_with_options( self, request: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityRequest, headers: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.label_id): body['labelId'] = request.label_id if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse(), self.do_roarequest('UpdatePartnerVisibility', 'exclusive_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/exclusive/partnerDepartments/visibilityPartners', 'boolean', req, runtime) ) async def update_partner_visibility_with_options_async( self, request: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityRequest, headers: dingtalkexclusive__1__0_models.UpdatePartnerVisibilityHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.label_id): body['labelId'] = request.label_id if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.UpdatePartnerVisibilityResponse(), await self.do_roarequest_async('UpdatePartnerVisibility', 'exclusive_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/exclusive/partnerDepartments/visibilityPartners', 'boolean', req, runtime) ) def get_ding_report_dept_summary( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDingReportDeptSummaryHeaders() return self.get_ding_report_dept_summary_with_options(data_id, request, headers, runtime) async def get_ding_report_dept_summary_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryRequest, ) -> dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetDingReportDeptSummaryHeaders() return await self.get_ding_report_dept_summary_with_options_async(data_id, request, headers, runtime) def get_ding_report_dept_summary_with_options( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse(), self.do_roarequest('GetDingReportDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/report/dept/{data_id}', 'json', req, runtime) ) async def get_ding_report_dept_summary_with_options_async( self, data_id: str, request: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryRequest, headers: dingtalkexclusive__1__0_models.GetDingReportDeptSummaryHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.next_token): query['nextToken'] = request.next_token if not UtilClient.is_unset(request.max_results): query['maxResults'] = request.max_results real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetDingReportDeptSummaryResponse(), await self.do_roarequest_async('GetDingReportDeptSummary', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/report/dept/{data_id}', 'json', req, runtime) ) def get_in_active_user_list( self, request: dingtalkexclusive__1__0_models.GetInActiveUserListRequest, ) -> dingtalkexclusive__1__0_models.GetInActiveUserListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetInActiveUserListHeaders() return self.get_in_active_user_list_with_options(request, headers, runtime) async def get_in_active_user_list_async( self, request: dingtalkexclusive__1__0_models.GetInActiveUserListRequest, ) -> dingtalkexclusive__1__0_models.GetInActiveUserListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetInActiveUserListHeaders() return await self.get_in_active_user_list_with_options_async(request, headers, runtime) def get_in_active_user_list_with_options( self, request: dingtalkexclusive__1__0_models.GetInActiveUserListRequest, headers: dingtalkexclusive__1__0_models.GetInActiveUserListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetInActiveUserListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.stat_date): body['statDate'] = request.stat_date if not UtilClient.is_unset(request.service_id): body['serviceId'] = request.service_id if not UtilClient.is_unset(request.ding_oauth_app_id): body['dingOauthAppId'] = request.ding_oauth_app_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetInActiveUserListResponse(), self.do_roarequest('GetInActiveUserList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/inactives/users/query', 'json', req, runtime) ) async def get_in_active_user_list_with_options_async( self, request: dingtalkexclusive__1__0_models.GetInActiveUserListRequest, headers: dingtalkexclusive__1__0_models.GetInActiveUserListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetInActiveUserListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.stat_date): body['statDate'] = request.stat_date if not UtilClient.is_unset(request.service_id): body['serviceId'] = request.service_id if not UtilClient.is_unset(request.ding_oauth_app_id): body['dingOauthAppId'] = request.ding_oauth_app_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.dept_ids): body['deptIds'] = request.dept_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetInActiveUserListResponse(), await self.do_roarequest_async('GetInActiveUserList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/inactives/users/query', 'json', req, runtime) ) def get_trust_device_list( self, request: dingtalkexclusive__1__0_models.GetTrustDeviceListRequest, ) -> dingtalkexclusive__1__0_models.GetTrustDeviceListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetTrustDeviceListHeaders() return self.get_trust_device_list_with_options(request, headers, runtime) async def get_trust_device_list_async( self, request: dingtalkexclusive__1__0_models.GetTrustDeviceListRequest, ) -> dingtalkexclusive__1__0_models.GetTrustDeviceListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetTrustDeviceListHeaders() return await self.get_trust_device_list_with_options_async(request, headers, runtime) def get_trust_device_list_with_options( self, request: dingtalkexclusive__1__0_models.GetTrustDeviceListRequest, headers: dingtalkexclusive__1__0_models.GetTrustDeviceListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetTrustDeviceListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetTrustDeviceListResponse(), self.do_roarequest('GetTrustDeviceList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/trustedDevices/query', 'json', req, runtime) ) async def get_trust_device_list_with_options_async( self, request: dingtalkexclusive__1__0_models.GetTrustDeviceListRequest, headers: dingtalkexclusive__1__0_models.GetTrustDeviceListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetTrustDeviceListResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.user_ids): body['userIds'] = request.user_ids real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetTrustDeviceListResponse(), await self.do_roarequest_async('GetTrustDeviceList', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/trustedDevices/query', 'json', req, runtime) ) def list_mini_app_available_version( self, request: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionRequest, ) -> dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionHeaders() return self.list_mini_app_available_version_with_options(request, headers, runtime) async def list_mini_app_available_version_async( self, request: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionRequest, ) -> dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionHeaders() return await self.list_mini_app_available_version_with_options_async(request, headers, runtime) def list_mini_app_available_version_with_options( self, request: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionRequest, headers: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.version_type_set): body['versionTypeSet'] = request.version_type_set if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse(), self.do_roarequest('ListMiniAppAvailableVersion', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/availableLists', 'json', req, runtime) ) async def list_mini_app_available_version_with_options_async( self, request: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionRequest, headers: dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.version_type_set): body['versionTypeSet'] = request.version_type_set if not UtilClient.is_unset(request.page_size): body['pageSize'] = request.page_size if not UtilClient.is_unset(request.page_number): body['pageNumber'] = request.page_number if not UtilClient.is_unset(request.ding_isv_org_id): body['dingIsvOrgId'] = request.ding_isv_org_id if not UtilClient.is_unset(request.ding_org_id): body['dingOrgId'] = request.ding_org_id if not UtilClient.is_unset(request.ding_suite_key): body['dingSuiteKey'] = request.ding_suite_key if not UtilClient.is_unset(request.ding_corp_id): body['dingCorpId'] = request.ding_corp_id if not UtilClient.is_unset(request.ding_client_id): body['dingClientId'] = request.ding_client_id if not UtilClient.is_unset(request.ding_token_grant_type): body['dingTokenGrantType'] = request.ding_token_grant_type if not UtilClient.is_unset(request.mini_app_id): body['miniAppId'] = request.mini_app_id real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.ListMiniAppAvailableVersionResponse(), await self.do_roarequest_async('ListMiniAppAvailableVersion', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/miniApps/versions/availableLists', 'json', req, runtime) ) def search_org_inner_group_info( self, request: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoRequest, ) -> dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoHeaders() return self.search_org_inner_group_info_with_options(request, headers, runtime) async def search_org_inner_group_info_async( self, request: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoRequest, ) -> dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoHeaders() return await self.search_org_inner_group_info_with_options_async(request, headers, runtime) def search_org_inner_group_info_with_options( self, request: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoRequest, headers: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.group_members_count_end): query['groupMembersCountEnd'] = request.group_members_count_end if not UtilClient.is_unset(request.sync_to_dingpan): query['syncToDingpan'] = request.sync_to_dingpan if not UtilClient.is_unset(request.group_owner): query['groupOwner'] = request.group_owner if not UtilClient.is_unset(request.create_time_end): query['createTimeEnd'] = request.create_time_end if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size if not UtilClient.is_unset(request.create_time_start): query['createTimeStart'] = request.create_time_start if not UtilClient.is_unset(request.uuid): query['uuid'] = request.uuid if not UtilClient.is_unset(request.group_members_count_start): query['groupMembersCountStart'] = request.group_members_count_start if not UtilClient.is_unset(request.last_active_time_end): query['lastActiveTimeEnd'] = request.last_active_time_end if not UtilClient.is_unset(request.operator_user_id): query['operatorUserId'] = request.operator_user_id if not UtilClient.is_unset(request.group_name): query['groupName'] = request.group_name if not UtilClient.is_unset(request.page_start): query['pageStart'] = request.page_start if not UtilClient.is_unset(request.last_active_time_start): query['lastActiveTimeStart'] = request.last_active_time_start real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse(), self.do_roarequest('SearchOrgInnerGroupInfo', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/securities/orgGroupInfos', 'json', req, runtime) ) async def search_org_inner_group_info_with_options_async( self, request: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoRequest, headers: dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.group_members_count_end): query['groupMembersCountEnd'] = request.group_members_count_end if not UtilClient.is_unset(request.sync_to_dingpan): query['syncToDingpan'] = request.sync_to_dingpan if not UtilClient.is_unset(request.group_owner): query['groupOwner'] = request.group_owner if not UtilClient.is_unset(request.create_time_end): query['createTimeEnd'] = request.create_time_end if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size if not UtilClient.is_unset(request.create_time_start): query['createTimeStart'] = request.create_time_start if not UtilClient.is_unset(request.uuid): query['uuid'] = request.uuid if not UtilClient.is_unset(request.group_members_count_start): query['groupMembersCountStart'] = request.group_members_count_start if not UtilClient.is_unset(request.last_active_time_end): query['lastActiveTimeEnd'] = request.last_active_time_end if not UtilClient.is_unset(request.operator_user_id): query['operatorUserId'] = request.operator_user_id if not UtilClient.is_unset(request.group_name): query['groupName'] = request.group_name if not UtilClient.is_unset(request.page_start): query['pageStart'] = request.page_start if not UtilClient.is_unset(request.last_active_time_start): query['lastActiveTimeStart'] = request.last_active_time_start real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SearchOrgInnerGroupInfoResponse(), await self.do_roarequest_async('SearchOrgInnerGroupInfo', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/securities/orgGroupInfos', 'json', req, runtime) ) def send_invitation( self, request: dingtalkexclusive__1__0_models.SendInvitationRequest, ) -> dingtalkexclusive__1__0_models.SendInvitationResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SendInvitationHeaders() return self.send_invitation_with_options(request, headers, runtime) async def send_invitation_async( self, request: dingtalkexclusive__1__0_models.SendInvitationRequest, ) -> dingtalkexclusive__1__0_models.SendInvitationResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.SendInvitationHeaders() return await self.send_invitation_with_options_async(request, headers, runtime) def send_invitation_with_options( self, request: dingtalkexclusive__1__0_models.SendInvitationRequest, headers: dingtalkexclusive__1__0_models.SendInvitationHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SendInvitationResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.dept_id): body['deptId'] = request.dept_id if not UtilClient.is_unset(request.partner_num): body['partnerNum'] = request.partner_num if not UtilClient.is_unset(request.partner_label_id): body['partnerLabelId'] = request.partner_label_id if not UtilClient.is_unset(request.phone): body['phone'] = request.phone if not UtilClient.is_unset(request.org_alias): body['orgAlias'] = request.org_alias real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SendInvitationResponse(), self.do_roarequest('SendInvitation', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/partnerDepartments/invitations/send', 'none', req, runtime) ) async def send_invitation_with_options_async( self, request: dingtalkexclusive__1__0_models.SendInvitationRequest, headers: dingtalkexclusive__1__0_models.SendInvitationHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.SendInvitationResponse: UtilClient.validate_model(request) body = {} if not UtilClient.is_unset(request.dept_id): body['deptId'] = request.dept_id if not UtilClient.is_unset(request.partner_num): body['partnerNum'] = request.partner_num if not UtilClient.is_unset(request.partner_label_id): body['partnerLabelId'] = request.partner_label_id if not UtilClient.is_unset(request.phone): body['phone'] = request.phone if not UtilClient.is_unset(request.org_alias): body['orgAlias'] = request.org_alias real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, body=OpenApiUtilClient.parse_to_map(body) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.SendInvitationResponse(), await self.do_roarequest_async('SendInvitation', 'exclusive_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/exclusive/partnerDepartments/invitations/send', 'none', req, runtime) ) def get_group_active_info( self, request: dingtalkexclusive__1__0_models.GetGroupActiveInfoRequest, ) -> dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGroupActiveInfoHeaders() return self.get_group_active_info_with_options(request, headers, runtime) async def get_group_active_info_async( self, request: dingtalkexclusive__1__0_models.GetGroupActiveInfoRequest, ) -> dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetGroupActiveInfoHeaders() return await self.get_group_active_info_with_options_async(request, headers, runtime) def get_group_active_info_with_options( self, request: dingtalkexclusive__1__0_models.GetGroupActiveInfoRequest, headers: dingtalkexclusive__1__0_models.GetGroupActiveInfoHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.stat_date): query['statDate'] = request.stat_date if not UtilClient.is_unset(request.ding_group_id): query['dingGroupId'] = request.ding_group_id if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse(), self.do_roarequest('GetGroupActiveInfo', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/activeGroups', 'json', req, runtime) ) async def get_group_active_info_with_options_async( self, request: dingtalkexclusive__1__0_models.GetGroupActiveInfoRequest, headers: dingtalkexclusive__1__0_models.GetGroupActiveInfoHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.stat_date): query['statDate'] = request.stat_date if not UtilClient.is_unset(request.ding_group_id): query['dingGroupId'] = request.ding_group_id if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetGroupActiveInfoResponse(), await self.do_roarequest_async('GetGroupActiveInfo', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/data/activeGroups', 'json', req, runtime) ) def get_comment_list( self, publisher_id: str, request: dingtalkexclusive__1__0_models.GetCommentListRequest, ) -> dingtalkexclusive__1__0_models.GetCommentListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetCommentListHeaders() return self.get_comment_list_with_options(publisher_id, request, headers, runtime) async def get_comment_list_async( self, publisher_id: str, request: dingtalkexclusive__1__0_models.GetCommentListRequest, ) -> dingtalkexclusive__1__0_models.GetCommentListResponse: runtime = util_models.RuntimeOptions() headers = dingtalkexclusive__1__0_models.GetCommentListHeaders() return await self.get_comment_list_with_options_async(publisher_id, request, headers, runtime) def get_comment_list_with_options( self, publisher_id: str, request: dingtalkexclusive__1__0_models.GetCommentListRequest, headers: dingtalkexclusive__1__0_models.GetCommentListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetCommentListResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetCommentListResponse(), self.do_roarequest('GetCommentList', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/publishers/{publisher_id}/comments/list', 'json', req, runtime) ) async def get_comment_list_with_options_async( self, publisher_id: str, request: dingtalkexclusive__1__0_models.GetCommentListRequest, headers: dingtalkexclusive__1__0_models.GetCommentListHeaders, runtime: util_models.RuntimeOptions, ) -> dingtalkexclusive__1__0_models.GetCommentListResponse: UtilClient.validate_model(request) query = {} if not UtilClient.is_unset(request.page_number): query['pageNumber'] = request.page_number if not UtilClient.is_unset(request.page_size): query['pageSize'] = request.page_size real_headers = {} if not UtilClient.is_unset(headers.common_headers): real_headers = headers.common_headers if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token): real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token req = open_api_models.OpenApiRequest( headers=real_headers, query=OpenApiUtilClient.query(query) ) return TeaCore.from_map( dingtalkexclusive__1__0_models.GetCommentListResponse(), await self.do_roarequest_async('GetCommentList', 'exclusive_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/exclusive/publishers/{publisher_id}/comments/list', 'json', req, runtime) )

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0.902641

0

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116,140

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51.940966

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null

0

1

0

1

0

null

0

8

cd84dc75becec4bcf4b7a2f3f2dcd0c4fd101927

1,234

py

Python

poke_django/teams/migrations/0002_auto_20201229_1837.py

XrossFox/Poke-Django-Api-Test

2e272b055fbeb633edb64fb2a3d6a720a50045f8

[ "MIT" ]

null

poke_django/teams/migrations/0002_auto_20201229_1837.py

XrossFox/Poke-Django-Api-Test

2e272b055fbeb633edb64fb2a3d6a720a50045f8

[ "MIT" ]

null

poke_django/teams/migrations/0002_auto_20201229_1837.py

XrossFox/Poke-Django-Api-Test

2e272b055fbeb633edb64fb2a3d6a720a50045f8

[ "MIT" ]

null

# Generated by Django 3.1.4 on 2020-12-30 00:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('teams', '0001_initial'), ] operations = [ migrations.AlterField( model_name='team', name='slot_1_dex_id', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='team', name='slot_2_dex_id', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='team', name='slot_3_dex_id', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='team', name='slot_4_dex_id', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='team', name='slot_5_dex_id', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='team', name='slot_6_dex_id', field=models.IntegerField(blank=True, null=True), ), ]

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1,234

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0

1

0

1

0

1

0

null

0

8

26955a95196dde9594c595f3ca163135bd65e6e2

10,176

py

Python

trash-classify/models/model1.py

scp19801980/Trash-classify-M_b-Xception

1cfb0bc111a356770fcade17bc8b83e13c4838f5

[ "MIT" ]

5

2020-08-08T11:38:54.000Z

2021-11-04T02:15:06.000Z

trash-classify/models/model1.py

scp19801980/Trash-classify-M_b-Xception

1cfb0bc111a356770fcade17bc8b83e13c4838f5

[ "MIT" ]

null

trash-classify/models/model1.py

scp19801980/Trash-classify-M_b-Xception

1cfb0bc111a356770fcade17bc8b83e13c4838f5

[ "MIT" ]

1

2020-08-08T11:41:00.000Z

#Xception from keras import layers from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization from keras.layers import Activation, Input, Dense, SeparableConv2D from keras.models import Model from keras.regularizers import l2 def Xception(input_shape, num_classes): l2_reg = 1e-4 img_input = Input(shape=input_shape) x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(img_input) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(64, (3, 3), use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) x = layers.add([x, residual]) residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) x = layers.add([x, residual]) residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = x x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = layers.add([x, residual]) residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) residual = BatchNormalization()(residual) x = Activation('relu')(x) x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x) x = layers.add([x, residual]) x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, kernel_regularizer=l2(l2_reg), kernel_initializer='he_normal')(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = GlobalAveragePooling2D()(x) x = Dense(num_classes, activation='softmax')(x) model = Model(img_input, x) return model #input_shape = (229, 229, 3) #num_classes = 6 #Xception(input_shape, num_classes).summary()

44.436681

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0.595814

1,230

10,176

4.769919

0.045528

0.040225

0.081814

0.116584

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10,176

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44.436681

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1

0

1

0

null

0

7

f8c39a3a832ec12d600e09a3b4aa6062b09f2f7b

5,773

py

Python

config.py

seo3650/video-question-answering

fd796a5b470bb1303ebeb3d8de957451ec83700d

[ "MIT" ]

59

2017-09-19T03:02:18.000Z

2019-11-28T01:57:48.000Z

config.py

seo3650/video-question-answering

fd796a5b470bb1303ebeb3d8de957451ec83700d

[ "MIT" ]

null

config.py

seo3650/video-question-answering

fd796a5b470bb1303ebeb3d8de957451ec83700d

[ "MIT" ]

16

2017-11-01T00:16:36.000Z

2019-10-20T08:43:56.000Z

"""Configuration for models.""" import os import tensorflow as tf CONFIG = { 'esa': { 'msvd_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 4000, 'pretrained_embedding': 'data/msvd_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 32, 'reg_coeff': 1e-5, 'learning_rate': 0.001 } } }, 'msrvtt_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 8000, 'pretrained_embedding': 'data/msrvtt_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 64, 'reg_coeff': 1e-6, 'learning_rate': 0.001 } } } }, 'emn': { 'msvd_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 4000, 'pretrained_embedding': 'data/msvd_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'memory_dim': 256 }, 'train': { 'batch_size': 32, 'reg_coeff': 1e-5, 'learning_rate': 0.001 } } }, 'msrvtt_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 8000, 'pretrained_embedding': 'data/msrvtt_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'memory_dim': 256 }, 'train': { 'batch_size': 64, 'reg_coeff': 1e-6, 'learning_rate': 0.001 } } } }, 'evqa': { 'msvd_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 4000, 'pretrained_embedding': 'data/msvd_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 32, 'reg_coeff': 1e-5, 'learning_rate': 0.001 } } }, 'msrvtt_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 8000, 'pretrained_embedding': 'data/msrvtt_qa/word_embedding.npy', 'video_feature_dim': 4096, 'video_feature_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 64, 'reg_coeff': 1e-6, 'learning_rate': 0.001 } } } }, 'gra': { 'msvd_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 4000, 'pretrained_embedding': 'data/msvd_qa/word_embedding.npy', 'appear_dim': 4096, 'frame_num': 20, 'motion_dim': 4096, 'clip_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 32, 'reg_coeff': 1e-6, 'shu_coeff': 1e-5, 'learning_rate': 0.001 } } }, 'msrvtt_qa': { '0': { 'model': { 'word_dim': 300, 'vocab_num': 8000, 'pretrained_embedding': 'data/msrvtt_qa/word_embedding.npy', 'appear_dim': 4096, 'frame_num': 20, 'motion_dim': 4096, 'clip_num': 20, 'answer_num': 1000, 'common_dim': 256 }, 'train': { 'batch_size': 64, 'reg_coeff': 1e-7, 'shu_coeff': 1e-7, 'learning_rate': 0.001 } } } } } def get(model, dataset, config_id, gpu_list): """Generate configuration.""" config = {} if dataset == 'msvd_qa': config['preprocess_dir'] = 'data/msvd_qa' elif dataset == 'msrvtt_qa': config['preprocess_dir'] = 'data/msrvtt_qa' config['model'] = CONFIG[model][dataset][config_id]['model'] config['train'] = CONFIG[model][dataset][config_id]['train'] sess_config = tf.ConfigProto() sess_config.gpu_options.allow_growth = True sess_config.gpu_options.visible_device_list = gpu_list config['session'] = sess_config return config

31.037634

80

0.364802

455

5,773

4.316484

0.171429

0.07332

0.032587

0.04888

0.809572

0.757637

0

0.08908

0.517755

5,773

185

81

31.205405

0.616379

0.008488

0

0.657143

0

0.26711

0.04481

0

1

0.005714

false

0

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null

0

1

0

1

0

null

0

7

3e44ce3ad007c9a9079e2a432648dcd4ccaebe52

5,734

py

Python

ljdata/ds/JPEGBaseline/__init__.py

scaramallion/pylibjpeg-data

2ab4b8a65b070656eca2582bd23197a3d01cdccd

[ "MIT" ]

3

2020-03-07T21:54:09.000Z

2020-06-11T02:23:58.000Z

ljdata/ds/JPEGBaseline/__init__.py

scaramallion/pylibjpeg-data

2ab4b8a65b070656eca2582bd23197a3d01cdccd

[ "MIT" ]

2

2020-03-08T00:35:51.000Z

2020-07-19T23:42:13.000Z

ljdata/ds/JPEGBaseline/__init__.py

scaramallion/pylibjpeg-data

2ab4b8a65b070656eca2582bd23197a3d01cdccd

[ "MIT" ]

1

2020-03-07T21:49:07.000Z

"""1.2.840.10008.1.2.4.50 - JPEG Baseline (Process 1)""" INDEX = { "color3d_jpeg_baseline.dcm" : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL_422'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '120'), 'Rows' : ('US', 480), 'Columns' : ('US', 640), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, "JPEGBaseline_1s_1f_u_08_08.dcm" : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 1), 'PhotometricInterpretation' : ('CS', 'MONOCHROME2'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), 'ImageComments' : ( 'LT', ( "Created from SC_rgb_jpeg_dcmtk.dcm using IJG's cjpeg with " "-grayscale and -baseline flags" ) ), }, 'SC_rgb_dcmtk_+eb+cr.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'RGB'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_dcmtk_+eb+cy+n1.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_dcmtk_+eb+cy+n2.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_dcmtk_+eb+cy+np.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL_422'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_dcmtk_+eb+cy+s2.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL_422'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_dcmtk_+eb+cy+s4.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_jpeg_dcmtk.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_jpeg_lossy_gdcm.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'RGB'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 100), 'Columns' : ('US', 100), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, 'SC_rgb_small_odd_jpeg.dcm' : { 'TransferSyntaxUID' : ('UI', '1.2.840.10008.1.2.4.50'), 'SamplesPerPixel' : ('US', 3), 'PhotometricInterpretation' : ('CS', 'YBR_FULL'), 'PlanarConfiguration' : ('US', 0), 'NumberOfFrames' : ('IS', '1'), 'Rows' : ('US', 3), 'Columns' : ('US', 3), 'BitsAllocated' : ('US', 8), 'BitsStored' : ('US', 8), 'HighBit' : ('US', 7), 'PixelRepresentation' : ('US', 0), }, }

36.993548

76

0.465992

551

5,734

4.758621

0.128857

0.018307

0.022883

0.045767

0.903509

0.895118

0.889397

0

0.084985

0.296128

5,734

154

77

37.233766

0.564668

0.00872

0

0.75

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0.461958

0.144065

0

1

0

false

0

null

0

1

0

1

0

null

0

7

3e6022f20ff980ed2ff22e2f8e9f752cf95febf2

112

py

Python

example.py

EMCain/python-demo-dates

d4d198cd916bd395ea89744af2057610eef64cb0

[ "MIT" ]

null

example.py

EMCain/python-demo-dates

d4d198cd916bd395ea89744af2057610eef64cb0

[ "MIT" ]

null

example.py

EMCain/python-demo-dates

d4d198cd916bd395ea89744af2057610eef64cb0

[ "MIT" ]

null

from src.info_string import this_year_info_string if __name__ == "__main__": print(this_year_info_string())

28

49

0.794643

17

112

4.352941

0.647059

0.405405

0.324324

0.486486

0

0.116071

112

4

50

28

0.747475

0

0.070796

0

1

0

true

0

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

7

3e7cf7c8821734d25608263070cef557f49ca488

23,674

py

Python

sdk/python/pulumi_cloudflare/logpush_job.py

pulumi/pulumi-cloudflare

d444af2fab6101b388a15cf2e3933e45e9935cc6

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

35

2019-03-14T21:29:29.000Z

2022-03-30T00:00:59.000Z

sdk/python/pulumi_cloudflare/logpush_job.py

pulumi/pulumi-cloudflare

d444af2fab6101b388a15cf2e3933e45e9935cc6

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

128

2019-03-08T23:45:58.000Z

2022-03-31T21:05:22.000Z

sdk/python/pulumi_cloudflare/logpush_job.py

pulumi/pulumi-cloudflare

d444af2fab6101b388a15cf2e3933e45e9935cc6

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

6

2019-05-10T12:52:56.000Z

2020-03-24T15:02:14.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['LogpushJobArgs', 'LogpushJob'] @pulumi.input_type class LogpushJobArgs: def __init__(__self__, *, dataset: pulumi.Input[str], destination_conf: pulumi.Input[str], zone_id: pulumi.Input[str], enabled: Optional[pulumi.Input[bool]] = None, logpull_options: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_challenge: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a LogpushJob resource. :param pulumi.Input[str] dataset: Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. :param pulumi.Input[str] destination_conf: Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). :param pulumi.Input[str] zone_id: The zone ID where the logpush job should be created. :param pulumi.Input[bool] enabled: Whether to enable the job. :param pulumi.Input[str] logpull_options: Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). :param pulumi.Input[str] name: The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. :param pulumi.Input[str] ownership_challenge: Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). """ pulumi.set(__self__, "dataset", dataset) pulumi.set(__self__, "destination_conf", destination_conf) pulumi.set(__self__, "zone_id", zone_id) if enabled is not None: pulumi.set(__self__, "enabled", enabled) if logpull_options is not None: pulumi.set(__self__, "logpull_options", logpull_options) if name is not None: pulumi.set(__self__, "name", name) if ownership_challenge is not None: pulumi.set(__self__, "ownership_challenge", ownership_challenge) @property @pulumi.getter def dataset(self) -> pulumi.Input[str]: """ Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. """ return pulumi.get(self, "dataset") @dataset.setter def dataset(self, value: pulumi.Input[str]): pulumi.set(self, "dataset", value) @property @pulumi.getter(name="destinationConf") def destination_conf(self) -> pulumi.Input[str]: """ Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). """ return pulumi.get(self, "destination_conf") @destination_conf.setter def destination_conf(self, value: pulumi.Input[str]): pulumi.set(self, "destination_conf", value) @property @pulumi.getter(name="zoneId") def zone_id(self) -> pulumi.Input[str]: """ The zone ID where the logpush job should be created. """ return pulumi.get(self, "zone_id") @zone_id.setter def zone_id(self, value: pulumi.Input[str]): pulumi.set(self, "zone_id", value) @property @pulumi.getter def enabled(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable the job. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="logpullOptions") def logpull_options(self) -> Optional[pulumi.Input[str]]: """ Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). """ return pulumi.get(self, "logpull_options") @logpull_options.setter def logpull_options(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "logpull_options", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="ownershipChallenge") def ownership_challenge(self) -> Optional[pulumi.Input[str]]: """ Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). """ return pulumi.get(self, "ownership_challenge") @ownership_challenge.setter def ownership_challenge(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ownership_challenge", value) @pulumi.input_type class _LogpushJobState: def __init__(__self__, *, dataset: Optional[pulumi.Input[str]] = None, destination_conf: Optional[pulumi.Input[str]] = None, enabled: Optional[pulumi.Input[bool]] = None, logpull_options: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_challenge: Optional[pulumi.Input[str]] = None, zone_id: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering LogpushJob resources. :param pulumi.Input[str] dataset: Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. :param pulumi.Input[str] destination_conf: Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). :param pulumi.Input[bool] enabled: Whether to enable the job. :param pulumi.Input[str] logpull_options: Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). :param pulumi.Input[str] name: The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. :param pulumi.Input[str] ownership_challenge: Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). :param pulumi.Input[str] zone_id: The zone ID where the logpush job should be created. """ if dataset is not None: pulumi.set(__self__, "dataset", dataset) if destination_conf is not None: pulumi.set(__self__, "destination_conf", destination_conf) if enabled is not None: pulumi.set(__self__, "enabled", enabled) if logpull_options is not None: pulumi.set(__self__, "logpull_options", logpull_options) if name is not None: pulumi.set(__self__, "name", name) if ownership_challenge is not None: pulumi.set(__self__, "ownership_challenge", ownership_challenge) if zone_id is not None: pulumi.set(__self__, "zone_id", zone_id) @property @pulumi.getter def dataset(self) -> Optional[pulumi.Input[str]]: """ Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. """ return pulumi.get(self, "dataset") @dataset.setter def dataset(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "dataset", value) @property @pulumi.getter(name="destinationConf") def destination_conf(self) -> Optional[pulumi.Input[str]]: """ Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). """ return pulumi.get(self, "destination_conf") @destination_conf.setter def destination_conf(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "destination_conf", value) @property @pulumi.getter def enabled(self) -> Optional[pulumi.Input[bool]]: """ Whether to enable the job. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="logpullOptions") def logpull_options(self) -> Optional[pulumi.Input[str]]: """ Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). """ return pulumi.get(self, "logpull_options") @logpull_options.setter def logpull_options(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "logpull_options", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="ownershipChallenge") def ownership_challenge(self) -> Optional[pulumi.Input[str]]: """ Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). """ return pulumi.get(self, "ownership_challenge") @ownership_challenge.setter def ownership_challenge(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ownership_challenge", value) @property @pulumi.getter(name="zoneId") def zone_id(self) -> Optional[pulumi.Input[str]]: """ The zone ID where the logpush job should be created. """ return pulumi.get(self, "zone_id") @zone_id.setter def zone_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "zone_id", value) class LogpushJob(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, dataset: Optional[pulumi.Input[str]] = None, destination_conf: Optional[pulumi.Input[str]] = None, enabled: Optional[pulumi.Input[bool]] = None, logpull_options: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_challenge: Optional[pulumi.Input[str]] = None, zone_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Create a LogpushJob resource with the given unique name, props, and options. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] dataset: Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. :param pulumi.Input[str] destination_conf: Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). :param pulumi.Input[bool] enabled: Whether to enable the job. :param pulumi.Input[str] logpull_options: Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). :param pulumi.Input[str] name: The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. :param pulumi.Input[str] ownership_challenge: Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). :param pulumi.Input[str] zone_id: The zone ID where the logpush job should be created. """ ... @overload def __init__(__self__, resource_name: str, args: LogpushJobArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Create a LogpushJob resource with the given unique name, props, and options. :param str resource_name: The name of the resource. :param LogpushJobArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(LogpushJobArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, dataset: Optional[pulumi.Input[str]] = None, destination_conf: Optional[pulumi.Input[str]] = None, enabled: Optional[pulumi.Input[bool]] = None, logpull_options: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_challenge: Optional[pulumi.Input[str]] = None, zone_id: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = LogpushJobArgs.__new__(LogpushJobArgs) if dataset is None and not opts.urn: raise TypeError("Missing required property 'dataset'") __props__.__dict__["dataset"] = dataset if destination_conf is None and not opts.urn: raise TypeError("Missing required property 'destination_conf'") __props__.__dict__["destination_conf"] = destination_conf __props__.__dict__["enabled"] = enabled __props__.__dict__["logpull_options"] = logpull_options __props__.__dict__["name"] = name __props__.__dict__["ownership_challenge"] = ownership_challenge if zone_id is None and not opts.urn: raise TypeError("Missing required property 'zone_id'") __props__.__dict__["zone_id"] = zone_id super(LogpushJob, __self__).__init__( 'cloudflare:index/logpushJob:LogpushJob', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, dataset: Optional[pulumi.Input[str]] = None, destination_conf: Optional[pulumi.Input[str]] = None, enabled: Optional[pulumi.Input[bool]] = None, logpull_options: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, ownership_challenge: Optional[pulumi.Input[str]] = None, zone_id: Optional[pulumi.Input[str]] = None) -> 'LogpushJob': """ Get an existing LogpushJob resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] dataset: Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. :param pulumi.Input[str] destination_conf: Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). :param pulumi.Input[bool] enabled: Whether to enable the job. :param pulumi.Input[str] logpull_options: Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). :param pulumi.Input[str] name: The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. :param pulumi.Input[str] ownership_challenge: Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). :param pulumi.Input[str] zone_id: The zone ID where the logpush job should be created. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _LogpushJobState.__new__(_LogpushJobState) __props__.__dict__["dataset"] = dataset __props__.__dict__["destination_conf"] = destination_conf __props__.__dict__["enabled"] = enabled __props__.__dict__["logpull_options"] = logpull_options __props__.__dict__["name"] = name __props__.__dict__["ownership_challenge"] = ownership_challenge __props__.__dict__["zone_id"] = zone_id return LogpushJob(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def dataset(self) -> pulumi.Output[str]: """ Which type of dataset resource to use. Available values are `"firewall_events"`, `"http_requests"`, `"spectrum_events"` and `"nel_reports"`. """ return pulumi.get(self, "dataset") @property @pulumi.getter(name="destinationConf") def destination_conf(self) -> pulumi.Output[str]: """ Uniquely identifies a resource (such as an s3 bucket) where data will be pushed. Additional configuration parameters supported by the destination may be included. See [Logpush destination documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#destination). """ return pulumi.get(self, "destination_conf") @property @pulumi.getter def enabled(self) -> pulumi.Output[Optional[bool]]: """ Whether to enable the job. """ return pulumi.get(self, "enabled") @property @pulumi.getter(name="logpullOptions") def logpull_options(self) -> pulumi.Output[Optional[str]]: """ Configuration string for the Logshare API. It specifies things like requested fields and timestamp formats. See [Logpull options documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#options). """ return pulumi.get(self, "logpull_options") @property @pulumi.getter def name(self) -> pulumi.Output[Optional[str]]: """ The name of the logpush job to create. Must match the regular expression `^[a-zA-Z0-9\-\.]*$`. """ return pulumi.get(self, "name") @property @pulumi.getter(name="ownershipChallenge") def ownership_challenge(self) -> pulumi.Output[Optional[str]]: """ Ownership challenge token to prove destination ownership, required when destination is Amazon S3, Google Cloud Storage, Microsoft Azure or Sumo Logic. See [Developer documentation](https://developers.cloudflare.com/logs/logpush/logpush-configuration-api/understanding-logpush-api/#usage). """ return pulumi.get(self, "ownership_challenge") @property @pulumi.getter(name="zoneId") def zone_id(self) -> pulumi.Output[str]: """ The zone ID where the logpush job should be created. """ return pulumi.get(self, "zone_id")

54.050228

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0.678339

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5.612671

0.074874

0.067022

0.071832

0.063494

0.892124

0.873461

0.857491

0.838956

0.834531

0.820357

0

0.001561

0.215131

23,674

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368

54.173913

0.837576

0.441032

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0.722433

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false

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null

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null

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8

e4911746a360397602d4823372d2b73e519e7dfb

149

py

Python

tests/distributed/test_against_external_daemon/tf_encoder_ws/tf_encoder.py

Karnak123/jina

9eba4feb3afa2e49c779b46f77c5022bdcb944aa

[ "Apache-2.0" ]

4

2021-06-29T04:14:31.000Z

2021-08-01T07:01:08.000Z

tests/distributed/test_against_external_daemon/tf_encoder_ws/tf_encoder.py

Karnak123/jina

9eba4feb3afa2e49c779b46f77c5022bdcb944aa

[ "Apache-2.0" ]

1

2021-12-25T09:06:13.000Z

tests/distributed/test_against_external_daemon/tf_encoder_ws/tf_encoder.py

Karnak123/jina

9eba4feb3afa2e49c779b46f77c5022bdcb944aa

[ "Apache-2.0" ]

1

2021-06-29T17:03:24.000Z

from jina import Executor, requests class TFEncoder(Executor): @requests def foo(*args, **kwargs): import tensorflow pass

14.9

35

0.644295

16

149

6

0.8125

0.333333

0

0.275168

149

9

36

16.555556

0.888889

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0.166667

true

0.166667

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null

1

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1

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null

0

1

0

1

0

7

e49f2695797f3975634eb3a99ddc39ebcff08381

8,158

py

Python

scheduler/period_scheduler.py

CptnDuras/saltshaker_api

c89d91b95a8930364c125e286b94f1e7f060fef0

[ "MIT" ]

115

2018-03-26T11:27:49.000Z

2019-06-06T16:03:43.000Z

scheduler/period_scheduler.py

CptnDuras/saltshaker_api

c89d91b95a8930364c125e286b94f1e7f060fef0

[ "MIT" ]

16

2019-07-10T10:09:23.000Z

2021-07-02T10:30:48.000Z

scheduler/period_scheduler.py

CptnDuras/saltshaker_api

c89d91b95a8930364c125e286b94f1e7f060fef0

[ "MIT" ]

52

2019-06-27T02:58:30.000Z

2021-11-12T03:36:05.000Z

# -*- coding:utf-8 -*- from common.log import loggers from extensions import scheduler import time logger = loggers() # 添加一次性定时执行 def scheduler_timing_add(period_id, product_id, user, run_date): # run_date="2018-07-04 14:01:00" try: scheduler.add_job(func="tasks.tasks:job", trigger='date', run_date=run_date, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add timing scheduler error: %s" % e) return {"status": False, "message": str(e)} # 修改一次性定时执行 def scheduler_timing_modify(period_id, product_id, user, run_date): # run_date="2018-07-04 14:01:00" try: scheduler.modify_job(func="tasks.tasks:job", trigger='date', run_date=run_date, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify timing scheduler error: %s" % e) # 如果之前的scheduler已经不存在，重新添加定时任务 scheduler_result = scheduler_timing_add(period_id, product_id, user, run_date) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} # 添加周期间隔任务 def scheduler_interval_add(period_id, product_id, user, run_interval, interval): if interval == "second": try: scheduler.add_job(func="tasks.tasks:job", trigger='interval', seconds=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add second period scheduler error: %s" % e) return {"status": False, "message": str(e)} elif interval == "minute": try: scheduler.add_job(func="tasks.tasks:job", trigger='interval', minutes=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add minute period scheduler error: %s" % e) return {"status": False, "message": str(e)} elif interval == "hour": try: scheduler.add_job(func="tasks.tasks:job", trigger='interval', hours=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add hour period scheduler error: %s" % e) return {"status": False, "message": str(e)} elif interval == "day": try: scheduler.add_job(func="tasks.tasks:job", trigger='interval', days=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add day period scheduler error: %s" % e) return {"status": False, "message": str(e)} elif interval == "week": try: scheduler.add_job(func="tasks.tasks:job", trigger='interval', weeks=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Add week period scheduler error: %s" % e) return {"status": False, "message": str(e)} else: return {"status": False, "message": "No interval specified"} # 修改周期间隔任务 def scheduler_interval_modify(period_id, product_id, user, run_interval, interval): if interval == "second": try: scheduler.modify_job(func="tasks.tasks:job", trigger='interval', seconds=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify second period scheduler error: %s" % e) scheduler_result = scheduler_interval_add(period_id, product_id, user, run_interval, interval) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} elif interval == "minute": try: scheduler.modify_job(func="tasks.tasks:job", trigger='interval', minutes=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify minute period scheduler error: %s" % e) scheduler_result = scheduler_interval_add(period_id, product_id, user, run_interval, interval) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} elif interval == "hour": try: scheduler.modify_job(func="tasks.tasks:job", trigger='interval', hours=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify hour period scheduler error: %s" % e) scheduler_result = scheduler_interval_add(period_id, product_id, user, run_interval, interval) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} elif interval == "day": try: scheduler.modify_job(func="tasks.tasks:job", trigger='interval', days=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify day period scheduler error: %s" % e) scheduler_result = scheduler_interval_add(period_id, product_id, user, run_interval, interval) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} elif interval == "week": try: scheduler.modify_job(func="tasks.tasks:job", trigger='interval', weeks=run_interval, args=[period_id, product_id, user], id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Modify week period scheduler error: %s" % e) scheduler_result = scheduler_interval_add(period_id, product_id, user, run_interval, interval) if scheduler_result.get("status") is not True: return {"status": False, "message": scheduler_result.get("message")} else: return {"status": True, "message": ""} else: return {"status": False, "message": "No interval specified"} # 删除任务 def scheduler_delete(period_id): try: scheduler.delete_job(id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Delete period scheduler error: %s" % e) return {"status": False, "message": str(e)} # 暂停任务 def scheduler_pause(period_id): try: scheduler.pause_job(id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Pause period scheduler error: %s" % e) return {"status": False, "message": str(e)} # 恢复任务 def scheduler_resume(period_id): try: scheduler.resume_job(id=period_id) return {"status": True, "message": ""} except Exception as e: logger.error("Resume period scheduler error: %s" % e) return {"status": False, "message": str(e)}

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46.090395

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90397596389f9c7fffcb6bf3d24b40cd9afa2767

63,181

py

Python

great_international/migrations/0082_auto_20190913_1339.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2018-03-20T11:19:07.000Z

2021-10-05T07:53:11.000Z

great_international/migrations/0082_auto_20190913_1339.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

802

2018-02-05T14:16:13.000Z

2022-02-10T10:59:21.000Z

great_international/migrations/0082_auto_20190913_1339.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2019-01-22T13:19:37.000Z

2019-07-01T10:35:26.000Z

# Generated by Django 2.2.4 on 2019-09-13 13:39 import core.model_fields from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('great_international', '0081_auto_20190910_1233'), ] operations = [ migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_four_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_one_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_three_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='case_study_two_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_eight_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_five_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_four_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_one_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_seven_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_six_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_three_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='companies_list_item_image_alt_two_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='opportunity_list_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_ar', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_de', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_en_gb', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_es', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_fr', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_ja', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_pt', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_one_video_transcript_zh_hans', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.'), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_ar', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_de', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_en_gb', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_es', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_fr', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_ja', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_pt', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_image_alt_zh_hans', field=models.TextField(blank=True, help_text='If this image adds extra information to the page that is not already provided by the text (e.g. if the image is a diagram, chart, or has text on it) then an image description must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_ar', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_de', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_en_gb', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_es', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_fr', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_ja', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_pt', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), migrations.AddField( model_name='investhighpotentialopportunitydetailpage', name='proposition_two_video_transcript_zh_hans', field=core.model_fields.MarkdownField(blank=True, help_text='If the video is present, a transcript must be provided.', null=True), ), ]

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0.22301

63,181

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0

9

5f55ec135d26727d3b92658cb2ff2ab7f6200696

43,480

py

Python

Video-Person-ReID/models/ResNet.py

anurag3/2019-CVPR-AIC-Track-2-UWIPL

61ee2c96611e10fe51a52033b1cd0e2804d544ca

[ "MIT" ]

20

2019-06-05T08:43:26.000Z

2021-12-07T08:48:18.000Z

Video-Person-ReID/models/ResNet.py

yizhou-wang/2019-CVPR-AIC-Track-2-UWIPL-ETRI

387924b1e33e0594977cd095c26a147e4a7f8192

[ "MIT" ]

8

2019-10-05T11:17:11.000Z

2020-04-04T00:40:20.000Z

Video-Person-ReID/models/ResNet.py

yizhou-wang/2019-CVPR-AIC-Track-2-UWIPL-ETRI

387924b1e33e0594977cd095c26a147e4a7f8192

[ "MIT" ]

14

2019-06-16T23:09:15.000Z

2021-09-13T08:36:50.000Z

from __future__ import absolute_import import torch from torch import nn from torch.nn import functional as F from torch.autograd import Variable import torchvision #from models.lightcnn import * # JR #import torch.nn.parallel.data_parallel as DataParallel # JR from collections import OrderedDict # JR __all__ = ['ResNet50TP', 'ResNet50TA', 'myResNet50TA', 'ResNet50RNN', 'ResNet50TP_ORIENTATION', 'ResNet50TP_ORIENTATION_IOU', 'ResNet50TA_ORIENTATION', 'ResNet50TA_ORIENTATION_IOU', 'ResNet50TA_SURFACE', 'ResNet50TA_SURFACE_NU', 'ResNet50TA_SURFACE_NU4', 'ResNet50TA_SURFACE_NU2', 'ResNet50TA_SURFACE_NU2F1', 'ResNet50TA_SURFACE_N1', 'ResNet50TA_SURFACE_N2'] class ResNet50TP_ORIENTATION_IOU(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TP_ORIENTATION_IOU, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.feat_dim = 2048 self.classifier = nn.Linear(self.feat_dim + 2, num_classes) def forward(self, x, orientation, frame_IOU): b = x.size(0) t = x.size(1) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) x = F.avg_pool2d(x, x.size()[2:]) x = x.view(b, t, -1) x = x.permute(0, 2, 1) f = F.avg_pool1d(x, t) f = f.view(b, self.feat_dim) orientation = orientation.view(f.size(0), -1) orientation = orientation.float() frame_IOU = frame_IOU.view(f.size(0), -1) frame_IOU = frame_IOU.float() f = torch.cat((f, orientation, frame_IOU), 1) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TP_ORIENTATION(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TP_ORIENTATION, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.feat_dim = 2048 self.classifier = nn.Linear(self.feat_dim + 1, num_classes) def forward(self, x, orientation): b = x.size(0) t = x.size(1) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) x = F.avg_pool2d(x, x.size()[2:]) x = x.view(b, t, -1) x = x.permute(0, 2, 1) f = F.avg_pool1d(x, t) f = f.view(b, self.feat_dim) #f = torch.LongTensor(f) #f = f.long() #import pdb; pdb.set_trace() orientation = orientation.view(f.size(0), -1) #import pdb; pdb.set_trace() orientation = orientation.float() #import pdb; pdb.set_trace() #import pdb; pdb.set_trace() f = torch.cat((f, orientation), 1) #import pdb; pdb.set_trace() if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TP(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TP, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.feat_dim = 2048 self.classifier = nn.Linear(self.feat_dim, num_classes) def forward(self, x): b = x.size(0) t = x.size(1) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) x = F.avg_pool2d(x, x.size()[2:]) x = x.view(b, t, -1) x = x.permute(0, 2, 1) f = F.avg_pool1d(x, t) f = f.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_ORIENTATION_IOU1(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_ORIENTATION_IOU, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension # self.classifier = nn.Linear(self.feat_dim+8*8, num_classes) #seq leng=4 is +8 self.classifier = nn.Linear(self.feat_dim + 8, num_classes) # seq leng=4 is +8 # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5, 3]) # 7,4 cooresponds to 224, 112 input image size self.attention_tconv = nn.Conv1d(self.middle_dim, 1, 3, padding=1) def forward(self, x, orientation, frame_IOU): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() x = x.view(b * t, x.size(2), x.size(3), x.size(4)) # print("X2",x.shape) #import pdb; pdb.set_trace() x = self.base(x) # print("X3",x.shape) # print("Ori",orientation.shape) #import pdb; pdb.set_trace() a = F.relu(self.attention_conv(x)) # print("a1",a.shape) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) a = a.permute(0, 2, 1) #import pdb; pdb.set_trace() a = F.relu(self.attention_tconv(a)) # print("a2",a.shape) #import pdb; pdb.set_trace() a = a.view(b, t) # print("a3",a.shape) x = F.avg_pool2d(x, x.size()[2:]) # print("x1",x.shape) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) #import pdb; pdb.set_trace() #x = x.view(b,-1) #import pdb; pdb.set_trace() #x = torch.cat((x,orientation,frame_IOU),1) # print("a4",a.shape) x = x.view(b, t, -1) # print("x2",x.shape) orientation = orientation.view(x.size(0), -1) # print("ori2",orientation.shape) #import pdb; pdb.set_trace() orientation = orientation.float() frame_IOU = frame_IOU.view(x.size(0), -1) frame_IOU = frame_IOU.float() for i in range(1, t + 1): orientation = torch.stack((orientation, orientation)) frame_IOU = torch.stack((frame_IOU, frame_IOU)) orientation = orientation.view(b, t, -1) frame_IOU = frame_IOU.view(b, t, -1) #import pdb; pdb.set_trace() x = torch.cat((x, orientation, frame_IOU), 2) #import pdb; pdb.set_trace() a = torch.unsqueeze(a, -1) #a = a.expand(b, t, self.feat_dim) a = a.expand(b, t, x.size(2)) #import pdb; pdb.set_trace() att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) #f = att_x.view(b,self.feat_dim) f = att_x.view(b, x.size(2)) #import pdb; pdb.set_trace() if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_ORIENTATION_IOU(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_ORIENTATION_IOU, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.classifier = nn.Linear(self.feat_dim + 8, num_classes) # seq leng=4 is +8 =8 is 8*8 # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5, 3]) # 7,4 cooresponds to 224, 112 input image size self.attention_tconv = nn.Conv1d(self.middle_dim, 1, 3, padding=1) def forward(self, x, orientation, frame_IOU): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() x = x.view(b * t, x.size(2), x.size(3), x.size(4)) #import pdb; pdb.set_trace() x = self.base(x) #import pdb; pdb.set_trace() a = F.relu(self.attention_conv(x)) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) a = a.permute(0, 2, 1) #import pdb; pdb.set_trace() a = F.relu(self.attention_tconv(a)) #import pdb; pdb.set_trace() a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) #import pdb; pdb.set_trace() #x = x.view(b,-1) #import pdb; pdb.set_trace() #x = torch.cat((x,orientation,frame_IOU),1) x = x.view(b, t, -1) orientation = orientation.view(x.size(0), -1) orientation = orientation.float() frame_IOU = frame_IOU.view(x.size(0), -1) frame_IOU = frame_IOU.float() for i in range(1, t + 1): orientation = torch.stack((orientation, orientation)) frame_IOU = torch.stack((frame_IOU, frame_IOU)) orientation = orientation.view(b, t, -1) frame_IOU = frame_IOU.view(b, t, -1) #import pdb; pdb.set_trace() x = torch.cat((x, orientation, frame_IOU), 2) #import pdb; pdb.set_trace() a = torch.unsqueeze(a, -1) #a = a.expand(b, t, self.feat_dim) a = a.expand(b, t, x.size(2)) #import pdb; pdb.set_trace() att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) #f = att_x.view(b,self.feat_dim) f = att_x.view(b, x.size(2)) #import pdb; pdb.set_trace() if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_ORIENTATION(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_ORIENTATION, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.classifier = nn.Linear(self.feat_dim + 1, num_classes) self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 4]) # 7,4 cooresponds to 224, 112 input image size self.attention_tconv = nn.Conv1d(self.middle_dim, 1, 3, padding=1) def forward(self, x, orientation): b = x.size(0) t = x.size(1) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) a = a.view(b, t, self.middle_dim) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) orientation = orientation.view(x.size(0), -1) orientation = orientation.float() orientation = torch.unsqueeze(orientation, -1) x = torch.cat((x, orientation), 1) #import pdb; pdb.set_trace() a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE(nn.Module): def __init__(self, num_classes, loss={'xent'}, augf_surface=False, **kwargs): super(ResNet50TA_SURFACE, self).__init__() self.loss = loss self.augf_surface = augf_surface resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #m = nn.ReLU6() ####### !!!!! optional scale #a = m(self.attention_conv(x)) ####### !!!!! optional scale #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.mul(surface, 6) ####### !!!!! optional scale a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.augf_surface: surface = surface.permute(0, 2, 1) surface = F.avg_pool1d(surface, kernel_size=t) surface = surface.view(b, self.surface_dim) if self.loss == {'xent'}: return y if not self.augf_surface else (y, surface) elif self.loss == {'xent', 'htri'}: return (y, f) if not self.augf_surface else (y, f, surface) elif self.loss == {'cent'}: return (y, f) if not self.augf_surface else (y, f, surface) else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_NU(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_NU, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) self.nu_surface = nn.Linear(self.surface_dim, self.feat_dim, bias=True) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #m = nn.ReLU6() ####### !!!!! optional scale #a = m(self.attention_conv(x)) ####### !!!!! optional scale #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.mul(surface, 6) ####### !!!!! optional scale a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) ## surface_nu surface = surface.view(b*t, self.surface_dim) surface_nu = self.nu_surface(surface) surface_nu = surface_nu.view(b, t, -1) #surface_nu = torch.mul(surface_nu, 100) # optional scaling x = x.add(surface_nu) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_NU4(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_NU4, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) self.nu_surface = nn.Linear(self.surface_dim, self.feat_dim, bias=True) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #m = nn.ReLU6() ####### !!!!! optional scale #a = m(self.attention_conv(x)) ####### !!!!! optional scale #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.mul(surface, 6) ####### !!!!! optional scale a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) ## surface_nu surface = surface.view(b*t, self.surface_dim) surface_nu = F.relu(self.nu_surface(surface)) ### the only difference surface_nu = surface_nu.view(b, t, -1) #surface_nu = torch.mul(surface_nu, 100) # optional scaling x = x.add(surface_nu) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_NU2(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_NU2, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.surface_dim_middle = 256 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) self.nu_surface_1 = nn.Linear(self.surface_dim, self.surface_dim_middle, bias=True) self.nu_surface_2 = nn.Linear(self.surface_dim_middle, self.feat_dim, bias=True) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #m = nn.ReLU6() ####### !!!!! optional scale #a = m(self.attention_conv(x)) ####### !!!!! optional scale #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.mul(surface, 6) ####### !!!!! optional scale a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) ## surface_nu surface = surface.view(b*t, self.surface_dim) surface_nu = F.relu(self.nu_surface_1(surface)) surface_nu = F.relu(self.nu_surface_2(surface_nu)) surface_nu = surface_nu.view(b, t, -1) #surface_nu = torch.mul(surface_nu, 100) # optional scaling x = x.add(surface_nu) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_NU2F1(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_NU2F1, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.surface_dim_middle = 256 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) self.nu_surface_1 = nn.Linear(self.surface_dim, self.surface_dim_middle, bias=True) self.nu_surface_2 = nn.Linear(self.surface_dim_middle, self.feat_dim, bias=True) self.nu_surface_f1 = nn.Linear(self.feat_dim, self.feat_dim, bias=True) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #m = nn.ReLU6() ####### !!!!! optional scale #a = m(self.attention_conv(x)) ####### !!!!! optional scale #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.mul(surface, 6) ####### !!!!! optional scale a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) ## surface_nu surface = surface.view(b*t, self.surface_dim) surface_nu = F.relu(self.nu_surface_1(surface)) surface_nu = F.relu(self.nu_surface_2(surface_nu)) surface_nu = surface_nu.view(b, t, -1) #surface_nu = torch.mul(surface_nu, 100) # optional scaling x = x.add(surface_nu) x = F.relu(self.nu_surface_f1(x)) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_N1(nn.Module): # add surface by 1 def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_N1, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim, 1, 3, padding=1) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) #surface = torch.add(surface, 1) # add surface by 1 # obsolete m = torch.nn.Threshold(0, 0) # take positive surface = m(surface) surface = torch.mul(surface, pow(2, 0.5)) # multiply by 2^0.5 a = torch.cat((a, surface), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA_SURFACE_N2(nn.Module): # take abs and expand size def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA_SURFACE_N2, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.surface_dim = 18 # surface feature dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_tconv = nn.Conv1d(self.middle_dim + self.surface_dim*2, 1, 3, padding=1) def forward(self, x, surface): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) # append surface feature surface = surface.view(b, t, self.surface_dim) surface_n = torch.mul(surface, -1) # multiply by -1 surface_c = torch.cat((surface, surface_n), 2) m = torch.nn.Threshold(0, 0) # take positive surface_c = m(surface_c) a = torch.cat((a, surface_c), 2) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50TA(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50TA, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.classifier = nn.Linear(self.feat_dim, num_classes) # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size # self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [5,3]) # 5,3 cooresponds to 150, 75 input image size #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [3, 5]) # 3,5 cooresponds to 75, 150 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7, 7]) # 7,7 cooresponds to 224, 224 input image size self.attention_tconv = nn.Conv1d(self.middle_dim, 1, 3, padding=1) def forward(self, x): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() # print(x) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) a = F.relu(self.attention_conv(x)) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) a = a.permute(0, 2, 1) a = F.relu(self.attention_tconv(a)) a = a.view(b, t) x = F.avg_pool2d(x, x.size()[2:]) if self. att_gen == 'softmax': a = F.softmax(a, dim=1) elif self.att_gen == 'sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) a = torch.unsqueeze(a, -1) a = a.expand(b, t, self.feat_dim) att_x = torch.mul(x, a) att_x = torch.sum(att_x, 1) f = att_x.view(b, self.feat_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) class ResNet50RNN(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(ResNet50r, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) self.base = nn.Sequential(*list(resnet50.children())[:-2]) self.hidden_dim = 512 self.feat_dim = 2048 self.classifier = nn.Linear(self.hidden_dim, num_classes) self.lstm = nn.LSTM(input_size=self.feat_dim, hidden_size=self.hidden_dim, num_layers=1, batch_first=True) def forward(self, x): b = x.size(0) t = x.size(1) x = x.view(b * t, x.size(2), x.size(3), x.size(4)) x = self.base(x) x = F.avg_pool2d(x, x.size()[2:]) x = x.view(b, t, -1) output, (h_n, c_n) = self.lstm(x) output = output.permute(0, 2, 1) f = F.avg_pool1d(output, t) f = f.view(b, self.hidden_dim) if not self.training: return f y = self.classifier(f) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss)) # JR class myResNet50TA(nn.Module): def __init__(self, num_classes, loss={'xent'}, **kwargs): super(myResNet50TA, self).__init__() self.loss = loss resnet50 = torchvision.models.resnet50(pretrained=True) #state_dict=torch.load('/hd/Jiarui/pytorch-multi-label-classifier-master/aic/trainer_aic0225/Train/epoch_25_snapshot.pth') state_dict=torch.load('/media/twhuang/NewVolume1/0219AIC_attentionReID/Video-Person-ReID-master2/epoch_25_snapshot.pth') new_state_dict = OrderedDict() for k, v in state_dict.items(): if 'base' in k: name = k[10:] # remove `module. new_state_dict[name] = v else: pass resnet50.load_state_dict(new_state_dict) self.base = nn.Sequential(*list(resnet50.children())[:-2]) print(self.base) self.att_gen = 'softmax' # method for attention generation: softmax or sigmoid self.feat_dim = 2048 # feature dimension self.middle_dim = 256 # middle layer dimension self.classifier = nn.Linear(self.feat_dim, num_classes) #self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,4]) # 7,4 cooresponds to 224, 112 input image size self.attention_conv = nn.Conv2d(self.feat_dim, self.middle_dim, [7,7]) # 7,4 cooresponds to 150, 75 input image size self.attention_tconv = nn.Conv1d(self.middle_dim, 1, 3, padding=1) def forward(self, x): b = x.size(0) t = x.size(1) #import pdb; pdb.set_trace() #print(1,x.shape) x = x.view(b*t, x.size(2), x.size(3), x.size(4)) #print(2,x.shape) x = self.base(x) # print(3,x.shape) a = F.relu(self.attention_conv(x)) #print(4,a.shape) #import pdb; pdb.set_trace() a = a.view(b, t, self.middle_dim) a = a.permute(0,2,1) #print(5,a.shape) a = F.relu(self.attention_tconv(a)) #print(6,a.shape) a = a.view(b, t) #print(7,a.shape) x = F.avg_pool2d(x, x.size()[2:]) #print(8,x.shape) if self. att_gen=='softmax': a = F.softmax(a, dim=1) #print(9,a.shape) elif self.att_gen=='sigmoid': a = F.sigmoid(a) a = F.normalize(a, p=1, dim=1) #print(10,a.shape) else: raise KeyError("Unsupported attention generation function: {}".format(self.att_gen)) x = x.view(b, t, -1) #print(11,x.shape) a = torch.unsqueeze(a, -1) #print(12,a.shape) a = a.expand(b, t, self.feat_dim) #print(13,a.shape) att_x = torch.mul(x,a) #print(14,att_x.shape) att_x = torch.sum(att_x,1) #print(15,att_x.shape) f = att_x.view(b,self.feat_dim) #print(16,f.shape) if not self.training: return f y = self.classifier(f) #print(17,y.shape) if self.loss == {'xent'}: return y elif self.loss == {'xent', 'htri'}: return y, f elif self.loss == {'cent'}: return y, f else: raise KeyError("Unsupported loss: {}".format(self.loss))

40.673527

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0.036919

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0.046886

0.030416

0.924809

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0.912477

0.901883

0.890006

0.879081

0

0.038333

0.281831

43,480

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359

40.673527

0.735541

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0

null

0

1

0

1

0

null

0

7

39e19e887f2c1880198e960cb1e07ce109ee0727

6,410

py

Python

analyzeapp/assets.py

topdeveloper424/DataAnalyzer-Flask-

94724d77d993617d362a9c0cfc3f6be458089723

[ "BSD-2-Clause" ]

1

2019-07-21T20:03:30.000Z

analyzeapp/assets.py

topdeveloper424/DataAnalyzer-Flask-

94724d77d993617d362a9c0cfc3f6be458089723

[ "BSD-2-Clause" ]

2

2019-07-05T14:59:22.000Z

2019-07-05T15:26:52.000Z

analyzeapp/assets.py

harrybenit424/DataAnalyzer

cce6aca1050512374938ae74d0fbea6aeb8b948f

[ "BSD-2-Clause" ]

null

from flask_assets import Bundle common_css = Bundle( 'pages/waves/css/waves.min.css', 'bower_components/bootstrap/css/bootstrap.min.css', 'pages/waves/css/waves.min.css', 'icon/themify-icons/themify-icons.css', 'icon/font-awesome/css/font-awesome.min.css', 'css/jquery.mCustomScrollbar.css', 'pages/chart/radial/css/radial.css', 'css/style.css', filters='cssmin', output='public/css/common.css' ) common_js = Bundle( 'bower_components/jquery/js/jquery.min.js', 'bower_components/jquery-ui/js/jquery-ui.min.js', 'bower_components/popper.js/js/popper.min.js', 'bower_components/bootstrap/js/bootstrap.min.js', 'pages/widget/excanvas.js', 'pages/waves/js/waves.min.js', 'bower_components/jquery-slimscroll/js/jquery.slimscroll.js', 'bower_components/modernizr/js/modernizr.js', 'js/SmoothScroll.js', 'js/jquery.mCustomScrollbar.concat.min.js', 'bower_components/chart.js/js/Chart.js', filters='jsmin', output = 'public/js/common.js' ) jschart = Bundle( 'pages/widget/amchart/gauge.min.js', 'pages/widget/amchart/serial.min.js', 'pages/widget/amchart/light.min.js', 'pages/widget/amchart/pie.min.js', filters='jsmin', output='public/js/chart.js' ) jslayout = Bundle( 'js/pcoded.min.js', 'js/vertical/vertical-layout.min.js', 'pages/dashboard/custom-dashboard.js', 'js/script.js', filters = 'jsmin', output = 'public/js/layout.js' ) #file page------------------------------------------------------------------------------- files_css = Bundle( 'bower_components/bootstrap/css/bootstrap.min.css', 'pages/waves/css/waves.min.css', 'icon/themify-icons/themify-icons.css', 'icon/icofont/css/icofont.css', 'icon/font-awesome/css/font-awesome.min.css', 'css/component.css', 'bower_components/datatables.net-bs4/css/dataTables.bootstrap4.min.css', 'pages/data-table/css/buttons.dataTables.min.css', 'bower_components/datatables.net-responsive-bs4/css/responsive.bootstrap4.min.css', 'css/style.css', 'css/jquery.mCustomScrollbar.css', filters = 'cssmin', output = 'public/js/files.css' ) files_js = Bundle( 'bower_components/jquery/js/jquery.min.js', 'bower_components/jquery/js/jquery.min.js', 'bower_components/popper.js/js/popper.min.js', 'bower_components/bootstrap/js/bootstrap.min.js', 'pages/waves/js/waves.min.js', 'bower_components/jquery-slimscroll/js/jquery.slimscroll.js', 'bower_components/modernizr/js/modernizr.js', 'bower_components/modernizr/js/css-scrollbars.js', 'bower_components/datatables.net/js/jquery.dataTables.min.js', 'bower_components/datatables.net-buttons/js/dataTables.buttons.min.js', 'pages/data-table/js/jszip.min.js', 'pages/data-table/js/pdfmake.min.js', 'pages/data-table/js/vfs_fonts.js', 'bower_components/datatables.net-buttons/js/buttons.print.min.js', 'bower_components/datatables.net-buttons/js/buttons.html5.min.js', 'bower_components/datatables.net-bs4/js/dataTables.bootstrap4.min.js', 'bower_components/datatables.net-responsive/js/dataTables.responsive.min.js', 'bower_components/datatables.net-responsive-bs4/js/responsive.bootstrap4.min.js', 'pages/data-table/js/data-table-custom.js', 'js/pcoded.min.js', 'js/vertical/vertical-layout.min.js', 'js/jquery.mCustomScrollbar.concat.min.js', 'js/script.js', filters = 'jsmin', output = 'public/js/files.js' ) settings_css = Bundle( 'bower_components/bootstrap/css/bootstrap.min.css', 'pages/waves/css/waves.min.css', 'icon/themify-icons/themify-icons.css', 'icon/icofont/css/icofont.css', 'icon/font-awesome/css/font-awesome.min.css', 'css/component.css', 'css/style.css', 'css/jquery.mCustomScrollbar.css', filters = 'cssmin', output = 'public/js/settings.css' ) settings_js = Bundle( 'bower_components/jquery/js/jquery.min.js', 'bower_components/jquery/js/jquery.min.js', 'bower_components/popper.js/js/popper.min.js', 'bower_components/bootstrap/js/bootstrap.min.js', 'pages/waves/js/waves.min.js', 'bower_components/jquery-slimscroll/js/jquery.slimscroll.js', 'bower_components/modernizr/js/modernizr.js', 'bower_components/modernizr/js/css-scrollbars.js', 'bower_components/bootstrap-tagsinput/js/bootstrap-tagsinput.js', 'bower_components/bootstrap-maxlength/js/bootstrap-maxlength.js', 'js/pcoded.min.js', 'js/vertical/vertical-layout.min.js', 'js/jquery.mCustomScrollbar.concat.min.js', 'js/script.js', filters = 'jsmin', output = 'public/js/files.js' ) analyize_css = Bundle( 'bower_components/bootstrap/css/bootstrap.min.css', 'pages/waves/css/waves.min.css', 'icon/themify-icons/themify-icons.css', 'icon/icofont/css/icofont.css', 'icon/font-awesome/css/font-awesome.min.css', 'bower_components/select2/css/select2.min.css', 'css/component.css', 'bower_components/datedropper/css/datedropper.min.css', 'bower_components/datatables.net-bs4/css/dataTables.bootstrap4.min.css', 'pages/data-table/css/buttons.dataTables.min.css', 'css/style.css', 'css/jquery.mCustomScrollbar.css', filters = 'cssmin', output = 'public/js/analyize.css' ) analyize_js = Bundle( 'bower_components/jquery/js/jquery.min.js', 'bower_components/jquery-ui/js/jquery-ui.min.js', 'bower_components/popper.js/js/popper.min.js', 'bower_components/bootstrap/js/bootstrap.min.js', 'pages/widget/excanvas.js', 'pages/waves/js/waves.min.js', 'bower_components/jquery-slimscroll/js/jquery.slimscroll.js', 'bower_components/modernizr/js/modernizr.js', 'bower_components/modernizr/js/css-scrollbars.js', 'bower_components/select2/js/select2.full.min.js', 'bower_components/multiselect/js/jquery.multi-select.js', 'js/jquery.quicksearch.js', 'pages/advance-elements/select2-custom.js', 'pages/advance-elements/moment-with-locales.min.js', 'bower_components/datedropper/js/datedropper.min.js', 'bower_components/datatables.net/js/jquery.dataTables.min.js', 'bower_components/datatables.net-bs4/js/dataTables.bootstrap4.min.js', 'js/pcoded.min.js', 'js/vertical/vertical-layout.min.js', 'js/jquery.mCustomScrollbar.concat.min.js', 'js/script.js', filters = 'jsmin', output = 'public/js/analyize.js' )

37.267442

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0.701872

857

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0.10035

0.172375

0.145561

0.11717

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0.677783

0.66922

0

0.003191

0.119969

6,410

172

90

37.267442

0.783549

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0

0.636943

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0

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0

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0

null

0

1

0

1

0

1

null

0

7

39f339d8c8042c589643013f683e7ed947bd4025

61

py

Python

bcwalletx/__init__.py

dalijolijo/bcwallet

eeb00c3b7a0c4aa12261d1a1e290b289767ec7e1

[ "Apache-2.0" ]

null

bcwalletx/__init__.py

dalijolijo/bcwallet

eeb00c3b7a0c4aa12261d1a1e290b289767ec7e1

[ "Apache-2.0" ]

null

bcwalletx/__init__.py

dalijolijo/bcwallet

eeb00c3b7a0c4aa12261d1a1e290b289767ec7e1

[ "Apache-2.0" ]

null

from .bcwalletx import cli from .bcwalletx import invoke_cli

20.333333

33

0.836066

9

61

5.555556

0.555556

0.52

0.76

0

0.131148

61

2

34

30.5

0.943396

0

1

0

true

0

1

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1

0

1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

ffc1c927ef32f552bd46a72686f9fd9f2c9b4165

4,907

py

Python

payloads/service_devices_mapping.py

bcsr0009/pdtf

5d3cc7933ac07457f6b4b59f2d4d70e0de1ffaec

[ "MIT" ]

null

payloads/service_devices_mapping.py

bcsr0009/pdtf

5d3cc7933ac07457f6b4b59f2d4d70e0de1ffaec

[ "MIT" ]

null

payloads/service_devices_mapping.py

bcsr0009/pdtf

5d3cc7933ac07457f6b4b59f2d4d70e0de1ffaec

[ "MIT" ]

null

devices_per_service_mapping = { "1-test": [ { "deviceusername": "admin", "devicepassword": "admin", "requestmethod": "POST", "createurl": "http://localhost:8080/api/running/dmz/services", "deleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "devicename": "iso0", "nsoipaddress": "127.0.0.1", "nsosshport": 2026, "nsousername": "admin", "nsopassword": "admin", "islsa": false, "device_running_config_cmd": "enable\nshow running-config", "ncs_running_config_cmd": "switch cli\nshow running-config devices device {0}", "device_address_cmd": "switch cli\nshow running-config devices device {0} address", "device_port_cmd": "switch cli\nshow running-config devices device {0} port", "issetup": false, "setupcreateurl": "http://localhost:8080/api/running/dmz/services", "setupdeleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "setuprequestmethod": "POST" } ], "3-test": [ { "deviceusername": "admin", "devicepassword": "admin", "requestmethod": "POST", "createurl": "http://localhost:8080/api/running/dmz/services", "deleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "devicename": "iso0", "nsoipaddress": "127.0.0.1", "nsosshport": 2026, "nsousername": "admin", "nsopassword": "admin", "islsa": false, "device_running_config_cmd": "enable\nshow running-config", "ncs_running_config_cmd": "switch cli\nshow running-config devices device {0}", "device_address_cmd": "switch cli\nshow running-config devices device {0} address", "device_port_cmd": "switch cli\nshow running-config devices device {0} port", "issetup": false, "setupcreateurl": "http://localhost:8080/api/running/dmz/services", "setupdeleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "setuprequestmethod": "POST" } ], "5-test": [ { "deviceusername": "admin", "devicepassword": "admin", "requestmethod": "POST", "createurl": "http://localhost:8080/api/running/dmz/services", "deleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "devicename": "iso0", "nsoipaddress": "127.0.0.1", "nsosshport": 2026, "nsousername": "admin", "nsopassword": "admin", "islsa": false, "device_running_config_cmd": "enable\nshow running-config", "ncs_running_config_cmd": "switch cli\nshow running-config devices device {0}", "device_address_cmd": "switch cli\nshow running-config devices device {0} address", "device_port_cmd": "switch cli\nshow running-config devices device {0} port", "issetup": false, "setupcreateurl": "http://localhost:8080/api/running/dmz/services", "setupdeleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "setuprequestmethod": "POST" } ], "2-test": [ { "deviceusername": "admin", "devicepassword": "admin", "requestmethod": "POST", "createurl": "http://localhost:8080/api/running/dmz/services", "deleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "devicename": "iso0", "nsoipaddress": "127.0.0.1", "nsosshport": 2026, "nsousername": "admin", "nsopassword": "admin", "islsa": false, "device_running_config_cmd": "enable\nshow running-config", "ncs_running_config_cmd": "switch cli\nshow running-config devices device {0}", "device_address_cmd": "switch cli\nshow running-config devices device {0} address", "device_port_cmd": "switch cli\nshow running-config devices device {0} port", "issetup": false, "setupcreateurl": "http://localhost:8080/api/running/dmz/services", "setupdeleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "setuprequestmethod": "POST" } ], "4-test": [ { "deviceusername": "admin", "devicepassword": "admin", "requestmethod": "POST", "createurl": "http://localhost:8080/api/running/dmz/services", "deleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "devicename": "iso0", "nsoipaddress": "127.0.0.1", "nsosshport": 2026, "nsousername": "admin", "nsopassword": "admin", "islsa": false, "device_running_config_cmd": "enable\nshow running-config", "ncs_running_config_cmd": "switch cli\nshow running-config devices device {0}", "device_address_cmd": "switch cli\nshow running-config devices device {0} address", "device_port_cmd": "switch cli\nshow running-config devices device {0} port", "issetup": false, "setupcreateurl": "http://localhost:8080/api/running/dmz/services", "setupdeleteurl": "http://localhost:8080/api/running/dmz/services/interface-tunnel/Headend,Remote0/", "setuprequestmethod": "POST" } ] }

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104

0.694314

559

4,907

6

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0.119261

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null

0

1

0

9

ffc2170a49598682f9b92ef5cfd2b1248d12bd7d

3,469

py

Python

src/training/dataloader.py

manhhabui/personalized-breath

e8cbb2cd68dbfe78662701922826996b8e42f75e

[ "Apache-2.0" ]

2

2020-04-09T15:25:07.000Z

2020-04-16T08:28:10.000Z

src/training/dataloader.py

hamanhbui/personalized-breath

1627476ce94b81d1daa1da3f71f80b7274031afd

[ "Apache-2.0" ]

null

src/training/dataloader.py

hamanhbui/personalized-breath

1627476ce94b81d1daa1da3f71f80b7274031afd

[ "Apache-2.0" ]

null

import numpy as np from torch.utils.data import Dataset class Audio_Dataset(Dataset): def __init__(self,root_dir,filenamelist,old_new_name_map, object_id = None): self.filenamelist = filenamelist self.root_dir=root_dir self.old_new_name_map=old_new_name_map self.type_name = "Audio" self.object_id = object_id def __len__(self): return len(self.filenamelist) def data_extracted_loader(self,file_name): audio_file_name=file_name.replace("processed","processed/audio") return np.load(audio_file_name+".npy") def __getitem__(self, index): if len(self.old_new_name_map)==0: subject_name=int(self.filenamelist[index].split("/")[2].split("_")[0])-1 else: subject_name=self.old_new_name_map[self.filenamelist[index].split("/")[2].split("_")[0]] if self.object_id != None: if subject_name == self.object_id: subject_name = 0 else: subject_name = 1 return self.type_name, self.data_extracted_loader(self.root_dir+self.filenamelist[index]), subject_name class Acce_Gyro_Dataset(Dataset): def __init__(self,root_dir,filenamelist,old_new_name_map, object_id = None): self.filenamelist = filenamelist self.root_dir=root_dir self.old_new_name_map=old_new_name_map self.type_name = "Acce_Gyro" self.object_id = object_id def __len__(self): return len(self.filenamelist) def data_extracted_loader(self,file_name): sensor_file_name=file_name.replace("processed","processed/acce-gyro") return np.load(sensor_file_name+".npy") def __getitem__(self, index): if len(self.old_new_name_map)==0: subject_name=int(self.filenamelist[index].split("/")[2].split("_")[0])-1 else: subject_name=self.old_new_name_map[self.filenamelist[index].split("/")[2].split("_")[0]] if self.object_id != None: if subject_name == self.object_id: subject_name = 0 else: subject_name = 1 return self.type_name, self.data_extracted_loader(self.root_dir+self.filenamelist[index]),subject_name class Multimodality_Dataset(Dataset): def __init__(self,root_dir,filenamelist,old_new_name_map, object_id = None): self.filenamelist = filenamelist self.root_dir=root_dir self.old_new_name_map=old_new_name_map self.object_id = object_id self.type_name = "Multimodality" def __len__(self): return len(self.filenamelist) def data_extracted_loader(self,file_name): audio_file_name=file_name.replace("processed","processed/audio") sensor_file_name=file_name.replace("processed","processed/acce-gyro") return np.load(sensor_file_name+".npy"), np.load(audio_file_name+".npy") def __getitem__(self, index): if len(self.old_new_name_map)==0: subject_name=int(self.filenamelist[index].split("/")[2].split("_")[0])-1 else: subject_name=self.old_new_name_map[self.filenamelist[index].split("/")[2].split("_")[0]] if self.object_id != None: if subject_name == self.object_id: subject_name = 0 else: subject_name = 1 return self.type_name, self.data_extracted_loader(self.root_dir+self.filenamelist[index]),subject_name

38.544444

111

0.656962

464

3,469

4.528017

0.103448

0.094241

0.071395

0.092813

0.942408

0.934793

0

0.008949

0.226867

3,469

90

112

38.544444

0.774422

0

0.847222

0

0.045821

0

1

0.166667

false

0

0.027778

0.041667

0.361111

0

null

0

1

0

1

0

null

0

7

0814d70812281c7e9eaab0da815bcd04328b2bba

48,612

py

Python

openapi_client/api/default_api.py

brighthive/jdx-client-api-python

ed94c578a6c9a5e9aadf8764439c22783ac1d9d5

[ "Apache-2.0" ]

null

openapi_client/api/default_api.py

brighthive/jdx-client-api-python

ed94c578a6c9a5e9aadf8764439c22783ac1d9d5

[ "Apache-2.0" ]

null

openapi_client/api/default_api.py

brighthive/jdx-client-api-python

ed94c578a6c9a5e9aadf8764439c22783ac1d9d5

[ "Apache-2.0" ]

null

# coding: utf-8 """ JDX reference application API This is a collection of schemas and endpoints for the various JDX, Concentric Sky facing REST endpoints, the schemas define an API contract of sorts between the request and response expectations of the JDX reference application. This API is to be mutually developed by Concentric Sky and BrightHive. # noqa: E501 The version of the OpenAPI document: 0.0.17 Contact: engineering@brighthive.io Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from openapi_client.api_client import ApiClient from openapi_client.exceptions import ( ApiTypeError, ApiValueError ) class DefaultApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def framework_recommendations_post(self, **kwargs): # noqa: E501 """Get framework recommendations based on the uploaded job descripton and context. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.framework_recommendations_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get framework-recommendations for a given Pipeline ID. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: FrameworkRecommendationResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.framework_recommendations_post_with_http_info(**kwargs) # noqa: E501 def framework_recommendations_post_with_http_info(self, **kwargs): # noqa: E501 """Get framework recommendations based on the uploaded job descripton and context. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.framework_recommendations_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get framework-recommendations for a given Pipeline ID. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(FrameworkRecommendationResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method framework_recommendations_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'request' in local_var_params: body_params = local_var_params['request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/framework-recommendations', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FrameworkRecommendationResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def framework_selections_post(self, **kwargs): # noqa: E501 """The user indicates what frameworks they selected # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.framework_selections_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param FrameworkSelectionRequest framework_selection_request: framework selections :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Response If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.framework_selections_post_with_http_info(**kwargs) # noqa: E501 def framework_selections_post_with_http_info(self, **kwargs): # noqa: E501 """The user indicates what frameworks they selected # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.framework_selections_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param FrameworkSelectionRequest framework_selection_request: framework selections :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(Response, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['framework_selection_request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method framework_selections_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'framework_selection_request' in local_var_params: body_params = local_var_params['framework_selection_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/framework-selections', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Response', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def generate_job_schema_plus_post(self, **kwargs): # noqa: E501 """Generate JobSchema+ # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.generate_job_schema_plus_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Generate JobSchema+ from a given pipeline_id :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: GenerateJobSchemaPlusResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.generate_job_schema_plus_post_with_http_info(**kwargs) # noqa: E501 def generate_job_schema_plus_post_with_http_info(self, **kwargs): # noqa: E501 """Generate JobSchema+ # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.generate_job_schema_plus_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Generate JobSchema+ from a given pipeline_id :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(GenerateJobSchemaPlusResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method generate_job_schema_plus_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'request' in local_var_params: body_params = local_var_params['request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/generate-job-schema-plus', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='GenerateJobSchemaPlusResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_score_post(self, **kwargs): # noqa: E501 """Provides a scored based on how much metadata you provide and the quality of that data. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_score_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get score for a given Pipeline ID. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: PipelineScoreResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_score_post_with_http_info(**kwargs) # noqa: E501 def get_score_post_with_http_info(self, **kwargs): # noqa: E501 """Provides a scored based on how much metadata you provide and the quality of that data. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_score_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get score for a given Pipeline ID. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(PipelineScoreResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_score_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'request' in local_var_params: body_params = local_var_params['request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/get-score', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PipelineScoreResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_get(self, **kwargs): # noqa: E501 """Health Check # noqa: E501 The health check endpoint can be used to check if the API is up. If the API is running it will return a 200 OK response. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_get(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: HealthResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.health_get_with_http_info(**kwargs) # noqa: E501 def health_get_with_http_info(self, **kwargs): # noqa: E501 """Health Check # noqa: E501 The health check endpoint can be used to check if the API is up. If the API is running it will return a 200 OK response. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(HealthResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method health_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/health', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def match_table_post(self, **kwargs): # noqa: E501 """Get the match table associated with the provided `pipelineID` # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.match_table_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param MatchTableRequest match_table_request: Get framework-recommendations for a given Pipeline ID. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: MatchTableResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.match_table_post_with_http_info(**kwargs) # noqa: E501 def match_table_post_with_http_info(self, **kwargs): # noqa: E501 """Get the match table associated with the provided `pipelineID` # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.match_table_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param MatchTableRequest match_table_request: Get framework-recommendations for a given Pipeline ID. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(MatchTableResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['match_table_request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method match_table_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'match_table_request' in local_var_params: body_params = local_var_params['match_table_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/match-table', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MatchTableResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def preview_post(self, **kwargs): # noqa: E501 """Get preview of job description with tagged matches. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.preview_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get preview of job description wth tagged matches. :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: PreviewResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.preview_post_with_http_info(**kwargs) # noqa: E501 def preview_post_with_http_info(self, **kwargs): # noqa: E501 """Get preview of job description with tagged matches. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.preview_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Request request: Get preview of job description wth tagged matches. :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(PreviewResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method preview_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'request' in local_var_params: body_params = local_var_params['request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/preview', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PreviewResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_job_description_context_post(self, **kwargs): # noqa: E501 """Provide job description context (e.g metadata) on the job description # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_job_description_context_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param JobDescriptionContextRequest job_description_context_request: job description context :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: JobDescriptionContextResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.upload_job_description_context_post_with_http_info(**kwargs) # noqa: E501 def upload_job_description_context_post_with_http_info(self, **kwargs): # noqa: E501 """Provide job description context (e.g metadata) on the job description # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_job_description_context_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param JobDescriptionContextRequest job_description_context_request: job description context :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(JobDescriptionContextResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['job_description_context_request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method upload_job_description_context_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'job_description_context_request' in local_var_params: body_params = local_var_params['job_description_context_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/upload-job-description-context', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='JobDescriptionContextResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_job_description_file_post(self, **kwargs): # noqa: E501 """Upload a raw job description file. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_job_description_file_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param file file: The file to upload :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: RawJobDescriptionResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.upload_job_description_file_post_with_http_info(**kwargs) # noqa: E501 def upload_job_description_file_post_with_http_info(self, **kwargs): # noqa: E501 """Upload a raw job description file. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_job_description_file_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param file file: The file to upload :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(RawJobDescriptionResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['file'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method upload_job_description_file_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} if 'file' in local_var_params: local_var_files['file'] = local_var_params['file'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/upload-job-description-file', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='RawJobDescriptionResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def user_actions_post(self, **kwargs): # noqa: E501 """Provide the user responses as a list of user actions # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.user_actions_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param UserActionRequest user_action_request: Contains a list of user responses :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Response If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.user_actions_post_with_http_info(**kwargs) # noqa: E501 def user_actions_post_with_http_info(self, **kwargs): # noqa: E501 """Provide the user responses as a list of user actions # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.user_actions_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param UserActionRequest user_action_request: Contains a list of user responses :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(Response, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['user_action_request'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method user_actions_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'user_action_request' in local_var_params: body_params = local_var_params['user_action_request'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/user-actions', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Response', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

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7

f273d805dc542d49b7657c334d5add4cb1cfda75

148,695

py

Python

octopus_deploy_swagger_client/octopus_deploy_client/tenants_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

octopus_deploy_swagger_client/octopus_deploy_client/tenants_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

octopus_deploy_swagger_client/octopus_deploy_client/tenants_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

# coding: utf-8 """ Octopus Server API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 2019.6.7+Branch.tags-2019.6.7.Sha.aa18dc6809953218c66f57eff7d26481d9b23d6a Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from octopus_deploy_swagger_client.api_client import ApiClient class TenantsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_response_descriptor_tenants_tenant_tenant_resource(self, **kwargs): # noqa: E501 """Create a TenantResource # noqa: E501 Creates a new tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_response_descriptor_tenants_tenant_tenant_resource(async_req=True) >>> result = thread.get() :param async_req bool :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 else: (data) = self.create_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 return data def create_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, **kwargs): # noqa: E501 """Create a TenantResource # noqa: E501 Creates a new tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_response_descriptor_tenants_tenant_tenant_resource_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['tenant_resource'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'tenant_resource' in params: body_params = params['tenant_resource'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def create_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, **kwargs): # noqa: E501 """Create a TenantResource # noqa: E501 Creates a new tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.create_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def create_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """Create a TenantResource # noqa: E501 Creates a new tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'tenant_resource'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `create_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'tenant_resource' in params: body_params = params['tenant_resource'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder # noqa: E501 Gets the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: file If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder # noqa: E501 Gets the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: file If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['image/png']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/logo', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='file', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces # noqa: E501 Gets the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: file If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces # noqa: E501 Gets the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: file If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_get_responder_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['image/png']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/logo', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='file', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/logo', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0 # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0 # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_0`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/logo', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/logo', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0 # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0 # noqa: E501 Updates the logo associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_logo_put_responder_spaces_0`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/logo', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action # noqa: E501 Checks tenants for matching tags rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_with_http_info(**kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_with_http_info(**kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_with_http_info(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action # noqa: E501 Checks tenants for matching tags rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/tag-test', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces # noqa: E501 Checks tenants for matching tags rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces # noqa: E501 Checks tenants for matching tags rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_tag_test_action_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/tag-test', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action # noqa: E501 Checks tenants for matching variable set rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_with_http_info(**kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_with_http_info(**kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_with_http_info(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action # noqa: E501 Checks tenants for matching variable set rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/variableset-test', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces # noqa: E501 Checks tenants for matching variable set rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces # noqa: E501 Checks tenants for matching variable set rule NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variable_set_test_action_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/variableset-test', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder # noqa: E501 Gets all the available variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder # noqa: E501 Gets all the available variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/variables', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces # noqa: E501 Gets all the available variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces # noqa: E501 Gets all the available variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_get_responder_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/variables', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action # noqa: E501 Returns list of tenants who are missing required variables NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_with_http_info(**kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_with_http_info(**kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_with_http_info(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action # noqa: E501 Returns list of tenants who are missing required variables NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: None If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/variables-missing', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces # noqa: E501 Returns list of tenants who are missing required variables NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces # noqa: E501 Returns list of tenants who are missing required variables NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_missing_action_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/variables-missing', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/variables', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0 # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0_with_http_info(id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0_with_http_info(self, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0 # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_0`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}/variables', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/variables', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0 # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0 # noqa: E501 Updates the variables associated with the tenant. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the resource (required) :return: TenantVariableResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenant_variables_put_responder_spaces_0`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}/variables', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantVariableResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action # noqa: E501 Reports back the status of multi-tenancy NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action(async_req=True) >>> result = thread.get() :param async_req bool :return: MultiTenancyStatusResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_with_http_info(**kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_with_http_info(**kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_with_http_info(self, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action # noqa: E501 Reports back the status of multi-tenancy NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: MultiTenancyStatusResource If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/status', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MultiTenancyStatusResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces # noqa: E501 Reports back the status of multi-tenancy NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: MultiTenancyStatusResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces # noqa: E501 Reports back the status of multi-tenancy NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: MultiTenancyStatusResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_tenants_configuration_get_action_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/status', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='MultiTenancyStatusResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_on_background_response_descriptor_tenants_tenant_tenant_resource(self, id, **kwargs): # noqa: E501 """Delete a TenantResource by ID # noqa: E501 Deletes an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_on_background_response_descriptor_tenants_tenant_tenant_resource(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to delete (required) :return: TaskResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 return data def delete_on_background_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, id, **kwargs): # noqa: E501 """Delete a TenantResource by ID # noqa: E501 Deletes an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to delete (required) :return: TaskResource If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_on_background_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `delete_on_background_response_descriptor_tenants_tenant_tenant_resource`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TaskResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """Delete a TenantResource by ID # noqa: E501 Deletes an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to delete (required) :return: TaskResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """Delete a TenantResource by ID # noqa: E501 Deletes an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to delete (required) :return: TaskResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `delete_on_background_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TaskResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def index_response_descriptor_tenants_tenant_tenant_resource(self, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name, and returned 30 at a time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.index_response_descriptor_tenants_tenant_tenant_resource(async_req=True) >>> result = thread.get() :param async_req bool :param int skip: Number of items to skip :param int take: Number of items to take :return: ResourceCollectionTenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.index_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 else: (data) = self.index_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 return data def index_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name, and returned 30 at a time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.index_response_descriptor_tenants_tenant_tenant_resource_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int skip: Number of items to skip :param int take: Number of items to take :return: ResourceCollectionTenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['skip', 'take'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method index_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'skip' in params: query_params.append(('skip', params['skip'])) # noqa: E501 if 'take' in params: query_params.append(('take', params['take'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ResourceCollectionTenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def index_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name, and returned 30 at a time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.index_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param int skip: Number of items to skip :param int take: Number of items to take :return: ResourceCollectionTenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.index_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.index_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def index_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name, and returned 30 at a time. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.index_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param int skip: Number of items to skip :param int take: Number of items to take :return: ResourceCollectionTenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'skip', 'take'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method index_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `index_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] if 'skip' in params: query_params.append(('skip', params['skip'])) # noqa: E501 if 'take' in params: query_params.append(('take', params['take'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ResourceCollectionTenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_all_response_descriptor_tenants_tenant_tenant_resource(self, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_all_response_descriptor_tenants_tenant_tenant_resource(async_req=True) >>> result = thread.get() :param async_req bool :return: list[TenantResource] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.list_all_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 else: (data) = self.list_all_response_descriptor_tenants_tenant_tenant_resource_with_http_info(**kwargs) # noqa: E501 return data def list_all_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_all_response_descriptor_tenants_tenant_tenant_resource_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[TenantResource] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_all_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/all', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[TenantResource]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_all_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_all_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: list[TenantResource] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.list_all_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 else: (data) = self.list_all_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501 return data def list_all_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501 """Get a list of TenantResources # noqa: E501 Lists all of the tenants in the supplied Octopus Deploy Space. The results will be sorted alphabetically by name. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_all_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :return: list[TenantResource] If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_all_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `list_all_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/all', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[TenantResource]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def load_response_descriptor_tenants_tenant_tenant_resource(self, id, **kwargs): # noqa: E501 """Get a TenantResource by ID # noqa: E501 Gets a single tenant by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.load_response_descriptor_tenants_tenant_tenant_resource(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to load (required) :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.load_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.load_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 return data def load_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, id, **kwargs): # noqa: E501 """Get a TenantResource by ID # noqa: E501 Gets a single tenant by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.load_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to load (required) :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method load_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `load_response_descriptor_tenants_tenant_tenant_resource`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def load_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """Get a TenantResource by ID # noqa: E501 Gets a single tenant by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.load_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to load (required) :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.load_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.load_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def load_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """Get a TenantResource by ID # noqa: E501 Gets a single tenant by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.load_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to load (required) :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method load_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `load_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `load_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def modify_response_descriptor_tenants_tenant_tenant_resource(self, id, **kwargs): # noqa: E501 """Modify a TenantResource by ID # noqa: E501 Modifies an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_response_descriptor_tenants_tenant_tenant_resource(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to modify (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.modify_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.modify_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, **kwargs) # noqa: E501 return data def modify_response_descriptor_tenants_tenant_tenant_resource_with_http_info(self, id, **kwargs): # noqa: E501 """Modify a TenantResource by ID # noqa: E501 Modifies an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_response_descriptor_tenants_tenant_tenant_resource_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: ID of the TenantResource to modify (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'tenant_resource'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method modify_response_descriptor_tenants_tenant_tenant_resource" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `modify_response_descriptor_tenants_tenant_tenant_resource`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'tenant_resource' in params: body_params = params['tenant_resource'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/tenants/{id}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def modify_response_descriptor_tenants_tenant_tenant_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501 """Modify a TenantResource by ID # noqa: E501 Modifies an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_response_descriptor_tenants_tenant_tenant_resource_spaces(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to modify (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.modify_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 else: (data) = self.modify_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501 return data def modify_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501 """Modify a TenantResource by ID # noqa: E501 Modifies an existing tenant. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_response_descriptor_tenants_tenant_tenant_resource_spaces_with_http_info(base_space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param str base_space_id: ID of the space (required) :param str id: ID of the TenantResource to modify (required) :param TenantResource tenant_resource: The TenantResource resource to create :return: TenantResource If the method is called asynchronously, returns the request thread. """ all_params = ['base_space_id', 'id', 'tenant_resource'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method modify_response_descriptor_tenants_tenant_tenant_resource_spaces" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'base_space_id' is set if ('base_space_id' not in params or params['base_space_id'] is None): raise ValueError("Missing the required parameter `base_space_id` when calling `modify_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `modify_response_descriptor_tenants_tenant_tenant_resource_spaces`") # noqa: E501 collection_formats = {} path_params = {} if 'base_space_id' in params: path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501 if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'tenant_resource' in params: body_params = params['tenant_resource'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501 return self.api_client.call_api( '/api/{baseSpaceId}/tenants/{id}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TenantResource', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

47.612872

212

0.667541

17,853

148,695

5.194925

0.013275

0.03942

0.028465

0.06534

0.994469

0.99324

0.993024

0.992884

0

0.013265

0.257285

148,695

3,122

213

47.628123

0.826527

0.350301

0

0.839317

1

0

0.218049

0.098458

0

1

0.038258

false

0

0.002354

0

0.097705

0

null

0

1

0

1

0

null

0

8

4b2b8f70d6bb6436bc9cb976f782e83edcd7cb1c

166

py

Python

tests/test_global.py

Jakob-Daugherty/ftlengine

a655589b560c16c0c3509d2bd9f7c70a0ed321cc

[ "MIT" ]

1

2021-06-17T16:03:13.000Z

tests/test_global.py

Jakob-Daugherty/ftlengine

a655589b560c16c0c3509d2bd9f7c70a0ed321cc

[ "MIT" ]

null

tests/test_global.py

Jakob-Daugherty/ftlengine

a655589b560c16c0c3509d2bd9f7c70a0ed321cc

[ "MIT" ]

null

from src import __version__, __version_info__ def test_version(): assert len(__version__) == 6 def test_version_info(): assert len(__version_info__) == 3

16.6

45

0.73494

22

166

4.590909

0.5

0.326733

0.277228

0

0.014599

0.174699

166

9

46

18.444444

0.722628

0

0.4

1

0.4

true

0

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

4bbb6949bc19ba48d18243b754f90483bb904c06

411,618

py

Python

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_clns_isis_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_clns_isis_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_clns_isis_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

import re import collections from enum import Enum from ydk._core._dm_meta_info import _MetaInfoClassMember, _MetaInfoClass, _MetaInfoEnum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk._core._dm_meta_info import ATTRIBUTE, REFERENCE_CLASS, REFERENCE_LIST, REFERENCE_LEAFLIST, REFERENCE_IDENTITY_CLASS, REFERENCE_ENUM_CLASS, REFERENCE_BITS, REFERENCE_UNION from ydk.errors import YPYError, YPYModelError from ydk.providers._importer import _yang_ns _meta_table = { 'IsisSnpAuthEnum' : _MetaInfoEnum('IsisSnpAuthEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'send-only':'send_only', 'full':'full', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibMaxAreaAddressMismatchBooleanEnum' : _MetaInfoEnum('IsisMibMaxAreaAddressMismatchBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibLspTooLargeToPropagateBooleanEnum' : _MetaInfoEnum('IsisMibLspTooLargeToPropagateBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibSequenceNumberSkipBooleanEnum' : _MetaInfoEnum('IsisMibSequenceNumberSkipBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisInterfaceFrrTiebreakerEnum' : _MetaInfoEnum('IsisInterfaceFrrTiebreakerEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'node-protecting':'node_protecting', 'srlg-disjoint':'srlg_disjoint', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisAuthenticationAlgorithmEnum' : _MetaInfoEnum('IsisAuthenticationAlgorithmEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'cleartext':'cleartext', 'hmac-md5':'hmac_md5', 'keychain':'keychain', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisOverloadBitModeEnum' : _MetaInfoEnum('IsisOverloadBitModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'permanently-set':'permanently_set', 'startup-period':'startup_period', 'wait-for-bgp':'wait_for_bgp', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibRejectedAdjacencyBooleanEnum' : _MetaInfoEnum('IsisMibRejectedAdjacencyBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibCorruptedLspDetectedBooleanEnum' : _MetaInfoEnum('IsisMibCorruptedLspDetectedBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisAdjCheckEnum' : _MetaInfoEnum('IsisAdjCheckEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'disabled':'disabled', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisispfStateEnum' : _MetaInfoEnum('IsisispfStateEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'enabled':'enabled', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisfrrLoadSharingEnum' : _MetaInfoEnum('IsisfrrLoadSharingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'disable':'disable', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMibAuthenticationFailureBooleanEnum' : _MetaInfoEnum('IsisMibAuthenticationFailureBooleanEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'false':'false', 'true':'true', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisInterfaceStateEnum' : _MetaInfoEnum('IsisInterfaceStateEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'shutdown':'shutdown', 'suppressed':'suppressed', 'passive':'passive', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisTracingModeEnum' : _MetaInfoEnum('IsisTracingModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'off':'off', 'basic':'basic', 'enhanced':'enhanced', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisMetricStyleEnum' : _MetaInfoEnum('IsisMetricStyleEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'old-metric-style':'old_metric_style', 'new-metric-style':'new_metric_style', 'both-metric-style':'both_metric_style', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'IsisNsfFlavorEnum' : _MetaInfoEnum('IsisNsfFlavorEnum', 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'Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces.ExcludeInterface' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces.ExcludeInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the interface to be excluded ''', 'interface_name', 'Cisco-IOS-XR-clns-isis-cfg', True), ], 'Cisco-IOS-XR-clns-isis-cfg', 'exclude-interface', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces', False, [ _MetaInfoClassMember('exclude-interface', REFERENCE_LIST, 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_MetaInfoClassMember('prefix-limit', ATTRIBUTE, 'int' , None, None, [('1', '28000')], [], ''' Max number of prefixes ''', 'prefix_limit', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'max-redist-prefix', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes', False, [ _MetaInfoClassMember('max-redist-prefix', REFERENCE_LIST, 'MaxRedistPrefix' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes.MaxRedistPrefix', [], [], ''' An upper limit on the number of redistributed prefixes which may be included in the local system's LSP ''', 'max_redist_prefix', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'max-redist-prefixes', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Propagations.Propagation' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Propagations.Propagation', False, [ _MetaInfoClassMember('destination-level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Destination level for routes. 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''', 'instance_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('levels', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Levels to redistribute routes into ''', 'levels', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('0', '16777215')], [], ''' Metric for redistributed routes: <0-63> for narrow, <0-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-type', REFERENCE_ENUM_CLASS, 'IsisMetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisMetricEnum', [], [], ''' IS-IS metric type ''', 'metric_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-route-type', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' OSPF route types to redistribute. May only be specified if Protocol is OSPF. ''', 'ospf_route_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Route policy to control redistribution. ''', 'route_policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'ospf-or-ospfv3-or-isis-or-application', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Bgp' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Bgp', False, [ _MetaInfoClassMember('as-xx', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' First half of BGP AS number in XX.YY format. Mandatory if Protocol is BGP and must not be specified otherwise. Must be a non-zero value if second half is zero. ''', 'as_xx', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('as-yy', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Second half of BGP AS number in XX.YY format. Mandatory if Protocol is BGP and must not be specified otherwise. Must be a non-zero value if first half is zero. ''', 'as_yy', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('levels', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Levels to redistribute routes into ''', 'levels', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('0', '16777215')], [], ''' Metric for redistributed routes: <0-63> for narrow, <0-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-type', REFERENCE_ENUM_CLASS, 'IsisMetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisMetricEnum', [], [], ''' IS-IS metric type ''', 'metric_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-route-type', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' OSPF route types to redistribute. May only be specified if Protocol is OSPF. ''', 'ospf_route_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Route policy to control redistribution. ''', 'route_policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'bgp', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Eigrp' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Eigrp', False, [ _MetaInfoClassMember('as-zz', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' Eigrp as number. ''', 'as_zz', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('levels', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Levels to redistribute routes into ''', 'levels', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('0', '16777215')], [], ''' Metric for redistributed routes: <0-63> for narrow, <0-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-type', REFERENCE_ENUM_CLASS, 'IsisMetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisMetricEnum', [], [], ''' IS-IS metric type ''', 'metric_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-route-type', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' OSPF route types to redistribute. May only be specified if Protocol is OSPF. ''', 'ospf_route_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Route policy to control redistribution. ''', 'route_policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'eigrp', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution', False, [ _MetaInfoClassMember('protocol-name', REFERENCE_ENUM_CLASS, 'IsisRedistProtoEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisRedistProtoEnum', [], [], ''' The protocol to be redistributed. OSPFv3 may not be specified for an IPv4 topology and OSPF may not be specified for an IPv6 topology. ''', 'protocol_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('bgp', REFERENCE_LIST, 'Bgp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Bgp', [], [], ''' bgp ''', 'bgp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('connected-or-static-or-rip-or-subscriber-or-mobile', REFERENCE_CLASS, 'ConnectedOrStaticOrRipOrSubscriberOrMobile' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.ConnectedOrStaticOrRipOrSubscriberOrMobile', [], [], ''' connected or static or rip or subscriber or mobile ''', 'connected_or_static_or_rip_or_subscriber_or_mobile', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('eigrp', REFERENCE_LIST, 'Eigrp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Eigrp', [], [], ''' eigrp ''', 'eigrp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-or-ospfv3-or-isis-or-application', REFERENCE_LIST, 'OspfOrOspfv3OrIsisOrApplication' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.OspfOrOspfv3OrIsisOrApplication', [], [], ''' ospf or ospfv3 or isis or application ''', 'ospf_or_ospfv3_or_isis_or_application', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'redistribution', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Redistributions', False, [ _MetaInfoClassMember('redistribution', REFERENCE_LIST, 'Redistribution' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution', [], [], ''' Redistribution of other 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_yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals', False, [ _MetaInfoClassMember('spf-periodic-interval', REFERENCE_LIST, 'SpfPeriodicInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals.SpfPeriodicInterval', [], [], ''' Maximum interval between spf runs ''', 'spf_periodic_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-periodic-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals.SpfInterval' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals.SpfInterval', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('initial-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Initial wait before running a route calculation in milliseconds ''', 'initial_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('maximum-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Maximum wait before running a route calculation in milliseconds ''', 'maximum_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('secondary-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Secondary wait before running a route calculation in milliseconds ''', 'secondary_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-interval', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals', False, [ _MetaInfoClassMember('spf-interval', REFERENCE_LIST, 'SpfInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals.SpfInterval', [], [], ''' Route calculation scheduling parameters ''', 'spf_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.MonitorConvergence' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.MonitorConvergence', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable convergence monitoring ''', 'enable', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('prefix-list', ATTRIBUTE, 'str' , None, None, [], [], ''' Enable the monitoring of individual prefixes (prefix list name) ''', 'prefix_list', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('track-ip-frr', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable the Tracking of IP-Frr Convergence ''', 'track_ip_frr', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'monitor-convergence', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.DefaultInformation' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.DefaultInformation', False, [ _MetaInfoClassMember('external', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Flag to indicate that the default prefix should be originated as an external route ''', 'external', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Policy name ''', 'policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('use-policy', ATTRIBUTE, 'bool' , None, None, [], [], ''' Flag to indicate whether default origination is controlled using a policy ''', 'use_policy', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'default-information', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.AdminDistances.AdminDistance' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.AdminDistances.AdminDistance', False, [ _MetaInfoClassMember('address-prefix', REFERENCE_UNION, 'str' , None, None, [], [], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True, [ _MetaInfoClassMember('address-prefix', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('address-prefix', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True), ]), _MetaInfoClassMember('distance', ATTRIBUTE, 'int' , None, None, [('1', '255')], [], ''' Administrative distance ''', 'distance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('prefix-list', ATTRIBUTE, 'str' , None, None, [], [], ''' List of prefixes to which this distance applies ''', 'prefix_list', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-distance', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.AdminDistances' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.AdminDistances', False, [ _MetaInfoClassMember('admin-distance', REFERENCE_LIST, 'AdminDistance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.AdminDistances.AdminDistance', [], [], ''' Administrative distance configuration. The supplied distance is applied to all routes discovered from the specified source, or only those that match the supplied prefix list if this is specified ''', 'admin_distance', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-distances', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Ispf.States.State' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Ispf.States.State', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('state', REFERENCE_ENUM_CLASS, 'IsisispfStateEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisispfStateEnum', [], [], ''' State ''', 'state', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'state', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Ispf.States' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Ispf.States', False, [ _MetaInfoClassMember('state', REFERENCE_LIST, 'State' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Ispf.States.State', [], [], ''' Enable/disable ISPF ''', 'state', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'states', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Ispf' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Ispf', False, [ _MetaInfoClassMember('states', REFERENCE_CLASS, 'States' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Ispf.States', [], [], ''' ISPF state (enable/disable) ''', 'states', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'ispf', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.MplsLdpGlobal' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.MplsLdpGlobal', False, [ _MetaInfoClassMember('auto-config', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, LDP will be enabled onall IS-IS interfaces enabled for this address-family ''', 'auto_config', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'mpls-ldp-global', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Mpls.RouterId' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Mpls.RouterId', False, [ _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPv4 address to be used as a router ID. Precisely one of Address and Interface must be specified. ''', 'address', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Interface with designated stable IP address to be used as a router ID. This must be a Loopback interface. Precisely one of Address and Interface must be specified. ''', 'interface_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'router-id', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Mpls' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Mpls', False, [ _MetaInfoClassMember('igp-intact', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Install TE and non-TE nexthops in the RIB ''', 'igp_intact', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Enable MPLS for an IS-IS at the given levels ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('multicast-intact', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Install non-TE nexthops in the RIB for use by multicast ''', 'multicast_intact', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('router-id', REFERENCE_CLASS, 'RouterId' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Mpls.RouterId', [], [], ''' Traffic Engineering stable IP address for system ''', 'router_id', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'mpls', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Metrics.Metric.MetricEnum' : _MetaInfoEnum('MetricEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'maximum':'maximum', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'Isis.Instances.Instance.Afs.Af.AfData.Metrics.Metric' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Metrics.Metric', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('metric', REFERENCE_UNION, 'str' , None, None, [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False, [ _MetaInfoClassMember('metric', REFERENCE_ENUM_CLASS, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum', [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ]), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metric', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Metrics' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Metrics', False, [ _MetaInfoClassMember('metric', REFERENCE_LIST, 'Metric' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Metrics.Metric', [], [], ''' Metric configuration. Legal value depends on the metric-style specified for the topology. If the metric-style defined is narrow, then only a value between <1-63> is allowed and if the metric-style is defined as wide, then a value between <1-16777215> is allowed as the metric value. All routers exclude links with the maximum wide metric (16777215) from their SPF ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metrics', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Weights.Weight' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Weights.Weight', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('weight', ATTRIBUTE, 'int' , None, None, [('1', '16777214')], [], ''' Weight to be configured under interface for Load Balancing. Allowed weight: <1-16777215> ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weight', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData.Weights' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData.Weights', False, [ _MetaInfoClassMember('weight', REFERENCE_LIST, 'Weight' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Weights.Weight', [], [], ''' Weight configuration under interface for load balancing ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weights', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.AfData' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.AfData', False, [ _MetaInfoClassMember('adjacency-check', REFERENCE_ENUM_CLASS, 'IsisAdjCheckEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisAdjCheckEnum', [], [], ''' Suppress check for consistent AF support on received IIHs ''', 'adjacency_check', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('admin-distances', REFERENCE_CLASS, 'AdminDistances' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.AdminDistances', [], [], ''' Per-route administrative distanceconfiguration ''', 'admin_distances', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('advertise-link-attributes', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, advertise additional link attributes in our LSP ''', 'advertise_link_attributes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('advertise-passive-only', ATTRIBUTE, 'Empty' , None, None, [], [], ''' If enabled, advertise prefixes of passive interfaces only ''', 'advertise_passive_only', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('apply-weight', REFERENCE_ENUM_CLASS, 'IsisApplyWeightEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisApplyWeightEnum', [], [], ''' Apply weights to UCMP or ECMP only ''', 'apply_weight', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('attached-bit', REFERENCE_ENUM_CLASS, 'IsisAttachedBitEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisAttachedBitEnum', [], [], ''' Set the attached bit in this router's level 1 System LSP ''', 'attached_bit', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('default-admin-distance', ATTRIBUTE, 'int' , None, None, [('1', '255')], [], ''' Default IS-IS administrative distance configuration. ''', 'default_admin_distance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('default-information', REFERENCE_CLASS, 'DefaultInformation' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.DefaultInformation', [], [], ''' Control origination of a default route with the option of using a policy. If no policy is specified the default route is advertised with zero cost in level 2 only. ''', 'default_information', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('frr-table', REFERENCE_CLASS, 'FrrTable' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.FrrTable', [], [], ''' Fast-ReRoute configuration ''', 'frr_table', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ignore-attached-bit', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, Ignore other routers attached bit ''', 'ignore_attached_bit', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ispf', REFERENCE_CLASS, 'Ispf' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Ispf', [], [], ''' ISPF configuration ''', 'ispf', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('max-redist-prefixes', REFERENCE_CLASS, 'MaxRedistPrefixes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes', [], [], ''' Maximum number of redistributed prefixesconfiguration ''', 'max_redist_prefixes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('maximum-paths', ATTRIBUTE, 'int' , None, None, [('1', '64')], [], ''' Maximum number of active parallel paths per route ''', 'maximum_paths', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-styles', REFERENCE_CLASS, 'MetricStyles' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.MetricStyles', [], [], ''' Metric-style configuration ''', 'metric_styles', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metrics', REFERENCE_CLASS, 'Metrics' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Metrics', [], [], ''' Metric configuration ''', 'metrics', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('micro-loop-avoidance', REFERENCE_CLASS, 'MicroLoopAvoidance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.MicroLoopAvoidance', [], [], ''' Micro Loop Avoidance configuration ''', 'micro_loop_avoidance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('monitor-convergence', REFERENCE_CLASS, 'MonitorConvergence' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.MonitorConvergence', [], [], ''' Enable convergence monitoring ''', 'monitor_convergence', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mpls', REFERENCE_CLASS, 'Mpls' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData.Mpls', [], [], ''' MPLS configuration. 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''', 'ospf_route_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Route policy to control redistribution. ''', 'route_policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'bgp', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.Eigrp' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.Eigrp', False, [ _MetaInfoClassMember('as-zz', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' Eigrp as number. ''', 'as_zz', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('levels', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Levels to redistribute routes into ''', 'levels', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('0', '16777215')], [], ''' Metric for redistributed routes: <0-63> for narrow, <0-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-type', REFERENCE_ENUM_CLASS, 'IsisMetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisMetricEnum', [], [], ''' IS-IS metric type ''', 'metric_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-route-type', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' OSPF route types to redistribute. May only be specified if Protocol is OSPF. ''', 'ospf_route_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Route policy to control redistribution. ''', 'route_policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'eigrp', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution', False, [ _MetaInfoClassMember('protocol-name', REFERENCE_ENUM_CLASS, 'IsisRedistProtoEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisRedistProtoEnum', [], [], ''' The protocol to be redistributed. OSPFv3 may not be specified for an IPv4 topology and OSPF may not be specified for an IPv6 topology. ''', 'protocol_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('bgp', REFERENCE_LIST, 'Bgp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.Bgp', [], [], ''' bgp ''', 'bgp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('connected-or-static-or-rip-or-subscriber-or-mobile', REFERENCE_CLASS, 'ConnectedOrStaticOrRipOrSubscriberOrMobile' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.ConnectedOrStaticOrRipOrSubscriberOrMobile', [], [], ''' connected or static or rip or subscriber or mobile ''', 'connected_or_static_or_rip_or_subscriber_or_mobile', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('eigrp', REFERENCE_LIST, 'Eigrp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.Eigrp', [], [], ''' eigrp ''', 'eigrp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ospf-or-ospfv3-or-isis-or-application', REFERENCE_LIST, 'OspfOrOspfv3OrIsisOrApplication' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution.OspfOrOspfv3OrIsisOrApplication', [], [], ''' ospf or ospfv3 or isis or application ''', 'ospf_or_ospfv3_or_isis_or_application', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'redistribution', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions', False, [ _MetaInfoClassMember('redistribution', REFERENCE_LIST, 'Redistribution' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions.Redistribution', [], [], ''' Redistribution of other protocols into this IS-IS instance ''', 'redistribution', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'redistributions', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals.SpfPeriodicInterval' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals.SpfPeriodicInterval', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('periodic-interval', ATTRIBUTE, 'int' , None, None, [('0', '3600')], [], ''' Maximum interval in between SPF runs in seconds ''', 'periodic_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-periodic-interval', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals', False, [ _MetaInfoClassMember('spf-periodic-interval', REFERENCE_LIST, 'SpfPeriodicInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals.SpfPeriodicInterval', [], [], ''' Maximum interval between spf runs ''', 'spf_periodic_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-periodic-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals.SpfInterval' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals.SpfInterval', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('initial-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Initial wait before running a route calculation in milliseconds ''', 'initial_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('maximum-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Maximum wait before running a route calculation in milliseconds ''', 'maximum_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('secondary-wait', ATTRIBUTE, 'int' , None, None, [('0', '120000')], [], ''' Secondary wait before running a route calculation in milliseconds ''', 'secondary_wait', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-interval', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals', False, [ _MetaInfoClassMember('spf-interval', REFERENCE_LIST, 'SpfInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals.SpfInterval', [], [], ''' Route calculation scheduling parameters ''', 'spf_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'spf-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.MonitorConvergence' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.MonitorConvergence', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable convergence monitoring ''', 'enable', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('prefix-list', ATTRIBUTE, 'str' , None, None, [], [], ''' Enable the monitoring of individual prefixes (prefix list name) ''', 'prefix_list', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('track-ip-frr', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable the Tracking of IP-Frr Convergence ''', 'track_ip_frr', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'monitor-convergence', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.DefaultInformation' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.DefaultInformation', False, [ _MetaInfoClassMember('external', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Flag to indicate that the default prefix should be originated as an external route ''', 'external', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('policy-name', ATTRIBUTE, 'str' , None, None, [], [], ''' Policy name ''', 'policy_name', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('use-policy', ATTRIBUTE, 'bool' , None, None, [], [], ''' Flag to indicate whether default origination is controlled using a policy ''', 'use_policy', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'default-information', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances.AdminDistance' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances.AdminDistance', False, [ _MetaInfoClassMember('address-prefix', REFERENCE_UNION, 'str' , None, None, [], [], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True, [ _MetaInfoClassMember('address-prefix', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])/(([0-9])|([1-2][0-9])|(3[0-2]))'], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('address-prefix', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(/(([0-9])|([0-9]{2})|(1[0-1][0-9])|(12[0-8])))'], ''' IP route source prefix ''', 'address_prefix', 'Cisco-IOS-XR-clns-isis-cfg', True), ]), _MetaInfoClassMember('distance', ATTRIBUTE, 'int' , None, None, [('1', '255')], [], ''' Administrative distance ''', 'distance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('prefix-list', ATTRIBUTE, 'str' , None, None, [], [], ''' List of prefixes to which this distance applies ''', 'prefix_list', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-distance', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances', False, [ _MetaInfoClassMember('admin-distance', REFERENCE_LIST, 'AdminDistance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances.AdminDistance', [], [], ''' Administrative distance configuration. The supplied distance is applied to all routes discovered from the specified source, or only those that match the supplied prefix list if this is specified ''', 'admin_distance', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-distances', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States.State' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States.State', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('state', REFERENCE_ENUM_CLASS, 'IsisispfStateEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisispfStateEnum', [], [], ''' State ''', 'state', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'state', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States', False, [ _MetaInfoClassMember('state', REFERENCE_LIST, 'State' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States.State', [], [], ''' Enable/disable ISPF ''', 'state', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'states', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Ispf', False, [ _MetaInfoClassMember('states', REFERENCE_CLASS, 'States' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf.States', [], [], ''' ISPF state (enable/disable) ''', 'states', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'ispf', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.MplsLdpGlobal' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.MplsLdpGlobal', False, [ _MetaInfoClassMember('auto-config', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, LDP will be enabled onall IS-IS interfaces enabled for this address-family ''', 'auto_config', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'mpls-ldp-global', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Mpls.RouterId' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Mpls.RouterId', False, [ _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPv4 address to be used as a router ID. Precisely one of Address and Interface must be specified. ''', 'address', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Interface with designated stable IP address to be used as a router ID. This must be a Loopback interface. Precisely one of Address and Interface must be specified. ''', 'interface_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'router-id', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Mpls' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Mpls', False, [ _MetaInfoClassMember('igp-intact', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Install TE and non-TE nexthops in the RIB ''', 'igp_intact', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Enable MPLS for an IS-IS at the given levels ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('multicast-intact', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Install non-TE nexthops in the RIB for use by multicast ''', 'multicast_intact', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('router-id', REFERENCE_CLASS, 'RouterId' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Mpls.RouterId', [], [], ''' Traffic Engineering stable IP address for system ''', 'router_id', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'mpls', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Metrics.Metric.MetricEnum' : _MetaInfoEnum('MetricEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'maximum':'maximum', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'Isis.Instances.Instance.Afs.Af.TopologyName.Metrics.Metric' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Metrics.Metric', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('metric', REFERENCE_UNION, 'str' , None, None, [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False, [ _MetaInfoClassMember('metric', REFERENCE_ENUM_CLASS, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum', [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ]), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metric', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Metrics' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Metrics', False, [ _MetaInfoClassMember('metric', REFERENCE_LIST, 'Metric' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Metrics.Metric', [], [], ''' Metric configuration. Legal value depends on the metric-style specified for the topology. If the metric-style defined is narrow, then only a value between <1-63> is allowed and if the metric-style is defined as wide, then a value between <1-16777215> is allowed as the metric value. All routers exclude links with the maximum wide metric (16777215) from their SPF ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metrics', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Weights.Weight' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Weights.Weight', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('weight', ATTRIBUTE, 'int' , None, None, [('1', '16777214')], [], ''' Weight to be configured under interface for Load Balancing. Allowed weight: <1-16777215> ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weight', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName.Weights' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName.Weights', False, [ _MetaInfoClassMember('weight', REFERENCE_LIST, 'Weight' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Weights.Weight', [], [], ''' Weight configuration under interface for load balancing ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weights', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af.TopologyName' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af.TopologyName', False, [ _MetaInfoClassMember('topology-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Topology Name ''', 'topology_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('adjacency-check', REFERENCE_ENUM_CLASS, 'IsisAdjCheckEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisAdjCheckEnum', [], [], ''' Suppress check for consistent AF support on received IIHs ''', 'adjacency_check', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('admin-distances', REFERENCE_CLASS, 'AdminDistances' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances', [], [], ''' Per-route administrative distanceconfiguration ''', 'admin_distances', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('advertise-link-attributes', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, advertise additional link attributes in our LSP ''', 'advertise_link_attributes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('advertise-passive-only', ATTRIBUTE, 'Empty' , None, None, [], [], ''' If enabled, advertise prefixes of passive interfaces only ''', 'advertise_passive_only', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('apply-weight', REFERENCE_ENUM_CLASS, 'IsisApplyWeightEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisApplyWeightEnum', [], [], ''' Apply weights to UCMP or ECMP only ''', 'apply_weight', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('attached-bit', REFERENCE_ENUM_CLASS, 'IsisAttachedBitEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisAttachedBitEnum', [], [], ''' Set the attached bit in this router's level 1 System LSP ''', 'attached_bit', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('default-admin-distance', ATTRIBUTE, 'int' , None, None, [('1', '255')], [], ''' Default IS-IS administrative distance configuration. ''', 'default_admin_distance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('default-information', REFERENCE_CLASS, 'DefaultInformation' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.DefaultInformation', [], [], ''' Control origination of a default route with the option of using a policy. If no policy is specified the default route is advertised with zero cost in level 2 only. ''', 'default_information', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('frr-table', REFERENCE_CLASS, 'FrrTable' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.FrrTable', [], [], ''' Fast-ReRoute configuration ''', 'frr_table', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ignore-attached-bit', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, Ignore other routers attached bit ''', 'ignore_attached_bit', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ispf', REFERENCE_CLASS, 'Ispf' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Ispf', [], [], ''' ISPF configuration ''', 'ispf', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('max-redist-prefixes', REFERENCE_CLASS, 'MaxRedistPrefixes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.MaxRedistPrefixes', [], [], ''' Maximum number of redistributed prefixesconfiguration ''', 'max_redist_prefixes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('maximum-paths', ATTRIBUTE, 'int' , None, None, [('1', '64')], [], ''' Maximum number of active parallel paths per route ''', 'maximum_paths', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric-styles', REFERENCE_CLASS, 'MetricStyles' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.MetricStyles', [], [], ''' Metric-style configuration ''', 'metric_styles', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metrics', REFERENCE_CLASS, 'Metrics' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Metrics', [], [], ''' Metric configuration ''', 'metrics', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('micro-loop-avoidance', REFERENCE_CLASS, 'MicroLoopAvoidance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.MicroLoopAvoidance', [], [], ''' Micro Loop Avoidance configuration ''', 'micro_loop_avoidance', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('monitor-convergence', REFERENCE_CLASS, 'MonitorConvergence' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.MonitorConvergence', [], [], ''' Enable convergence monitoring ''', 'monitor_convergence', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mpls', REFERENCE_CLASS, 'Mpls' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Mpls', [], [], ''' MPLS configuration. MPLS configuration will only be applied for the IPv4-unicast address-family. ''', 'mpls', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mpls-ldp-global', REFERENCE_CLASS, 'MplsLdpGlobal' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.MplsLdpGlobal', [], [], ''' MPLS LDP configuration. MPLS LDP configuration will only be applied for the IPv4-unicast address-family. ''', 'mpls_ldp_global', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('propagations', REFERENCE_CLASS, 'Propagations' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Propagations', [], [], ''' Route propagation configuration ''', 'propagations', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('redistributions', REFERENCE_CLASS, 'Redistributions' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions', [], [], ''' Protocol redistribution configuration ''', 'redistributions', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('route-source-first-hop', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, routes will be installed with the IP address of the first-hop node as the source instead of the originating node ''', 'route_source_first_hop', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('router-id', REFERENCE_CLASS, 'RouterId' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.RouterId', [], [], ''' Stable IP address for system. Will only be applied for the unicast sub-address-family. ''', 'router_id', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('segment-routing', REFERENCE_CLASS, 'SegmentRouting' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SegmentRouting', [], [], ''' Enable Segment Routing configuration ''', 'segment_routing', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('single-topology', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Run IPv6 Unicast using the standard (IPv4 Unicast) topology ''', 'single_topology', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('spf-intervals', REFERENCE_CLASS, 'SpfIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals', [], [], ''' SPF-interval configuration ''', 'spf_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('spf-periodic-intervals', REFERENCE_CLASS, 'SpfPeriodicIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals', [], [], ''' Peoridic SPF configuration ''', 'spf_periodic_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('spf-prefix-priorities', REFERENCE_CLASS, 'SpfPrefixPriorities' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SpfPrefixPriorities', [], [], ''' SPF Prefix Priority configuration ''', 'spf_prefix_priorities', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('summary-prefixes', REFERENCE_CLASS, 'SummaryPrefixes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.SummaryPrefixes', [], [], ''' Summary-prefix configuration ''', 'summary_prefixes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('topology-id', ATTRIBUTE, 'int' , None, None, [('6', '4095')], [], ''' Set the topology ID for a named (non-default) topology. This object must be set before any other configuration is supplied for a named (non-default) topology , and must be the last configuration object to be removed. This item should not be supplied for the non-named default topologies. ''', 'topology_id', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ucmp', REFERENCE_CLASS, 'Ucmp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Ucmp', [], [], ''' UCMP (UnEqual Cost MultiPath) configuration ''', 'ucmp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('weights', REFERENCE_CLASS, 'Weights' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.TopologyName.Weights', [], [], ''' Weight configuration ''', 'weights', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'topology-name', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Afs.Af' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Afs.Af', False, [ _MetaInfoClassMember('af-name', REFERENCE_ENUM_CLASS, 'IsisAddressFamilyEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisAddressFamilyEnum', [], [], ''' Address family ''', 'af_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('saf-name', REFERENCE_ENUM_CLASS, 'IsisSubAddressFamilyEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisSubAddressFamilyEnum', [], [], ''' Sub address family ''', 'saf_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('af-data', REFERENCE_CLASS, 'AfData' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs.Af.AfData', [], [], ''' Data container. 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''', 'auto_metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'auto-metrics', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags.AdminTag' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags.AdminTag', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('admin-tag', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Tag to associate with connected routes ''', 'admin_tag', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-tag', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags', False, [ _MetaInfoClassMember('admin-tag', REFERENCE_LIST, 'AdminTag' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags.AdminTag', [], [], ''' Admin tag for advertised interface connected routes ''', 'admin_tag', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'admin-tags', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.InterfaceLinkGroup' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.InterfaceLinkGroup', False, [ _MetaInfoClassMember('level', ATTRIBUTE, 'int' , None, None, [('0', '2')], [], ''' Level in which link group will be effective ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('link-group', ATTRIBUTE, 'str' , None, None, [(0, 40)], [], ''' Link Group ''', 'link_group', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'interface-link-group', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum' : _MetaInfoEnum('MetricEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'maximum':'maximum', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('metric', REFERENCE_UNION, 'str' , None, None, [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False, [ _MetaInfoClassMember('metric', REFERENCE_ENUM_CLASS, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric.MetricEnum', [], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metric', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Allowed metric: <1-63> for narrow, <1-16777215> for wide ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ]), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metric', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics', False, [ _MetaInfoClassMember('metric', REFERENCE_LIST, 'Metric' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics.Metric', [], [], ''' Metric configuration. Legal value depends on the metric-style specified for the topology. If the metric-style defined is narrow, then only a value between <1-63> is allowed and if the metric-style is defined as wide, then a value between <1-16777215> is allowed as the metric value. All routers exclude links with the maximum wide metric (16777215) from their SPF ''', 'metric', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'metrics', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights.Weight' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights.Weight', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('weight', ATTRIBUTE, 'int' , None, None, [('1', '16777214')], [], ''' Weight to be configured under interface for Load Balancing. Allowed weight: <1-16777215> ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weight', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights', False, [ _MetaInfoClassMember('weight', REFERENCE_LIST, 'Weight' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights.Weight', [], [], ''' Weight configuration under interface for load balancing ''', 'weight', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'weights', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName', False, [ _MetaInfoClassMember('topology-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Topology Name ''', 'topology_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('admin-tags', REFERENCE_CLASS, 'AdminTags' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AdminTags', [], [], ''' admin-tag configuration ''', 'admin_tags', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('auto-metrics', REFERENCE_CLASS, 'AutoMetrics' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.AutoMetrics', [], [], ''' AutoMetric configuration ''', 'auto_metrics', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-af-state', REFERENCE_ENUM_CLASS, 'IsisInterfaceAfStateEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisInterfaceAfStateEnum', [], [], ''' Interface state ''', 'interface_af_state', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-frr-table', REFERENCE_CLASS, 'InterfaceFrrTable' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.InterfaceFrrTable', [], [], ''' Fast-ReRoute configuration ''', 'interface_frr_table', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-link-group', REFERENCE_CLASS, 'InterfaceLinkGroup' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.InterfaceLinkGroup', [], [], ''' Provide link group name and level ''', 'interface_link_group', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('metrics', REFERENCE_CLASS, 'Metrics' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Metrics', [], [], ''' Metric configuration ''', 'metrics', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mpls-ldp', REFERENCE_CLASS, 'MplsLdp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.MplsLdp', [], [], ''' MPLS LDP configuration ''', 'mpls_ldp', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('prefix-sid', REFERENCE_CLASS, 'PrefixSid' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.PrefixSid', [], [], ''' Assign prefix SID to an interface, ISISPHPFlag will be rejected if set to disable, ISISEXPLICITNULLFlag will override the value of ISISPHPFlag ''', 'prefix_sid', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('running', ATTRIBUTE, 'Empty' , None, None, [], [], ''' The presence of this object allows an address-family to be run over the interface in question.This must be the first object created under the InterfaceAddressFamily container, and the last one deleted ''', 'running', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('weights', REFERENCE_CLASS, 'Weights' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName.Weights', [], [], ''' Weight configuration ''', 'weights', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'topology-name', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf', False, [ _MetaInfoClassMember('af-name', REFERENCE_ENUM_CLASS, 'IsisAddressFamilyEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisAddressFamilyEnum', [], [], ''' Address family ''', 'af_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('saf-name', REFERENCE_ENUM_CLASS, 'IsisSubAddressFamilyEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisSubAddressFamilyEnum', [], [], ''' Sub address family ''', 'saf_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('interface-af-data', REFERENCE_CLASS, 'InterfaceAfData' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData', [], [], ''' Data container. ''', 'interface_af_data', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('topology-name', REFERENCE_LIST, 'TopologyName' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.TopologyName', [], [], ''' keys: topology-name ''', 'topology_name', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'interface-af', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs', False, [ _MetaInfoClassMember('interface-af', REFERENCE_LIST, 'InterfaceAf' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf', [], [], ''' Configuration for an IS-IS address-family on a single interface. If a named (non-default) topology is being created it must be multicast. Also the topology ID mustbe set first and delete last in the router configuration. ''', 'interface_af', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'interface-afs', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals.CsnpInterval' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals.CsnpInterval', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('interval', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Seconds ''', 'interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'csnp-interval', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals', False, [ _MetaInfoClassMember('csnp-interval', REFERENCE_LIST, 'CsnpInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals.CsnpInterval', [], [], ''' CSNP-interval configuration. No fixed default value as this depends on the media type of the interface. ''', 'csnp_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'csnp-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.LspIntervals.LspInterval' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.LspIntervals.LspInterval', False, [ _MetaInfoClassMember('level', REFERENCE_ENUM_CLASS, 'IsisInternalLevelEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_datatypes', 'IsisInternalLevelEnum', [], [], ''' Level to which configuration applies ''', 'level', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('interval', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Milliseconds ''', 'interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'lsp-interval', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.LspIntervals' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface.LspIntervals', False, [ _MetaInfoClassMember('lsp-interval', REFERENCE_LIST, 'LspInterval' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.LspIntervals.LspInterval', [], [], ''' Interval between transmission of LSPs on interface. ''', 'lsp_interval', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'lsp-intervals', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces.Interface.MeshGroupEnum' : _MetaInfoEnum('MeshGroupEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', { 'blocked':'blocked', }, 'Cisco-IOS-XR-clns-isis-cfg', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg']), 'Isis.Instances.Instance.Interfaces.Interface' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces.Interface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Interface name ''', 'interface_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('bfd', REFERENCE_CLASS, 'Bfd' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.Bfd', [], [], ''' BFD configuration ''', 'bfd', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('circuit-type', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' Configure circuit type for interface ''', 'circuit_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('csnp-intervals', REFERENCE_CLASS, 'CsnpIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.CsnpIntervals', [], [], ''' CSNP-interval configuration ''', 'csnp_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('hello-accept-passwords', REFERENCE_CLASS, 'HelloAcceptPasswords' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.HelloAcceptPasswords', [], [], ''' IIH accept password configuration ''', 'hello_accept_passwords', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('hello-intervals', REFERENCE_CLASS, 'HelloIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.HelloIntervals', [], [], ''' Hello-interval configuration ''', 'hello_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('hello-multipliers', REFERENCE_CLASS, 'HelloMultipliers' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.HelloMultipliers', [], [], ''' Hello-multiplier configuration ''', 'hello_multipliers', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('hello-paddings', REFERENCE_CLASS, 'HelloPaddings' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.HelloPaddings', [], [], ''' Hello-padding configuration ''', 'hello_paddings', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('hello-passwords', REFERENCE_CLASS, 'HelloPasswords' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.HelloPasswords', [], [], ''' IIH password configuration ''', 'hello_passwords', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interface-afs', REFERENCE_CLASS, 'InterfaceAfs' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs', [], [], ''' Per-interface address-family configuration ''', 'interface_afs', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('link-down-fast-detect', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Configure high priority detection of interface down event ''', 'link_down_fast_detect', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-fast-flood-thresholds', REFERENCE_CLASS, 'LspFastFloodThresholds' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.LspFastFloodThresholds', [], [], ''' LSP fast flood threshold configuration ''', 'lsp_fast_flood_thresholds', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-intervals', REFERENCE_CLASS, 'LspIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.LspIntervals', [], [], ''' LSP-interval configuration ''', 'lsp_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-retransmit-intervals', REFERENCE_CLASS, 'LspRetransmitIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.LspRetransmitIntervals', [], [], ''' LSP-retransmission-interval configuration ''', 'lsp_retransmit_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-retransmit-throttle-intervals', REFERENCE_CLASS, 'LspRetransmitThrottleIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.LspRetransmitThrottleIntervals', [], [], ''' LSP-retransmission-throttle-interval configuration ''', 'lsp_retransmit_throttle_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mesh-group', REFERENCE_UNION, 'str' , None, None, [], [], ''' Mesh-group configuration ''', 'mesh_group', 'Cisco-IOS-XR-clns-isis-cfg', False, [ _MetaInfoClassMember('mesh-group', REFERENCE_ENUM_CLASS, 'Isis.Instances.Instance.Interfaces.Interface.MeshGroupEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.MeshGroupEnum', [], [], ''' Mesh-group configuration ''', 'mesh_group', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('mesh-group', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Mesh-group configuration ''', 'mesh_group', 'Cisco-IOS-XR-clns-isis-cfg', False), ]), _MetaInfoClassMember('point-to-point', ATTRIBUTE, 'Empty' , None, None, [], [], ''' IS-IS will attempt to form point-to-point over LAN adjacencies over this interface. ''', 'point_to_point', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('priorities', REFERENCE_CLASS, 'Priorities' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface.Priorities', [], [], ''' DIS-election priority configuration ''', 'priorities', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('running', ATTRIBUTE, 'Empty' , None, None, [], [], ''' This object must be set before any other configuration is supplied for an interface, and must be the last per-interface configuration object to be removed. ''', 'running', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('state', REFERENCE_ENUM_CLASS, 'IsisInterfaceStateEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisInterfaceStateEnum', [], [], ''' Enable/Disable routing ''', 'state', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'interface', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance.Interfaces' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance.Interfaces', False, [ _MetaInfoClassMember('interface', REFERENCE_LIST, 'Interface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces.Interface', [], [], ''' Configuration for an IS-IS interface ''', 'interface', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'interfaces', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances.Instance' : { 'meta_info' : _MetaInfoClass('Isis.Instances.Instance', False, [ _MetaInfoClassMember('instance-name', ATTRIBUTE, 'str' , None, None, [(0, 40)], [], ''' Instance identifier ''', 'instance_name', 'Cisco-IOS-XR-clns-isis-cfg', True), _MetaInfoClassMember('afs', REFERENCE_CLASS, 'Afs' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Afs', [], [], ''' Per-address-family configuration ''', 'afs', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('distribute', REFERENCE_CLASS, 'Distribute' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Distribute', [], [], ''' IS-IS Distribute BGP-LS configuration ''', 'distribute', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('dynamic-host-name', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, dynamic hostname resolution is disabled, and system IDs will always be displayed by show and debug output. ''', 'dynamic_host_name', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('ignore-lsp-errors', ATTRIBUTE, 'bool' , None, None, [], [], ''' If TRUE, LSPs recieved with bad checksums will result in the purging of that LSP from the LSP DB. If FALSE or not set, the received LSP will just be ignored. ''', 'ignore_lsp_errors', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('instance-id', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Instance ID of the IS-IS process ''', 'instance_id', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('interfaces', REFERENCE_CLASS, 'Interfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Interfaces', [], [], ''' Per-interface configuration ''', 'interfaces', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('is-type', REFERENCE_ENUM_CLASS, 'IsisConfigurableLevelsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisConfigurableLevelsEnum', [], [], ''' IS type of the IS-IS process ''', 'is_type', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('link-groups', REFERENCE_CLASS, 'LinkGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LinkGroups', [], [], ''' Link Group ''', 'link_groups', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('log-adjacency-changes', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Log changes in adjacency state ''', 'log_adjacency_changes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('log-pdu-drops', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Log PDU drops ''', 'log_pdu_drops', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-accept-passwords', REFERENCE_CLASS, 'LspAcceptPasswords' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspAcceptPasswords', [], [], ''' LSP/SNP accept password configuration ''', 'lsp_accept_passwords', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-arrival-times', REFERENCE_CLASS, 'LspArrivalTimes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspArrivalTimes', [], [], ''' LSP arrival time configuration ''', 'lsp_arrival_times', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-check-intervals', REFERENCE_CLASS, 'LspCheckIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspCheckIntervals', [], [], ''' LSP checksum check interval configuration ''', 'lsp_check_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-generation-intervals', REFERENCE_CLASS, 'LspGenerationIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspGenerationIntervals', [], [], ''' LSP generation-interval configuration ''', 'lsp_generation_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-lifetimes', REFERENCE_CLASS, 'LspLifetimes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspLifetimes', [], [], ''' LSP lifetime configuration ''', 'lsp_lifetimes', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-mtus', REFERENCE_CLASS, 'LspMtus' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspMtus', [], [], ''' LSP MTU configuration ''', 'lsp_mtus', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-passwords', REFERENCE_CLASS, 'LspPasswords' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspPasswords', [], [], ''' LSP/SNP password configuration ''', 'lsp_passwords', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('lsp-refresh-intervals', REFERENCE_CLASS, 'LspRefreshIntervals' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.LspRefreshIntervals', [], [], ''' LSP refresh-interval configuration ''', 'lsp_refresh_intervals', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('max-link-metrics', REFERENCE_CLASS, 'MaxLinkMetrics' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.MaxLinkMetrics', [], [], ''' Max Link Metric configuration ''', 'max_link_metrics', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('nets', REFERENCE_CLASS, 'Nets' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Nets', [], [], ''' NET configuration ''', 'nets', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('nsf', REFERENCE_CLASS, 'Nsf' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Nsf', [], [], ''' IS-IS NSF configuration ''', 'nsf', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('nsr', ATTRIBUTE, 'Empty' , None, None, [], [], ''' IS-IS NSR configuration ''', 'nsr', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('overload-bits', REFERENCE_CLASS, 'OverloadBits' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.OverloadBits', [], [], ''' LSP overload-bit configuration ''', 'overload_bits', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('running', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Flag to indicate that instance should be running. This must be the first object created when an IS-IS instance is configured, and the last object deleted when it is deconfigured. When this object is deleted, the IS-IS instance will exit. ''', 'running', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('srgb', REFERENCE_CLASS, 'Srgb' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.Srgb', [], [], ''' Segment Routing Global Block configuration ''', 'srgb', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('trace-buffer-size', REFERENCE_CLASS, 'TraceBufferSize' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance.TraceBufferSize', [], [], ''' Trace buffer size configuration ''', 'trace_buffer_size', 'Cisco-IOS-XR-clns-isis-cfg', False), _MetaInfoClassMember('tracing-mode', REFERENCE_ENUM_CLASS, 'IsisTracingModeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'IsisTracingModeEnum', [], [], ''' Tracing mode configuration ''', 'tracing_mode', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'instance', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis.Instances' : { 'meta_info' : _MetaInfoClass('Isis.Instances', False, [ _MetaInfoClassMember('instance', REFERENCE_LIST, 'Instance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances.Instance', [], [], ''' Configuration for a single IS-IS instance ''', 'instance', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'instances', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, 'Isis' : { 'meta_info' : _MetaInfoClass('Isis', False, [ _MetaInfoClassMember('instances', REFERENCE_CLASS, 'Instances' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg', 'Isis.Instances', [], [], ''' IS-IS instance configuration ''', 'instances', 'Cisco-IOS-XR-clns-isis-cfg', False), ], 'Cisco-IOS-XR-clns-isis-cfg', 'isis', _yang_ns._namespaces['Cisco-IOS-XR-clns-isis-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_clns_isis_cfg' ), }, } _meta_table['Isis.Instances.Instance.LspGenerationIntervals.LspGenerationInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.LspGenerationIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.LspArrivalTimes.LspArrivalTime']['meta_info'].parent =_meta_table['Isis.Instances.Instance.LspArrivalTimes']['meta_info'] _meta_table['Isis.Instances.Instance.MaxLinkMetrics.MaxLinkMetric']['meta_info'].parent =_meta_table['Isis.Instances.Instance.MaxLinkMetrics']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SegmentRouting.PrefixSidMap']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.SegmentRouting']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MetricStyles.MetricStyle']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.MetricStyles']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrLoadSharings.FrrLoadSharing']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrLoadSharings']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.PriorityLimits.PriorityLimit']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.PriorityLimits']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrRemoteLfaPrefixes.FrrRemoteLfaPrefix']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrRemoteLfaPrefixes']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrTiebreakers.FrrTiebreaker']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrTiebreakers']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrUseCandOnlies.FrrUseCandOnly']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrUseCandOnlies']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrLoadSharings']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.PriorityLimits']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrRemoteLfaPrefixes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrTiebreakers']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable.FrrUseCandOnlies']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPrefixPriorities.SpfPrefixPriority']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPrefixPriorities']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SummaryPrefixes.SummaryPrefix']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.SummaryPrefixes']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces.ExcludeInterface']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp.Enable']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp.ExcludeInterfaces']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes.MaxRedistPrefix']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Propagations.Propagation']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Propagations']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.ConnectedOrStaticOrRipOrSubscriberOrMobile']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.OspfOrOspfv3OrIsisOrApplication']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Bgp']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution.Eigrp']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions.Redistribution']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals.SpfPeriodicInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals.SpfInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.AdminDistances.AdminDistance']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.AdminDistances']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ispf.States.State']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ispf.States']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ispf.States']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ispf']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Mpls.RouterId']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Mpls']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Metrics.Metric']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Metrics']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Weights.Weight']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData.Weights']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SegmentRouting']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MetricStyles']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.FrrTable']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.RouterId']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPrefixPriorities']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SummaryPrefixes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MicroLoopAvoidance']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Ucmp']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MaxRedistPrefixes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Propagations']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.Redistributions']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfPeriodicIntervals']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.SpfIntervals']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData.MonitorConvergence']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'] 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_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.MetricStyles']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.FrrTable']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.RouterId']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.SpfPrefixPriorities']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.SummaryPrefixes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.MicroLoopAvoidance']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Ucmp']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.MaxRedistPrefixes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Propagations']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Redistributions']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.SpfPeriodicIntervals']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.SpfIntervals']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.MonitorConvergence']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.DefaultInformation']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.AdminDistances']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Ispf']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.MplsLdpGlobal']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Mpls']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Metrics']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName.Weights']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.AfData']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af.TopologyName']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs.Af']['meta_info'] _meta_table['Isis.Instances.Instance.Afs.Af']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Afs']['meta_info'] _meta_table['Isis.Instances.Instance.LspRefreshIntervals.LspRefreshInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.LspRefreshIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.LspAcceptPasswords.LspAcceptPassword']['meta_info'].parent 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_meta_table['Isis.Instances.Instance.OverloadBits.OverloadBit']['meta_info'].parent =_meta_table['Isis.Instances.Instance.OverloadBits']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.LspRetransmitThrottleIntervals.LspRetransmitThrottleInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.LspRetransmitThrottleIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.LspRetransmitIntervals.LspRetransmitInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.LspRetransmitIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.Priorities.Priority']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.Priorities']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloAcceptPasswords.HelloAcceptPassword']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloAcceptPasswords']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloPasswords.HelloPassword']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloPasswords']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloPaddings.HelloPadding']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloPaddings']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloMultipliers.HelloMultiplier']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloMultipliers']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.LspFastFloodThresholds.LspFastFloodThreshold']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.LspFastFloodThresholds']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloIntervals.HelloInterval']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.HelloIntervals']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrlfaCandidateInterfaces.FrrlfaCandidateInterface']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrlfaCandidateInterfaces']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrRemoteLfaMaxMetrics.FrrRemoteLfaMaxMetric']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrRemoteLfaMaxMetrics']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrTypes.FrrType']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrTypes']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrRemoteLfaTypes.FrrRemoteLfaType']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrRemoteLfaTypes']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.InterfaceFrrTiebreakerDefaults.InterfaceFrrTiebreakerDefault']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.InterfaceFrrTiebreakerDefaults']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrtilfaTypes.FrrtilfaType']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrtilfaTypes']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrExcludeInterfaces.FrrExcludeInterface']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrExcludeInterfaces']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.InterfaceFrrTiebreakers.InterfaceFrrTiebreaker']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.InterfaceFrrTiebreakers']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrlfaCandidateInterfaces']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrRemoteLfaMaxMetrics']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable']['meta_info'] _meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable.FrrTypes']['meta_info'].parent =_meta_table['Isis.Instances.Instance.Interfaces.Interface.InterfaceAfs.InterfaceAf.InterfaceAfData.InterfaceFrrTable']['meta_info'] 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29a4b28a3428e8a1d9d8be56e9b6795d1756e451

3,455

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Python

tests/processing/pipeline/test_varia.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

133

2018-05-18T13:54:10.000Z

2022-02-15T02:14:20.000Z

tests/processing/pipeline/test_varia.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

68

2018-06-03T16:42:09.000Z

2021-01-29T10:58:30.000Z

tests/processing/pipeline/test_varia.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

37

2018-11-02T02:40:29.000Z

2021-11-30T07:44:50.000Z

import numpy as np from audiomate.processing import pipeline class TestAddContext: def test_compute_with_left_frames(self): data = np.array([[1, 2], [3, 4], [5, 6]]) chunk = pipeline.Chunk(data, offset=0, is_last=True) step = pipeline.AddContext(left_frames=2, right_frames=0) result = step.compute(chunk, 16000) assert np.array_equal(result, np.array([ [0, 0, 0, 0, 1, 2], [0, 0, 1, 2, 3, 4], [1, 2, 3, 4, 5, 6] ])) def test_compute_with_right_frames(self): data = np.array([[1, 2], [3, 4], [5, 6]]) chunk = pipeline.Chunk(data, offset=0, is_last=True) step = pipeline.AddContext(left_frames=0, right_frames=2) result = step.compute(chunk, 16000) assert np.array_equal(result, np.array([ [1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 0, 0], [5, 6, 0, 0, 0, 0] ])) def test_compute_with_left_and_right_frames(self): data = np.array([[1, 2], [3, 4], [5, 6]]) chunk = pipeline.Chunk(data, offset=0, is_last=True) step = pipeline.AddContext(left_frames=2, right_frames=2) result = step.compute(chunk, 16000) assert np.array_equal(result, np.array([ [0, 0, 0, 0, 1, 2, 3, 4, 5, 6], [0, 0, 1, 2, 3, 4, 5, 6, 0, 0], [1, 2, 3, 4, 5, 6, 0, 0, 0, 0] ])) def test_compute_online_with_left_frames(self): step = pipeline.AddContext(left_frames=2, right_frames=0) # FIRST CHUNK data = np.array([[1, 2], [3, 4]]) result = step.process_frames(data, 16000, offset=0, last=False) assert np.array_equal(result, np.array([ [0, 0, 0, 0, 1, 2], [0, 0, 1, 2, 3, 4], ])) # SECOND CHUNK data = np.array([[5, 6], [7, 8]]) result = step.process_frames(data, 16000, offset=2, last=True) assert np.array_equal(result, np.array([ [1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8] ])) def test_compute_online_with_right_frames(self): step = pipeline.AddContext(left_frames=0, right_frames=2) # FIRST CHUNK data = np.array([[1, 2], [3, 4]]) result = step.process_frames(data, 16000, offset=0, last=False) # Since it waits on context, no output is expected assert result is None # SECOND CHUNK data = np.array([[5, 6], [7, 8]]) result = step.process_frames(data, 16000, offset=2, last=True) assert np.array_equal(result, np.array([ [1, 2, 3, 4, 5, 6], [3, 4, 5, 6, 7, 8], [5, 6, 7, 8, 0, 0], [7, 8, 0, 0, 0, 0] ])) def test_compute_online_with_left_and_right_frames(self): step = pipeline.AddContext(left_frames=2, right_frames=2) # FIRST CHUNK data = np.array([[1, 2], [3, 4]]) result = step.process_frames(data, 16000, offset=0, last=False) # Since it waits on context, no output is expected assert result is None # SECOND CHUNK data = np.array([[5, 6], [7, 8]]) result = step.process_frames(data, 16000, offset=2, last=True) assert np.array_equal(result, np.array([ [0, 0, 0, 0, 1, 2, 3, 4, 5, 6], [0, 0, 1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8, 0, 0], [3, 4, 5, 6, 7, 8, 0, 0, 0, 0] ]))

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0.031017

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null

0

1

0

1

0

null

0

7

29b079de363644052e9f050b85bccb4f6f3b431f

5,817

py

Python

quality_check.py

Veggente/soybean-network

c624a0721db13a86c455fb4f7f2721beae737104

[ "MIT" ]

null

quality_check.py

Veggente/soybean-network

c624a0721db13a86c455fb4f7f2721beae737104

[ "MIT" ]

null

quality_check.py

Veggente/soybean-network

c624a0721db13a86c455fb4f7f2721beae737104

[ "MIT" ]

null

#!/usr/bin/env python3 """Quality check for weighted GRN reconstruction. Functions: show_histograms: Save or plot edge visibility histograms. hist_w_correct: Save or plot edge visibility histograms with ground truth. """ import networkx as nx import matplotlib.pyplot as plt plt.style.use("ggplot") import numpy as np def show_histograms( graphml_file, ylim=0, self_edge=False, output="", display=False, figsize=None, show_mean=False, dpi=300, ): """Show histograms of the edge weights. Args: graphml_file: A dictionary of GraphML files. ylim: Y-axis limit. 0 means default limit. self_edge: Indicator for self-edges. output: Output filename. display: Indicates whether to display the plot. figsize: Figure size. show_mean: Indicator for showing the mean visibility with a black vertical line. dpi: DPI for png figure. Default is 300. Returns: Plots the histograms. """ num_graphs = len(graphml_file) fig, ax = plt.subplots(num_graphs, sharey=True, figsize=figsize) bins = np.linspace(0, 1, 101) width = 0.01 for idx_gf, alg in enumerate(graphml_file): network = nx.read_graphml(graphml_file[alg]) num_genes = len(network.nodes()) if self_edge: total_num_edges = num_genes ** 2 else: total_num_edges = num_genes * (num_genes - 1) if self_edge: weights = [data["weight"] for u, v, data in network.edges(data=True)] else: weights = [ data["weight"] for u, v, data in network.edges(data=True) if u != v ] hist, _ = np.histogram(weights, bins=bins) # Assign to edges that never appear the weight of 0. hist[0] = total_num_edges - sum(hist) if num_graphs == 1: ax = [ax] ax[idx_gf].bar(bins[:-1] + width / 2, hist / total_num_edges, width) ax[idx_gf].set_title(alg) if ylim: ax[idx_gf].set_ylim(0, ylim) if show_mean: ax[idx_gf].axvline(np.mean(weights), color="k") plt.tight_layout() if output: fig.savefig(output, dpi=dpi) if display: fig.show() return def hist_w_correct( graphml_file, ground_truth, ylim=0, self_edge=False, output="", display=False, figsize=None, show_mean=False, dpi=300, ): """Histogram with correctness. Args: graphml_file: A dictionary of GraphML files. ground_truth: Adjacency matrix file for the ground truth. ylim: Y-axis limit. 0 means default limit. self_edge: Indicator for self-edges. output: Output filename. display: Indicates whether to display the plot. figsize: Figure size. show_mean: Indicator for showing the mean visibility with a black vertical line. dpi: DPI for png figure. Default is 300. Returns: Plots the histograms or saves as a file. """ num_graphs = len(graphml_file) fig, ax = plt.subplots(num_graphs, sharey=True, figsize=figsize) bins = np.linspace(0, 1, 101) width = 0.01 adj_mat = np.loadtxt(ground_truth, delimiter=" ") for idx_gf, alg in enumerate(graphml_file): network = nx.read_graphml(graphml_file[alg]) num_genes = len(network.nodes()) if self_edge: total_num_edges = num_genes ** 2 else: total_num_edges = num_genes * (num_genes - 1) if self_edge: weights = [data["weight"] for u, v, data in network.edges(data=True)] # Here we assume the gene ID for the ith gene is # 'Gene'+i, for 0 <= i <= n-1. weights_correct = [ data["weight"] for u, v, data in network.edges(data=True) if data["sign"] == np.sign(adj_mat[int(u[4:]), int(v[4:])]) ] false_positive = [ True for u, v, data in network.edges(data=True) if not adj_mat[int(u[4:]), int(v[4:])] ] else: weights = [ data["weight"] for u, v, data in network.edges(data=True) if u != v ] # Here we assume the gene ID for the ith gene is # 'Gene'+i, for 0 <= i <= n-1. weights_correct = [ data["weight"] for u, v, data in network.edges(data=True) if data["sign"] == np.sign(adj_mat[int(u[4:]), int(v[4:])]) and u != v ] false_positive = [ True for u, v, data in network.edges(data=True) if not adj_mat[int(u[4:]), int(v[4:])] and u != v ] hist, _ = np.histogram(weights, bins=bins) hist_correct, _ = np.histogram(weights_correct, bins=bins) # Assign to edges that never appear the weight of 0. hist[0] = total_num_edges - sum(hist) hist_correct[0] = ( total_num_edges - np.sum(adj_mat != 0) - np.sum(false_positive) ) if num_graphs == 1: ax = [ax] ax[idx_gf].bar( bins[:-1] + width / 2, hist / total_num_edges, width, label="False" ) ax[idx_gf].bar( bins[:-1] + width / 2, hist_correct / total_num_edges, width, color="g", label="True", ) ax[idx_gf].legend() if ylim: ax[idx_gf].set_ylim(0, ylim) if show_mean: ax[idx_gf].axvline(np.mean(weights), color="k") ax[idx_gf].set_title(alg) plt.tight_layout() if output: fig.savefig(output, dpi=dpi) if display: fig.show() return

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d9bcb6569a2fdafd64f19b0c6bb4c14d064979e2

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Python

tests/erica_legacy/request_processing/test_requests_controller.py

digitalservice4germany/erica

7e07d88f3db78ab6e4f7cccad8dfef2a4b3a71b2

[ "MIT" ]

3

2022-01-31T15:17:17.000Z

2022-03-01T16:15:47.000Z

tests/erica_legacy/request_processing/test_requests_controller.py

digitalservice4germany/erica

7e07d88f3db78ab6e4f7cccad8dfef2a4b3a71b2

[ "MIT" ]

59

2022-01-31T14:04:20.000Z

2022-03-31T20:08:47.000Z

tests/erica_legacy/request_processing/test_requests_controller.py

digitalservice4germany/erica

7e07d88f3db78ab6e4f7cccad8dfef2a4b3a71b2

[ "MIT" ]

1

2022-03-10T09:24:28.000Z

import base64 import unittest from datetime import date from unittest.mock import patch, MagicMock, call import pytest from erica.domain.FreischaltCode.FreischaltCode import FreischaltCodeActivatePayload, FreischaltCodeRevocatePayload from erica.domain.tax_number_validation.check_tax_number import CheckTaxNumberPayload from erica.erica_legacy.pyeric.check_elster_request_id import SPECIAL_TESTMERKER_IDNR from erica.erica_legacy.pyeric.eric_errors import InvalidBufaNumberError from erica.erica_legacy.pyeric.pyeric_response import PyericResponse from erica.erica_legacy.request_processing.eric_mapper import EstEricMapping, UnlockCodeRequestEricMapper from erica.erica_legacy.request_processing.erica_input.v1.erica_input import UnlockCodeRequestData, \ UnlockCodeActivationData, \ UnlockCodeRevocationData, GetAddressData from erica.erica_legacy.request_processing.requests_controller import UnlockCodeRequestController, \ UnlockCodeActivationRequestController, EstRequestController, EstValidationRequestController, \ UnlockCodeRevocationRequestController, GetAddressRequestController, \ GetBelegeRequestController, CheckTaxNumberRequestController from tests.erica_legacy.utils import create_est, missing_cert, missing_pyeric_lib, replace_text_in_xml, \ replace_subtree_in_xml, TEST_EST_VERANLAGUNGSJAHR from tests.utils import read_text_from_sample class TestEstValidationRequestProcess(unittest.TestCase): def test_pyeric_controller_is_initialised_with_correct_arguments(self): est_validation_request = EstValidationRequestController(create_est(correct_form_data=True)) xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.__init__', MagicMock(return_value=None)) \ as pyeric_controller_init, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.elster_xml.est_mapping.check_and_generate_entries'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.EstValidationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_est_xml', MagicMock(return_value=xml)): est_validation_request.process() pyeric_controller_init.assert_called_with(xml, TEST_EST_VERANLAGUNGSJAHR) def test_pyeric_get_eric_response_is_called(self): est_validation_request = EstValidationRequestController(create_est(correct_form_data=True)) xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.__init__', MagicMock(return_value=None)), \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response') \ as pyeric_controller_get_response, \ patch( 'erica.erica_legacy.request_processing.requests_controller.EstValidationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_est_xml', MagicMock(return_value=xml)): est_validation_request.process() pyeric_controller_get_response.assert_called() class TestEstRequestInit(unittest.TestCase): def test_if_no_include_param_given_then_set_include_false(self): created_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=False) self.assertFalse(created_request.include_elster_responses) def test_if_include_param_true_then_set_include_true(self): created_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=True) self.assertTrue(created_request.include_elster_responses) class TestEstRequestProcess(unittest.TestCase): def test_check_and_generate_entries_is_called_with_eric_mapped_object(self): eric_mapped_object = EstEricMapping.parse_obj(create_est(correct_form_data=True).est_data) with patch('erica.erica_legacy.request_processing.eric_mapper', MagicMock(return_value=eric_mapped_object)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.est_mapping.check_and_generate_entries') as generate_entries, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_est_xml'): EstRequestController(create_est(correct_form_data=True)).process() assert generate_entries.mock_calls == [call(eric_mapped_object.__dict__)] def test_pyeric_controller_is_initialised_with_correct_arguments(self): est_request = EstRequestController(create_est(correct_form_data=True)) xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.__init__', MagicMock(return_value=None)) \ as pyeric_controller_init, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_est_xml', MagicMock(return_value=xml)): est_request.process() pyeric_controller_init.assert_called_with(xml, TEST_EST_VERANLAGUNGSJAHR) def test_pyeric_get_eric_response_is_called(self): est_request = EstRequestController(create_est(correct_form_data=True)) xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.__init__', MagicMock(return_value=None)), \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response') as pyeric_get_response, \ patch( 'erica.erica_legacy.request_processing.requests_controller.est_mapping.check_and_generate_entries'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_est_xml', MagicMock(return_value=xml)): est_request.process() pyeric_get_response.assert_called() def test_if_use_testmerker_env_false_and_special_idnr_then_create_xml_is_called_with_use_testmerker_set_true(self): correct_est = create_est(correct_form_data=True) correct_est.est_data.person_a_idnr = SPECIAL_TESTMERKER_IDNR[0] with patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_est_xml') as generate_xml_fun, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'): est_request = EstRequestController(correct_est) est_request.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_use_testmerker_env_false_and_not_special_idnr_then_create_xml_is_called_with_use_testmerker_set_false( self): correct_est = create_est(correct_form_data=True) correct_est.est_data.person_a_idnr = '02293417683' with patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_est_xml') as generate_xml_fun, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'): est_request = EstRequestController(correct_est) est_request.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_submission_without_tax_nr_then_generate_vorsatz_without_tax_nr_is_called(self): empfaenger = '9198' correct_est = create_est(correct_form_data=True, with_tax_number=False) correct_est.est_data.bufa_nr = empfaenger with patch( 'erica.erica_legacy.request_processing.requests_controller.generate_vorsatz_without_tax_number') as generate_vorsatz_without_tax_number, \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_est_xml') as generate_xml_fun, \ patch('erica.erica_legacy.pyeric.pyeric_controller.EstPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.EstRequestController.generate_json'): est_request = EstRequestController(correct_est) est_request.process() generate_vorsatz_without_tax_number.assert_called() self.assertEqual(empfaenger, generate_xml_fun.call_args.args[-1]) # empfaenger should be the last args @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_full_form_then_return_not_none_response(self): est_request = EstRequestController(create_est(correct_form_data=True)) response = est_request.process() self.assertIsNotNone(response) @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_submission_without_tax_nr_then_return_not_none_response(self): est_request = EstRequestController(create_est(correct_form_data=True, with_tax_number=False)) response = est_request.process() self.assertIsNotNone(response) @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_full_form_and_include_elster_responses_then_return_response_only_with_correct_keys(self): expected_keys = ['transferticket', 'pdf', 'eric_response', 'server_response'] est_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=True) response = est_request.process() self.assertEqual(set(expected_keys), set(response.keys())) @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_full_form_and_not_include_elster_responses_then_return_response_with_correct_keys(self): expected_keys = ['transferticket', 'pdf'] est_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=False) response = est_request.process() self.assertEqual(expected_keys, list(response.keys())) class TestEstRequestGenerateJson(unittest.TestCase): def setUp(self): self.expected_transferticket = 'J-KLAPAUCIUS' self.pdf_bytes = b"Our lives begin the day we become silent about things that matter" self.expected_pdf = base64.b64encode(self.pdf_bytes).decode('utf-8') self.expected_eric_response = "We are now faced with the fact that tomorrow is today." response_with_correct_transferticket = replace_text_in_xml( read_text_from_sample('sample_est_response_server.xml'), 'TransferTicket', self.expected_transferticket) self.expected_server_response = response_with_correct_transferticket def test_if_id_given_and_include_true_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'pdf': self.expected_pdf, 'eric_response': self.expected_eric_response, 'server_response': self.expected_server_response } est_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=True) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response, self.pdf_bytes) actual_response = est_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_id_given_and_include_false_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'pdf': self.expected_pdf } est_request = EstRequestController(create_est(correct_form_data=True), include_elster_responses=False) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response, self.pdf_bytes) actual_response = est_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) class TestUnlockCodeRequestInit(unittest.TestCase): def test_if_idnr_given_then_set_idnr_as_attribute_correctly(self): expected_idnr = "09952417688" created_request = UnlockCodeRequestController(UnlockCodeRequestData(idnr=expected_idnr, dob=date(1969, 7, 20))) self.assertEqual(expected_idnr, created_request.input_data.tax_id_number) def test_if_no_include_param_given_then_set_include_false(self): created_request = UnlockCodeRequestController(UnlockCodeRequestData(idnr="09952417688", dob=date(1969, 7, 20))) self.assertFalse(created_request.include_elster_responses) def test_if_include_param_true_then_set_include_true(self): created_request = UnlockCodeRequestController(UnlockCodeRequestData( idnr="09952417688", dob=date(1969, 7, 20)), include_elster_responses=True) self.assertTrue(created_request.include_elster_responses) class TestUnlockCodeRequestProcess(unittest.TestCase): def setUp(self): self.unlock_request_with_valid_input = UnlockCodeRequestController(UnlockCodeRequestData( idnr="02293417683", dob=date(1985, 1, 1))) self.unlock_request_with_valid_input_with_special_idnr = UnlockCodeRequestController(UnlockCodeRequestData( idnr=SPECIAL_TESTMERKER_IDNR[0], dob=date(1985, 1, 1))) def test_pyeric_controller_is_initialised_with_correct_arguments(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.__init__', MagicMock(return_value=None)) \ as pyeric_controller_init, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_vast_request_xml', MagicMock(return_value=xml)): self.unlock_request_with_valid_input.process() pyeric_controller_init.assert_called_with(xml) def test_pyeric_get_eric_response_is_called(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.__init__', MagicMock(return_value=None)), \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.get_eric_response') \ as pyeric_controller_get_response, \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_vast_request_xml', MagicMock(return_value=xml)): self.unlock_request_with_valid_input.process() pyeric_controller_get_response.assert_called() def test_if_special_idnr_then_create_xml_is_called_with_use_testmerker_set_true(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_request_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRequestController.generate_json'): self.unlock_request_with_valid_input_with_special_idnr.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_not_special_idnr_then_create_xml_is_called_with_use_testmerker_set_false(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_request_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRequestPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRequestController.generate_json'): self.unlock_request_with_valid_input.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_processed_called_then_elster_request_id_added_to_cache_list(self): elster_request_id = "1234" with patch('erica.erica_legacy.request_processing.requests_controller.TransferticketRequestController.process', MagicMock(return_value={'elster_request_id': elster_request_id})),\ patch('erica.erica_legacy.request_processing.requests_controller.add_new_request_id_to_cache_list') as add_to_cache_list: self.unlock_request_with_valid_input.process() add_to_cache_list.assert_called_once_with(elster_request_id) class TestUnlockCodeRequestGenerateFullXml(unittest.TestCase): def test_if_dob_date_given_then_call_generate_full_xml_with_unlock_code_eric_mapping(self): unlock_code_eric_mapping = UnlockCodeRequestEricMapper(tax_id_number="09952417688", date_of_birth=date(1969, 7, 20)) created_request = UnlockCodeRequestController(UnlockCodeRequestData(idnr="09952417688", dob=date(1969, 7, 20))) with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_request_xml') as generate_full_xml: created_request.generate_full_xml(use_testmerker=True) assert generate_full_xml.mock_calls == [call(unlock_code_eric_mapping.__dict__, use_testmerker=True)] class TestUnlockCodeRequestGenerateJson(unittest.TestCase): def setUp(self): self.expected_request_id = 'J-KLAPAUCIUS' self.expected_transferticket = 'Transferiato' self.expected_idnr = "123456789" self.expected_eric_response = "We are now faced with the fact that tomorrow is today." response_with_correct_transferticket = replace_text_in_xml( read_text_from_sample('sample_vast_request_response.xml'), 'TransferTicket', self.expected_transferticket) self.expected_server_response = response_with_correct_transferticket def test_if_id_given_and_include_true_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id, 'idnr': self.expected_idnr, 'eric_response': self.expected_eric_response, 'server_response': self.expected_server_response } unlock_code_request = UnlockCodeRequestController(UnlockCodeRequestData( idnr=self.expected_idnr, dob=date(1985, 1, 1)), include_elster_responses=True) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_id_given_and_include_false_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id, 'idnr': self.expected_idnr, } unlock_code_request = UnlockCodeRequestController(UnlockCodeRequestData( idnr=self.expected_idnr, dob=date(1985, 1, 1)), include_elster_responses=False) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_eric_process_successful_then_return_correct_elster_request_id(self): unlock_code_request = UnlockCodeRequestController(UnlockCodeRequestData( idnr=self.expected_idnr, dob=date(1985, 1, 1)), include_elster_responses=False) expected_elster_request_id = "PizzaAndApplePie" successful_server_response = replace_text_in_xml(read_text_from_sample('sample_vast_request_response.xml', 'r'), "AntragsID", expected_elster_request_id) pyeric_response = PyericResponse('eric_response', successful_server_response) actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_elster_request_id, actual_response['elster_request_id']) def test_if_eric_process_successful_then_return_correct_transferticket(self): unlock_code_request = UnlockCodeRequestController(UnlockCodeRequestData( idnr=self.expected_idnr, dob=date(1985, 1, 1)), include_elster_responses=False) expected_transferticket = "PizzaAndNutCake" successful_server_response = replace_text_in_xml(read_text_from_sample('sample_vast_request_response.xml', 'r'), "TransferTicket", expected_transferticket) pyeric_response = PyericResponse('eric_response', successful_server_response) actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_transferticket, actual_response['transferticket']) class TestUnlockCodeActivationProcess(unittest.TestCase): def setUp(self): self.known_idnr = '02293417683' self.unlock_activation_with_valid_input = UnlockCodeActivationRequestController(UnlockCodeActivationData( idnr=self.known_idnr, unlock_code='1985-T67D-K89O', elster_request_id='42')) self.unlock_activation_with_valid_input_with_special_idnr = UnlockCodeActivationRequestController( UnlockCodeActivationData( idnr=SPECIAL_TESTMERKER_IDNR[0], unlock_code='1985-T67D-K89O', elster_request_id='42')) self.unlock_activation_with_unknown_idnr = UnlockCodeActivationRequestController(UnlockCodeActivationData( idnr="123456789", unlock_code='1985-T67D-K89O', elster_request_id='42')) self.unlock_activation_without_idnr = UnlockCodeActivationRequestController(FreischaltCodeActivatePayload( freischalt_code='1985-T67D-K89O', elster_request_id='42')) def test_pyeric_controller_is_initialised_with_correct_argument(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.__init__', MagicMock(return_value=None)) \ as pyeric_controller_init, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.' 'generate_full_vast_activation_xml', MagicMock(return_value=xml)): self.unlock_activation_with_valid_input.process() pyeric_controller_init.assert_called_with(xml) def test_pyeric_get_eric_response_is_called(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.__init__', MagicMock(return_value=None)), \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response') \ as pyeric_controller_get_response, \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_activation_xml', MagicMock(return_value=xml)): self.unlock_activation_with_valid_input.process() pyeric_controller_get_response.assert_called() def test_if_special_idnr_then_create_xml_is_called_with_use_testmerker_set_true(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_activation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'): self.unlock_activation_with_valid_input_with_special_idnr.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_not_special_idnr_then_create_xml_is_called_with_use_testmerker_set_false(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_activation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'): self.unlock_activation_with_valid_input.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_idnr_and_request_needs_test_merker_then_create_xml_is_called_with_true(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_activation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.request_needs_testmerker', MagicMock(return_value=True)): self.unlock_activation_without_idnr.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_idnr_and_request_needs_test_merker_then_create_xml_is_called_with_false(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_activation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeActivationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeActivationRequestController.generate_json'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.request_needs_testmerker', MagicMock(return_value=False)): self.unlock_activation_without_idnr.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) class TestUnlockCodeActivationGenerateJson(unittest.TestCase): def setUp(self): self.expected_idnr = "123456789" self.expected_request_id = 'J-KLAPAUCIUS' self.expected_transferticket = 'Transfiguration' self.expected_eric_response = "We are now faced with the fact that tomorrow is today." response_with_correct_transferticket = replace_text_in_xml( read_text_from_sample('sample_vast_activation_response.xml'), 'TransferTicket', self.expected_transferticket) self.expected_server_response = response_with_correct_transferticket def test_if_id_given_and_include_true_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id, 'idnr': self.expected_idnr, 'eric_response': self.expected_eric_response, 'server_response': self.expected_server_response } unlock_code_request = UnlockCodeActivationRequestController(UnlockCodeActivationData( idnr=self.expected_idnr, unlock_code='1985-T67D-K89O', elster_request_id='42'), include_elster_responses=True) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_id_given_and_include_false_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id, 'idnr': self.expected_idnr, } unlock_code_request = UnlockCodeActivationRequestController(UnlockCodeActivationData( idnr=self.expected_idnr, unlock_code='1985-T67D-K89O', elster_request_id='42'), include_elster_responses=False) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_eric_process_successful_then_return_correct_transferticket(self): expected_transferticket = "PizzaAndNutCake" unlock_code_activation = UnlockCodeActivationRequestController(UnlockCodeActivationData( idnr=self.expected_idnr, unlock_code='1985-T67D-K89O', elster_request_id='42'), include_elster_responses=False) successful_server_response = replace_text_in_xml(read_text_from_sample('sample_vast_activation_response.xml'), "TransferTicket", expected_transferticket) pyeric_response = PyericResponse('eric_response', successful_server_response) actual_response = unlock_code_activation.generate_json(pyeric_response) self.assertEqual(expected_transferticket, actual_response['transferticket']) class TestUnlockCodeRevocationProcess(unittest.TestCase): def setUp(self): self.known_idnr = '02293417683' self.unlock_revocation_with_valid_input = UnlockCodeRevocationRequestController(UnlockCodeRevocationData( idnr=self.known_idnr, elster_request_id='lookanotherrequestid')) self.unlock_revocation_with_valid_input_and_special_idnr = UnlockCodeRevocationRequestController( UnlockCodeRevocationData( idnr=SPECIAL_TESTMERKER_IDNR[0], elster_request_id='lookanotherrequestid')) self.unlock_revocation_with_unknown_idnr = UnlockCodeRevocationRequestController(UnlockCodeRevocationData( idnr="123456789", elster_request_id='lookyetanotherrequestid')) self.unlock_revocation_without_idnr = UnlockCodeRevocationRequestController(FreischaltCodeRevocatePayload( elster_request_id='lookyetanotherrequestid')) def test_pyeric_controller_is_initialised_with_correct_arguments(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.__init__', MagicMock(return_value=None)) \ as pyeric_controller_init, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml', MagicMock(return_value=xml)): self.unlock_revocation_with_valid_input.process() pyeric_controller_init.assert_called_with(xml) def test_pyeric_get_eric_response_is_called(self): xml = '<xml></xml>' with patch('erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.__init__', MagicMock(return_value=None)), \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response') \ as pyeric_controller_get_response, \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'), \ patch('erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml', MagicMock(return_value=xml)): self.unlock_revocation_with_valid_input.process() pyeric_controller_get_response.assert_called() def test_if_special_idnr_then_create_xml_is_called_with_use_testmerker_set_true(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'): self.unlock_revocation_with_valid_input_and_special_idnr.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_not_special_idnr_then_create_xml_is_called_with_use_testmerker_set_false(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'): self.unlock_revocation_with_valid_input.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_idnr_and_request_needs_test_merker_then_create_xml_is_called_with_true(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.request_needs_testmerker', MagicMock(return_value=True)): self.unlock_revocation_without_idnr.process() self.assertTrue(generate_xml_fun.call_args.kwargs['use_testmerker']) def test_if_idnr_and_request_needs_test_merker_then_create_xml_is_called_with_false(self): with patch( 'erica.erica_legacy.elster_xml.elster_xml_generator.generate_full_vast_revocation_xml') as generate_xml_fun, \ patch( 'erica.erica_legacy.pyeric.pyeric_controller.UnlockCodeRevocationPyericProcessController.get_eric_response'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.UnlockCodeRevocationRequestController.generate_json'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.request_needs_testmerker', MagicMock(return_value=False)): self.unlock_revocation_without_idnr.process() self.assertFalse(generate_xml_fun.call_args.kwargs['use_testmerker']) class TestUnlockCodeRevocationGenerateJson(unittest.TestCase): def setUp(self): self.expected_idnr = "123456789" self.expected_request_id = 'J-KLAPAUCIUS' self.expected_transferticket = 'The time is always right to do what is right.' self.expected_eric_response = "We are now faced with the fact that tomorrow is today." response_with_correct_transferticket = replace_text_in_xml( read_text_from_sample('sample_vast_revocation_response.xml'), 'TransferTicket', self.expected_transferticket) self.expected_server_response = response_with_correct_transferticket def test_if_id_given_and_include_true_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id, 'eric_response': self.expected_eric_response, 'server_response': self.expected_server_response } unlock_code_request = UnlockCodeRevocationRequestController(UnlockCodeRevocationData( idnr=self.expected_idnr, elster_request_id='lookanotherrequestid'), include_elster_responses=True) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_id_given_and_include_false_then_return_json_with_correct_info(self): expected_output = { 'transferticket': self.expected_transferticket, 'elster_request_id': self.expected_request_id } unlock_code_request = UnlockCodeRevocationRequestController(UnlockCodeRevocationData( idnr=self.expected_idnr, elster_request_id='lookanotherrequestid'), include_elster_responses=False) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_antrag_id_from_xml', MagicMock(return_value=self.expected_request_id)): actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_eric_process_successful_then_return_correct_transferticket(self): expected_transferticket = "PizzaAndNutCake" unlock_code_revocation = UnlockCodeRevocationRequestController(UnlockCodeRevocationData(idnr=self.expected_idnr, elster_request_id='42'), include_elster_responses=False) successful_server_response = replace_text_in_xml(read_text_from_sample('sample_vast_revocation_response.xml'), "TransferTicket", expected_transferticket) pyeric_response = PyericResponse('eric_response', successful_server_response) actual_response = unlock_code_revocation.generate_json(pyeric_response) self.assertEqual(expected_transferticket, actual_response['transferticket']) class TestCheckTaxNumberRequestControllerProcess: @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_tax_number_is_valid_then_return_json_with_is_valid_true(self): state_abbreviation = "by" valid_tax_number = "19811310010" input_data = CheckTaxNumberPayload(state_abbreviation=state_abbreviation, tax_number=valid_tax_number) result = CheckTaxNumberRequestController(input_data).process() assert result == {'is_valid': True} @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_tax_number_is_invalid_then_return_json_with_is_valid_false(self): state_abbreviation = "by" invalid_tax_number = "19811310011" # is invalid because of incorrect check sum (last digit should be 0) input_data = CheckTaxNumberPayload(state_abbreviation=state_abbreviation, tax_number=invalid_tax_number) result = CheckTaxNumberRequestController(input_data).process() assert result == {'is_valid': False} @pytest.mark.skipif(missing_cert(), reason="skipped because of missing cert.pfx; see pyeric/README.md") @pytest.mark.skipif(missing_pyeric_lib(), reason="skipped because of missing eric lib; see pyeric/README.md") def test_if_generate_electronic_steuernummer_raises_invalid_bufa_nr_then_return_json_with_is_valid_false(self): state_abbreviation = "by" valid_tax_number = "19811310010" input_data = CheckTaxNumberPayload(state_abbreviation=state_abbreviation, tax_number=valid_tax_number) with patch('erica.erica_legacy.request_processing.requests_controller.generate_electronic_steuernummer', MagicMock(side_effect=InvalidBufaNumberError)): result = CheckTaxNumberRequestController(input_data).process() assert result == {'is_valid': False} class TestGetBelegeRequestController(unittest.TestCase): def setUp(self): self.idnr = '04452397687' self.input_data = GetAddressData.parse_obj({'idnr': self.idnr}) self.request_xml = '<Anfrage>' self.sample_beleg_ids = ['vg3071ovc201t97gdvyy1851qrutaheh'] self.sample_encrypted_belege = [read_text_from_sample('sample_encrypted_beleg.xml')] def test_get_beleg_ids_calls_correct_pyeric_controller_with_correct_argument(self): with patch( 'erica.erica_legacy.request_processing.requests_controller.BelegIdRequestPyericProcessController.__init__', MagicMock(return_value=None)) as pyeric_controller_mock, \ patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_ids_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegIdRequestPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.get_relevant_beleg_ids'): GetBelegeRequestController(self.input_data)._request_beleg_ids() pyeric_controller_mock.assert_called_once_with(self.request_xml) def test_get_beleg_ids_calls_get_eric_response(self): with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_ids_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegIdRequestPyericProcessController.get_eric_response') as fun_get_eric_response, \ patch('erica.erica_legacy.request_processing.requests_controller.get_relevant_beleg_ids'): GetBelegeRequestController(self.input_data)._request_beleg_ids() fun_get_eric_response.assert_called_once() def test_get_beleg_ids_returns_relevant_beleg_ids_from_pyeric_response_for_given_beleg_art(self): mocked_pyeric_response = PyericResponse('', read_text_from_sample('sample_beleg_id_response.xml')) with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_ids_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegIdRequestPyericProcessController.get_eric_response', MagicMock(return_value=mocked_pyeric_response)): request_controller = GetBelegeRequestController(self.input_data) request_controller._NEEDED_BELEG_ART = 'VaSt_Pers1' returned_beleg_ids = request_controller._request_beleg_ids() self.assertEqual(['vg3071ovc201t97gdvyy1851qrutaheh'], returned_beleg_ids) def test_request_encrypted_belege_calls_correct_pyeric_controller_with_correct_argument(self): with patch( 'erica.erica_legacy.request_processing.requests_controller.BelegRequestPyericProcessController.__init__', MagicMock(return_value=None)) as pyeric_controller_mock, \ patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegRequestPyericProcessController.get_eric_response'), \ patch('erica.erica_legacy.request_processing.requests_controller.get_elements_text_from_xml'): GetBelegeRequestController(self.input_data)._request_encrypted_belege(self.sample_beleg_ids) pyeric_controller_mock.assert_called_once_with(self.request_xml) def test_request_encrypted_belege_calls_get_eric_response(self): with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegRequestPyericProcessController.get_eric_response') as fun_get_eric_response, \ patch('erica.erica_legacy.request_processing.requests_controller.get_elements_text_from_xml'): GetBelegeRequestController(self.input_data)._request_encrypted_belege(self.sample_beleg_ids) fun_get_eric_response.assert_called_once() def test_request_encrypted_belege_returns_relevant_beleg_ids_from_pyeric_response(self): sample_encrypted_beleg = 'SpeakFriendAndEnter' sample_response_with_encrypted_beleg = replace_text_in_xml( read_text_from_sample('sample_encrypted_beleg_response.xml'), 'Datenpaket', sample_encrypted_beleg) mocked_pyeric_response = PyericResponse('', sample_response_with_encrypted_beleg) with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_request_xml', MagicMock(return_value=self.request_xml)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegRequestPyericProcessController.get_eric_response', MagicMock(return_value=mocked_pyeric_response)): request_controller = GetBelegeRequestController(self.input_data) returned_encrypted_belege = request_controller._request_encrypted_belege(self.sample_beleg_ids) self.assertEqual([sample_encrypted_beleg], returned_encrypted_belege) def test_request_decrypted_belege_calls_get_decrypted_belege_of_correct_pyeric_controller(self): with patch( 'erica.erica_legacy.request_processing.requests_controller.DecryptBelegePyericController.get_decrypted_belege') as fun_get_eric_response, \ patch('erica.erica_legacy.request_processing.requests_controller.get_belege_xml'): GetBelegeRequestController(self.input_data)._request_decrypted_belege(self.sample_encrypted_belege) fun_get_eric_response.assert_called_once_with(self.sample_encrypted_belege) def test_request_decrypted_belege_returns_decrypted_belege(self): sample_beleg_xml = read_text_from_sample('sample_decrypted_beleg_response.xml') with patch( 'erica.erica_legacy.request_processing.requests_controller.DecryptBelegePyericController.get_decrypted_belege', MagicMock(return_value=[sample_beleg_xml])): request_controller = GetBelegeRequestController(self.input_data) returned_decrypted_belege_xml = request_controller._request_decrypted_belege( self.sample_encrypted_belege) len_of_namespace_intro = len( '<?xml version="1.0" encoding="ISO-8859-15" ?><VaSt_RBM xmlns="http://finkonsens.de/elster/elstervast/vastrbm/v202001" version="202001">') self.assertIn(sample_beleg_xml[len_of_namespace_intro], returned_decrypted_belege_xml) self.assertIn('<Belege', returned_decrypted_belege_xml) class TestGetAddressProcess(unittest.TestCase): def setUp(self): self.known_idnr = '02293417683' self.get_address_with_valid_input = GetAddressRequestController(GetAddressData(idnr=self.known_idnr)) def test_calls_get_relevant_beleg_ids_with_correct_arguments(self): mock_server_response = 'server_response' mock_pyeric_response = PyericResponse('', mock_server_response) with patch( 'erica.erica_legacy.request_processing.requests_controller.elster_xml_generator.generate_full_vast_beleg_ids_request_xml'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.BelegIdRequestPyericProcessController.get_eric_response', MagicMock(return_value=mock_pyeric_response)), \ patch( 'erica.erica_legacy.request_processing.requests_controller.get_relevant_beleg_ids') as fun_get_beleg_ids, \ patch( 'erica.erica_legacy.request_processing.requests_controller.GetBelegeRequestController._request_encrypted_belege'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.GetBelegeRequestController._request_decrypted_belege'), \ patch( 'erica.erica_legacy.request_processing.requests_controller.GetAddressRequestController.generate_json'): self.get_address_with_valid_input.process() fun_get_beleg_ids.assert_called_once_with(mock_server_response, ['VaSt_Pers1']) class TestGetAddressGenerateJson(unittest.TestCase): def setUp(self): self.expected_idnr = "123456789" self.expected_address = '<Str>Musterstraße</Str>' self.expected_eric_response = "We are now faced with the fact that tomorrow is today." response_with_correct_address = replace_subtree_in_xml(read_text_from_sample('sample_beleg_address_response.xml'), 'AdrKette', self.expected_address) self.expected_server_response = response_with_correct_address def test_if_id_given_and_include_true_then_return_json_with_correct_info(self): expected_output = { 'address': self.expected_address, 'eric_response': self.expected_eric_response, 'server_response': self.expected_server_response } get_address_request = GetAddressRequestController(GetAddressData(idnr=self.expected_idnr), include_elster_responses=True) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_address_from_xml', MagicMock(return_value=self.expected_address)): actual_response = get_address_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response) def test_if_id_given_and_include_false_then_return_json_with_correct_info(self): expected_output = { 'address': self.expected_address } unlock_code_request = GetAddressRequestController(GetAddressData(idnr=self.expected_idnr), include_elster_responses=False) pyeric_response = PyericResponse(self.expected_eric_response, self.expected_server_response) with patch('erica.erica_legacy.request_processing.requests_controller.get_address_from_xml', MagicMock(return_value=self.expected_address)): actual_response = unlock_code_request.generate_json(pyeric_response) self.assertEqual(expected_output, actual_response)

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43,005

py

Python

code/python/IRNConfiguration/v1/fds/sdk/IRNConfiguration/api/contacts___types_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

6

2022-02-07T16:34:18.000Z

2022-03-30T08:04:57.000Z

code/python/IRNConfiguration/v1/fds/sdk/IRNConfiguration/api/contacts___types_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

2

2022-02-07T05:25:57.000Z

2022-03-07T14:18:04.000Z

code/python/IRNConfiguration/v1/fds/sdk/IRNConfiguration/api/contacts___types_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

null

""" IRN API v1 Allows users to extract, create, update and configure IRN data. # noqa: E501 The version of the OpenAPI document: 1 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from multiprocessing.pool import ApplyResult import typing from fds.sdk.IRNConfiguration.api_client import ApiClient, Endpoint as _Endpoint from fds.sdk.IRNConfiguration.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from fds.sdk.IRNConfiguration.exceptions import ApiException from fds.sdk.IRNConfiguration.model.contact_type_dto import ContactTypeDto from fds.sdk.IRNConfiguration.model.new_item_dto import NewItemDto from fds.sdk.IRNConfiguration.model.problem_details import ProblemDetails class ContactsTypesApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.create_contact_type_endpoint = _Endpoint( settings={ 'response_type': ( { 201: (NewItemDto,), 400: (ProblemDetails,), 0: (ProblemDetails,), }, None ), 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/v1/contact-types', 'operation_id': 'create_contact_type', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'body', ], 'required': [ 'body', ], 'nullable': [ ], 'enum': [ ], 'validation': [ 'body', ] }, root_map={ 'validations': { ('body',): { 'max_length': 50, 'min_length': 0, }, }, 'allowed_values': { }, 'openapi_types': { 'body': (str,), }, 'attribute_map': { }, 'location_map': { 'body': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json-patch+json', 'application/json', 'text/json', 'application/*+json' ] }, api_client=api_client ) self.delete_contact_type_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/v1/contact-types/{contactTypeId}', 'operation_id': 'delete_contact_type', 'http_method': 'DELETE', 'servers': None, }, params_map={ 'all': [ 'contact_type_id', ], 'required': [ 'contact_type_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'contact_type_id': (str,), }, 'attribute_map': { 'contact_type_id': 'contactTypeId', }, 'location_map': { 'contact_type_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_contact_types_endpoint = _Endpoint( settings={ 'response_type': ( { 200: ([ContactTypeDto],), }, None ), 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/v1/contact-types', 'operation_id': 'get_contact_types', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { }, 'attribute_map': { }, 'location_map': { }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.update_contact_type_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/v1/contact-types/{contactTypeId}', 'operation_id': 'update_contact_type', 'http_method': 'PUT', 'servers': None, }, params_map={ 'all': [ 'contact_type_id', 'body', ], 'required': [ 'contact_type_id', 'body', ], 'nullable': [ ], 'enum': [ ], 'validation': [ 'body', ] }, root_map={ 'validations': { ('body',): { 'max_length': 50, 'min_length': 0, }, }, 'allowed_values': { }, 'openapi_types': { 'contact_type_id': (str,), 'body': (str,), }, 'attribute_map': { 'contact_type_id': 'contactTypeId', }, 'location_map': { 'contact_type_id': 'path', 'body': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json-patch+json', 'application/json', 'text/json', 'application/*+json' ] }, api_client=api_client ) @staticmethod def apply_kwargs_defaults(kwargs, return_http_data_only, async_req): kwargs["async_req"] = async_req kwargs["_return_http_data_only"] = return_http_data_only kwargs["_preload_content"] = kwargs.get("_preload_content", True) kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) kwargs["_content_type"] = kwargs.get("_content_type") kwargs["_host_index"] = kwargs.get("_host_index") def create_contact_type( self, body, **kwargs ) -> NewItemDto: """Create contact types # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Args: body (str): Contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: NewItemDto Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) kwargs['body'] = \ body return self.create_contact_type_endpoint.call_with_http_info(**kwargs) def create_contact_type_with_http_info( self, body, **kwargs ) -> typing.Tuple[NewItemDto, int, typing.MutableMapping]: """Create contact types # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Args: body (str): Contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: NewItemDto Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) kwargs['body'] = \ body return self.create_contact_type_endpoint.call_with_http_info(**kwargs) def create_contact_type_async( self, body, **kwargs ) -> "ApplyResult[NewItemDto]": """Create contact types # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Args: body (str): Contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[NewItemDto] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) kwargs['body'] = \ body return self.create_contact_type_endpoint.call_with_http_info(**kwargs) def create_contact_type_with_http_info_async( self, body, **kwargs ) -> "ApplyResult[typing.Tuple[NewItemDto, int, typing.MutableMapping]]": """Create contact types # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Args: body (str): Contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[(NewItemDto, int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) kwargs['body'] = \ body return self.create_contact_type_endpoint.call_with_http_info(**kwargs) def delete_contact_type( self, contact_type_id, **kwargs ) -> None: """Delete a contact type # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Args: contact_type_id (str): contactTypeId to delete associated record Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: None Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) kwargs['contact_type_id'] = \ contact_type_id return self.delete_contact_type_endpoint.call_with_http_info(**kwargs) def delete_contact_type_with_http_info( self, contact_type_id, **kwargs ) -> typing.Tuple[None, int, typing.MutableMapping]: """Delete a contact type # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Args: contact_type_id (str): contactTypeId to delete associated record Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: None Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) kwargs['contact_type_id'] = \ contact_type_id return self.delete_contact_type_endpoint.call_with_http_info(**kwargs) def delete_contact_type_async( self, contact_type_id, **kwargs ) -> "ApplyResult[None]": """Delete a contact type # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Args: contact_type_id (str): contactTypeId to delete associated record Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[None] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) kwargs['contact_type_id'] = \ contact_type_id return self.delete_contact_type_endpoint.call_with_http_info(**kwargs) def delete_contact_type_with_http_info_async( self, contact_type_id, **kwargs ) -> "ApplyResult[typing.Tuple[None, int, typing.MutableMapping]]": """Delete a contact type # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Args: contact_type_id (str): contactTypeId to delete associated record Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[(None, int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) kwargs['contact_type_id'] = \ contact_type_id return self.delete_contact_type_endpoint.call_with_http_info(**kwargs) def get_contact_types( self, **kwargs ) -> [ContactTypeDto]: """Get list of the contact types configured in your group # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: [ContactTypeDto] Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) return self.get_contact_types_endpoint.call_with_http_info(**kwargs) def get_contact_types_with_http_info( self, **kwargs ) -> typing.Tuple[[ContactTypeDto], int, typing.MutableMapping]: """Get list of the contact types configured in your group # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: [ContactTypeDto] Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) return self.get_contact_types_endpoint.call_with_http_info(**kwargs) def get_contact_types_async( self, **kwargs ) -> "ApplyResult[[ContactTypeDto]]": """Get list of the contact types configured in your group # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[[ContactTypeDto]] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) return self.get_contact_types_endpoint.call_with_http_info(**kwargs) def get_contact_types_with_http_info_async( self, **kwargs ) -> "ApplyResult[typing.Tuple[[ContactTypeDto], int, typing.MutableMapping]]": """Get list of the contact types configured in your group # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[([ContactTypeDto], int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) return self.get_contact_types_endpoint.call_with_http_info(**kwargs) def update_contact_type( self, contact_type_id, body, **kwargs ) -> None: """Edit a contact type # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Args: contact_type_id (str): contactTypeId to update associated record body (str): Updated contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: None Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) kwargs['contact_type_id'] = \ contact_type_id kwargs['body'] = \ body return self.update_contact_type_endpoint.call_with_http_info(**kwargs) def update_contact_type_with_http_info( self, contact_type_id, body, **kwargs ) -> typing.Tuple[None, int, typing.MutableMapping]: """Edit a contact type # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Args: contact_type_id (str): contactTypeId to update associated record body (str): Updated contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: None Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) kwargs['contact_type_id'] = \ contact_type_id kwargs['body'] = \ body return self.update_contact_type_endpoint.call_with_http_info(**kwargs) def update_contact_type_async( self, contact_type_id, body, **kwargs ) -> "ApplyResult[None]": """Edit a contact type # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Args: contact_type_id (str): contactTypeId to update associated record body (str): Updated contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[None] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) kwargs['contact_type_id'] = \ contact_type_id kwargs['body'] = \ body return self.update_contact_type_endpoint.call_with_http_info(**kwargs) def update_contact_type_with_http_info_async( self, contact_type_id, body, **kwargs ) -> "ApplyResult[typing.Tuple[None, int, typing.MutableMapping]]": """Edit a contact type # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Args: contact_type_id (str): contactTypeId to update associated record body (str): Updated contact type name Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[(None, int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) kwargs['contact_type_id'] = \ contact_type_id kwargs['body'] = \ body return self.update_contact_type_endpoint.call_with_http_info(**kwargs)

42.411243

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0.566167

4,756

43,005

4.955845

0.046468

0.036657

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0.025796

0.934663

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0.902673

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43,005

1,013

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null

0

7

d9fbdb5a7651e603942e32a197a0e2c5cc010f39

7,832

py

Python

apps/secure_url/tests/tests_secured_entity_model.py

fryta/sercure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

null

apps/secure_url/tests/tests_secured_entity_model.py

fryta/sercure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

7

2020-02-11T23:49:48.000Z

2022-01-13T01:05:42.000Z

apps/secure_url/tests/tests_secured_entity_model.py

fryta/secure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

null

import tempfile from datetime import timedelta from django.conf import settings from django.contrib.auth import get_user_model from django.test import TestCase from django.urls.base import reverse from ..constants import SecuredEntityTypes from ..models import SecuredEntity class SecuredEntityModelTest(TestCase): def setUp(self): username = 'test' password = '123qweasd' self.user = get_user_model().objects.create_user(username=username, password=password) self.url = 'https://www.facebook.com/' self.tmp_file = tempfile.NamedTemporaryFile(suffix='.jpg') def test_new_model_instance_has_proper_type__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertEqual(secured_entity.type, SecuredEntityTypes.LINK) def test_new_model_instance_has_proper_password_salt__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertEqual(len(secured_entity.password_salt), 32) def test_new_model_instance_has_proper_password__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertEqual(len(secured_entity.password), 12) def test_new_model_instance_has_empty_file_and_not_empty_url__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertFalse(secured_entity.file) self.assertTrue(secured_entity.url) def test_new_model_instance_is_accessible_after_create__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertTrue(secured_entity.is_accessible) def test_new_model_instance_is_accessible_just_before_expire__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() SecuredEntity.objects.filter(pk=secured_entity.pk).update( created=secured_entity.created - settings.SECURED_ENTITY_ACCESSIBLE_TIME + timedelta(seconds=1)) secured_entity = SecuredEntity.objects.get(pk=secured_entity.pk) self.assertTrue(secured_entity.is_accessible) def test_new_model_instance_is_not_accessible_just_after_expire__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() SecuredEntity.objects.filter(pk=secured_entity.pk).update( created=secured_entity.created - settings.SECURED_ENTITY_ACCESSIBLE_TIME - timedelta(seconds=1)) secured_entity = SecuredEntity.objects.get(pk=secured_entity.pk) self.assertFalse(secured_entity.is_accessible) def test_new_model_instance_generates_proper_absolute_url__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertEqual(secured_entity.get_absolute_url(), reverse('secure_url:secured-entity-detail-view', kwargs={'pk': secured_entity.pk})) def test_new_model_instance_generates_proper_redirect_url__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() self.assertEqual(secured_entity.get_redirect_url(), self.url) def test_new_model_instance_regenerates_password_properly__url(self): secured_entity = SecuredEntity(user=self.user, url=self.url) secured_entity.save() old_password = secured_entity.password secured_entity.regenerate_password() self.assertNotEqual(old_password, secured_entity.password) def test_new_model_instance_has_proper_type__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertEqual(secured_entity.type, SecuredEntityTypes.FILE) def test_new_model_instance_has_proper_password_salt__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertEqual(len(secured_entity.password_salt), 32) def test_new_model_instance_has_proper_password__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertEqual(len(secured_entity.password), 12) def test_new_model_instance_has_empty_url_and_not_empty_file__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertTrue(secured_entity.file) self.assertFalse(secured_entity.url) def test_new_model_instance_is_accessible_after_create__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertTrue(secured_entity.is_accessible) def test_new_model_instance_is_accessible_just_before_expire__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() SecuredEntity.objects.filter(pk=secured_entity.pk).update( created=secured_entity.created - settings.SECURED_ENTITY_ACCESSIBLE_TIME + timedelta(seconds=1)) secured_entity = SecuredEntity.objects.get(pk=secured_entity.pk) self.assertTrue(secured_entity.is_accessible) def test_new_model_instance_is_not_accessible_just_after_expire__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() SecuredEntity.objects.filter(pk=secured_entity.pk).update( created=secured_entity.created - settings.SECURED_ENTITY_ACCESSIBLE_TIME - timedelta(seconds=1)) secured_entity = SecuredEntity.objects.get(pk=secured_entity.pk) self.assertFalse(secured_entity.is_accessible) def test_new_model_instance_generates_proper_absolute_url__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertEqual(secured_entity.get_absolute_url(), reverse('secure_url:secured-entity-detail-view', kwargs={'pk': secured_entity.pk})) def test_new_model_instance_generates_proper_redirect_url__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() self.assertEqual(secured_entity.get_redirect_url(), '{}{}'.format(settings.MEDIA_URL.rstrip('/'), self.tmp_file.name)) def test_new_model_instance_regenerates_password_properly__file(self): secured_entity = SecuredEntity(user=self.user, file=self.tmp_file.name) secured_entity.save() old_password = secured_entity.password secured_entity.regenerate_password() self.assertNotEqual(old_password, secured_entity.password)

40.580311

108

0.661389

870

7,832

5.581609

0.103448

0.240939

0.128501

0.061779

0.876647

0.87603

0.84061

0.813427

0

0.002587

0.259704

7,832

192

109

40.791667

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0

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0

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0.009448

0

0.161765

1

0.154412

false

0.132353

0.058824

0

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0

null

1

0

1

0

null

0

1

0

8

8a10019ea7edea9d64abaa2ea33aa5ff7766b4f0

191

py

Python

stroylux/main/payment/admin.py

vladkoblynsky/shop

aaf027f4111605772624a868a0243b221b97c857

[ "BSD-3-Clause" ]

null

stroylux/main/payment/admin.py

vladkoblynsky/shop

aaf027f4111605772624a868a0243b221b97c857

[ "BSD-3-Clause" ]

7

2020-09-19T16:24:46.000Z

2022-01-13T03:19:46.000Z

stroylux/main/payment/admin.py

vladkoblynsky/shop

aaf027f4111605772624a868a0243b221b97c857

[ "BSD-3-Clause" ]

null

from django.contrib import admin from .models import Payment, Transaction, PaymentMethod @admin.register(Payment, Transaction, PaymentMethod) class ProductAdmin(admin.ModelAdmin): pass

23.875

55

0.811518

21

191

7.380952

0.666667

0.232258

0.4

0

0.115183

191

7

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27.285714

0.91716

0

1

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true

0.2

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0.6

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1

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null

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1

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null

0

1

0

1

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8

8a14fdae11a88b577d8fecdb4123028e9d29d430

4,560

py

Python

tests/Composition/test_Composition__grad_species_mole_fractions.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

tests/Composition/test_Composition__grad_species_mole_fractions.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

tests/Composition/test_Composition__grad_species_mole_fractions.py

kamilazdybal/multipy

ebdcddb63bfb1cd647ca99bbf9002b04a9b50ed9

[ "MIT" ]

null

import unittest import numpy as np import multipy ################################################################################ ################################################################################ #### #### Class: Composition #### ################################################################################ ################################################################################ class Composition(unittest.TestCase): def test_Composition__grad_species_mole_fractions__allowed_calls(self): species_mole_fractions = np.random.rand(5,100) try: comp = multipy.Composition() gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01) (n_species, n_observations) = np.shape(gradients) self.assertTrue(n_species==5) self.assertTrue(n_observations==100) except Exception: self.assertTrue(False) try: comp = multipy.Composition() gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=1) (n_species, n_observations) = np.shape(gradients) self.assertTrue(n_species==5) self.assertTrue(n_observations==100) except Exception: self.assertTrue(False) try: comp = multipy.Composition() gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01, edge_order=2) (n_species, n_observations) = np.shape(gradients) self.assertTrue(n_species==5) self.assertTrue(n_observations==100) except Exception: self.assertTrue(False) ################################################################################ ################################################################################ def test_Composition__grad_species_mole_fractions__not_allowed_calls(self): species_mole_fractions = np.random.rand(5,) comp = multipy.Composition() with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01) with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions([1,2,3], delta=0.01) species_mole_fractions = np.random.rand(5,100) with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=[1]) with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta='nones') with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01, edge_order=0) with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01, edge_order=3) with self.assertRaises(ValueError): gradients = comp.grad_species_mole_fractions(species_mole_fractions, delta=0.01, edge_order=[1]) ################################################################################ ################################################################################ def test_Composition__grad_species_mole_fractions__computation(self): x = np.linspace(1,2,100) delta_x = x[2] - x[1] quad = x**2 cub = x**3 quad_grad = 2*x cub_grad = 3*x**2 data = np.vstack((quad[None,:], cub[None,:])) expected_result = np.vstack((quad_grad[None,:], cub_grad[None,:])) try: comp = multipy.Composition() gradient_data = comp.grad_species_mole_fractions(data, delta_x) for i in range(1, 99): self.assertTrue(abs(gradient_data[0,i] - expected_result[0,i]) < 10e-10) self.assertTrue(abs(gradient_data[0,0] - expected_result[0,0]) < 0.1) self.assertTrue(abs(gradient_data[0,-1] - expected_result[0,-1]) < 0.1) except Exception: self.assertTrue(False) try: comp = multipy.Composition() gradient_data = comp.grad_species_mole_fractions(data, delta_x, edge_order=2) for i in range(0, 100): self.assertTrue(abs(gradient_data[0,i] - expected_result[0,i]) < 10e-10) except Exception: self.assertTrue(False) ################################################################################ ################################################################################

42.616822

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0.533114

454

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0.233867

0.155912

0.845821

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0.80511

0.750541

0.733651

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0

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4,560

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109

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0

null

0

1

0

1

0

null

0

7

8a74f9e9bff9958655e6c64a71b270a4a10f8b2c

747

py

Python

tests/parser/ancestor.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/ancestor.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/ancestor.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

input = """ married(X,Y) :- husband_of(X,Y). married(Y,X) :- husband_of(X,Y). married(X,Y) :- wife_of(X,Y). married(Y,X) :- wife_of(X,Y). parent(X,Y) :- father_of(X,Y). parent(X,Y) :- mother_of(X,Y). parent(X,Y) :- married(X,Z), father_of(Z,Y). parent(X,Y) :- married(X,Z), mother_of(Z,Y). ancestor(X,Y) :- parent(X,Y). ancestor(X,Y) :- ancestor(X,U), ancestor(U,Y). """ output = """ married(X,Y) :- husband_of(X,Y). married(Y,X) :- husband_of(X,Y). married(X,Y) :- wife_of(X,Y). married(Y,X) :- wife_of(X,Y). parent(X,Y) :- father_of(X,Y). parent(X,Y) :- mother_of(X,Y). parent(X,Y) :- married(X,Z), father_of(Z,Y). parent(X,Y) :- married(X,Z), mother_of(Z,Y). ancestor(X,Y) :- parent(X,Y). ancestor(X,Y) :- ancestor(X,U), ancestor(U,Y). """

25.758621

46

0.603748

156

747

2.788462

0.089744

0.137931

0.110345

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0.974713

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747

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false

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null

0

1

0

1

0

1

0

null

0

9

8aab6cfc5942919e91a614b86494a5015efefe2c

68,663

py

Python

benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_ml/backup_results_unknownr/EightThreads_leslie3d/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_ml/backup_results_unknownr/EightThreads_leslie3d/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_ml/backup_results_unknownr/EightThreads_leslie3d/power.py

TugberkArkose/MLScheduler

e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061

[ "Unlicense" ]

null

power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0651675, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.253874, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.337578, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.1891, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.327453, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.187804, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.704358, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.135163, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 5.74045, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0637757, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00685504, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0745517, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0506972, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.138327, 'Execution Unit/Register Files/Runtime Dynamic': 0.0575522, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.198001, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.52265, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155, 'Execution Unit/Runtime Dynamic': 1.94381, 'Execution Unit/Subthreshold Leakage': 1.83518, 'Execution Unit/Subthreshold Leakage with power gating': 0.709678, 'Gate Leakage': 0.372997, 'Instruction Fetch Unit/Area': 5.86007, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000473479, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000473479, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000409802, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.00015722, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000728269, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00208503, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0046325, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0590479, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0487365, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 3.10006, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.135894, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.165531, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 5.47133, 'Instruction Fetch Unit/Runtime Dynamic': 0.356879, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932587, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.108484, 'L2/Runtime Dynamic': 0.0316228, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80969, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 3.14656, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.970754, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0351387, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0617749, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.061775, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 3.43946, 'Load Store Unit/Runtime Dynamic': 1.33718, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.152326, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.304653, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591622, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283406, 'Memory Management Unit/Area': 0.434579, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0540612, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0556718, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00813591, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.19275, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0223336, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.44471, 'Memory Management Unit/Runtime Dynamic': 0.0780054, 'Memory Management Unit/Subthreshold Leakage': 0.0769113, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462, 'Peak Dynamic': 19.7661, 'Renaming Unit/Area': 0.369768, 'Renaming Unit/FP Front End RAT/Area': 0.168486, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.222499, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925, 'Renaming Unit/Free List/Area': 0.0414755, 'Renaming Unit/Free List/Gate Leakage': 4.15911e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0401324, 'Renaming Unit/Free List/Runtime Dynamic': 0.0123469, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987, 'Renaming Unit/Gate Leakage': 0.00863632, 'Renaming Unit/Int Front End RAT/Area': 0.114751, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0951093, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781, 'Renaming Unit/Peak Dynamic': 4.56169, 'Renaming Unit/Runtime Dynamic': 0.329955, 'Renaming Unit/Subthreshold Leakage': 0.070483, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779, 'Runtime Dynamic': 4.07746, 'Subthreshold Leakage': 6.21877, 'Subthreshold Leakage with power gating': 2.58311}, {'Area': 32.0201, 'Execution Unit/Area': 7.68434, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0277673, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.224498, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.14255, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0699257, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.112788, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0569314, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.239645, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.0581209, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 4.19185, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0269307, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.002933, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0319061, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0216913, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.0588368, 'Execution Unit/Register Files/Runtime Dynamic': 0.0246243, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0741606, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.196406, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 1.09055, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000210663, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000210663, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000183811, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 7.1333e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000311598, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.000916735, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00200827, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0208524, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.32639, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0576799, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0708242, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 3.60928, 'Instruction Fetch Unit/Runtime Dynamic': 0.152282, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932286, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0451777, 'L2/Runtime Dynamic': 0.0132532, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80901, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 2.06334, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.419427, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0267302, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0267302, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 2.18957, 'Load Store Unit/Runtime Dynamic': 0.577981, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate 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'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.0283227, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.224934, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.146998, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate 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'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0576608, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.242715, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.058463, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 4.19953, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.027771, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00297058, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0323268, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0219692, 'Execution Unit/Register 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'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00183086, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0211196, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.34339, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0568315, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0717316, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 3.6271, 'Instruction Fetch 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power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0653942, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction 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0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00017651, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000154036, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 5.97916e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000291405, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00079846, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00168179, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0195011, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.24044, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.051339, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 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'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0262019, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0262019, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 2.17074, 'Load Store Unit/Runtime Dynamic': 0.564227, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.0646094, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.129219, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0229301, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0235835, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.0771258, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.00844603, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.272624, 'Memory Management Unit/Runtime Dynamic': 0.0320295, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 13.7624, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0656771, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.00374968, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0324145, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.101841, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 1.9111, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 8.578190758719012, 'Runtime Dynamic': 8.578190758719012, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.385642, 'Runtime Dynamic': 0.146137, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 61.7981, 'Peak Power': 94.9104, 'Runtime Dynamic': 10.1255, 'Subthreshold 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76dc8f520fd7c8a220895dafa224af379640bff4

36,328

py

Python

tests/test_base.py

bgorr/instrupy

e3dca871ce2dcd2ef279898fcc36bf9d18f0c243

[ "Apache-2.0" ]

null

tests/test_base.py

bgorr/instrupy

e3dca871ce2dcd2ef279898fcc36bf9d18f0c243

[ "Apache-2.0" ]

null

tests/test_base.py

bgorr/instrupy

e3dca871ce2dcd2ef279898fcc36bf9d18f0c243

[ "Apache-2.0" ]

null

"""Unit tests for instrupy.base. """ import unittest import numpy as np import random from deepdiff import DeepDiff from instrupy import InstrumentModelFactory, Instrument from instrupy.basic_sensor_model import BasicSensorModel from instrupy.passive_optical_scanner_model import PassiveOpticalScannerModel from instrupy.synthetic_aperture_radar_model import SyntheticApertureRadarModel from instrupy.radiometer_model import RadiometerModel from instrupy.util import SphericalGeometry, Orientation, ViewGeometry, Maneuver class TestInstrumentModelFactory(unittest.TestCase): class DummyNewInstrument: def __init__(self, *args, **kwargs): pass def from_dict(self): return TestInstrumentModelFactory.DummyNewInstrument() def test___init__(self): factory = InstrumentModelFactory() # test the built-in instrumnet models are registered self.assertIn('Basic Sensor', factory._creators) self.assertEqual(factory._creators['Basic Sensor'], BasicSensorModel) self.assertIn('Passive Optical Scanner', factory._creators) self.assertEqual(factory._creators['Passive Optical Scanner'], PassiveOpticalScannerModel) self.assertIn('Synthetic Aperture Radar', factory._creators) self.assertEqual(factory._creators['Synthetic Aperture Radar'], SyntheticApertureRadarModel) self.assertIn('Radiometer', factory._creators) self.assertEqual(factory._creators['Radiometer'], RadiometerModel) def test_register_instrument_model(self): factory = InstrumentModelFactory() factory.register_instrument_model('New Instrument 2021', TestInstrumentModelFactory.DummyNewInstrument) self.assertIn('New Instrument 2021', factory._creators) self.assertEqual(factory._creators['New Instrument 2021'], TestInstrumentModelFactory.DummyNewInstrument) # test the built-in instrumnet models remain registered self.assertIn('Basic Sensor', factory._creators) self.assertEqual(factory._creators['Basic Sensor'], BasicSensorModel) self.assertIn('Passive Optical Scanner', factory._creators) self.assertEqual(factory._creators['Passive Optical Scanner'], PassiveOpticalScannerModel) self.assertIn('Synthetic Aperture Radar', factory._creators) self.assertEqual(factory._creators['Synthetic Aperture Radar'], SyntheticApertureRadarModel) self.assertIn('Radiometer', factory._creators) self.assertEqual(factory._creators['Radiometer'], RadiometerModel) def test_get_instrument_model(self): factory = InstrumentModelFactory() # register a dummy instrument model factory.register_instrument_model('New Instrument 2021', TestInstrumentModelFactory.DummyNewInstrument) # test the instrument model classes can be obtained depending on the input specifications # basic sensor model specs = {"@type": 'Basic Sensor'} # in practice additional instrument specs shall be present in the dictionary bs_model = factory.get_instrument_model(specs) self.assertIsInstance(bs_model, BasicSensorModel) # PassiveOpticalScannerModel, specs = {"@type": 'Passive Optical Scanner', 'scanTechnique': 'PUSHBROOM', "numberDetectorRows":1, "numberDetectorCols":500, "fieldOfViewGeometry":{"shape":"rectangular", "angleWidth": 5, "angleHeight": 5}} # in practice additional instrument specs shall be present in the dictionary ps_model = factory.get_instrument_model(specs) self.assertIsInstance(ps_model, PassiveOpticalScannerModel) # SyntheticApertureRadarModel specs = {"@type": 'Synthetic Aperture Radar', "minimumPRF": 2000, "maximumPRF": 8000, "operatingFrequency": 9.6e9, "antennaHeight":5, "antennaWidth":0.5} # in practice additional instrument specs shall be present in the dictionary sar_model = factory.get_instrument_model(specs) self.assertIsInstance(sar_model, SyntheticApertureRadarModel) # RadiometerModel specs = {"@type": 'Radiometer'} # in practice additional instrument specs shall be present in the dictionary rad_model = factory.get_instrument_model(specs) self.assertIsInstance(rad_model, RadiometerModel) # DummyNewInstrument specs = {"@type": 'New Instrument 2021'} # in practice additional instrument specs shall be present in the dictionary di_model = factory.get_instrument_model(specs) self.assertIsInstance(di_model, TestInstrumentModelFactory.DummyNewInstrument) class TestInstrument(unittest.TestCase): bs1 = Instrument.from_json('{"name": "Alpha", "mass":10, "volume":12.45, "dataRate": 40, "bitsPerPixel": 8, "power": 12, \ "orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, \ "fieldOfViewGeometry": {"shape": "CIRCULAR", "diameter":2.5 }, \ "sceneFieldOfViewGeometry": {"shape": "CIRCULAR", "diameter":5 }, \ "maneuver":{"maneuverType": "CIRCULAR", "diameter":10}, \ "pointingOption": [{"referenceFrame": "NADIR_POINTING", "convention": "XYZ", "xRotation":0, "yRotation":2.5, "zRotation":0}, \ {"referenceFrame": "NADIR_POINTING", "convention": "XYZ", "xRotation":0, "yRotation":-2.5, "zRotation":0} \ ], \ "numberDetectorRows":5, "numberDetectorCols":10, "@id":"bs1", "@type":"Basic Sensor" \ }') bs2 = Instrument.from_json('{"name": "Beta", "mass":10, "volume":12.45, "dataRate": 40, "bitsPerPixel": 8, "power": 12, \ "fieldOfViewGeometry": {"shape": "CIRCULAR", "diameter":5 }, \ "maneuver":{"maneuverType": "SINGLE_ROLL_ONLY", "A_rollMin":10, "A_rollMax":15}, \ "mode": [{"@id":101, "orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}} \ ], \ "numberDetectorRows":5, "numberDetectorCols":10, "@type":"Basic Sensor" \ }') bs3 = Instrument.from_json('{"name": "Gamma", "mass":10, "volume":12.45, "dataRate": 40, "bitsPerPixel": 8, "power": 12, \ "fieldOfViewGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }, \ "sceneFieldOfViewGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }, \ "maneuver":{"maneuverType": "Double_Roll_Only", "A_rollMin":10, "A_rollMax":15, "B_rollMin":-15, "B_rollMax":-10}, \ "pointingOption": [{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle":10}, \ {"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle":15} \ ], \ "mode": [{"@id":0, "orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}}, \ {"@id":1, "orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": 25}}, \ { "orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": -25}} \ ], \ "numberDetectorRows":5, "numberDetectorCols":10, "@id": "bs3", "@type":"Basic Sensor" \ }') def test_from_json_basic_sensor(self): # test initialization with no mode specification self.assertEqual(TestInstrument.bs1.name, "Alpha") self.assertEqual(TestInstrument.bs1._id, "bs1") self.assertEqual(TestInstrument.bs1._type, "Basic Sensor") self.assertIsInstance(TestInstrument.bs1, Instrument) self.assertEqual(len(TestInstrument.bs1.mode), 1) self.assertIsInstance(TestInstrument.bs1.mode[0], BasicSensorModel) mode0 = TestInstrument.bs1.mode[0] self.assertEqual(mode0._id, "0") self.assertEqual(mode0.mass, 10) self.assertEqual(mode0.volume, 12.45) self.assertEqual(mode0.dataRate, 40) self.assertEqual(mode0.bitsPerPixel, 8) self.assertEqual(mode0.power, 12) self.assertEqual(mode0.numberDetectorRows, 5) self.assertEqual(mode0.numberDetectorCols, 10) self.assertEqual(mode0.orientation, Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) self.assertEqual(mode0.fieldOfView, ViewGeometry.from_dict({"orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter":2.5 }})) self.assertEqual(mode0.sceneFieldOfView, ViewGeometry.from_dict({"orientation": {"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter":5 }})) self.assertEqual(mode0.maneuver, Maneuver.from_json('{"maneuverType": "CIRCULAR", "diameter": 10}')) self.assertEqual(mode0.fieldOfRegard, [ViewGeometry.from_dict({"orientation": {"referenceFrame": "NADIR_POINTING", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter":15 }})]) self.assertEqual(mode0.pointingOption, [Orientation.from_dict({"referenceFrame": "NADIR_POINTING", "convention": "XYZ", "xRotation":0, "yRotation":2.5, "zRotation":0}), Orientation.from_dict({"referenceFrame": "NADIR_POINTING", "convention": "XYZ", "xRotation":0, "yRotation":-2.5, "zRotation":0})]) # test initialization with single mode specification self.assertEqual(TestInstrument.bs2.name, "Beta") self.assertIsNotNone(TestInstrument.bs2._id) # a random id shall be assigned self.assertEqual(TestInstrument.bs2._type, "Basic Sensor") self.assertIsInstance(TestInstrument.bs2, Instrument) self.assertEqual(len(TestInstrument.bs2.mode), 1) self.assertIsInstance(TestInstrument.bs2.mode[0], BasicSensorModel) mode0 = TestInstrument.bs2.mode[0] self.assertEqual(mode0._id, 101) self.assertEqual(mode0.mass, 10) self.assertEqual(mode0.volume, 12.45) self.assertEqual(mode0.dataRate, 40) self.assertEqual(mode0.bitsPerPixel, 8) self.assertEqual(mode0.power, 12) self.assertEqual(mode0.numberDetectorRows, 5) self.assertEqual(mode0.numberDetectorCols, 10) self.assertEqual(mode0.orientation, Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) self.assertEqual(mode0.fieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "CIRCULAR", "diameter": 5}})) self.assertEqual(mode0.sceneFieldOfView, mode0.fieldOfView) self.assertEqual(mode0.maneuver, Maneuver.from_json('{"maneuverType": "single_ROLL_ONLY", "A_rollMin": 10, "A_rollMax":15}')) self.assertIsNone(mode0.pointingOption) # test initialization with multiple mode specifications self.assertEqual(TestInstrument.bs3.name, "Gamma") self.assertEqual(TestInstrument.bs3._id, "bs3") self.assertEqual(TestInstrument.bs3._type, "Basic Sensor") self.assertIsInstance(TestInstrument.bs3, Instrument) self.assertEqual(len(TestInstrument.bs3.mode), 3) self.assertIsInstance(TestInstrument.bs3.mode[0], BasicSensorModel) # mode0 mode0 = TestInstrument.bs3.mode[0] self.assertEqual(mode0._id, 0) self.assertEqual(mode0.mass, 10) self.assertEqual(mode0.volume, 12.45) self.assertEqual(mode0.dataRate, 40) self.assertEqual(mode0.bitsPerPixel, 8) self.assertEqual(mode0.power, 12) self.assertEqual(mode0.numberDetectorRows, 5) self.assertEqual(mode0.numberDetectorCols, 10) self.assertEqual(mode0.orientation, Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) self.assertEqual(mode0.fieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10}})) self.assertEqual(mode0.sceneFieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10}})) self.assertEqual(mode0.maneuver, Maneuver.from_json('{"maneuverType": "double_roll_only", "A_rollMin": 10, "A_rollMax":15, "B_rollMin":-15, "B_rollMax":-10}')) ddiff = DeepDiff(mode0.fieldOfRegard, [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}), ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":-12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}) ], significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) self.assertEqual(mode0.pointingOption, [Orientation.from_dict({"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle":10}), Orientation.from_dict({"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle":15})]) # mode1 mode1 = TestInstrument.bs3.mode[1] self.assertEqual(mode1._id, 1) self.assertEqual(mode1.mass, 10) self.assertEqual(mode1.volume, 12.45) self.assertEqual(mode1.dataRate, 40) self.assertEqual(mode1.bitsPerPixel, 8) self.assertEqual(mode1.power, 12) self.assertEqual(mode1.numberDetectorRows, 5) self.assertEqual(mode1.numberDetectorCols, 10) self.assertEqual(mode0.fieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10}})) self.assertEqual(mode0.sceneFieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10}})) self.assertEqual(mode0.maneuver, Maneuver.from_json('{"maneuverType": "double_roll_only", "A_rollMin": 10, "A_rollMax":15, "B_rollMin":-15, "B_rollMax":-10}')) ddiff = DeepDiff(mode0.fieldOfRegard, [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}), ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":-12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}) ], significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) self.assertEqual(mode1.pointingOption, mode0.pointingOption) # mode2 mode2 = TestInstrument.bs3.mode[2] self.assertIsNotNone(mode2._id) self.assertEqual(mode2.mass, 10) self.assertEqual(mode2.volume, 12.45) self.assertEqual(mode2.dataRate, 40) self.assertEqual(mode2.bitsPerPixel, 8) self.assertEqual(mode2.power, 12) self.assertEqual(mode2.numberDetectorRows, 5) self.assertEqual(mode2.numberDetectorCols, 10) self.assertEqual(mode2.orientation, Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": -25}')) self.assertEqual(mode0.fieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10}})) self.assertEqual(mode0.sceneFieldOfView, ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10}})) self.assertEqual(mode0.maneuver, Maneuver.from_json('{"maneuverType": "double_roll_only", "A_rollMin": 10, "A_rollMax":15, "B_rollMin":-15, "B_rollMax":-10}')) ddiff = DeepDiff(mode0.fieldOfRegard, [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}), ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_Look", "sideLookAngle":-12.5}, "sphericalGeometry":{"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15}}) ], significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) self.assertEqual(mode2.pointingOption, mode0.pointingOption) def test_get_type(self): self.assertEqual(TestInstrument.bs1.get_type(), 'Basic Sensor') self.assertEqual(TestInstrument.bs2.get_type(), 'Basic Sensor') self.assertEqual(TestInstrument.bs3.get_type(), 'Basic Sensor') def test_get_id(self): self.assertEqual(TestInstrument.bs1.get_id(), "bs1") self.assertIsNotNone(TestInstrument.bs2.get_id()) self.assertEqual(TestInstrument.bs3.get_id(), "bs3") def test_get_mode_id(self): #@TODO pass def test_get_mode(self): #@TODO pass def test_get_field_of_regard(self): #@TODO # bs1 # no input mode-id self.assertEqual(TestInstrument.bs1.get_field_of_regard(), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter": 15}})]) # input correct mode-id self.assertEqual(TestInstrument.bs1.get_field_of_regard(mode_id="0"), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter": 15}})]) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs1.get_field_of_regard(mode_id="abc"), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "REF_FRAME_ALIGNED"}, "sphericalGeometry": {"shape": "CIRCULAR", "diameter": 15}})]) # bs2 # no input mode-id self.assertEqual(TestInstrument.bs2.get_field_of_regard(), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": 12.5},"sphericalGeometry": {"shape": "rectangular", "angleHeight": 5, "angleWidth":10}})]) # input correct mode-id ddiff = DeepDiff(TestInstrument.bs2.get_field_of_regard(mode_id=101), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": 12.5},"sphericalGeometry": {"shape": "rectangular", "angleHeight": 5, "angleWidth":10}})], ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs2.get_field_of_regard(mode_id="abc"), [ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": 12.5},"sphericalGeometry": {"shape": "rectangular", "angleHeight": 5, "angleWidth":10}})]) # bs3, all modes have the same field of regard # no input mode-id ddiff = DeepDiff(TestInstrument.bs3.get_field_of_regard()[0], ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": 12.5},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15 }}), significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) ddiff = DeepDiff(TestInstrument.bs3.get_field_of_regard()[1], ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": -12.5},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15 }}), significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) # input correct mode-id ddiff = DeepDiff(TestInstrument.bs3.get_field_of_regard(mode_id=0)[0], ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": 12.5},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15 }}), significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) ddiff = DeepDiff(TestInstrument.bs3.get_field_of_regard(mode_id=0)[1], ViewGeometry.from_dict({"orientation":{"referenceFrame": "NADIR_POINTING", "convention": "SIDE_LOOK", "sideLookAngle": -12.5},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":15 }}), significant_digits=7, ignore_numeric_type_changes=True) self.assertEqual(ddiff, {}, msg=ddiff) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs3.get_field_of_regard(mode_id='abc'), TestInstrument.bs3.get_field_of_regard(mode_id=0)) # next mode self.assertEqual(TestInstrument.bs3.get_field_of_regard(mode_id=1), TestInstrument.bs3.get_field_of_regard(mode_id=0)) # next mode, mode_id = TestInstrument.bs3.mode_id[2] self.assertEqual(TestInstrument.bs3.get_field_of_regard(mode_id=mode_id), TestInstrument.bs3.get_field_of_regard(mode_id=0)) def test_get_field_of_view(self): # bs1 # no input mode-id self.assertEqual(TestInstrument.bs1.get_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 2.5}})) # input correct mode-id self.assertEqual(TestInstrument.bs1.get_field_of_view(mode_id="0"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 2.5}})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs1.get_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 2.5}})) # bs2 # no input mode-id self.assertEqual(TestInstrument.bs2.get_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input correct mode-id self.assertEqual(TestInstrument.bs2.get_field_of_view(mode_id=101), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs2.get_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # bs3 # no input mode-id self.assertEqual(TestInstrument.bs3.get_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }})) # input correct mode-id self.assertEqual(TestInstrument.bs3.get_field_of_view(mode_id=0), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs3.get_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }})) # next mode self.assertEqual(TestInstrument.bs3.get_field_of_view(mode_id=1), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": 25},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }})) # next mode mode_id = TestInstrument.bs3.mode_id[2] self.assertEqual(TestInstrument.bs3.get_field_of_view(mode_id=mode_id), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": -25},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":0.25, "angleWidth":10 }})) def test_get_scene_field_of_view(self): # bs1 # no input mode-id self.assertEqual(TestInstrument.bs1.get_scene_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input correct mode-id self.assertEqual(TestInstrument.bs1.get_scene_field_of_view(mode_id="0"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs1.get_scene_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # bs2 # no input mode-id self.assertEqual(TestInstrument.bs2.get_scene_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input correct mode-id self.assertEqual(TestInstrument.bs2.get_scene_field_of_view(mode_id=101), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs2.get_scene_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "CIRCULAR", "diameter": 5}})) # bs3 # no input mode-id self.assertEqual(TestInstrument.bs3.get_scene_field_of_view(), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }})) # input correct mode-id self.assertEqual(TestInstrument.bs3.get_scene_field_of_view(mode_id=0), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }})) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs3.get_scene_field_of_view(mode_id="abc"), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }})) # next mode self.assertEqual(TestInstrument.bs3.get_scene_field_of_view(mode_id=1), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": 25},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }})) # next mode mode_id = TestInstrument.bs3.mode_id[2] self.assertEqual(TestInstrument.bs3.get_scene_field_of_view(mode_id=mode_id), ViewGeometry.from_dict({"orientation":{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": -25},"sphericalGeometry": {"shape": "RECTANGULAR", "angleHeight":5, "angleWidth":10 }})) def test_get_orientation(self): # bs1 # no input mode-id self.assertEqual(TestInstrument.bs1.get_orientation(), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input correct mode-id self.assertEqual(TestInstrument.bs1.get_orientation(mode_id="0"), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs1.get_orientation(mode_id="abc"), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # bs2 # no input mode-id self.assertEqual(TestInstrument.bs2.get_orientation(), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input correct mode-id self.assertEqual(TestInstrument.bs2.get_orientation(mode_id=101), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs2.get_orientation(mode_id="abc"), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # bs3 # no input mode-id self.assertEqual(TestInstrument.bs3.get_orientation(), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input correct mode-id self.assertEqual(TestInstrument.bs3.get_orientation(mode_id=0), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # input incorrect mode-id, should default to first mode self.assertEqual(TestInstrument.bs3.get_orientation(mode_id="abc"), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "REF_FRAME_ALIGNED"}')) # next mode self.assertEqual(TestInstrument.bs3.get_orientation(mode_id=1), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": 25}')) # next mode mode_id = TestInstrument.bs3.mode_id[2] self.assertEqual(TestInstrument.bs3.get_orientation(mode_id=mode_id), Orientation.from_json('{"referenceFrame": "SC_BODY_FIXED", "convention": "SIDE_LOOK", "sideLookAngle": -25}')) def test_pixel_config(self): #@TODO pass def test_calc_data_metrics_bs1(self): #@TODO """ Simple test involving satellite above POI at (lat = 0,lon = 0). Date chosen so that ECEF and ECI frames are aligned. Sensor specs do not influence the below calcs. They do however shall influence the coverage calcs (which is not covered by this test). """ epoch_JDUT1 = 2458543.06088 # 2019 Feb 28 13:27:40 is time at which the ECEF and ECI frames approximately align, hence ECEF to ECI rotation is identity. See <https://www.celnav.de/longterm.htm> online calculator of GMST. SpacecraftOrbitState = {'time [JDUT1]':epoch_JDUT1, 'x [km]': 6878.137, 'y [km]': 0, 'z [km]': 0, 'vx [km/s]': 0, 'vy [km/s]': 7.6126, 'vz [km/s]': 0} # altitude 500 km TargetCoords = {'lat [deg]': 0, 'lon [deg]': 0} # no input mode-id obsv_metrics = TestInstrument.bs1.calc_data_metrics(None, SpacecraftOrbitState, TargetCoords) self.assertAlmostEqual(obsv_metrics["observation range [km]"], 500, delta = 1) self.assertAlmostEqual(obsv_metrics["incidence angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["look angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["solar zenith [deg]"], 20.335, delta = 0.1) # precomputed value at the epoch and (lat=0, lon=0) position # correct mode-id obsv_metrics = TestInstrument.bs1.calc_data_metrics("0", SpacecraftOrbitState, TargetCoords) self.assertAlmostEqual(obsv_metrics["observation range [km]"], 500, delta = 1) self.assertAlmostEqual(obsv_metrics["incidence angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["look angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["solar zenith [deg]"], 20.335, delta = 0.1) # precomputed value at the epoch and (lat=0, lon=0) position # incorrect mode-id obsv_metrics = TestInstrument.bs1.calc_data_metrics("abc", SpacecraftOrbitState, TargetCoords) self.assertAlmostEqual(obsv_metrics["observation range [km]"], 500, delta = 1) self.assertAlmostEqual(obsv_metrics["incidence angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["look angle [deg]"], 0, delta = 0.1) self.assertAlmostEqual(obsv_metrics["solar zenith [deg]"], 20.335, delta = 0.1) # precomputed value at the epoch and (lat=0, lon=0) position def test_synthesize_observation(self): #@TODO pass def test_get_pointing_option(self): #@TODO pass

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0

7

76ea7cd965e12ce48fdc6aecacf01b4fc0b9826d

10,120

py

Python

ui_data/gui_study_3.py

LeeDaeil/PyQt5_study

ecdd22ce2809ce6f01c8691a7ca75ef1771b7202

[ "MIT" ]

1

2020-03-22T14:35:11.000Z

ui_data/gui_study_3.py

LeeDaeil/PyQt5_study

ecdd22ce2809ce6f01c8691a7ca75ef1771b7202

[ "MIT" ]

null

ui_data/gui_study_3.py

LeeDaeil/PyQt5_study

ecdd22ce2809ce6f01c8691a7ca75ef1771b7202

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'study_3.ui' # # Created by: PyQt5 UI code generator 5.11.3 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Dialog(object): def setupUi(self, Dialog): Dialog.setObjectName("Dialog") Dialog.resize(400, 300) self.pushButton = QtWidgets.QPushButton(Dialog) self.pushButton.setGeometry(QtCore.QRect(50, 60, 241, 181)) palette = QtGui.QPalette() brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(224, 159, 134)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(186, 132, 111)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(99, 70, 59)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(202, 180, 172)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(224, 159, 134)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(186, 132, 111)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(99, 70, 59)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(202, 180, 172)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush) brush = QtGui.QBrush(QtGui.QColor(224, 159, 134)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush) brush = QtGui.QBrush(QtGui.QColor(186, 132, 111)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush) brush = QtGui.QBrush(QtGui.QColor(99, 70, 59)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush) brush = QtGui.QBrush(QtGui.QColor(74, 53, 44)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush) brush = QtGui.QBrush(QtGui.QColor(42, 234, 255)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush) brush = QtGui.QBrush(QtGui.QColor(149, 106, 89)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush) brush = QtGui.QBrush(QtGui.QColor(255, 255, 220)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush) brush = QtGui.QBrush(QtGui.QColor(0, 0, 0)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush) self.pushButton.setPalette(palette) self.pushButton.setAutoFillBackground(False) self.pushButton.setStyleSheet("background-color: rgb(42, 234, 255);\n" "image: url(qt_resource/Biological_Treatment.png);") self.pushButton.setInputMethodHints(QtCore.Qt.ImhNone) icon = QtGui.QIcon() icon.addPixmap(QtGui.QPixmap("../../../도장.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton.setIcon(icon) self.pushButton.setIconSize(QtCore.QSize(30, 30)) self.pushButton.setCheckable(False) self.pushButton.setAutoRepeat(False) self.pushButton.setAutoDefault(False) self.pushButton.setDefault(True) self.pushButton.setFlat(False) self.pushButton.setObjectName("pushButton") self.pushButton_2 = QtWidgets.QPushButton(Dialog) self.pushButton_2.setGeometry(QtCore.QRect(160, 120, 75, 23)) self.pushButton_2.setObjectName("pushButton_2") self.retranslateUi(Dialog) QtCore.QMetaObject.connectSlotsByName(Dialog) def retranslateUi(self, Dialog): _translate = QtCore.QCoreApplication.translate Dialog.setWindowTitle(_translate("Dialog", "Dialog")) self.pushButton.setText(_translate("Dialog", "PushButton")) self.pushButton_2.setText(_translate("Dialog", "PushButton"))

56.222222

93

0.694664

1,196

10,120

5.867057

0.121237

0.168591

0.102608

0.134673

0.824426

0.813026

0.802622

0

0.041843

0.180534

10,120

179

94

56.536313

0.804293

0.017885

0

0.529762

1

0

0.017519

0.004229

0

1

0.011905

false

0

0.005952

0

0.02381

0

null

0

1

0

1

0

null

0

7

0a069f77d031de0acacff4e0462c0bf5f0016c7c

30,959

py

Python

python/Model_Files/LFV_2/decays.py

ZAKI1905/HEP-Phen

bc06fecb2aa6bf108b59f76794e63c29eb37a35a

[ "MIT" ]

1

2019-10-21T08:25:46.000Z

python/Model_Files/LFV_2/decays.py

ZAKI1905/HEP-Phen

bc06fecb2aa6bf108b59f76794e63c29eb37a35a

[ "MIT" ]

null

python/Model_Files/LFV_2/decays.py

ZAKI1905/HEP-Phen

bc06fecb2aa6bf108b59f76794e63c29eb37a35a

[ "MIT" ]

null

# This file was automatically created by FeynRules 2.3.32 # Mathematica version: 11.3.0 for Mac OS X x86 (64-bit) (March 7, 2018) # Date: Sat 21 Apr 2018 20:44:59 from object_library import all_decays, Decay import particles as P Decay_b = Decay(name = 'Decay_b', particle = P.b, partial_widths = {(P.W__minus__,P.c):'(((3*CKM2x3*ee**2*MB**2*complexconjugate(CKM2x3))/(2.*sw**2) + (3*CKM2x3*ee**2*MC**2*complexconjugate(CKM2x3))/(2.*sw**2) + (3*CKM2x3*ee**2*MB**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) - (3*CKM2x3*ee**2*MB**2*MC**2*complexconjugate(CKM2x3))/(MW**2*sw**2) + (3*CKM2x3*ee**2*MC**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) - (3*CKM2x3*ee**2*MW**2*complexconjugate(CKM2x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MC**2 + MC**4 - 2*MB**2*MW**2 - 2*MC**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MB)**3)', (P.W__minus__,P.t):'(((3*CKM3x3*ee**2*MB**2*complexconjugate(CKM3x3))/(2.*sw**2) + (3*CKM3x3*ee**2*MT**2*complexconjugate(CKM3x3))/(2.*sw**2) + (3*CKM3x3*ee**2*MB**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) - (3*CKM3x3*ee**2*MB**2*MT**2*complexconjugate(CKM3x3))/(MW**2*sw**2) + (3*CKM3x3*ee**2*MT**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) - (3*CKM3x3*ee**2*MW**2*complexconjugate(CKM3x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MT**2 + MT**4 - 2*MB**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MB)**3)', (P.W__minus__,P.u):'(((3*CKM1x3*ee**2*MB**2*complexconjugate(CKM1x3))/(2.*sw**2) + (3*CKM1x3*ee**2*MU**2*complexconjugate(CKM1x3))/(2.*sw**2) + (3*CKM1x3*ee**2*MB**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) - (3*CKM1x3*ee**2*MB**2*MU**2*complexconjugate(CKM1x3))/(MW**2*sw**2) + (3*CKM1x3*ee**2*MU**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) - (3*CKM1x3*ee**2*MW**2*complexconjugate(CKM1x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MU**2 + MU**4 - 2*MB**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MB)**3)'}) Decay_c = Decay(name = 'Decay_c', particle = P.c, partial_widths = {(P.W__plus__,P.b):'(((3*CKM2x3*ee**2*MB**2*complexconjugate(CKM2x3))/(2.*sw**2) + (3*CKM2x3*ee**2*MC**2*complexconjugate(CKM2x3))/(2.*sw**2) + (3*CKM2x3*ee**2*MB**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) - (3*CKM2x3*ee**2*MB**2*MC**2*complexconjugate(CKM2x3))/(MW**2*sw**2) + (3*CKM2x3*ee**2*MC**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) - (3*CKM2x3*ee**2*MW**2*complexconjugate(CKM2x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MC**2 + MC**4 - 2*MB**2*MW**2 - 2*MC**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MC)**3)', (P.W__plus__,P.d):'(((3*CKM2x1*ee**2*MC**2*complexconjugate(CKM2x1))/(2.*sw**2) + (3*CKM2x1*ee**2*MD**2*complexconjugate(CKM2x1))/(2.*sw**2) + (3*CKM2x1*ee**2*MC**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) - (3*CKM2x1*ee**2*MC**2*MD**2*complexconjugate(CKM2x1))/(MW**2*sw**2) + (3*CKM2x1*ee**2*MD**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) - (3*CKM2x1*ee**2*MW**2*complexconjugate(CKM2x1))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MD**2 + MD**4 - 2*MC**2*MW**2 - 2*MD**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MC)**3)', (P.W__plus__,P.s):'(((3*CKM2x2*ee**2*MC**2*complexconjugate(CKM2x2))/(2.*sw**2) + (3*CKM2x2*ee**2*MS**2*complexconjugate(CKM2x2))/(2.*sw**2) + (3*CKM2x2*ee**2*MC**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) - (3*CKM2x2*ee**2*MC**2*MS**2*complexconjugate(CKM2x2))/(MW**2*sw**2) + (3*CKM2x2*ee**2*MS**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) - (3*CKM2x2*ee**2*MW**2*complexconjugate(CKM2x2))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MS**2 + MS**4 - 2*MC**2*MW**2 - 2*MS**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MC)**3)'}) Decay_d = Decay(name = 'Decay_d', particle = P.d, partial_widths = {(P.W__minus__,P.c):'(((3*CKM2x1*ee**2*MC**2*complexconjugate(CKM2x1))/(2.*sw**2) + (3*CKM2x1*ee**2*MD**2*complexconjugate(CKM2x1))/(2.*sw**2) + (3*CKM2x1*ee**2*MC**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) - (3*CKM2x1*ee**2*MC**2*MD**2*complexconjugate(CKM2x1))/(MW**2*sw**2) + (3*CKM2x1*ee**2*MD**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) - (3*CKM2x1*ee**2*MW**2*complexconjugate(CKM2x1))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MD**2 + MD**4 - 2*MC**2*MW**2 - 2*MD**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MD)**3)', (P.W__minus__,P.t):'(((3*CKM3x1*ee**2*MD**2*complexconjugate(CKM3x1))/(2.*sw**2) + (3*CKM3x1*ee**2*MT**2*complexconjugate(CKM3x1))/(2.*sw**2) + (3*CKM3x1*ee**2*MD**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) - (3*CKM3x1*ee**2*MD**2*MT**2*complexconjugate(CKM3x1))/(MW**2*sw**2) + (3*CKM3x1*ee**2*MT**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) - (3*CKM3x1*ee**2*MW**2*complexconjugate(CKM3x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MT**2 + MT**4 - 2*MD**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MD)**3)', (P.W__minus__,P.u):'(((3*CKM1x1*ee**2*MD**2*complexconjugate(CKM1x1))/(2.*sw**2) + (3*CKM1x1*ee**2*MU**2*complexconjugate(CKM1x1))/(2.*sw**2) + (3*CKM1x1*ee**2*MD**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) - (3*CKM1x1*ee**2*MD**2*MU**2*complexconjugate(CKM1x1))/(MW**2*sw**2) + (3*CKM1x1*ee**2*MU**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) - (3*CKM1x1*ee**2*MW**2*complexconjugate(CKM1x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MU**2 + MU**4 - 2*MD**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MD)**3)'}) Decay_e__minus__ = Decay(name = 'Decay_e__minus__', particle = P.e__minus__, partial_widths = {(P.fi,P.mu__minus__):'((Me*MMU*vev**2*yf1x2*yf2x1 - 50*vev**2*yf1x2*complexconjugate(yf1x2) + (Me**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. + (MMU**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. - 50*vev**2*yf2x1*complexconjugate(yf2x1) + (Me**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. + (MMU**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. + Me*MMU*vev**2*complexconjugate(yf1x2)*complexconjugate(yf2x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MMU**2 - 2*Me**2*MMU**2 + MMU**4))/(32.*cmath.pi*abs(Me)**3)', (P.fi,P.ta__minus__):'((Me*MTA*vev**2*yf1x3*yf3x1 - 50*vev**2*yf1x3*complexconjugate(yf1x3) + (Me**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. + (MTA**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. - 50*vev**2*yf3x1*complexconjugate(yf3x1) + (Me**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. + (MTA**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. + Me*MTA*vev**2*complexconjugate(yf1x3)*complexconjugate(yf3x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MTA**2 - 2*Me**2*MTA**2 + MTA**4))/(32.*cmath.pi*abs(Me)**3)', (P.W__minus__,P.ve):'((Me**2 - MW**2)*((ee**2*Me**2)/(2.*sw**2) + (ee**2*Me**4)/(2.*MW**2*sw**2) - (ee**2*MW**2)/sw**2))/(32.*cmath.pi*abs(Me)**3)'}) Decay_fi = Decay(name = 'Decay_fi', particle = P.fi, partial_widths = {(P.e__minus__,P.e__plus__):'((-(Me**2*vev**2*yf1x1**2) + 100*vev**2*yf1x1*complexconjugate(yf1x1) - 2*Me**2*vev**2*yf1x1*complexconjugate(yf1x1) - Me**2*vev**2*complexconjugate(yf1x1)**2)*cmath.sqrt(10000 - 400*Me**2))/(16000.*cmath.pi)', (P.e__minus__,P.mu__plus__):'((-(Me*MMU*vev**2*yf1x2*yf2x1) + 50*vev**2*yf1x2*complexconjugate(yf1x2) - (Me**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. - (MMU**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. + 50*vev**2*yf2x1*complexconjugate(yf2x1) - (Me**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. - (MMU**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. - Me*MMU*vev**2*complexconjugate(yf1x2)*complexconjugate(yf2x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MMU**2 - 2*Me**2*MMU**2 + MMU**4))/(16000.*cmath.pi)', (P.e__minus__,P.ta__plus__):'((-(Me*MTA*vev**2*yf1x3*yf3x1) + 50*vev**2*yf1x3*complexconjugate(yf1x3) - (Me**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. - (MTA**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. + 50*vev**2*yf3x1*complexconjugate(yf3x1) - (Me**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. - (MTA**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. - Me*MTA*vev**2*complexconjugate(yf1x3)*complexconjugate(yf3x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MTA**2 - 2*Me**2*MTA**2 + MTA**4))/(16000.*cmath.pi)', (P.mu__minus__,P.e__plus__):'((-(Me*MMU*vev**2*yf1x2*yf2x1) + 50*vev**2*yf1x2*complexconjugate(yf1x2) - (Me**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. - (MMU**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. + 50*vev**2*yf2x1*complexconjugate(yf2x1) - (Me**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. - (MMU**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. - Me*MMU*vev**2*complexconjugate(yf1x2)*complexconjugate(yf2x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MMU**2 - 2*Me**2*MMU**2 + MMU**4))/(16000.*cmath.pi)', (P.mu__minus__,P.mu__plus__):'((-(MMU**2*vev**2*yf2x2**2) + 100*vev**2*yf2x2*complexconjugate(yf2x2) - 2*MMU**2*vev**2*yf2x2*complexconjugate(yf2x2) - MMU**2*vev**2*complexconjugate(yf2x2)**2)*cmath.sqrt(10000 - 400*MMU**2))/(16000.*cmath.pi)', (P.mu__minus__,P.ta__plus__):'((-(MMU*MTA*vev**2*yf2x3*yf3x2) + 50*vev**2*yf2x3*complexconjugate(yf2x3) - (MMU**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. - (MTA**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. + 50*vev**2*yf3x2*complexconjugate(yf3x2) - (MMU**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. - (MTA**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. - MMU*MTA*vev**2*complexconjugate(yf2x3)*complexconjugate(yf3x2))*cmath.sqrt(10000 - 200*MMU**2 + MMU**4 - 200*MTA**2 - 2*MMU**2*MTA**2 + MTA**4))/(16000.*cmath.pi)', (P.ta__minus__,P.e__plus__):'((-(Me*MTA*vev**2*yf1x3*yf3x1) + 50*vev**2*yf1x3*complexconjugate(yf1x3) - (Me**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. - (MTA**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. + 50*vev**2*yf3x1*complexconjugate(yf3x1) - (Me**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. - (MTA**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. - Me*MTA*vev**2*complexconjugate(yf1x3)*complexconjugate(yf3x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MTA**2 - 2*Me**2*MTA**2 + MTA**4))/(16000.*cmath.pi)', (P.ta__minus__,P.mu__plus__):'((-(MMU*MTA*vev**2*yf2x3*yf3x2) + 50*vev**2*yf2x3*complexconjugate(yf2x3) - (MMU**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. - (MTA**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. + 50*vev**2*yf3x2*complexconjugate(yf3x2) - (MMU**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. - (MTA**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. - MMU*MTA*vev**2*complexconjugate(yf2x3)*complexconjugate(yf3x2))*cmath.sqrt(10000 - 200*MMU**2 + MMU**4 - 200*MTA**2 - 2*MMU**2*MTA**2 + MTA**4))/(16000.*cmath.pi)', (P.ta__minus__,P.ta__plus__):'((-(MTA**2*vev**2*yf3x3**2) + 100*vev**2*yf3x3*complexconjugate(yf3x3) - 2*MTA**2*vev**2*yf3x3*complexconjugate(yf3x3) - MTA**2*vev**2*complexconjugate(yf3x3)**2)*cmath.sqrt(10000 - 400*MTA**2))/(16000.*cmath.pi)'}) Decay_H = Decay(name = 'Decay_H', particle = P.H, partial_widths = {(P.a,P.a):'(AH**2*MH**6)/(64.*cmath.pi*abs(MH)**3)', (P.b,P.b__tilde__):'((-12*MB**2*yb**2 + 3*MH**2*yb**2)*cmath.sqrt(-4*MB**2*MH**2 + MH**4))/(16.*cmath.pi*abs(MH)**3)', (P.c,P.c__tilde__):'((-12*MC**2*yc**2 + 3*MH**2*yc**2)*cmath.sqrt(-4*MC**2*MH**2 + MH**4))/(16.*cmath.pi*abs(MH)**3)', (P.d,P.d__tilde__):'((-12*MD**2*ydo**2 + 3*MH**2*ydo**2)*cmath.sqrt(-4*MD**2*MH**2 + MH**4))/(16.*cmath.pi*abs(MH)**3)', (P.e__minus__,P.e__plus__):'((-4*Me**2*ye**2 + MH**2*ye**2)*cmath.sqrt(-4*Me**2*MH**2 + MH**4))/(16.*cmath.pi*abs(MH)**3)', (P.fi,P.fi):'(kq**2*vev**2*cmath.sqrt(-400*MH**2 + MH**4))/(32.*cmath.pi*abs(MH)**3)', (P.g,P.g):'(GH**2*MH**6)/(8.*cmath.pi*abs(MH)**3)', (P.mu__minus__,P.mu__plus__):'((MH**2*ym**2 - 4*MMU**2*ym**2)*cmath.sqrt(MH**4 - 4*MH**2*MMU**2))/(16.*cmath.pi*abs(MH)**3)', (P.s,P.s__tilde__):'((3*MH**2*ys**2 - 12*MS**2*ys**2)*cmath.sqrt(MH**4 - 4*MH**2*MS**2))/(16.*cmath.pi*abs(MH)**3)', (P.t,P.t__tilde__):'((3*MH**2*yt**2 - 12*MT**2*yt**2)*cmath.sqrt(MH**4 - 4*MH**2*MT**2))/(16.*cmath.pi*abs(MH)**3)', (P.ta__minus__,P.ta__plus__):'((MH**2*ytau**2 - 4*MTA**2*ytau**2)*cmath.sqrt(MH**4 - 4*MH**2*MTA**2))/(16.*cmath.pi*abs(MH)**3)', (P.u,P.u__tilde__):'((3*MH**2*yup**2 - 12*MU**2*yup**2)*cmath.sqrt(MH**4 - 4*MH**2*MU**2))/(16.*cmath.pi*abs(MH)**3)', (P.W__minus__,P.W__plus__):'(((3*ee**4*vev**2)/(4.*sw**4) + (ee**4*MH**4*vev**2)/(16.*MW**4*sw**4) - (ee**4*MH**2*vev**2)/(4.*MW**2*sw**4))*cmath.sqrt(MH**4 - 4*MH**2*MW**2))/(16.*cmath.pi*abs(MH)**3)', (P.Z,P.Z):'(((9*ee**4*vev**2)/2. + (3*ee**4*MH**4*vev**2)/(8.*MZ**4) - (3*ee**4*MH**2*vev**2)/(2.*MZ**2) + (3*cw**4*ee**4*vev**2)/(4.*sw**4) + (cw**4*ee**4*MH**4*vev**2)/(16.*MZ**4*sw**4) - (cw**4*ee**4*MH**2*vev**2)/(4.*MZ**2*sw**4) + (3*cw**2*ee**4*vev**2)/sw**2 + (cw**2*ee**4*MH**4*vev**2)/(4.*MZ**4*sw**2) - (cw**2*ee**4*MH**2*vev**2)/(MZ**2*sw**2) + (3*ee**4*sw**2*vev**2)/cw**2 + (ee**4*MH**4*sw**2*vev**2)/(4.*cw**2*MZ**4) - (ee**4*MH**2*sw**2*vev**2)/(cw**2*MZ**2) + (3*ee**4*sw**4*vev**2)/(4.*cw**4) + (ee**4*MH**4*sw**4*vev**2)/(16.*cw**4*MZ**4) - (ee**4*MH**2*sw**4*vev**2)/(4.*cw**4*MZ**2))*cmath.sqrt(MH**4 - 4*MH**2*MZ**2))/(32.*cmath.pi*abs(MH)**3)'}) Decay_h1 = Decay(name = 'Decay_h1', particle = P.h1, partial_widths = {(P.g,P.g):'(Gphi**2*MP**6)/(8.*cmath.pi*abs(MP)**3)'}) Decay_mu__minus__ = Decay(name = 'Decay_mu__minus__', particle = P.mu__minus__, partial_widths = {(P.fi,P.e__minus__):'((Me*MMU*vev**2*yf1x2*yf2x1 - 50*vev**2*yf1x2*complexconjugate(yf1x2) + (Me**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. + (MMU**2*vev**2*yf1x2*complexconjugate(yf1x2))/2. - 50*vev**2*yf2x1*complexconjugate(yf2x1) + (Me**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. + (MMU**2*vev**2*yf2x1*complexconjugate(yf2x1))/2. + Me*MMU*vev**2*complexconjugate(yf1x2)*complexconjugate(yf2x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MMU**2 - 2*Me**2*MMU**2 + MMU**4))/(32.*cmath.pi*abs(MMU)**3)', (P.fi,P.ta__minus__):'((MMU*MTA*vev**2*yf2x3*yf3x2 - 50*vev**2*yf2x3*complexconjugate(yf2x3) + (MMU**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. + (MTA**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. - 50*vev**2*yf3x2*complexconjugate(yf3x2) + (MMU**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. + (MTA**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. + MMU*MTA*vev**2*complexconjugate(yf2x3)*complexconjugate(yf3x2))*cmath.sqrt(10000 - 200*MMU**2 + MMU**4 - 200*MTA**2 - 2*MMU**2*MTA**2 + MTA**4))/(32.*cmath.pi*abs(MMU)**3)', (P.W__minus__,P.vm):'((MMU**2 - MW**2)*((ee**2*MMU**2)/(2.*sw**2) + (ee**2*MMU**4)/(2.*MW**2*sw**2) - (ee**2*MW**2)/sw**2))/(32.*cmath.pi*abs(MMU)**3)'}) Decay_s = Decay(name = 'Decay_s', particle = P.s, partial_widths = {(P.W__minus__,P.c):'(((3*CKM2x2*ee**2*MC**2*complexconjugate(CKM2x2))/(2.*sw**2) + (3*CKM2x2*ee**2*MS**2*complexconjugate(CKM2x2))/(2.*sw**2) + (3*CKM2x2*ee**2*MC**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) - (3*CKM2x2*ee**2*MC**2*MS**2*complexconjugate(CKM2x2))/(MW**2*sw**2) + (3*CKM2x2*ee**2*MS**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) - (3*CKM2x2*ee**2*MW**2*complexconjugate(CKM2x2))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MS**2 + MS**4 - 2*MC**2*MW**2 - 2*MS**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MS)**3)', (P.W__minus__,P.t):'(((3*CKM3x2*ee**2*MS**2*complexconjugate(CKM3x2))/(2.*sw**2) + (3*CKM3x2*ee**2*MT**2*complexconjugate(CKM3x2))/(2.*sw**2) + (3*CKM3x2*ee**2*MS**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) - (3*CKM3x2*ee**2*MS**2*MT**2*complexconjugate(CKM3x2))/(MW**2*sw**2) + (3*CKM3x2*ee**2*MT**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) - (3*CKM3x2*ee**2*MW**2*complexconjugate(CKM3x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MT**2 + MT**4 - 2*MS**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MS)**3)', (P.W__minus__,P.u):'(((3*CKM1x2*ee**2*MS**2*complexconjugate(CKM1x2))/(2.*sw**2) + (3*CKM1x2*ee**2*MU**2*complexconjugate(CKM1x2))/(2.*sw**2) + (3*CKM1x2*ee**2*MS**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) - (3*CKM1x2*ee**2*MS**2*MU**2*complexconjugate(CKM1x2))/(MW**2*sw**2) + (3*CKM1x2*ee**2*MU**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) - (3*CKM1x2*ee**2*MW**2*complexconjugate(CKM1x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MU**2 + MU**4 - 2*MS**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MS)**3)'}) Decay_t = Decay(name = 'Decay_t', particle = P.t, partial_widths = {(P.W__plus__,P.b):'(((3*CKM3x3*ee**2*MB**2*complexconjugate(CKM3x3))/(2.*sw**2) + (3*CKM3x3*ee**2*MT**2*complexconjugate(CKM3x3))/(2.*sw**2) + (3*CKM3x3*ee**2*MB**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) - (3*CKM3x3*ee**2*MB**2*MT**2*complexconjugate(CKM3x3))/(MW**2*sw**2) + (3*CKM3x3*ee**2*MT**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) - (3*CKM3x3*ee**2*MW**2*complexconjugate(CKM3x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MT**2 + MT**4 - 2*MB**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MT)**3)', (P.W__plus__,P.d):'(((3*CKM3x1*ee**2*MD**2*complexconjugate(CKM3x1))/(2.*sw**2) + (3*CKM3x1*ee**2*MT**2*complexconjugate(CKM3x1))/(2.*sw**2) + (3*CKM3x1*ee**2*MD**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) - (3*CKM3x1*ee**2*MD**2*MT**2*complexconjugate(CKM3x1))/(MW**2*sw**2) + (3*CKM3x1*ee**2*MT**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) - (3*CKM3x1*ee**2*MW**2*complexconjugate(CKM3x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MT**2 + MT**4 - 2*MD**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MT)**3)', (P.W__plus__,P.s):'(((3*CKM3x2*ee**2*MS**2*complexconjugate(CKM3x2))/(2.*sw**2) + (3*CKM3x2*ee**2*MT**2*complexconjugate(CKM3x2))/(2.*sw**2) + (3*CKM3x2*ee**2*MS**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) - (3*CKM3x2*ee**2*MS**2*MT**2*complexconjugate(CKM3x2))/(MW**2*sw**2) + (3*CKM3x2*ee**2*MT**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) - (3*CKM3x2*ee**2*MW**2*complexconjugate(CKM3x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MT**2 + MT**4 - 2*MS**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MT)**3)'}) Decay_ta__minus__ = Decay(name = 'Decay_ta__minus__', particle = P.ta__minus__, partial_widths = {(P.fi,P.e__minus__):'((Me*MTA*vev**2*yf1x3*yf3x1 - 50*vev**2*yf1x3*complexconjugate(yf1x3) + (Me**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. + (MTA**2*vev**2*yf1x3*complexconjugate(yf1x3))/2. - 50*vev**2*yf3x1*complexconjugate(yf3x1) + (Me**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. + (MTA**2*vev**2*yf3x1*complexconjugate(yf3x1))/2. + Me*MTA*vev**2*complexconjugate(yf1x3)*complexconjugate(yf3x1))*cmath.sqrt(10000 - 200*Me**2 + Me**4 - 200*MTA**2 - 2*Me**2*MTA**2 + MTA**4))/(32.*cmath.pi*abs(MTA)**3)', (P.fi,P.mu__minus__):'((MMU*MTA*vev**2*yf2x3*yf3x2 - 50*vev**2*yf2x3*complexconjugate(yf2x3) + (MMU**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. + (MTA**2*vev**2*yf2x3*complexconjugate(yf2x3))/2. - 50*vev**2*yf3x2*complexconjugate(yf3x2) + (MMU**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. + (MTA**2*vev**2*yf3x2*complexconjugate(yf3x2))/2. + MMU*MTA*vev**2*complexconjugate(yf2x3)*complexconjugate(yf3x2))*cmath.sqrt(10000 - 200*MMU**2 + MMU**4 - 200*MTA**2 - 2*MMU**2*MTA**2 + MTA**4))/(32.*cmath.pi*abs(MTA)**3)', (P.W__minus__,P.vt):'((MTA**2 - MW**2)*((ee**2*MTA**2)/(2.*sw**2) + (ee**2*MTA**4)/(2.*MW**2*sw**2) - (ee**2*MW**2)/sw**2))/(32.*cmath.pi*abs(MTA)**3)'}) Decay_u = Decay(name = 'Decay_u', particle = P.u, partial_widths = {(P.W__plus__,P.b):'(((3*CKM1x3*ee**2*MB**2*complexconjugate(CKM1x3))/(2.*sw**2) + (3*CKM1x3*ee**2*MU**2*complexconjugate(CKM1x3))/(2.*sw**2) + (3*CKM1x3*ee**2*MB**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) - (3*CKM1x3*ee**2*MB**2*MU**2*complexconjugate(CKM1x3))/(MW**2*sw**2) + (3*CKM1x3*ee**2*MU**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) - (3*CKM1x3*ee**2*MW**2*complexconjugate(CKM1x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MU**2 + MU**4 - 2*MB**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MU)**3)', (P.W__plus__,P.d):'(((3*CKM1x1*ee**2*MD**2*complexconjugate(CKM1x1))/(2.*sw**2) + (3*CKM1x1*ee**2*MU**2*complexconjugate(CKM1x1))/(2.*sw**2) + (3*CKM1x1*ee**2*MD**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) - (3*CKM1x1*ee**2*MD**2*MU**2*complexconjugate(CKM1x1))/(MW**2*sw**2) + (3*CKM1x1*ee**2*MU**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) - (3*CKM1x1*ee**2*MW**2*complexconjugate(CKM1x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MU**2 + MU**4 - 2*MD**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MU)**3)', (P.W__plus__,P.s):'(((3*CKM1x2*ee**2*MS**2*complexconjugate(CKM1x2))/(2.*sw**2) + (3*CKM1x2*ee**2*MU**2*complexconjugate(CKM1x2))/(2.*sw**2) + (3*CKM1x2*ee**2*MS**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) - (3*CKM1x2*ee**2*MS**2*MU**2*complexconjugate(CKM1x2))/(MW**2*sw**2) + (3*CKM1x2*ee**2*MU**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) - (3*CKM1x2*ee**2*MW**2*complexconjugate(CKM1x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MU**2 + MU**4 - 2*MS**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(96.*cmath.pi*abs(MU)**3)'}) Decay_W__plus__ = Decay(name = 'Decay_W__plus__', particle = P.W__plus__, partial_widths = {(P.c,P.b__tilde__):'(((-3*CKM2x3*ee**2*MB**2*complexconjugate(CKM2x3))/(2.*sw**2) - (3*CKM2x3*ee**2*MC**2*complexconjugate(CKM2x3))/(2.*sw**2) - (3*CKM2x3*ee**2*MB**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) + (3*CKM2x3*ee**2*MB**2*MC**2*complexconjugate(CKM2x3))/(MW**2*sw**2) - (3*CKM2x3*ee**2*MC**4*complexconjugate(CKM2x3))/(2.*MW**2*sw**2) + (3*CKM2x3*ee**2*MW**2*complexconjugate(CKM2x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MC**2 + MC**4 - 2*MB**2*MW**2 - 2*MC**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.c,P.d__tilde__):'(((-3*CKM2x1*ee**2*MC**2*complexconjugate(CKM2x1))/(2.*sw**2) - (3*CKM2x1*ee**2*MD**2*complexconjugate(CKM2x1))/(2.*sw**2) - (3*CKM2x1*ee**2*MC**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) + (3*CKM2x1*ee**2*MC**2*MD**2*complexconjugate(CKM2x1))/(MW**2*sw**2) - (3*CKM2x1*ee**2*MD**4*complexconjugate(CKM2x1))/(2.*MW**2*sw**2) + (3*CKM2x1*ee**2*MW**2*complexconjugate(CKM2x1))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MD**2 + MD**4 - 2*MC**2*MW**2 - 2*MD**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.c,P.s__tilde__):'(((-3*CKM2x2*ee**2*MC**2*complexconjugate(CKM2x2))/(2.*sw**2) - (3*CKM2x2*ee**2*MS**2*complexconjugate(CKM2x2))/(2.*sw**2) - (3*CKM2x2*ee**2*MC**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) + (3*CKM2x2*ee**2*MC**2*MS**2*complexconjugate(CKM2x2))/(MW**2*sw**2) - (3*CKM2x2*ee**2*MS**4*complexconjugate(CKM2x2))/(2.*MW**2*sw**2) + (3*CKM2x2*ee**2*MW**2*complexconjugate(CKM2x2))/sw**2)*cmath.sqrt(MC**4 - 2*MC**2*MS**2 + MS**4 - 2*MC**2*MW**2 - 2*MS**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.t,P.b__tilde__):'(((-3*CKM3x3*ee**2*MB**2*complexconjugate(CKM3x3))/(2.*sw**2) - (3*CKM3x3*ee**2*MT**2*complexconjugate(CKM3x3))/(2.*sw**2) - (3*CKM3x3*ee**2*MB**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) + (3*CKM3x3*ee**2*MB**2*MT**2*complexconjugate(CKM3x3))/(MW**2*sw**2) - (3*CKM3x3*ee**2*MT**4*complexconjugate(CKM3x3))/(2.*MW**2*sw**2) + (3*CKM3x3*ee**2*MW**2*complexconjugate(CKM3x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MT**2 + MT**4 - 2*MB**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.t,P.d__tilde__):'(((-3*CKM3x1*ee**2*MD**2*complexconjugate(CKM3x1))/(2.*sw**2) - (3*CKM3x1*ee**2*MT**2*complexconjugate(CKM3x1))/(2.*sw**2) - (3*CKM3x1*ee**2*MD**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) + (3*CKM3x1*ee**2*MD**2*MT**2*complexconjugate(CKM3x1))/(MW**2*sw**2) - (3*CKM3x1*ee**2*MT**4*complexconjugate(CKM3x1))/(2.*MW**2*sw**2) + (3*CKM3x1*ee**2*MW**2*complexconjugate(CKM3x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MT**2 + MT**4 - 2*MD**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.t,P.s__tilde__):'(((-3*CKM3x2*ee**2*MS**2*complexconjugate(CKM3x2))/(2.*sw**2) - (3*CKM3x2*ee**2*MT**2*complexconjugate(CKM3x2))/(2.*sw**2) - (3*CKM3x2*ee**2*MS**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) + (3*CKM3x2*ee**2*MS**2*MT**2*complexconjugate(CKM3x2))/(MW**2*sw**2) - (3*CKM3x2*ee**2*MT**4*complexconjugate(CKM3x2))/(2.*MW**2*sw**2) + (3*CKM3x2*ee**2*MW**2*complexconjugate(CKM3x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MT**2 + MT**4 - 2*MS**2*MW**2 - 2*MT**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.u,P.b__tilde__):'(((-3*CKM1x3*ee**2*MB**2*complexconjugate(CKM1x3))/(2.*sw**2) - (3*CKM1x3*ee**2*MU**2*complexconjugate(CKM1x3))/(2.*sw**2) - (3*CKM1x3*ee**2*MB**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) + (3*CKM1x3*ee**2*MB**2*MU**2*complexconjugate(CKM1x3))/(MW**2*sw**2) - (3*CKM1x3*ee**2*MU**4*complexconjugate(CKM1x3))/(2.*MW**2*sw**2) + (3*CKM1x3*ee**2*MW**2*complexconjugate(CKM1x3))/sw**2)*cmath.sqrt(MB**4 - 2*MB**2*MU**2 + MU**4 - 2*MB**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.u,P.d__tilde__):'(((-3*CKM1x1*ee**2*MD**2*complexconjugate(CKM1x1))/(2.*sw**2) - (3*CKM1x1*ee**2*MU**2*complexconjugate(CKM1x1))/(2.*sw**2) - (3*CKM1x1*ee**2*MD**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) + (3*CKM1x1*ee**2*MD**2*MU**2*complexconjugate(CKM1x1))/(MW**2*sw**2) - (3*CKM1x1*ee**2*MU**4*complexconjugate(CKM1x1))/(2.*MW**2*sw**2) + (3*CKM1x1*ee**2*MW**2*complexconjugate(CKM1x1))/sw**2)*cmath.sqrt(MD**4 - 2*MD**2*MU**2 + MU**4 - 2*MD**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.u,P.s__tilde__):'(((-3*CKM1x2*ee**2*MS**2*complexconjugate(CKM1x2))/(2.*sw**2) - (3*CKM1x2*ee**2*MU**2*complexconjugate(CKM1x2))/(2.*sw**2) - (3*CKM1x2*ee**2*MS**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) + (3*CKM1x2*ee**2*MS**2*MU**2*complexconjugate(CKM1x2))/(MW**2*sw**2) - (3*CKM1x2*ee**2*MU**4*complexconjugate(CKM1x2))/(2.*MW**2*sw**2) + (3*CKM1x2*ee**2*MW**2*complexconjugate(CKM1x2))/sw**2)*cmath.sqrt(MS**4 - 2*MS**2*MU**2 + MU**4 - 2*MS**2*MW**2 - 2*MU**2*MW**2 + MW**4))/(48.*cmath.pi*abs(MW)**3)', (P.ve,P.e__plus__):'((-Me**2 + MW**2)*(-(ee**2*Me**2)/(2.*sw**2) - (ee**2*Me**4)/(2.*MW**2*sw**2) + (ee**2*MW**2)/sw**2))/(48.*cmath.pi*abs(MW)**3)', (P.vm,P.mu__plus__):'((-MMU**2 + MW**2)*(-(ee**2*MMU**2)/(2.*sw**2) - (ee**2*MMU**4)/(2.*MW**2*sw**2) + (ee**2*MW**2)/sw**2))/(48.*cmath.pi*abs(MW)**3)', (P.vt,P.ta__plus__):'((-MTA**2 + MW**2)*(-(ee**2*MTA**2)/(2.*sw**2) - (ee**2*MTA**4)/(2.*MW**2*sw**2) + (ee**2*MW**2)/sw**2))/(48.*cmath.pi*abs(MW)**3)'}) Decay_Z = Decay(name = 'Decay_Z', particle = P.Z, partial_widths = {(P.b,P.b__tilde__):'((-7*ee**2*MB**2 + ee**2*MZ**2 - (3*cw**2*ee**2*MB**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) - (17*ee**2*MB**2*sw**2)/(6.*cw**2) + (5*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MB**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.c,P.c__tilde__):'((-11*ee**2*MC**2 - ee**2*MZ**2 - (3*cw**2*ee**2*MC**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*MC**2*sw**2)/(6.*cw**2) + (17*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MC**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.d,P.d__tilde__):'((-7*ee**2*MD**2 + ee**2*MZ**2 - (3*cw**2*ee**2*MD**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) - (17*ee**2*MD**2*sw**2)/(6.*cw**2) + (5*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MD**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.e__minus__,P.e__plus__):'((-5*ee**2*Me**2 - ee**2*MZ**2 - (cw**2*ee**2*Me**2)/(2.*sw**2) + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*Me**2*sw**2)/(2.*cw**2) + (5*ee**2*MZ**2*sw**2)/(2.*cw**2))*cmath.sqrt(-4*Me**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.mu__minus__,P.mu__plus__):'((-5*ee**2*MMU**2 - ee**2*MZ**2 - (cw**2*ee**2*MMU**2)/(2.*sw**2) + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*MMU**2*sw**2)/(2.*cw**2) + (5*ee**2*MZ**2*sw**2)/(2.*cw**2))*cmath.sqrt(-4*MMU**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.s,P.s__tilde__):'((-7*ee**2*MS**2 + ee**2*MZ**2 - (3*cw**2*ee**2*MS**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) - (17*ee**2*MS**2*sw**2)/(6.*cw**2) + (5*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MS**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.t,P.t__tilde__):'((-11*ee**2*MT**2 - ee**2*MZ**2 - (3*cw**2*ee**2*MT**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*MT**2*sw**2)/(6.*cw**2) + (17*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MT**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.ta__minus__,P.ta__plus__):'((-5*ee**2*MTA**2 - ee**2*MZ**2 - (cw**2*ee**2*MTA**2)/(2.*sw**2) + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*MTA**2*sw**2)/(2.*cw**2) + (5*ee**2*MZ**2*sw**2)/(2.*cw**2))*cmath.sqrt(-4*MTA**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.u,P.u__tilde__):'((-11*ee**2*MU**2 - ee**2*MZ**2 - (3*cw**2*ee**2*MU**2)/(2.*sw**2) + (3*cw**2*ee**2*MZ**2)/(2.*sw**2) + (7*ee**2*MU**2*sw**2)/(6.*cw**2) + (17*ee**2*MZ**2*sw**2)/(6.*cw**2))*cmath.sqrt(-4*MU**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)', (P.ve,P.ve__tilde__):'(MZ**2*(ee**2*MZ**2 + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (ee**2*MZ**2*sw**2)/(2.*cw**2)))/(48.*cmath.pi*abs(MZ)**3)', (P.vm,P.vm__tilde__):'(MZ**2*(ee**2*MZ**2 + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (ee**2*MZ**2*sw**2)/(2.*cw**2)))/(48.*cmath.pi*abs(MZ)**3)', (P.vt,P.vt__tilde__):'(MZ**2*(ee**2*MZ**2 + (cw**2*ee**2*MZ**2)/(2.*sw**2) + (ee**2*MZ**2*sw**2)/(2.*cw**2)))/(48.*cmath.pi*abs(MZ)**3)', (P.W__minus__,P.W__plus__):'(((-12*cw**2*ee**2*MW**2)/sw**2 - (17*cw**2*ee**2*MZ**2)/sw**2 + (4*cw**2*ee**2*MZ**4)/(MW**2*sw**2) + (cw**2*ee**2*MZ**6)/(4.*MW**4*sw**2))*cmath.sqrt(-4*MW**2*MZ**2 + MZ**4))/(48.*cmath.pi*abs(MZ)**3)'})

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11

0a217410b98cfc852d24d4bfb32c93bb3ad5f318

9,310

py

Python

app_files/main/draw.py

ammaralt/fyp-project

1edd9cd01762c392b338c486b5aba932130f2a77

[ "Apache-2.0" ]

3

2021-12-27T06:52:07.000Z

2022-02-19T20:58:48.000Z

app_files/main/draw.py

ammaralt/fyp-project

1edd9cd01762c392b338c486b5aba932130f2a77

[ "Apache-2.0" ]

1

2022-03-03T13:02:40.000Z

app_files/main/draw.py

ammaralt/fyp-project

1edd9cd01762c392b338c486b5aba932130f2a77

[ "Apache-2.0" ]

2

2022-03-04T04:23:10.000Z

2022-03-07T19:57:19.000Z

import cv2 as cv def draw_landmarks(image, landmark_point): if len(landmark_point) > 0: cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]), (255, 255, 255), 2) cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), (0, 0, 0), 6) cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]), (255, 255, 255), 2) for index, landmark in enumerate(landmark_point): if index == 0: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 1: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 2: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 3: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 4: cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) if index == 5: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 6: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 7: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 8: cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) if index == 9: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 10: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 11: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 12: cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) if index == 13: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 14: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 15: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 16: cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) if index == 17: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 18: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 19: cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1) if index == 20: cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255), -1) cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1) return image def draw_info_text(image, handedness, hand_sign_text): info_text = handedness.classification[0].label[0:] if hand_sign_text != "": info_text = "Predicted Text" + ':' + hand_sign_text cv.putText(image, info_text, (10, 60), cv.FONT_HERSHEY_SIMPLEX, 1.0, (196, 255, 255), 2, cv.LINE_AA) return image

47.258883

104

0.497852

1,270

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3.569291

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47.258883

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null

1

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1

0

1

0

null

0

9

0a2541a673a0eb06c7fe04fcdc66de461976f9bb

75

py

Python

tests/testapp/scripts/invalid_import_script.py

behconsci/django-extensions

716832fb9fbe78c6970930b378e0c0962beccd2e

[ "MIT" ]

1

2019-04-15T10:28:42.000Z

tests/testapp/scripts/invalid_import_script.py

behconsci/django-extensions

716832fb9fbe78c6970930b378e0c0962beccd2e

[ "MIT" ]

null

tests/testapp/scripts/invalid_import_script.py

behconsci/django-extensions

716832fb9fbe78c6970930b378e0c0962beccd2e

[ "MIT" ]

null

# -*- coding: utf-8 -*- import invalidpackage # NOQA def run(): pass

10.714286

28

0.586667

9

75

4.888889

1

0

0.017544

0.24

75

6

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12.5

0.754386

0.346667

0

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0.333333

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0.333333

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null

0

1

0

null

0

1

0

1

0

7

0a36a397bf11280d75fd71c3df712a63db55b62b

119

py

Python

modules/junk_test.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

null

modules/junk_test.py

mwanakijiji/rrlfe2

0637b348b8d3e54ff34c56caa8b4c6fdac1c699e

[ "MIT" ]

18

2022-01-13T14:43:57.000Z

2022-03-24T12:52:41.000Z

modules/junk_test.py

mwanakijiji/rrlyrae_metallicity

1aa867eb9c96dba433271207efdf758cc7849360

[ "MIT" ]

null

import astropy #from modules2 import * #from modules2 import create_spec_realizations def junk_fcn(): return "1"

14.875

46

0.764706

16

119

5.5

0.75

0.272727

0.409091

0

0.030303

0.168067

119

7

47

17

0.858586

0.563025

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0.02

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0.333333

true

0

0.333333

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1

0

null

0

1

0

1

0

9

0a6f0f76784a7f2e00ca4a6af4cf86a27566ba58

2,310

py

Python

common/http_util.py

mozhumz/machine_learning_py

880f6778ac16b0a16a80b31972a35304caa91dc1

[ "MulanPSL-1.0" ]

null

common/http_util.py

mozhumz/machine_learning_py

880f6778ac16b0a16a80b31972a35304caa91dc1

[ "MulanPSL-1.0" ]

null

common/http_util.py

mozhumz/machine_learning_py

880f6778ac16b0a16a80b31972a35304caa91dc1

[ "MulanPSL-1.0" ]

null

from http.cookies import SimpleCookie def parse_cookie(cookie): sck=SimpleCookie(cookie) return {v.key:v.value for k,v in sck.items()} if __name__ == '__main__': cookie = '_zap=da65d76c-fbcb-4c76-b731-1bbff7fb04ec; d_c0="AJCaOogwaxGPTjuq8M-UoT-tMeRndcdBNWM=|1592031146"; _ga=GA1.2.2020371219.1592031151; _xsrf=FC5ygIfy9Id687u5zuQ2rhDZD26rAfS2; __snaker__id=YqPDWFm68rU5FPKm; _9755xjdesxxd_=32; YD00517437729195%3AWM_TID=n6EMpKJWJsFFQERRAUMqyz%2FmzH62puZU; gdxidpyhxdE=bCfVQYiVxphcSI1VuWp82ViBonGYx%2B94%2Bc59UNOInO1gyEZtm5VejBMyUUSuk7whM5XUv7ESISl6dpSEd2hv%5CsoERW3E4Xm%2FQ2dLV9iEvwJP1ITidZXEe%5CYTyX67LjVowQlI4k%5CLs9fh%2Bvzxgh9yfSgEoSAkGJmjPz89r6V3cmuOKjIQ%3A1633443302320; YD00517437729195%3AWM_NI=Nv46%2F8vUXvDaBROGu%2BpcxIzpu%2B6QFUlVro4L7PuPWimUEgml5RJ%2BMJKE48N0qX6J6IbAwUaTzO4zsqYix5x8oRkI2VVLuYXeMxss5Ic5I2RMrDr7%2FKvASeb0ILD2gQyheG8%3D; YD00517437729195%3AWM_NIKE=9ca17ae2e6ffcda170e2e6eed1aa34b6b782d4b5748d868fa7c14f929b8b84f87c85868b91c453a1b09fd6cf2af0fea7c3b92a8eaafcd7ae34b68b9897c76b828e81a8f821b4bdb8aaf97cb7f5fab2c55fb08abe90b748f590bbb1d663af8b8b88f068f78e86ccd042899bfcd9cd3cb388faabb543a29fb9b6e460ba91fc9bf27b9bb7b8d6fb34ad8ea5aab15e9694bbbadb6f8fa8bd94b843a1eb87d3d147b29cbed8ae41b69f9d86e13c8b948cd3e65bbbb8aba8bb37e2a3; z_c0="2|1:0|10:1633442413|4:z_c0|92:Mi4xT3pVSUJBQUFBQUFBa0pvNmlEQnJFU1lBQUFCZ0FsVk5iYWhKWWdBZDg4OG1RT2t6Q1NNX1N4TU5zVUtFcHd1Q0F3|5fcf3e2ca5ef5c2ab208793aa1a0c1fcf44564aff46faea55c815141169af26e"; tst=r; q_c1=be1793119d154b20846b05d3dad2418f|1636173236000|1636173236000; __utmc=51854390; __utmz=51854390.1636173236.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utmv=51854390.100--|2=registration_date=20170201=1^3=entry_date=20170201=1; SESSIONID=iszLu5J1gez7KZsprLPb2iITVrMF816iqGlupSPDSg8; JOID=UVoXBklMBnK8rgJVfEpGLPNz_dJsJHUP99o2H0oqYgiM4lknDPQc9NuuA1J86RYP8NUH8ljHVZGZIRi_BLLKpbc=; osd=UlERB0lPDXS9rgFeektGL_h1_NJvL3MO99k9GUsqYQOK41kkB_Id9NilBVN86h0J8dUE-V7GVZKSJxm_B7nMpLc=; Hm_lvt_98beee57fd2ef70ccdd5ca52b9740c49=1636173082,1636173200,1636181886; NOT_UNREGISTER_WAITING=1; __utma=51854390.2020371219.1592031151.1636173610.1636184011.4; __utmb=51854390.0.10.1636184011; Hm_lpvt_98beee57fd2ef70ccdd5ca52b9740c49=1636184127; KLBRSID=d1f07ca9b929274b65d830a00cbd719a|1636184217|1636173080' print('"%s"'%parse_cookie(cookie).get('d_c0'))

256.666667

2,087

0.898268

177

2,310

11.435028

0.762712

0.029644

0.016798

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2,310

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2,088

256.666667

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0

1

null

0

1

0

1

0

1

null

0

7

0a709c9c302fb4bac026340e77977bab3b9c4204

61

py

Python

wikicodename/__init__.py

bartlomiej-zdrojewski/wikicodename

256483479e9124323c1e14db9fdb4ed23117a2fc

[ "MIT" ]

null

wikicodename/__init__.py

bartlomiej-zdrojewski/wikicodename

256483479e9124323c1e14db9fdb4ed23117a2fc

[ "MIT" ]

null

wikicodename/__init__.py

bartlomiej-zdrojewski/wikicodename

256483479e9124323c1e14db9fdb4ed23117a2fc

[ "MIT" ]

null

from .wiki_data import Cache from .wiki_data import WikiData

20.333333

31

0.836066

10

61

4.9

0.6

0.326531

0.489796

0.734694

0

0.131148

61

2

32

30.5

0.924528

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

0a7ab96b2a1419e3fdbf053cddc1cb763c486286

130

py

Python

env/lib/python3.6/site-packages/import_export/signals.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

132

2015-01-06T21:43:42.000Z

2021-04-05T10:01:49.000Z

env/lib/python3.6/site-packages/import_export/signals.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

87

2015-01-04T08:24:19.000Z

2017-05-08T18:09:45.000Z

env/lib/python3.6/site-packages/import_export/signals.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

[ "MIT" ]

95

2015-01-01T22:30:15.000Z

2021-06-25T14:54:16.000Z

from django.dispatch import Signal post_export = Signal(providing_args=["model"]) post_import = Signal(providing_args=["model"])

26

46

0.784615

17

130

5.764706

0.588235

0.244898

0.387755

0.489796

0

0.084615

130

4

47

32.5

0.823529

0

0.076923

0

1

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false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

0a9a967880c9255e71296c5c383c13d8b9cdab5c

107

py

Python

rpython/jit/backend/x86/test/test_zrpy_gc_boehm.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

381

2018-08-18T03:37:22.000Z

2022-02-06T23:57:36.000Z

rpython/jit/backend/x86/test/test_zrpy_gc_boehm.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

16

2018-09-22T18:12:47.000Z

2022-02-22T20:03:59.000Z

rpython/jit/backend/x86/test/test_zrpy_gc_boehm.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

55

2015-08-16T02:41:30.000Z

2022-03-20T20:33:35.000Z

from rpython.jit.backend.llsupport.test.zrpy_gc_boehm_test import compile_boehm_test as test_compile_boehm

53.5

106

0.897196

18

107

4.944444

0.666667

0.202247

0

0.056075

107

1

107

0.881188

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true

0

1

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null

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1

0

1

0

null

0

1

0

1

0

1

0

7

0a9a9efe5a790eb96299aec21b2331b2cb80da34

82,448

py

Python

routeTracker/UI.py

bt530/routeTracker

8d546836e42293ec8444da201555378dbe85a820

[ "MIT" ]

1

2021-06-26T08:07:33.000Z

routeTracker/UI.py

bt530/routeTracker

8d546836e42293ec8444da201555378dbe85a820

[ "MIT" ]

1

2021-05-27T00:58:59.000Z

routeTracker/UI.py

bt530/routeTracker

8d546836e42293ec8444da201555378dbe85a820

[ "MIT" ]

1

2021-05-19T08:37:59.000Z

import tkinter as tk import ctypes import time import math import pickle import winsound from tkinter.filedialog import askopenfilename, askdirectory from tkinter import messagebox import os import pyperclip import webbrowser import traceback import datetime import sys if getattr(sys, 'frozen', False): # Running as compiled running_dir = sys._MEIPASS + "/files/" # Same path name than pyinstaller option else: running_dir = "./" # Path name when run with Python interpreter ICON = running_dir + "carrier.ico" class POINT(ctypes.Structure): _fields_ = [("x", ctypes.c_ulong), ("y", ctypes.c_ulong)] def mousePosition(): pt = POINT() ctypes.windll.user32.GetCursorPos(ctypes.byref(pt)) return int(pt.x), int(pt.y) class UserInterface(): def __init__(self, reader, debug=False): user32 = ctypes.windll.user32 width, height = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) dataTemplate = {'window position': [width / 2 - 250, height / 4], 'route positions': {}, 'showType': 'show', 'topmost': 1, 'alarm': True, 'logLocation': '', 'shipCargo': 0, 'carrierCargo': 0, 'more': False, 'jumpStart': '00:00', 'jumpEnd': '23:59' } self.exiting = False self.debug = debug # self.logReader=reader self.maxCountdown = 60 * 21 self.logCheck = 5 self.logReader = reader self.scroll = 0 self.dragOffset = [0, 0] self.scrolling = False self.stopLocations = [] self.pristineRings = [] self.countdown = self.maxCountdown self.countdownStart = time.time() self.logStart = 0 self.currentFileDataKeys = {} self.currentFileData = [['unknown']] self.system = None self.nextSystem = 'unknown' self.currentFile = None self.position = 0 self.dragging = False self.draggingPos = [width / 2 - 250, height / 4] self.hovering = False self.scrollTop = [0, 0] self.scrollBottom = [0, 0] try: with open("trackerData.txt", "rb") as f: self.data = pickle.load(f) except FileNotFoundError: self.data = dataTemplate with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) added = False dataKeys = list(self.data.keys()) for i in list(dataTemplate.keys()): if i not in dataKeys: self.data[i] = dataTemplate[i] added = True if added: with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) if "current file" in list(self.data.keys()): self.currentFile = self.data["current file"] self.openFile(dialogue=False) if self.data['logLocation'] != '': self.logReader.folderLocation = self.data['logLocation'] self.createWindow() def mainLoop(self): timeLoop = time.time() while True: time.sleep(0.01) try: pyperclip.paste() if self.exiting: self.saveData() self.window.destroy() self.root.destroy() try: self.settingsWindow.destroy() except AttributeError: pass except tk._tkinter.TclError: pass break # self.menu.update() currentTime = time.time() if currentTime - self.logStart > self.logCheck and self.currentFileData != None: self.logStart = currentTime self.logReader.updateLog() # print(self.logReader.oldSystem,self.logReader.currentSystem) if self.logReader.oldSystem != self.logReader.currentSystem: # print("Jumped to "+self.logReader.currentSystem) self.nextSystem = 'unknown' for i in range(self.position, len(self.currentFileData) - 1): ##print(i) ##print(ui.currentFileData[i]) if self.currentFileData[i][self.currentFileDataKeys['System Name']] == self.logReader.currentSystem: # print('copied ' + self.nextSystem + ' to clipboard') if self.currentFileData[i + 1][self.currentFileDataKeys['System Name']] == \ self.currentFileData[i][self.currentFileDataKeys['System Name']]: self.position = i + 1 # print('double') else: self.position = i self.nextSystem = self.currentFileData[self.position + 1][ self.currentFileDataKeys['System Name']] pyperclip.copy(self.nextSystem) self.data['route positions'][self.currentFile] = self.position self.saveData() # try: self.clear() """ except Exception as e: #print(e)""" break # try: self.root.update() x, y = mousePosition() if self.hovering: self.clear() self.hovering = False if self.dragging: self.data['window position'] = [x - self.dragOffset[0], y - self.dragOffset[1]] self.clear() elif self.scrolling and self.scrollLength < len(self.currentFileData): proportion = (y - self.barCentre - self.scrollTop[1]) / self.scrollHeight self.scroll = round(proportion * len(self.currentFileData) - self.position) self.limitScroll() self.clear() elif currentTime - timeLoop > 1: self.clear() timeLoop = currentTime """ if self.data['topmost'] == 0: if not self.window.focus_displayof(): if topSet != 0: self.window.attributes('-topmost', 0) topSet=0 elif topSet != 1: self.window.attributes('-topmost', 1) topSet=1 ##print(topSet) """ """ except Exception as e: if e == SystemExit: break else: self.exiting=True #print(e)""" try: self.settingsWindow.update() except AttributeError: pass except tk._tkinter.TclError: pass except pyperclip.PyperclipWindowsException: time.sleep(2) def openFile(self, dialogue=True): self.scroll = 0 if dialogue: self.currentFile = askopenfilename() self.data["current file"] = self.currentFile if self.currentFile != '': # print(self.currentFile) # print(self.data) if self.currentFile in list(self.data['route positions'].keys()): self.position = self.data['route positions'][self.currentFile] else: self.position = 0 self.data['route positions'][self.currentFile] = self.position self.saveData() try: with open(self.currentFile, 'r') as f: self.currentFileData = f.read() self.currentFileData = "".join(self.currentFileData.split("\"")) self.currentFileData = self.currentFileData.split("\n") self.currentFileData = [i.split(",") for i in self.currentFileData] ##print(currentFileData) self.currentFileDataKeys = {} for i in range(len(self.currentFileData[0])): self.currentFileDataKeys[self.currentFileData[0][i]] = i del self.currentFileData[0] if [''] in self.currentFileData: self.currentFileData.remove(['']) self.stopLocations = [] self.pristineRings=[] for i in range(len(self.currentFileData) - 1): if self.currentFileData[i][self.currentFileDataKeys['System Name']] == self.currentFileData[i + 1][ self.currentFileDataKeys['System Name']]: self.stopLocations.append(i) if self.currentFileData[i][self.currentFileDataKeys['Pristine']] == 'Yes': self.pristineRings.append(i) ##print(self.currentFileData[i]) ##print(self.stopLocations) except FileNotFoundError as e: messagebox.showerror("Import Error", e) if self.data['showType'] == 'show': self.logReader.resetValues() self.logStart = 0 self.createWindow() def saveData(self, values=None): with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) # overlay functions def clear(self): # all to change with new UI try: self.canvas.destroy() except: pass clip = pyperclip.paste() x, y = self.data['window position'][0], self.data['window position'][1] self.canvas = tk.Canvas(self.window, bg="pink", bd=0, highlightthickness=0, relief='ridge') self.canvas.pack(fill="both", expand=True) self.canvas.create_rectangle(x, y, x + 520, y + 30, fill='black') if self.logReader.currentSystem == clip: self.canvas.create_text(x + 5, y + 5, text=self.logReader.currentSystem, font="Ebrima 13 bold", fill='green', anchor='nw') else: self.canvas.create_text(x + 5, y + 5, text=self.logReader.currentSystem, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_rectangle(x + 150, y, x + 500, y + 30, fill='black') self.canvas.create_text(x + 158, y + 5, text='>> ', font="Ebrima 13 bold", fill='orange', anchor='nw') if self.nextSystem == clip: self.canvas.create_text(x + 190, y + 5, text=self.nextSystem, font="Ebrima 13 bold", fill='green', anchor='nw') else: self.canvas.create_text(x + 190, y + 5, text=self.nextSystem, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_rectangle(x + 340, y, x + 500, y + 30, fill='black') timeSince = time.time() - self.logReader.lastJumpRequest timeSince = self.maxCountdown - timeSince if timeSince > 0: if timeSince < 10 and self.data['alarm']: winsound.Beep(3000, 100) mins = str(round(timeSince // 60)) seconds = str(math.floor(timeSince % 60)) if len(mins) == 1: mins = '0' + mins if len(seconds) == 1: seconds = '0' + seconds text = mins + ':' + seconds else: text = 'Ready' text = '| ' + text + ' |' self.canvas.create_text(x + 350, y + 5, text=text, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 420, y + 5, text='☰', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 440, y + 5, text='📁', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 463, y + 5, text='⚙', font="Ebrima 13 bold", fill='orange', anchor='nw') if self.data['topmost'] == 1: self.canvas.create_text(x + 485, y + 5, text='⮝', font="Ebrima 13 bold", fill='orange', anchor='nw') else: self.canvas.create_text(x + 485, y + 5, text='⮟', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 500, y + 5, text='✘', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_line(x, y, x + 520, y, fill='orange') self.canvas.create_line(x, y + 30, x + 520, y + 30, fill='orange') if self.data['more']: self.createDashboard() def createDashboard(self): mouseX, mouseY = mousePosition() x, y = self.data['window position'][0], self.data['window position'][1] try: self.canvas.create_rectangle(x, y + 35, x + 520, y + 600, fill='black', outline='orange') # pannel backgrounds self.canvas.create_rectangle(x + 10, y + 40, x + 510, y + 150, fill='#111111', outline='#333333') self.canvas.create_rectangle(x + 10, y + 160, x + 510, y + 270, fill='#111111', outline='#333333') self.canvas.create_rectangle(x + 10, y + 280, x + 510, y + 540, fill='#111111', outline='#333333') horPos = self.position / len(self.currentFileData) * 480 + 20 self.canvas.create_line(x + 20, y + 80, x + horPos, y + 80, fill='orange', width=2, dash=10) self.canvas.create_line(x + horPos, y + 80, x + 500, y + 80, fill='orange', width=2) above = False for i in [0] + self.stopLocations: horPos = i / len(self.currentFileData) * 480 + 20 if i in self.pristineRings: colour = '#a1c5ff' else: colour = 'orange' if i in self.stopLocations: size=3 else: size=1 if above and (mouseX - (x + horPos)) ** 2 + (mouseY - (y + 80)) ** 2 < size ** 2: if horPos < 250: anchor = 'w' else: anchor = 'e' self.canvas.create_line(x + horPos, y + 70, x + horPos, y + 80, fill=colour) jumps=i - self.position if jumps > 0: eta = ' | ' + self.getETA(jumps=jumps) else: eta='' self.canvas.create_text(x + horPos, y + 60, text=self.currentFileData[i][self.currentFileDataKeys['System Name']] + eta, font="Ebrima 10 bold", fill=colour, anchor=anchor) self.canvas.create_oval(x + horPos - size*1.5, y + 80 - size*1.5, x + horPos + size*1.5, y + 80 + size*1.5, fill=colour, outline=colour) elif not above: self.canvas.create_rectangle(x + horPos - 8, y + 80, x + 500, y + 120, fill='#111111', outline='#111111') self.canvas.create_line(x + horPos, y + 80, x + horPos, y + 90, fill='orange') self.canvas.create_text(x + horPos, y + 95, text=self.currentFileData[i][self.currentFileDataKeys['System Name']], font="Ebrima 10 bold", fill='orange', anchor='w') self.canvas.create_oval(x + horPos - size, y + 80 - size, x + horPos + 5, y + 80 + size, fill=colour, outline=colour) above = True horPos = 500 jumps = len(self.currentFileData) - 1 - self.position if jumps > 0: eta = ' | ' + self.getETA(jumps=jumps) else: eta = '' #self.canvas.create_rectangle(x + horPos - 10, y + 80, x + 500, y + 120, fill='#111111', outline='#111111') self.canvas.create_line(x + horPos, y + 80, x + horPos, y + 90, fill='orange') self.canvas.create_text(x + horPos, y + 95, text=" " + self.currentFileData[-1][self.currentFileDataKeys['System Name']] + eta, font="Ebrima 10 bold", fill='orange', anchor='e') ##print(self.stopLocations) self.canvas.create_oval(x + 15, y + 75, x + 25, y + 85, fill='orange', outline='orange') self.canvas.create_oval(x + 495, y + 75, x + 505, y + 85, fill='orange', outline='orange') self.canvas.create_text(x + 20, y + 130, text="Jumps | Completed: " + str(self.position), font="Ebrima 13 bold", fill='orange', anchor='w') found = False for i in self.stopLocations: diff = i - self.position if diff >= 0: self.canvas.create_text(x + 220, y + 130, text="| To Waypoint: " + str(diff), font="Ebrima 13 bold", fill='orange', anchor='w') found = True break if not found: self.canvas.create_text(x + 220, y + 130, text="| To Waypoint: " + str(len(self.currentFileData) - self.position - 1), font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 380, y + 130, text="| Left: " + str(len(self.currentFileData) - self.position - 1), font="Ebrima 13 bold", fill='orange', anchor='w') """ for i in self.stopLocations + self.pristineRings: if i in self.stopLocations: fill = 'orange' outline = 'orange' else: fill = 'orange' outline = 'orange' horPos = i / len(self.currentFileData) * 480 + 20 self.canvas.create_oval(x + horPos - 3, y + 77, x + horPos + 3, y + 83, fill=fill, outline=outline) ##print('h',horPos) ##print(self.stopLocations) """ horPos = self.position / len(self.currentFileData) * 480 + 20 self.canvas.create_polygon(x + horPos - 5, y + 85, x + horPos, y + 75, x + horPos + 5, y + 85, fill='#00ff00', outline='#00ff00') try: reqFuel = self.currentFileData[self.position][self.currentFileDataKeys['Tritium in market']] reqFuel = int(reqFuel) if reqFuel > 0: reqFuel += 1000 else: for i in range(self.position, len(self.currentFileData)): reqFuel += int(self.currentFileData[i][self.currentFileDataKeys['Fuel Used']]) reqFuel -= int(self.currentFileData[self.position][self.currentFileDataKeys['Fuel Used']]) except IndexError: reqFuel = 'Error' tankFuel = self.logReader.carrierFuel shipFuel = self.logReader.shipInventory - self.data['shipCargo'] carrierFuel = self.logReader.carrierInventory - self.data['carrierCargo'] self.canvas.create_text(x + 20, y + 180, text="Tritium | ", font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 95, y + 180, text="Tank: " + str(tankFuel), font="Ebrima 13 bold", fill='green', anchor='w') self.canvas.create_text(x + 190, y + 180, text="| Ship: " + str(shipFuel), font="Ebrima 13 bold", fill='blue', anchor='w') self.canvas.create_text(x + 280, y + 180, text="| Cargo: " + str(carrierFuel), font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 400, y + 180, text="| Min: " + str(reqFuel), font="Ebrima 13 bold", fill='red', anchor='w') fuelTotal = tankFuel + shipFuel + carrierFuel if reqFuel == 'Error': reqFuel = 0 width = max(fuelTotal, reqFuel) / 480 self.canvas.create_rectangle(x + 20, y + 210, x + 20 + reqFuel / width, y + 230, fill='red', outline='red', stipple='gray25') self.canvas.create_rectangle(x + 20, y + 210, x + 20 + tankFuel / width, y + 230, fill='green', outline='green') self.canvas.create_rectangle(x + 20 + tankFuel / width, y + 210, x + 20 + shipFuel / width + tankFuel / width, y + 230, fill='blue', outline='blue') self.canvas.create_rectangle(x + 20 + shipFuel / width + tankFuel / width, y + 210, x + 20 + shipFuel / width + tankFuel / width + carrierFuel / width, y + 230, fill='orange', outline='orange') self.canvas.create_rectangle(x + 20 + reqFuel / width - 2, y + 210, x + 20 + reqFuel / width, y + 230, fill='red', outline='red') diff = fuelTotal - reqFuel if diff >= 0: self.canvas.create_text(x + 260, y + 250, text="You are " + str(diff) + " Tritium in excess", font="Ebrima 13 bold", fill='green') else: self.canvas.create_text(x + 260, y + 250, text="Warning! You are " + str(-diff) + " Tritium short!", font="Ebrima 13 bold", fill='red') self.canvas.create_text(x + 260, y + 197, text="Please note you need to open the carrier management page to update this.", font="Ebrima 8 bold", fill='orange') # routeList length = 10 self.scrollLength = length verticalSpacing = 25 self.verticalSpacing = verticalSpacing boxHeight = 20 self.boxHeight = boxHeight startY = 290 self.scrollHeight = verticalSpacing * (length - 1) + boxHeight barHeight = min(length / len(self.currentFileData) * self.scrollHeight, self.scrollHeight) self.barCentre = barHeight / 2 barPosition = y + (self.position + self.scroll) / len(self.currentFileData) * self.scrollHeight + startY clipboard = pyperclip.paste() for i in range(length): if self.position + self.scroll + i < len(self.currentFileData): if self.currentFileData[self.position + self.scroll + i][ self.currentFileDataKeys['System Name']] == clipboard: boxFill = 'green' textFill = 'black' elif self.scroll + i == 0: boxFill = 'orange' textFill = 'black' elif self.position + self.scroll + i in self.stopLocations or self.position + self.scroll + i - 1 in self.stopLocations: boxFill = 'red' textFill = 'black' else: boxFill = 'black' textFill = 'orange' if self.position + self.scroll + i in self.pristineRings: textFill = '#a1c5ff' self.canvas.create_rectangle(x + 15, y + startY + verticalSpacing * i, x + 490, y + startY + verticalSpacing * i + boxHeight, fill=boxFill, outline='orange') self.canvas.create_text(x + 17, y + startY + verticalSpacing * i, text=self.currentFileData[self.position + self.scroll + i][ self.currentFileDataKeys['System Name']], font="Ebrima 12 bold", fill=textFill, anchor='nw') #print(self.scroll + i - self.position) if self.scroll + i > 0: #print('eta') eta = self.getETA(jumps=self.scroll + i) self.canvas.create_text(x + 490, y + startY + verticalSpacing * i, text=eta, font="Ebrima 12 bold", fill=textFill, anchor='ne') self.canvas.create_rectangle(x + 497, y + startY, x + 505, y + startY + self.scrollHeight, fill='black', outline='orange') self.scrollTop = [x + 497, y + startY] self.scrollBottom = [x + 505, y + startY + verticalSpacing * (length - 1) + boxHeight] self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + barHeight, fill='orange', outline='orange') for i in self.pristineRings: barPosition = y + i / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + 1, fill='#a1c5ff', outline='#a1c5ff') for i in self.stopLocations: barPosition = y + i / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + 1, fill='red', outline='red') for i in self.pristineRings: if i in self.stopLocations: barPosition = y + i / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 498, barPosition + 1, fill='#a1c5ff', outline='#a1c5ff') self.canvas.create_rectangle(x + 499, barPosition, x + 500, barPosition + 1, fill='#a1c5ff', outline='#a1c5ff') self.canvas.create_rectangle(x + 501, barPosition, x + 502, barPosition + 1, fill='#a1c5ff', outline='#a1c5ff') self.canvas.create_rectangle(x + 503, barPosition, x + 504, barPosition + 1, fill='#a1c5ff', outline='#a1c5ff') barPosition = y + self.position / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + 1, fill='orange', outline='orange') except Exception as e: self.canvas.create_rectangle(x, y + 35, x + 520, y + 600, fill='black', outline='orange') self.canvas.create_text(x + 260, y + 250, text=traceback.format_exc(), font="Ebrima 13 bold", fill='red') def getETA(self, jumps): start = self.data['jumpStart'] end = self.data['jumpEnd'] # print(start,end) start = start.split(':') end = end.split(':') start = sum([int(val) * 60 ** (1 - i) for i, val in enumerate(start)]) end = sum([int(val) * 60 ** (1 - i) for i, val in enumerate(end)]) if end < start: end += 1440 gap = 1440 - (end - start) alreadyQueued = time.time() - self.logReader.lastJumpRequest if alreadyQueued < 21 * 60: #print('from last jump') now = datetime.datetime.utcfromtimestamp(self.logReader.lastJumpRequest) #print(now) else: now = datetime.datetime.now(datetime.timezone.utc) nowReset = now - datetime.timedelta(hours=now.hour,minutes=now.minute) todayMinutes = sum([int(val) * 60 ** (1 - i) for i, val in enumerate([now.hour, now.minute])]) #print(todayMinutes) #print(start) if todayMinutes + 1440 < end and todayMinutes < start: #print('shift') end -= 1440 start -= 1440 todayStart = max(todayMinutes, start) #print('ts',todayStart) todayAvailableJumps = (end - todayStart) // 21 + 1 regularAvailableJumps = (end - start) // 21 + 1 if jumps <= todayAvailableJumps: eta = nowReset + datetime.timedelta(minutes = todayStart + jumps * 21 - 5) elif (jumps - todayAvailableJumps) % regularAvailableJumps != 0: eta = nowReset + datetime.timedelta(minutes = 1440 + start + (jumps - todayAvailableJumps) // regularAvailableJumps * 1440 + (( jumps - todayAvailableJumps) % regularAvailableJumps) * 21 - 5) else: eta = nowReset + datetime.timedelta( minutes=1440 + start + ((jumps - todayAvailableJumps) // regularAvailableJumps) * 1440 + 15) #print(todayStart - todayMinutes) #eta += datetime.timedelta(minutes=max(0,todayStart - todayMinutes)) eta = eta.strftime("%H:%M - %d/%m/%Y") return eta # print(start,end) def mouseDown(self, values): ##print(values) self.startDrag = time.time() if self.scrollTop[0] <= values.x and values.x <= self.scrollBottom[0] and self.scrollTop[1] <= values.y and \ self.scrollBottom[1] >= values.y and not self.dragging: self.scrolling = True elif not self.scrolling: self.dragging = True self.dragOffset = [values.x - self.data['window position'][0], values.y - self.data['window position'][1]] def endDrag(self, values): self.dragging = False self.scrolling = False relX = values.x - self.data['window position'][0] if time.time() - self.startDrag < 0.3 and values.y - self.data['window position'][1] < 30: if relX < 150: pyperclip.copy(self.logReader.currentSystem) # print('copied ' + self.logReader.currentSystem + ' to clipboard') elif relX > 190 and relX < 340: pyperclip.copy(self.nextSystem) # print('copied ' + self.nextSystem + ' to clipboard') # more elif relX > 420 and relX < 440: self.data['more'] = not self.data['more'] pass # open route elif relX > 440 and relX < 463: self.openFile() # settings elif relX > 463 and relX < 485: self.settings() pass # minimise elif relX > 485 and relX < 500: self.data['topmost'] = -self.data['topmost'] + 1 self.createWindow() # close elif relX > 500 and relX < 520: self.exiting = True self.saveData() elif time.time() - self.startDrag < 0.3 and 15 < relX and 490 > relX and values.y - self.scrollTop[1] > 0 and \ self.scrollBottom[1] - values.y > 0: proportion = (values.y - self.scrollTop[1]) / self.scrollHeight clickedOn = proportion * self.scrollLength pyperclip.copy(self.currentFileData[math.floor(self.position + self.scroll + clickedOn)][ self.currentFileDataKeys['System Name']]) self.clear() def wheel(self, values): if self.scrollLength < len(self.currentFileData): self.scroll += round(-values.delta / 100) self.limitScroll() self.clear() def limitScroll(self): if self.scroll + self.position < 0: self.scroll = -self.position if self.scroll + self.position >= len(self.currentFileData) - self.scrollLength: self.scroll = len(self.currentFileData) - self.position - self.scrollLength def hover(self, values): if not self.dragging and abs(values.y - (self.data['window position'][1] + 80)) < 5: self.hovering = True def createWindow(self, onTop=1): try: self.root.destroy() self.window.destroy() except AttributeError: pass except tk._tkinter.TclError: pass self.hidden = False user32 = ctypes.windll.user32 width, height = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) self.root = tk.Tk() self.root.title('routeTracker') self.root.attributes('-alpha', 0.0) # For icon # self.root.lower() self.root.iconify() if self.data['topmost'] == 1: self.window = tk.Toplevel(self.root, highlightthickness=0) self.root.iconbitmap(ICON) setWindowIcon=False else: self.window = tk.Tk() setWindowIcon=True self.window.title('routeTracker') self.window.config(bg="pink") self.window.geometry(str(width) + "x" + str(height)) # Whatever size #self.root.iconbitmap(ICON) if self.data['topmost'] == 1: self.window.overrideredirect(1) # Remove border self.window.attributes('-topmost', 1) else: self.window.wm_attributes('-fullscreen', 'true') self.root.overrideredirect(1) self.window.wm_attributes("-transparentcolor", "pink") self.window.bind('<ButtonPress-1>', self.mouseDown) self.window.bind('<ButtonRelease-1>', self.endDrag) self.window.bind("<MouseWheel>", self.wheel) self.window.bind("<Motion>", self.hover) self.clear() if setWindowIcon: self.window.iconbitmap(ICON) # settings window def alarm(self): self.data['alarm'] = not self.data['alarm'] self.saveData() self.alarmButton.config(text='Alarm: ' + str(self.data['alarm'])) def logLocation(self): self.data['logLocation'] = askdirectory() # print(self.data['logLocation']) if self.data['logLocation'] != '': self.logReader.folderLocation = self.data['logLocation'] else: self.logReader.defaultLocation() self.saveData() self.logLocationLabel.config(text=self.logReader.folderLocation) def change(self, values): value = self.carrierGoodsEntry.get() try: value = int(value) self.data['carrierCargo'] = value except ValueError: pass value = self.shipGoodsEntry.get() try: value = int(value) self.data['shipCargo'] = value except ValueError: pass jumpStart = self.jumpStartEntry.get() try: check = jumpStart.split(':') check[0] = int(check[0]) check[1] = int(check[1]) if 0 <= check[0] and check[0] < 24 and 0 <= check[1] and check[1] < 60 and len(check) == 2: self.data['jumpStart'] = jumpStart except ValueError: pass except IndexError: pass jumpEnd = self.jumpEndEntry.get() try: check = jumpEnd.split(':') check[0] = int(check[0]) check[1] = int(check[1]) if 0 <= check[0] and check[0] < 24 and 0 <= check[1] and check[1] < 60 and len(check) == 2: self.data['jumpEnd'] = jumpEnd except ValueError: pass except IndexError: pass self.saveData() def settings(self): try: self.settingsWindow.destroy() except AttributeError: pass except tk._tkinter.TclError: pass # print('settings window does not yet exist') self.settingsWindow = tk.Tk() self.settingsWindow.title('Settings') self.settingsWindow.config(bg='black') settingsLabel = tk.Label(self.settingsWindow, text='Settings\n', font="Ebrima 15 bold", fg='orange', bg='black') settingsLabel.grid(row=0, column=0, columnspan=2) # log reader file path openBrowserButton = tk.Button(self.settingsWindow, text='Log File Location', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=25, command=self.logLocation) openBrowserButton.grid(row=1, column=0) self.logLocationLabel = tk.Label(self.settingsWindow, text=self.logReader.folderLocation, font="Ebrima 15 bold", fg='orange', bg='black') self.logLocationLabel.grid(row=1, column=1) # alarm self.alarmButton = tk.Button(self.settingsWindow, text='Alarm: ' + str(self.data['alarm']), font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, command=self.alarm) self.alarmButton.grid(row=2, column=0) # non tritium goods in carrier carrierGoods = tk.Button(self.settingsWindow, text='Carrier Goods', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=25, ) carrierGoods.grid(row=3, column=0) self.carrierGoodsEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.carrierGoodsEntry.insert(0, str(self.data['carrierCargo'])) self.carrierGoodsEntry.grid(row=3, column=1) # non tritium goods in ship shipGoods = tk.Button(self.settingsWindow, text='Ship Goods', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) shipGoods.grid(row=4, column=0) self.shipGoodsEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.shipGoodsEntry.insert(0, str(self.data['shipCargo'])) self.shipGoodsEntry.grid(row=4, column=1) # jump start/end jumpStart = tk.Button(self.settingsWindow, text='Start Time', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) jumpStart.grid(row=5, column=0) self.jumpStartEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.jumpStartEntry.insert(0, str(self.data['jumpStart'])) self.jumpStartEntry.grid(row=5, column=1) jumpEnd = tk.Button(self.settingsWindow, text='End Time', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) jumpEnd.grid(row=6, column=0) self.jumpEndEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.jumpEndEntry.insert(0, str(self.data['jumpEnd'])) self.jumpEndEntry.grid(row=6, column=1) # Thanks invite = tk.Button(self.settingsWindow, text="With thanks to the Fleet Carrier Owner's Club (Especially Ed, NalloVint and Brandstaetter)", font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=90, command=lambda: webbrowser.open('https://discord.gg/tcMPHfh')) invite.grid(row=7, column=0, columnspan=2) self.settingsWindow.iconbitmap(ICON) self.settingsWindow.bind("<KeyRelease>", self.change) if __name__ == '__main__': from logReader import * reader = logReader() ui = UserInterface(reader=reader) # print('t') ui.getETA(18) ui.mainLoop() ##print(countdownMessage) # window.mainloop() self.scrollTop = [0, 0] self.scrollBottom = [0, 0] try: with open("trackerData.txt", "rb") as f: self.data = pickle.load(f) except FileNotFoundError: self.data = dataTemplate with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) added = False dataKeys = list(self.data.keys()) for i in list(dataTemplate.keys()): if i not in dataKeys: self.data[i] = dataTemplate[i] added = True if added: with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) if "current file" in list(self.data.keys()): self.currentFile = self.data["current file"] self.openFile(dialogue=False) if self.data['logLocation'] != '': self.logReader.folderLocation = self.data['logLocation'] self.createWindow() def mainLoop(self): timeLoop = time.time() while True: time.sleep(0.01) try: pyperclip.paste() if self.exiting: self.saveData() self.window.destroy() self.root.destroy() try: self.settingsWindow.destroy() except AttributeError: pass except tk._tkinter.TclError: pass break # self.menu.update() currentTime = time.time() if currentTime - self.logStart > self.logCheck and self.currentFileData != None: self.logStart = currentTime self.logReader.updateLog() # print(self.logReader.oldSystem,self.logReader.currentSystem) if self.logReader.oldSystem != self.logReader.currentSystem: # print("Jumped to "+self.logReader.currentSystem) self.nextSystem = 'unknown' for i in range(self.position, len(self.currentFileData) - 1): ##print(i) ##print(ui.currentFileData[i]) if self.currentFileData[i][self.currentFileDataKeys['System Name']] == self.logReader.currentSystem: # print('copied ' + self.nextSystem + ' to clipboard') if self.currentFileData[i + 1][self.currentFileDataKeys['System Name']] == \ self.currentFileData[i][self.currentFileDataKeys['System Name']]: self.position = i + 1 # print('double') else: self.position = i self.nextSystem = self.currentFileData[self.position + 1][ self.currentFileDataKeys['System Name']] pyperclip.copy(self.nextSystem) self.data['route positions'][self.currentFile] = self.position self.saveData() # try: self.clear() """ except Exception as e: #print(e)""" break # try: self.root.update() x, y = mousePosition() if self.hovering: self.clear() self.hovering = False if self.dragging: self.data['window position'] = [x - self.dragOffset[0], y - self.dragOffset[1]] self.clear() elif self.scrolling and self.scrollLength < len(self.currentFileData): proportion = (y - self.barCentre - self.scrollTop[1]) / self.scrollHeight self.scroll = round(proportion * len(self.currentFileData) - self.position) self.limitScroll() self.clear() elif currentTime - timeLoop > 1: self.clear() timeLoop = currentTime """ if self.data['topmost'] == 0: if not self.window.focus_displayof(): if topSet != 0: self.window.attributes('-topmost', 0) topSet=0 elif topSet != 1: self.window.attributes('-topmost', 1) topSet=1 ##print(topSet) """ """ except Exception as e: if e == SystemExit: break else: self.exiting=True #print(e)""" try: self.settingsWindow.update() except AttributeError: pass except tk._tkinter.TclError: pass except pyperclip.PyperclipWindowsException: time.sleep(2) def openFile(self, dialogue=True): self.scroll = 0 if dialogue: self.currentFile = askopenfilename() self.data["current file"] = self.currentFile if self.currentFile != '': # print(self.currentFile) # print(self.data) if self.currentFile in list(self.data['route positions'].keys()): self.position = self.data['route positions'][self.currentFile] else: self.position = 0 self.data['route positions'][self.currentFile] = self.position self.saveData() try: with open(self.currentFile, 'r') as f: self.currentFileData = f.read() self.currentFileData = "".join(self.currentFileData.split("\"")) self.currentFileData = self.currentFileData.split("\n") self.currentFileData = [i.split(",") for i in self.currentFileData] ##print(currentFileData) self.currentFileDataKeys = {} for i in range(len(self.currentFileData[0])): self.currentFileDataKeys[self.currentFileData[0][i]] = i del self.currentFileData[0] if [''] in self.currentFileData: self.currentFileData.remove(['']) self.stopLocations = [] for i in range(len(self.currentFileData) - 1): if self.currentFileData[i][self.currentFileDataKeys['System Name']] == self.currentFileData[i + 1][ self.currentFileDataKeys['System Name']]: self.stopLocations.append(i) ##print(self.currentFileData[i]) ##print(self.stopLocations) except FileNotFoundError as e: messagebox.showerror("Import Error", e) if self.data['showType'] == 'show': self.logReader.resetValues() self.logStart = 0 self.createWindow() def saveData(self, values=None): with open("trackerData.txt", "wb") as f: pickle.dump(self.data, f) # overlay functions def clear(self): # all to change with new UI try: self.canvas.destroy() except: pass clip = pyperclip.paste() x, y = self.data['window position'][0], self.data['window position'][1] self.canvas = tk.Canvas(self.window, bg="pink", bd=0, highlightthickness=0, relief='ridge') self.canvas.pack(fill="both", expand=True) self.canvas.create_rectangle(x, y, x + 520, y + 30, fill='black') if self.logReader.currentSystem == clip: self.canvas.create_text(x + 5, y + 5, text=self.logReader.currentSystem, font="Ebrima 13 bold", fill='green', anchor='nw') else: self.canvas.create_text(x + 5, y + 5, text=self.logReader.currentSystem, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_rectangle(x + 150, y, x + 500, y + 30, fill='black') self.canvas.create_text(x + 158, y + 5, text='>> ', font="Ebrima 13 bold", fill='orange', anchor='nw') if self.nextSystem == clip: self.canvas.create_text(x + 190, y + 5, text=self.nextSystem, font="Ebrima 13 bold", fill='green', anchor='nw') else: self.canvas.create_text(x + 190, y + 5, text=self.nextSystem, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_rectangle(x + 340, y, x + 500, y + 30, fill='black') timeSince = time.time() - self.logReader.lastJumpRequest timeSince = self.maxCountdown - timeSince if timeSince > 0: if timeSince < 10 and self.data['alarm']: winsound.Beep(3000, 100) mins = str(round(timeSince // 60)) seconds = str(math.floor(timeSince % 60)) if len(mins) == 1: mins = '0' + mins if len(seconds) == 1: seconds = '0' + seconds text = mins + ':' + seconds else: text = 'Ready' text = '| ' + text + ' |' self.canvas.create_text(x + 350, y + 5, text=text, font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 420, y + 5, text='☰', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 440, y + 5, text='📁', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 463, y + 5, text='⚙', font="Ebrima 13 bold", fill='orange', anchor='nw') if self.data['topmost'] == 1: self.canvas.create_text(x + 485, y + 5, text='⮝', font="Ebrima 13 bold", fill='orange', anchor='nw') else: self.canvas.create_text(x + 485, y + 5, text='⮟', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_text(x + 500, y + 5, text='✘', font="Ebrima 13 bold", fill='orange', anchor='nw') self.canvas.create_line(x, y, x + 520, y, fill='orange') self.canvas.create_line(x, y + 30, x + 520, y + 30, fill='orange') if self.data['more']: self.createDashboard() def createDashboard(self): mouseX, mouseY = mousePosition() x, y = self.data['window position'][0], self.data['window position'][1] try: self.canvas.create_rectangle(x, y + 35, x + 520, y + 600, fill='black', outline='orange') # pannel backgrounds self.canvas.create_rectangle(x + 10, y + 40, x + 510, y + 150, fill='#111111', outline='#333333') self.canvas.create_rectangle(x + 10, y + 160, x + 510, y + 270, fill='#111111', outline='#333333') self.canvas.create_rectangle(x + 10, y + 280, x + 510, y + 540, fill='#111111', outline='#333333') above = False for i in [0] + self.stopLocations: horPos = i / len(self.currentFileData) * 480 + 20 if above and (mouseX - (x + horPos)) ** 2 + (mouseY - (y + 80)) ** 2 < 25: if horPos < 250: anchor = 'w' else: anchor = 'e' self.canvas.create_rectangle(x + horPos - 8, y + 45, x + 500, y + 80, fill='#111111', outline='#111111') self.canvas.create_line(x + horPos, y + 70, x + horPos, y + 80, fill='orange') jumps=i - self.position if jumps > 0: eta = ' | ' + self.getETA(jumps=jumps) else: eta='' self.canvas.create_text(x + horPos, y + 60, text=self.currentFileData[i][self.currentFileDataKeys['System Name']] + eta, font="Ebrima 10 bold", fill='orange', anchor=anchor) self.canvas.create_oval(x + horPos - 5, y + 75, x + horPos + 5, y + 85, fill='orange', outline='orange') elif not above: self.canvas.create_rectangle(x + horPos - 8, y + 80, x + 500, y + 120, fill='#111111', outline='#111111') self.canvas.create_line(x + horPos, y + 80, x + horPos, y + 90, fill='orange') self.canvas.create_text(x + horPos, y + 95, text=self.currentFileData[i][self.currentFileDataKeys['System Name']], font="Ebrima 10 bold", fill='orange', anchor='w') above = True horPos = 500 jumps = len(self.currentFileData) - 1 - self.position if jumps > 0: eta = ' | ' + self.getETA(jumps=jumps) else: eta = '' self.canvas.create_rectangle(x + horPos - 10, y + 80, x + 500, y + 120, fill='#111111', outline='#111111') self.canvas.create_line(x + horPos, y + 80, x + horPos, y + 90, fill='orange') self.canvas.create_text(x + horPos, y + 95, text=" " + self.currentFileData[-1][self.currentFileDataKeys['System Name']] + eta, font="Ebrima 10 bold", fill='orange', anchor='e') ##print(self.stopLocations) horPos = self.position / len(self.currentFileData) * 480 + 20 self.canvas.create_line(x + 20, y + 80, x + horPos, y + 80, fill='orange', width=2, dash=10) self.canvas.create_line(x + horPos, y + 80, x + 500, y + 80, fill='orange', width=2) self.canvas.create_oval(x + 15, y + 75, x + 25, y + 85, fill='orange', outline='orange') self.canvas.create_oval(x + 495, y + 75, x + 505, y + 85, fill='orange', outline='orange') self.canvas.create_text(x + 20, y + 130, text="Jumps | Completed: " + str(self.position), font="Ebrima 13 bold", fill='orange', anchor='w') found = False for i in self.stopLocations: diff = i - self.position if diff >= 0: self.canvas.create_text(x + 220, y + 130, text="| To Waypoint: " + str(diff), font="Ebrima 13 bold", fill='orange', anchor='w') found = True break if not found: self.canvas.create_text(x + 220, y + 130, text="| To Waypoint: " + str(len(self.currentFileData) - self.position - 1), font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 380, y + 130, text="| Left: " + str(len(self.currentFileData) - self.position - 1), font="Ebrima 13 bold", fill='orange', anchor='w') for i in self.stopLocations: if i < self.position: fill = 'orange' outline = 'orange' else: fill = 'orange' outline = 'orange' horPos = i / len(self.currentFileData) * 480 + 20 self.canvas.create_oval(x + horPos - 3, y + 77, x + horPos + 3, y + 83, fill=fill, outline=outline) ##print('h',horPos) ##print(self.stopLocations) horPos = self.position / len(self.currentFileData) * 480 + 20 self.canvas.create_polygon(x + horPos - 5, y + 85, x + horPos, y + 75, x + horPos + 5, y + 85, fill='#00ff00', outline='#00ff00') try: reqFuel = self.currentFileData[self.position][self.currentFileDataKeys['Tritium in market']] reqFuel = int(reqFuel) if reqFuel > 0: reqFuel += 1000 else: for i in range(self.position, len(self.currentFileData)): reqFuel += int(self.currentFileData[i][self.currentFileDataKeys['Fuel Used']]) reqFuel -= int(self.currentFileData[self.position][self.currentFileDataKeys['Fuel Used']]) except IndexError: reqFuel = 'Error' tankFuel = self.logReader.carrierFuel shipFuel = self.logReader.shipInventory - self.data['shipCargo'] carrierFuel = self.logReader.carrierInventory - self.data['carrierCargo'] self.canvas.create_text(x + 20, y + 180, text="Tritium | ", font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 95, y + 180, text="Tank: " + str(tankFuel), font="Ebrima 13 bold", fill='green', anchor='w') self.canvas.create_text(x + 190, y + 180, text="| Ship: " + str(shipFuel), font="Ebrima 13 bold", fill='blue', anchor='w') self.canvas.create_text(x + 280, y + 180, text="| Cargo: " + str(carrierFuel), font="Ebrima 13 bold", fill='orange', anchor='w') self.canvas.create_text(x + 400, y + 180, text="| Min: " + str(reqFuel), font="Ebrima 13 bold", fill='red', anchor='w') fuelTotal = tankFuel + shipFuel + carrierFuel if reqFuel == 'Error': reqFuel = 0 width = max(fuelTotal, reqFuel) / 480 self.canvas.create_rectangle(x + 20, y + 210, x + 20 + reqFuel / width, y + 230, fill='red', outline='red', stipple='gray25') self.canvas.create_rectangle(x + 20, y + 210, x + 20 + tankFuel / width, y + 230, fill='green', outline='green') self.canvas.create_rectangle(x + 20 + tankFuel / width, y + 210, x + 20 + shipFuel / width + tankFuel / width, y + 230, fill='blue', outline='blue') self.canvas.create_rectangle(x + 20 + shipFuel / width + tankFuel / width, y + 210, x + 20 + shipFuel / width + tankFuel / width + carrierFuel / width, y + 230, fill='orange', outline='orange') self.canvas.create_rectangle(x + 20 + reqFuel / width - 2, y + 210, x + 20 + reqFuel / width, y + 230, fill='red', outline='red') diff = fuelTotal - reqFuel if diff >= 0: self.canvas.create_text(x + 260, y + 250, text="You are " + str(diff) + " Tritium in excess", font="Ebrima 13 bold", fill='green') else: self.canvas.create_text(x + 260, y + 250, text="Warning! You are " + str(-diff) + " Tritium short!", font="Ebrima 13 bold", fill='red') self.canvas.create_text(x + 260, y + 197, text="Please note you need to open the carrier management page to update this.", font="Ebrima 8 bold", fill='orange') # routeList length = 10 self.scrollLength = length verticalSpacing = 25 self.verticalSpacing = verticalSpacing boxHeight = 20 self.boxHeight = boxHeight startY = 290 self.scrollHeight = verticalSpacing * (length - 1) + boxHeight barHeight = min(length / len(self.currentFileData) * self.scrollHeight, self.scrollHeight) self.barCentre = barHeight / 2 barPosition = y + (self.position + self.scroll) / len(self.currentFileData) * self.scrollHeight + startY clipboard = pyperclip.paste() for i in range(length): if self.position + self.scroll + i < len(self.currentFileData): if self.currentFileData[self.position + self.scroll + i][ self.currentFileDataKeys['System Name']] == clipboard: boxFill = 'green' textFill = 'black' elif self.scroll + i == 0: boxFill = 'orange' textFill = 'black' elif self.position + self.scroll + i in self.stopLocations or self.position + self.scroll + i - 1 in self.stopLocations: boxFill = 'red' textFill = 'black' else: boxFill = 'black' textFill = 'orange' self.canvas.create_rectangle(x + 15, y + startY + verticalSpacing * i, x + 490, y + startY + verticalSpacing * i + boxHeight, fill=boxFill, outline='orange') self.canvas.create_text(x + 17, y + startY + verticalSpacing * i, text=self.currentFileData[self.position + self.scroll + i][ self.currentFileDataKeys['System Name']], font="Ebrima 12 bold", fill=textFill, anchor='nw') #print(self.scroll + i - self.position) if self.scroll + i > 0: #print('eta') eta = self.getETA(jumps=self.scroll + i) self.canvas.create_text(x + 490, y + startY + verticalSpacing * i, text=eta, font="Ebrima 12 bold", fill=textFill, anchor='ne') self.canvas.create_rectangle(x + 497, y + startY, x + 505, y + startY + self.scrollHeight, fill='black', outline='orange') self.scrollTop = [x + 497, y + startY] self.scrollBottom = [x + 505, y + startY + verticalSpacing * (length - 1) + boxHeight] self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + barHeight, fill='orange', outline='orange') for i in self.stopLocations: barPosition = y + i / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + 1, fill='red', outline='red') barPosition = y + self.position / len(self.currentFileData) * self.scrollHeight + startY self.canvas.create_rectangle(x + 497, barPosition, x + 505, barPosition + 1, fill='orange', outline='orange') except Exception as e: self.canvas.create_rectangle(x, y + 35, x + 520, y + 600, fill='black', outline='orange') self.canvas.create_text(x + 260, y + 250, text=traceback.format_exc(), font="Ebrima 13 bold", fill='red') def getETA(self, jumps): start = self.data['jumpStart'] end = self.data['jumpEnd'] # print(start,end) start = start.split(':') end = end.split(':') start = sum([int(val) * 60 ** (1 - i) for i, val in enumerate(start)]) end = sum([int(val) * 60 ** (1 - i) for i, val in enumerate(end)]) if end < start: end += 1440 gap = 1440 - (end - start) alreadyQueued = time.time() - self.logReader.lastJumpRequest if alreadyQueued < 21 * 60: #print('from last jump') now = datetime.datetime.utcfromtimestamp(self.logReader.lastJumpRequest) #print(now) else: now = datetime.datetime.now(datetime.timezone.utc) nowReset = now - datetime.timedelta(hours=now.hour,minutes=now.minute) todayMinutes = sum([int(val) * 60 ** (1 - i) for i, val in enumerate([now.hour, now.minute])]) #print(todayMinutes) #print(start) if todayMinutes + 1440 < end and todayMinutes < start: #print('shift') end -= 1440 start -= 1440 todayStart = max(todayMinutes, start) #print('ts',todayStart) todayAvailableJumps = (end - todayStart) // 21 + 1 regularAvailableJumps = (end - start) // 21 + 1 if jumps <= todayAvailableJumps: eta = nowReset + datetime.timedelta(minutes = todayStart + jumps * 21 - 5) elif (jumps - todayAvailableJumps) % regularAvailableJumps != 0: eta = nowReset + datetime.timedelta(minutes = 1440 + start + (jumps - todayAvailableJumps) // regularAvailableJumps * 1440 + (( jumps - todayAvailableJumps) % regularAvailableJumps) * 21 - 5) else: eta = nowReset + datetime.timedelta( minutes=1440 + start + ((jumps - todayAvailableJumps) // regularAvailableJumps) * 1440 + 15) #print(todayStart - todayMinutes) #eta += datetime.timedelta(minutes=max(0,todayStart - todayMinutes)) eta = eta.strftime("%H:%M - %d/%m/%Y") return eta # print(start,end) def mouseDown(self, values): ##print(values) self.startDrag = time.time() if self.scrollTop[0] <= values.x and values.x <= self.scrollBottom[0] and self.scrollTop[1] <= values.y and \ self.scrollBottom[1] >= values.y and not self.dragging: self.scrolling = True elif not self.scrolling: self.dragging = True self.dragOffset = [values.x - self.data['window position'][0], values.y - self.data['window position'][1]] def endDrag(self, values): self.dragging = False self.scrolling = False relX = values.x - self.data['window position'][0] if time.time() - self.startDrag < 0.3 and values.y - self.data['window position'][1] < 30: if relX < 150: pyperclip.copy(self.logReader.currentSystem) # print('copied ' + self.logReader.currentSystem + ' to clipboard') elif relX > 190 and relX < 340: pyperclip.copy(self.nextSystem) # print('copied ' + self.nextSystem + ' to clipboard') # more elif relX > 420 and relX < 440: self.data['more'] = not self.data['more'] pass # open route elif relX > 440 and relX < 463: self.openFile() # settings elif relX > 463 and relX < 485: self.settings() pass # minimise elif relX > 485 and relX < 500: self.data['topmost'] = -self.data['topmost'] + 1 self.createWindow() # close elif relX > 500 and relX < 520: self.exiting = True self.saveData() elif time.time() - self.startDrag < 0.3 and 15 < relX and 490 > relX and values.y - self.scrollTop[1] > 0 and \ self.scrollBottom[1] - values.y > 0: proportion = (values.y - self.scrollTop[1]) / self.scrollHeight clickedOn = proportion * self.scrollLength pyperclip.copy(self.currentFileData[math.floor(self.position + self.scroll + clickedOn)][ self.currentFileDataKeys['System Name']]) self.clear() def wheel(self, values): if self.scrollLength < len(self.currentFileData): self.scroll += round(-values.delta / 100) self.limitScroll() self.clear() def limitScroll(self): if self.scroll + self.position < 0: self.scroll = -self.position if self.scroll + self.position >= len(self.currentFileData) - self.scrollLength: self.scroll = len(self.currentFileData) - self.position - self.scrollLength def hover(self, values): if not self.dragging and abs(values.y - (self.data['window position'][1] + 80)) < 5: self.hovering = True def createWindow(self, onTop=1): try: self.root.destroy() self.window.destroy() except AttributeError: pass except tk._tkinter.TclError: pass self.hidden = False user32 = ctypes.windll.user32 width, height = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) self.root = tk.Tk() self.root.title('routeTracker') self.root.attributes('-alpha', 0.0) # For icon # self.root.lower() self.root.iconify() if self.data['topmost'] == 1: self.window = tk.Toplevel(self.root, highlightthickness=0) self.root.iconbitmap(ICON) setWindowIcon=False else: self.window = tk.Tk() setWindowIcon=True self.window.title('routeTracker') self.window.config(bg="pink") self.window.geometry(str(width) + "x" + str(height)) # Whatever size #self.root.iconbitmap(ICON) if self.data['topmost'] == 1: self.window.overrideredirect(1) # Remove border self.window.attributes('-topmost', 1) else: self.window.wm_attributes('-fullscreen', 'true') self.root.overrideredirect(1) self.window.wm_attributes("-transparentcolor", "pink") self.window.bind('<ButtonPress-1>', self.mouseDown) self.window.bind('<ButtonRelease-1>', self.endDrag) self.window.bind("<MouseWheel>", self.wheel) self.window.bind("<Motion>", self.hover) self.clear() if setWindowIcon: self.window.iconbitmap(ICON) # settings window def alarm(self): self.data['alarm'] = not self.data['alarm'] self.saveData() self.alarmButton.config(text='Alarm: ' + str(self.data['alarm'])) def logLocation(self): self.data['logLocation'] = askdirectory() # print(self.data['logLocation']) if self.data['logLocation'] != '': self.logReader.folderLocation = self.data['logLocation'] else: self.logReader.defaultLocation() self.saveData() self.logLocationLabel.config(text=self.logReader.folderLocation) def change(self, values): value = self.carrierGoodsEntry.get() try: value = int(value) self.data['carrierCargo'] = value except ValueError: pass value = self.shipGoodsEntry.get() try: value = int(value) self.data['shipCargo'] = value except ValueError: pass jumpStart = self.jumpStartEntry.get() try: check = jumpStart.split(':') check[0] = int(check[0]) check[1] = int(check[1]) if 0 <= check[0] and check[0] < 24 and 0 <= check[1] and check[1] < 60 and len(check) == 2: self.data['jumpStart'] = jumpStart except ValueError: pass except IndexError: pass jumpEnd = self.jumpEndEntry.get() try: check = jumpEnd.split(':') check[0] = int(check[0]) check[1] = int(check[1]) if 0 <= check[0] and check[0] < 24 and 0 <= check[1] and check[1] < 60 and len(check) == 2: self.data['jumpEnd'] = jumpEnd except ValueError: pass except IndexError: pass self.saveData() def settings(self): try: self.settingsWindow.destroy() except AttributeError: pass except tk._tkinter.TclError: pass # print('settings window does not yet exist') self.settingsWindow = tk.Tk() self.settingsWindow.title('Settings') self.settingsWindow.config(bg='black') settingsLabel = tk.Label(self.settingsWindow, text='Settings\n', font="Ebrima 15 bold", fg='orange', bg='black') settingsLabel.grid(row=0, column=0, columnspan=2) # log reader file path openBrowserButton = tk.Button(self.settingsWindow, text='Log File Location', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=25, command=self.logLocation) openBrowserButton.grid(row=1, column=0) self.logLocationLabel = tk.Label(self.settingsWindow, text=self.logReader.folderLocation, font="Ebrima 15 bold", fg='orange', bg='black') self.logLocationLabel.grid(row=1, column=1) # alarm self.alarmButton = tk.Button(self.settingsWindow, text='Alarm: ' + str(self.data['alarm']), font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, command=self.alarm) self.alarmButton.grid(row=2, column=0) # non tritium goods in carrier carrierGoods = tk.Button(self.settingsWindow, text='Carrier Goods', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=25, ) carrierGoods.grid(row=3, column=0) self.carrierGoodsEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.carrierGoodsEntry.insert(0, str(self.data['carrierCargo'])) self.carrierGoodsEntry.grid(row=3, column=1) # non tritium goods in ship shipGoods = tk.Button(self.settingsWindow, text='Ship Goods', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) shipGoods.grid(row=4, column=0) self.shipGoodsEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.shipGoodsEntry.insert(0, str(self.data['shipCargo'])) self.shipGoodsEntry.grid(row=4, column=1) # jump start/end jumpStart = tk.Button(self.settingsWindow, text='Start Time', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) jumpStart.grid(row=5, column=0) self.jumpStartEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.jumpStartEntry.insert(0, str(self.data['jumpStart'])) self.jumpStartEntry.grid(row=5, column=1) jumpEnd = tk.Button(self.settingsWindow, text='End Time', font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#333333', activebackground='#222222', width=25, ) jumpEnd.grid(row=6, column=0) self.jumpEndEntry = tk.Entry(self.settingsWindow, bg='#222222', fg='orange', bd=0, font="Ebrima 13 bold") self.jumpEndEntry.insert(0, str(self.data['jumpEnd'])) self.jumpEndEntry.grid(row=6, column=1) # Thanks invite = tk.Button(self.settingsWindow, text="With thanks to the Fleet Carrier Owner's Club (Especially Ed, NalloVint and Brandstaetter)", font="Ebrima 13 bold", fg='orange', activeforeground='orange', bg='#222222', activebackground='#111111', width=90, command=lambda: webbrowser.open('https://discord.gg/tcMPHfh')) invite.grid(row=7, column=0, columnspan=2) self.settingsWindow.iconbitmap(ICON) self.settingsWindow.bind("<KeyRelease>", self.change) if __name__ == '__main__': from logReader import * reader = logReader() ui = UserInterface(reader=reader) # print('t') ui.getETA(18) ui.mainLoop() ##print(countdownMessage) # window.mainloop()

43.692634

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4.996461

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0.034655

0.05302

0.028331

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0

1

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null

0

1

0

8

0ac0c3467e25e61d29d2fbdd76a179f64537e1b2

10,804

py

Python

applications/medical_history/tests/test_views.py

szypkiwonsz/Physiotherapy-Management-System

36decab47890e2f4be259c8796f47324ffad28fe

[ "MIT" ]

null

applications/medical_history/tests/test_views.py

szypkiwonsz/Physiotherapy-Management-System

36decab47890e2f4be259c8796f47324ffad28fe

[ "MIT" ]

8

2020-08-17T14:36:02.000Z

2022-03-12T00:33:50.000Z

applications/medical_history/tests/test_views.py

szypkiwonsz/Physiotherapy-Management-System

36decab47890e2f4be259c8796f47324ffad28fe

[ "MIT" ]

null

from datetime import datetime from django.test import TestCase, Client from django.urls import reverse from django.utils import timezone from applications.appointments.models import Appointment, Service from applications.medical_history.models import MedicalHistory from applications.office_panel.models import Patient from applications.users.models import User, UserOffice class TestOfficeMedicalHistoryViews(TestCase): def setUp(self): self.client = Client() self.medical_history_url = reverse('office_panel:medical_history:list') self.make_medical_history_url = reverse('office_panel:medical_history:make') self.detail_medical_history_url = reverse('office_panel:medical_history:detail', args=[1]) self.update_medical_history_url = reverse('office_panel:medical_history:update', args=[1]) self.delete_medical_history_url = reverse('office_panel:medical_history:delete', args=[1]) self.patient1 = User.objects.create_user( 'patient', 'patient@gmail.com', 'patientpassword', is_patient=True ) self.office1 = User.objects.create_user( 'office', 'office@gmail.com', 'officepassword', is_office=True ) self.office_patient1 = Patient.objects.create( owner=self.office1, first_name='firstname', last_name='lastname', email='patient@gmail.com' ) self.medical_history1 = MedicalHistory.objects.create( owner=self.office1, patient=self.office_patient1, description='description', recommendations='recommendations', date_selected=timezone.now(), ) self.appointment_office1 = UserOffice.objects.create( user=self.office1, name='name', address='address', city='City', phone_number='000000000', website='www.website.com' ) self.service = Service.objects.create( office=self.appointment_office1, name='Konsultacja', duration=10 ) self.appointment1 = Appointment.objects.create( owner=self.patient1, office=self.appointment_office1, date=datetime(2012, 1, 13, 16, 00, 00), first_name='Kacper', last_name='Sawicki', date_selected=datetime(2012, 1, 13, 23, 51, 34), phone_number='000000000', confirmed=False, service=self.service ) def test_medical_history_list_GET_not_logged_in(self): response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history.html') def test_medical_history_list_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history.html') def test_medical_history_list_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateUsed(response, 'medical_history/office/medical_history.html') def test_make_medical_history_create_GET_not_logged_in(self): response = self.client.get(self.make_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_make_medical_history_create_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.make_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_make_medical_history_create_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.make_medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_make_medical_history_create_POST(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.post(self.make_medical_history_url, { 'patient': self.office_patient1.pk, 'appointment': self.appointment1.pk, 'description': 'description', 'recommendations': 'recommendations' }) medical_history2 = MedicalHistory.objects.get(id=2) self.assertEquals(response.status_code, 302) self.assertEquals(medical_history2.description, 'description') def test_medical_history_detail_GET_not_logged_in(self): response = self.client.get(self.detail_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_medical_history_detail_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.detail_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_medical_history_detail_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.detail_medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateUsed(response, 'medical_history/office/medical_history_detail_form.html') def test_medical_history_update_GET_not_logged_in(self): response = self.client.get(self.update_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_update_form.html') def test_medical_history_update_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.update_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_update_form.html') def test_medical_history_update_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.update_medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateUsed(response, 'medical_history/office/medical_history_update_form.html') def test_medical_history_update_POST(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.post(self.update_medical_history_url, { 'patient': self.medical_history1.patient.pk, 'appointment': self.appointment1.pk, 'description': self.medical_history1.description, 'recommendations': 'newrecommendations' }) medical_history_update = MedicalHistory.objects.get(id=1) self.assertEquals(response.status_code, 302) self.assertEquals(medical_history_update.recommendations, 'newrecommendations') def test_medical_history_delete_GET_not_logged_in(self): response = self.client.get(self.delete_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_delete_confirm.html') def test_medical_history_delete_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.delete_medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/office/medical_history_delete_confirm.html') def test_medical_history_delete_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.delete_medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateUsed(response, 'medical_history/office/medical_history_delete_confirm.html') def test_medical_history_delete_POST(self): self.client.login(username='office@gmail.com', password='officepassword') response_with_post = self.client.get(self.detail_medical_history_url) self.assertEquals(response_with_post.status_code, 200) response = self.client.post(self.delete_medical_history_url) response_with_deleted_post = self.client.get(self.detail_medical_history_url) self.assertEquals(response_with_deleted_post.status_code, 404) class TestPatientMedicalHistoryViews(TestCase): def setUp(self): self.client = Client() self.medical_history_url = reverse('patient_panel:medical_history:list') self.patient1 = User.objects.create_user( 'patient', 'patient@gmail.com', 'patientpassword', is_patient=True ) self.office1 = User.objects.create_user( 'office', 'office@gmail.com', 'officepassword', is_office=True ) def test_medical_history_GET_not_logged_in(self): response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/patient/medical_history.html') def test_medical_history_GET_logged_as_office(self): self.client.login(username='office@gmail.com', password='officepassword') response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 302) self.assertTemplateNotUsed(response, 'medical_history/patient/medical_history.html') def test_medical_history_GET_logged_as_patient(self): self.client.login(username='patient@gmail.com', password='patientpassword') response = self.client.get(self.medical_history_url) self.assertEquals(response.status_code, 200) self.assertTemplateUsed(response, 'medical_history/patient/medical_history.html')

46.973913

106

0.727231

1,234

10,804

6.079417

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0.065716

0.045321

0.805918

0.771394

0.749667

0.746334

0.712877

0.695415

0

0.015606

0.175583

10,804

229

107

47.179039

0.826653

0

0.483516

0

0.188819

0.105054

0

0.230769

1

0.126374

false

0.104396

0.043956

0

0.181319

0

null

0

1

0

null

0

1

0

7

7c1d59a937cc24395907fc29662cf9001dae8e62

24,142

py

Python

lang/python/github/com/metaprov/modelaapi/services/k8score/v1/k8score_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

5

2022-02-18T03:40:10.000Z

2022-03-01T16:11:24.000Z

lang/python/github/com/metaprov/modelaapi/services/k8score/v1/k8score_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

1

2022-01-07T19:59:25.000Z

2022-02-04T01:21:14.000Z

lang/python/github/com/metaprov/modelaapi/services/k8score/v1/k8score_pb2_grpc.py

metaprov/modeldapi

ee05693832051dcd990ee4f061715d7ae0787340

[ "Apache-2.0" ]

1

2022-03-25T10:21:43.000Z

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from github.com.metaprov.modelaapi.services.k8score.v1 import k8score_pb2 as github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2 class CoreK8sServiceStub(object): """///////////////////////////// Jobs The Core K8s service is used to list and get a kubernetes object """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.ListK8sSecrets = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sSecrets', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretResponse.FromString, ) self.GetK8sSecret = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sSecret', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretResponse.FromString, ) self.ListK8sServices = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sServices', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesResponse.FromString, ) self.GetK8sService = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sService', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceResponse.FromString, ) self.ListK8sDeployments = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sDeployments', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsResponse.FromString, ) self.GetK8sDeployment = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sDeployment', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentResponse.FromString, ) self.ListK8sPods = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sPods', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsResponse.FromString, ) self.GetK8sPod = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sPod', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodResponse.FromString, ) self.ListK8sJobs = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sJobs', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsResponse.FromString, ) self.GetK8sJob = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sJob', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobResponse.FromString, ) self.ListEvents = channel.unary_unary( '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListEvents', request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsRequest.SerializeToString, response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsResponse.FromString, ) class CoreK8sServiceServicer(object): """///////////////////////////// Jobs The Core K8s service is used to list and get a kubernetes object """ def ListK8sSecrets(self, request, context): """//////////////// secret """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetK8sSecret(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListK8sServices(self, request, context): """//////////////// service """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetK8sService(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListK8sDeployments(self, request, context): """//////////////// deployment """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetK8sDeployment(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListK8sPods(self, request, context): """//////////////// pod """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetK8sPod(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListK8sJobs(self, request, context): """////////////// jobs """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetK8sJob(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListEvents(self, request, context): """Events """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_CoreK8sServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'ListK8sSecrets': grpc.unary_unary_rpc_method_handler( servicer.ListK8sSecrets, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretResponse.SerializeToString, ), 'GetK8sSecret': grpc.unary_unary_rpc_method_handler( servicer.GetK8sSecret, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretResponse.SerializeToString, ), 'ListK8sServices': grpc.unary_unary_rpc_method_handler( servicer.ListK8sServices, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesResponse.SerializeToString, ), 'GetK8sService': grpc.unary_unary_rpc_method_handler( servicer.GetK8sService, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceResponse.SerializeToString, ), 'ListK8sDeployments': grpc.unary_unary_rpc_method_handler( servicer.ListK8sDeployments, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsResponse.SerializeToString, ), 'GetK8sDeployment': grpc.unary_unary_rpc_method_handler( servicer.GetK8sDeployment, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentResponse.SerializeToString, ), 'ListK8sPods': grpc.unary_unary_rpc_method_handler( servicer.ListK8sPods, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsResponse.SerializeToString, ), 'GetK8sPod': grpc.unary_unary_rpc_method_handler( servicer.GetK8sPod, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodResponse.SerializeToString, ), 'ListK8sJobs': grpc.unary_unary_rpc_method_handler( servicer.ListK8sJobs, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsResponse.SerializeToString, ), 'GetK8sJob': grpc.unary_unary_rpc_method_handler( servicer.GetK8sJob, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobResponse.SerializeToString, ), 'ListEvents': grpc.unary_unary_rpc_method_handler( servicer.ListEvents, request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsRequest.FromString, response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class CoreK8sService(object): """///////////////////////////// Jobs The Core K8s service is used to list and get a kubernetes object """ @staticmethod def ListK8sSecrets(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sSecrets', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListSecretResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetK8sSecret(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sSecret', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetSecretResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListK8sServices(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sServices', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListServicesResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetK8sService(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sService', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetServiceResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListK8sDeployments(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sDeployments', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListDeploymentsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetK8sDeployment(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sDeployment', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetDeploymentResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListK8sPods(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sPods', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListPodsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetK8sPod(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sPod', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetPodResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListK8sJobs(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListK8sJobs', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListJobsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetK8sJob(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/GetK8sJob', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.GetJobResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListEvents(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.k8score.v1.CoreK8sService/ListEvents', github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsRequest.SerializeToString, github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_k8score_dot_v1_dot_k8score__pb2.ListEventsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

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py

Python

sdk/python/pulumi_aws/batch/compute_environment.py

dixler/pulumi-aws

88838ed6d412c092717a916b0b5b154f68226c3a

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

null

sdk/python/pulumi_aws/batch/compute_environment.py

dixler/pulumi-aws

88838ed6d412c092717a916b0b5b154f68226c3a

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

null

sdk/python/pulumi_aws/batch/compute_environment.py

dixler/pulumi-aws

88838ed6d412c092717a916b0b5b154f68226c3a

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class ComputeEnvironment(pulumi.CustomResource): arn: pulumi.Output[str] """ The Amazon Resource Name (ARN) of the compute environment. """ compute_environment_name: pulumi.Output[str] compute_environment_name_prefix: pulumi.Output[str] """ Creates a unique compute environment name beginning with the specified prefix. Conflicts with `compute_environment_name`. """ compute_resources: pulumi.Output[dict] """ Details of the compute resources managed by the compute environment. This parameter is required for managed compute environments. See details below. * `allocationStrategy` (`str`) - The allocation strategy to use for the compute resource in case not enough instances of the best fitting instance type can be allocated. Valid items are `BEST_FIT_PROGRESSIVE`, `SPOT_CAPACITY_OPTIMIZED` or `BEST_FIT`. Defaults to `BEST_FIT`. See [AWS docs](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html) for details. * `bidPercentage` (`float`) - Integer of minimum percentage that a Spot Instance price must be when compared with the On-Demand price for that instance type before instances are launched. For example, if your bid percentage is 20% (`20`), then the Spot price must be below 20% of the current On-Demand price for that EC2 instance. This parameter is required for SPOT compute environments. * `desiredVcpus` (`float`) - The desired number of EC2 vCPUS in the compute environment. * `ec2KeyPair` (`str`) - The EC2 key pair that is used for instances launched in the compute environment. * `imageId` (`str`) - The Amazon Machine Image (AMI) ID used for instances launched in the compute environment. * `instanceRole` (`str`) - The Amazon ECS instance role applied to Amazon EC2 instances in a compute environment. * `instanceTypes` (`list`) - A list of instance types that may be launched. * `launch_template` (`dict`) - The launch template to use for your compute resources. See details below. * `launchTemplateId` (`str`) - ID of the launch template. You must specify either the launch template ID or launch template name in the request, but not both. * `launchTemplateName` (`str`) - Name of the launch template. * `version` (`str`) - The version number of the launch template. Default: The default version of the launch template. * `maxVcpus` (`float`) - The maximum number of EC2 vCPUs that an environment can reach. * `minVcpus` (`float`) - The minimum number of EC2 vCPUs that an environment should maintain. * `securityGroupIds` (`list`) - A list of EC2 security group that are associated with instances launched in the compute environment. * `spotIamFleetRole` (`str`) - The Amazon Resource Name (ARN) of the Amazon EC2 Spot Fleet IAM role applied to a SPOT compute environment. This parameter is required for SPOT compute environments. * `subnets` (`list`) - A list of VPC subnets into which the compute resources are launched. * `tags` (`dict`) - Key-value pair tags to be applied to resources that are launched in the compute environment. * `type` (`str`) - The type of compute environment. Valid items are `EC2` or `SPOT`. """ ecs_cluster_arn: pulumi.Output[str] """ The Amazon Resource Name (ARN) of the underlying Amazon ECS cluster used by the compute environment. """ service_role: pulumi.Output[str] """ The full Amazon Resource Name (ARN) of the IAM role that allows AWS Batch to make calls to other AWS services on your behalf. """ state: pulumi.Output[str] """ The state of the compute environment. If the state is `ENABLED`, then the compute environment accepts jobs from a queue and can scale out automatically based on queues. Valid items are `ENABLED` or `DISABLED`. Defaults to `ENABLED`. """ status: pulumi.Output[str] """ The current status of the compute environment (for example, CREATING or VALID). """ status_reason: pulumi.Output[str] """ A short, human-readable string to provide additional details about the current status of the compute environment. """ type: pulumi.Output[str] """ The type of compute environment. Valid items are `EC2` or `SPOT`. """ def __init__(__self__, resource_name, opts=None, compute_environment_name=None, compute_environment_name_prefix=None, compute_resources=None, service_role=None, state=None, type=None, __props__=None, __name__=None, __opts__=None): """ Creates a AWS Batch compute environment. Compute environments contain the Amazon ECS container instances that are used to run containerized batch jobs. For information about AWS Batch, see [What is AWS Batch?][1] . For information about compute environment, see [Compute Environments][2] . > **Note:** To prevent a race condition during environment deletion, make sure to set `depends_on` to the related `iam.RolePolicyAttachment`; otherwise, the policy may be destroyed too soon and the compute environment will then get stuck in the `DELETING` state, see [Troubleshooting AWS Batch][3] . :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] compute_environment_name_prefix: Creates a unique compute environment name beginning with the specified prefix. Conflicts with `compute_environment_name`. :param pulumi.Input[dict] compute_resources: Details of the compute resources managed by the compute environment. This parameter is required for managed compute environments. See details below. :param pulumi.Input[str] service_role: The full Amazon Resource Name (ARN) of the IAM role that allows AWS Batch to make calls to other AWS services on your behalf. :param pulumi.Input[str] state: The state of the compute environment. If the state is `ENABLED`, then the compute environment accepts jobs from a queue and can scale out automatically based on queues. Valid items are `ENABLED` or `DISABLED`. Defaults to `ENABLED`. :param pulumi.Input[str] type: The type of compute environment. Valid items are `EC2` or `SPOT`. The **compute_resources** object supports the following: * `allocationStrategy` (`pulumi.Input[str]`) - The allocation strategy to use for the compute resource in case not enough instances of the best fitting instance type can be allocated. Valid items are `BEST_FIT_PROGRESSIVE`, `SPOT_CAPACITY_OPTIMIZED` or `BEST_FIT`. Defaults to `BEST_FIT`. See [AWS docs](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html) for details. * `bidPercentage` (`pulumi.Input[float]`) - Integer of minimum percentage that a Spot Instance price must be when compared with the On-Demand price for that instance type before instances are launched. For example, if your bid percentage is 20% (`20`), then the Spot price must be below 20% of the current On-Demand price for that EC2 instance. This parameter is required for SPOT compute environments. * `desiredVcpus` (`pulumi.Input[float]`) - The desired number of EC2 vCPUS in the compute environment. * `ec2KeyPair` (`pulumi.Input[str]`) - The EC2 key pair that is used for instances launched in the compute environment. * `imageId` (`pulumi.Input[str]`) - The Amazon Machine Image (AMI) ID used for instances launched in the compute environment. * `instanceRole` (`pulumi.Input[str]`) - The Amazon ECS instance role applied to Amazon EC2 instances in a compute environment. * `instanceTypes` (`pulumi.Input[list]`) - A list of instance types that may be launched. * `launch_template` (`pulumi.Input[dict]`) - The launch template to use for your compute resources. See details below. * `launchTemplateId` (`pulumi.Input[str]`) - ID of the launch template. You must specify either the launch template ID or launch template name in the request, but not both. * `launchTemplateName` (`pulumi.Input[str]`) - Name of the launch template. * `version` (`pulumi.Input[str]`) - The version number of the launch template. Default: The default version of the launch template. * `maxVcpus` (`pulumi.Input[float]`) - The maximum number of EC2 vCPUs that an environment can reach. * `minVcpus` (`pulumi.Input[float]`) - The minimum number of EC2 vCPUs that an environment should maintain. * `securityGroupIds` (`pulumi.Input[list]`) - A list of EC2 security group that are associated with instances launched in the compute environment. * `spotIamFleetRole` (`pulumi.Input[str]`) - The Amazon Resource Name (ARN) of the Amazon EC2 Spot Fleet IAM role applied to a SPOT compute environment. This parameter is required for SPOT compute environments. * `subnets` (`pulumi.Input[list]`) - A list of VPC subnets into which the compute resources are launched. * `tags` (`pulumi.Input[dict]`) - Key-value pair tags to be applied to resources that are launched in the compute environment. * `type` (`pulumi.Input[str]`) - The type of compute environment. Valid items are `EC2` or `SPOT`. > This content is derived from https://github.com/terraform-providers/terraform-provider-aws/blob/master/website/docs/r/batch_compute_environment.html.markdown. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['compute_environment_name'] = compute_environment_name __props__['compute_environment_name_prefix'] = compute_environment_name_prefix __props__['compute_resources'] = compute_resources if service_role is None: raise TypeError("Missing required property 'service_role'") __props__['service_role'] = service_role __props__['state'] = state if type is None: raise TypeError("Missing required property 'type'") __props__['type'] = type __props__['arn'] = None __props__['ecs_cluster_arn'] = None __props__['status'] = None __props__['status_reason'] = None super(ComputeEnvironment, __self__).__init__( 'aws:batch/computeEnvironment:ComputeEnvironment', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, arn=None, compute_environment_name=None, compute_environment_name_prefix=None, compute_resources=None, ecs_cluster_arn=None, service_role=None, state=None, status=None, status_reason=None, type=None): """ Get an existing ComputeEnvironment resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] arn: The Amazon Resource Name (ARN) of the compute environment. :param pulumi.Input[str] compute_environment_name_prefix: Creates a unique compute environment name beginning with the specified prefix. Conflicts with `compute_environment_name`. :param pulumi.Input[dict] compute_resources: Details of the compute resources managed by the compute environment. This parameter is required for managed compute environments. See details below. :param pulumi.Input[str] ecs_cluster_arn: The Amazon Resource Name (ARN) of the underlying Amazon ECS cluster used by the compute environment. :param pulumi.Input[str] service_role: The full Amazon Resource Name (ARN) of the IAM role that allows AWS Batch to make calls to other AWS services on your behalf. :param pulumi.Input[str] state: The state of the compute environment. If the state is `ENABLED`, then the compute environment accepts jobs from a queue and can scale out automatically based on queues. Valid items are `ENABLED` or `DISABLED`. Defaults to `ENABLED`. :param pulumi.Input[str] status: The current status of the compute environment (for example, CREATING or VALID). :param pulumi.Input[str] status_reason: A short, human-readable string to provide additional details about the current status of the compute environment. :param pulumi.Input[str] type: The type of compute environment. Valid items are `EC2` or `SPOT`. The **compute_resources** object supports the following: * `allocationStrategy` (`pulumi.Input[str]`) - The allocation strategy to use for the compute resource in case not enough instances of the best fitting instance type can be allocated. Valid items are `BEST_FIT_PROGRESSIVE`, `SPOT_CAPACITY_OPTIMIZED` or `BEST_FIT`. Defaults to `BEST_FIT`. See [AWS docs](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html) for details. * `bidPercentage` (`pulumi.Input[float]`) - Integer of minimum percentage that a Spot Instance price must be when compared with the On-Demand price for that instance type before instances are launched. For example, if your bid percentage is 20% (`20`), then the Spot price must be below 20% of the current On-Demand price for that EC2 instance. This parameter is required for SPOT compute environments. * `desiredVcpus` (`pulumi.Input[float]`) - The desired number of EC2 vCPUS in the compute environment. * `ec2KeyPair` (`pulumi.Input[str]`) - The EC2 key pair that is used for instances launched in the compute environment. * `imageId` (`pulumi.Input[str]`) - The Amazon Machine Image (AMI) ID used for instances launched in the compute environment. * `instanceRole` (`pulumi.Input[str]`) - The Amazon ECS instance role applied to Amazon EC2 instances in a compute environment. * `instanceTypes` (`pulumi.Input[list]`) - A list of instance types that may be launched. * `launch_template` (`pulumi.Input[dict]`) - The launch template to use for your compute resources. See details below. * `launchTemplateId` (`pulumi.Input[str]`) - ID of the launch template. You must specify either the launch template ID or launch template name in the request, but not both. * `launchTemplateName` (`pulumi.Input[str]`) - Name of the launch template. * `version` (`pulumi.Input[str]`) - The version number of the launch template. Default: The default version of the launch template. * `maxVcpus` (`pulumi.Input[float]`) - The maximum number of EC2 vCPUs that an environment can reach. * `minVcpus` (`pulumi.Input[float]`) - The minimum number of EC2 vCPUs that an environment should maintain. * `securityGroupIds` (`pulumi.Input[list]`) - A list of EC2 security group that are associated with instances launched in the compute environment. * `spotIamFleetRole` (`pulumi.Input[str]`) - The Amazon Resource Name (ARN) of the Amazon EC2 Spot Fleet IAM role applied to a SPOT compute environment. This parameter is required for SPOT compute environments. * `subnets` (`pulumi.Input[list]`) - A list of VPC subnets into which the compute resources are launched. * `tags` (`pulumi.Input[dict]`) - Key-value pair tags to be applied to resources that are launched in the compute environment. * `type` (`pulumi.Input[str]`) - The type of compute environment. Valid items are `EC2` or `SPOT`. > This content is derived from https://github.com/terraform-providers/terraform-provider-aws/blob/master/website/docs/r/batch_compute_environment.html.markdown. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["arn"] = arn __props__["compute_environment_name"] = compute_environment_name __props__["compute_environment_name_prefix"] = compute_environment_name_prefix __props__["compute_resources"] = compute_resources __props__["ecs_cluster_arn"] = ecs_cluster_arn __props__["service_role"] = service_role __props__["state"] = state __props__["status"] = status __props__["status_reason"] = status_reason __props__["type"] = type return ComputeEnvironment(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

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Python

test/features/test_actions_publiques.py

loic-simon/lg-rez

c5eb8af7b2146fca110ee50a9209529f0ecfca1a

[ "MIT" ]

4

2020-08-13T17:07:51.000Z

2021-04-21T00:29:33.000Z

test/features/test_actions_publiques.py

loic-simon/lg-rez

c5eb8af7b2146fca110ee50a9209529f0ecfca1a

[ "MIT" ]

9

2021-03-22T00:52:18.000Z

2021-10-05T23:46:38.000Z

test/features/test_actions_publiques.py

loic-simon/lg-rez

c5eb8af7b2146fca110ee50a9209529f0ecfca1a

[ "MIT" ]

1

2020-10-17T15:04:23.000Z

import unittest from unittest import mock from lgrez import config, bdd from lgrez.features import actions_publiques from test import mock_discord, mock_bdd, mock_env class TestActionsPubliques(unittest.IsolatedAsyncioTestCase): """Unit tests for lgrez.features.actions_publiques commands.""" def setUp(self): mock_discord.mock_config() self.cog = actions_publiques.ActionsPubliques(config.bot) def tearDown(self): mock_discord.unmock_config() @mock_bdd.patch_db # Empty database for this method @mock.patch("lgrez.config.Channel.haros.send") @mock.patch("lgrez.config.Channel.debats.send") @mock_discord.interact() async def test_haro(self, send_debats_patch, send_haros_patch): """Unit tests for !haro command.""" # async def haro(self, ctx, *, cible=None) haro = self.cog.haro mock_bdd.add_campsroles() joueur = bdd.Joueur(discord_id=1, chan_id_=11, nom="Joueur1", vote_condamne_=None) joueur.add() cible = bdd.Joueur(discord_id=2, chan_id_=21, nom="Joueur2") cible.add() # no vote ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) await ctx.invoke() ctx.assert_sent("Pas de vote") self.assertFalse(bdd.CandidHaro.query.all()) # no haro created send_debats_patch.assert_not_called() send_haros_patch.assert_not_called() # cible = dead joueur.vote_condamne_ = "bzzzt" cible.statut = bdd.Statut.mort cible.update() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) await ctx.invoke() ctx.assert_sent("pas assez souffert") self.assertFalse(bdd.CandidHaro.query.all()) # no haro created send_debats_patch.assert_not_called() send_haros_patch.assert_not_called() # cible = immortel cible.statut = bdd.Statut.immortel cible.update() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) await ctx.invoke() ctx.assert_sent("Comment oses-tu") self.assertFalse(bdd.CandidHaro.query.all()) # no haro created send_debats_patch.assert_not_called() send_haros_patch.assert_not_called() # cible = MV : ok cible.statut = bdd.Statut.MV cible.update() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message(".")), ("yes_no", False)): await ctx.invoke() ctx.assert_sent("quelle est la raison", "", "") self.assertFalse(bdd.CandidHaro.query.all()) # no haro created send_debats_patch.assert_not_called() send_haros_patch.assert_not_called() # ok, and abort cible.statut = bdd.Statut.vivant cible.update() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("pakontan")), ("yes_no", False)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("quelle est la raison", "tout bon ?", "Mission aborted") embed = ctx.send.call_args_list[1].kwargs["embed"] self.assertIn("contre Joueur2", embed.title) self.assertIn("pakontan", embed.description) self.assertIn("Joueur1", embed.author.name) self.assertFalse(bdd.CandidHaro.query.all()) # no haro created send_debats_patch.assert_not_called() send_haros_patch.assert_not_called() # validate ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("pakontan")), ("yes_no", True)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("quelle est la raison", "tout bon ?", "c'est parti") embed = ctx.send.call_args_list[1].kwargs["embed"] haros = bdd.CandidHaro.query.all() self.assertEqual(len(haros), 2) # 2 haros created self.assertEqual({haro.joueur for haro in haros}, {joueur, cible}) self.assertEqual(haros[0].type, bdd.CandidHaroType.haro) self.assertEqual(haros[1].type, bdd.CandidHaroType.haro) send_haros_patch.assert_called_once() self.assertEqual(send_haros_patch.call_args.kwargs, {"embed": embed}) send_haros_patch.reset_mock() send_debats_patch.assert_called_once() send_debats_patch.reset_mock() # validate, but haroted already registered bdd.CandidHaro.delete(*haros) self.assertFalse(bdd.CandidHaro.query.all()) # no haros bdd.CandidHaro(joueur=cible, type=bdd.CandidHaroType.haro).add() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("pakontan")), ("yes_no", True)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("", "", "") haros = bdd.CandidHaro.query.all() self.assertEqual(len(haros), 2) # 2 haros created self.assertEqual({haro.joueur for haro in haros}, {joueur, cible}) self.assertEqual(haros[0].type, bdd.CandidHaroType.haro) self.assertEqual(haros[1].type, bdd.CandidHaroType.haro) send_haros_patch.assert_called_once() send_haros_patch.reset_mock() send_debats_patch.assert_called_once() send_debats_patch.reset_mock() # validate, but haroter already registered bdd.CandidHaro.delete(*haros) self.assertFalse(bdd.CandidHaro.query.all()) # no haros bdd.CandidHaro(joueur=joueur, type=bdd.CandidHaroType.haro).add() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("pakontan")), ("yes_no", True)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("", "", "") haros = bdd.CandidHaro.query.all() self.assertEqual(len(haros), 2) # 2 haros created self.assertEqual({haro.joueur for haro in haros}, {joueur, cible}) self.assertEqual(haros[0].type, bdd.CandidHaroType.haro) self.assertEqual(haros[1].type, bdd.CandidHaroType.haro) send_haros_patch.assert_called_once() send_haros_patch.reset_mock() send_debats_patch.assert_called_once() send_debats_patch.reset_mock() # validate, but haroter and haroted already registered bdd.CandidHaro.delete(*haros) self.assertFalse(bdd.CandidHaro.query.all()) # no haros bdd.CandidHaro(joueur=joueur, type=bdd.CandidHaroType.haro).add() bdd.CandidHaro(joueur=cible, type=bdd.CandidHaroType.haro).add() ctx = mock_discord.get_ctx(haro, cible="Joueur2", _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("pakontan")), ("yes_no", True)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("", "", "") haros = bdd.CandidHaro.query.all() self.assertEqual(len(haros), 2) # 2 haros created self.assertEqual({haro.joueur for haro in haros}, {joueur, cible}) self.assertEqual(haros[0].type, bdd.CandidHaroType.haro) self.assertEqual(haros[1].type, bdd.CandidHaroType.haro) send_haros_patch.assert_called_once() send_haros_patch.reset_mock() send_debats_patch.assert_called_once() send_debats_patch.reset_mock() @mock_bdd.patch_db # Empty database for this method @mock.patch("lgrez.config.Channel.haros.send") @mock.patch("lgrez.config.Channel.debats.send") @mock_discord.interact() async def test_candid(self, send_debats_patch, send_candids_patch): """Unit tests for !candid command.""" # async def candid(self, ctx) candid = self.cog.candid mock_bdd.add_campsroles() joueur = bdd.Joueur(discord_id=1, chan_id_=11, nom="Joueur1", vote_maire_=None) joueur.add() # no vote ctx = mock_discord.get_ctx(candid, _caller_id=1) await ctx.invoke() ctx.assert_sent("Pas de vote") self.assertFalse(bdd.CandidHaro.query.all()) # no candid created send_debats_patch.assert_not_called() send_candids_patch.assert_not_called() # joueur already candidated joueur.vote_maire_ = "bzzzt" cand = bdd.CandidHaro(joueur=joueur, type=bdd.CandidHaroType.candidature) cand.add() ctx = mock_discord.get_ctx(candid, _caller_id=1) await ctx.invoke() ctx.assert_sent("déjà présenté") self.assertEqual(len(bdd.CandidHaro.query.all()), 1) # none created send_debats_patch.assert_not_called() send_candids_patch.assert_not_called() # ok, and abort cand.delete() ctx = mock_discord.get_ctx(candid, _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("votépourmoi")), ("yes_no", False)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("Quel est ton programme", "tout bon ?", "Mission aborted") embed = ctx.send.call_args_list[1].kwargs["embed"] self.assertIn("Joueur1", embed.title) self.assertIn("votépourmoi", embed.description) self.assertIn("Joueur1", embed.author.name) self.assertFalse(bdd.CandidHaro.query.all()) # no candid created send_debats_patch.assert_not_called() send_candids_patch.assert_not_called() # validate ctx = mock_discord.get_ctx(candid, _caller_id=1) with mock_discord.interact( ("wait_for_message_here", ctx.new_message("votépourmoi")), ("yes_no", True)): # give reason then abort (answer "no" at check) await ctx.invoke() ctx.assert_sent("Quel est ton programme", "tout bon ?", "c'est parti") embed = ctx.send.call_args_list[1].kwargs["embed"] candids = bdd.CandidHaro.query.all() self.assertEqual(len(candids), 1) # 2 candids created self.assertEqual(candids[0].joueur, joueur) self.assertEqual(candids[0].type, bdd.CandidHaroType.candidature) send_candids_patch.assert_called_once() self.assertEqual(send_candids_patch.call_args.kwargs, {"embed": embed}) send_candids_patch.reset_mock() send_debats_patch.assert_called_once() send_debats_patch.reset_mock()

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6b0235507612ab5a34c7a0e17512b97c66e0cf5f

16,502

py

Python

backend/resource/migrations/0001_initial.py

sebaskun/budget_app

08d67be17a512d91ee49a4e9088c32392410be46

[ "Unlicense" ]

null

backend/resource/migrations/0001_initial.py

sebaskun/budget_app

08d67be17a512d91ee49a4e9088c32392410be46

[ "Unlicense" ]

null

backend/resource/migrations/0001_initial.py

sebaskun/budget_app

08d67be17a512d91ee49a4e9088c32392410be46

[ "Unlicense" ]

null

# Generated by Django 2.0.2 on 2019-05-29 07:08 import backend.core.utils from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('core', '0001_initial'), ] operations = [ migrations.CreateModel( name='CategoryEquipment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('name', models.CharField(max_length=150, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('image', models.ImageField(blank=True, null=True, upload_to='image_category', verbose_name='imagen')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categoryequipment_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categoryequipment_modified', to=settings.AUTH_USER_MODEL)), ('padre', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='category_padre', to='resource.CategoryEquipment')), ], options={ 'verbose_name': 'categoría de equipo', 'verbose_name_plural': 'categorías de equipos', 'ordering': ('name',), }, ), migrations.CreateModel( name='CategoryManpower', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('name', models.CharField(max_length=150, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('image', models.ImageField(blank=True, null=True, upload_to='image_category', verbose_name='imagen')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categorymanpower_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categorymanpower_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'categoría de mano de obra', 'verbose_name_plural': 'categorías de mano de obra', 'ordering': ('name',), }, ), migrations.CreateModel( name='CategoryMaterial', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('name', models.CharField(max_length=150, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('image', models.ImageField(blank=True, null=True, upload_to='image_category', verbose_name='imagen')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categorymaterial_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='categorymaterial_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'categoría de material', 'verbose_name_plural': 'categorías de materiales', }, ), migrations.CreateModel( name='Equipment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('code', backend.core.utils.CharNullField(blank=True, max_length=30, null=True, unique=True, verbose_name='código')), ('name', models.CharField(max_length=200, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('unit', models.CharField(blank=True, max_length=15, null=True, verbose_name='unidad de costo')), ('currency', models.CharField(choices=[('S', 'S/'), ('D', '$')], default='D', max_length=1, verbose_name='moneda')), ('cost', models.DecimalField(decimal_places=6, default=0, max_digits=15, verbose_name='costo unitario')), ('pull_in_new_budget', models.BooleanField(default=False)), ('hours_equipment_operation', models.DecimalField(decimal_places=6, default=8, max_digits=15, verbose_name='horas de operación del equipo')), ('potencia', models.DecimalField(blank=True, decimal_places=2, max_digits=6, null=True, verbose_name='potencia del equipo')), ('image', models.ImageField(blank=True, null=True, upload_to='equipment', verbose_name='image')), ('has_combustible', models.BooleanField(default=False)), ('tipo_combustible', models.CharField(blank=True, choices=[('GO', 'GasOil'), ('GS', 'Gasolina')], max_length=2, null=True, verbose_name='tipo de combustible')), ('is_deleted', models.BooleanField(default=False)), ('category', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='resource.CategoryEquipment')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='equipment_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='equipment_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'equipo', 'verbose_name_plural': 'equipos', }, ), migrations.CreateModel( name='Manpower', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('code', backend.core.utils.CharNullField(blank=True, max_length=30, null=True, unique=True, verbose_name='código')), ('name', models.CharField(max_length=200, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('unit', models.CharField(blank=True, max_length=15, null=True, verbose_name='unidad de costo')), ('currency', models.CharField(choices=[('S', 'S/'), ('D', '$')], default='D', max_length=1, verbose_name='moneda')), ('cost', models.DecimalField(decimal_places=6, default=0, max_digits=15, verbose_name='costo unitario')), ('pull_in_new_budget', models.BooleanField(default=False)), ('sueldo_bruto', models.DecimalField(decimal_places=6, default=0, max_digits=15, verbose_name='sueldo bruto')), ('type_cost', models.CharField(choices=[('D', 'DIRECTO'), ('I', 'INDIRECTO')], default='D', max_length=1, verbose_name='tipo de costo')), ('allows_ratio', models.PositiveIntegerField(default=1, verbose_name='Permite coeficiente?')), ('tipo_regimen', models.CharField(choices=[('C', 'COMÚN'), ('V', 'CIVIL')], default='C', max_length=1, verbose_name='tipo de regimen')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='manpower_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='manpower_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'mano de obra', 'verbose_name_plural': 'manos de obra', 'ordering': ('code', 'name'), }, ), migrations.CreateModel( name='Material', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('code', backend.core.utils.CharNullField(blank=True, max_length=30, null=True, unique=True, verbose_name='código')), ('name', models.CharField(max_length=200, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('unit', models.CharField(blank=True, max_length=15, null=True, verbose_name='unidad de costo')), ('currency', models.CharField(choices=[('S', 'S/'), ('D', '$')], default='D', max_length=1, verbose_name='moneda')), ('cost', models.DecimalField(decimal_places=6, default=0, max_digits=15, verbose_name='costo unitario')), ('pull_in_new_budget', models.BooleanField(default=False)), ('image', models.ImageField(blank=True, null=True, upload_to='material', verbose_name='image')), ('class_cost', models.CharField(choices=[('S', 'Estandar'), ('M', 'Medico'), ('W', 'Taller'), ('O', 'Operativo'), ('V', 'Varios'), ('I', 'Insumo médico'), ('E', 'EPP')], default='S', max_length=1, verbose_name='clase de costo')), ('is_subcontract', models.BooleanField(default=False)), ('is_deleted', models.BooleanField(default=False)), ('category', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='resource.CategoryMaterial')), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='material_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='material_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'material', 'verbose_name_plural': 'materiales', 'ordering': ['name', 'code'], }, ), migrations.CreateModel( name='Subcontract', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('code', backend.core.utils.CharNullField(blank=True, max_length=30, null=True, unique=True, verbose_name='código')), ('name', models.CharField(max_length=200, verbose_name='nombre')), ('description', models.TextField(blank=True, null=True, verbose_name='descripción')), ('unit', models.CharField(blank=True, max_length=15, null=True, verbose_name='unidad de costo')), ('currency', models.CharField(choices=[('S', 'S/'), ('D', '$')], default='D', max_length=1, verbose_name='moneda')), ('cost', models.DecimalField(decimal_places=6, default=0, max_digits=15, verbose_name='costo unitario')), ('pull_in_new_budget', models.BooleanField(default=False)), ('is_deleted', models.BooleanField(default=False)), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='subcontract_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='subcontract_modified', to=settings.AUTH_USER_MODEL)), ], options={ 'verbose_name': 'subcontrato', 'verbose_name_plural': 'subcontratos', 'ordering': ['name', 'code'], }, ), migrations.CreateModel( name='Vacuna', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('nombre', models.CharField(max_length=50, unique=True, verbose_name='nombre')), ('moneda', models.CharField(choices=[('S', 'S/'), ('D', '$')], default='D', max_length=1, verbose_name='moneda')), ('is_deleted', models.BooleanField(default=False)), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='vacuna_created', to=settings.AUTH_USER_MODEL)), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='vacuna_modified', to=settings.AUTH_USER_MODEL)), ('ubicacion', models.ForeignKey(blank=True, limit_choices_to={'grupo': 'UBC'}, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ubicaciones_vacuna', to='core.GruposVarios', verbose_name='ubicacion')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='VacunaDetail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, null=True)), ('modified', models.DateTimeField(auto_now=True, null=True)), ('quantity', models.DecimalField(decimal_places=6, default=1, max_digits=15, verbose_name='cantidad')), ('observacion', models.TextField(blank=True, null=True, verbose_name='observacion')), ('is_deleted', models.BooleanField(default=False)), ('created_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='vacunadetail_created', to=settings.AUTH_USER_MODEL)), ('material', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='material_vacunas_budget', to='resource.Material', verbose_name='vacunas material')), ('modified_by', models.ForeignKey(blank=True, editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='vacunadetail_modified', to=settings.AUTH_USER_MODEL)), ('vacuna', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='details_vacuna', to='resource.Vacuna', verbose_name='Vacuna')), ], options={ 'ordering': ('material',), }, ), migrations.AlterUniqueTogether( name='vacunadetail', unique_together={('vacuna', 'material')}, ), ]

76.046083

245

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1,797

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5.59488

0.102949

0.079869

0.040581

0.054705

0.81291

0.789238

0.755719

0.749552

0.745276

0.71663

0

0.007585

0.217004

16,502

216

246

76.398148

0.770529

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0

null

0

1

0

1

0

null

0

7

6b1181a42095f821851ef03c45db417fb3dc1acf

93,188

py

Python

venv/lib/python3.7/site-packages/slackclient_cli/slack_api_specific.py

gigumbrajaguru/SlackTats

77fe93711ae3502f833bf3ba340f36d7da82f9cf

[ "Apache-2.0" ]

2

2018-10-04T06:12:38.000Z

2021-08-02T16:39:12.000Z

venv/lib/python3.7/site-packages/slackclient_cli/slack_api_specific.py

gigumbrajaguru/SlackTats

77fe93711ae3502f833bf3ba340f36d7da82f9cf

[ "Apache-2.0" ]

null

venv/lib/python3.7/site-packages/slackclient_cli/slack_api_specific.py

gigumbrajaguru/SlackTats

77fe93711ae3502f833bf3ba340f36d7da82f9cf

[ "Apache-2.0" ]

1

2021-11-05T22:16:58.000Z

# If there is an API that understands the specification this code will be very short and it will dynamically change with specification change def get(): return { # unavailable: slackclient needs token # "api.test": [ # { # "Argument": "error", # "Required": "Optional", # "Description": "Error response to return" # }, # { # "Argument": "foo", # "Required": "Optional", # "Description": "example property to return" # }, # ], "auth.revoke": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token." }, { "Argument": "test", "Required": "Optional", "Description": "Setting this parameter to 1 triggers a testing mode where the specified token will not actually be revoked.", }, ], "auth.test": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: identify" }, ], "bots.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: users:read" }, { "Argument": "bot", "Required": "Optional", "Description": "Bot user to get info on", }, ], "channels.archive": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to archive" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to archive" }, ], "channels.create": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "name", "Required": "Required", "Description": "Name of channel to create" }, ], "channels.history": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to fetch history for." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to fetch history for." }, { "Argument": "latest", "Required": "Optional", "Description": "End of time range of messages to include in results. default=now" }, { "Argument": "oldest", "Required": "Optional", "Description": "Start of time range of messages to include in results. default=0" }, { "Argument": "inclusive", "Required": "Optional", "Description": "Include messages with latest or oldest timestamp in results. default=0" }, { "Argument": "count", "Required": "Optional", "Description": "Number of messages to return, between 1 and 1000. default=100" }, { "Argument": "unreads", "Required": "Optional", "Description": "Include unread_count_display in the output? default=0" }, ], "channels.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: channels:read" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to get info on" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to get info on" }, ], "channels.invite": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to invite user to." }, { "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to invite user to." }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to invite to channel." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to invite to channel." }, ], "channels.join": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "name", "Required": "Required", "Description": "Name of channel to join" }, ], "channels.kick": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to remove user from." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to remove user from." }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to remove from channel." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to remove from channel." }, ], "channels.leave": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to leave" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to leave" }, ], "channels.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: channels:read" }, { "Argument": "exclude_archived", "Required": "Optional", "Description": "Don't return archived channels. default=false" }, ], "channels.mark": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to set reading cursor in." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to set reading cursor in." }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the most recently seen message." }, ], "channels.rename": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to rename" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to rename" }, { "Argument": "name", "Required": "Required", "Description": "New name for channel." }, ], "channels.replies": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to fetch thread from" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to fetch thread from" }, { "Argument": "thread_ts", "Required": "Required", "Description": "Unique identifier of a thread's parent message" }, ], "channels.setPurpose": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to set the purpose of" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to set the purpose of" }, { "Argument": "purpose", "Required": "Required", "Description": "The new purpose" }, ], "channels.setTopic": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to set the topic of" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to set the topic of" }, { "Argument": "topic", "Required": "Required", "Description": "The new topic" }, ], "channels.unarchive": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: channels:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to unarchive" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to unarchive" }, ], "chat.delete": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: chat:write:bot or chat:write:user" }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the message to be updated." }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID containing the message to be updated." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name containing the message to be updated." }, { "Argument": "as_user", "Required": "Optional", "Description": "Pass true to update the message as the authed user. Bot users in this context are considered authed users." }, ], "chat.meMessage": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: chat:write:user" }, { "Argument": "channel", "Required": "Required", "Description": "Channel to send message to. Can be a public channel, private group or IM channel. Can be an encoded ID, or a name." }, { "Argument": "text", "Required": "Required", "Description": "Text of the message to send." }, ], "chat.postMessage": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: chat:write:bot or chat:write:user" }, { "Argument": "channel", "Required": "Required", "Description": "Channel, private group, or IM channel to send message to. Can be an encoded ID, or a name. See below for more details." }, { "Argument": "text", "Required": "Required", "Description": "Text of the message to send. See below for an explanation of formatting. This field is usually required, unless you're providing only attachments instead." }, { "Argument": "parse", "Required": "Optional", "Description": "Text of the message to send. See below for an explanation of formatting. This field is usually required, unless you're providing only attachments instead.Change how messages are treated. Defaults to none. See below." }, { "Argument": "link_names", "Required": "Optional", "Description": "Find and link channel names and usernames." }, { "Argument": "attachments", "Required": "Optional", "Description": "Structured message attachments." }, { "Argument": "unfurl_links", "Required": "Optional", "Description": "Pass true to enable unfurling of primarily text-based content." }, { "Argument": "unfurl_media", "Required": "Optional", "Description": "Pass false to disable unfurling of media content." }, { "Argument": "username", "Required": "Optional", "Description": "Set your bot's user name. Must be used in conjunction with as_user set to false, otherwise ignored. See authorship below." }, { "Argument": "as_user", "Required": "Optional", "Description": "Pass true to post the message as the authed user, instead of as a bot. Defaults to false. See authorship below." }, { "Argument": "icon_url", "Required": "Optional", "Description": "URL to an image to use as the icon for this message. Must be used in conjunction with as_user set to false, otherwise ignored. See authorship below." }, { "Argument": "icon_emoji", "Required": "Optional", "Description": "Emoji to use as the icon for this message. Overrides icon_url. Must be used in conjunction with as_user set to false, otherwise ignored. See authorship below." }, { "Argument": "thread_ts", "Required": "Optional", "Description": "Provide another message's ts value to make this message a reply. Avoid using a reply's ts value; use its parent instead." }, { "Argument": "reply_broadcast", "Required": "Optional", "Description": "Used in conjunction with thread_ts and indicates whether reply should be made visible to everyone in the channel or conversation. Defaults to false." }, ], "chat.update": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: chat:write:bot or chat:write:user" }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the message to be updated." }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID containing the message to be updated." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name containing the message to be updated." }, { "Argument": "text", "Required": "Required", "Description": "New text for the message, using the default formatting rules." }, { "Argument": "attachments", "Required": "Optional", "Description": "Structured message attachments." }, { "Argument": "parse", "Required": "Optional", "Description": "Change how messages are treated. Defaults to client, unlike chat.postMessage." }, { "Argument": "link_names", "Required": "Optional", "Description": "Find and link channel names and usernames. Defaults to none. This parameter should be used in conjunction with parse. To set link_names to 1, specify a parse mode of full." }, { "Argument": "as_user", "Required": "Optional", "Description": "Pass true to update the message as the authed user. Bot users in this context are considered authed users." }, ], "dnd.endDnd": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: dnd:write" }, ], "dnd.endSnooze": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: dnd:write" }, ], "dnd.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: dnd:read" }, { "Argument": "user", "Required": "Optional", "Description": "User ID to fetch status for (defaults to current user)" }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to fetch status for (defaults to current user)" }, ], "dnd.setSnooze": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: dnd:write" }, { "Argument": "num_minutes", "Required": "Required", "Description": "Number of minutes, from now, to snooze until." }, ], "dnd.teamInfo": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: dnd:read" }, { "Argument": "users", "Required": "Optional", "Description": "Comma-separated list of user IDs to fetch Do Not Disturb status for" }, { "extra_function": "user_names_to_ids", "Argument": "ex-users", "Required": "Optional", "Description": "Comma-separated list of user names to fetch Do Not Disturb status for" }, ], "emoji.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: emoji:read" }, ], "files.comments.add": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "File to add a comment to." }, { "Argument": "comment", "Required": "Required", "Description": "Text of the comment to add." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel id (encoded) of which location to associate with the new comment." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name of which location to associate with the new comment." }, ], "files.comments.delete": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "File to delete a comment from." }, { "Argument": "id", "Required": "Required", "Description": "The comment to delete." }, ], "files.comments.edit": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "File containing the comment to edit." }, { "Argument": "id", "Required": "Required", "Description": "The comment to edit." }, { "Argument": "comment", "Required": "Required", "Description": "Text of the comment to edit." }, ], "files.delete": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "ID of file to delete." }, ], "files.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:read" }, { "Argument": "file", "Required": "Required", "Description": "Specify a file by providing its ID." }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=100" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "files.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:read" }, { "Argument": "user", "Required": "Optional", "Description": "Filter files created by a single user ID." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "Filter files created by a single user name." }, { "Argument": "channel", "Required": "Optional", "Description": "Filter files appearing in a specific channel, indicated by its ID." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Filter files appearing in a specific channel, indicated by its name." }, { "Argument": "ts_from", "Required": "Optional", "Description": "Filter files created after this timestamp (inclusive). default=0" }, { "Argument": "ts_to", "Required": "Optional", "Description": "Filter files created before this timestamp (inclusive). default=now" }, { "Argument": "types", "Required": "Optional", "Description": "Filter files by type: /all - All files/spaces - Posts/snippets - Snippets/images - Image files/gdocs - Google docs/zips - Zip files/pdfs - PDF files/You can pass multiple values in the types argument, like types=spaces,snippets.The default value is all, which does not filter the list. default=all" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=100" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "files.revokePublicURL": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "File to revoke" }, ], "files.sharedPublicURL": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:write:user" }, { "Argument": "file", "Required": "Required", "Description": "File to share" }, ], "files.upload": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token.Requires scope: files:write:user" }, { "Argument": "file", "Required": "Optional", "Description": "File contents via multipart/form-data. If omitting this parameter, you must submit content." }, { "Argument": "content", "Required": "Optional", "Description": "File contents via a POST variable. If omitting this parameter, you must provide a file." }, { "extra_function": "file_upload_by_filename", "Argument": "ex-content", "Required": "Optional", "Description": "File name to be uploaded. This is a proprietary option, and the content of the file is included in the 'content' argument" }, { "Argument": "filetype", "Required": "Optional", "Description": "A file type identifier." }, { "Argument": "filename", "Required": "Optional", "Description": "Filename of file" }, { "Argument": "title", "Required": "Optional", "Description": "Title of file." }, { "Argument": "initial_comment", "Required": "Optional", "Description": "Initial comment to add to file." }, { "Argument": "channels", "Required": "Optional", "Description": "Comma-separated list of channel names or IDs where the file will be shared." }, { "extra_function": "channel_names_to_ids", "Argument": "ex-channels", "Required": "Optional", "Description": "Comma-separated list of channel names or IDs where the file will be shared." }, ], "groups.archive": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to archive" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to archive" }, ], "groups.close": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to close." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to close." }, ], "groups.create": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "name", "Required": "Required", "Description": "Name of private channel to creat" }, ], "groups.createChild": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to clone and archive." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to clone and archive." }, ], "groups.history": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to fetch history for." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to fetch history for." }, { "Argument": "latest", "Required": "Optional", "Description": "End of time range of messages to include in results. default=now" }, { "Argument": "oldest", "Required": "Optional", "Description": "Start of time range of messages to include in results. default=0" }, { "Argument": "inclusive", "Required": "Optional", "Description": "Include messages with latest or oldest timestamp in results. default=0" }, { "Argument": "count", "Required": "Optional", "Description": "Number of messages to return, between 1 and 1000. default=100" }, { "Argument": "unreads", "Required": "Optional", "Description": "Include unread_count_display in the output? default=0" }, ], "groups.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:read" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to get info on" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to get info on" }, ], "groups.invite": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to invite user to." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to invite user to." }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to invite." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to invite." }, ], "groups.kick": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to remove user from." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to remove user from." }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to remove from private channel." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to remove from private channel." }, ], "groups.leave": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to leave" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to leave" }, ], "groups.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:read" }, { "Argument": "exclude_archived", "Required": "Optional", "Description": "Don't return archived private channels. default=0" }, ], "groups.mark": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to set reading cursor in." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to set reading cursor in." }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the most recently seen message." }, ], "groups.open": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to open." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to open." }, ], "groups.rename": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to rename." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to rename." }, { "Argument": "name", "Required": "Required", "Description": "New name for private channel." }, ], "groups.replies": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to fetch thread from" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to fetch thread from" }, { "Argument": "thread_ts", "Required": "Required", "Description": "Unique identifier of a thread's parent message" }, ], "groups.setPurpose": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to set the purpose of" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to set the purpose of" }, { "Argument": "purpose", "Required": "Required", "Description": "The new purpose" }, ], "groups.setTopic": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to set the topic of" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to set the topic of" }, { "Argument": "topic", "Required": "Required", "Description": "The new topic" }, ], "groups.unarchive": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: groups:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Private channel ID to unarchive" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Private channel name to unarchive" }, ], "im.close": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Direct message channel ID to close." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Direct message channel name to close." }, ], "im.history": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Direct message channel ID to fetch history for." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Direct message channel name to fetch history for." }, { "Argument": "latest", "Required": "Optional", "Description": "End of time range of messages to include in results. default=now" }, { "Argument": "oldest", "Required": "Optional", "Description": "Start of time range of messages to include in results. default=0" }, { "Argument": "inclusive", "Required": "Optional", "Description": "Include messages with latest or oldest timestamp in results. default=0" }, { "Argument": "count", "Required": "Optional", "Description": "Number of messages to return, between 1 and 1000. default=100" }, { "Argument": "unreads", "Required": "Optional", "Description": "Include unread_count_display in the output? default=0" }, ], "im.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:read" }, ], "im.mark": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Direct message channel ID to set reading cursor in" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Direct message channel name to set reading cursor in" }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the most recently seen message." }, ], "im.open": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:write" }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to open a direct message channel with." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to open a direct message channel with." }, { "Argument": "return_im", "Required": "Optional", "Description": "Boolean, indicates you want the full IM channel definition in the response." }, ], "im.replies": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: im:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Direct message channel ID to fetch thread from" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Direct message channel name to fetch thread from" }, { "Argument": "thread_ts", "Required": "Required", "Description": "Unique identifier of a thread's parent message" }, ], "mpim.close": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "MPIM to close." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "MPIM channel name to close." }, ], "mpim.history": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Multiparty direct message (channel id) to fetch history for." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Multiparty direct message (channel name) to fetch history for." }, { "Argument": "latest", "Required": "Optional", "Description": "End of time range of messages to include in results. default=now" }, { "Argument": "oldest", "Required": "Optional", "Description": "Start of time range of messages to include in results. default=0" }, { "Argument": "inclusive", "Required": "Optional", "Description": "Include messages with latest or oldest timestamp in results. default=0" }, { "Argument": "count", "Required": "Optional", "Description": "Number of messages to return, between 1 and 1000. default=100" }, { "Argument": "unreads", "Required": "Optional", "Description": "Include unread_count_display in the output? default=0" }, ], "mpim.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:read" }, ], "mpim.mark": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "multiparty direct message channel ID to set reading cursor in." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "multiparty direct message channel name to set reading cursor in." }, { "Argument": "ts", "Required": "Required", "Description": "Timestamp of the most recently seen message." }, ], "mpim.open": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:write" }, { "Argument": "users", # "Required": "Required", "Required": "Optional", "Description": "Comma separated lists of user IDs. The ordering of the users is preserved whenever a MPIM group is returned." }, { "extra_function": "user_names_to_ids", "Argument": "ex-users", "Required": "Optional", "Description": "Comma separated lists of user names. The ordering of the users is preserved whenever a MPIM group is returned." }, ], "mpim.replies": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: mpim:history" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Multiparty direct message channel ID to fetch thread from." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Multiparty direct message channel name to fetch thread from." }, { "Argument": "thread_ts", "Required": "Required", "Description": "Unique identifier of a thread's parent message." }, ], "oauth.access": [ { "Argument": "client_id", "Required": "Required", "Description": "Issued when you created your application." }, { "Argument": "client_secret", "Required": "Required", "Description": "Issued when you created your application." }, { "Argument": "code", "Required": "Required", "Description": "The code param returned via the OAuth callback." }, { "Argument": "redirect_uri", "Required": "Optional", "Description": "This must match the originally submitted URI (if one was sent)." }, ], "pins.add": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: pins:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to pin the item in." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to pin the item in." }, { "Argument": "file", "Required": "Optional", "Description": "Channel to pin the item in." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to pin." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to pin." }, ], "pins.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: pins:read" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID to get pinned items for." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to get pinned items for." }, ], "pins.remove": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: pins:write" }, { "Argument": "channel", # "Required": "Required", "Required": "Optional", "Description": "Channel ID where the item is pinned to." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name where the item is pinned to." }, { "Argument": "file", "Required": "Optional", "Description": "File to un-pin." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to un-pin." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to un-pin." }, ], "reactions.add": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reactions:write" }, { "Argument": "name", "Required": "Required", "Description": "Reaction (emoji) name." }, { "Argument": "file", "Required": "Optional", "Description": "File to add reaction to." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to add reaction to." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel ID where the message to add reaction to was posted." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name where the message to add reaction to was posted." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to add reaction to." }, ], "reactions.get": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reactions:read" }, { "Argument": "file", "Required": "Optional", "Description": "File to get reactions for." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to get reactions for." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel ID where the message to get reactions for was posted." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name where the message to get reactions for was posted." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to get reactions for." }, { "Argument": "full", "Required": "Optional", "Description": "If true always return the complete reaction list." }, ], "reactions.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reactions:read" }, { "Argument": "user", "Required": "Optional", "Description": "Show reactions made by this user ID. Defaults to the authed user." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "Show reactions made by this user name." }, { "Argument": "full", "Required": "Optional", "Description": "If true always return the complete reaction list." }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=100" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "reactions.remove": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reactions:write" }, { "Argument": "name", "Required": "Required", "Description": "Reaction (emoji) name." }, { "Argument": "file", "Required": "Optional", "Description": "File to remove reaction from." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to remove reaction from." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel ID where the message to remove reaction from was posted." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name where the message to remove reaction from was posted." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to remove reaction from." }, ], "reminders.add": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reminders:write" }, { "Argument": "text", "Required": "Required", "Description": "The content of the reminder" }, { "Argument": "time", "Required": "Required", "Description": 'When this reminder should happen: the Unix timestamp (up to five years from now), the number of seconds until the reminder (if within 24 hours), or a natural language description (Ex. "in 15 minutes," or "every Thursday")' }, { "Argument": "user", "Required": "Optional", "Description": "The user ID who will receive the reminder. If no user is specified, the reminder will go to user who created it." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "The user name who will receive the reminder." }, ], "reminders.complete": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reminders:write" }, { "Argument": "reminder", "Required": "Required", "Description": "The ID of the reminder to be marked as complet" }, ], "reminders.delete": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reminders:write" }, { "Argument": "reminder", "Required": "Required", "Description": "The ID of the reminder" }, ], "reminders.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reminders:read" }, { "Argument": "reminder", "Required": "Required", "Description": "The ID of the reminder" }, ], "reminders.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: reminders:read" }, ], "search.all": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: search:read" }, { "Argument": "query", "Required": "Required", "Description": "Search query. May contains booleans, etc" }, { "Argument": "sort", "Required": "Optional", "Description": "Return matches sorted by either score or timestamp. default=score" }, { "Argument": "sort_dir", "Required": "Optional", "Description": "Change sort direction to ascending (asc) or descending (desc). default=desc" }, { "Argument": "highlight", "Required": "Optional", "Description": "Pass a value of true to enable query highlight markers" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=20" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "search.files": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: search:read" }, { "Argument": "query", "Required": "Required", "Description": "Search query. May contains booleans, etc" }, { "Argument": "sort", "Required": "Optional", "Description": "Return matches sorted by either score or timestamp. default=score" }, { "Argument": "sort_dir", "Required": "Optional", "Description": "Change sort direction to ascending (asc) or descending (desc). default=desc" }, { "Argument": "highlight", "Required": "Optional", "Description": "Pass a value of true to enable query highlight markers" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=20" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "search.messages": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: search:read" }, { "Argument": "query", "Required": "Required", "Description": "Search query. May contains booleans, etc" }, { "Argument": "sort", "Required": "Optional", "Description": "Return matches sorted by either score or timestamp. default=score" }, { "Argument": "sort_dir", "Required": "Optional", "Description": "Change sort direction to ascending (asc) or descending (desc). default=desc" }, { "Argument": "highlight", "Required": "Optional", "Description": "Pass a value of true to enable query highlight markers" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=20" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "stars.add": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: stars:write" }, { "Argument": "file", "Required": "Optional", "Description": "File to add star to." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to add star to." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel ID to add star to, or channel where the message to add star to was posted (used with timestamp)" }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to add star to, or channel where the message to add star to was posted (used with timestamp)" }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to add star to." }, ], "stars.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: stars:read" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=100" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, ], "stars.remove": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: stars:write" }, { "Argument": "file", "Required": "Optional", "Description": "File to remove star from." }, { "Argument": "file_comment", "Required": "Optional", "Description": "File comment to remove star from." }, { "Argument": "channel", "Required": "Optional", "Description": "Channel ID to remove star from, or channel where the message to remove star from was posted (used with timestamp)." }, { "extra_function": "channel_name_to_id", "Argument": "ex-channel", "Required": "Optional", "Description": "Channel name to remove star from, or channel where the message to remove star from was posted (used with timestamp)." }, { "Argument": "timestamp", "Required": "Optional", "Description": "Timestamp of the message to remove star from." }, ], "team.accessLogs": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: admin" }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page. default=100" }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return. default=1" }, { "Argument": "before", "Required": "Optional", "Description": "End of time range of logs to include in results (inclusive). default=now" }, ], "team.billableInfo": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: admin" }, { "Argument": "user", "Required": "Optional", "Description": "A user ID to retrieve the billable information for. Defaults to all users." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "A user name to retrieve the billable information for." }, ], "team.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: team:read" }, ], "team.integrationLogs": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: admin" }, { "Argument": "service_id", "Required": "Optional", "Description": "Filter logs to this service. Defaults to all logs." }, { "Argument": "app_id", "Required": "Optional", "Description": "Filter logs to this Slack app. Defaults to all logs." }, { "Argument": "user", "Required": "Optional", "Description": "Filter logs generated by this user's (ID) actions. Defaults to all logs." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "Filter logs generated by this user's (name) actions. Defaults to all logs." }, { "Argument": "change_type", "Required": "Optional", "Description": "Filter logs with this change type. Defaults to all logs." }, { "Argument": "count", "Required": "Optional", "Description": "Number of items to return per page." }, { "Argument": "page", "Required": "Optional", "Description": "Page number of results to return." }, ], "team.profile.get": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users.profile:read" }, { "Argument": "visibility", "Required": "Optional", "Description": "Filter by visibility." }, ], "usergroups.create": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:write" }, { "Argument": "name", "Required": "Required", "Description": "A name for the User Group. Must be unique among User Groups." }, { "Argument": "handle", "Required": "Optional", "Description": "A mention handle. Must be unique among channels, users and User Groups." }, { "Argument": "description", "Required": "Optional", "Description": "A short description of the User Group." }, { "Argument": "channels", "Required": "Optional", "Description": "A comma separated string of encoded channel IDs for which the User Group uses as a default." }, { "extra_function": "channel_names_to_ids", "Argument": "ex-channels", "Required": "Optional", "Description": "A comma separated string of channel names for which the User Group uses as a default." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in each User Group." }, ], "usergroups.disable": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:write" }, { "Argument": "usergroup", "Required": "Required", "Description": "The encoded ID of the User Group to disable." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in the User Group." }, ], "usergroups.enable": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:write" }, { "Argument": "usergroup", "Required": "Required", "Description": "The encoded ID of the User Group to disable." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in the User Group." }, ], "usergroups.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:read" }, { "Argument": "include_disabled", "Required": "Optional", "Description": "Include disabled User Groups." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in the User Group." }, { "Argument": "include_users", "Required": "Optional", "Description": "Include the list of users for each User Group." }, ], "usergroups.update": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:write" }, { "Argument": "usergroup", "Required": "Required", "Description": "The encoded ID of the User Group to update." }, { "Argument": "name", "Required": "Optional", "Description": "A name for the User Group. Must be unique among User Groups." }, { "Argument": "handle", "Required": "Optional", "Description": "A mention handle. Must be unique among channels, users and User Groups." }, { "Argument": "description", "Required": "Optional", "Description": "A short description of the User Group." }, { "Argument": "channels", "Required": "Optional", "Description": "A comma separated string of encoded channel IDs for which the User Group uses as a default." }, { "extra_function": "channel_names_to_ids", "Argument": "ex-channels", "Required": "Optional", "Description": "A comma separated string of channel names for which the User Group uses as a default." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in the User Group." }, ], "usergroups.users.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:read" }, { "Argument": "usergroup", "Required": "Required", "Description": "The encoded ID of the User Group to update." }, { "Argument": "include_disabled", "Required": "Optional", "Description": "Allow results that involve disabled User Groups." }, ], "usergroups.users.update": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: usergroups:write" }, { "Argument": "usergroup", "Required": "Required", "Description": "The encoded ID of the User Group to update." }, { "Argument": "users", # "Required": "Required", "Required": "Optional", "Description": "A comma separated string of encoded user IDs that represent the entire list of users for the User Group." }, { "extra_function": "user_names_to_ids", "Argument": "ex-users", "Required": "Optional", "Description": "A comma separated string of user names that represent the entire list of users for the User Group." }, { "Argument": "include_count", "Required": "Optional", "Description": "Include the number of users in the User Group." }, ], "users.deletePhoto": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users.profile:write" }, ], "users.getPresence": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users:read" }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to get presence info on. Defaults to the authed user." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to get presence info on. Defaults to the authed user." }, ], "users.identity": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: identity.basic" }, ], "users.info": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users:read" }, { "Argument": "user", # "Required": "Required", "Required": "Optional", "Description": "User ID to get info on" }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to get info on" }, ], "users.list": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users:read" }, { "Argument": "presence", "Required": "Optional", "Description": "Whether to include presence data in the output" }, ], "users.setActive": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users:write" }, ], "users.setPhoto": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users.profile:write" }, { "Argument": "image", "Required": "Required", "Description": "File contents via multipart/form-data." }, { "Argument": "crop_x", "Required": "Optional", "Description": "X coordinate of top-left corner of crop box" }, { "Argument": "crop_y", "Required": "Optional", "Description": "Y coordinate of top-left corner of crop box" }, { "Argument": "crop_w", "Required": "Optional", "Description": "Width/height of crop box (always square)" }, ], "users.setPresence": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users:write" }, { "Argument": "presence", "Required": "Required", "Description": "Either auto or away" }, ], "users.profile.get": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users.profile:read" }, { "Argument": "user", "Required": "Optional", "Description": "User ID to retrieve profile info for" }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "User name to retrieve profile info for" }, { "Argument": "include_labels", "Required": "Optional", "Description": "Include labels for each ID in custom profile fields. default=false" }, ], "users.profile.set": [ { "Argument": "token", "Required": "Required", "Description": "Authentication token. Requires scope: users.profile:write" }, { "Argument": "user", "Required": "Optional", "Description": "ID of user to change. This argument may only be specified by team admins on paid teams." }, { "extra_function": "user_name_to_id", "Argument": "ex-user", "Required": "Optional", "Description": "name of user to change. This argument may only be specified by team admins on paid teams." }, { "Argument": "profile", "Required": "Optional", "Description": "Collection of key:value pairs presented as a URL-encoded JSON hash." }, { "Argument": "name", "Required": "Optional", "Description": "Name of a single key to set. Usable only if profile is not passed." }, { "Argument": "value", "Required": "Optional", "Description": "Value to set a single key to. Usable only if profile is not passed." }, ], }

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6b158e40cced29033e11197f7a23c2b3f08d8955

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py

Python

Platforms/Web/Processing/Api/Discord/Configs/Whitelistedlinks/__init__.py

The-CJ/Phaazebot

83a9563d210718071d4e2cdcca3b212c87abaf51

[ "MIT" ]

2

2017-09-14T08:07:55.000Z

2021-05-18T05:05:05.000Z

Platforms/Web/Processing/Api/Discord/Configs/Whitelistedlinks/__init__.py

The-CJ/Phaazebot

83a9563d210718071d4e2cdcca3b212c87abaf51

[ "MIT" ]

111

2018-04-15T14:32:14.000Z

2021-03-28T21:06:29.000Z

Platforms/Web/Processing/Api/Discord/Configs/Whitelistedlinks/__init__.py

The-CJ/Phaazebot

83a9563d210718071d4e2cdcca3b212c87abaf51

[ "MIT" ]

1

2018-04-15T13:24:44.000Z

import Platforms.Web.Processing.Api.Discord.Configs.Whitelistedlinks.create as create import Platforms.Web.Processing.Api.Discord.Configs.Whitelistedlinks.delete as delete import Platforms.Web.Processing.Api.Discord.Configs.Whitelistedlinks.errors as errors import Platforms.Web.Processing.Api.Discord.Configs.Whitelistedlinks.get as get import Platforms.Web.Processing.Api.Discord.Configs.Whitelistedlinks.main as main

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6b19304816bda17461d2876c28ba4a8106b37a44

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gyp

Python

cloud_print/virtual_driver/win/install/virtual_driver_install.gyp

kjthegod/chromium

cf940f7f418436b77e15b1ea23e6fa100ca1c91a

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

8

2016-02-08T11:59:31.000Z

2020-05-31T15:19:54.000Z

cloud_print/virtual_driver/win/install/virtual_driver_install.gyp

kjthegod/chromium

cf940f7f418436b77e15b1ea23e6fa100ca1c91a

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

1

2021-05-05T11:11:31.000Z

cloud_print/virtual_driver/win/install/virtual_driver_install.gyp

kjthegod/chromium

cf940f7f418436b77e15b1ea23e6fa100ca1c91a

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

7

2016-02-09T09:28:14.000Z

2020-07-25T19:03:36.000Z

# Copyright (c) 2012 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. { 'target_defaults': { 'variables': { 'chromium_code': 1, }, 'include_dirs': [ '<(DEPTH)', ], }, 'targets' : [ { 'target_name': 'virtual_driver_setup', 'type': 'executable', 'include_dirs': [ # To allow including "version.h" '<(SHARED_INTERMEDIATE_DIR)', ], 'dependencies': [ '../virtual_driver.gyp:virtual_driver_lib', '<(DEPTH)/base/base.gyp:base', '<(DEPTH)/cloud_print/common/common.gyp:cloud_print_install_lib', '<(DEPTH)/cloud_print/cloud_print_resources.gyp:cloud_print_version_resources', 'virtual_driver_setup_resources', ], 'sources': [ 'setup.cc', '<(SHARED_INTERMEDIATE_DIR)/cloud_print/virtual_driver_setup_exe_version.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ar.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_bg.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_bn.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ca.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_cs.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_da.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_de.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_el.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_en.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_en-GB.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_es.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_es-419.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_et.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_fa.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_fi.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_fil.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_fr.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_gu.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_he.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_hi.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_hr.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_hu.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_id.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_it.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ja.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_kn.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ko.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_lt.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_lv.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ml.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_mr.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ms.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_nb.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_nl.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_pl.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_pt-BR.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_pt-PT.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ro.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_ru.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_sk.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_sl.rc', '<(SHARED_INTERMEDIATE_DIR)/virtual_driver_setup_resources/virtual_driver_setup_resources_sr.rc', 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866c92352c243e46ca4530671fe001fcc7758fa2

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py

Python

utils.py

coasxu/FedMA

21f4d32338fd2563ebd97c737e3b9f4f470029d9

[ "MIT" ]

254

2020-02-14T07:45:36.000Z

2022-03-30T01:36:07.000Z

utils.py

coasxu/FedMA

21f4d32338fd2563ebd97c737e3b9f4f470029d9

[ "MIT" ]

14

2020-05-01T18:21:06.000Z

2022-02-21T03:50:52.000Z

utils.py

coasxu/FedMA

21f4d32338fd2563ebd97c737e3b9f4f470029d9

[ "MIT" ]

72

2020-02-20T12:16:25.000Z

2022-02-19T09:59:59.000Z

import os import argparse import json import numpy as np import torch import torch.optim as optim import torch.nn as nn import logging import torchvision import torchvision.transforms as transforms import torch.utils.data as data from itertools import product import math import copy import time from sklearn.metrics import confusion_matrix # we've changed to a faster solver #from scipy.optimize import linear_sum_assignment import logging from torch.autograd import Variable import torch.nn.functional as F import torch.nn as nn import torch.nn.init as init from datasets import MNIST_truncated, CIFAR10_truncated, ImageFolderTruncated, CIFAR10ColorGrayScaleTruncated from combine_nets import prepare_uniform_weights, prepare_sanity_weights, prepare_weight_matrix, normalize_weights, get_weighted_average_pred from vgg import * from model import * logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) def mkdirs(dirpath): try: os.makedirs(dirpath) except Exception as _: pass def parse_class_dist(net_class_config): cls_net_map = {} for net_idx, net_classes in enumerate(net_class_config): for net_cls in net_classes: if net_cls not in cls_net_map: cls_net_map[net_cls] = [] cls_net_map[net_cls].append(net_idx) return cls_net_map def record_net_data_stats(y_train, net_dataidx_map, logdir): net_cls_counts = {} for net_i, dataidx in net_dataidx_map.items(): unq, unq_cnt = np.unique(y_train[dataidx], return_counts=True) tmp = {unq[i]: unq_cnt[i] for i in range(len(unq))} net_cls_counts[net_i] = tmp logging.debug('Data statistics: %s' % str(net_cls_counts)) return net_cls_counts def partition_data(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': X_train, y_train, X_test, y_test = load_mnist_data(datadir) n_train = X_train.shape[0] elif dataset == 'cifar10': X_train, y_train, X_test, y_test = load_cifar10_data(datadir) n_train = X_train.shape[0] elif dataset == 'cinic10': _train_dir = './data/cinic10/cinic-10-trainlarge/train' cinic_mean = [0.47889522, 0.47227842, 0.43047404] cinic_std = [0.24205776, 0.23828046, 0.25874835] trainset = ImageFolderTruncated(_train_dir, transform=transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: F.pad(Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=cinic_mean,std=cinic_std), ])) y_train = trainset.get_train_labels n_train = y_train.shape[0] if partition == "homo": idxs = np.random.permutation(n_train) batch_idxs = np.array_split(idxs, n_nets) net_dataidx_map = {i: batch_idxs[i] for i in range(n_nets)} elif partition == "hetero-dir": min_size = 0 K = 10 N = y_train.shape[0] net_dataidx_map = {} while min_size < 10: idx_batch = [[] for _ in range(n_nets)] # for each class in the dataset for k in range(K): idx_k = np.where(y_train == k)[0] np.random.shuffle(idx_k) proportions = np.random.dirichlet(np.repeat(alpha, n_nets)) ## Balance proportions = np.array([p*(len(idx_j)<N/n_nets) for p,idx_j in zip(proportions,idx_batch)]) proportions = proportions/proportions.sum() proportions = (np.cumsum(proportions)*len(idx_k)).astype(int)[:-1] idx_batch = [idx_j + idx.tolist() for idx_j,idx in zip(idx_batch,np.split(idx_k,proportions))] min_size = min([len(idx_j) for idx_j in idx_batch]) for j in range(n_nets): np.random.shuffle(idx_batch[j]) net_dataidx_map[j] = idx_batch[j] elif partition == "hetero-fbs": # in this part we conduct a experimental study on exploring the effect of increasing the number of batches # but the number of data points are approximately fixed for each batch # the arguments we need to use here are: `args.partition_step_size`, `args.local_points`, `args.partition_step`(step can be {0, 1, ..., args.partition_step_size - 1}). # Note that `args.partition` need to be fixed as "hetero-fbs" where fbs means fixed batch size net_dataidx_map = {} # stage 1st: homo partition idxs = np.random.permutation(n_train) total_num_batches = int(n_train/args.local_points) # e.g. currently we have 180k, we want each local batch has 5k data points the `total_num_batches` becomes 36 step_batch_idxs = np.array_split(idxs, args.partition_step_size) sub_partition_size = int(total_num_batches / args.partition_step_size) # e.g. for `total_num_batches` at 36 and `args.partition_step_size` at 6, we have `sub_partition_size` at 6 # stage 2nd: hetero partition n_batches = (args.partition_step + 1) * sub_partition_size min_size = 0 K = 10 #N = len(step_batch_idxs[args.step]) baseline_indices = np.concatenate([step_batch_idxs[i] for i in range(args.partition_step + 1)]) y_train = y_train[baseline_indices] N = y_train.shape[0] while min_size < 10: idx_batch = [[] for _ in range(n_batches)] # for each class in the dataset for k in range(K): idx_k = np.where(y_train == k)[0] np.random.shuffle(idx_k) proportions = np.random.dirichlet(np.repeat(alpha, n_batches)) ## Balance proportions = np.array([p*(len(idx_j)<N/n_batches) for p,idx_j in zip(proportions,idx_batch)]) proportions = proportions/proportions.sum() proportions = (np.cumsum(proportions)*len(idx_k)).astype(int)[:-1] idx_batch = [idx_j + idx.tolist() for idx_j,idx in zip(idx_batch,np.split(idx_k,proportions))] min_size = min([len(idx_j) for idx_j in idx_batch]) # we leave this to the end for j in range(n_batches): np.random.shuffle(idx_batch[j]) net_dataidx_map[j] = idx_batch[j] traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, baseline_indices traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) #return (X_train, y_train, X_test, y_test, net_dataidx_map, traindata_cls_counts) return y_train, net_dataidx_map, traindata_cls_counts def partition_data_dist_skew(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] entire_gray_scale_indices_train = [] entire_gray_scale_indices_test = [] # we start an adjust version here: ######################################################################################################################################### # in this setting, we corelate the majority / minory with the class # i.e. we firstly do an extreme version where we randomly sample 5 out of 10 groups s.t. in those groups there are only grayscale images # for the other five groups, we leave all images to be colored images ######################################################################################################################################### grayscale_dominate_classes = np.random.choice(np.arange(10), 5, replace=False) logger.info("Grayscale image dominated classes are : {}".format(grayscale_dominate_classes)) # we split all grayscale dominate classes to client 0 and all color dominate classes to client1 client0_indices = [] client1_indices = [] for i in range(10): if i in grayscale_dominate_classes: logger.info("Grayscale dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] # we fix this to be one first ### # this is the extreme case, we now change to a relatexed case ### #num_of_gray_scale_per_class_train = int(1.0 * class_indices_train.shape[0]) num_of_gray_scale_per_class_train = int(0.95 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) client0_indices.append(class_indices_train) else: logger.info("Color dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] num_of_gray_scale_per_class_train = int(0.05 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) client1_indices.append(class_indices_train) entire_gray_scale_indices_train.append(class_gray_scale_indices_train) entire_gray_scale_indices_train = np.concatenate(entire_gray_scale_indices_train) client0_indices = np.concatenate(client0_indices) client1_indices = np.concatenate(client1_indices) ### # extreme case: ### for i in range(10): class_indices_test = np.where(np.array(testset.targets) == i)[0] # training set contains skewness, but in test set colored and gray-scale images are evenly distributed num_of_gray_scale_per_class_test = int(0.5 * class_indices_test.shape[0]) class_gray_scale_indices_test = np.random.choice(class_indices_test, num_of_gray_scale_per_class_test, replace=False) entire_gray_scale_indices_test.append(class_gray_scale_indices_test) logger.info("Num of gray scale image per class test: {}".format(class_gray_scale_indices_test.shape[0])) entire_gray_scale_indices_test = np.concatenate(entire_gray_scale_indices_test) logger.info("Total Num of gray scale image test: {}".format(entire_gray_scale_indices_test.shape[0])) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) indices_colored = np.array([i for i in idxs if i not in entire_gray_scale_indices_train]) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = client0_indices net_dataidx_map[1] = client1_indices elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test def partition_data_dist_skew_baseline(dataset, datadir, logdir, partition, n_nets, alpha, args): ''' This is for one of the baseline we're going to use for rebuttal of ICLR2020 Where the entire training dataset and the entire test dataset are with all grayscale images ''' if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] n_test = testset.data.shape[0] entire_gray_scale_indices_train = np.arange(n_train) entire_gray_scale_indices_test = np.arange(n_test) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) indices_colored = np.array([i for i in idxs if i not in entire_gray_scale_indices_train]) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = np.arange(n_train) elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test #return y_train, entire_gray_scale_indices_train, entire_gray_scale_indices_test def partition_data_dist_skew_normal(dataset, datadir, logdir, partition, n_nets, alpha, args): ''' This is for one of the baseline we're going to use for rebuttal of ICLR2020 Where the entire training dataset and the entire test dataset are with all grayscale images ''' if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] n_test = testset.data.shape[0] entire_gray_scale_indices_train = [] entire_gray_scale_indices_test = np.arange(n_test) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) indices_colored = np.array([i for i in idxs if i not in entire_gray_scale_indices_train]) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = np.arange(n_train) elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test def partition_data_dist_skew_baseline_balanced(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] entire_gray_scale_indices_train = [] entire_gray_scale_indices_test = [] # we start an adjust version here: ######################################################################################################################################### # in this setting, we corelate the majority / minory with the class # i.e. we firstly do an extreme version where we randomly sample 5 out of 10 groups s.t. in those groups there are only grayscale images # for the other five groups, we leave all images to be colored images ######################################################################################################################################### grayscale_dominate_classes = np.random.choice(np.arange(10), 5, replace=False) logger.info("Grayscale image dominated classes are : {}".format(grayscale_dominate_classes)) # we split all grayscale dominate classes to client 0 and all color dominate classes to client1 for i in range(10): if i in grayscale_dominate_classes: logger.info("Grayscale dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] # we fix this to be one first ### # this is the extreme case, we now change to a relatexed case ### #num_of_gray_scale_per_class_train = int(1.0 * class_indices_train.shape[0]) num_of_gray_scale_per_class_train = int(0.5 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) else: logger.info("Color dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] num_of_gray_scale_per_class_train = int(0.5 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) entire_gray_scale_indices_train.append(class_gray_scale_indices_train) entire_gray_scale_indices_train = np.concatenate(entire_gray_scale_indices_train) #################### # in this test case #################### for i in range(10): class_indices_test = np.where(np.array(testset.targets) == i)[0] # training set contains skewness, but in test set colored and gray-scale images are evenly distributed num_of_gray_scale_per_class_test = int(0.5 * class_indices_test.shape[0]) class_gray_scale_indices_test = np.random.choice(class_indices_test, num_of_gray_scale_per_class_test, replace=False) entire_gray_scale_indices_test.append(class_gray_scale_indices_test) logger.info("Num of gray scale image per class test: {}".format(class_gray_scale_indices_test.shape[0])) entire_gray_scale_indices_test = np.concatenate(entire_gray_scale_indices_test) logger.info("Total Num of gray scale image test: {}".format(entire_gray_scale_indices_test.shape[0])) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) net_dataidx_map[0] = np.arange(n_train) elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test #return y_train, entire_gray_scale_indices_train, entire_gray_scale_indices_test def partition_data_dist_skew_baseline_oversampled(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] entire_gray_scale_indices_train = [] entire_gray_scale_indices_test = [] # we start an adjust version here: ######################################################################################################################################### # in this setting, we corelate the majority / minory with the class # i.e. we firstly do an extreme version where we randomly sample 5 out of 10 groups s.t. in those groups there are only grayscale images # for the other five groups, we leave all images to be colored images ######################################################################################################################################### grayscale_dominate_classes = np.random.choice(np.arange(10), 5, replace=False) logger.info("Grayscale image dominated classes are : {}".format(grayscale_dominate_classes)) # we split all grayscale dominate classes to client 0 and all color dominate classes to client1 entire_indices = [] for i in range(10): if i in grayscale_dominate_classes: logger.info("Grayscale dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] # we fix this to be one first ### # this is the extreme case, we now change to a relatexed case ### # here we created the dominated images num_of_gray_scale_per_class_train = int(0.95 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) entire_indices.append(class_indices_train) # here we need to oversample the underrepresented images class_color_indices_train = [idx for idx in class_indices_train if idx not in class_gray_scale_indices_train] logger.info("Length gray scale image train: {}".format(len(class_gray_scale_indices_train))) logger.info("Length gray scale image test: {}".format(len(class_color_indices_train))) # we use this way to mitigate the data bias by oversampling for i in range(int(0.95/0.05)): entire_indices.append(class_color_indices_train) else: logger.info("Color dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] num_of_gray_scale_per_class_train = int(0.05 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) logger.info("Length gray scale image train: {}".format(len(class_gray_scale_indices_train))) entire_indices.append(class_indices_train) # we use this way to mitigate the data bias by oversampling for i in range(int(0.95/0.05)): entire_indices.append(class_gray_scale_indices_train) entire_gray_scale_indices_train.append(class_gray_scale_indices_train) entire_gray_scale_indices_train = np.concatenate(entire_gray_scale_indices_train) entire_indices = np.concatenate(entire_indices) logger.info("Entire indices: {}".format(len(entire_indices))) ### # extreme case: ### for i in range(10): class_indices_test = np.where(np.array(testset.targets) == i)[0] # training set contains skewness, but in test set colored and gray-scale images are evenly distributed num_of_gray_scale_per_class_test = int(0.5 * class_indices_test.shape[0]) class_gray_scale_indices_test = np.random.choice(class_indices_test, num_of_gray_scale_per_class_test, replace=False) entire_gray_scale_indices_test.append(class_gray_scale_indices_test) logger.info("Num of gray scale image per class test: {}".format(class_gray_scale_indices_test.shape[0])) entire_gray_scale_indices_test = np.concatenate(entire_gray_scale_indices_test) logger.info("Total Num of gray scale image test: {}".format(entire_gray_scale_indices_test.shape[0])) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) indices_colored = np.array([i for i in idxs if i not in entire_gray_scale_indices_train]) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = entire_indices elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test #return y_train, net_dataidx_map, entire_gray_scale_indices_train, entire_gray_scale_indices_test def partition_data_viz(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': pass elif dataset == 'cifar10': # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] # we start an adjust version here: ############################################################################################### # Our strategy is like the following: # we split the data of CIFAR=10 dataset to two different clients # and those two clients only share on common class ############################################################################################### # the class 5 (i.e. dog) is shared across the two clients #classes_client1 = [class_index for class_index in range(0, 7)] #classes_client2 = [class_index for class_index in range(2, 10)] #classes_client1 = [0, 1] #classes_client2 = [8, 9] classes_client1 = [0, 1, 2, 3] classes_client2 = [6, 7, 8, 9] # we split all grayscale dominate classes to client 0 and all color dominate classes to client1 client0_indices = [] client1_indices = [] for ci in range(10): class_indices_train = np.where(np.array(training_set.targets) == ci)[0] #logger.info("############# class index: {}, class_indices: {}".format(ci, class_indices_train)) if ci in classes_client1: logger.info("Client 1 exclusive classes: {}".format(ci)) client0_indices.append(class_indices_train) elif ci in classes_client2: logger.info("Client 2 exclusive classes: {}".format(ci)) client1_indices.append(class_indices_train) else: # here we handel the shared class num_of_dp_per_client = int(0.5 * class_indices_train.shape[0]) shared_class_indices_client0 = np.random.choice(class_indices_train, num_of_dp_per_client, replace=False) shared_class_indices_client1 = [idx for idx in class_indices_train if idx not in shared_class_indices_client0] client0_indices.append(shared_class_indices_client0) client1_indices.append(shared_class_indices_client1) logger.info("shared_class_indices_client0: {}, length: {}, shared_class_indices_client1: {}, length: {}".format( shared_class_indices_client0, len(shared_class_indices_client0), shared_class_indices_client1, len(shared_class_indices_client1))) client0_indices = np.concatenate(client0_indices) client1_indices = np.concatenate(client1_indices) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = client0_indices net_dataidx_map[1] = client1_indices elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts def partition_data_viz2(dataset, datadir, logdir, partition, n_nets, alpha, args): if dataset == 'mnist': pass elif dataset == 'cifar10': normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([ transforms.ToTensor(), normalize]) # load training and test set here: training_set = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True, transform=None) testset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=True, transform=None) y_train = np.array(copy.deepcopy(training_set.targets)) n_train = training_set.data.shape[0] entire_gray_scale_indices_train = [] entire_gray_scale_indices_test = [] # we start an adjust version here: ############################################################################################### # in this setting, we corelate the majority / minory with the class # i.e. we firstly do an extreme version where we randomly sample 5 out of 10 groups # s.t. in those groups there are only grayscale images # for the other five groups, we leave all images to be colored images ############################################################################################### #grayscale_dominate_classes = np.random.choice(np.arange(10), 5, replace=False) grayscale_dominate_classes = np.arange(0, 6) logger.info("Grayscale image dominated classes are : {}".format(grayscale_dominate_classes)) # we split all grayscale dominate classes to client 0 and all color dominate classes to client1 client0_indices = [] client1_indices = [] for i in range(10): if i in grayscale_dominate_classes: logger.info("Grayscale dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] # we fix this to be one first ### # this is the extreme case, we now change to a relatexed case ### num_of_gray_scale_per_class_train = int(1.0 * class_indices_train.shape[0]) #num_of_gray_scale_per_class_train = int(0.95 * class_indices_train.shape[0]) class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) client0_indices.append(class_indices_train) entire_gray_scale_indices_train.append(class_gray_scale_indices_train) else: logger.info("Color dominate class index: {}".format(i)) class_indices_train = np.where(np.array(training_set.targets) == i)[0] #num_of_gray_scale_per_class_train = int(0.05 * class_indices_train.shape[0]) num_of_gray_scale_per_class_train = int(0.0 * class_indices_train.shape[0]) #class_gray_scale_indices_train = np.random.choice(class_indices_train, num_of_gray_scale_per_class_train, replace=False) client1_indices.append(class_indices_train) #entire_gray_scale_indices_train.append(class_gray_scale_indices_train) entire_gray_scale_indices_train = np.concatenate(entire_gray_scale_indices_train) client0_indices = np.concatenate(client0_indices) client1_indices = np.concatenate(client1_indices) ### # extreme case: ### for i in range(10): class_indices_test = np.where(np.array(testset.targets) == i)[0] # training set contains skewness, but in test set colored and gray-scale images are evenly distributed num_of_gray_scale_per_class_test = int(0.5 * class_indices_test.shape[0]) class_gray_scale_indices_test = np.random.choice(class_indices_test, num_of_gray_scale_per_class_test, replace=False) entire_gray_scale_indices_test.append(class_gray_scale_indices_test) logger.info("Num of gray scale image per class test: {}".format(class_gray_scale_indices_test.shape[0])) entire_gray_scale_indices_test = np.concatenate(entire_gray_scale_indices_test) logger.info("Total Num of gray scale image test: {}".format(entire_gray_scale_indices_test.shape[0])) elif dataset == 'cinic10': pass if partition == "homo": net_dataidx_map = {} idxs = np.arange(n_train) indices_colored = np.array([i for i in idxs if i not in entire_gray_scale_indices_train]) # we split grayscale and colored images on two workers entirely net_dataidx_map[0] = client0_indices net_dataidx_map[1] = client1_indices elif partition == "hetero-dir": pass elif partition == "hetero-fbs": pass traindata_cls_counts = record_net_data_stats(y_train, net_dataidx_map, logdir) return y_train, net_dataidx_map, traindata_cls_counts, entire_gray_scale_indices_train, entire_gray_scale_indices_test def load_mnist_data(datadir): transform = transforms.Compose([transforms.ToTensor()]) mnist_train_ds = MNIST_truncated(datadir, train=True, download=True, transform=transform) mnist_test_ds = MNIST_truncated(datadir, train=False, download=True, transform=transform) X_train, y_train = mnist_train_ds.data, mnist_train_ds.target X_test, y_test = mnist_test_ds.data, mnist_test_ds.target X_train = X_train.data.numpy() y_train = y_train.data.numpy() X_test = X_test.data.numpy() y_test = y_test.data.numpy() return (X_train, y_train, X_test, y_test) def load_cifar10_data(datadir): transform = transforms.Compose([transforms.ToTensor()]) cifar10_train_ds = CIFAR10_truncated(datadir, train=True, download=True, transform=transform) cifar10_test_ds = CIFAR10_truncated(datadir, train=False, download=True, transform=transform) X_train, y_train = cifar10_train_ds.data, cifar10_train_ds.target X_test, y_test = cifar10_test_ds.data, cifar10_test_ds.target return (X_train, y_train, X_test, y_test) def compute_accuracy(model, dataloader, get_confusion_matrix=False, device="cpu"): was_training = False if model.training: model.eval() was_training = True true_labels_list, pred_labels_list = np.array([]), np.array([]) correct, total = 0, 0 with torch.no_grad(): for batch_idx, (x, target) in enumerate(dataloader): x, target = x.to(device), target.to(device) out = model(x) _, pred_label = torch.max(out.data, 1) total += x.data.size()[0] correct += (pred_label == target.data).sum().item() if device == "cpu": pred_labels_list = np.append(pred_labels_list, pred_label.numpy()) true_labels_list = np.append(true_labels_list, target.data.numpy()) else: pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy()) true_labels_list = np.append(true_labels_list, target.data.cpu().numpy()) if get_confusion_matrix: conf_matrix = confusion_matrix(true_labels_list, pred_labels_list) if was_training: model.train() if get_confusion_matrix: return correct/float(total), conf_matrix return correct/float(total) def init_cnns(net_configs, n_nets): ''' Initialize the local CNNs Please note that this part is hard coded right now ''' input_size = (16 * 5 * 5) # hard coded, defined by the SimpleCNN useds output_size = net_configs[-1] # hidden_sizes = [120, 84] cnns = {net_i: None for net_i in range(n_nets)} # we add this book keeping to store meta data of model weights model_meta_data = [] layer_type = [] for cnn_i in range(n_nets): cnn = SimpleCNN(input_size, hidden_sizes, output_size) cnns[cnn_i] = cnn for (k, v) in cnns[0].state_dict().items(): model_meta_data.append(v.shape) layer_type.append(k) #logger.info("Layer name: {}, layer shape: {}".format(k, v.shape)) return cnns, model_meta_data, layer_type def init_models(net_configs, n_nets, args): ''' Initialize the local LeNets Please note that this part is hard coded right now ''' cnns = {net_i: None for net_i in range(n_nets)} # we add this book keeping to store meta data of model weights model_meta_data = [] layer_type = [] for cnn_i in range(n_nets): if args.model == "lenet": cnn = LeNet() elif args.model == "vgg": cnn = vgg11() elif args.model == "simple-cnn": if args.dataset in ("cifar10", "cinic10"): cnn = SimpleCNN(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=10) elif args.dataset == "mnist": cnn = SimpleCNNMNIST(input_dim=(16 * 4 * 4), hidden_dims=[120, 84], output_dim=10) elif args.model == "moderate-cnn": if args.dataset == "mnist": cnn = ModerateCNNMNIST() elif args.dataset in ("cifar10", "cinic10"): cnn = ModerateCNN() cnns[cnn_i] = cnn for (k, v) in cnns[0].state_dict().items(): model_meta_data.append(v.shape) layer_type.append(k) #logger.info("{} ::: Layer name: {}, layer shape: {}".format(args.model, k, v.shape)) return cnns, model_meta_data, layer_type def save_model(model, model_index): logger.info("saving local model-{}".format(model_index)) with open("trained_local_model"+str(model_index), "wb") as f_: torch.save(model.state_dict(), f_) return def load_model(model, model_index, rank=0, device="cpu"): # with open("trained_local_model"+str(model_index), "rb") as f_: model.load_state_dict(torch.load(f_)) model.to(device) return model def save_model_dist_skew(model, model_index): logger.info("saving local model-{} dist skew".format(model_index)) with open("trained_local_model_dist_skew"+str(model_index), "wb") as f_: torch.save(model.state_dict(), f_) return def load_model_dist_skew(model, model_index, device="cpu"): logger.info("loading local model-{} dist skew".format(model_index)) with open("trained_local_model_dist_skew"+str(model_index), "rb") as f_: model.load_state_dict(torch.load(f_)) model.to(device) return model def save_model_viz(model, model_index): logger.info("saving local model-{} visulization".format(model_index)) with open("trained_local_model_viz{}_new".format(model_index), "wb") as f_: torch.save(model.state_dict(), f_) return def load_model_viz(model, model_index, device="cpu"): logger.info("loading local model-{} visulization".format(model_index)) with open("trained_local_model_viz"+str(model_index), "rb") as f_: model.load_state_dict(torch.load(f_)) model.to(device) return model def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None): if dataset in ('mnist', 'cifar10'): if dataset == 'mnist': dl_obj = MNIST_truncated transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) elif dataset == 'cifar10': dl_obj = CIFAR10_truncated normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) # data prep for test set transform_test = transforms.Compose([transforms.ToTensor(),normalize]) train_ds = dl_obj(datadir, dataidxs=dataidxs, train=True, transform=transform_train, download=True) test_ds = dl_obj(datadir, train=False, transform=transform_test, download=True) train_dl = data.DataLoader(dataset=train_ds, batch_size=train_bs, shuffle=True) test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False) elif dataset == 'cinic10': # statistic for normalizing the dataset cinic_mean = [0.47889522, 0.47227842, 0.43047404] cinic_std = [0.24205776, 0.23828046, 0.25874835] cinic_directory = './data/cinic10' training_set = ImageFolderTruncated(cinic_directory + '/cinic-10-trainlarge/train', dataidxs=dataidxs, transform=transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: F.pad(Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=cinic_mean,std=cinic_std), ])) train_dl = torch.utils.data.DataLoader(training_set, batch_size=train_bs, shuffle=True) logger.info("Len of training set: {}, len of imgs in training set: {}, len of train dl: {}".format(len(training_set), len(training_set.imgs), len(train_dl))) test_dl = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(cinic_directory + '/cinic-10-trainlarge/test', transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=cinic_mean,std=cinic_std)])), batch_size=test_bs, shuffle=False) return train_dl, test_dl def get_dataloader_dist_skew(dataset, datadir, train_bs, test_bs, dataidxs=None, gray_scale_indices_train=None, gray_scale_indices_test=None): if dataset in ('mnist', 'cifar10'): if dataset == 'mnist': pass elif dataset == 'cifar10': normalize_colored = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], std=[x/255.0 for x in [63.0, 62.1, 66.7]]) normalize_gray_scale = transforms.Normalize(mean=[x/255.0 for x in [125.3, 125.3, 125.3]], std=[x/255.0 for x in [63.0, 63.0, 63.0]]) transform_train_color = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize_colored, ]) transform_train_gray_scale = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: F.pad( Variable(x.unsqueeze(0), requires_grad=False), (4,4,4,4),mode='reflect').data.squeeze()), transforms.ToPILImage(), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize_gray_scale, ]) # data prep for test set transform_test_color = transforms.Compose([ transforms.ToTensor(), normalize_colored]) transform_test_gray_scale = transforms.Compose([ transforms.ToTensor(), normalize_gray_scale]) train_ds = CIFAR10ColorGrayScaleTruncated(datadir, dataidxs=dataidxs, gray_scale_indices=gray_scale_indices_train, train=True, transform_color=transform_train_color, transofrm_gray_scale=transform_train_gray_scale, download=True) test_ds = CIFAR10ColorGrayScaleTruncated(datadir, gray_scale_indices=gray_scale_indices_test, train=False, transform_color=transform_test_color, transofrm_gray_scale=transform_test_gray_scale, download=True) train_dl = data.DataLoader(dataset=train_ds, batch_size=train_bs, shuffle=True) test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False) elif dataset == 'cinic10': pass return train_dl, test_dl def pdm_prepare_full_weights_cnn(nets, device="cpu"): """ we extract all weights of the conv nets out here: """ weights = [] for net_i, net in enumerate(nets): net_weights = [] statedict = net.state_dict() for param_id, (k, v) in enumerate(statedict.items()): if device == "cpu": if 'fc' in k or 'classifier' in k: if 'weight' in k: net_weights.append(v.numpy().T) else: net_weights.append(v.numpy()) elif 'conv' in k or 'features' in k: if 'weight' in k: _weight_shape = v.size() if len(_weight_shape) == 4: net_weights.append(v.numpy().reshape(_weight_shape[0], _weight_shape[1]*_weight_shape[2]*_weight_shape[3])) else: pass else: net_weights.append(v.numpy()) else: if 'fc' in k or 'classifier' in k: if 'weight' in k: net_weights.append(v.cpu().numpy().T) else: net_weights.append(v.cpu().numpy()) elif 'conv' in k or 'features' in k: if 'weight' in k: _weight_shape = v.size() if len(_weight_shape) == 4: net_weights.append(v.cpu().numpy().reshape(_weight_shape[0], _weight_shape[1]*_weight_shape[2]*_weight_shape[3])) else: pass else: net_weights.append(v.cpu().numpy()) weights.append(net_weights) return weights def pdm_prepare_weights_vggs(nets, device="cpu"): """ Note that we only handle the FC parts and leave the conv layers as is """ weights = [] for net_i, net in enumerate(nets): layer_i = 0 statedict = net.state_dict() net_weights = [] for i, (k,v) in enumerate(statedict.items()): if "classifier" in k: if "weight" in k: if device == "cpu": net_weights.append(statedict[k].numpy().T) else: net_weights.append(statedict[k].cpu().numpy().T) elif "bias" in k: if device == "cpu": net_weights.append(statedict[k].numpy()) else: net_weights.append(statedict[k].cpu().numpy()) net_weights.insert(0, np.zeros(net_weights[0].shape[0], dtype=np.float32)) weights.append(net_weights) return weights def pdm_prepare_freq(cls_freqs, n_classes): freqs = [] for net_i in sorted(cls_freqs.keys()): net_freqs = [0] * n_classes for cls_i in cls_freqs[net_i]: net_freqs[cls_i] = cls_freqs[net_i][cls_i] freqs.append(np.array(net_freqs)) return freqs def compute_ensemble_accuracy(models: list, dataloader, n_classes, train_cls_counts=None, uniform_weights=False, sanity_weights=False, device="cpu"): correct, total = 0, 0 true_labels_list, pred_labels_list = np.array([]), np.array([]) was_training = [False]*len(models) for i, model in enumerate(models): if model.training: was_training[i] = True model.eval() if uniform_weights is True: weights_list = prepare_uniform_weights(n_classes, len(models)) elif sanity_weights is True: weights_list = prepare_sanity_weights(n_classes, len(models)) else: weights_list = prepare_weight_matrix(n_classes, train_cls_counts) weights_norm = normalize_weights(weights_list) with torch.no_grad(): for batch_idx, (x, target) in enumerate(dataloader): x, target = x.to(device), target.to(device) target = target.long() out = get_weighted_average_pred(models, weights_norm, x, device=device) _, pred_label = torch.max(out, 1) total += x.data.size()[0] correct += (pred_label == target.data).sum().item() if device == "cpu": pred_labels_list = np.append(pred_labels_list, pred_label.numpy()) true_labels_list = np.append(true_labels_list, target.data.numpy()) else: pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy()) true_labels_list = np.append(true_labels_list, target.data.cpu().numpy()) #logger.info(correct, total) conf_matrix = confusion_matrix(true_labels_list, pred_labels_list) for i, model in enumerate(models): if was_training[i]: model.train() return correct / float(total), conf_matrix class ModerateCNNContainerConvBlocks(nn.Module): def __init__(self, num_filters, output_dim=10): super(ModerateCNNContainerConvBlocks, self).__init__() self.conv_layer = nn.Sequential( # Conv Layer block 1 nn.Conv2d(in_channels=3, out_channels=num_filters[0], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=num_filters[0], out_channels=num_filters[1], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Conv Layer block 2 nn.Conv2d(in_channels=num_filters[1], out_channels=num_filters[2], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=num_filters[2], out_channels=num_filters[3], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Dropout2d(p=0.05), # Conv Layer block 3 nn.Conv2d(in_channels=num_filters[3], out_channels=num_filters[4], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=num_filters[4], out_channels=num_filters[5], kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) def forward(self, x): x = self.conv_layer(x) return x

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null

0

1

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1

0

null

0

7

86778a4a64ad33dbb6efe9043e32a6e880a7e4ab

9,476

py

Python

pyathena/classic/plot_tools/plot_slices.py

changgoo/pyathena-1

c461ac3390d773537ce52393e3ebf68a3282aa46

[ "MIT" ]

1

2019-10-03T13:59:14.000Z

pyathena/classic/plot_tools/plot_slices.py

changgoo/pyathena-1

c461ac3390d773537ce52393e3ebf68a3282aa46

[ "MIT" ]

3

2020-09-23T23:36:17.000Z

2022-01-11T06:16:56.000Z

pyathena/classic/plot_tools/plot_slices.py

changgoo/pyathena-1

c461ac3390d773537ce52393e3ebf68a3282aa46

[ "MIT" ]

2

2019-06-10T04:26:16.000Z

2019-12-04T22:27:02.000Z

import glob import os import string import pickle import numpy as np import matplotlib.colorbar as colorbar import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.colors import LogNorm,SymLogNorm,NoNorm,Normalize from .scatter_sp import scatter_sp from ..vtk_reader import read_starvtk from ..utils import texteffect from ..set_units import set_units unit=set_units(muH=1.4271) to_Myr=unit['time'].to('Myr').value def slice(slcfname,starfname,fields_to_draw,zoom=1.,aux={},\ writefile=True,tstamp=True,stars=True,field_label=True,norm_factor=2): plt.rc('font',size=14) plt.rc('xtick',labelsize=14) plt.rc('ytick',labelsize=14) slc_data=pickle.load(open(slcfname,'rb')) x0=slc_data['yextent'][0] y0=slc_data['yextent'][3] Lx=slc_data['yextent'][1]-slc_data['yextent'][0] Lz=slc_data['yextent'][3]-slc_data['yextent'][2] Lz=Lz/zoom ix=2 iz=ix*Lz/Lx nf=len(fields_to_draw) fig=plt.figure(1,figsize=(ix*nf,iz+ix*2)) gs = gridspec.GridSpec(2,nf,height_ratios=[iz,ix]) gs.update(left=0.10,right=0.90,wspace=0,hspace=0) sp=read_starvtk(starfname) if 'time' in slc_data: tMyr=slc_data['time'] else: time,sp=read_starvtk(starfname,time_out=True) tMyr=time*Myr images=[] for i,axis in enumerate(['y','z']): for j,f in enumerate(fields_to_draw): data=slc_data[axis][f] ax=plt.subplot(gs[i,j]) im=ax.imshow(data,origin='lower') if aux.has_key(f): if aux[f].has_key('norm'): im.set_norm(aux[f]['norm']) if aux[f].has_key('cmap'): im.set_cmap(aux[f]['cmap']) if aux[f].has_key('clim'): im.set_clim(aux[f]['clim']) extent=slc_data[axis+'extent'] im.set_extent(extent) images.append(im) if stars: if j == 0: scatter_sp(sp,ax,axis=axis,runaway=False,norm_factor=norm_factor) elif j == 1: scatter_sp(sp,ax,axis=axis,norm_factor=norm_factor) ax.set_xlim(extent[0],extent[1]) ax.set_ylim(extent[2],extent[3]) gs2 = gridspec.GridSpec(nf+2+stars,1) gs2.update(left=0.91,right=0.93,hspace=0.05) for j,(im,f) in enumerate(zip(images,fields_to_draw)): cax=plt.subplot(gs2[j+stars]) cbar = fig.colorbar(im,cax=cax,orientation='vertical') if aux.has_key(f): if 'label' in aux[f]: cbar.set_label(aux[f]['label']) if 'cticks' in aux[f]: cbar.set_ticks(aux[f]['cticks']) if stars: cax=plt.subplot(gs2[0]) cbar = colorbar.ColorbarBase(cax, ticks=[0,20,40], cmap=plt.cm.cool_r, norm=Normalize(vmin=0,vmax=40), orientation='vertical') cbar.set_label(r'${\rm age [Myr]}$') axes=fig.axes[:2*nf] if field_label: for ax,f in zip(axes[:nf],fields_to_draw): if aux.has_key(f): if 'label' in aux[f]: lab=aux[f]['label'] label=lab[:lab.rfind(r'\;')]+'$' ax.text(0.5,0.95,label,size=20,horizontalalignment='center', transform = ax.transAxes,**(texteffect())) if stars: s1=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e3)/norm_factor,color='k', alpha=.8,label=r'$10^3 M_\odot$') s2=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e4)/norm_factor,color='k', alpha=.8,label=r'$10^4 M_\odot$') s3=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e5)/norm_factor, color='k',alpha=.8,label=r'$10^5 M_\odot$') ax.set_xlim(x0,x0+Lx) ax.set_ylim(y0,y0+Lz); legend=ax.legend((s1,s2,s3),(r'$10^3 M_\odot$',r'$10^4 M_\odot$',r'$10^5 M_\odot$'), scatterpoints = 1, loc='lower left',fontsize='medium',frameon=True) plt.setp([ax.get_xticklabels() for ax in axes[:2*nf]],visible=False) plt.setp([ax.get_yticklabels() for ax in axes[:2*nf]],visible=False) plt.setp(axes[:nf],'ylim',(slc_data['yextent'][2]/zoom,slc_data['yextent'][3]/zoom)) plt.setp(axes[nf:2*nf],'xlabel','x [kpc]') plt.setp(axes[0],'ylabel','z [kpc]') if tstamp: plt.setp(axes[0],'title','t=%3d Myr' % tMyr) plt.setp(axes[nf],'ylabel','y [kpc]') plt.setp([ax.get_xticklabels() for ax in axes[nf:]], visible=True) plt.setp([ax.get_yticklabels() for ax in axes[:2*nf:nf]], visible=True) plt.setp([ax.xaxis.get_majorticklabels() for ax in axes[nf:2*nf]], rotation=45 ) pngfname=slcfname[:-1]+'png' #canvas = mpl.backends.backend_agg.FigureCanvasAgg(fig) #canvas.print_figure(pngfname,num=1,dpi=150,bbox_inches='tight') if writefile: plt.savefig(pngfname,bbox_inches='tight',num=0,dpi=150) plt.close() else: return fig def slice2(slcfname,starfname,fields_to_draw,zoom=1.,aux={},\ writefile=True,tstamp=True,stars=True,field_label=True,norm_factor=2): plt.rc('font',size=14) plt.rc('xtick',labelsize=14) plt.rc('ytick',labelsize=14) slc_data=pickle.load(open(slcfname,'rb')) x0=slc_data['yextent'][0] y0=slc_data['yextent'][3] Lx=slc_data['yextent'][1]-slc_data['yextent'][0] Lz=slc_data['yextent'][3]-slc_data['yextent'][2] Lz=Lz/zoom ix=2 iz=ix*Lz/Lx nf=len(fields_to_draw) fig=plt.figure(1,figsize=(ix*nf,iz+ix*1.2)) gs = gridspec.GridSpec(2,nf,height_ratios=[iz,ix]) gs.update(top=0.95,left=0.10,right=0.95,wspace=0.05,hspace=0) if stars: sp=read_starvtk(starfname) if 'time' in slc_data: tMyr=slc_data['time'] else: time,sp=read_starvtk(starfname,time_out=True) tMyr=time*Myr images=[] for i,axis in enumerate(['y','z']): for j,f in enumerate(fields_to_draw): ax=plt.subplot(gs[i,j]) if f == 'star_particles': scatter_sp(sp,ax,axis=axis,norm_factor=norm_factor,type='surf') if axis == 'y': ax.set_xlim(x0,x0+Lx) ax.set_ylim(y0,y0+Lz); if axis == 'z': ax.set_xlim(x0,x0+Lx) ax.set_ylim(x0,x0+Lx) ax.set_aspect(1.0) else: data=slc_data[axis][f] im=ax.imshow(data,origin='lower',interpolation='bilinear') if aux.has_key(f): if aux[f].has_key('norm'): im.set_norm(aux[f]['norm']) if aux[f].has_key('cmap'): im.set_cmap(aux[f]['cmap']) if aux[f].has_key('clim'): im.set_clim(aux[f]['clim']) extent=slc_data[axis+'extent'] im.set_extent(extent) images.append(im) ax.set_xlim(extent[0],extent[1]) ax.set_ylim(extent[2],extent[3]) for j,(im,f) in enumerate(zip(images,fields_to_draw[1:])): ax=plt.subplot(gs[0,j+1]) divider = make_axes_locatable(ax) cax = divider.append_axes("top", "3%", pad="1%") cbar = fig.colorbar(im,cax=cax,orientation='horizontal') if aux.has_key(f): if 'label' in aux[f]: cbar.set_label(aux[f]['label']) if 'cticks' in aux[f]: cbar.set_ticks(aux[f]['cticks']) cax.xaxis.tick_top() cax.xaxis.set_label_position('top') ax=plt.subplot(gs[0,0]) divider = make_axes_locatable(ax) cax = divider.append_axes("top", "3%", pad="1%") cbar = colorbar.ColorbarBase(cax, ticks=[0,20,40], cmap=plt.cm.cool_r, norm=Normalize(vmin=0,vmax=40), orientation='horizontal') cax.xaxis.tick_top() cax.xaxis.set_label_position('top') cbar.set_label(r'${\rm age [Myr]}$') s1=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e3)/norm_factor,color='k', alpha=.8,label=r'$10^3 M_\odot$') s2=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e4)/norm_factor,color='k', alpha=.8,label=r'$10^4 M_\odot$') s3=ax.scatter(Lx*2,Lz*2, s=np.sqrt(1.e5)/norm_factor, color='k',alpha=.8,label=r'$10^5 M_\odot$') ax.set_xlim(x0,x0+Lx) ax.set_ylim(y0,y0+Lz); legend=ax.legend((s1,s2,s3),(r'$10^3 M_\odot$',r'$10^4 M_\odot$',r'$10^5 M_\odot$'), scatterpoints = 1, loc='lower left',fontsize='medium',frameon=True) axes=fig.axes plt.setp([ax.get_xticklabels() for ax in axes[:2*nf]],visible=False) plt.setp([ax.get_yticklabels() for ax in axes[:2*nf]],visible=False) plt.setp(axes[:nf],'ylim',(slc_data['yextent'][2]/zoom,slc_data['yextent'][3]/zoom)) plt.setp(axes[nf:2*nf],'xlabel','x [kpc]') plt.setp(axes[0],'ylabel','z [kpc]') if tstamp: ax=axes[0] ax.text(0.5,0.95,'t=%3d Myr' % tMyr,size=16,horizontalalignment='center', transform = ax.transAxes,**(texteffect())) plt.setp(axes[nf],'ylabel','y [kpc]') plt.setp([ax.get_xticklabels() for ax in axes[nf:]], visible=True) plt.setp([ax.get_yticklabels() for ax in axes[:2*nf:nf]], visible=True) plt.setp([ax.xaxis.get_majorticklabels() for ax in axes[nf:2*nf]], rotation=45 ) pngfname=slcfname+'ng' #canvas = mpl.backends.backend_agg.FigureCanvasAgg(fig) #canvas.print_figure(pngfname,num=1,dpi=150,bbox_inches='tight') if writefile: plt.savefig(pngfname,bbox_inches='tight',num=0,dpi=150) plt.close() else: return fig

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0.151434

0.033573

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1

0

1

0

null

0

7

86ecd81a5abe0273f3121e18a3e0acfab9f865c8

86

py

Python

utils/random_send.py

Lisovq/markov-chain-bot

b7fb8c1e47c0abda6ccc9dfed1d0b35b8a6eaff1

[ "MIT" ]

null

utils/random_send.py

Lisovq/markov-chain-bot

b7fb8c1e47c0abda6ccc9dfed1d0b35b8a6eaff1

[ "MIT" ]

null

utils/random_send.py

Lisovq/markov-chain-bot

b7fb8c1e47c0abda6ccc9dfed1d0b35b8a6eaff1

[ "MIT" ]

null

from random import randint def randomize() -> bool: return randint(0, 33) == 24

14.333333

31

0.662791

12

86

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5

32

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true

0

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1

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null

0

1

0

null

0

1

0

1

0

7

d4abaee803c70af9a4655798a481ba367692f435

174,357

py

Python

core/domain/suggestion_services_test.py

jlau323/oppia

37438a2c9bf7e66892fb9a6a93a1fe4ca7a82691

[ "Apache-2.0" ]

2

2021-04-08T01:06:08.000Z

2021-06-02T08:20:13.000Z

core/domain/suggestion_services_test.py

jlau323/oppia

37438a2c9bf7e66892fb9a6a93a1fe4ca7a82691

[ "Apache-2.0" ]

null

core/domain/suggestion_services_test.py

jlau323/oppia

37438a2c9bf7e66892fb9a6a93a1fe4ca7a82691

[ "Apache-2.0" ]

1

2020-12-11T06:56:31.000Z

# Copyright 2018 The Oppia Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for suggestion related services.""" from __future__ import absolute_import # pylint: disable=import-only-modules from __future__ import unicode_literals # pylint: disable=import-only-modules import datetime from constants import constants from core.domain import exp_domain from core.domain import exp_fetchers from core.domain import exp_services from core.domain import feedback_services from core.domain import question_domain from core.domain import rights_domain from core.domain import rights_manager from core.domain import skill_services from core.domain import state_domain from core.domain import story_domain from core.domain import story_services from core.domain import suggestion_registry from core.domain import suggestion_services from core.domain import topic_services from core.domain import user_services from core.platform import models from core.tests import test_utils import feconf import python_utils import utils (suggestion_models, feedback_models, user_models) = ( models.Registry.import_models( [models.NAMES.suggestion, models.NAMES.feedback, models.NAMES.user] ) ) class SuggestionServicesUnitTests(test_utils.GenericTestBase): """Test the functions in suggestion_services.""" score_category = ( suggestion_models.SCORE_TYPE_CONTENT + suggestion_models.SCORE_CATEGORY_DELIMITER + 'Algebra') target_id = 'exp1' target_id_2 = 'exp2' target_id_3 = 'exp3' target_version_at_submission = 1 change = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state_1', 'new_value': { 'content_id': 'content', 'html': 'new suggestion content' } } AUTHOR_EMAIL = 'author@example.com' REVIEWER_EMAIL = 'reviewer@example.com' NORMAL_USER_EMAIL = 'normal@example.com' THREAD_ID = 'exploration.exp1.thread_1' COMMIT_MESSAGE = 'commit message' EMPTY_COMMIT_MESSAGE = ' ' suggestion_id = THREAD_ID suggestion_id_2 = 'exploration.exp2.thread_2' suggestion_id_3 = 'exploration.exp3.thread_3' def setUp(self): super(SuggestionServicesUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email(self.AUTHOR_EMAIL) self.signup(self.REVIEWER_EMAIL, 'reviewer') self.reviewer_id = self.get_user_id_from_email(self.REVIEWER_EMAIL) self.signup(self.NORMAL_USER_EMAIL, 'normaluser') self.normal_user_id = self.get_user_id_from_email( self.NORMAL_USER_EMAIL) self.save_new_valid_exploration( self.target_id, self.author_id, category='Algebra') def assert_suggestion_status(self, suggestion_id, status): """Assert the status of the suggestion with suggestion_id.""" suggestion = suggestion_services.get_suggestion_by_id(suggestion_id) self.assertEqual(suggestion.status, status) def mock_accept_suggestion( self, suggestion_id, reviewer_id, commit_message, review_message): """Sets up the appropriate mocks to successfully call accept_suggestion. """ with self.swap( exp_services, 'update_exploration', self.mock_update_exploration): with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): with self.swap( suggestion_registry.SuggestionEditStateContent, 'pre_accept_validate', self.mock_pre_accept_validate_does_nothing): with self.swap( suggestion_registry.SuggestionEditStateContent, 'get_change_list_for_accepting_suggestion', self.mock_get_change_list_does_nothing): suggestion_services.accept_suggestion( suggestion_id, reviewer_id, commit_message, review_message) def mock_create_suggestion(self, target_id): """Sets up the appropriate mocks to successfully call create_suggestion. """ with self.swap( feedback_models.GeneralFeedbackThreadModel, 'generate_new_thread_id', self.mock_generate_new_thread_id): with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, target_id, self.target_version_at_submission, self.author_id, self.change, 'test description') def mock_generate_new_thread_id(self, entity_type, exp_id): thread_id = 'thread_%s' % exp_id[-1] return '.'.join([entity_type, exp_id, thread_id]) class MockExploration(python_utils.OBJECT): """Mocks an exploration. To be used only for testing.""" def __init__(self, exploration_id, states): self.id = exploration_id self.states = states self.category = 'Algebra' # All mock explorations created for testing. explorations = [ MockExploration('exp1', {'state_1': {}, 'state_2': {}}), MockExploration('exp2', {'state_1': {}, 'state_2': {}}), MockExploration('exp3', {'state_1': {}, 'state_2': {}}) ] def mock_get_exploration_by_id(self, exp_id): for exp in self.explorations: if exp.id == exp_id: return exp def mock_pre_accept_validate_does_nothing(self): pass def mock_get_change_list_does_nothing(self): pass def mock_accept_does_nothing(self, unused_arg): pass def test_create_new_suggestion_successfully(self): expected_suggestion_dict = { 'suggestion_id': 'exploration.exp1.thread_1', 'suggestion_type': ( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT), 'target_type': suggestion_models.TARGET_TYPE_EXPLORATION, 'target_id': self.target_id, 'target_version_at_submission': self.target_version_at_submission, 'status': suggestion_models.STATUS_IN_REVIEW, 'author_name': 'author', 'change': { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state_1', 'new_value': { 'content_id': 'content', 'html': 'new suggestion content' }, 'old_value': None }, 'score_category': self.score_category, 'language_code': None } self.mock_create_suggestion(self.target_id) observed_suggestion = suggestion_services.get_suggestion_by_id( self.suggestion_id) self.assertDictContainsSubset( expected_suggestion_dict, observed_suggestion.to_dict()) def test_cannot_create_suggestion_with_invalid_suggestion_type(self): with self.assertRaisesRegexp(Exception, 'Invalid suggestion type'): suggestion_services.create_suggestion( 'invalid_suggestion_type', suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, self.change, 'test description') def test_cannot_create_suggestion_with_invalid_author_id(self): with self.assertRaisesRegexp( Exception, 'Expected author_id to be in a valid user ID format'): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, 'invalid author ID', self.change, 'test description') def test_cannot_create_translation_suggestion_with_invalid_content_html_raise_error(self): # pylint: disable=line-too-long add_translation_change_dict = { 'cmd': 'add_translation', 'state_name': 'Introduction', 'content_id': 'content', 'language_code': 'hi', 'content_html': 'The invalid content html', 'translation_html': 'Translation for invalid content.' } with self.assertRaisesRegexp( Exception, 'The given content_html does not match the content of the ' 'exploration.'): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, add_translation_change_dict, 'test description') def test_get_all_stale_suggestion_ids(self): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, self.change, 'test description') with self.swap( suggestion_models, 'THRESHOLD_TIME_BEFORE_ACCEPT_IN_MSECS', 0): self.assertEqual( len(suggestion_services.get_all_stale_suggestion_ids()), 1) with self.swap( suggestion_models, 'THRESHOLD_TIME_BEFORE_ACCEPT_IN_MSECS', 7 * 24 * 60 * 60 * 1000): self.assertEqual( len(suggestion_services.get_all_stale_suggestion_ids()), 0) def mock_update_exploration( self, unused_user_id, unused_exploration_id, unused_change_list, commit_message, is_suggestion): self.assertTrue(is_suggestion) self.assertEqual( commit_message, 'Accepted suggestion by %s: %s' % ( 'author', self.COMMIT_MESSAGE)) def test_cannot_reject_suggestion_with_empty_review_message(self): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, self.change, 'test description') suggestion = suggestion_services.query_suggestions( [('author_id', self.author_id), ( 'target_id', self.target_id)])[0] self.assert_suggestion_status( suggestion.suggestion_id, suggestion_models.STATUS_IN_REVIEW) with self.assertRaisesRegexp( Exception, 'Review message cannot be empty.'): suggestion_services.reject_suggestion( suggestion.suggestion_id, self.reviewer_id, '') # Assert that the suggestion was not rejected. self.assert_suggestion_status( suggestion.suggestion_id, suggestion_models.STATUS_IN_REVIEW) def test_accept_suggestion_and_send_email_to_author(self): change_list = [exp_domain.ExplorationChange({ 'cmd': exp_domain.CMD_ADD_STATE, 'state_name': 'state 1', })] exp_services.update_exploration( self.author_id, self.target_id, change_list, 'Add state.') new_suggestion_content = state_domain.SubtitledHtml( 'content', 'new suggestion content html').to_dict() change_dict = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state 1', 'new_value': new_suggestion_content } suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, change_dict, 'test description') suggestion = suggestion_services.query_suggestions( [('author_id', self.author_id), ( 'target_id', self.target_id)])[0] self.assert_suggestion_status( suggestion.suggestion_id, suggestion_models.STATUS_IN_REVIEW) # Create a user proficiency model to verify that the # score and onboarding_email_sent fields have changed after the # suggestion has been accepted. user_models.UserContributionProficiencyModel.create( self.author_id, suggestion.score_category, 0) # An email is sent to users the first time that they pass the score # required to review a suggestion category. By default, when a # suggestion is accepted and the recording of scores is enabled, the # score of the author of that suggestion is increased by 1. Therefore, # by setting that increment to minimum score required to review, we can # ensure that the email is sent. with self.swap(feconf, 'ENABLE_RECORDING_OF_SCORES', True): with self.swap( feconf, 'SEND_SUGGESTION_REVIEW_RELATED_EMAILS', True): with self.swap( suggestion_models, 'INCREMENT_SCORE_OF_AUTHOR_BY', feconf.MINIMUM_SCORE_REQUIRED_TO_REVIEW): suggestion_services.accept_suggestion( suggestion.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Assert that the suggestion is now accepted. suggestion = suggestion_services.query_suggestions( [('author_id', self.author_id), ( 'target_id', self.target_id)])[0] self.assert_suggestion_status( suggestion.suggestion_id, suggestion_models.STATUS_ACCEPTED) # Assert that the email was sent and that the score increased by the # correct amount. user_proficiency_model = ( user_models.UserContributionProficiencyModel.get( self.author_id, suggestion.score_category ) ) self.assertTrue(user_proficiency_model.onboarding_email_sent) self.assertEqual( user_proficiency_model.score, feconf.MINIMUM_SCORE_REQUIRED_TO_REVIEW) def test_accept_suggestion_does_not_send_email_if_users_score_is_too_low( self): self.mock_create_suggestion(self.target_id) self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) # Create the user proficiency model to verify the score and # that the onboarding_email_sent field does not change after the # suggestion is accepted. user_models.UserContributionProficiencyModel.create( self.author_id, self.score_category, 0) # An email is sent to users the first time that they pass the score # required to review a suggestion category. By default, when a # suggestion is accepted and the recording of scores is enabled, the # score of the author of that suggestion is increased by 1. This is # less than the minimum score required to review so an email should not # be sent. with self.swap(feconf, 'ENABLE_RECORDING_OF_SCORES', True): with self.swap( feconf, 'SEND_SUGGESTION_REVIEW_RELATED_EMAILS', True): self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Assert that the suggestion is now accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) user_proficiency_model = ( user_models.UserContributionProficiencyModel.get( self.author_id, self.score_category ) ) # Assert that the users score was updated correctly. self.assertEqual( user_proficiency_model.score, suggestion_models.INCREMENT_SCORE_OF_AUTHOR_BY) # Assert that their score is not high enough to review the category. self.assertLess( user_proficiency_model.score, feconf.MINIMUM_SCORE_REQUIRED_TO_REVIEW) # Assert that the onboarding new reviewer email was not sent. self.assertFalse(user_proficiency_model.onboarding_email_sent) def test_accept_suggestion_creates_user_proficiency_model_if_it_is_none( self): self.mock_create_suggestion(self.target_id) self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) # Verify that a user proficiency model does not exist. self.assertIsNone(user_models.UserContributionProficiencyModel.get( self.author_id, self.score_category)) with self.swap(feconf, 'ENABLE_RECORDING_OF_SCORES', True): self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Verify that a user proficiency model now exists. self.assertIsNotNone(user_models.UserContributionProficiencyModel.get( self.author_id, self.score_category)) def test_accept_suggestion_successfully(self): self.mock_create_suggestion(self.target_id) self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Assert that the suggestion is now accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) suggestion = suggestion_services.get_suggestion_by_id( self.suggestion_id) self.assertEqual( suggestion.final_reviewer_id, self.reviewer_id) thread_messages = feedback_services.get_messages(self.THREAD_ID) last_message = thread_messages[len(thread_messages) - 1] self.assertEqual( last_message.text, 'review message') def test_accept_suggestion_raises_exception_if_suggestion_does_not_exist( self): expected_exception_regexp = ( 'You cannot accept the suggestion with id %s because it does not ' 'exist.' % (self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') def test_accept_suggestion_with_invalid_math_fails(self): """Test that the method for accepting suggestions raises error when a suggestion with invalid math-tags is tried to be accepted. """ change_dict = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state_1', 'new_value': { 'content_id': 'content', 'html': ( '<oppia-noninteractive-math raw_latex-with-value="&am' 'p;quot;(x - a_1)(x - a_2)(x - a_3)...(x - a_n)&q' 'uot;"></oppia-noninteractive-math>') } } with self.swap( feedback_models.GeneralFeedbackThreadModel, 'generate_new_thread_id', self.mock_generate_new_thread_id): with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, self.target_version_at_submission, self.author_id, change_dict, 'test description') self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) expected_exception_regexp = ( 'Invalid math tags found in the suggestion with id %s.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Assert that the status of the suggestion hasn't changed. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) def test_accept_suggestion_raises_exception_if_suggestion_already_accepted( self): self.mock_create_suggestion(self.target_id) # Accept the suggestion. self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, None) # Assert that the suggestion has been accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) expected_exception_regexp = ( 'The suggestion with id %s has already been accepted/rejected.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, None) def test_accept_suggestion_raises_exception_if_suggestion_already_rejected( self): self.mock_create_suggestion(self.target_id) # Reject the suggestion. suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message' ) # Assert that the suggestion has been rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_REJECTED) expected_exception_regexp = ( 'The suggestion with id %s has already been accepted/rejected.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, None) # Assert that the suggestion is still rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_REJECTED) def test_accept_suggestion_invalid_suggestion_failure(self): self.mock_create_suggestion(self.target_id) suggestion = suggestion_services.get_suggestion_by_id( self.suggestion_id) # Invalidating the suggestion. suggestion.score_category = 'invalid_score_category' with self.assertRaisesRegexp( utils.ValidationError, 'Expected score_category to be of the form ' 'score_type.score_sub_type, received ' 'invalid_score_category'): suggestion_services._update_suggestion(suggestion) # pylint: disable=protected-access def test_accept_suggestion_no_commit_message_failure(self): self.mock_create_suggestion(self.target_id) self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) with self.assertRaisesRegexp( Exception, 'Commit message cannot be empty.'): suggestion_services.accept_suggestion( self.suggestion_id, self.reviewer_id, self.EMPTY_COMMIT_MESSAGE, None) # Assert that the status of the suggestion didn't change. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) def test_reject_suggestion_successfully(self): self.mock_create_suggestion(self.target_id) self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message') # Assert that the suggestion has been rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_REJECTED) suggestion = suggestion_services.get_suggestion_by_id( self.suggestion_id) self.assertEqual( suggestion.final_reviewer_id, self.reviewer_id) thread_messages = feedback_services.get_messages(self.THREAD_ID) last_message = thread_messages[len(thread_messages) - 1] self.assertEqual(last_message.text, 'reject review message') def test_reject_suggestions_successfully(self): # Create the first suggestion to be rejected. self.mock_create_suggestion(self.target_id_2) self.assert_suggestion_status( self.suggestion_id_2, suggestion_models.STATUS_IN_REVIEW) # Create another suggestion to be rejected. self.mock_create_suggestion(self.target_id_3) self.assert_suggestion_status( self.suggestion_id_3, suggestion_models.STATUS_IN_REVIEW) suggestion_ids = [self.suggestion_id_2, self.suggestion_id_3] suggestion_services.reject_suggestions( suggestion_ids, self.reviewer_id, 'reject review message') for suggestion_id in suggestion_ids: # Assert that the statuses changed to rejected. self.assert_suggestion_status( suggestion_id, suggestion_models.STATUS_REJECTED) # Assert that the final reviewer id was updated. suggestion = suggestion_services.get_suggestion_by_id( suggestion_id) self.assertEqual( suggestion.final_reviewer_id, self.reviewer_id) # Assert that the messages were updated. thread_messages = feedback_services.get_messages(suggestion_id) last_message = thread_messages[len(thread_messages) - 1] self.assertEqual( last_message.text, 'reject review message') def test_reject_suggestion_raises_exception_if_suggestion_does_not_exist( self): expected_exception_regexp = ( 'You cannot reject the suggestion with id %s because it does not ' 'exist.' % (self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'review message') def test_reject_suggestion_raises_exception_if_suggestion_already_accepted( self): self.mock_create_suggestion(self.target_id) # Accept the suggestion. self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, None) # Assert that the suggestion has been accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) # Rejecting the suggestion should not work because the suggestion has # already been accepted. expected_exception_regexp = ( 'The suggestion with id %s has already been accepted/rejected.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message') # Assert that the suggestion's status did not change. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) def test_reject_suggestion_raises_exception_if_suggestion_already_rejected( self): self.mock_create_suggestion(self.target_id) # Reject the suggestion. suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message') # Assert that the suggestion has been rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_REJECTED) # Rejecting the suggestion should not work because the suggestion has # already been rejected. expected_exception_regexp = ( 'The suggestion with id %s has already been accepted/rejected.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message') def test_resubmit_rejected_suggestion_success(self): self.mock_create_suggestion(self.target_id) # Reject the suggestion. suggestion_services.reject_suggestion( self.suggestion_id, self.reviewer_id, 'reject review message') # Assert that the suggestion has been rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_REJECTED) # Create the new change for the resubmitted suggestion. resubmit_change_content = state_domain.SubtitledHtml( 'content', 'resubmit change content html').to_dict() resubmit_change = exp_domain.ExplorationChange( { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state_1', 'new_value': resubmit_change_content, 'old_value': self.change['new_value'] } ) # Resubmit rejected suggestion. suggestion_services.resubmit_rejected_suggestion( self.suggestion_id, 'resubmit summary message', self.author_id, resubmit_change) # The suggestion's status should now be in review instead of rejected. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_IN_REVIEW) # The suggestion's change should be updated. suggestion = suggestion_services.get_suggestion_by_id( self.suggestion_id) self.assertEqual( suggestion.change.new_value['html'], resubmit_change_content['html']) def test_resubmit_rejected_suggestion_raises_exception_for_empty_message( self): self.mock_create_suggestion(self.target_id) # Can't resubmit a rejected suggestion if the summary message is empty. with self.assertRaisesRegexp( Exception, 'Summary message cannot be empty.'): suggestion_services.resubmit_rejected_suggestion( self.suggestion_id, '', self.author_id, {}) def test_resubmit_rejected_suggestion_raises_exception_for_unhandled_input( self): self.mock_create_suggestion(self.target_id) # Can't resubmit a rejected suggestion if the suggestion hasn't been # rejected yet. expected_exception_regexp = ( 'The suggestion with id %s is not yet handled.' % ( self.suggestion_id) ) with self.assertRaisesRegexp(Exception, expected_exception_regexp): suggestion_services.resubmit_rejected_suggestion( self.suggestion_id, 'resubmit summary message', self.author_id, {} ) def test_resubmit_rejected_suggestion_raises_excep_for_accepted_suggestion( self): self.mock_create_suggestion(self.target_id) # Accept the suggestion. self.mock_accept_suggestion( self.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') # Verfiy that the suggestion has been accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) # Can't resubmit the suggestion if it's already accepted. expected_exception_regexp = ( 'The suggestion with id %s was accepted. Only rejected ' 'suggestions can be resubmitted.' % ( self.suggestion_id) ) with self.assertRaisesRegexp( Exception, expected_exception_regexp): suggestion_services.resubmit_rejected_suggestion( self.suggestion_id, 'resubmit summary message', self.author_id, {} ) # Verfiy that the suggestion is still accepted. self.assert_suggestion_status( self.suggestion_id, suggestion_models.STATUS_ACCEPTED) def test_check_can_resubmit_suggestion(self): self.mock_create_suggestion(self.target_id) can_resubmit = suggestion_services.check_can_resubmit_suggestion( self.suggestion_id, self.author_id) self.assertEqual(can_resubmit, True) can_resubmit = suggestion_services.check_can_resubmit_suggestion( self.suggestion_id, self.normal_user_id) self.assertEqual(can_resubmit, False) class SuggestionGetServicesUnitTests(test_utils.GenericTestBase): score_category = ( suggestion_models.SCORE_TYPE_TRANSLATION + suggestion_models.SCORE_CATEGORY_DELIMITER + 'English') target_id_1 = 'exp1' target_id_2 = 'exp2' target_id_3 = 'exp3' target_version_at_submission = 1 change = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'state_1', 'new_value': 'new suggestion content' } AUTHOR_EMAIL_1 = 'author1@example.com' REVIEWER_EMAIL_1 = 'reviewer1@example.com' AUTHOR_EMAIL_2 = 'author2@example.com' REVIEWER_EMAIL_2 = 'reviewer2@example.com' add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': 'state_1', 'content_id': 'content', 'language_code': 'hi', 'content_html': 'State name: state_1, Content id: content', 'translation_html': 'This is translated html.' } class MockExploration(python_utils.OBJECT): """Mocks an exploration. To be used only for testing.""" def __init__(self, exploration_id, states): self.id = exploration_id self.states = states self.category = 'Algebra' def get_content_html(self, state_name, content_id): """Used to mock the get_content_html method for explorations.""" # state_name and content_id are used here to suppress the unused # arguments warning. The main goal of this method is to just # produce content html for the tests. return 'State name: %s, Content id: %s' % ( state_name, content_id ) # All mock explorations created for testing. explorations = [ MockExploration('exp1', {'state_1': {}, 'state_2': {}}), MockExploration('exp2', {'state_1': {}, 'state_2': {}}), MockExploration('exp3', {'state_1': {}, 'state_2': {}}), ] def mock_get_exploration_by_id(self, exp_id): for exp in self.explorations: if exp.id == exp_id: return exp def _create_question_suggestion_with_skill_id(self, skill_id): """Creates a question suggestion with the given skill_id.""" suggestion_change = { 'cmd': ( question_domain .CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION), 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': 'en', 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, skill_id, 1, self.author_id_1, suggestion_change, 'test description' ) def _create_translation_suggestion_with_language_code(self, language_code): """Creates a translation suggestion with the language code given.""" add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': 'state_1', 'content_id': 'content', 'language_code': language_code, 'content_html': 'State name: state_1, Content id: content', 'translation_html': 'This is translated html.' } with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): with self.swap( exp_domain.Exploration, 'get_content_html', self.MockExploration.get_content_html): translation_suggestion = suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, 1, self.author_id_1, add_translation_change_dict, 'test description') return translation_suggestion def setUp(self): super(SuggestionGetServicesUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL_1, 'author1') self.author_id_1 = self.get_user_id_from_email(self.AUTHOR_EMAIL_1) self.signup(self.REVIEWER_EMAIL_1, 'reviewer1') self.reviewer_id_1 = self.get_user_id_from_email(self.REVIEWER_EMAIL_1) self.signup(self.AUTHOR_EMAIL_2, 'author2') self.author_id_2 = self.get_user_id_from_email(self.AUTHOR_EMAIL_2) self.signup(self.REVIEWER_EMAIL_2, 'reviewer2') self.reviewer_id_2 = self.get_user_id_from_email(self.REVIEWER_EMAIL_2) with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, self.target_version_at_submission, self.author_id_1, self.change, 'test description') suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, self.target_version_at_submission, self.author_id_1, self.change, 'test description') suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, self.target_version_at_submission, self.author_id_1, self.change, 'test description') suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, self.target_version_at_submission, self.author_id_2, self.change, 'test description') suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_2, self.target_version_at_submission, self.author_id_2, self.change, 'test description') def test_get_by_author(self): queries = [('author_id', self.author_id_1)] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 3) queries = [('author_id', self.author_id_2)] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 2) def test_get_by_target_id(self): queries = [ ('target_type', suggestion_models.TARGET_TYPE_EXPLORATION), ('target_id', self.target_id_1) ] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 4) queries = [ ('target_type', suggestion_models.TARGET_TYPE_EXPLORATION), ('target_id', self.target_id_2) ] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 1) def test_get_by_status(self): queries = [('status', suggestion_models.STATUS_IN_REVIEW)] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 5) def test_get_by_type(self): queries = [( 'suggestion_type', suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT)] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 5) def test_query_suggestions(self): queries = [ ('target_type', suggestion_models.TARGET_TYPE_EXPLORATION), ('target_id', self.target_id_1), ('author_id', self.author_id_2) ] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 1) queries = [ ('target_type', suggestion_models.TARGET_TYPE_EXPLORATION), ('target_id', self.target_id_1), ('author_id', self.author_id_1), ('status', suggestion_models.STATUS_IN_REVIEW) ] self.assertEqual(len(suggestion_services.query_suggestions(queries)), 3) queries = [ ('target_type', suggestion_models.TARGET_TYPE_EXPLORATION), ('target_id', self.target_id_1), ('invalid_field', 'value') ] with self.assertRaisesRegexp( Exception, 'Not allowed to query on field invalid_field'): suggestion_services.query_suggestions(queries) def test_get_translation_suggestion_ids_with_exp_ids_with_one_exp(self): # Create the translation suggestion associated with exploration id # target_id_1. with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): with self.swap( exp_domain.Exploration, 'get_content_html', self.MockExploration.get_content_html): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_1, 1, self.author_id_1, self.add_translation_change_dict, 'test description') # Assert that there is one translation suggestion with the given # exploration id found. self.assertEqual( len( suggestion_services .get_translation_suggestion_ids_with_exp_ids( [self.target_id_1])), 1) def test_get_translation_suggestion_ids_with_exp_ids_with_multiple_exps( self): # Create the translation suggestion associated with exploration id # target_id_2. with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): with self.swap( exp_domain.Exploration, 'get_content_html', self.MockExploration.get_content_html): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_2, 1, self.author_id_1, self.add_translation_change_dict, 'test description') # Create the translation suggestion associated with exploration id # target_id_3. with self.swap( exp_fetchers, 'get_exploration_by_id', self.mock_get_exploration_by_id): with self.swap( exp_domain.Exploration, 'get_content_html', self.MockExploration.get_content_html): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id_3, 1, self.author_id_1, self.add_translation_change_dict, 'test description') # Assert that there are two translation suggestions with the given # exploration ids found. self.assertEqual( len( suggestion_services .get_translation_suggestion_ids_with_exp_ids( [self.target_id_2, self.target_id_3])), 2) def test_get_translation_suggestion_ids_with_exp_ids_with_invalid_exp( self): # Assert that there are no translation suggestions with an invalid # exploration id found. self.assertEqual( len( suggestion_services .get_translation_suggestion_ids_with_exp_ids( ['invalid_exp_id'])), 0) def test_get_translation_suggestion_ids_with_exp_ids_with_empty_exp_list( self): # Assert that there are no translation suggestions found when we # use an empty exp_ids list. self.assertEqual( len( suggestion_services .get_translation_suggestion_ids_with_exp_ids([])), 0) def test_get_translation_suggestions_waiting_longest_for_review_per_lang( self): suggestion_1 = self._create_translation_suggestion_with_language_code( 'hi') suggestion_2 = self._create_translation_suggestion_with_language_code( 'hi') suggestion_3 = self._create_translation_suggestion_with_language_code( 'hi') suggestions = ( suggestion_services .get_translation_suggestions_waiting_longest_for_review( 'hi')) # Assert that the suggestions are in the order that they were created. self.assertEqual(len(suggestions), 3) self.assertEqual( suggestions[0].suggestion_id, suggestion_1.suggestion_id) self.assertEqual( suggestions[1].suggestion_id, suggestion_2.suggestion_id) self.assertEqual( suggestions[2].suggestion_id, suggestion_3.suggestion_id) for i in python_utils.RANGE(len(suggestions) - 1): self.assertLess( suggestions[i].last_updated, suggestions[i + 1].last_updated) def test_get_translation_suggestions_waiting_longest_for_review_wrong_lang( self): suggestions = ( suggestion_services .get_translation_suggestions_waiting_longest_for_review( 'wrong_language_code')) self.assertEqual(len(suggestions), 0) def test_get_question_suggestions_waiting_longest_for_review_keeps_order( self): """This test makes sure that if a suggestion is rejected and is then resubmitted, we count the time that the suggestion has been waiting for review from when it was resubmitted, not from when it was first submitted. """ suggestion_1 = self._create_question_suggestion_with_skill_id('skill1') suggestion_2 = self._create_question_suggestion_with_skill_id('skill2') # Verify that both suggestions are returned and in the right order. suggestions = ( suggestion_services .get_question_suggestions_waiting_longest_for_review() ) self.assertEqual(len(suggestions), 2) self.assertEqual( suggestions[0].suggestion_id, suggestion_1.suggestion_id) self.assertEqual( suggestions[1].suggestion_id, suggestion_2.suggestion_id) self.assertLess( suggestions[0].last_updated, suggestions[1].last_updated) # Reject the suggestion that was created first since it is the one that # has been waiting the longest for review. suggestion_services.reject_suggestion( suggestion_1.suggestion_id, self.reviewer_id_1, 'Reject message') # Verify that only the suggestion that was created second is returned. suggestions = ( suggestion_services .get_question_suggestions_waiting_longest_for_review() ) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].suggestion_id, suggestion_2.suggestion_id) # Change the question_dict of the question suggestion that got rejected # so we can resubmit the suggestion for review. resubmit_question_change = suggestion_1.change resubmit_question_change.question_dict['linked_skill_ids'] = ['skill1'] # Resubmit the rejected question suggestion. suggestion_services.resubmit_rejected_suggestion( suggestion_1.suggestion_id, 'resubmit summary message', self.author_id_1, resubmit_question_change ) # Verify that both suggestions are returned again and the suggestion # that was created second is now the first suggestion in the returned # list, since it has been waiting longer (due to it not being updated). suggestions = ( suggestion_services .get_question_suggestions_waiting_longest_for_review() ) self.assertEqual(len(suggestions), 2) self.assertEqual( suggestions[0].suggestion_id, suggestion_2.suggestion_id) self.assertEqual( suggestions[1].suggestion_id, suggestion_1.suggestion_id) self.assertLess( suggestions[0].last_updated, suggestions[1].last_updated) def test_get_question_suggestions_waiting_longest_for_review(self): suggestion_1 = self._create_question_suggestion_with_skill_id('skill1') suggestion_2 = self._create_question_suggestion_with_skill_id('skill2') suggestion_3 = self._create_question_suggestion_with_skill_id('skill3') suggestions = ( suggestion_services .get_question_suggestions_waiting_longest_for_review() ) # Assert that the suggestions are in the order that they were created. self.assertEqual(len(suggestions), 3) self.assertEqual( suggestions[0].suggestion_id, suggestion_1.suggestion_id) self.assertEqual( suggestions[1].suggestion_id, suggestion_2.suggestion_id) self.assertEqual( suggestions[2].suggestion_id, suggestion_3.suggestion_id) for i in python_utils.RANGE(len(suggestions) - 1): self.assertLess( suggestions[i].last_updated, suggestions[i + 1].last_updated) def test_query_suggestions_that_can_be_reviewed_by_user(self): # User proficiency models for user1. user_models.UserContributionProficiencyModel.create( 'user1', 'category1', 15) user_models.UserContributionProficiencyModel.create( 'user1', 'category2', 15) user_models.UserContributionProficiencyModel.create( 'user1', 'category3', 5) # User proficiency models for user2. user_models.UserContributionProficiencyModel.create( 'user2', 'category1', 5) user_models.UserContributionProficiencyModel.create( 'user2', 'category2', 5) user_models.UserContributionProficiencyModel.create( 'user2', 'category3', 5) suggestion_models.GeneralSuggestionModel.create( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, 'exp1', 1, suggestion_models.STATUS_IN_REVIEW, 'author_3', 'reviewer_2', self.change, 'category1', 'exploration.exp1.thread_1', None) suggestion_models.GeneralSuggestionModel.create( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, 'exp1', 1, suggestion_models.STATUS_IN_REVIEW, 'author_3', 'reviewer_2', self.change, 'category2', 'exploration.exp1.thread_2', None) suggestion_models.GeneralSuggestionModel.create( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, 'exp1', 1, suggestion_models.STATUS_IN_REVIEW, 'author_3', 'reviewer_2', self.change, 'category3', 'exploration.exp1.thread_3', None) # This suggestion does not count as a suggestion that can be reviewed # by a user because it has already been rejected. suggestion_models.GeneralSuggestionModel.create( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, 'exp1', 1, suggestion_models.STATUS_REJECTED, 'author_3', 'reviewer_2', self.change, 'category1', 'exploration.exp1.thread_4', None) suggestion_models.GeneralSuggestionModel.create( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, 'exp1', 1, suggestion_models.STATUS_IN_REVIEW, 'author_3', 'reviewer_2', self.change, 'category2', 'exploration.exp1.thread_5', None) self.assertEqual(len( suggestion_services .get_all_suggestions_that_can_be_reviewed_by_user('user1')), 3) self.assertEqual(len( suggestion_services .get_all_suggestions_that_can_be_reviewed_by_user('user2')), 0) class SuggestionIntegrationTests(test_utils.GenericTestBase): EXP_ID = 'exp1' TOPIC_ID = 'topic1' STORY_ID = 'story1' TRANSLATION_LANGUAGE_CODE = 'en' AUTHOR_EMAIL = 'author@example.com' score_category = ( suggestion_models.SCORE_TYPE_CONTENT + suggestion_models.SCORE_CATEGORY_DELIMITER + 'Algebra') THREAD_ID = 'exploration.exp1.thread_1' COMMIT_MESSAGE = 'commit message' def mock_generate_new_thread_id(self, unused_entity_type, unused_entity_id): return self.THREAD_ID def setUp(self): super(SuggestionIntegrationTests, self).setUp() self.signup(self.OWNER_EMAIL, self.OWNER_USERNAME) self.signup(self.EDITOR_EMAIL, self.EDITOR_USERNAME) self.signup(self.AUTHOR_EMAIL, 'author') self.owner_id = self.get_user_id_from_email(self.OWNER_EMAIL) self.editor_id = self.get_user_id_from_email(self.EDITOR_EMAIL) self.author_id = self.get_user_id_from_email(self.AUTHOR_EMAIL) self.reviewer_id = self.editor_id self.editor = user_services.UserActionsInfo(self.editor_id) # Login and create exploration and suggestions. self.login(self.EDITOR_EMAIL) # Create exploration. exploration = ( self.save_new_linear_exp_with_state_names_and_interactions( self.EXP_ID, self.editor_id, ['State 1', 'State 2'], ['TextInput'], category='Algebra')) self.old_content = state_domain.SubtitledHtml( 'content', 'old content').to_dict() recorded_voiceovers_dict = { 'voiceovers_mapping': { 'content': { self.TRANSLATION_LANGUAGE_CODE: { 'filename': 'filename3.mp3', 'file_size_bytes': 3000, 'needs_update': False, 'duration_secs': 42.43 } }, 'default_outcome': {}, 'ca_placeholder_0': {} } } self.old_recorded_voiceovers = ( state_domain.RecordedVoiceovers.from_dict(recorded_voiceovers_dict)) # Create content in State A with a single audio subtitle. exploration.states['State 1'].update_content( state_domain.SubtitledHtml.from_dict(self.old_content)) exploration.states['State 1'].update_recorded_voiceovers( self.old_recorded_voiceovers) exp_services._save_exploration(self.editor_id, exploration, '', []) # pylint: disable=protected-access rights_manager.publish_exploration(self.editor, self.EXP_ID) rights_manager.assign_role_for_exploration( self.editor, self.EXP_ID, self.owner_id, rights_domain.ROLE_EDITOR) self.new_content = state_domain.SubtitledHtml( 'content', 'new content').to_dict() self.change = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'State 1', 'new_value': self.new_content } self.target_version_at_submission = exploration.version # Set up for testing translation suggestions. Translation suggestions # correspond to a given topic, story and exploration. self.save_new_topic(self.TOPIC_ID, self.owner_id) self.save_new_story( self.STORY_ID, self.owner_id, self.TOPIC_ID, title='A story', description='Description', notes='Notes') # Adds the story to the topic. topic_services.add_canonical_story( self.owner_id, self.TOPIC_ID, self.STORY_ID) # Adds the exploration to the story. story_change_list_to_add_an_exp = [ story_domain.StoryChange({ 'cmd': 'add_story_node', 'node_id': 'node_1', 'title': 'Node1', }), story_domain.StoryChange({ 'cmd': 'update_story_node_property', 'property_name': 'exploration_id', 'node_id': 'node_1', 'old_value': None, 'new_value': self.EXP_ID }) ] story_services.update_story( self.owner_id, self.STORY_ID, story_change_list_to_add_an_exp, 'Added exploration.') def create_translation_suggestion_associated_with_exp( self, exp_id, author_id): """Creates a translation suggestion that is associated with an exploration with id exp_id. The author of the created suggestion is author_id. """ # Gets the html content in the exploration to be translated. exploration = exp_fetchers.get_exploration_by_id(exp_id) content_html = exploration.states['State 1'].content.html add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': 'State 1', 'content_id': 'content', 'language_code': 'hi', 'content_html': content_html, 'translation_html': 'This is translated html.' } suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, exp_id, 1, author_id, add_translation_change_dict, 'test description') def assert_created_suggestion_is_valid(self, target_id, author_id): """Assert that the created suggestion is in review and that only one suggestion with the given target_id and author_id exists. """ suggestions = suggestion_services.query_suggestions( [('author_id', author_id), ('target_id', target_id)]) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].status, suggestion_models.STATUS_IN_REVIEW) def test_create_and_accept_suggestion(self): with self.swap( feedback_models.GeneralFeedbackThreadModel, 'generate_new_thread_id', self.mock_generate_new_thread_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.EXP_ID, self.target_version_at_submission, self.author_id, self.change, 'test description') suggestion_id = self.THREAD_ID suggestion_services.accept_suggestion( suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, None) exploration = exp_fetchers.get_exploration_by_id(self.EXP_ID) self.assertEqual( exploration.states['State 1'].content.html, 'new content') suggestion = suggestion_services.get_suggestion_by_id(suggestion_id) self.assertEqual(suggestion.status, suggestion_models.STATUS_ACCEPTED) def test_create_and_reject_suggestion(self): with self.swap( feedback_models.GeneralFeedbackThreadModel, 'generate_new_thread_id', self.mock_generate_new_thread_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.EXP_ID, self.target_version_at_submission, self.author_id, self.change, 'test description') suggestion_id = self.THREAD_ID suggestion_services.reject_suggestion( suggestion_id, self.reviewer_id, 'Reject message') exploration = exp_fetchers.get_exploration_by_id(self.EXP_ID) thread_messages = feedback_services.get_messages(self.THREAD_ID) last_message = thread_messages[len(thread_messages) - 1] self.assertEqual( last_message.text, 'Reject message') self.assertEqual( exploration.states['State 1'].content.html, 'old content') suggestion = suggestion_services.get_suggestion_by_id(suggestion_id) self.assertEqual(suggestion.status, suggestion_models.STATUS_REJECTED) def test_create_and_accept_suggestion_with_message(self): with self.swap( feedback_models.GeneralFeedbackThreadModel, 'generate_new_thread_id', self.mock_generate_new_thread_id): suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.EXP_ID, self.target_version_at_submission, self.author_id, self.change, 'test description') suggestion_id = self.THREAD_ID suggestion_services.accept_suggestion( suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'Accept message') exploration = exp_fetchers.get_exploration_by_id(self.EXP_ID) thread_messages = feedback_services.get_messages(self.THREAD_ID) last_message = thread_messages[len(thread_messages) - 1] self.assertEqual( last_message.text, 'Accept message') self.assertEqual( exploration.states['State 1'].content.html, 'new content') suggestion = suggestion_services.get_suggestion_by_id(suggestion_id) self.assertEqual(suggestion.status, suggestion_models.STATUS_ACCEPTED) def test_delete_skill_rejects_question_suggestion(self): skill_id = skill_services.get_new_skill_id() self.save_new_skill(skill_id, self.author_id, description='description') suggestion_change = { 'cmd': ( question_domain .CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION), 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': 'en', 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': skill_id, 'skill_difficulty': 0.3 } suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, skill_id, 1, self.author_id, suggestion_change, 'test description') self.assert_created_suggestion_is_valid(skill_id, self.author_id) skill_services.delete_skill(self.author_id, skill_id) # Suggestion should be rejected after corresponding skill is deleted. suggestions = suggestion_services.query_suggestions( [('author_id', self.author_id), ('target_id', skill_id)]) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].status, suggestion_models.STATUS_REJECTED) def test_delete_topic_rejects_translation_suggestion(self): self.create_translation_suggestion_associated_with_exp( self.EXP_ID, self.author_id) self.assert_created_suggestion_is_valid(self.EXP_ID, self.author_id) topic_services.delete_topic(self.author_id, self.TOPIC_ID) # Suggestion should be rejected after the topic is deleted. suggestions = suggestion_services.query_suggestions( [('author_id', self.author_id), ('target_id', self.EXP_ID)]) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].status, suggestion_models.STATUS_REJECTED) def test_delete_story_rejects_translation_suggestion(self): self.create_translation_suggestion_associated_with_exp( self.EXP_ID, self.author_id) self.assert_created_suggestion_is_valid(self.EXP_ID, self.author_id) story_services.delete_story(self.author_id, self.STORY_ID) # Suggestion should be rejected after the story is deleted. suggestions = suggestion_services.query_suggestions( [('author_id', self.author_id), ('target_id', self.EXP_ID)]) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].status, suggestion_models.STATUS_REJECTED) def test_remove_exp_from_story_rejects_translation_suggestion(self): self.create_translation_suggestion_associated_with_exp( self.EXP_ID, self.author_id) self.assert_created_suggestion_is_valid(self.EXP_ID, self.author_id) # Removes the exploration from the story. story_services.update_story( self.owner_id, self.STORY_ID, [story_domain.StoryChange({ 'cmd': 'update_story_node_property', 'property_name': 'exploration_id', 'node_id': 'node_1', 'old_value': self.EXP_ID, 'new_value': None })], 'Removed exploration.') # Suggestion should be rejected after exploration is removed from the # story. suggestions = suggestion_services.query_suggestions( [('author_id', self.author_id), ('target_id', self.EXP_ID)]) self.assertEqual(len(suggestions), 1) self.assertEqual( suggestions[0].status, suggestion_models.STATUS_REJECTED) class UserContributionProficiencyUnitTests(test_utils.GenericTestBase): def setUp(self): super(UserContributionProficiencyUnitTests, self).setUp() self.signup('user1@example.com', 'user1') self.signup('user2@example.com', 'user2') self.user_1_id = self.get_user_id_from_email('user1@example.com') self.user_2_id = self.get_user_id_from_email('user2@example.com') def test_get_all_user_ids_who_are_allowed_to_review(self): user_models.UserContributionProficiencyModel.create( self.user_1_id, 'category1', 0) user_models.UserContributionProficiencyModel.create( self.user_1_id, 'category2', feconf.MINIMUM_SCORE_REQUIRED_TO_REVIEW) user_models.UserContributionProficiencyModel.create( self.user_2_id, 'category1', 0) user_models.UserContributionProficiencyModel.create( self.user_2_id, 'category2', 0) user_ids = ( suggestion_services.get_all_user_ids_who_are_allowed_to_review( 'category1')) self.assertEqual(user_ids, []) user_ids = ( suggestion_services.get_all_user_ids_who_are_allowed_to_review( 'category2')) self.assertEqual(user_ids, [self.user_1_id]) self.assertFalse(suggestion_services.can_user_review_category( self.user_1_id, 'category1')) self.assertTrue(suggestion_services.can_user_review_category( self.user_1_id, 'category2')) self.assertFalse(suggestion_services.can_user_review_category( self.user_2_id, 'category1')) self.assertFalse(suggestion_services.can_user_review_category( self.user_2_id, 'category1')) def test_get_all_scores_of_the_user_with_multiple_scores(self): user_models.UserContributionProficiencyModel.create( self.user_1_id, 'category1', 1) user_models.UserContributionProficiencyModel.create( self.user_1_id, 'category2', 2) user_models.UserContributionProficiencyModel.create( self.user_1_id, 'category3', 3) expected_scores_dict = {} for index in python_utils.RANGE(1, 4): key = 'category%s' % python_utils.UNICODE(index) expected_scores_dict[key] = index scores_dict = suggestion_services.get_all_scores_of_user( self.user_1_id) self.assertEqual(len(scores_dict), 3) self.assertDictEqual(scores_dict, expected_scores_dict) def test_get_all_scores_of_the_user_when_no_scores_exist(self): scores_dict = suggestion_services.get_all_scores_of_user( self.user_1_id) self.assertEqual(len(scores_dict), 0) self.assertDictEqual(scores_dict, {}) class VoiceoverApplicationServiceUnitTest(test_utils.GenericTestBase): """Tests for the ExplorationVoiceoverApplication class.""" def setUp(self): super(VoiceoverApplicationServiceUnitTest, self).setUp() self.signup('author@example.com', 'author') self.author_id = self.get_user_id_from_email('author@example.com') suggestion_models.GeneralVoiceoverApplicationModel( id='application_id', target_type='exploration', target_id='0', status='review', author_id=self.author_id, final_reviewer_id=None, language_code='en', filename='filename.mp3', content='content', rejection_message=None).put() self.voiceover_application_model = ( suggestion_models.GeneralVoiceoverApplicationModel.get_by_id( 'application_id')) def test_get_voiceover_application_from_model_with_invalid_type_raise_error( self): suggestion_services.get_voiceover_application( self.voiceover_application_model.id) self.voiceover_application_model.target_type = 'invalid_type' with self.assertRaisesRegexp( Exception, 'Invalid target type for voiceover application: invalid_type'): suggestion_services.get_voiceover_application( self.voiceover_application_model.id) class ReviewableSuggestionEmailInfoUnitTests( test_utils.GenericTestBase): """Tests the methods related to the ReviewableSuggestionEmailInfo class. """ target_id = 'exp1' skill_id = 'skill1' language_code = 'en' AUTHOR_EMAIL = 'author1@example.com' REVIEWER_EMAIL = 'reviewer@community.org' COMMIT_MESSAGE = 'commit message' def _create_translation_suggestion_with_translation_html( self, translation_html): """Creates a translation suggestion with the given translation_html.""" add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': feconf.DEFAULT_INIT_STATE_NAME, 'content_id': feconf.DEFAULT_NEW_STATE_CONTENT_ID, 'language_code': self.language_code, 'content_html': feconf.DEFAULT_INIT_STATE_CONTENT_STR, 'translation_html': translation_html } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_translation_change_dict, 'test description' ) def _create_question_suggestion_with_question_html_content( self, question_html_content): """Creates a question suggestion with the html content used for the question in the question suggestion. """ with self.swap( feconf, 'DEFAULT_INIT_STATE_CONTENT_STR', question_html_content): add_question_change_dict = { 'cmd': ( question_domain .CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION), 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': self.language_code, 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': self.skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, self.skill_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_question_change_dict, 'test description' ) def _create_edit_state_content_suggestion(self): """Creates an "edit state content" suggestion.""" edit_state_content_change_dict = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'Introduction', 'new_value': { 'content_id': 'content', 'html': 'new html content' }, 'old_value': { 'content_id': 'content', 'html': 'old html content' } } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, edit_state_content_change_dict, 'test description') def _assert_reviewable_suggestion_email_infos_are_equal( self, reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info): """Asserts that the reviewable suggestion email info is equal to the expected reviewable suggestion email info. """ self.assertEqual( reviewable_suggestion_email_info.suggestion_type, expected_reviewable_suggestion_email_info.suggestion_type) self.assertEqual( reviewable_suggestion_email_info.language_code, expected_reviewable_suggestion_email_info.language_code) self.assertEqual( reviewable_suggestion_email_info.suggestion_content, expected_reviewable_suggestion_email_info.suggestion_content) self.assertEqual( reviewable_suggestion_email_info.submission_datetime, expected_reviewable_suggestion_email_info.submission_datetime) def setUp(self): super( ReviewableSuggestionEmailInfoUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email( self.AUTHOR_EMAIL) self.signup(self.REVIEWER_EMAIL, 'reviewer') self.reviewer_id = self.get_user_id_from_email( self.REVIEWER_EMAIL) self.save_new_valid_exploration(self.target_id, self.author_id) def test_create_raises_for_suggestion_type_not_on_contributor_dashboard( self): edit_state_content_suggestion = ( self._create_edit_state_content_suggestion()) # Mocking the SUGGESTION_EMPHASIZED_TEXT_GETTER_FUNCTIONS dict in # suggestion services so that this test still passes if the # "edit state content" suggestion type is added to the Contributor # Dashboard in the future. suggestion_emphasized_text_getter_functions_mock = {} with self.swap( suggestion_services, 'SUGGESTION_EMPHASIZED_TEXT_GETTER_FUNCTIONS', suggestion_emphasized_text_getter_functions_mock): with self.assertRaisesRegexp( Exception, 'Expected suggestion type to be offered on the Contributor ' 'Dashboard, received: %s.' % ( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT)): ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( edit_state_content_suggestion) ) def test_contributor_suggestion_types_are_in_suggestion_text_getter_dict( self): # This test will fail if a new suggestion type is added to the # Contributor Dashboard but hasn't been added to # SUGGESTION_EMPHASIZED_TEXT_GETTER_FUNCTIONS. sorted_text_getter_dict_suggestion_types = sorted( suggestion_services .SUGGESTION_EMPHASIZED_TEXT_GETTER_FUNCTIONS.keys()) sorted_contributor_dashboard_suggestion_types = sorted( suggestion_models.CONTRIBUTOR_DASHBOARD_SUGGESTION_TYPES) self.assertListEqual( sorted_text_getter_dict_suggestion_types, sorted_contributor_dashboard_suggestion_types) def test_create_from_suggestion_returns_info_for_question_suggestion(self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( 'default question content')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'default question content', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_from_suggestion_returns_info_for_translation_suggestion( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( 'default translation content')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'default translation content', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_from_suggestion_returns_info_for_empty_html(self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( '')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, '', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_from_suggestion_returns_info_with_no_trailing_whitespace( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( ' test whitespace ')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'test whitespace', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_math_rte( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( 'translation with rte' '<oppia-noninteractive-math></oppia-noninteractive-math>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'translation with rte [Math]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_image_rte( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( 'translation with rte' '<oppia-noninteractive-image></oppia-noninteractive-image>' '')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'translation with rte [Image]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_link_rte( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( ' translation with rte' '<oppia-noninteractive-link></oppia-noninteractive-link> ')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'translation with rte [Link]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_rte_repeats( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( ' translation with rte' '<oppia-noninteractive-link></oppia-noninteractive-link>' '<oppia-noninteractive-link></oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'translation with rte [Link] [Link]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_multi_rte( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( ' translation with rte' '<oppia-noninteractive-link></oppia-noninteractive-link>' '<oppia-noninteractive-math></oppia-noninteractive-math>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, 'translation with rte [Link] [Math]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_translation_suggestion_if_html_rte_value( self): translation_suggestion = ( self._create_translation_suggestion_with_translation_html( '<oppia-noninteractive-link text-with-value="&quot;Test ' 'a tag&quot;" url-with-value="&quot;somelink&' 'quot;"></oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( translation_suggestion.suggestion_type, translation_suggestion.language_code, '[Link]', translation_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( translation_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_question_suggestion_if_html_has_math_rte( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( ' question with rte' '<oppia-noninteractive-math></oppia-noninteractive-math> ')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'question with rte [Math]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_question_suggestion_if_html_has_image_rte( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( ' question with rte' '<oppia-noninteractive-image></oppia-noninteractive-image>' '')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'question with rte [Image]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info) def test_create_returns_info_for_question_suggestion_if_html_has_link_rte( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( ' question with rte' '<oppia-noninteractive-link></oppia-noninteractive-link> ')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'question with rte [Link]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_question_suggestion_if_html_has_repeat_rte( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( ' question with rte' '<oppia-noninteractive-link></oppia-noninteractive-link>' '<oppia-noninteractive-link></oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'question with rte [Link] [Link]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_question_suggestion_if_html_has_multi_rte( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( ' question with rte' '<oppia-noninteractive-link></oppia-noninteractive-link>' '<oppia-noninteractive-math></oppia-noninteractive-math>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, 'question with rte [Link] [Math]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_question_suggestion_if_html_has_rte_value( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( '<oppia-noninteractive-link text-with-value="&quot;Test ' 'a tag&quot;" url-with-value="&quot;somelink&' 'quot;"></oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, '[Link]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_suggestion_if_html_has_rte_with_text( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( '<oppia-noninteractive-link text-with-value="&quot;Test ' 'a tag&quot;" url-with-value="&quot;somelink&' 'quot;">text</oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, '[Link]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_suggestion_if_html_has_rte_with_html( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( '<oppia-noninteractive-link text-with-value="&quot;Test ' 'a tag&quot;" url-with-value="&quot;somelink&' 'quot;">text</oppia-noninteractive-link>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, '[Link]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) def test_create_returns_info_for_suggestion_if_html_has_rte_with_multi_word( self): question_suggestion = ( self._create_question_suggestion_with_question_html_content( '<oppia-noninteractive-link-test text-with-value=' '"&quot;Test a tag&quot;" url-with-value="&quot;' 'somelink&quot;">text' '</oppia-noninteractive-link-test>')) expected_reviewable_suggestion_email_info = ( suggestion_registry.ReviewableSuggestionEmailInfo( question_suggestion.suggestion_type, question_suggestion.language_code, '[Link Test]', question_suggestion.last_updated )) reviewable_suggestion_email_info = ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( question_suggestion) ) self._assert_reviewable_suggestion_email_infos_are_equal( reviewable_suggestion_email_info, expected_reviewable_suggestion_email_info ) class GetSuggestionsWaitingForReviewInfoToNotifyReviewersUnitTests( test_utils.GenericTestBase): """Test the ability of the get_suggestions_waitng_for_review_info_to_notify_reviewers method in suggestion services, which is used to retrieve the information required to notify reviewers that there are suggestions that need review. """ target_id = 'exp1' language_code = 'en' AUTHOR_EMAIL = 'author1@example.com' REVIEWER_1_EMAIL = 'reviewer1@community.org' REVIEWER_2_EMAIL = 'reviewer2@community.org' COMMIT_MESSAGE = 'commit message' def _create_translation_suggestion_with_language_code_and_author( self, language_code, author_id): """Creates a translation suggestion in the given language_code with the given author id. """ add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': feconf.DEFAULT_INIT_STATE_NAME, 'content_id': feconf.DEFAULT_NEW_STATE_CONTENT_ID, 'language_code': language_code, 'content_html': feconf.DEFAULT_INIT_STATE_CONTENT_STR, 'translation_html': 'This is the translated content.' } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, author_id, add_translation_change_dict, 'test description' ) def _create_question_suggestion_with_skill_id_and_author_id( self, skill_id, author_id): """Creates a question suggestion with the given skill_id.""" add_question_change_dict = { 'cmd': question_domain.CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION, 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': self.language_code, 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, skill_id, feconf.CURRENT_STATE_SCHEMA_VERSION, author_id, add_question_change_dict, 'test description' ) def _create_reviewable_suggestion_email_infos_from_suggestions( self, suggestions): """Creates a list of ReviewableSuggestionEmailInfo objects from the given suggestions. """ return [ ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( suggestion) ) for suggestion in suggestions ] def _assert_reviewable_suggestion_email_infos_are_in_correct_order( self, reviewable_suggestion_email_infos, expected_reviewable_suggestion_email_infos): """Asserts that the reviewable suggestion email infos are equal to the expected reviewable suggestion email infos and that the reviewable suggestion email infos are sorted in descending order according to review wait time. """ self.assertEqual( len(reviewable_suggestion_email_infos), len(expected_reviewable_suggestion_email_infos) ) for index, reviewable_suggestion_email_info in enumerate( reviewable_suggestion_email_infos): self.assertEqual( reviewable_suggestion_email_info.suggestion_type, expected_reviewable_suggestion_email_infos[ index].suggestion_type) self.assertEqual( reviewable_suggestion_email_info.language_code, expected_reviewable_suggestion_email_infos[ index].language_code) self.assertEqual( reviewable_suggestion_email_info.suggestion_content, expected_reviewable_suggestion_email_infos[ index].suggestion_content) self.assertEqual( reviewable_suggestion_email_info.submission_datetime, expected_reviewable_suggestion_email_infos[ index].submission_datetime) for index in python_utils.RANGE( len(reviewable_suggestion_email_infos) - 1): self.assertLess( reviewable_suggestion_email_infos[index].submission_datetime, reviewable_suggestion_email_infos[ index + 1].submission_datetime ) def setUp(self): super( GetSuggestionsWaitingForReviewInfoToNotifyReviewersUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email(self.AUTHOR_EMAIL) self.signup(self.REVIEWER_1_EMAIL, 'reviewer1') self.reviewer_1_id = self.get_user_id_from_email( self.REVIEWER_1_EMAIL) self.signup(self.REVIEWER_2_EMAIL, 'reviewer2') self.reviewer_2_id = self.get_user_id_from_email( self.REVIEWER_2_EMAIL) self.save_new_valid_exploration(self.target_id, self.author_id) def test_get_returns_empty_for_reviewers_who_authored_the_suggestions(self): user_services.allow_user_to_review_question(self.reviewer_1_id) user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.reviewer_1_id) self._create_translation_suggestion_with_language_code_and_author( 'hi', self.reviewer_1_id) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_empty_for_question_reviewers_if_only_translation_exist( self): user_services.allow_user_to_review_question(self.reviewer_1_id) self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_empty_for_translation_reviewers_if_only_question_exist( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.reviewer_1_id) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_empty_for_accepted_suggestions(self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') translation_suggestion = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_services.accept_suggestion( translation_suggestion.suggestion_id, self.reviewer_1_id, self.COMMIT_MESSAGE, 'review message') reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_empty_for_rejected_suggestions(self): user_services.allow_user_to_review_question(self.reviewer_1_id) translation_suggestion = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_services.reject_suggestion( translation_suggestion.suggestion_id, self.reviewer_1_id, 'review message') reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_suggestion_infos_for_a_translation_reviewer_same_lang( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion_1, translation_suggestion_2])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_empty_for_a_translation_reviewer_with_diff_lang_rights( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self.assertEqual(reviewable_suggestion_email_infos, [[]]) def test_get_returns_suggestion_infos_for_translation_reviewer_multi_lang( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id)) translation_suggestion_3 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [ translation_suggestion_1, translation_suggestion_2, translation_suggestion_3])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_infos_for_translation_reviewer_past_limit_same_lang( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) # Create another translation suggestion so that we pass the # MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER limit. self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion_1])) with self.swap( suggestion_services, 'MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER', 1): reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_infos_for_translation_reviewer_past_limit_diff_lang( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id)) # Create another hindi and english translation suggestion so that we # reach the MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER limit for each # language code but continue to update which suggestions have been # waiting the longest (since the top two suggestions waiting the # longest are from different language codes). self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id) self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion_1, translation_suggestion_2])) with self.swap( suggestion_services, 'MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER', 2): reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_suggestion_infos_for_multiple_translation_reviewers( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') user_services.allow_user_to_review_translation_in_language( self.reviewer_2_id, 'hi') translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id)) translation_suggestion_3 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) expected_reviewable_suggestion_email_infos_reviewer_1 = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [ translation_suggestion_1, translation_suggestion_2, translation_suggestion_3])) expected_reviewable_suggestion_email_infos_reviewer_2 = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion_1, translation_suggestion_3])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id, self.reviewer_2_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 2) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos_reviewer_1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[1], expected_reviewable_suggestion_email_infos_reviewer_2) def test_get_returns_suggestion_infos_for_reviewer_with_multi_review_rights( self): user_services.allow_user_to_review_question(self.reviewer_1_id) user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') suggestion_1 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id)) suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_3 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_2', self.author_id)) suggestion_4 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_5 = ( self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id)) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [ suggestion_1, suggestion_2, suggestion_3, suggestion_4, suggestion_5])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_suggestion_infos_for_a_question_reviewer(self): user_services.allow_user_to_review_question(self.reviewer_1_id) question_suggestion_1 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id)) question_suggestion_2 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_2', self.author_id)) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [question_suggestion_1, question_suggestion_2])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_suggestion_infos_for_multi_question_reviewers(self): user_services.allow_user_to_review_question(self.reviewer_1_id) user_services.allow_user_to_review_question(self.reviewer_2_id) question_suggestion_1 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id)) question_suggestion_2 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_2', self.author_id)) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [question_suggestion_1, question_suggestion_2])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id, self.reviewer_2_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 2) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[1], expected_reviewable_suggestion_email_infos) def test_get_returns_suggestion_infos_for_question_reviewer_past_limit( self): user_services.allow_user_to_review_question(self.reviewer_1_id) question_suggestion_1 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id)) self._create_question_suggestion_with_skill_id_and_author_id( 'skill_2', self.author_id) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [question_suggestion_1])) with self.swap( suggestion_services, 'MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER', 1): reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) def test_get_returns_suggestion_infos_for_multi_reviewers_with_multi_rights( self): # Reviewer 1's permissions. user_services.allow_user_to_review_question(self.reviewer_1_id) user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'en') # Reviewer 2's permissions. user_services.allow_user_to_review_question(self.reviewer_2_id) user_services.allow_user_to_review_translation_in_language( self.reviewer_2_id, 'hi') user_services.allow_user_to_review_translation_in_language( self.reviewer_2_id, 'fr') suggestion_1 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id)) suggestion_2 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_3 = ( self._create_translation_suggestion_with_language_code_and_author( 'fr', self.author_id)) suggestion_4 = ( self._create_question_suggestion_with_skill_id_and_author_id( 'skill_2', self.author_id)) suggestion_5 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) suggestion_6 = ( self._create_translation_suggestion_with_language_code_and_author( 'en', self.author_id)) expected_reviewable_suggestion_email_infos_reviewer_1 = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [ suggestion_1, suggestion_2, suggestion_4, suggestion_5, suggestion_6])) expected_reviewable_suggestion_email_infos_reviewer_2 = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [ suggestion_1, suggestion_2, suggestion_3, suggestion_4, suggestion_5])) reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id, self.reviewer_2_id] ) ) self.assertEqual(len(reviewable_suggestion_email_infos), 2) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos_reviewer_1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[1], expected_reviewable_suggestion_email_infos_reviewer_2) def test_get_returns_infos_for_reviewer_with_multi_rights_past_limit( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_1_id, 'hi') user_services.allow_user_to_review_question(self.reviewer_1_id) translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id)) # Create additional suggestions so that we pass the # MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER limit regardless of # suggestion type. self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id) self._create_translation_suggestion_with_language_code_and_author( 'hi', self.author_id) self._create_question_suggestion_with_skill_id_and_author_id( 'skill_1', self.author_id) expected_reviewable_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion_1])) with self.swap( suggestion_services, 'MAX_NUMBER_OF_SUGGESTIONS_TO_EMAIL_REVIEWER', 1): reviewable_suggestion_email_infos = ( suggestion_services .get_suggestions_waiting_for_review_info_to_notify_reviewers( [self.reviewer_1_id])) self.assertEqual(len(reviewable_suggestion_email_infos), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( reviewable_suggestion_email_infos[0], expected_reviewable_suggestion_email_infos) class CommunityContributionStatsUnitTests(test_utils.GenericTestBase): """Test the functionality related to updating the community contribution stats. TODO(#10957): It is currently not possible to resubmit a rejected translation suggestion for review. As a result, there isn't a test for that case in this test class. If the functionality is added, a new test should be added here to cover that case. If the functionality is not going to be added then this can be removed. See issue #10957 for more context. """ target_id = 'exp1' skill_id = 'skill_123456' language_code = 'en' AUTHOR_EMAIL = 'author@example.com' REVIEWER_EMAIL = 'reviewer@community.org' COMMIT_MESSAGE = 'commit message' def _create_translation_suggestion_with_language_code(self, language_code): """Creates a translation suggestion in the given language_code.""" add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': feconf.DEFAULT_INIT_STATE_NAME, 'content_id': feconf.DEFAULT_NEW_STATE_CONTENT_ID, 'language_code': language_code, 'content_html': feconf.DEFAULT_INIT_STATE_CONTENT_STR, 'translation_html': 'This is the translated content.' } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_translation_change_dict, 'test description' ) def _create_question_suggestion(self): """Creates a question suggestion.""" add_question_change_dict = { 'cmd': question_domain.CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION, 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': self.language_code, 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': self.skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, self.skill_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_question_change_dict, 'test description' ) def _create_edit_state_content_suggestion(self): """Creates an "edit state content" suggestion.""" edit_state_content_change_dict = { 'cmd': exp_domain.CMD_EDIT_STATE_PROPERTY, 'property_name': exp_domain.STATE_PROPERTY_CONTENT, 'state_name': 'Introduction', 'new_value': { 'content_id': 'content', 'html': 'new html content' }, 'old_value': { 'content_id': 'content', 'html': 'old html content' } } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_EDIT_STATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, edit_state_content_change_dict, 'test description' ) def _assert_community_contribution_stats_is_in_default_state(self): """Checks if the community contribution stats is in its default state. """ community_contribution_stats = ( suggestion_services.get_community_contribution_stats() ) self.assertEqual( ( community_contribution_stats .translation_reviewer_counts_by_lang_code ), {}) self.assertEqual( ( community_contribution_stats .translation_suggestion_counts_by_lang_code ), {}) self.assertEqual( community_contribution_stats.question_reviewer_count, 0) self.assertEqual( community_contribution_stats.question_suggestion_count, 0) def setUp(self): super( CommunityContributionStatsUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email( self.AUTHOR_EMAIL) self.signup(self.REVIEWER_EMAIL, 'reviewer') self.reviewer_id = self.get_user_id_from_email( self.REVIEWER_EMAIL) self.save_new_valid_exploration(self.target_id, self.author_id) self.save_new_skill(self.skill_id, self.author_id) def test_create_edit_state_content_suggestion_does_not_change_the_counts( self): self._create_edit_state_content_suggestion() self._assert_community_contribution_stats_is_in_default_state() def test_accept_edit_state_content_suggestion_does_not_change_the_counts( self): edit_state_content_suggestion = ( self._create_edit_state_content_suggestion()) self._assert_community_contribution_stats_is_in_default_state() suggestion_services.accept_suggestion( edit_state_content_suggestion.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_edit_state_content_suggestion_does_not_change_the_counts( self): edit_state_content_suggestion = ( self._create_edit_state_content_suggestion()) self._assert_community_contribution_stats_is_in_default_state() suggestion_services.reject_suggestion( edit_state_content_suggestion.suggestion_id, self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_edit_state_content_suggestions_does_not_change_the_counts( self): edit_state_content_suggestion_1 = ( self._create_edit_state_content_suggestion()) edit_state_content_suggestion_2 = ( self._create_edit_state_content_suggestion()) self._assert_community_contribution_stats_is_in_default_state() suggestion_services.reject_suggestions( [ edit_state_content_suggestion_1.suggestion_id, edit_state_content_suggestion_2.suggestion_id ], self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_resubmit_edit_state_content_suggestion_does_not_change_the_counts( self): edit_state_content_suggestion = ( self._create_edit_state_content_suggestion()) suggestion_services.reject_suggestion( edit_state_content_suggestion.suggestion_id, self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() # Change the new_value of the html of the suggestion that got rejected # so we can resubmit the suggestion for review. resubmit_suggestion_change = edit_state_content_suggestion.change resubmit_suggestion_change.new_value['html'] = 'new html to resubmit' # Resubmit the rejected "edit state content" suggestion. suggestion_services.resubmit_rejected_suggestion( edit_state_content_suggestion.suggestion_id, 'resubmit summary message', self.author_id, resubmit_suggestion_change) self._assert_community_contribution_stats_is_in_default_state() def test_create_question_suggestion_increases_question_suggestion_count( self): self._create_question_suggestion() stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) def test_create_multi_question_suggestions_increases_question_count(self): self._create_question_suggestion() self._create_question_suggestion() stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 2) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) def test_accept_question_suggestion_decreases_question_suggestion_count( self): question_suggestion = self._create_question_suggestion() # Assert that the question suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_services.accept_suggestion( question_suggestion.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_question_suggestion_decreases_question_suggestion_count( self): question_suggestion = self._create_question_suggestion() # Assert that the question suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_services.reject_suggestion( question_suggestion.suggestion_id, self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_question_suggestions_decreases_question_suggestion_count( self): question_suggestion_1 = self._create_question_suggestion() question_suggestion_2 = self._create_question_suggestion() # Assert that the question suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 2) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_services.reject_suggestions( [ question_suggestion_1.suggestion_id, question_suggestion_2.suggestion_id ], self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_resubmit_question_suggestion_increases_question_suggestion_count( self): question_suggestion = self._create_question_suggestion() # Assert that the question suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_services.reject_suggestion( question_suggestion.suggestion_id, self.reviewer_id, 'review message') # Assert that the question suggestion decreased because the suggestion # was rejected. self._assert_community_contribution_stats_is_in_default_state() # Change the question_dict of the question suggestion that got rejected # so we can resubmit the suggestion for review. resubmit_question_change = question_suggestion.change resubmit_question_change.question_dict['linked_skill_ids'] = ['skill1'] # Resubmit the rejected question suggestion. suggestion_services.resubmit_rejected_suggestion( question_suggestion.suggestion_id, 'resubmit summary message', self.author_id, resubmit_question_change ) stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) def test_create_translation_suggestion_raises_translation_suggestion_count( self): self._create_translation_suggestion_with_language_code( self.language_code) stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {self.language_code: 1}) def test_create_translation_suggestions_diff_lang_raises_translation_counts( self): self._create_translation_suggestion_with_language_code('hi') self._create_translation_suggestion_with_language_code('en') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 1, 'en': 1}) def test_create_translation_suggestions_eq_lang_increases_translation_count( self): self._create_translation_suggestion_with_language_code('hi') self._create_translation_suggestion_with_language_code('hi') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 2}) def test_accept_translation_suggestion_lowers_translation_suggestion_count( self): translation_suggestion = ( self._create_translation_suggestion_with_language_code( self.language_code)) # Assert that the translation suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {self.language_code: 1}) suggestion_services.accept_suggestion( translation_suggestion.suggestion_id, self.reviewer_id, self.COMMIT_MESSAGE, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_translation_suggestion_lowers_translation_suggestion_count( self): translation_suggestion = ( self._create_translation_suggestion_with_language_code( self.language_code)) # Assert that the translation suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {self.language_code: 1}) suggestion_services.reject_suggestion( translation_suggestion.suggestion_id, self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_one_translation_suggestion_diff_lang_lowers_only_one_count( self): translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code('hi')) # Create a translation suggestion in a different language that won't be # rejected. self._create_translation_suggestion_with_language_code('en') # Assert that the translation suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 1, 'en': 1}) suggestion_services.reject_suggestion( translation_suggestion_1.suggestion_id, self.reviewer_id, 'review message') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'en': 1}) def test_reject_translation_suggestions_diff_lang_lowers_translation_count( self): translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code('hi')) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code('en')) # Assert that the translation suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 1, 'en': 1}) suggestion_services.reject_suggestions( [ translation_suggestion_1.suggestion_id, translation_suggestion_2.suggestion_id ], self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_translation_suggestions_same_lang_lowers_translation_count( self): translation_suggestion_1 = ( self._create_translation_suggestion_with_language_code( self.language_code)) translation_suggestion_2 = ( self._create_translation_suggestion_with_language_code( self.language_code)) # Assert that the translation suggestion count increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {self.language_code: 2}) suggestion_services.reject_suggestions( [ translation_suggestion_1.suggestion_id, translation_suggestion_2.suggestion_id ], self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_reject_suggestions_diff_type_decreases_suggestion_counts(self): suggestion_1 = ( self._create_translation_suggestion_with_language_code('hi')) suggestion_2 = ( self._create_translation_suggestion_with_language_code('en')) suggestion_3 = self._create_edit_state_content_suggestion() suggestion_4 = self._create_question_suggestion() # Assert that the suggestion counts increased. stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 1, 'en': 1}) suggestion_services.reject_suggestions( [ suggestion_1.suggestion_id, suggestion_2.suggestion_id, suggestion_3.suggestion_id, suggestion_4.suggestion_id ], self.reviewer_id, 'review message') self._assert_community_contribution_stats_is_in_default_state() def test_create_suggestions_diff_type_increases_suggestion_counts(self): self._create_translation_suggestion_with_language_code('hi') self._create_translation_suggestion_with_language_code('en') self._create_question_suggestion() stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'hi': 1, 'en': 1}) class GetSuggestionsWaitingTooLongForReviewInfoForAdminsUnitTests( test_utils.GenericTestBase): """Test the ability of the get_info_about_suggestions_waiting_too_long_for_review method in suggestion services, which is used to retrieve the information required to notify admins if there are suggestions that have waited longer than suggestion_models.SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS days for a review on the Contributor Dashboard. """ target_id = 'exp1' skill_id = 'skill_123456' language_code = 'en' AUTHOR_EMAIL = 'author@example.com' REVIEWER_1_EMAIL = 'reviewer1@community.org' REVIEWER_2_EMAIL = 'reviewer2@community.org' COMMIT_MESSAGE = 'commit message' mocked_datetime_utcnow = datetime.datetime(2020, 6, 15, 5) def _create_translation_suggestion(self): """Creates a translation suggestion.""" add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': feconf.DEFAULT_INIT_STATE_NAME, 'content_id': feconf.DEFAULT_NEW_STATE_CONTENT_ID, 'language_code': self.language_code, 'content_html': feconf.DEFAULT_INIT_STATE_CONTENT_STR, 'translation_html': 'This is the translated content.' } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_translation_change_dict, 'test description' ) def _create_question_suggestion(self): """Creates a question suggestion.""" add_question_change_dict = { 'cmd': question_domain.CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION, 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': self.language_code, 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': self.skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, self.skill_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_question_change_dict, 'test description' ) def _create_reviewable_suggestion_email_infos_from_suggestions( self, suggestions): """Creates a list of ReviewableSuggestionEmailInfo objects from the given suggestions. """ return [ ( suggestion_services .create_reviewable_suggestion_email_info_from_suggestion( suggestion) ) for suggestion in suggestions ] def _assert_reviewable_suggestion_email_infos_are_in_correct_order( self, reviewable_suggestion_email_infos, expected_reviewable_suggestion_email_infos): """Asserts that the reviewable suggestion email infos are equal to the expected reviewable suggestion email infos and that the reviewable suggestion email infos are sorted in descending order according to review wait time. """ self.assertEqual( len(reviewable_suggestion_email_infos), len(expected_reviewable_suggestion_email_infos) ) for index, reviewable_suggestion_email_info in enumerate( reviewable_suggestion_email_infos): self.assertEqual( reviewable_suggestion_email_info.suggestion_type, expected_reviewable_suggestion_email_infos[ index].suggestion_type) self.assertEqual( reviewable_suggestion_email_info.language_code, expected_reviewable_suggestion_email_infos[ index].language_code) self.assertEqual( reviewable_suggestion_email_info.suggestion_content, expected_reviewable_suggestion_email_infos[ index].suggestion_content) self.assertEqual( reviewable_suggestion_email_info.submission_datetime, expected_reviewable_suggestion_email_infos[ index].submission_datetime) for index in python_utils.RANGE( len(reviewable_suggestion_email_infos) - 1): self.assertLess( reviewable_suggestion_email_infos[index].submission_datetime, reviewable_suggestion_email_infos[ index + 1].submission_datetime ) def setUp(self): super( GetSuggestionsWaitingTooLongForReviewInfoForAdminsUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email(self.AUTHOR_EMAIL) self.signup(self.REVIEWER_1_EMAIL, 'reviewer1') self.reviewer_1_id = self.get_user_id_from_email( self.REVIEWER_1_EMAIL) self.signup(self.REVIEWER_2_EMAIL, 'reviewer2') self.reviewer_2_id = self.get_user_id_from_email( self.REVIEWER_2_EMAIL) self.save_new_valid_exploration(self.target_id, self.author_id) self.save_new_skill(self.skill_id, self.author_id) def test_get_returns_empty_for_suggestion_type_not_on_contributor_dashboard( self): self._create_translation_suggestion() # This mocked list cannot be empty because then the storage query in the # get_suggestions_waiting_too_long_for_review method will fail. mocked_contributor_dashboard_suggestion_types = [ suggestion_models.SUGGESTION_TYPE_ADD_QUESTION] with self.swap( suggestion_models, 'CONTRIBUTOR_DASHBOARD_SUGGESTION_TYPES', mocked_contributor_dashboard_suggestion_types): with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', 0): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 0) def test_get_returns_empty_if_suggestion_review_wait_time_diff_is_negative( self): self._create_translation_suggestion() # Make sure the threshold is nonzero. with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', 1): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 0) def test_get_returns_empty_if_suggestions_have_waited_less_than_threshold( self): with self.mock_datetime_utcnow(self.mocked_datetime_utcnow): self._create_translation_suggestion() self._create_question_suggestion() mocked_threshold_review_wait_time_in_days = 2 mocked_datetime_less_than_review_wait_time_threshold = ( self.mocked_datetime_utcnow + datetime.timedelta(days=1)) with self.mock_datetime_utcnow( mocked_datetime_less_than_review_wait_time_threshold): with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', mocked_threshold_review_wait_time_in_days): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 0) def test_get_returns_empty_if_suggestions_have_waited_threshold_review_time( self): with self.mock_datetime_utcnow(self.mocked_datetime_utcnow): self._create_translation_suggestion() mocked_threshold_review_wait_time_in_days = 2 mocked_datetime_eq_review_wait_time_threshold = ( self.mocked_datetime_utcnow + datetime.timedelta( days=mocked_threshold_review_wait_time_in_days)) with self.mock_datetime_utcnow( mocked_datetime_eq_review_wait_time_threshold): with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', mocked_threshold_review_wait_time_in_days): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 0) def test_get_returns_suggestion_waited_long_if_their_wait_is_past_threshold( self): with self.mock_datetime_utcnow(self.mocked_datetime_utcnow): translation_suggestion = self._create_translation_suggestion() # Give the question suggestion a slightly different review submission # time so that the suggestions are not indistinguishable, in terms of # their review submission time. with self.mock_datetime_utcnow( self.mocked_datetime_utcnow + datetime.timedelta(minutes=5)): question_suggestion = self._create_question_suggestion() expected_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion, question_suggestion])) mocked_threshold_review_wait_time_in_days = 1 mocked_datetime_past_review_wait_time_threshold = ( self.mocked_datetime_utcnow + datetime.timedelta(days=2)) with self.mock_datetime_utcnow( mocked_datetime_past_review_wait_time_threshold): with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', mocked_threshold_review_wait_time_in_days): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 2) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( info_about_suggestions_waiting_too_long_for_review, expected_suggestion_email_infos ) def test_get_only_returns_suggestions_that_have_waited_past_wait_threshold( self): with self.mock_datetime_utcnow(self.mocked_datetime_utcnow): translation_suggestion = self._create_translation_suggestion() with self.mock_datetime_utcnow( self.mocked_datetime_utcnow + datetime.timedelta(days=2)): self._create_question_suggestion() expected_suggestion_email_infos = ( self._create_reviewable_suggestion_email_infos_from_suggestions( [translation_suggestion])) mocked_threshold_review_wait_time_in_days = 3 mocked_datetime_past_review_wait_time_threshold = ( self.mocked_datetime_utcnow + datetime.timedelta(days=4)) with self.mock_datetime_utcnow( mocked_datetime_past_review_wait_time_threshold): with self.swap( suggestion_models, 'SUGGESTION_REVIEW_WAIT_TIME_THRESHOLD_IN_DAYS', mocked_threshold_review_wait_time_in_days): info_about_suggestions_waiting_too_long_for_review = ( suggestion_services .get_info_about_suggestions_waiting_too_long_for_review() ) # The question suggestion was created 2 days after the translation # suggestion, so it has only waited 1 day for a review, which is less # than 3, the mocked review wait time threshold. Therefore, only the # translation suggestion has waited too long for review. self.assertEqual( len(info_about_suggestions_waiting_too_long_for_review), 1) self._assert_reviewable_suggestion_email_infos_are_in_correct_order( info_about_suggestions_waiting_too_long_for_review, expected_suggestion_email_infos ) class GetSuggestionTypesThatNeedReviewersUnitTests(test_utils.GenericTestBase): """Tests for the get_suggestion_types_that_need_reviewers method.""" sample_language_code = 'en' target_id = 'exp1' skill_id = 'skill_123456' language_code = 'en' AUTHOR_EMAIL = 'author@example.com' REVIEWER_EMAIL = 'reviewer@community.org' def _create_translation_suggestion_with_language_code(self, language_code): """Creates a translation suggestion in the given language_code.""" add_translation_change_dict = { 'cmd': exp_domain.CMD_ADD_TRANSLATION, 'state_name': feconf.DEFAULT_INIT_STATE_NAME, 'content_id': feconf.DEFAULT_NEW_STATE_CONTENT_ID, 'language_code': language_code, 'content_html': feconf.DEFAULT_INIT_STATE_CONTENT_STR, 'translation_html': 'This is the translated content.' } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT, suggestion_models.TARGET_TYPE_EXPLORATION, self.target_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_translation_change_dict, 'test description' ) def _create_question_suggestion(self): """Creates a question suggestion.""" add_question_change_dict = { 'cmd': question_domain.CMD_CREATE_NEW_FULLY_SPECIFIED_QUESTION, 'question_dict': { 'question_state_data': self._create_valid_question_data( 'default_state').to_dict(), 'language_code': constants.DEFAULT_LANGUAGE_CODE, 'question_state_data_schema_version': ( feconf.CURRENT_STATE_SCHEMA_VERSION), 'linked_skill_ids': ['skill_1'], 'inapplicable_skill_misconception_ids': ['skillid12345-1'] }, 'skill_id': self.skill_id, 'skill_difficulty': 0.3 } return suggestion_services.create_suggestion( suggestion_models.SUGGESTION_TYPE_ADD_QUESTION, suggestion_models.TARGET_TYPE_SKILL, self.skill_id, feconf.CURRENT_STATE_SCHEMA_VERSION, self.author_id, add_question_change_dict, 'test description' ) def _assert_community_contribution_stats_is_in_default_state(self): """Checks if the community contribution stats is in its default state. """ community_contribution_stats = ( suggestion_services.get_community_contribution_stats()) self.assertEqual( ( community_contribution_stats .translation_reviewer_counts_by_lang_code ), {}) self.assertEqual( ( community_contribution_stats .translation_suggestion_counts_by_lang_code ), {}) self.assertEqual( community_contribution_stats.question_reviewer_count, 0) self.assertEqual( community_contribution_stats.question_suggestion_count, 0) def setUp(self): super( GetSuggestionTypesThatNeedReviewersUnitTests, self).setUp() self.signup(self.AUTHOR_EMAIL, 'author') self.author_id = self.get_user_id_from_email(self.AUTHOR_EMAIL) self.save_new_valid_exploration(self.target_id, self.author_id) self.save_new_skill(self.skill_id, self.author_id) self.signup(self.REVIEWER_EMAIL, 'reviewer') self.reviewer_id = self.get_user_id_from_email( self.REVIEWER_EMAIL) def test_get_returns_no_reviewers_needed_if_no_suggestions_exist(self): self._assert_community_contribution_stats_is_in_default_state() suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual(suggestion_types_needing_reviewers, {}) def test_get_returns_no_reviewers_needed_if_question_reviewer_no_question( self): user_services.allow_user_to_review_question(self.reviewer_id) stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 1) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual(suggestion_types_needing_reviewers, {}) def test_get_returns_not_needed_if_translation_reviewers_but_no_translation( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_id, 'en') user_services.allow_user_to_review_translation_in_language( self.reviewer_id, 'fr') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {'en': 1, 'fr': 1}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual(suggestion_types_needing_reviewers, {}) def test_get_returns_no_reviewers_needed_if_enough_translation_reviewers( self): user_services.allow_user_to_review_translation_in_language( self.reviewer_id, 'en') user_services.allow_user_to_review_translation_in_language( self.reviewer_id, 'fr') self._create_translation_suggestion_with_language_code('en') self._create_translation_suggestion_with_language_code('fr') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {'en': 1, 'fr': 1}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, { 'en': 1, 'fr': 1}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual(suggestion_types_needing_reviewers, {}) def test_get_returns_no_reviewers_needed_if_enough_question_reviewers( self): user_services.allow_user_to_review_question(self.reviewer_id) self._create_question_suggestion() stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 1) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual(suggestion_types_needing_reviewers, {}) def test_get_returns_reviewers_needed_if_question_but_no_reviewers( self): self._create_question_suggestion() stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual( suggestion_types_needing_reviewers, {suggestion_models.SUGGESTION_TYPE_ADD_QUESTION: {}}) def test_get_returns_reviewers_needed_if_translation_for_a_lang_no_reviewer( self): self._create_translation_suggestion_with_language_code( self.sample_language_code) stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, { self.sample_language_code: 1}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual( suggestion_types_needing_reviewers, {suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT: { self.sample_language_code}}) def test_get_returns_reviewers_needed_if_translation_for_langs_no_reviewers( self): self._create_translation_suggestion_with_language_code('en') self._create_translation_suggestion_with_language_code('fr') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 0) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, { 'en': 1, 'fr': 1}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual( suggestion_types_needing_reviewers, {suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT: {'en', 'fr'}}) def test_get_returns_reviewers_needed_if_multi_suggestion_types_no_reviewer( self): self._create_question_suggestion() self._create_translation_suggestion_with_language_code('en') self._create_translation_suggestion_with_language_code('fr') stats = suggestion_services.get_community_contribution_stats() self.assertEqual(stats.question_reviewer_count, 0) self.assertEqual(stats.question_suggestion_count, 1) self.assertDictEqual( stats.translation_reviewer_counts_by_lang_code, {}) self.assertDictEqual( stats.translation_suggestion_counts_by_lang_code, {'en': 1, 'fr': 1}) suggestion_types_needing_reviewers = ( suggestion_services.get_suggestion_types_that_need_reviewers()) self.assertDictEqual( suggestion_types_needing_reviewers, { suggestion_models.SUGGESTION_TYPE_TRANSLATE_CONTENT: { 'en', 'fr'}, suggestion_models.SUGGESTION_TYPE_ADD_QUESTION: {} })

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077dfbe10301b4060c770e725c7c1a699cb212d3

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Python

sdk/storage/azure-storage-file-share/azure/storage/fileshare/_generated/operations/_share_operations.py

aiven/azure-sdk-for-python

8764dc07423beca46ed0b51212d81289d9e52c60

[ "MIT" ]

1

2020-03-05T18:10:35.000Z

sdk/storage/azure-storage-file-share/azure/storage/fileshare/_generated/operations/_share_operations.py

aiven/azure-sdk-for-python

8764dc07423beca46ed0b51212d81289d9e52c60

[ "MIT" ]

2

2020-03-03T23:11:13.000Z

2020-03-30T18:50:55.000Z

sdk/storage/azure-storage-file-share/azure/storage/fileshare/_generated/operations/_share_operations.py

aiven/azure-sdk-for-python

8764dc07423beca46ed0b51212d81289d9e52c60

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from azure.core.exceptions import map_error from .. import models class ShareOperations(object): """ShareOperations operations. You should not instantiate directly this class, but create a Client instance that will create it for you and attach it as attribute. :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. :ivar restype: . Constant value: "share". """ models = models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config self.restype = "share" def create(self, timeout=None, metadata=None, quota=None, access_tier=None, enabled_protocols=None, root_squash=None, cls=None, **kwargs): """Creates a new share under the specified account. If the share with the same name already exists, the operation fails. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param metadata: A name-value pair to associate with a file storage object. :type metadata: str :param quota: Specifies the maximum size of the share, in gigabytes. :type quota: int :param access_tier: Specifies the access tier of the share. Possible values include: 'TransactionOptimized', 'Hot', 'Cool' :type access_tier: str or ~azure.storage.fileshare.models.ShareAccessTier :param enabled_protocols: Protocols to enable on the share. :type enabled_protocols: str :param root_squash: Root squash to set on the share. Only valid for NFS shares. Possible values include: 'NoRootSquash', 'RootSquash', 'AllSquash' :type root_squash: str or ~azure.storage.fileshare.models.ShareRootSquash :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) # Construct URL url = self.create.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} if metadata is not None: header_parameters['x-ms-meta'] = self._serialize.header("metadata", metadata, 'str') if quota is not None: header_parameters['x-ms-share-quota'] = self._serialize.header("quota", quota, 'int', minimum=1) if access_tier is not None: header_parameters['x-ms-access-tier'] = self._serialize.header("access_tier", access_tier, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if enabled_protocols is not None: header_parameters['x-ms-enabled-protocols'] = self._serialize.header("enabled_protocols", enabled_protocols, 'str') if root_squash is not None: header_parameters['x-ms-root-squash'] = self._serialize.header("root_squash", root_squash, 'ShareRootSquash') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) create.metadata = {'url': '/{shareName}'} def get_properties(self, sharesnapshot=None, timeout=None, lease_access_conditions=None, cls=None, **kwargs): """Returns all user-defined metadata and system properties for the specified share or share snapshot. The data returned does not include the share's list of files. :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id # Construct URL url = self.get_properties.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-meta': self._deserialize('{str}', response.headers.get('x-ms-meta')), 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-share-quota': self._deserialize('int', response.headers.get('x-ms-share-quota')), 'x-ms-share-provisioned-iops': self._deserialize('int', response.headers.get('x-ms-share-provisioned-iops')), 'x-ms-share-provisioned-ingress-mbps': self._deserialize('int', response.headers.get('x-ms-share-provisioned-ingress-mbps')), 'x-ms-share-provisioned-egress-mbps': self._deserialize('int', response.headers.get('x-ms-share-provisioned-egress-mbps')), 'x-ms-share-next-allowed-quota-downgrade-time': self._deserialize('rfc-1123', response.headers.get('x-ms-share-next-allowed-quota-downgrade-time')), 'x-ms-lease-duration': self._deserialize(models.LeaseDurationType, response.headers.get('x-ms-lease-duration')), 'x-ms-lease-state': self._deserialize(models.LeaseStateType, response.headers.get('x-ms-lease-state')), 'x-ms-lease-status': self._deserialize(models.LeaseStatusType, response.headers.get('x-ms-lease-status')), 'x-ms-access-tier': self._deserialize('str', response.headers.get('x-ms-access-tier')), 'x-ms-access-tier-change-time': self._deserialize('rfc-1123', response.headers.get('x-ms-access-tier-change-time')), 'x-ms-access-tier-transition-state': self._deserialize('str', response.headers.get('x-ms-access-tier-transition-state')), 'x-ms-enabled-protocols': self._deserialize('str', response.headers.get('x-ms-enabled-protocols')), 'x-ms-root-squash': self._deserialize(models.ShareRootSquash, response.headers.get('x-ms-root-squash')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) get_properties.metadata = {'url': '/{shareName}'} def delete(self, sharesnapshot=None, timeout=None, delete_snapshots=None, lease_access_conditions=None, cls=None, **kwargs): """Operation marks the specified share or share snapshot for deletion. The share or share snapshot and any files contained within it are later deleted during garbage collection. :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param delete_snapshots: Specifies the option include to delete the base share and all of its snapshots. Possible values include: 'include', 'include-leased' :type delete_snapshots: str or ~azure.storage.fileshare.models.DeleteSnapshotsOptionType :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id # Construct URL url = self.delete.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if delete_snapshots is not None: header_parameters['x-ms-delete-snapshots'] = self._serialize.header("delete_snapshots", delete_snapshots, 'DeleteSnapshotsOptionType') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) delete.metadata = {'url': '/{shareName}'} def acquire_lease(self, timeout=None, duration=None, proposed_lease_id=None, sharesnapshot=None, request_id=None, cls=None, **kwargs): """The Lease Share operation establishes and manages a lock on a share, or the specified snapshot for set and delete share operations. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param duration: Specifies the duration of the lease, in seconds, or negative one (-1) for a lease that never expires. A non-infinite lease can be between 15 and 60 seconds. A lease duration cannot be changed using renew or change. :type duration: int :param proposed_lease_id: Proposed lease ID, in a GUID string format. The File service returns 400 (Invalid request) if the proposed lease ID is not in the correct format. See Guid Constructor (String) for a list of valid GUID string formats. :type proposed_lease_id: str :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "lease" action = "acquire" # Construct URL url = self.acquire_lease.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} if duration is not None: header_parameters['x-ms-lease-duration'] = self._serialize.header("duration", duration, 'int') if proposed_lease_id is not None: header_parameters['x-ms-proposed-lease-id'] = self._serialize.header("proposed_lease_id", proposed_lease_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-lease-action'] = self._serialize.header("action", action, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-lease-id': self._deserialize('str', response.headers.get('x-ms-lease-id')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) acquire_lease.metadata = {'url': '/{shareName}'} def release_lease(self, lease_id, timeout=None, sharesnapshot=None, request_id=None, cls=None, **kwargs): """The Lease Share operation establishes and manages a lock on a share, or the specified snapshot for set and delete share operations. :param lease_id: Specifies the current lease ID on the resource. :type lease_id: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "lease" action = "release" # Construct URL url = self.release_lease.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-lease-action'] = self._serialize.header("action", action, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) release_lease.metadata = {'url': '/{shareName}'} def change_lease(self, lease_id, timeout=None, proposed_lease_id=None, sharesnapshot=None, request_id=None, cls=None, **kwargs): """The Lease Share operation establishes and manages a lock on a share, or the specified snapshot for set and delete share operations. :param lease_id: Specifies the current lease ID on the resource. :type lease_id: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param proposed_lease_id: Proposed lease ID, in a GUID string format. The File service returns 400 (Invalid request) if the proposed lease ID is not in the correct format. See Guid Constructor (String) for a list of valid GUID string formats. :type proposed_lease_id: str :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "lease" action = "change" # Construct URL url = self.change_lease.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') if proposed_lease_id is not None: header_parameters['x-ms-proposed-lease-id'] = self._serialize.header("proposed_lease_id", proposed_lease_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-lease-action'] = self._serialize.header("action", action, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-lease-id': self._deserialize('str', response.headers.get('x-ms-lease-id')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) change_lease.metadata = {'url': '/{shareName}'} def renew_lease(self, lease_id, timeout=None, sharesnapshot=None, request_id=None, cls=None, **kwargs): """The Lease Share operation establishes and manages a lock on a share, or the specified snapshot for set and delete share operations. :param lease_id: Specifies the current lease ID on the resource. :type lease_id: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "lease" action = "renew" # Construct URL url = self.renew_lease.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-lease-action'] = self._serialize.header("action", action, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-lease-id': self._deserialize('str', response.headers.get('x-ms-lease-id')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) renew_lease.metadata = {'url': '/{shareName}'} def break_lease(self, timeout=None, break_period=None, request_id=None, sharesnapshot=None, lease_access_conditions=None, cls=None, **kwargs): """The Lease Share operation establishes and manages a lock on a share, or the specified snapshot for set and delete share operations. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param break_period: For a break operation, proposed duration the lease should continue before it is broken, in seconds, between 0 and 60. This break period is only used if it is shorter than the time remaining on the lease. If longer, the time remaining on the lease is used. A new lease will not be available before the break period has expired, but the lease may be held for longer than the break period. If this header does not appear with a break operation, a fixed-duration lease breaks after the remaining lease period elapses, and an infinite lease breaks immediately. :type break_period: int :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param sharesnapshot: The snapshot parameter is an opaque DateTime value that, when present, specifies the share snapshot to query. :type sharesnapshot: str :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "lease" action = "break" # Construct URL url = self.break_lease.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) if sharesnapshot is not None: query_parameters['sharesnapshot'] = self._serialize.query("sharesnapshot", sharesnapshot, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') # Construct headers header_parameters = {} if break_period is not None: header_parameters['x-ms-lease-break-period'] = self._serialize.header("break_period", break_period, 'int') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') header_parameters['x-ms-lease-action'] = self._serialize.header("action", action, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-lease-time': self._deserialize('int', response.headers.get('x-ms-lease-time')), 'x-ms-lease-id': self._deserialize('str', response.headers.get('x-ms-lease-id')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) break_lease.metadata = {'url': '/{shareName}'} def create_snapshot(self, timeout=None, metadata=None, cls=None, **kwargs): """Creates a read-only snapshot of a share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param metadata: A name-value pair to associate with a file storage object. :type metadata: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "snapshot" # Construct URL url = self.create_snapshot.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} if metadata is not None: header_parameters['x-ms-meta'] = self._serialize.header("metadata", metadata, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-snapshot': self._deserialize('str', response.headers.get('x-ms-snapshot')), 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) create_snapshot.metadata = {'url': '/{shareName}'} def create_permission(self, share_permission, timeout=None, cls=None, **kwargs): """Create a permission (a security descriptor). :param share_permission: A permission (a security descriptor) at the share level. :type share_permission: ~azure.storage.fileshare.models.SharePermission :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "filepermission" # Construct URL url = self.create_permission.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/json; charset=utf-8' header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') # Construct body body_content = self._serialize.body(share_permission, 'SharePermission', is_xml=False) # Construct and send request request = self._client.put(url, query_parameters, header_parameters, body_content) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-file-permission-key': self._deserialize('str', response.headers.get('x-ms-file-permission-key')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) create_permission.metadata = {'url': '/{shareName}'} def get_permission(self, file_permission_key, timeout=None, cls=None, **kwargs): """Returns the permission (security descriptor) for a given key. :param file_permission_key: Key of the permission to be set for the directory/file. :type file_permission_key: str :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param callable cls: A custom type or function that will be passed the direct response :return: SharePermission or the result of cls(response) :rtype: ~azure.storage.fileshare.models.SharePermission :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "filepermission" # Construct URL url = self.get_permission.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['Accept'] = 'application/json' header_parameters['x-ms-file-permission-key'] = self._serialize.header("file_permission_key", file_permission_key, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) header_dict = {} deserialized = None if response.status_code == 200: deserialized = self._deserialize('SharePermission', response) header_dict = { 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } if cls: return cls(response, deserialized, header_dict) return deserialized get_permission.metadata = {'url': '/{shareName}'} def set_properties(self, timeout=None, quota=None, access_tier=None, root_squash=None, lease_access_conditions=None, cls=None, **kwargs): """Sets properties for the specified share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param quota: Specifies the maximum size of the share, in gigabytes. :type quota: int :param access_tier: Specifies the access tier of the share. Possible values include: 'TransactionOptimized', 'Hot', 'Cool' :type access_tier: str or ~azure.storage.fileshare.models.ShareAccessTier :param root_squash: Root squash to set on the share. Only valid for NFS shares. Possible values include: 'NoRootSquash', 'RootSquash', 'AllSquash' :type root_squash: str or ~azure.storage.fileshare.models.ShareRootSquash :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "properties" # Construct URL url = self.set_properties.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if quota is not None: header_parameters['x-ms-share-quota'] = self._serialize.header("quota", quota, 'int', minimum=1) if access_tier is not None: header_parameters['x-ms-access-tier'] = self._serialize.header("access_tier", access_tier, 'str') if root_squash is not None: header_parameters['x-ms-root-squash'] = self._serialize.header("root_squash", root_squash, 'ShareRootSquash') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) set_properties.metadata = {'url': '/{shareName}'} def set_metadata(self, timeout=None, metadata=None, lease_access_conditions=None, cls=None, **kwargs): """Sets one or more user-defined name-value pairs for the specified share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param metadata: A name-value pair to associate with a file storage object. :type metadata: str :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "metadata" # Construct URL url = self.set_metadata.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} if metadata is not None: header_parameters['x-ms-meta'] = self._serialize.header("metadata", metadata, 'str') header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) set_metadata.metadata = {'url': '/{shareName}'} def get_access_policy(self, timeout=None, lease_access_conditions=None, cls=None, **kwargs): """Returns information about stored access policies specified on the share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: list or the result of cls(response) :rtype: list[~azure.storage.fileshare.models.SignedIdentifier] :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "acl" # Construct URL url = self.get_access_policy.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['Accept'] = 'application/xml' header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) header_dict = {} deserialized = None if response.status_code == 200: deserialized = self._deserialize('[SignedIdentifier]', response) header_dict = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } if cls: return cls(response, deserialized, header_dict) return deserialized get_access_policy.metadata = {'url': '/{shareName}'} def set_access_policy(self, share_acl=None, timeout=None, lease_access_conditions=None, cls=None, **kwargs): """Sets a stored access policy for use with shared access signatures. :param share_acl: The ACL for the share. :type share_acl: list[~azure.storage.fileshare.models.SignedIdentifier] :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "acl" # Construct URL url = self.set_access_policy.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['Content-Type'] = 'application/xml; charset=utf-8' header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct body serialization_ctxt = {'xml': {'name': 'SignedIdentifiers', 'itemsName': 'SignedIdentifier', 'wrapped': True}} if share_acl is not None: body_content = self._serialize.body(share_acl, '[SignedIdentifier]', serialization_ctxt=serialization_ctxt) else: body_content = None # Construct and send request request = self._client.put(url, query_parameters, header_parameters, body_content) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) set_access_policy.metadata = {'url': '/{shareName}'} def get_statistics(self, timeout=None, lease_access_conditions=None, cls=None, **kwargs): """Retrieves statistics related to the share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param lease_access_conditions: Additional parameters for the operation :type lease_access_conditions: ~azure.storage.fileshare.models.LeaseAccessConditions :param callable cls: A custom type or function that will be passed the direct response :return: ShareStats or the result of cls(response) :rtype: ~azure.storage.fileshare.models.ShareStats :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) lease_id = None if lease_access_conditions is not None: lease_id = lease_access_conditions.lease_id comp = "stats" # Construct URL url = self.get_statistics.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['Accept'] = 'application/xml' header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if lease_id is not None: header_parameters['x-ms-lease-id'] = self._serialize.header("lease_id", lease_id, 'str') # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) header_dict = {} deserialized = None if response.status_code == 200: deserialized = self._deserialize('ShareStats', response) header_dict = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } if cls: return cls(response, deserialized, header_dict) return deserialized get_statistics.metadata = {'url': '/{shareName}'} def restore(self, timeout=None, request_id=None, deleted_share_name=None, deleted_share_version=None, cls=None, **kwargs): """Restores a previously deleted Share. :param timeout: The timeout parameter is expressed in seconds. For more information, see <a href="https://docs.microsoft.com/en-us/rest/api/storageservices/Setting-Timeouts-for-File-Service-Operations?redirectedfrom=MSDN">Setting Timeouts for File Service Operations.</a> :type timeout: int :param request_id: Provides a client-generated, opaque value with a 1 KB character limit that is recorded in the analytics logs when storage analytics logging is enabled. :type request_id: str :param deleted_share_name: Specifies the name of the preivously-deleted share. :type deleted_share_name: str :param deleted_share_version: Specifies the version of the preivously-deleted share. :type deleted_share_version: str :param callable cls: A custom type or function that will be passed the direct response :return: None or the result of cls(response) :rtype: None :raises: :class:`StorageErrorException<azure.storage.fileshare.models.StorageErrorException>` """ error_map = kwargs.pop('error_map', None) comp = "undelete" # Construct URL url = self.restore.metadata['url'] path_format_arguments = { 'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True) } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} if timeout is not None: query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0) query_parameters['restype'] = self._serialize.query("self.restype", self.restype, 'str') query_parameters['comp'] = self._serialize.query("comp", comp, 'str') # Construct headers header_parameters = {} header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str') if request_id is not None: header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id", request_id, 'str') if deleted_share_name is not None: header_parameters['x-ms-deleted-share-name'] = self._serialize.header("deleted_share_name", deleted_share_name, 'str') if deleted_share_version is not None: header_parameters['x-ms-deleted-share-version'] = self._serialize.header("deleted_share_version", deleted_share_version, 'str') # Construct and send request request = self._client.put(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise models.StorageErrorException(response, self._deserialize) if cls: response_headers = { 'ETag': self._deserialize('str', response.headers.get('ETag')), 'Last-Modified': self._deserialize('rfc-1123', response.headers.get('Last-Modified')), 'x-ms-request-id': self._deserialize('str', response.headers.get('x-ms-request-id')), 'x-ms-client-request-id': self._deserialize('str', response.headers.get('x-ms-client-request-id')), 'x-ms-version': self._deserialize('str', response.headers.get('x-ms-version')), 'Date': self._deserialize('rfc-1123', response.headers.get('Date')), 'x-ms-error-code': self._deserialize('str', response.headers.get('x-ms-error-code')), } return cls(response, None, response_headers) restore.metadata = {'url': '/{shareName}'}

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0.043473

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0.048333

0.045604

0.907341

0.884409

0.86767

0.865613

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0.225784

72,401

1,350

165

53.63037

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0

null

0

1

0

1

0

null

0

7

079b8f98b49a5182fb021080823718b367bdc393

5,390

py

Python

tests/test_parser_delete_extracts.py

playkazoomedia/tabcmd2

a89db9be6047d95379a7c88264236e9cb3e78189

[ "MIT" ]

11

2020-09-02T03:41:01.000Z

2022-01-20T12:38:20.000Z

tests/test_parser_delete_extracts.py

playkazoomedia/tabcmd2

a89db9be6047d95379a7c88264236e9cb3e78189

[ "MIT" ]

19

2020-09-03T04:54:47.000Z

2022-01-31T17:41:19.000Z

tests/test_parser_delete_extracts.py

playkazoomedia/tabcmd2

a89db9be6047d95379a7c88264236e9cb3e78189

[ "MIT" ]

6

2020-11-21T15:45:51.000Z

2022-01-24T12:26:20.000Z

import unittest try: from unittest import mock except ImportError: import mock import argparse from pythontabcmd2.parsers.delete_extracts_parser import DeleteExtractsParser class DeleteExtractsParserTest(unittest.TestCase): @mock.patch('argparse.ArgumentParser.parse_args', return_value=argparse.Namespace(datasource="testproject", parent_project_path="abcdef", embedded_datasources='desc', url="1234", encrypt=None, project="test123", workbook="workbooktest", include_all="test", username="helloworld", site="", logging_level="info", password="testing123", no_prompt=True, token=None, token_name=None, cookie=True, no_cookie=False, prompt=False, )) def test_delete_extract_parser_optional_arguments(self, mock_args): args = DeleteExtractsParser.delete_extracts_parser() assert args == argparse.Namespace(datasource="testproject", parent_project_path="abcdef", embedded_datasources='desc', url="1234", encrypt=None, project="test123", workbook="workbooktest", include_all="test", username="helloworld", site="", logging_level="info", password="testing123", no_prompt=True, token=None, token_name=None, cookie=True, no_cookie=False, prompt=False) @mock.patch('argparse.ArgumentParser.parse_args', return_value=argparse.Namespace()) def test_delete_extract_parser_missing_all_args(self, mock_args): with self.assertRaises(AttributeError): args = DeleteExtractsParser.delete_extracts_parser() @mock.patch('argparse.ArgumentParser.parse_args', return_value=argparse.Namespace(datasource="testproject", embedded_datasources='desc', url="1234", encrypt=None, project="test123", workbook="workbooktest", include_all="test", username="helloworld", site="", logging_level="info", password="testing123", no_prompt=True, token=None, token_name=None, cookie=True, no_cookie=False, prompt=False, )) def test_delete_extract_parser_missing_project_path(self, mock_args): args = DeleteExtractsParser.delete_extracts_parser() with self.assertRaises(AssertionError): assert args != argparse.Namespace(datasource="testproject", embedded_datasources='desc', url="1234", encrypt=None, project="test123", workbook="workbooktest", include_all="test", username="helloworld", site="", logging_level="info", password="testing123", no_prompt=True, token=None, token_name=None, cookie=True, no_cookie=False, prompt=False)

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5,390

94

78

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1

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1

0

null

0

8

07a8b5e3c831808e6bc7bc89d714679657d91b6c

16,926

py

Python

src/datamigration/azext_datamigration/generated/_help.py

calvin197/azure-cli-extensions

65fac44b8dd0e1523c3866be5ec92eeb9b3a54f5

[ "MIT" ]

1

2022-02-18T00:16:47.000Z

src/datamigration/azext_datamigration/generated/_help.py

calvin197/azure-cli-extensions

65fac44b8dd0e1523c3866be5ec92eeb9b3a54f5

[ "MIT" ]

null

src/datamigration/azext_datamigration/generated/_help.py

calvin197/azure-cli-extensions

65fac44b8dd0e1523c3866be5ec92eeb9b3a54f5

[ "MIT" ]

null

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=too-many-lines from knack.help_files import helps helps['datamigration'] = ''' type: group short-summary: Manage Data Migration ''' helps['datamigration sql-managed-instance'] = """ type: group short-summary: Manage database migrations to SQL Managed Instance. """ helps['datamigration sql-managed-instance show'] = """ type: command short-summary: "Retrieve the specified database migration for a given SQL Managed Instance." examples: - name: Get Database Migration resource. text: |- az datamigration sql-managed-instance show --managed-instance-name "managedInstance1" --resource-group \ "testrg" --target-db-name "db1" """ helps['datamigration sql-managed-instance create'] = """ type: command short-summary: "Create a new database migration to a given SQL Managed Instance." parameters: - name: --source-sql-connection short-summary: "Source SQL Server connection details." long-summary: | Usage: --source-sql-connection data-source=XX authentication=XX user-name=XX password=XX \ encrypt-connection=XX trust-server-certificate=XX data-source: Data source. authentication: Authentication type. user-name: User name to connect to source SQL. password: Password to connect to source SQL. encrypt-connection: Whether to encrypt connection or not. trust-server-certificate: Whether to trust server certificate or not. - name: --offline-configuration short-summary: "Offline configuration." long-summary: | Usage: --offline-configuration offline=XX last-backup-name=XX offline: Offline migration last-backup-name: Last backup name for offline migration. This is optional for migrations from file share. \ If it is not provided, then the service will determine the last backup file name based on latest backup files present \ in file share. - name: --target-location short-summary: "Target location for copying backups." long-summary: | Usage: --target-location storage-account-resource-id=XX account-key=XX storage-account-resource-id: Resource Id of the storage account copying backups. account-key: Storage Account Key. examples: - name: Create or Update Database Migration resource with Maximum parameters. text: |- az datamigration sql-managed-instance create --managed-instance-name "managedInstance1" \ --source-location "{\\"fileShare\\":{\\"path\\":\\"C:\\\\\\\\aaa\\\\\\\\bbb\\\\\\\\ccc\\",\\"password\\":\\"placeholder\ \\",\\"username\\":\\"name\\"}}" --target-location account-key="abcd" storage-account-resource-id="account.database.win\ dows.net" --migration-service "/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/testrg/providers/Micr\ osoft.DataMigration/sqlMigrationServices/testagent" --offline-configuration last-backup-name="last_backup_file_name" \ offline=true --scope "/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/testrg/providers/Microsoft.Sql\ /managedInstances/instance" --source-database-name "aaa" --source-sql-connection authentication="WindowsAuthentication"\ data-source="aaa" encrypt-connection=true password="placeholder" trust-server-certificate=true user-name="bbb" \ --resource-group "testrg" --target-db-name "db1" - name: Create or Update Database Migration resource with Minimum parameters. text: |- az datamigration sql-managed-instance create --managed-instance-name "managedInstance1" \ --source-location "{\\"fileShare\\":{\\"path\\":\\"C:\\\\\\\\aaa\\\\\\\\bbb\\\\\\\\ccc\\",\\"password\\":\\"placeholder\ \\",\\"username\\":\\"name\\"}}" --target-location account-key="abcd" storage-account-resource-id="account.database.win\ dows.net" --migration-service "/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/testrg/providers/Micr\ osoft.DataMigration/sqlMigrationServices/testagent" --offline-configuration last-backup-name="last_backup_file_name" \ offline=true --scope "/subscriptions/00000000-1111-2222-3333-444444444444/resourceGroups/testrg/providers/Microsoft.Sql\ /managedInstances/instance" --source-database-name "aaa" --source-sql-connection authentication="WindowsAuthentication"\ data-source="aaa" encrypt-connection=true password="placeholder" trust-server-certificate=true user-name="bbb" \ --resource-group "testrg" --target-db-name "db1" """ helps['datamigration sql-managed-instance cancel'] = """ type: command short-summary: "Stop in-progress database migration to SQL Managed Instance." examples: - name: Stop ongoing migration for the database. text: |- az datamigration sql-managed-instance cancel --managed-instance-name "managedInstance1" \ --migration-operation-id "4124fe90-d1b6-4b50-b4d9-46d02381f59a" --resource-group "testrg" --target-db-name "db1" """ helps['datamigration sql-managed-instance cutover'] = """ type: command short-summary: "Initiate cutover for in-progress online database migration to SQL Managed Instance." examples: - name: Cutover online migration operation for the database. text: |- az datamigration sql-managed-instance cutover --managed-instance-name "managedInstance1" \ --migration-operation-id "4124fe90-d1b6-4b50-b4d9-46d02381f59a" --resource-group "testrg" --target-db-name "db1" """ helps['datamigration sql-managed-instance wait'] = """ type: command short-summary: Place the CLI in a waiting state until a condition of the datamigration sql-managed-instance is \ met. examples: - name: Pause executing next line of CLI script until the datamigration sql-managed-instance is successfully \ created. text: |- az datamigration sql-managed-instance wait --managed-instance-name "managedInstance1" --resource-group \ "testrg" --target-db-name "db1" --created """ helps['datamigration sql-vm'] = """ type: group short-summary: Manage database migrations to SQL VM. """ helps['datamigration sql-vm show'] = """ type: command short-summary: "Retrieve the specified database migration for a given SQL VM." examples: - name: Get Database Migration resource. text: |- az datamigration sql-vm show --resource-group "testrg" --sql-vm-name "testvm" --target-db-name "db1" """ helps['datamigration sql-vm create'] = """ type: command short-summary: "Create a new database migration to a given SQL VM." parameters: - name: --source-sql-connection short-summary: "Source SQL Server connection details." long-summary: | Usage: --source-sql-connection data-source=XX authentication=XX user-name=XX password=XX \ encrypt-connection=XX trust-server-certificate=XX data-source: Data source. authentication: Authentication type. user-name: User name to connect to source SQL. password: Password to connect to source SQL. encrypt-connection: Whether to encrypt connection or not. trust-server-certificate: Whether to trust server certificate or not. - name: --offline-configuration short-summary: "Offline configuration." long-summary: | Usage: --offline-configuration offline=XX last-backup-name=XX offline: Offline migration last-backup-name: Last backup name for offline migration. This is optional for migrations from file share. \ If it is not provided, then the service will determine the last backup file name based on latest backup files present \ in file share. - name: --target-location short-summary: "Target location for copying backups." long-summary: | Usage: --target-location storage-account-resource-id=XX account-key=XX storage-account-resource-id: Resource Id of the storage account copying backups. account-key: Storage Account Key. examples: - name: Create or Update Database Migration resource with Maximum parameters. text: |- az datamigration sql-vm create --source-location "{\\"fileShare\\":{\\"path\\":\\"C:\\\\\\\\aaa\\\\\\\\b\ bb\\\\\\\\ccc\\",\\"password\\":\\"placeholder\\",\\"username\\":\\"name\\"}}" --target-location account-key="abcd" \ storage-account-resource-id="account.database.windows.net" --migration-service "/subscriptions/00000000-1111-2222-3333-\ 444444444444/resourceGroups/testrg/providers/Microsoft.DataMigration/sqlMigrationServices/testagent" \ --offline-configuration last-backup-name="last_backup_file_name" offline=true --scope "/subscriptions/00000000-1111-222\ 2-3333-444444444444/resourceGroups/testrg/providers/Microsoft.SqlVirtualMachine/sqlVirtualMachines/testvm" \ --source-database-name "aaa" --source-sql-connection authentication="WindowsAuthentication" data-source="aaa" \ encrypt-connection=true password="placeholder" trust-server-certificate=true user-name="bbb" --resource-group "testrg" \ --sql-vm-name "testvm" --target-db-name "db1" - name: Create or Update Database Migration resource with Minimum parameters. text: |- az datamigration sql-vm create --source-location "{\\"fileShare\\":{\\"path\\":\\"C:\\\\\\\\aaa\\\\\\\\b\ bb\\\\\\\\ccc\\",\\"password\\":\\"placeholder\\",\\"username\\":\\"name\\"}}" --target-location account-key="abcd" \ storage-account-resource-id="account.database.windows.net" --migration-service "/subscriptions/00000000-1111-2222-3333-\ 444444444444/resourceGroups/testrg/providers/Microsoft.DataMigration/sqlMigrationServices/testagent" \ --offline-configuration last-backup-name="last_backup_file_name" offline=true --scope "/subscriptions/00000000-1111-222\ 2-3333-444444444444/resourceGroups/testrg/providers/Microsoft.SqlVirtualMachine/sqlVirtualMachines/testvm" \ --source-database-name "aaa" --source-sql-connection authentication="WindowsAuthentication" data-source="aaa" \ encrypt-connection=true password="placeholder" trust-server-certificate=true user-name="bbb" --resource-group "testrg" \ --sql-vm-name "testvm" --target-db-name "db1" """ helps['datamigration sql-vm cancel'] = """ type: command short-summary: "Stop in-progress database migration to SQL VM." examples: - name: Stop ongoing migration for the database. text: |- az datamigration sql-vm cancel --migration-operation-id "4124fe90-d1b6-4b50-b4d9-46d02381f59a" \ --resource-group "testrg" --sql-vm-name "testvm" --target-db-name "db1" """ helps['datamigration sql-vm cutover'] = """ type: command short-summary: "Initiate cutover for in-progress online database migration to SQL VM." examples: - name: Cutover online migration operation for the database. text: |- az datamigration sql-vm cutover --migration-operation-id "4124fe90-d1b6-4b50-b4d9-46d02381f59a" \ --resource-group "testrg" --sql-vm-name "testvm" --target-db-name "db1" """ helps['datamigration sql-vm wait'] = """ type: command short-summary: Place the CLI in a waiting state until a condition of the datamigration sql-vm is met. examples: - name: Pause executing next line of CLI script until the datamigration sql-vm is successfully created. text: |- az datamigration sql-vm wait --resource-group "testrg" --sql-vm-name "testvm" --target-db-name "db1" \ --created """ helps['datamigration sql-service'] = """ type: group short-summary: Manage Database Migration Service. """ helps['datamigration sql-service list'] = """ type: command short-summary: "Retrieve all Database Migration Services in the resource group. And Retrieve all Database \ Migration Services in the subscription." examples: - name: Get Migration Services in the Resource Group. text: |- az datamigration sql-service list --resource-group "testrg" - name: Get Services in the Subscriptions. text: |- az datamigration sql-service list """ helps['datamigration sql-service show'] = """ type: command short-summary: "Retrieve the Database Migration Service." examples: - name: Get Migration Service. text: |- az datamigration sql-service show --resource-group "testrg" --name "service1" """ helps['datamigration sql-service create'] = """ type: command short-summary: "Create Database Migration Service." examples: - name: Create or Update SQL Migration Service with maximum parameters. text: |- az datamigration sql-service create --location "northeurope" --resource-group "testrg" --name \ "testagent" - name: Create or Update SQL Migration Service with minimum parameters. text: |- az datamigration sql-service create --location "northeurope" --resource-group "testrg" --name \ "testagent" """ helps['datamigration sql-service update'] = """ type: command short-summary: "Update Database Migration Service." examples: - name: Update SQL Migration Service. text: |- az datamigration sql-service update --tags mytag="myval" --resource-group "testrg" --name "testagent" """ helps['datamigration sql-service delete'] = """ type: command short-summary: "Delete Database Migration Service." examples: - name: Delete SQL Migration Service. text: |- az datamigration sql-service delete --resource-group "testrg" --name "service1" """ helps['datamigration sql-service delete-node'] = """ type: command short-summary: "Delete the integration runtime node." examples: - name: Delete the integration runtime node. text: |- az datamigration sql-service delete-node --ir-name "IRName" --node-name "nodeName" --resource-group \ "testrg" --name "service1" """ helps['datamigration sql-service list-auth-key'] = """ type: command short-summary: "Retrieve the List of Authentication Keys for Self Hosted Integration Runtime." examples: - name: Retrieve the List of Authentication Keys. text: |- az datamigration sql-service list-auth-key --resource-group "testrg" --name "service1" """ helps['datamigration sql-service list-integration-runtime-metric'] = """ type: command short-summary: "Retrieve the registered Integration Runtine nodes and their monitoring data for a given Database \ Migration Service." examples: - name: Retrieve the Monitoring Data. text: |- az datamigration sql-service list-integration-runtime-metric --resource-group "testrg" --name \ "service1" """ helps['datamigration sql-service list-migration'] = """ type: command short-summary: "Retrieve the List of database migrations attached to the service." examples: - name: List database migrations attached to the service. text: |- az datamigration sql-service list-migration --resource-group "testrg" --name "service1" """ helps['datamigration sql-service regenerate-auth-key'] = """ type: command short-summary: "Regenerate a new set of Authentication Keys for Self Hosted Integration Runtime." examples: - name: Regenerate the of Authentication Keys. text: |- az datamigration sql-service regenerate-auth-key --key-name "authKey1" --resource-group "testrg" --name \ "service1" """ helps['datamigration sql-service wait'] = """ type: command short-summary: Place the CLI in a waiting state until a condition of the datamigration sql-service is met. examples: - name: Pause executing next line of CLI script until the datamigration sql-service is successfully created. text: |- az datamigration sql-service wait --resource-group "testrg" --name "service1" --created - name: Pause executing next line of CLI script until the datamigration sql-service is successfully updated. text: |- az datamigration sql-service wait --resource-group "testrg" --name "service1" --updated - name: Pause executing next line of CLI script until the datamigration sql-service is successfully deleted. text: |- az datamigration sql-service wait --resource-group "testrg" --name "service1" --deleted """

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Python

src/utils/bounding_box_utils.py

aashish2000/Head-Tracking

7092263b4f9d99d98f605888eb9cf81ad407adcb

[ "MIT" ]

1

2022-02-16T20:23:30.000Z

SSD/bounding_box_utils/bounding_box_utils.py

labITS-stt-eesc/Axle_Detection

ea8ed4557c3319dfb125f6c62b7c5de41a922133

[ "MIT" ]

2

2021-03-23T12:19:07.000Z

2021-06-24T14:34:58.000Z

SSD/bounding_box_utils/bounding_box_utils.py

labITS-stt-eesc/Axle_Detection

ea8ed4557c3319dfb125f6c62b7c5de41a922133

[ "MIT" ]

null

''' Includes: * Function to compute the IoU similarity for axis-aligned, rectangular, 2D bounding boxes * Function for coordinate conversion for axis-aligned, rectangular, 2D bounding boxes Copyright (C) 2018 Pierluigi Ferrari Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' from __future__ import division import numpy as np def convert_coordinates(tensor, start_index, conversion, border_pixels='half'): ''' Convert coordinates for axis-aligned 2D boxes between two coordinate formats. Creates a copy of `tensor`, i.e. does not operate in place. Currently there are three supported coordinate formats that can be converted from and to each other: 1) (xmin, xmax, ymin, ymax) - the 'minmax' format 2) (xmin, ymin, xmax, ymax) - the 'corners' format 2) (cx, cy, w, h) - the 'centroids' format Arguments: tensor (array): A Numpy nD array containing the four consecutive coordinates to be converted somewhere in the last axis. start_index (int): The index of the first coordinate in the last axis of `tensor`. conversion (str, optional): The conversion direction. Can be 'minmax2centroids', 'centroids2minmax', 'corners2centroids', 'centroids2corners', 'minmax2corners', or 'corners2minmax'. border_pixels (str, optional): How to treat the border pixels of the bounding boxes. Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong to the boxes. If 'exclude', the border pixels do not belong to the boxes. If 'half', then one of each of the two horizontal and vertical borders belong to the boxex, but not the other. Returns: A Numpy nD array, a copy of the input tensor with the converted coordinates in place of the original coordinates and the unaltered elements of the original tensor elsewhere. ''' if border_pixels == 'half': d = 0 elif border_pixels == 'include': d = 1 elif border_pixels == 'exclude': d = -1 ind = start_index tensor1 = np.copy(tensor).astype(np.float) if conversion == 'minmax2centroids': tensor1[..., ind] = (tensor[..., ind] + tensor[..., ind+1]) / 2.0 # Set cx tensor1[..., ind+1] = (tensor[..., ind+2] + tensor[..., ind+3]) / 2.0 # Set cy tensor1[..., ind+2] = tensor[..., ind+1] - tensor[..., ind] + d # Set w tensor1[..., ind+3] = tensor[..., ind+3] - tensor[..., ind+2] + d # Set h elif conversion == 'centroids2minmax': tensor1[..., ind] = tensor[..., ind] - tensor[..., ind+2] / 2.0 # Set xmin tensor1[..., ind+1] = tensor[..., ind] + tensor[..., ind+2] / 2.0 # Set xmax tensor1[..., ind+2] = tensor[..., ind+1] - tensor[..., ind+3] / 2.0 # Set ymin tensor1[..., ind+3] = tensor[..., ind+1] + tensor[..., ind+3] / 2.0 # Set ymax elif conversion == 'corners2centroids': tensor1[..., ind] = (tensor[..., ind] + tensor[..., ind+2]) / 2.0 # Set cx tensor1[..., ind+1] = (tensor[..., ind+1] + tensor[..., ind+3]) / 2.0 # Set cy tensor1[..., ind+2] = tensor[..., ind+2] - tensor[..., ind] + d # Set w tensor1[..., ind+3] = tensor[..., ind+3] - tensor[..., ind+1] + d # Set h elif conversion == 'centroids2corners': tensor1[..., ind] = tensor[..., ind] - tensor[..., ind+2] / 2.0 # Set xmin tensor1[..., ind+1] = tensor[..., ind+1] - tensor[..., ind+3] / 2.0 # Set ymin tensor1[..., ind+2] = tensor[..., ind] + tensor[..., ind+2] / 2.0 # Set xmax tensor1[..., ind+3] = tensor[..., ind+1] + tensor[..., ind+3] / 2.0 # Set ymax elif (conversion == 'minmax2corners') or (conversion == 'corners2minmax'): tensor1[..., ind+1] = tensor[..., ind+2] tensor1[..., ind+2] = tensor[..., ind+1] else: raise ValueError("Unexpected conversion value. Supported values are 'minmax2centroids', 'centroids2minmax', 'corners2centroids', 'centroids2corners', 'minmax2corners', and 'corners2minmax'.") return tensor1 def convert_coordinates2(tensor, start_index, conversion): ''' A matrix multiplication implementation of `convert_coordinates()`. Supports only conversion between the 'centroids' and 'minmax' formats. This function is marginally slower on average than `convert_coordinates()`, probably because it involves more (unnecessary) arithmetic operations (unnecessary because the two matrices are sparse). For details please refer to the documentation of `convert_coordinates()`. ''' ind = start_index tensor1 = np.copy(tensor).astype(np.float) if conversion == 'minmax2centroids': M = np.array([[0.5, 0. , -1., 0.], [0.5, 0. , 1., 0.], [0. , 0.5, 0., -1.], [0. , 0.5, 0., 1.]]) tensor1[..., ind:ind+4] = np.dot(tensor1[..., ind:ind+4], M) elif conversion == 'centroids2minmax': M = np.array([[ 1. , 1. , 0. , 0. ], [ 0. , 0. , 1. , 1. ], [-0.5, 0.5, 0. , 0. ], [ 0. , 0. , -0.5, 0.5]]) # The multiplicative inverse of the matrix above tensor1[..., ind:ind+4] = np.dot(tensor1[..., ind:ind+4], M) else: raise ValueError("Unexpected conversion value. Supported values are 'minmax2centroids' and 'centroids2minmax'.") return tensor1 def intersection_area(boxes1, boxes2, coords='centroids', mode='outer_product', border_pixels='half'): ''' Computes the intersection areas of two sets of axis-aligned 2D rectangular boxes. Let `boxes1` and `boxes2` contain `m` and `n` boxes, respectively. In 'outer_product' mode, returns an `(m,n)` matrix with the intersection areas for all possible combinations of the boxes in `boxes1` and `boxes2`. In 'element-wise' mode, `m` and `n` must be broadcast-compatible. Refer to the explanation of the `mode` argument for details. Arguments: boxes1 (array): Either a 1D Numpy array of shape `(4, )` containing the coordinates for one box in the format specified by `coords` or a 2D Numpy array of shape `(m, 4)` containing the coordinates for `m` boxes. If `mode` is set to 'element_wise', the shape must be broadcast-compatible with `boxes2`. boxes2 (array): Either a 1D Numpy array of shape `(4, )` containing the coordinates for one box in the format specified by `coords` or a 2D Numpy array of shape `(n, 4)` containing the coordinates for `n` boxes. If `mode` is set to 'element_wise', the shape must be broadcast-compatible with `boxes1`. coords (str, optional): The coordinate format in the input arrays. Can be either 'centroids' for the format `(cx, cy, w, h)`, 'minmax' for the format `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`. mode (str, optional): Can be one of 'outer_product' and 'element-wise'. In 'outer_product' mode, returns an `(m,n)` matrix with the intersection areas for all possible combinations of the `m` boxes in `boxes1` with the `n` boxes in `boxes2`. In 'element-wise' mode, returns a 1D array and the shapes of `boxes1` and `boxes2` must be boadcast-compatible. If both `boxes1` and `boxes2` have `m` boxes, then this returns an array of length `m` where the i-th position contains the intersection area of `boxes1[i]` with `boxes2[i]`. border_pixels (str, optional): How to treat the border pixels of the bounding boxes. Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong to the boxes. If 'exclude', the border pixels do not belong to the boxes. If 'half', then one of each of the two horizontal and vertical borders belong to the boxex, but not the other. Returns: A 1D or 2D Numpy array (refer to the `mode` argument for details) of dtype float containing values with the intersection areas of the boxes in `boxes1` and `boxes2`. ''' # Make sure the boxes have the right shapes. if boxes1.ndim > 2: raise ValueError("boxes1 must have rank either 1 or 2, but has rank {}.".format(boxes1.ndim)) if boxes2.ndim > 2: raise ValueError("boxes2 must have rank either 1 or 2, but has rank {}.".format(boxes2.ndim)) if boxes1.ndim == 1: boxes1 = np.expand_dims(boxes1, axis=0) if boxes2.ndim == 1: boxes2 = np.expand_dims(boxes2, axis=0) if not (boxes1.shape[1] == boxes2.shape[1] == 4): raise ValueError("All boxes must consist of 4 coordinates, but the boxes in `boxes1` and `boxes2` have {} and {} coordinates, respectively.".format(boxes1.shape[1], boxes2.shape[1])) if not mode in {'outer_product', 'element-wise'}: raise ValueError("`mode` must be one of 'outer_product' and 'element-wise', but got '{}'.",format(mode)) # Convert the coordinates if necessary. if coords == 'centroids': boxes1 = convert_coordinates(boxes1, start_index=0, conversion='centroids2corners') boxes2 = convert_coordinates(boxes2, start_index=0, conversion='centroids2corners') coords = 'corners' elif not (coords in {'minmax', 'corners'}): raise ValueError("Unexpected value for `coords`. Supported values are 'minmax', 'corners' and 'centroids'.") m = boxes1.shape[0] # The number of boxes in `boxes1` n = boxes2.shape[0] # The number of boxes in `boxes2` # Set the correct coordinate indices for the respective formats. if coords == 'corners': xmin = 0 ymin = 1 xmax = 2 ymax = 3 elif coords == 'minmax': xmin = 0 xmax = 1 ymin = 2 ymax = 3 if border_pixels == 'half': d = 0 elif border_pixels == 'include': d = 1 # If border pixels are supposed to belong to the bounding boxes, we have to add one pixel to any difference `xmax - xmin` or `ymax - ymin`. elif border_pixels == 'exclude': d = -1 # If border pixels are not supposed to belong to the bounding boxes, we have to subtract one pixel from any difference `xmax - xmin` or `ymax - ymin`. # Compute the intersection areas. if mode == 'outer_product': # For all possible box combinations, get the greater xmin and ymin values. # This is a tensor of shape (m,n,2). min_xy = np.maximum(np.tile(np.expand_dims(boxes1[:,[xmin,ymin]], axis=1), reps=(1, n, 1)), np.tile(np.expand_dims(boxes2[:,[xmin,ymin]], axis=0), reps=(m, 1, 1))) # For all possible box combinations, get the smaller xmax and ymax values. # This is a tensor of shape (m,n,2). max_xy = np.minimum(np.tile(np.expand_dims(boxes1[:,[xmax,ymax]], axis=1), reps=(1, n, 1)), np.tile(np.expand_dims(boxes2[:,[xmax,ymax]], axis=0), reps=(m, 1, 1))) # Compute the side lengths of the intersection rectangles. side_lengths = np.maximum(0, max_xy - min_xy + d) return side_lengths[:,:,0] * side_lengths[:,:,1] elif mode == 'element-wise': min_xy = np.maximum(boxes1[:,[xmin,ymin]], boxes2[:,[xmin,ymin]]) max_xy = np.minimum(boxes1[:,[xmax,ymax]], boxes2[:,[xmax,ymax]]) # Compute the side lengths of the intersection rectangles. side_lengths = np.maximum(0, max_xy - min_xy + d) return side_lengths[:,0] * side_lengths[:,1] def intersection_area_(boxes1, boxes2, coords='corners', mode='outer_product', border_pixels='half'): ''' The same as 'intersection_area()' but for internal use, i.e. without all the safety checks. ''' m = boxes1.shape[0] # The number of boxes in `boxes1` n = boxes2.shape[0] # The number of boxes in `boxes2` # Set the correct coordinate indices for the respective formats. if coords == 'corners': xmin = 0 ymin = 1 xmax = 2 ymax = 3 elif coords == 'minmax': xmin = 0 xmax = 1 ymin = 2 ymax = 3 if border_pixels == 'half': d = 0 elif border_pixels == 'include': d = 1 # If border pixels are supposed to belong to the bounding boxes, we have to add one pixel to any difference `xmax - xmin` or `ymax - ymin`. elif border_pixels == 'exclude': d = -1 # If border pixels are not supposed to belong to the bounding boxes, we have to subtract one pixel from any difference `xmax - xmin` or `ymax - ymin`. # Compute the intersection areas. if mode == 'outer_product': # For all possible box combinations, get the greater xmin and ymin values. # This is a tensor of shape (m,n,2). min_xy = np.maximum(np.tile(np.expand_dims(boxes1[:,[xmin,ymin]], axis=1), reps=(1, n, 1)), np.tile(np.expand_dims(boxes2[:,[xmin,ymin]], axis=0), reps=(m, 1, 1))) # For all possible box combinations, get the smaller xmax and ymax values. # This is a tensor of shape (m,n,2). max_xy = np.minimum(np.tile(np.expand_dims(boxes1[:,[xmax,ymax]], axis=1), reps=(1, n, 1)), np.tile(np.expand_dims(boxes2[:,[xmax,ymax]], axis=0), reps=(m, 1, 1))) # Compute the side lengths of the intersection rectangles. side_lengths = np.maximum(0, max_xy - min_xy + d) return side_lengths[:,:,0] * side_lengths[:,:,1] elif mode == 'element-wise': min_xy = np.maximum(boxes1[:,[xmin,ymin]], boxes2[:,[xmin,ymin]]) max_xy = np.minimum(boxes1[:,[xmax,ymax]], boxes2[:,[xmax,ymax]]) # Compute the side lengths of the intersection rectangles. side_lengths = np.maximum(0, max_xy - min_xy + d) return side_lengths[:,0] * side_lengths[:,1] def iou(boxes1, boxes2, coords='centroids', mode='outer_product', border_pixels='half'): ''' Computes the intersection-over-union similarity (also known as Jaccard similarity) of two sets of axis-aligned 2D rectangular boxes. Let `boxes1` and `boxes2` contain `m` and `n` boxes, respectively. In 'outer_product' mode, returns an `(m,n)` matrix with the IoUs for all possible combinations of the boxes in `boxes1` and `boxes2`. In 'element-wise' mode, `m` and `n` must be broadcast-compatible. Refer to the explanation of the `mode` argument for details. Arguments: boxes1 (array): Either a 1D Numpy array of shape `(4, )` containing the coordinates for one box in the format specified by `coords` or a 2D Numpy array of shape `(m, 4)` containing the coordinates for `m` boxes. If `mode` is set to 'element_wise', the shape must be broadcast-compatible with `boxes2`. boxes2 (array): Either a 1D Numpy array of shape `(4, )` containing the coordinates for one box in the format specified by `coords` or a 2D Numpy array of shape `(n, 4)` containing the coordinates for `n` boxes. If `mode` is set to 'element_wise', the shape must be broadcast-compatible with `boxes1`. coords (str, optional): The coordinate format in the input arrays. Can be either 'centroids' for the format `(cx, cy, w, h)`, 'minmax' for the format `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`. mode (str, optional): Can be one of 'outer_product' and 'element-wise'. In 'outer_product' mode, returns an `(m,n)` matrix with the IoU overlaps for all possible combinations of the `m` boxes in `boxes1` with the `n` boxes in `boxes2`. In 'element-wise' mode, returns a 1D array and the shapes of `boxes1` and `boxes2` must be boadcast-compatible. If both `boxes1` and `boxes2` have `m` boxes, then this returns an array of length `m` where the i-th position contains the IoU overlap of `boxes1[i]` with `boxes2[i]`. border_pixels (str, optional): How to treat the border pixels of the bounding boxes. Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong to the boxes. If 'exclude', the border pixels do not belong to the boxes. If 'half', then one of each of the two horizontal and vertical borders belong to the boxex, but not the other. Returns: A 1D or 2D Numpy array (refer to the `mode` argument for details) of dtype float containing values in [0,1], the Jaccard similarity of the boxes in `boxes1` and `boxes2`. 0 means there is no overlap between two given boxes, 1 means their coordinates are identical. ''' # Make sure the boxes have the right shapes. if boxes1.ndim > 2: raise ValueError("boxes1 must have rank either 1 or 2, but has rank {}.".format(boxes1.ndim)) if boxes2.ndim > 2: raise ValueError("boxes2 must have rank either 1 or 2, but has rank {}.".format(boxes2.ndim)) if boxes1.ndim == 1: boxes1 = np.expand_dims(boxes1, axis=0) if boxes2.ndim == 1: boxes2 = np.expand_dims(boxes2, axis=0) if not (boxes1.shape[1] == boxes2.shape[1] == 4): raise ValueError("All boxes must consist of 4 coordinates, but the boxes in `boxes1` and `boxes2` have {} and {} coordinates, respectively.".format(boxes1.shape[1], boxes2.shape[1])) if not mode in {'outer_product', 'element-wise'}: raise ValueError("`mode` must be one of 'outer_product' and 'element-wise', but got '{}'.".format(mode)) # Convert the coordinates if necessary. if coords == 'centroids': boxes1 = convert_coordinates(boxes1, start_index=0, conversion='centroids2corners') boxes2 = convert_coordinates(boxes2, start_index=0, conversion='centroids2corners') coords = 'corners' elif not (coords in {'minmax', 'corners'}): raise ValueError("Unexpected value for `coords`. Supported values are 'minmax', 'corners' and 'centroids'.") # Compute the IoU. # Compute the interesection areas. intersection_areas = intersection_area_(boxes1, boxes2, coords=coords, mode=mode) m = boxes1.shape[0] # The number of boxes in `boxes1` n = boxes2.shape[0] # The number of boxes in `boxes2` # Compute the union areas. # Set the correct coordinate indices for the respective formats. if coords == 'corners': xmin = 0 ymin = 1 xmax = 2 ymax = 3 elif coords == 'minmax': xmin = 0 xmax = 1 ymin = 2 ymax = 3 if border_pixels == 'half': d = 0 elif border_pixels == 'include': d = 1 # If border pixels are supposed to belong to the bounding boxes, we have to add one pixel to any difference `xmax - xmin` or `ymax - ymin`. elif border_pixels == 'exclude': d = -1 # If border pixels are not supposed to belong to the bounding boxes, we have to subtract one pixel from any difference `xmax - xmin` or `ymax - ymin`. if mode == 'outer_product': boxes1_areas = np.tile(np.expand_dims((boxes1[:,xmax] - boxes1[:,xmin] + d) * (boxes1[:,ymax] - boxes1[:,ymin] + d), axis=1), reps=(1,n)) boxes2_areas = np.tile(np.expand_dims((boxes2[:,xmax] - boxes2[:,xmin] + d) * (boxes2[:,ymax] - boxes2[:,ymin] + d), axis=0), reps=(m,1)) elif mode == 'element-wise': boxes1_areas = (boxes1[:,xmax] - boxes1[:,xmin] + d) * (boxes1[:,ymax] - boxes1[:,ymin] + d) boxes2_areas = (boxes2[:,xmax] - boxes2[:,xmin] + d) * (boxes2[:,ymax] - boxes2[:,ymin] + d) union_areas = boxes1_areas + boxes2_areas - intersection_areas return intersection_areas / union_areas

52.4047

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0.631857

2,833

20,071

4.434169

0.106601

0.032479

0.013135

0.011384

0.806082

0.784986

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0.242091

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52.4047

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null

0

1

0

1

0

null

0

7

ed41f3cd70fa1dd4c9e784b84412f66d6746fcd5

4,935

py

Python

src/ralph/discovery/tests/plugins/samples/donpedro.py

jjagodzinski/ralph

000a22bcc934dc2051e7a09ab1e84bd1c25a9e73

[ "Apache-2.0" ]

null

src/ralph/discovery/tests/plugins/samples/donpedro.py

jjagodzinski/ralph

000a22bcc934dc2051e7a09ab1e84bd1c25a9e73

[ "Apache-2.0" ]

null

src/ralph/discovery/tests/plugins/samples/donpedro.py

jjagodzinski/ralph

000a22bcc934dc2051e7a09ab1e84bd1c25a9e73

[ "Apache-2.0" ]

null

data = """{"data":{"storage": [{"sn":"03da1030-f25b-47","mountpoint":"C:","size":"40957","label":"XENSRC PVDISK SCSI Disk Device"}], "ethernets": [{"mac":"6A:77:98:51:05:13","speed":"1000000000","label":"Citrix PV Ethernet Adapter #0","ipaddress":"10.100.0.10"}], "memory": [{"size":"3068","speed":"","index":"DIMM 0","sn":"","caption":"Physical Memory","label":"Physical Memory"}], "operating_system": {"memory":"3067","storage":"40957","corescount":"2","label":"Microsoft Windows Server 2008 R2 Standard"}, "processors": [{"speed":"2667","cores":"1","index":"CPU0","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"},{"speed":"2667","cores":"1","index":"CPU1","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"}], "device": {"sn":"6ddaaa4a-dc00-de38-e683-da037fd729ca","caption":"Computer System Product","version":"4.1.2","vendor":"Xen","label":"HVM domU"}, "fcs": [{"physicalid":"0","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"},{"physicalid":"1","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"}], "shares": [{"volume":"3PARdata VV Multi-Path Disk Device","sn":"25D304C1","label":"3PARdata VV Multi-Path Disk Device"}, {"volume":"3PARdata VV Multi-Path Disk Device","sn":"80C804C1","label":"3PARdata VV Multi-Path Disk Device"}], "software":[{"vendor":"Vendor 1","label":"Soft 1","version":"1.2.33"}, {"vendor":"Vendor 2","label":"Soft 2","version":"0.8.99"}] }} """ incomplete_data = """{"data":{"storage": [{"sn":"03da1030-f25b-47","mountpoint":"C:","size":"40957","label":"XENSRC PVDISK SCSI Disk Device"}], "ethernets": [], "memory": [{"size":"3068","speed":"","index":"DIMM 0","sn":"","caption":"Physical Memory","label":"Physical Memory"}], "operating_system": {"memory":"3067","storage":"40957","corescount":"8","label":"Microsoft Windows Server 2008 R2 Standard"}, "processors": [{"speed":"2667","cores":"4","index":"CPU0","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"},{"speed":"2667","cores":"4","index":"CPU1","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"}], "device": {"sn":"","caption":"Computer System Product","version":"4.1.2","vendor":"Test 1","label":"HVM domU"}, "fcs": [{"physicalid":"0","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"},{"physicalid":"1","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"}], "shares": [{"volume":"3PARdata VV Multi-Path Disk Device","sn":"25D304C1","label":"3PARdata VV Multi-Path Disk Device"}, {"volume":"3PARdata VV Multi-Path Disk Device","sn":"80C804C1","label":"3PARdata VV Multi-Path Disk Device"}] }} """ no_eth_data = """{"data":{"storage": [{"sn":"03da1030-f25b-48","mountpoint":"C:","size":"40957","label":"XENSRC PVDISK SCSI Disk Device"}], "ethernets": [], "memory": [{"size":"3068","speed":"","index":"DIMM 0","sn":"","caption":"Physical Memory","label":"Physical Memory"}], "operating_system": {"memory":"3067","storage":"40957","corescount":"8","label":"Microsoft Windows Server 2008 R2 Standard"}, "processors": [{"speed":"2667","cores":"4","index":"CPU0","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"},{"speed":"2667","cores":"4","index":"CPU1","description":"Intel64 Family 6 Model 44 Stepping 2","numberoflogicalprocessors":"1","caption":"Intel64 Family 6 Model 44 Stepping 2","label":"Intel(R) Xeon(R) CPU E5640 @ 2.67GHz"}], "device": {"sn":"7ddaaa4a-dc00-de38-e683-da037fd729ac","caption":"Computer System Product","version":"4.1.2","vendor":"Test 2","label":"HVM domU"}, "fcs": [{"physicalid":"0","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"},{"physicalid":"1","model":"QMH2462","sn":"MY572520SK","manufacturer":"QLogic Corporation","label":"QLogic QMH2462 Fibre Channel Adapter"}], "shares": [{"volume":"3PARdata VV Multi-Path Disk Device","sn":"25D304C2","label":"3PARdata VV Multi-Path Disk Device"}, {"volume":"3PARdata VV Multi-Path Disk Device","sn":"80C804C2","label":"3PARdata VV Multi-Path Disk Device"}] }} """

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0.66464

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4,935

5.155906

0.181102

0.045816

0.051313

0.06964

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0.888821

0

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null

0

1

0

1

0

1

null

0

10

71f4054d31efdcfd0220f8bcfdd5726d2c0f20ec

178

py

Python

events/contrib/plugins/form_handlers/db_store/urls/__init__.py

mansonul/events

4f6ca37bc600dcba3f74400d299826882d53b7d2

[ "MIT" ]

null

events/contrib/plugins/form_handlers/db_store/urls/__init__.py

mansonul/events

4f6ca37bc600dcba3f74400d299826882d53b7d2

[ "MIT" ]

null

events/contrib/plugins/form_handlers/db_store/urls/__init__.py

mansonul/events

4f6ca37bc600dcba3f74400d299826882d53b7d2

[ "MIT" ]

null

# Only import from .form_handlers module to ensure backwards compatibility. # Importing from .form_wizard_handlers module should be done explicitly. from .form_handlers import *

44.5

75

0.825843

24

178

5.958333

0.666667

0.167832

0.223776

0

0.129213

178

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0.808989

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null

0

1

0

1

0

1

0

7

9c3301d2e804e5ed406339cee7dd12db606c7cf4

3,235

py

Python

covgen/localsearch/trace.py

greenmonn/sbst-input-generator

3d3234af94a39ca2e3467431b84269e15f31db6f

[ "MIT" ]

7

2019-05-16T06:58:44.000Z

2020-08-08T09:22:15.000Z

covgen/localsearch/trace.py

greenmonn/sbst-input-generator

3d3234af94a39ca2e3467431b84269e15f31db6f

[ "MIT" ]

null

covgen/localsearch/trace.py

greenmonn/sbst-input-generator

3d3234af94a39ca2e3467431b84269e15f31db6f

[ "MIT" ]

null

import covgen.localsearch.distance_functions as df class Trace(): def __init__(self): self.executed_branches = [] def get_executed_branches(self): return self.executed_branches def is_true(self, id, exp): result = self.equals(id, exp, True) distance_to_alternative = 0 if result: distance_to_alternative = df.equals_bool(exp, False) else: distance_to_alternative = df.equals_bool(exp, True) self.executed_branches.append((id, result, distance_to_alternative)) return result def is_false(self, id, exp): result = self.equals(id, exp, False) distance_to_alternative = 0 if result: distance_to_alternative = df.equals_bool(exp, True) else: distance_to_alternative = df.equals_bool(exp, False) self.executed_branches.append((id, result, distance_to_alternative)) return result def equals(self, id, lhs, rhs): result = lhs == rhs distance_to_alternative = 0 if result: distance_to_alternative = df.not_equals_num(lhs, rhs) else: distance_to_alternative = df.equals_num(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result def not_equals(self, id, lhs, rhs): result = lhs != rhs distance_to_alternative = 0 if result: distance_to_alternative = df.equals_num(lhs, rhs) else: distance_to_alternative = df.not_equals_num(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result def less_than(self, id, lhs, rhs): result = lhs < rhs distance_to_alternative = 0 if result: distance_to_alternative = df.greater_than_or_equals(lhs, rhs) else: distance_to_alternative = df.less_than(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result def less_than_or_equals(self, id, lhs, rhs): result = lhs <= rhs distance_to_alternative = 0 if result: distance_to_alternative = df.greater_than(lhs, rhs) else: distance_to_alternative = df.less_than_or_equals(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result def greater_than(self, id, lhs, rhs): result = lhs > rhs distance_to_alternative = 0 if result: distance_to_alternative = df.less_than_or_equals(lhs, rhs) else: distance_to_alternative = df.greater_than(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result def greater_than_or_equals(self, id, lhs, rhs): result = lhs >= rhs distance_to_alternative = 0 if result: distance_to_alternative = df.less_than(lhs, rhs) else: distance_to_alternative = df.greater_than_or_equals(lhs, rhs) self.executed_branches.append((id, result, distance_to_alternative)) return result

25.472441

76

0.631221

393

3,235

4.895674

0.096692

0.16632

0.349272

0.224532

0.914241

0.832121

0.772349

0

0.003478

0.289026

3,235

126

77

25.674603

0.833043

0

0.717949

0

1

0.128205

false

0

0.012821

0.269231

0

null

0

1

0

1

0

null

0

9

9c7a7d7f77db4b9185d504465aeeff1dade200f6

2,862

py

Python

dashboard/tests/test_signals.py

Tomasz-Kluczkowski/Bnice

75eb66a94a3bf3225691ed6802e674fbcf108571

[ "MIT" ]

null

dashboard/tests/test_signals.py

Tomasz-Kluczkowski/Bnice

75eb66a94a3bf3225691ed6802e674fbcf108571

[ "MIT" ]

60

2018-04-20T21:32:21.000Z

2021-09-07T23:53:31.000Z

dashboard/tests/test_signals.py

Tomasz-Kluczkowski/Bnice

75eb66a94a3bf3225691ed6802e674fbcf108571

[ "MIT" ]

null

from django.contrib.contenttypes.models import ContentType from django.db.models import Q from guardian.models import UserObjectPermission class TestSmileySignals: def test_add_smiley_object_permissions_parent_user(self, parent_user, child, smiley_custom_description): assert parent_user.has_perm('dashboard.view_smiley_instance', smiley_custom_description) assert parent_user.has_perm('dashboard.edit_smiley_instance', smiley_custom_description) assert parent_user.has_perm('dashboard.delete_smiley_instance', smiley_custom_description) def test_add_smiley_object_permissions_child_user(self, parent_user, child, child_user, smiley_custom_description): assert child_user.has_perm('dashboard.view_smiley_instance', smiley_custom_description) assert not child_user.has_perm('dashboard.edit_smiley_instance', smiley_custom_description) assert not child_user.has_perm('dashboard.delete_smiley_instance', smiley_custom_description) def test_smiley_object_permissions_removed_after_smiley_deleted(self, parent_user, child, smiley_custom_description): assert parent_user.has_perm('dashboard.view_smiley_instance', smiley_custom_description) filters = Q(content_type=ContentType.objects.get_for_model(smiley_custom_description), object_pk=smiley_custom_description.pk) smiley_custom_description.delete() assert not UserObjectPermission.objects.filter(filters).exists() class TestOopsySignals: def test_add_oopsy_object_permissions_parent_user(self, parent_user, child, oopsy_custom_description): assert parent_user.has_perm('dashboard.view_oopsy_instance', oopsy_custom_description) assert parent_user.has_perm('dashboard.edit_oopsy_instance', oopsy_custom_description) assert parent_user.has_perm('dashboard.delete_oopsy_instance', oopsy_custom_description) def test_add_oopsy_object_permissions_child_user(self, parent_user, child, child_user, oopsy_custom_description): assert child_user.has_perm('dashboard.view_oopsy_instance', oopsy_custom_description) assert not child_user.has_perm('dashboard.edit_oopsy_instance', oopsy_custom_description) assert not child_user.has_perm('dashboard.delete_oopsy_instance', oopsy_custom_description) def test_oopsy_object_permissions_removed_after_oopsy_deleted(self, parent_user, child, oopsy_custom_description): assert parent_user.has_perm('dashboard.view_oopsy_instance', oopsy_custom_description) filters = Q(content_type=ContentType.objects.get_for_model(oopsy_custom_description), object_pk=oopsy_custom_description.pk) oopsy_custom_description.delete() assert not UserObjectPermission.objects.filter(filters).exists()

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92cc1ddd048789eb160e6826cc9b908fb674dd2d

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py

Python

sdk/python/pulumi_aws/elasticache/cluster.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

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

null

sdk/python/pulumi_aws/elasticache/cluster.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

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

null

sdk/python/pulumi_aws/elasticache/cluster.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class Cluster(pulumi.CustomResource): apply_immediately: pulumi.Output[bool] """ Specifies whether any database modifications are applied immediately, or during the next maintenance window. Default is `false`. See [Amazon ElastiCache Documentation for more information.][1] (Available since v0.6.0) """ arn: pulumi.Output[str] availability_zone: pulumi.Output[str] """ The Availability Zone for the cache cluster. If you want to create cache nodes in multi-az, use `preferred_availability_zones` instead. Default: System chosen Availability Zone. """ az_mode: pulumi.Output[str] """ Specifies whether the nodes in this Memcached node group are created in a single Availability Zone or created across multiple Availability Zones in the cluster's region. Valid values for this parameter are `single-az` or `cross-az`, default is `single-az`. If you want to choose `cross-az`, `num_cache_nodes` must be greater than `1` """ cache_nodes: pulumi.Output[list] """ List of node objects including `id`, `address`, `port` and `availability_zone`. Referenceable e.g. as `${aws_elasticache_cluster.bar.cache_nodes.0.address}` * `address` (`str`) * `availability_zone` (`str`) - The Availability Zone for the cache cluster. If you want to create cache nodes in multi-az, use `preferred_availability_zones` instead. Default: System chosen Availability Zone. * `id` (`str`) * `port` (`float`) - The port number on which each of the cache nodes will accept connections. For Memcache the default is 11211, and for Redis the default port is 6379. Cannot be provided with `replication_group_id`. """ cluster_address: pulumi.Output[str] """ (Memcached only) The DNS name of the cache cluster without the port appended. """ cluster_id: pulumi.Output[str] """ Group identifier. ElastiCache converts this name to lowercase """ configuration_endpoint: pulumi.Output[str] """ (Memcached only) The configuration endpoint to allow host discovery. """ engine: pulumi.Output[str] """ Name of the cache engine to be used for this cache cluster. Valid values for this parameter are `memcached` or `redis` """ engine_version: pulumi.Output[str] """ Version number of the cache engine to be used. See [Describe Cache Engine Versions](https://docs.aws.amazon.com/cli/latest/reference/elasticache/describe-cache-engine-versions.html) in the AWS Documentation center for supported versions """ maintenance_window: pulumi.Output[str] """ Specifies the weekly time range for when maintenance on the cache cluster is performed. The format is `ddd:hh24:mi-ddd:hh24:mi` (24H Clock UTC). The minimum maintenance window is a 60 minute period. Example: `sun:05:00-sun:09:00` """ node_type: pulumi.Output[str] """ The compute and memory capacity of the nodes. See [Available Cache Node Types](https://aws.amazon.com/elasticache/details#Available_Cache_Node_Types) for supported node types """ notification_topic_arn: pulumi.Output[str] """ An Amazon Resource Name (ARN) of an SNS topic to send ElastiCache notifications to. Example: `arn:aws:sns:us-east-1:012345678999:my_sns_topic` """ num_cache_nodes: pulumi.Output[float] """ The initial number of cache nodes that the cache cluster will have. For Redis, this value must be 1. For Memcache, this value must be between 1 and 20. If this number is reduced on subsequent runs, the highest numbered nodes will be removed. """ parameter_group_name: pulumi.Output[str] """ Name of the parameter group to associate with this cache cluster """ port: pulumi.Output[float] """ The port number on which each of the cache nodes will accept connections. For Memcache the default is 11211, and for Redis the default port is 6379. Cannot be provided with `replication_group_id`. """ preferred_availability_zones: pulumi.Output[list] """ A list of the Availability Zones in which cache nodes are created. If you are creating your cluster in an Amazon VPC you can only locate nodes in Availability Zones that are associated with the subnets in the selected subnet group. The number of Availability Zones listed must equal the value of `num_cache_nodes`. If you want all the nodes in the same Availability Zone, use `availability_zone` instead, or repeat the Availability Zone multiple times in the list. Default: System chosen Availability Zones. Detecting drift of existing node availability zone is not currently supported. Updating this argument by itself to migrate existing node availability zones is not currently supported and will show a perpetual difference. """ replication_group_id: pulumi.Output[str] """ The ID of the replication group to which this cluster should belong. If this parameter is specified, the cluster is added to the specified replication group as a read replica; otherwise, the cluster is a standalone primary that is not part of any replication group. """ security_group_ids: pulumi.Output[list] """ One or more VPC security groups associated with the cache cluster """ security_group_names: pulumi.Output[list] """ List of security group names to associate with this cache cluster """ snapshot_arns: pulumi.Output[list] """ A single-element string list containing an Amazon Resource Name (ARN) of a Redis RDB snapshot file stored in Amazon S3. Example: `arn:aws:s3:::my_bucket/snapshot1.rdb` """ snapshot_name: pulumi.Output[str] """ The name of a snapshot from which to restore data into the new node group. Changing the `snapshot_name` forces a new resource. """ snapshot_retention_limit: pulumi.Output[float] """ The number of days for which ElastiCache will retain automatic cache cluster snapshots before deleting them. For example, if you set SnapshotRetentionLimit to 5, then a snapshot that was taken today will be retained for 5 days before being deleted. If the value of SnapshotRetentionLimit is set to zero (0), backups are turned off. Please note that setting a `snapshot_retention_limit` is not supported on cache.t1.micro or cache.t2.* cache nodes """ snapshot_window: pulumi.Output[str] """ The daily time range (in UTC) during which ElastiCache will begin taking a daily snapshot of your cache cluster. Example: 05:00-09:00 """ subnet_group_name: pulumi.Output[str] """ Name of the subnet group to be used for the cache cluster. """ tags: pulumi.Output[dict] """ A mapping of tags to assign to the resource """ def __init__(__self__, resource_name, opts=None, apply_immediately=None, availability_zone=None, az_mode=None, cluster_id=None, engine=None, engine_version=None, maintenance_window=None, node_type=None, notification_topic_arn=None, num_cache_nodes=None, parameter_group_name=None, port=None, preferred_availability_zones=None, replication_group_id=None, security_group_ids=None, security_group_names=None, snapshot_arns=None, snapshot_name=None, snapshot_retention_limit=None, snapshot_window=None, subnet_group_name=None, tags=None, __props__=None, __name__=None, __opts__=None): """ Provides an ElastiCache Cluster resource, which manages a Memcached cluster or Redis instance. For working with Redis (Cluster Mode Enabled) replication groups, see the [`elasticache.ReplicationGroup` resource](https://www.terraform.io/docs/providers/aws/r/elasticache_replication_group.html). > **Note:** When you change an attribute, such as `node_type`, by default it is applied in the next maintenance window. Because of this, this provider may report a difference in its planning phase because the actual modification has not yet taken place. You can use the `apply_immediately` flag to instruct the service to apply the change immediately. Using `apply_immediately` can result in a brief downtime as the server reboots. See the AWS Docs on [Modifying an ElastiCache Cache Cluster][2] for more information. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] apply_immediately: Specifies whether any database modifications are applied immediately, or during the next maintenance window. Default is `false`. See [Amazon ElastiCache Documentation for more information.][1] (Available since v0.6.0) :param pulumi.Input[str] availability_zone: The Availability Zone for the cache cluster. If you want to create cache nodes in multi-az, use `preferred_availability_zones` instead. Default: System chosen Availability Zone. :param pulumi.Input[str] az_mode: Specifies whether the nodes in this Memcached node group are created in a single Availability Zone or created across multiple Availability Zones in the cluster's region. Valid values for this parameter are `single-az` or `cross-az`, default is `single-az`. If you want to choose `cross-az`, `num_cache_nodes` must be greater than `1` :param pulumi.Input[str] cluster_id: Group identifier. ElastiCache converts this name to lowercase :param pulumi.Input[str] engine: Name of the cache engine to be used for this cache cluster. Valid values for this parameter are `memcached` or `redis` :param pulumi.Input[str] engine_version: Version number of the cache engine to be used. See [Describe Cache Engine Versions](https://docs.aws.amazon.com/cli/latest/reference/elasticache/describe-cache-engine-versions.html) in the AWS Documentation center for supported versions :param pulumi.Input[str] maintenance_window: Specifies the weekly time range for when maintenance on the cache cluster is performed. The format is `ddd:hh24:mi-ddd:hh24:mi` (24H Clock UTC). The minimum maintenance window is a 60 minute period. Example: `sun:05:00-sun:09:00` :param pulumi.Input[str] node_type: The compute and memory capacity of the nodes. See [Available Cache Node Types](https://aws.amazon.com/elasticache/details#Available_Cache_Node_Types) for supported node types :param pulumi.Input[str] notification_topic_arn: An Amazon Resource Name (ARN) of an SNS topic to send ElastiCache notifications to. Example: `arn:aws:sns:us-east-1:012345678999:my_sns_topic` :param pulumi.Input[float] num_cache_nodes: The initial number of cache nodes that the cache cluster will have. For Redis, this value must be 1. For Memcache, this value must be between 1 and 20. If this number is reduced on subsequent runs, the highest numbered nodes will be removed. :param pulumi.Input[str] parameter_group_name: Name of the parameter group to associate with this cache cluster :param pulumi.Input[float] port: The port number on which each of the cache nodes will accept connections. For Memcache the default is 11211, and for Redis the default port is 6379. Cannot be provided with `replication_group_id`. :param pulumi.Input[list] preferred_availability_zones: A list of the Availability Zones in which cache nodes are created. If you are creating your cluster in an Amazon VPC you can only locate nodes in Availability Zones that are associated with the subnets in the selected subnet group. The number of Availability Zones listed must equal the value of `num_cache_nodes`. If you want all the nodes in the same Availability Zone, use `availability_zone` instead, or repeat the Availability Zone multiple times in the list. Default: System chosen Availability Zones. Detecting drift of existing node availability zone is not currently supported. Updating this argument by itself to migrate existing node availability zones is not currently supported and will show a perpetual difference. :param pulumi.Input[str] replication_group_id: The ID of the replication group to which this cluster should belong. If this parameter is specified, the cluster is added to the specified replication group as a read replica; otherwise, the cluster is a standalone primary that is not part of any replication group. :param pulumi.Input[list] security_group_ids: One or more VPC security groups associated with the cache cluster :param pulumi.Input[list] security_group_names: List of security group names to associate with this cache cluster :param pulumi.Input[list] snapshot_arns: A single-element string list containing an Amazon Resource Name (ARN) of a Redis RDB snapshot file stored in Amazon S3. Example: `arn:aws:s3:::my_bucket/snapshot1.rdb` :param pulumi.Input[str] snapshot_name: The name of a snapshot from which to restore data into the new node group. Changing the `snapshot_name` forces a new resource. :param pulumi.Input[float] snapshot_retention_limit: The number of days for which ElastiCache will retain automatic cache cluster snapshots before deleting them. For example, if you set SnapshotRetentionLimit to 5, then a snapshot that was taken today will be retained for 5 days before being deleted. If the value of SnapshotRetentionLimit is set to zero (0), backups are turned off. Please note that setting a `snapshot_retention_limit` is not supported on cache.t1.micro or cache.t2.* cache nodes :param pulumi.Input[str] snapshot_window: The daily time range (in UTC) during which ElastiCache will begin taking a daily snapshot of your cache cluster. Example: 05:00-09:00 :param pulumi.Input[str] subnet_group_name: Name of the subnet group to be used for the cache cluster. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['apply_immediately'] = apply_immediately __props__['availability_zone'] = availability_zone __props__['az_mode'] = az_mode __props__['cluster_id'] = cluster_id __props__['engine'] = engine __props__['engine_version'] = engine_version __props__['maintenance_window'] = maintenance_window __props__['node_type'] = node_type __props__['notification_topic_arn'] = notification_topic_arn __props__['num_cache_nodes'] = num_cache_nodes __props__['parameter_group_name'] = parameter_group_name __props__['port'] = port __props__['preferred_availability_zones'] = preferred_availability_zones __props__['replication_group_id'] = replication_group_id __props__['security_group_ids'] = security_group_ids __props__['security_group_names'] = security_group_names __props__['snapshot_arns'] = snapshot_arns __props__['snapshot_name'] = snapshot_name __props__['snapshot_retention_limit'] = snapshot_retention_limit __props__['snapshot_window'] = snapshot_window __props__['subnet_group_name'] = subnet_group_name __props__['tags'] = tags __props__['arn'] = None __props__['cache_nodes'] = None __props__['cluster_address'] = None __props__['configuration_endpoint'] = None super(Cluster, __self__).__init__( 'aws:elasticache/cluster:Cluster', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, apply_immediately=None, arn=None, availability_zone=None, az_mode=None, cache_nodes=None, cluster_address=None, cluster_id=None, configuration_endpoint=None, engine=None, engine_version=None, maintenance_window=None, node_type=None, notification_topic_arn=None, num_cache_nodes=None, parameter_group_name=None, port=None, preferred_availability_zones=None, replication_group_id=None, security_group_ids=None, security_group_names=None, snapshot_arns=None, snapshot_name=None, snapshot_retention_limit=None, snapshot_window=None, subnet_group_name=None, tags=None): """ Get an existing Cluster resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] apply_immediately: Specifies whether any database modifications are applied immediately, or during the next maintenance window. Default is `false`. See [Amazon ElastiCache Documentation for more information.][1] (Available since v0.6.0) :param pulumi.Input[str] availability_zone: The Availability Zone for the cache cluster. If you want to create cache nodes in multi-az, use `preferred_availability_zones` instead. Default: System chosen Availability Zone. :param pulumi.Input[str] az_mode: Specifies whether the nodes in this Memcached node group are created in a single Availability Zone or created across multiple Availability Zones in the cluster's region. Valid values for this parameter are `single-az` or `cross-az`, default is `single-az`. If you want to choose `cross-az`, `num_cache_nodes` must be greater than `1` :param pulumi.Input[list] cache_nodes: List of node objects including `id`, `address`, `port` and `availability_zone`. Referenceable e.g. as `${aws_elasticache_cluster.bar.cache_nodes.0.address}` :param pulumi.Input[str] cluster_address: (Memcached only) The DNS name of the cache cluster without the port appended. :param pulumi.Input[str] cluster_id: Group identifier. ElastiCache converts this name to lowercase :param pulumi.Input[str] configuration_endpoint: (Memcached only) The configuration endpoint to allow host discovery. :param pulumi.Input[str] engine: Name of the cache engine to be used for this cache cluster. Valid values for this parameter are `memcached` or `redis` :param pulumi.Input[str] engine_version: Version number of the cache engine to be used. See [Describe Cache Engine Versions](https://docs.aws.amazon.com/cli/latest/reference/elasticache/describe-cache-engine-versions.html) in the AWS Documentation center for supported versions :param pulumi.Input[str] maintenance_window: Specifies the weekly time range for when maintenance on the cache cluster is performed. The format is `ddd:hh24:mi-ddd:hh24:mi` (24H Clock UTC). The minimum maintenance window is a 60 minute period. Example: `sun:05:00-sun:09:00` :param pulumi.Input[str] node_type: The compute and memory capacity of the nodes. See [Available Cache Node Types](https://aws.amazon.com/elasticache/details#Available_Cache_Node_Types) for supported node types :param pulumi.Input[str] notification_topic_arn: An Amazon Resource Name (ARN) of an SNS topic to send ElastiCache notifications to. Example: `arn:aws:sns:us-east-1:012345678999:my_sns_topic` :param pulumi.Input[float] num_cache_nodes: The initial number of cache nodes that the cache cluster will have. For Redis, this value must be 1. For Memcache, this value must be between 1 and 20. If this number is reduced on subsequent runs, the highest numbered nodes will be removed. :param pulumi.Input[str] parameter_group_name: Name of the parameter group to associate with this cache cluster :param pulumi.Input[float] port: The port number on which each of the cache nodes will accept connections. For Memcache the default is 11211, and for Redis the default port is 6379. Cannot be provided with `replication_group_id`. :param pulumi.Input[list] preferred_availability_zones: A list of the Availability Zones in which cache nodes are created. If you are creating your cluster in an Amazon VPC you can only locate nodes in Availability Zones that are associated with the subnets in the selected subnet group. The number of Availability Zones listed must equal the value of `num_cache_nodes`. If you want all the nodes in the same Availability Zone, use `availability_zone` instead, or repeat the Availability Zone multiple times in the list. Default: System chosen Availability Zones. Detecting drift of existing node availability zone is not currently supported. Updating this argument by itself to migrate existing node availability zones is not currently supported and will show a perpetual difference. :param pulumi.Input[str] replication_group_id: The ID of the replication group to which this cluster should belong. If this parameter is specified, the cluster is added to the specified replication group as a read replica; otherwise, the cluster is a standalone primary that is not part of any replication group. :param pulumi.Input[list] security_group_ids: One or more VPC security groups associated with the cache cluster :param pulumi.Input[list] security_group_names: List of security group names to associate with this cache cluster :param pulumi.Input[list] snapshot_arns: A single-element string list containing an Amazon Resource Name (ARN) of a Redis RDB snapshot file stored in Amazon S3. Example: `arn:aws:s3:::my_bucket/snapshot1.rdb` :param pulumi.Input[str] snapshot_name: The name of a snapshot from which to restore data into the new node group. Changing the `snapshot_name` forces a new resource. :param pulumi.Input[float] snapshot_retention_limit: The number of days for which ElastiCache will retain automatic cache cluster snapshots before deleting them. For example, if you set SnapshotRetentionLimit to 5, then a snapshot that was taken today will be retained for 5 days before being deleted. If the value of SnapshotRetentionLimit is set to zero (0), backups are turned off. Please note that setting a `snapshot_retention_limit` is not supported on cache.t1.micro or cache.t2.* cache nodes :param pulumi.Input[str] snapshot_window: The daily time range (in UTC) during which ElastiCache will begin taking a daily snapshot of your cache cluster. Example: 05:00-09:00 :param pulumi.Input[str] subnet_group_name: Name of the subnet group to be used for the cache cluster. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource The **cache_nodes** object supports the following: * `address` (`pulumi.Input[str]`) * `availability_zone` (`pulumi.Input[str]`) - The Availability Zone for the cache cluster. If you want to create cache nodes in multi-az, use `preferred_availability_zones` instead. Default: System chosen Availability Zone. * `id` (`pulumi.Input[str]`) * `port` (`pulumi.Input[float]`) - The port number on which each of the cache nodes will accept connections. For Memcache the default is 11211, and for Redis the default port is 6379. Cannot be provided with `replication_group_id`. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["apply_immediately"] = apply_immediately __props__["arn"] = arn __props__["availability_zone"] = availability_zone __props__["az_mode"] = az_mode __props__["cache_nodes"] = cache_nodes __props__["cluster_address"] = cluster_address __props__["cluster_id"] = cluster_id __props__["configuration_endpoint"] = configuration_endpoint __props__["engine"] = engine __props__["engine_version"] = engine_version __props__["maintenance_window"] = maintenance_window __props__["node_type"] = node_type __props__["notification_topic_arn"] = notification_topic_arn __props__["num_cache_nodes"] = num_cache_nodes __props__["parameter_group_name"] = parameter_group_name __props__["port"] = port __props__["preferred_availability_zones"] = preferred_availability_zones __props__["replication_group_id"] = replication_group_id __props__["security_group_ids"] = security_group_ids __props__["security_group_names"] = security_group_names __props__["snapshot_arns"] = snapshot_arns __props__["snapshot_name"] = snapshot_name __props__["snapshot_retention_limit"] = snapshot_retention_limit __props__["snapshot_window"] = snapshot_window __props__["subnet_group_name"] = subnet_group_name __props__["tags"] = tags return Cluster(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

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null

0

7

130c51b2c8ae68247d06e21a13fb0e08a71f6976

473,295

py

Python

dev/services/wms/ows/wms_cfg.py

ASVincent/dea-config

ca1edee807de706057d8d3f54f0166ee8e5325d8

[ "Apache-2.0" ]

null

dev/services/wms/ows/wms_cfg.py

ASVincent/dea-config

ca1edee807de706057d8d3f54f0166ee8e5325d8

[ "Apache-2.0" ]

null

dev/services/wms/ows/wms_cfg.py

ASVincent/dea-config

ca1edee807de706057d8d3f54f0166ee8e5325d8

[ "Apache-2.0" ]

null

# Static config for the wms metadata. response_cfg = { "Access-Control-Allow-Origin": "*", # CORS header } service_cfg = { ## Which web service(s) should be supported by this instance "wcs": True, "wms": True, "wmts": True, ## Required config for WMS and/or WCS # Service title - appears e.g. in Terria catalog "title": "Digital Earth Australia - OGC Web Services", # Service URL. Should a fully qualified URL "url": [ "https://ows.services.dea.ga.gov.au", "https://ows.services.devkube.dea.ga.gov.au", "https://nrt.services.dea.ga.gov.au", "https://geomedian.services.dea.ga.gov.au", "https://geomedianau.dea.ga.gov.au", "https://geomedian.dea.ga.gov.au", "https://nrt.dea.ga.gov.au", "https://nrt-au.dea.ga.gov.au"], "human_url": "dea.ga.gov.au/", # Supported co-ordinate reference systems "published_CRSs": { "EPSG:3857": { # Web Mercator "geographic": False, "horizontal_coord": "x", "vertical_coord": "y", }, "EPSG:4326": { # WGS-84 "geographic": True, "vertical_coord_first": True }, "EPSG:3577": { # GDA-94, internal representation "geographic": False, "horizontal_coord": "x", "vertical_coord": "y", }, }, ## Required config for WCS # Must be a geographic CRS in the published_CRSs list. EPSG:4326 is recommended, but any geographic CRS should work. "default_geographic_CRS": "EPSG:4326", # Supported WCS formats "wcs_formats": { # Key is the format name, as used in DescribeCoverage XML "GeoTIFF": { # Renderer is the FQN of a Python function that takes: # * A WCS Request object # * Some ODC data to be rendered. "renderer": "datacube_wms.wcs_utils.get_tiff", # The MIME type of the image, as used in the Http Response. "mime": "image/geotiff", # The file extension to add to the filename. "extension": "tif", # Whether or not the file format supports multiple time slices. "multi-time": False }, "netCDF": { "renderer": "datacube_wms.wcs_utils.get_netcdf", "mime": "application/x-netcdf", "extension": "nc", "multi-time": True, } }, # The native wcs format must be declared in wcs_formats above. "native_wcs_format": "GeoTIFF", ## Optional config for instances supporting WMS "max_width": 512, "max_height": 512, # Optional config for all services (WMS and/or WCS) - may be set to blank/empty, no defaults "abstract": """Digital Earth Australia OGC Web Services""", "keywords": [ "geomedian", "WOfS", "mangrove", "bare-earth", "NIDEM", "HLTC", "landsat", "australia", "time-series", "fractional-cover" ], "contact_info": { "person": "Digital Earth Australia", "organisation": "Geoscience Australia", "position": "", "address": { "type": "postal", "address": "GPO Box 378", "city": "Canberra", "state": "ACT", "postcode": "2609", "country": "Australia", }, "telephone": "+61 2 6249 9111", "fax": "", "email": "earth.observation@ga.gov.au", }, "fees": "", "access_constraints": "© Commonwealth of Australia (Geoscience Australia) 2018. " "This product is released under the Creative Commons Attribution 4.0 International Licence. " "http://creativecommons.org/licenses/by/4.0/legalcode", "preauthenticate_s3": True, "geotiff_georeference_source": "INTERNAL" } layer_cfg = [ # Layer Config is a list of platform configs { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "Geomedian_AU_NBART", "title": "Surface Reflectance", "abstract": "", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "Landsat 8", # Included as a keyword for the layer "type": "Annual Geomedian", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "abstract": """ Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a an annual geomedian image is the statistical median of all observations for that pixel from a calendar year. Annual mosaics are available for the following years: Landsat 8: 2013 to 2017; For more information, see http://pid.geoscience.gov.au/dataset/ga/120374 For service status information, see https://status.dea.ga.gov.au""", "name": "ls8_nbart_geomedian_annual", # The Datacube name for the associated data product "product_name": "ls8_nbart_geomedian_annual", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "ls8_level1_usgs", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_manual_data_merge": True, # "data_manual_merge": True, # "pq_band": "quality", # "always_fetch_bands": [ "quality" ], # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [ ], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi"] }, "wcs_default_bands": ["red", "green", "blue"], # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, # # Examples of non-linear heat-mapped styles. { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, SWIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]), "needed_bands": ["green", "swir1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "blue", "title": "Blue - 480", "abstract": "Blue band, centered on 480nm", "components": { "red": { "blue": 1.0 }, "green": { "blue": 1.0 }, "blue": { "blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, centered on 560nm", "components": { "red": { "green": 1.0 }, "green": { "green": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 660", "abstract": "Red band, centered on 660nm", "components": { "red": { "red": 1.0 }, "green": { "red": 1.0 }, "blue": { "red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 870", "abstract": "Near infra-red band, centered on 870nm", "components": { "red": { "nir": 1.0 }, "green": { "nir": 1.0 }, "blue": { "nir": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1610", "abstract": "Short wave infra-red band 1, centered on 1610nm", "components": { "red": { "swir1": 1.0 }, "green": { "swir1": 1.0 }, "blue": { "swir1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2200", "abstract": "Short wave infra-red band 2, centered on 2200nm", "components": { "red": { "swir2": 1.0 }, "green": { "swir2": 1.0 }, "blue": { "swir2": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, { # Included as a keyword for the layer "label": "Landsat 7", # Included as a keyword for the layer "type": "Annual Geomedian", # Included as a keyword for the layer "variant": "25m", "abstract": """ Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a an annual geomedian image is the statistical median of all observations for that pixel from a calendar year. Annual mosaics are available for the following years: Landsat 7: 2000 to 2017; For more information, see http://pid.geoscience.gov.au/dataset/ga/120374 For service status information, see https://status.dea.ga.gov.au""", # The WMS name for the layer "name": "ls7_nbart_geomedian_annual", # The Datacube name for the associated data product "product_name": "ls7_nbart_geomedian_annual", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "ls8_level1_usgs", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_manual_data_merge": True, # "data_manual_merge": True, # "pq_band": "quality", # "always_fetch_bands": [ "quality" ], # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi"] }, "wcs_default_bands": ["red", "green", "blue"], # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, # # Examples of non-linear heat-mapped styles. { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, SWIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]), "needed_bands": ["green", "swir1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "blue", "title": "Blue - 490", "abstract": "Blue band, centered on 490nm", "components": { "red": { "blue": 1.0 }, "green": { "blue": 1.0 }, "blue": { "blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, centered on 560nm", "components": { "red": { "green": 1.0 }, "green": { "green": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 660", "abstract": "Red band, centered on 660nm", "components": { "red": { "red": 1.0 }, "green": { "red": 1.0 }, "blue": { "red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 840", "abstract": "Near infra-red band, centered on 840nm", "components": { "red": { "nir": 1.0 }, "green": { "nir": 1.0 }, "blue": { "nir": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1650", "abstract": "Short wave infra-red band 1, centered on 1650nm", "components": { "red": { "swir1": 1.0 }, "green": { "swir1": 1.0 }, "blue": { "swir1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2220", "abstract": "Short wave infra-red band 2, centered on 2220nm", "components": { "red": { "swir2": 1.0 }, "green": { "swir2": 1.0 }, "blue": { "swir2": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, { # Included as a keyword for the layer "label": "Landsat 5", # Included as a keyword for the layer "type": "Annual Geomedian", # Included as a keyword for the layer "variant": "25m", "abstract": """ Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a an annual geomedian image is the statistical median of all observations for that pixel from a calendar year. Annual mosaics are available for the following years: Landsat 5: 1988 to 1999, 2004 to 2007, 2009 to 2011; For more information, see http://pid.geoscience.gov.au/dataset/ga/120374 For service status information, see https://status.dea.ga.gov.au""", # The WMS name for the layer "name": "ls5_nbart_geomedian_annual", # The Datacube name for the associated data product "product_name": "ls5_nbart_geomedian_annual", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "ls8_level1_usgs", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_manual_data_merge": True, # "data_manual_merge": True, # "pq_band": "quality", # "always_fetch_bands": [ "quality" ], # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi"] }, "wcs_default_bands": ["red", "green", "blue"], # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, # # Examples of non-linear heat-mapped styles. { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, SWIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]), "needed_bands": ["green", "swir1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "blue", "title": "Blue - 490", "abstract": "Blue band, centered on 490nm", "components": { "red": { "blue": 1.0 }, "green": { "blue": 1.0 }, "blue": { "blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, centered on 560nm", "components": { "red": { "green": 1.0 }, "green": { "green": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 660", "abstract": "Red band, centered on 660nm", "components": { "red": { "red": 1.0 }, "green": { "red": 1.0 }, "blue": { "red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 840", "abstract": "Near infra-red band, centered on 840nm", "components": { "red": { "nir": 1.0 }, "green": { "nir": 1.0 }, "blue": { "nir": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1650", "abstract": "Short wave infra-red band 1, centered on 1650nm", "components": { "red": { "swir1": 1.0 }, "green": { "swir1": 1.0 }, "blue": { "swir1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2220", "abstract": "Short wave infra-red band 2, centered on 2220nm", "components": { "red": { "swir2": 1.0 }, "green": { "swir2": 1.0 }, "blue": { "swir2": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", } ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "landsat8_barest_earth", "title": "Barest Earth", "abstract": """ A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. Mosaics are available for the following years: Landsat 8: 2013 to 2017; """, # Link removed until eCat record is "published_external", not "published_internal" # For more information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/122573 # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "Landsat 8", # Included as a keyword for the layer "type": "Barest Earth", # Included as a keyword for the layer "variant": "25m", "abstract": """ A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. Mosaics are available for the following years: Landsat 8: 2013 to 2017; For service status information, see https://status.dea.ga.gov.au""", # Link removed until eCat record is "published_external", not "published_internal" # For more information, see the dataset record: http://pid.geoscience.gov.au/dataset/ga/122573 # The WMS name for the layer "name": "ls8_barest_earth_mosaic", # The Datacube name for the associated data product "product_name": "ls8_barest_earth_albers", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, #"max_datasets_wms": 1000, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi"] }, "wcs_default_bands": ["red", "green", "blue"], # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "blue", "title": "Blue - 480", "abstract": "Blue band, centered on 480nm", "components": { "red": { "blue": 1.0 }, "green": { "blue": 1.0 }, "blue": { "blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, centered on 560nm", "components": { "red": { "green": 1.0 }, "green": { "green": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 660", "abstract": "Red band, centered on 660nm", "components": { "red": { "red": 1.0 }, "green": { "red": 1.0 }, "blue": { "red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 870", "abstract": "Near infra-red band, centered on 870nm", "components": { "red": { "nir": 1.0 }, "green": { "nir": 1.0 }, "blue": { "nir": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1610", "abstract": "Short wave infra-red band 1, centered on 1610nm", "components": { "red": { "swir1": 1.0 }, "green": { "swir1": 1.0 }, "blue": { "swir1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2200", "abstract": "Short wave infra-red band 2, centered on 2200nm", "components": { "red": { "swir2": 1.0 }, "green": { "swir2": 1.0 }, "blue": { "swir2": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", } ] }, { "name": "mangrove_cover", "title": "Mangrove Canopy Cover", "abstract": "", "products": [ { "label": "Mangrove Canopy Cover", "abstract": """ Mangrove canopy cover version 1, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each year between 1987 and 2016 for the entire Australian coastline. The canopy cover classes are: 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 25 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency. The mangrove canopy cover version 1 product has the following caveats: it underestimates the overall extent of mangroves. it doesn’t detect small mangrove communities i.e. smaller estuaries in NSW and Victoria that there is localised confusion between mangroves and wooded freshwater wetlands i.e. Melaleuca swamps, and in some locations dense dwarf/shrub mangroves that are less than 2 metres tall may be mis-labelled as woodland/open forest/closed forest. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "mangrove_cover", "product_name": "mangrove_cover", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data["extent"] == 1, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["extent"], "apply_solar_corrections": False, "legend": { "styles": ["mangrove"] }, "wcs_default_bands": ["canopy_cover_class", "extent"], "styles": [ { "name": "mangrove", "title": "Mangrove Cover", "abstract": "", "value_map": { "canopy_cover_class": [ { "title": "Woodland", "abstract": "(20% - 50% cover)", "flags": { "woodland": True }, "color": "#9FFF4C" }, { "title": "Open Forest", "abstract": "(50% - 80% cover)", "flags": { "open_forest": True }, "color": "#5ECC00" }, { "title": "Closed Forest", "abstract": "(>80% cover)", "flags": { "closed_forest": True }, "color": "#3B7F00" }, ] } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "mangrove", }, ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "WOfS", "title": "Water Observations from Space", "abstract": "WOfS", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "WOfS Filtered Statistics", # Included as a keyword for the layer "type": "Filtered Water Summary", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_filtered_summary", # The Datacube name for the associated data product "product_name": "wofs_filtered_summary", "abstract": """ Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. The Filtered Water Summary layer is a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist. For more information please see: https://data.dea.ga.gov.au/?prefix=WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": [ "WOfS_filtered_frequency", "WOfS_filtered_frequency_blues_transparent"] }, "wcs_default_bands": ["wofs_filtered_summary"], "styles": [ { "name": "WOfS_filtered_frequency", "title": "Filtered Water Summary", "abstract": "WOfS filtered summary showing the frequency of Wetness", "needed_bands": ["wofs_filtered_summary"], "color_ramp": [ { "value": 0.0, "color": "#000000", "alpha": 0.0 }, { "value": 0.002, "color": "#000000", "alpha": 0.0 }, { "value": 0.005, "color": "#8e0101", "alpha": 0.25 }, { "value": 0.01, "color": "#cf2200", "alpha": 0.75 }, { "value": 0.02, "color": "#e38400" }, { "value": 0.05, "color": "#e3df00" }, { "value": 0.1, "color": "#a6e300" }, { "value": 0.2, "color": "#62e300" }, { "value": 0.3, "color": "#00e32d" }, { "value": 0.4, "color": "#00e384" }, { "value": 0.5, "color": "#00e3c8" }, { "value": 0.6, "color": "#00c5e3" }, { "value": 0.7, "color": "#0097e3" }, { "value": 0.8, "color": "#005fe3" }, { "value": 0.9, "color": "#000fe3" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, { "name": "WOfS_filtered_frequency_blues_transparent", "title": "Water Summary (Blue)", "abstract": "WOfS filtered summary showing the frequency of Wetness", "needed_bands": ["wofs_filtered_summary"], "color_ramp": [ { "value": 0.0, "color": "#ffffff", "alpha": 0.0, }, { "value": 0.001, "color": "#d5fef9", "alpha": 0.0, }, { "value": 0.02, "color": "#d5fef9", }, { "value": 0.2, "color": "#71e3ff" }, { "value": 0.4, "color": "#01ccff" }, { "value": 0.6, "color": "#0178ff" }, { "value": 0.8, "color": "#2701ff" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "WOfS_filtered_frequency", }, { # Included as a keyword for the layer "label": "WOfS Statistics", # Included as a keyword for the layer "type": "Wet Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_summary_wet", # The Datacube name for the associated data product "product_name": "wofs_summary", "abstract": """ Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Wet Count: how many times water was detected in observations that were clear. No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["water_observations"] }, "wcs_default_bands": ["count_wet"], "styles": [ { "name": "water_observations", "title": "Wet Count", "abstract": "WOfS summary showing the count of water observations", "needed_bands": ["count_wet"], "color_ramp": [ { "value": 0, "color": "#666666", "alpha": 0 }, { "value": 2, "color": "#890000" }, { "value": 5, "color": "#990000" }, { "value": 10, "color": "#E38400" }, { "value": 25, "color": "#E3DF00" }, { "value": 50, "color": "#A6E300" }, { "value": 100, "color": "#00E32D" }, { "value": 150, "color": "#00E3C8" }, { "value": 200, "color": "#0097E3" }, { "value": 250, "color": "#005FE3" }, { "value": 300, "color": "#000FE3" }, { "value": 350, "color": "#000EA9" }, { "value": 400, "color": "#5700E3", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 100 } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "water_observations", }, { # Included as a keyword for the layer "label": "WOfS Statistics", # Included as a keyword for the layer "type": "Clear Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_summary_clear", # The Datacube name for the associated data product "product_name": "wofs_summary", "abstract": """ Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products which help the understanding of surface water across Australia. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This layer contains Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["clear_observations"] }, "wcs_default_bands": ["count_clear"], "styles": [ { "name": "clear_observations", "title": "Clear Count", "abstract": "WOfS summary showing the count of clear observations", "needed_bands": ["count_clear"], "color_ramp": [ { "value": 0, "color": "#FFFFFF", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 10, "color": "#b21800", "alpha": 1 }, { "value": 100, "color": "#ef8500" }, { "value": 200, "color": "#ffb800" }, { "value": 300, "color": "#ffd300" }, { "value": 400, "color": "#ffe300" }, { "value": 500, "color": "#fff300" }, { "value": 600, "color": "#d0f800" }, { "value": 700, "color": "#a0fd00" }, { "value": 800, "color": "#6ee100" }, { "value": 901, "color": "#39a500" }, { "value": 1000, "color": "#026900", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 100, "axes_position": [0.05, 0.5, 0.89, 0.15] } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "clear_observations", }, { # Included as a keyword for the layer "label": "WOfS Statistics", # Included as a keyword for the layer "type": "Water Summary", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "Water Observations from Space Statistics", # The Datacube name for the associated data product "product_name": "wofs_summary", "abstract": """ Water Observations from Space (WOfS) Statistics is a set of statistical summaries of the WOfS product which combines WOfS observations into summary products that help the understanding of surface water across Australia. WOfS Statistics is calculated from the full depth time series (1986 – 2018). The water detected for each location is summed through time and then compared to the number of clear observations of that location. The result is a percentage value of the number of times water was observed at the location. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time (water summary). This layer contains the Water Summary: the percentage of clear observations which were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics Filtered Summary product, which provide a noise-reduced view of the water summary. For more information please see: https://data.dea.ga.gov.au/WOfS/summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["WOfS_frequency", "WOfS_frequency_blues_transparent"] }, "wcs_default_bands": ["frequency"], "styles": [ { "name": "WOfS_frequency", "title": " Water Summary", "abstract": "WOfS summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#000000", "alpha": 0.0 }, { "value": 0.002, "color": "#000000", "alpha": 0.0 }, { "value": 0.005, "color": "#8e0101", "alpha": 0.25 }, { "value": 0.01, "color": "#cf2200", "alpha": 0.75 }, { "value": 0.02, "color": "#e38400" }, { "value": 0.05, "color": "#e3df00" }, { "value": 0.1, "color": "#a6e300" }, { "value": 0.2, "color": "#62e300" }, { "value": 0.3, "color": "#00e32d" }, { "value": 0.4, "color": "#00e384" }, { "value": 0.5, "color": "#00e3c8" }, { "value": 0.6, "color": "#00c5e3" }, { "value": 0.7, "color": "#0097e3" }, { "value": 0.8, "color": "#005fe3" }, { "value": 0.9, "color": "#000fe3" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, { "name": "WOfS_frequency_blues_transparent", "title": "Water Summary (Blue)", "abstract": "WOfS summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#ffffff", "alpha": 0.0, }, { "value": 0.001, "color": "#d5fef9", "alpha": 0.0, }, { "value": 0.02, "color": "#d5fef9", }, { "value": 0.2, "color": "#71e3ff" }, { "value": 0.4, "color": "#01ccff" }, { "value": 0.6, "color": "#0178ff" }, { "value": 0.8, "color": "#2701ff" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "WOfS_frequency", }, { # Included as a keyword for the layer "label": "WOfS Filtered Statistics", # Included as a keyword for the layer "type": "Confidence", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_filtered_summary_confidence", # The Datacube name for the associated data product "product_name": "wofs_filtered_summary", "abstract": """ Water Observations from Space (WOfS) Filtered Statistics helps provide the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WOfS Filtered Statistics consists of a Confidence layer that compares the WOfS Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WOfS Statistics water summary where Confidence is low. This layer is Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. Areas where there is less than 1% confidence appears black, areas with confidence for between 1% 10% confidence are styled between black and red, areas with 25% confidence are styled yellow, areas with 75% confidence and above correspond to green. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. For more information please see: https://data.dea.ga.gov.au/WOfS/filtered_summary/v2.1.0/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["wofs_confidence"] }, "wcs_default_bands": ["confidence"], "styles": [ { "name": "wofs_confidence", "title": "Confidence", "abstract": "WOfS Confidence", "needed_bands": ["confidence"], "color_ramp": [ { "value": 0, "color": "#000000", }, { "value": 0.01, "color": "#000000" }, { "value": 0.02, "color": "#990000" }, { "value": 0.05, "color": "#CF2200" }, { "value": 0.1, "color": "#E38400" }, { "value": 0.25, "color": "#E3DF00" }, { "value": 0.5, "color": "#A6E300" }, { "value": 0.75, "color": "#62E300" }, { "value": 1.0, "color": "#00E32D" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.25 } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "wofs_confidence", }, { # Included as a keyword for the layer "label": "WOfS Annual Statistics", # Included as a keyword for the layer "type": "Wet Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_annual_summary_wet", # The Datacube name for the associated data product "product_name": "wofs_annual_summary", "abstract": """ Water Observations from Space - Annual Statistics is a set of annual statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - Annual Statistics, a set of annual statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. For more information please see: https://data.dea.ga.gov.au/WOfS/annual_summary/v2.1.5/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["annual_water_observations"] }, "wcs_default_bands": ["count_wet"], "styles": [ { "name": "annual_water_observations", "title": "Wet Count", "abstract": "WOfS annual summary showing the count of water observations", "needed_bands": ["count_wet"], "color_ramp": [ { "value": 0, "color": "#666666", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#990000", "alpha": 1 }, { "value": 2, "color": "#990000" }, { "value": 4, "color": "#E38400" }, { "value": 6, "color": "#E3DF00" }, { "value": 8, "color": "#00E32D" }, { "value": 10, "color": "#00E3C8" }, { "value": 12, "color": "#0097E3" }, { "value": 14, "color": "#005FE3" }, { "value": 16, "color": "#000FE3" }, { "value": 18, "color": "#000EA9" }, { "value": 20, "color": "#5700E3", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10 } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "annual_water_observations", }, { # Included as a keyword for the layer "label": "WOfS Annual Statistics", # Included as a keyword for the layer "type": "Clear Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_annual_summary_clear", # The Datacube name for the associated data product "product_name": "wofs_annual_summary", "abstract": """ Water Observations from Space - Annual Statistics is a set of annual statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - Annual Statistics, a set of annual statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. For more information please see: https://data.dea.ga.gov.au/WOfS/annual_summary/v2.1.5/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["annual_clear_observations"] }, "wcs_default_bands": ["count_clear"], "styles": [ { "name": "annual_clear_observations", "title": "Clear Count", "abstract": "WOfS annual summary showing the count of clear observations", "needed_bands": ["count_clear"], "color_ramp": [ { "value": 0, "color": "#FFFFFF", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#B21800", "alpha": 1 }, { "value": 1, "color": "#B21800" }, { "value": 4, "color": "#ef8500" }, { "value": 8, "color": "#ffb800" }, { "value": 10, "color": "#ffd000" }, { "value": 13, "color": "#fff300" }, { "value": 16, "color": "#fff300" }, { "value": 20, "color": "#c1ec00" }, { "value": 24, "color": "#6ee100" }, { "value": 28, "color": "#39a500" }, { "value": 30, "color": "#026900", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10, "axes_position": [0.05, 0.5, 0.89, 0.15] } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "annual_clear_observations", }, { # Included as a keyword for the layer "label": "WOfS Annual Statistics", # Included as a keyword for the layer "type": "Water Summary", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_annual_summary_statistics", # The Datacube name for the associated data product "product_name": "wofs_annual_summary", "abstract": """ Water Observations from Space - Annual Statistics is a set of annual statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - Annual Statistics, a set of annual statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. For more information please see: https://data.dea.ga.gov.au/WOfS/annual_summary/v2.1.5/Product%20Description.pdf For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["annual_WOfS_frequency", "annual_WOfS_frequency_blues_transparent"] }, "wcs_default_bands": ["frequency"], "styles": [ { "name": "annual_WOfS_frequency", "title": " Water Summary", "abstract": "WOfS annual summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#000000", "alpha": 0.0 }, { "value": 0.02, "color": "#000000", "alpha": 0.0 }, { "value": 0.05, "color": "#8e0101", "alpha": 0.25 }, { "value": 0.1, "color": "#cf2200", "alpha": 0.75 }, { "value": 0.2, "color": "#e38400" }, { "value": 0.3, "color": "#e3df00" }, { "value": 0.4, "color": "#62e300" }, { "value": 0.5, "color": "#00e32d" }, { "value": 0.6, "color": "#00e3c8" }, { "value": 0.7, "color": "#0097e3" }, { "value": 0.8, "color": "#005fe3" }, { "value": 0.9, "color": "#000fe3" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, { "name": "annual_WOfS_frequency_blues_transparent", "title": "Water Summary (Blue)", "abstract": "WOfS annual summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#ffffff", "alpha": 0.0, }, { "value": 0.001, "color": "#d5fef9", "alpha": 0.0, }, { "value": 0.02, "color": "#d5fef9", }, { "value": 0.2, "color": "#71e3ff" }, { "value": 0.4, "color": "#01ccff" }, { "value": 0.6, "color": "#0178ff" }, { "value": 0.8, "color": "#2701ff" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "annual_WOfS_frequency", }, { # Included as a keyword for the layer "label": "WOfS April - October Statistics", # Included as a keyword for the layer "type": "Wet Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_apr_oct_summary_wet", # The Datacube name for the associated data product "product_name": "wofs_apr_oct_summary", "abstract": """ Water Observations from Space - April to October Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - April to October Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_water_observations"] }, "wcs_default_bands": ["count_wet"], "styles": [ { "name": "seasonal_water_observations", "title": "Wet Count", "abstract": "WOfS seasonal summary showing the count of water observations", "needed_bands": ["count_wet"], "color_ramp": [ { "value": 0, "color": "#666666", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#990000", "alpha": 1 }, { "value": 2, "color": "#990000" }, { "value": 4, "color": "#E38400" }, { "value": 6, "color": "#E3DF00" }, { "value": 8, "color": "#00E32D" }, { "value": 10, "color": "#00E3C8" }, { "value": 12, "color": "#0097E3" }, { "value": 14, "color": "#005FE3" }, { "value": 16, "color": "#000FE3" }, { "value": 18, "color": "#000EA9" }, { "value": 20, "color": "#5700E3", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10 } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_water_observations", }, { # Included as a keyword for the layer "label": "WOfS April - October Summary Statistics", # Included as a keyword for the layer "type": "Clear Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_apr_oct_summary_clear", # The Datacube name for the associated data product "product_name": "wofs_apr_oct_summary", "abstract": """ Water Observations from Space - April to October Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - April to October Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_clear_observations"] }, "wcs_default_bands": ["count_clear"], "styles": [ { "name": "seasonal_clear_observations", "title": "Clear Count", "abstract": "WOfS seasonal summary showing the count of clear observations", "needed_bands": ["count_clear"], "color_ramp": [ { "value": 0, "color": "#FFFFFF", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#B21800", "alpha": 1 }, { "value": 1, "color": "#B21800" }, { "value": 4, "color": "#ef8500" }, { "value": 8, "color": "#ffb800" }, { "value": 10, "color": "#ffd000" }, { "value": 13, "color": "#fff300" }, { "value": 16, "color": "#fff300" }, { "value": 20, "color": "#c1ec00" }, { "value": 24, "color": "#6ee100" }, { "value": 28, "color": "#39a500" }, { "value": 30, "color": "#026900", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10, "axes_position": [0.05, 0.5, 0.89, 0.15] } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_clear_observations", }, { # Included as a keyword for the layer "label": "WOfS April - October Statistics", # Included as a keyword for the layer "type": "Water Summary", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_apr_oct_summary_statistics", # The Datacube name for the associated data product "product_name": "wofs_apr_oct_summary", "abstract": """ Water Observations from Space - Seasonal Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - April to October Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_WOfS_frequency", "seasonal_WOfS_frequency_blues_transparent"] }, "wcs_default_bands": ["frequency"], "styles": [ { "name": "seasonal_WOfS_frequency", "title": " Water Summary", "abstract": "WOfS seasonal summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#000000", "alpha": 0.0 }, { "value": 0.02, "color": "#000000", "alpha": 0.0 }, { "value": 0.05, "color": "#8e0101", "alpha": 0.25 }, { "value": 0.1, "color": "#cf2200", "alpha": 0.75 }, { "value": 0.2, "color": "#e38400" }, { "value": 0.3, "color": "#e3df00" }, { "value": 0.4, "color": "#62e300" }, { "value": 0.5, "color": "#00e32d" }, { "value": 0.6, "color": "#00e3c8" }, { "value": 0.7, "color": "#0097e3" }, { "value": 0.8, "color": "#005fe3" }, { "value": 0.9, "color": "#000fe3" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, { "name": "seasonal_WOfS_frequency_blues_transparent", "title": "Water Summary (Blue)", "abstract": "WOfS seasonal summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#ffffff", "alpha": 0.0, }, { "value": 0.001, "color": "#d5fef9", "alpha": 0.0, }, { "value": 0.02, "color": "#d5fef9", }, { "value": 0.2, "color": "#71e3ff" }, { "value": 0.4, "color": "#01ccff" }, { "value": 0.6, "color": "#0178ff" }, { "value": 0.8, "color": "#2701ff" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_WOfS_frequency", }, { # Included as a keyword for the layer "label": "WOfS November - March Statistics", # Included as a keyword for the layer "type": "Wet Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_nov_mar_summary_wet", # The Datacube name for the associated data product "product_name": "wofs_nov_mar_summary", "abstract": """ Water Observations from Space - November to March Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - November to March Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, 1-50 total clear observations of water correlate with red and yellow colours, 100 clear observations of water correlate with green, 200 clear observations of water correlates with light blue, 300 clear observations of water correlates to deep blue and 400 and over observations of clear water correlate to purple. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_water_observations"] }, "wcs_default_bands": ["count_wet"], "styles": [ { "name": "seasonal_water_observations", "title": "Wet Count", "abstract": "WOfS seasonal summary showing the count of water observations", "needed_bands": ["count_wet"], "color_ramp": [ { "value": 0, "color": "#666666", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#990000", "alpha": 1 }, { "value": 2, "color": "#990000" }, { "value": 4, "color": "#E38400" }, { "value": 6, "color": "#E3DF00" }, { "value": 8, "color": "#00E32D" }, { "value": 10, "color": "#00E3C8" }, { "value": 12, "color": "#0097E3" }, { "value": 14, "color": "#005FE3" }, { "value": 16, "color": "#000FE3" }, { "value": 18, "color": "#000EA9" }, { "value": 20, "color": "#5700E3", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10 } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_water_observations", }, { # Included as a keyword for the layer "label": "WOfS November - March Summary Statistics", # Included as a keyword for the layer "type": "Clear Count", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_nov_mar_summary_clear", # The Datacube name for the associated data product "product_name": "wofs_nov_mar_summary", "abstract": """ Water Observations from Space - November to March Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - November to March Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations causes an area to appear transparent, 1-300 total clear observations of water correlate with red and yellow colours, 400 clear observations correlates with light green, 800 clear observations and above correlates with dark green. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_clear_observations"] }, "wcs_default_bands": ["count_clear"], "styles": [ { "name": "seasonal_clear_observations", "title": "Clear Count", "abstract": "WOfS seasonal summary showing the count of clear observations", "needed_bands": ["count_clear"], "color_ramp": [ { "value": 0, "color": "#FFFFFF", "alpha": 0 }, { # purely for legend display # we should not get fractional # values in this styles "value": 0.2, "color": "#B21800", "alpha": 1 }, { "value": 1, "color": "#B21800" }, { "value": 4, "color": "#ef8500" }, { "value": 8, "color": "#ffb800" }, { "value": 10, "color": "#ffd000" }, { "value": 13, "color": "#fff300" }, { "value": 16, "color": "#fff300" }, { "value": 20, "color": "#c1ec00" }, { "value": 24, "color": "#6ee100" }, { "value": 28, "color": "#39a500" }, { "value": 30, "color": "#026900", "legend": { "prefix": ">" } } ], "legend": { "radix_point": 0, "scale_by": 1, "major_ticks": 10, "axes_position": [0.05, 0.5, 0.89, 0.15] } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_clear_observations", }, { # Included as a keyword for the layer "label": "WOfS November - March Statistics", # Included as a keyword for the layer "type": "Water Summary", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_nov_mar_summary_statistics", # The Datacube name for the associated data product "product_name": "wofs_nov_mar_summary", "abstract": """ Water Observations from Space - Seasonal Statistics is a set of seasonal statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time. This product is Water Observations from Space - November to March Statistics, a set of seasonal statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary. This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage). No clear observations of water causes an area to appear transparent, few clear observations of water correlate with red and yellow colours, deep blue and purple correspond to an area being wet through 90%-100% of clear observations. For service status information, see https://status.dea.ga.gov.au""", "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "legend": { # "url": "" "styles": ["seasonal_WOfS_frequency", "seasonal_WOfS_frequency_blues_transparent"] }, "wcs_default_bands": ["frequency"], "styles": [ { "name": "seasonal_WOfS_frequency", "title": " Water Summary", "abstract": "WOfS seasonal summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#000000", "alpha": 0.0 }, { "value": 0.02, "color": "#000000", "alpha": 0.0 }, { "value": 0.05, "color": "#8e0101", "alpha": 0.25 }, { "value": 0.1, "color": "#cf2200", "alpha": 0.75 }, { "value": 0.2, "color": "#e38400" }, { "value": 0.3, "color": "#e3df00" }, { "value": 0.4, "color": "#62e300" }, { "value": 0.5, "color": "#00e32d" }, { "value": 0.6, "color": "#00e3c8" }, { "value": 0.7, "color": "#0097e3" }, { "value": 0.8, "color": "#005fe3" }, { "value": 0.9, "color": "#000fe3" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, { "name": "seasonal_WOfS_frequency_blues_transparent", "title": "Water Summary (Blue)", "abstract": "WOfS seasonal summary showing the frequency of Wetness", "needed_bands": ["frequency"], "color_ramp": [ { "value": 0.0, "color": "#ffffff", "alpha": 0.0, }, { "value": 0.001, "color": "#d5fef9", "alpha": 0.0, }, { "value": 0.02, "color": "#d5fef9", }, { "value": 0.2, "color": "#71e3ff" }, { "value": 0.4, "color": "#01ccff" }, { "value": 0.6, "color": "#0178ff" }, { "value": 0.8, "color": "#2701ff" }, { "value": 1.0, "color": "#5700e3" } ], "legend": { "units": "%", "radix_point": 0, "scale_by": 100.0, "major_ticks": 0.1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "seasonal_WOfS_frequency", }, { # Included as a keyword for the layer "label": "WOfS Daily Observations", # Included as a keyword for the layer "type": "albers", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "wofs_albers", # The Datacube name for the associated data product "product_name": "wofs_albers", "abstract": """ Water Observations from Space (WOfS) provides surface water observations derived from satellite imagery for all of Australia. The current product (Version 2.1.5) includes observations taken from 1986 to the present, from the Landsat 5, 7 and 8 satellites. WOfS covers all of mainland Australia and Tasmania but excludes off-shore Territories. The WOfS product allows users to get a better understanding of where water is normally present in a landscape, where water is seldom observed, and where inundation has occurred occasionally. Data is provided as Water Observation Feature Layers (WOFLs), in a 1 to 1 relationship with the input satellite data. Hence there is one WOFL for each satellite dataset processed for the occurrence of water. The details of the WOfS algorithm and derived statistics are available at http://dx.doi.org/10.1016/j.rse.2015.11.003. For service status information, see https://status.dea.ga.gov.au""", #"pq_band": "water", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [200, 180, 180, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] & data[band].attrs['nodata']) == 0, # "pq_manual_merge": True, # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [ "nodata", "noncontiguous", ], # Include UTC dates for GSKY lookup "feature_info_include_utc_dates": True, "data_manual_merge": False, "always_fetch_bands": [ ], "apply_solar_corrections": False, "fuse_func": "datacube_wms.wms_utils.wofls_fuser", # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "legend": { "styles": ["observations"] }, "wcs_default_bands": ["water"], "styles": [ { "name": "observations", "title": "Observations", "abstract": "Observations", "value_map": { "water": [ { "title": "Invalid", "abstract": "Slope or Cloud", "flags": { "or": { "terrain_or_low_angle": True, "cloud_shadow": True, "cloud": True, "high_slope": True, "noncontiguous": True } }, "color": "#707070" }, { # Possible Sea Glint, also mark as invalid "title": "", "abstract": "", "flags": { "dry": True, "sea": True }, "color": "#707070" }, { "title": "Dry", "abstract": "Dry", "flags": { "dry": True, "sea": False, }, "color": "#D99694" }, { "title": "Wet", "abstract": "Wet or Sea", "flags": { "or": { "wet": True, "sea": True } }, "color": "#4F81BD" } ] } }, { "name": "wet", "title": "Wet Only", "abstract": "Wet Only", "value_map": { "water": [ { "title": "Invalid", "abstract": "Slope or Cloud", "flags": { "or": { "terrain_or_low_angle": True, "cloud_shadow": True, "cloud": True, "high_slope": True, "noncontiguous": True } }, "color": "#707070", "mask": True }, { # Possible Sea Glint, also mark as invalid "title": "", "abstract": "", "flags": { "dry": True, "sea": True }, "color": "#707070", "mask": True }, { "title": "Dry", "abstract": "Dry", "flags": { "dry": True, "sea": False, }, "color": "#D99694", "mask": True }, { "title": "Wet", "abstract": "Wet or Sea", "flags": { "or": { "wet": True, "sea": True } }, "color": "#4F81BD" } ] } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "observations", } ], }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "Sentinel-2 NRT", "title": "Near Real-Time", "abstract": "This is a 90-day rolling archive of daily Sentinel-2 Near Real Time data. " "The Near Real-Time capability provides analysis-ready data " "that is processed on receipt using the best-available ancillary information at the time to " "provide atmospheric corrections. For more information see " "http://pid.geoscience.gov.au/dataset/ga/122229", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "Sentinel 2 (A and B combined)", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "Surface Reflectance", "abstract":""" This is a 90-day rolling archive of daily Sentinel-2 Near Real Time data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections. For more information see http://pid.geoscience.gov.au/dataset/ga/122229 The Normalised Difference Chlorophyll Index (NDCI) is based on the method of Mishra & Mishra 2012, and adapted to bands on the Sentinel-2A & B sensors. The index indicates levels of chlorophyll-a (chl-a) concentrations in complex turbid productive waters such as those encountered in many inland water bodies. The index has not been validated in Australian waters, and there are a range of environmental conditions that may have an effect on the accuracy of the derived index values in this test implementation, including: - Influence on the remote sensing signal from nearby land and/or atmospheric effects - Optically shallow water - Cloud cover Mishra, S., Mishra, D.R., 2012. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, Remote Sensing of Urban Environments 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016 For service status information, see https://status.dea.ga.gov.au""", # The WMS name for the layer "name": "s2_nrt_granule_nbar_t", # The Datacube name for the associated data product "multi_product": True, "product_name": ["s2a_nrt_granule", "s2b_nrt_granule"], # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_nrt_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi", "ndci"] }, "wcs_default_bands": ["nbart_red", "nbart_green", "nbart_blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nbart_nir_1"] - data["nbart_red"]) / (data["nbart_nir_1"] + data["nbart_red"]), "needed_bands": ["nbart_red", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, NIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["nbart_green"] - data["nbart_nir_1"]) / ( data["nbart_nir_1"] + data["nbart_green"]), "needed_bands": ["nbart_green", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "ndci", "title": "NDCI - Red Edge, Red", "abstract": "Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll", "index_function": lambda data: (data["nbart_red_edge_1"] - data["nbart_red"]) / (data["nbart_red_edge_1"] + data["nbart_red"]).where(((data["nbart_green"] - data["nbart_swir_3"]) / (data["nbart_green"] + data["nbart_swir_3"])) > 0.1), "needed_bands": ["nbart_red_edge_1", "nbart_red", "nbart_green", "nbart_swir_3"], "color_ramp": [ { "value": -0.1, "color": "#1696FF", "legend": { "prefix" : "<" } }, { "value": -0.1, "color": "#1696FF" }, { "value": 0.0, "color": "#00FFDF", "legend": { } }, { "value": 0.1, "color": "#FFF50E", }, { "value": 0.2, "color": "#FFB50A", "legend": { } }, { "value": 0.4, "color": "#FF530D", }, { "value": 0.5, "color": "#FF0000", "legend": { "prefix": ">" } } ] }, { "name": "aerosol", "title": "Narrow Blue - 440", "abstract": "Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm", "components": { "red": { "nbart_coastal_aerosol": 1.0 }, "green": { "nbart_coastal_aerosol": 1.0 }, "blue": { "nbart_coastal_aerosol": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "blue", "title": "Blue - 490", "abstract": "Blue band, approximately 453nm to 511nm", "components": { "red": { "nbart_blue": 1.0 }, "green": { "nbart_blue": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, approximately 534nm to 588nm", "components": { "red": { "nbart_green": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 670", "abstract": "Red band, roughly 637nm to 672nm", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_red": 1.0 }, "blue": { "nbart_red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_1", "title": "Vegetation Red Edge - 710", "abstract": "Near infra-red band, centred on 710nm", "components": { "red": { "nbart_red_edge_1": 1.0 }, "green": { "nbart_red_edge_1": 1.0 }, "blue": { "nbart_red_edge_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_2", "title": "Vegetation Red Edge - 740", "abstract": "Near infra-red band, centred on 740nm", "components": { "red": { "nbart_red_edge_2": 1.0 }, "green": { "nbart_red_edge_2": 1.0 }, "blue": { "nbart_red_edge_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_3", "title": "Vegetation Red Edge - 780", "abstract": "Near infra-red band, centred on 780nm", "components": { "red": { "nbart_red_edge_3": 1.0 }, "green": { "nbart_red_edge_3": 1.0 }, "blue": { "nbart_red_edge_3": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 840", "abstract": "Near infra-red band, roughly 853nm to 876nm", "components": { "red": { "nbart_nir_1": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_nir_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "narrow_nir", "title": "Narrow Near Infrared - 870", "abstract": "Near infra-red band, centred on 865nm", "components": { "red": { "nbart_nir_2": 1.0 }, "green": { "nbart_nir_2": 1.0 }, "blue": { "nbart_nir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1610", "abstract": "Short wave infra-red band 1, roughly 1575nm to 1647nm", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_swir_2": 1.0 }, "blue": { "nbart_swir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2190", "abstract": "Short wave infra-red band 2, roughly 2117nm to 2285nm", "components": { "red": { "nbart_swir_3": 1.0 }, "green": { "nbart_swir_3": 1.0 }, "blue": { "nbart_swir_3": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, { # Included as a keyword for the layer "label": "Sentinel 2B", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "Surface Reflectance", "abstract":""" This is a 90-day rolling archive of daily Sentinel-2 Near Real Time data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections. For more information see http://pid.geoscience.gov.au/dataset/ga/122229 The Normalised Difference Chlorophyll Index (NDCI) is based on the method of Mishra & Mishra 2012, and adapted to bands on the Sentinel-2A & B sensors. The index indicates levels of chlorophyll-a (chl-a) concentrations in complex turbid productive waters such as those encountered in many inland water bodies. The index has not been validated in Australian waters, and there are a range of environmental conditions that may have an effect on the accuracy of the derived index values in this test implementation, including: - Influence on the remote sensing signal from nearby land and/or atmospheric effects - Optically shallow water - Cloud cover Mishra, S., Mishra, D.R., 2012. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, Remote Sensing of Urban Environments 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016 For service status information, see https://status.dea.ga.gov.au""", # The WMS name for the layer "name": "s2b_nrt_granule_nbar_t", # The Datacube name for the associated data product "product_name": "s2b_nrt_granule", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_nrt_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi", "ndci"] }, "wcs_default_bands": ["nbart_red", "nbart_green", "nbart_blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nbart_nir_1"] - data["nbart_red"]) / (data["nbart_nir_1"] + data["nbart_red"]), "needed_bands": ["nbart_red", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, NIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["nbart_green"] - data["nbart_nir_1"]) / ( data["nbart_nir_1"] + data["nbart_green"]), "needed_bands": ["nbart_green", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "ndci", "title": "NDCI - Red Edge, Red", "abstract": "Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll", "index_function": lambda data: (data["nbart_red_edge_1"] - data["nbart_red"]) / (data["nbart_red_edge_1"] + data["nbart_red"]).where(((data["nbart_green"] - data["nbart_swir_3"]) / (data["nbart_green"] + data["nbart_swir_3"])) > 0.1), "needed_bands": ["nbart_red_edge_1", "nbart_red", "nbart_green", "nbart_swir_3"], "color_ramp": [ { "value": -0.1, "color": "#1696FF", "legend": { "prefix" : "<" } }, { "value": -0.1, "color": "#1696FF" }, { "value": 0.0, "color": "#00FFDF", "legend": { } }, { "value": 0.1, "color": "#FFF50E", }, { "value": 0.2, "color": "#FFB50A", "legend": { } }, { "value": 0.4, "color": "#FF530D", }, { "value": 0.5, "color": "#FF0000", "legend": { "prefix": ">" } } ] }, { "name": "aerosol", "title": "Narrow Blue - 440", "abstract": "Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm", "components": { "red": { "nbart_coastal_aerosol": 1.0 }, "green": { "nbart_coastal_aerosol": 1.0 }, "blue": { "nbart_coastal_aerosol": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "blue", "title": "Blue - 490", "abstract": "Blue band, approximately 453nm to 511nm", "components": { "red": { "nbart_blue": 1.0 }, "green": { "nbart_blue": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, approximately 534nm to 588nm", "components": { "red": { "nbart_green": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 670", "abstract": "Red band, roughly 637nm to 672nm", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_red": 1.0 }, "blue": { "nbart_red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_1", "title": "Vegetation Red Edge - 710", "abstract": "Near infra-red band, centred on 710nm", "components": { "red": { "nbart_red_edge_1": 1.0 }, "green": { "nbart_red_edge_1": 1.0 }, "blue": { "nbart_red_edge_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_2", "title": "Vegetation Red Edge - 740", "abstract": "Near infra-red band, centred on 740nm", "components": { "red": { "nbart_red_edge_2": 1.0 }, "green": { "nbart_red_edge_2": 1.0 }, "blue": { "nbart_red_edge_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_3", "title": "Vegetation Red Edge - 780", "abstract": "Near infra-red band, centred on 780nm", "components": { "red": { "nbart_red_edge_3": 1.0 }, "green": { "nbart_red_edge_3": 1.0 }, "blue": { "nbart_red_edge_3": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 840", "abstract": "Near infra-red band, roughly 853nm to 876nm", "components": { "red": { "nbart_nir_1": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_nir_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "narrow_nir", "title": "Narrow Near Infrared - 870", "abstract": "Near infra-red band, centred on 865nm", "components": { "red": { "nbart_nir_2": 1.0 }, "green": { "nbart_nir_2": 1.0 }, "blue": { "nbart_nir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1610", "abstract": "Short wave infra-red band 1, roughly 1575nm to 1647nm", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_swir_2": 1.0 }, "blue": { "nbart_swir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2190", "abstract": "Short wave infra-red band 2, roughly 2117nm to 2285nm", "components": { "red": { "nbart_swir_3": 1.0 }, "green": { "nbart_swir_3": 1.0 }, "blue": { "nbart_swir_3": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, { # Included as a keyword for the layer "label": "Sentinel 2A", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "Surface Reflectance", "abstract": """ This is a 90-day rolling archive of daily Sentinel-2 Near Real Time data. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections. For more information see http://pid.geoscience.gov.au/dataset/ga/122229 The Normalised Difference Chlorophyll Index (NDCI) is based on the method of Mishra & Mishra 2012, and adapted to bands on the Sentinel-2A & B sensors. The index indicates levels of chlorophyll-a (chl-a) concentrations in complex turbid productive waters such as those encountered in many inland water bodies. The index has not been validated in Australian waters, and there are a range of environmental conditions that may have an effect on the accuracy of the derived index values in this test implementation, including: - Influence on the remote sensing signal from nearby land and/or atmospheric effects - Optically shallow water - Cloud cover Mishra, S., Mishra, D.R., 2012. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, Remote Sensing of Urban Environments 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016 For service status information, see https://status.dea.ga.gov.au""", # The WMS name for the layer "name": "s2a_nrt_granule_nbar_t", # The Datacube name for the associated data product "product_name": "s2a_nrt_granule", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_nrt_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 15.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi", "ndci"] }, "wcs_default_bands": ["nbart_red", "nbart_green", "nbart_blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nbart_nir_1"] - data["nbart_red"]) / ( data["nbart_nir_1"] + data["nbart_red"]), "needed_bands": ["nbart_red", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, NIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["nbart_green"] - data["nbart_nir_1"]) / ( data["nbart_nir_1"] + data["nbart_green"]), "needed_bands": ["nbart_green", "nbart_nir_1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, { "name": "ndci", "title": "NDCI - Red Edge, Red", "abstract": "Normalised Difference Chlorophyll Index - a derived index that correlates well with the existence of chlorophyll", "index_function": lambda data: (data["nbart_red_edge_1"] - data["nbart_red"]) / (data["nbart_red_edge_1"] + data["nbart_red"]).where(((data["nbart_green"] - data["nbart_swir_3"]) / (data["nbart_green"] + data["nbart_swir_3"])) > 0.1), "needed_bands": ["nbart_red_edge_1", "nbart_red", "nbart_green", "nbart_swir_3"], "color_ramp": [ { "value": -0.1, "color": "#1696FF", "legend": { "prefix" : "<" } }, { "value": -0.1, "color": "#1696FF" }, { "value": 0.0, "color": "#00FFDF", "legend": { } }, { "value": 0.1, "color": "#FFF50E", }, { "value": 0.2, "color": "#FFB50A", "legend": { } }, { "value": 0.4, "color": "#FF530D", }, { "value": 0.5, "color": "#FF0000", "legend": { "prefix": ">" } } ] }, { "name": "aerosol", "title": "Narrow Blue - 440", "abstract": "Coastal Aerosol or Narrow Blue band, approximately 435nm to 450nm", "components": { "red": { "nbart_coastal_aerosol": 1.0 }, "green": { "nbart_coastal_aerosol": 1.0 }, "blue": { "nbart_coastal_aerosol": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "blue", "title": "Blue - 490", "abstract": "Blue band, approximately 453nm to 511nm", "components": { "red": { "nbart_blue": 1.0 }, "green": { "nbart_blue": 1.0 }, "blue": { "nbart_blue": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "green", "title": "Green - 560", "abstract": "Green band, approximately 534nm to 588nm", "components": { "red": { "nbart_green": 1.0 }, "green": { "nbart_green": 1.0 }, "blue": { "nbart_green": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red", "title": "Red - 670", "abstract": "Red band, roughly 637nm to 672nm", "components": { "red": { "nbart_red": 1.0 }, "green": { "nbart_red": 1.0 }, "blue": { "nbart_red": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_1", "title": "Vegetation Red Edge - 710", "abstract": "Near infra-red band, centred on 710nm", "components": { "red": { "nbart_red_edge_1": 1.0 }, "green": { "nbart_red_edge_1": 1.0 }, "blue": { "nbart_red_edge_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_2", "title": "Vegetation Red Edge - 740", "abstract": "Near infra-red band, centred on 740nm", "components": { "red": { "nbart_red_edge_2": 1.0 }, "green": { "nbart_red_edge_2": 1.0 }, "blue": { "nbart_red_edge_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "red_edge_3", "title": "Vegetation Red Edge - 780", "abstract": "Near infra-red band, centred on 780nm", "components": { "red": { "nbart_red_edge_3": 1.0 }, "green": { "nbart_red_edge_3": 1.0 }, "blue": { "nbart_red_edge_3": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "nir", "title": "Near Infrared (NIR) - 840", "abstract": "Near infra-red band, roughly 853nm to 876nm", "components": { "red": { "nbart_nir_1": 1.0 }, "green": { "nbart_nir_1": 1.0 }, "blue": { "nbart_nir_1": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "narrow_nir", "title": "Narrow Near Infrared - 870", "abstract": "Near infra-red band, centred on 865nm", "components": { "red": { "nbart_nir_2": 1.0 }, "green": { "nbart_nir_2": 1.0 }, "blue": { "nbart_nir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir1", "title": "Shortwave Infrared (SWIR) - 1610", "abstract": "Short wave infra-red band 1, roughly 1575nm to 1647nm", "components": { "red": { "nbart_swir_2": 1.0 }, "green": { "nbart_swir_2": 1.0 }, "blue": { "nbart_swir_2": 1.0 } }, "scale_range": [0.0, 3000.0] }, { "name": "swir2", "title": "Shortwave Infrared (SWIR) - 2190", "abstract": "Short wave infra-red band 2, roughly 2117nm to 2285nm", "components": { "red": { "nbart_swir_3": 1.0 }, "green": { "nbart_swir_3": 1.0 }, "blue": { "nbart_swir_3": 1.0 } }, "scale_range": [0.0, 3000.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, ], }, { "name": "multi_scale_topographic_position", "title": "Multi-Scale Topographic Position", "abstract": "", "products": [ { "label": "Multi-Scale Topographic Position", "abstract": """A Multi-scale topographic position image of Australia has been generated by combining a topographic position index and topographic ruggedness. Topographic Position Index (TPI) measures the topographic slope position of landforms. Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain attributes are therefore scale dependent and will vary according to the size of the analysis window. Based on an algorithm developed by Lindsay et al. (2015) we have generated multi-scale topographic position model over the Australian continent using 3 second resolution (~90m) DEM derived from the Shuttle Radar Topography Mission (SRTM). The algorithm calculates topographic position scaled by the corresponding ruggedness across three spatial scales (window sizes) of 0.2-8.1 Km; 8.2-65.2 Km and 65.6-147.6 Km. The derived ternary image captures variations in topographic position across these spatial scales (blue local, green intermediate and red regional) and gives a rich representation of nested landform features that have broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Lindsay, J, B., Cockburn, J.M.H. and Russell, H.A.J. 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology 245, 51–61. For service status information, see https://status.dea.ga.gov.au""", "type": "1 degree tile", "variant": "", "name": "multi_scale_topographic_position", "product_name": "multi_scale_topographic_position", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["regional", "intermediate", "local"], "apply_solar_corrections": False, "legend": { "url": "https://data.dea.ga.gov.au/multi-scale-topographic-position/mstp_legend.png", # "styles": ["mstp_rgb"] }, "wcs_default_bands": ["regional", "intermediate", "local"], "styles": [ { "name": "mstp_rgb", "title": "Multi-scale Topographic Position", "abstract": "red regional, green intermediate and blue local", "components": { "red": { "regional": 1.0 }, "green": { "intermediate": 1.0 }, "blue": { "local": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 255.0] }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "mstp_rgb", }, ] }, { "name": "weathering_intensity", "title": "Weathering Intensity", "abstract": "", "products": [ { "label": "Weathering Intensity", "abstract": "Weathering intensity or the degree of weathering is an important characteristic of the " "earth’s surface that has a significant influence on the chemical and physical properties " "of surface materials. Weathering intensity largely controls the degree to which primary " "minerals are altered to secondary components including clay minerals and oxides. The " "degree of surface weathering is particularly important in Australia where variations in " "weathering intensity correspond to the nature and distribution of regolith (weathered " "bedrock and sediments) which mantles approximately 90% of the Australian continent. The " "weathering intensity prediction has been generated using the Random Forest decision tree " "machine learning algorithm. The algorithm is used to establish predictive relationships " "between field estimates of the degree of weathering and a comprehensive suite of " "covariate or predictive datasets. The covariates used to generate the model include " "satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. " "Correlations between the training dataset and the covariates were explored through the " "generation of 300 random tree models. An r-squared correlation of 0.85 is reported using " "5 K-fold cross-validation. The mean of the 300 models is used for predicting the " "weathering intensity and the uncertainty in the weathering intensity is estimated at " "each location via the standard deviation in the 300 model values. The predictive " "weathering intensity model is an estimate of the degree of surface weathering only. The " "interpretation of the weathering intensity is different for in-situ or residual " "landscapes compared with transported materials within depositional landscapes. In " "residual landscapes, weathering process are operating locally whereas in depositional " "landscapes the model is reflecting the degree of weathering either prior to erosion and " "subsequent deposition, or weathering of sediments after being deposited. The weathering " "intensity model has broad utility in assisting mineral exploration in variably weathered " "geochemical landscapes across the Australian continent, mapping chemical and physical " "attributes of soils in agricultural landscapes and in understanding the nature and " "distribution of weathering processes occurring within the upper regolith." "For service status information, see https://status.dea.ga.gov.au", "type": "1 degree tile", "variant": "", "name": "weathering_intensity", "product_name": "weathering_intensity", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["intensity"], "apply_solar_corrections": False, "legend": { "styles": ["wii"] }, "wcs_default_bands": ["intensity"], "styles": [ { "name": "wii", "title": "Weathering Intensity", "abstract": "Weather Intensity Index (0-6)", "needed_bands": ["intensity"], "color_ramp": [ { 'value': 0, 'color': '#ffffff', 'alpha': 0 }, { 'value': 1, 'color': '#2972a8', 'legend': { 'label': 'Low\nClass 1' } }, { 'value': 3.5, 'color': '#fcf24b' }, { 'value': 6, 'color': '#a02406', 'legend': { 'label': 'High\nClass 6' } } ], "legend": { "axes_position": [0.1, 0.5, 0.8, 0.15] } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "wii", }, ] }, { "name": "fcp_green_veg", "title": "Fractional Cover Percentiles - Green Vegetation", "abstract": "", "products": [ { "label": "Fractional Cover Percentiles - Green Vegetation", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program, for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover This contains the percentage of green vegetation per pixel at the 10th, 50th (median) and 90th percentiles for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_green_veg", "product_name": "fc_percentile_albers_annual", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [ ], "apply_solar_corrections": False, "legend": { "styles": ["green_veg_10"] }, "wcs_default_bands": ["PV_PC_10", "PV_PC_50", "PV_PC_90"], "styles": [ { "name": "green_veg_10", "title": "10th Percentile", "abstract": "10th Percentile of Green Vegetation", "needed_bands": ["PV_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc', 'legend': {} }, { 'value': 25, 'color': '#c2e699', 'legend': {} }, { 'value': 50, 'color': '#78c679', 'legend': {} }, { 'value': 75, 'color': '#31a354', 'legend': {} }, { 'value': 100, 'color': '#006837', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Green Vegetation", "rcParams": { "font.size": 9 } } }, { "name": "green_veg_50", "title": "50th Percentile", "abstract": "50th Percentile of Green Vegetation", "needed_bands": ["PV_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc' }, { 'value': 25, 'color': '#c2e699' }, { 'value': 50, 'color': '#78c679' }, { 'value': 75, 'color': '#31a354' }, { 'value': 100, 'color': '#006837' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "green_veg_90", "title": "90th Percentile", "abstract": "90th Percentile of Green Vegetation", "needed_bands": ["PV_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc' }, { 'value': 25, 'color': '#c2e699' }, { 'value': 50, 'color': '#78c679' }, { 'value': 75, 'color': '#31a354' }, { 'value': 100, 'color': '#006837' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "green_veg_10", }, ] }, { "name": "fcp_non_green_veg", "title": "Fractional Cover Percentiles - Non Green Vegetation", "abstract": "", "products": [ { "label": "Fractional Cover Percentiles - Non Green Vegetation", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program, for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover This contains the percentage of non-green vegetation per pixel at the 10th, 50th (median) and 90th percentiles for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_non_green_veg", "product_name": "fc_percentile_albers_annual", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "styles": ["non_green_veg_10"] }, "wcs_default_bands": ["NPV_PC_10", "NPV_PC_50", "NPV_PC_90"], "styles": [ { "name": "non_green_veg_10", "title": "10th Percentile", "abstract": "10th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4', 'legend': {} }, { 'value': 25, 'color': '#fed98e', 'legend': {} }, { 'value': 50, 'color': '#fe9929', 'legend': {} }, { 'value': 75, 'color': '#d95f0e', 'legend': {} }, { 'value': 100, 'color': '#993404', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Non-Green Vegetation", "rcParams": { "font.size": 9 } } }, { "name": "non_green_veg_50", "title": "50th Percentile", "abstract": "50th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4' }, { 'value': 25, 'color': '#fed98e' }, { 'value': 50, 'color': '#fe9929' }, { 'value': 75, 'color': '#d95f0e' }, { 'value': 100, 'color': '#993404' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "non_green_veg_90", "title": "90th Percentile", "abstract": "90th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4' }, { 'value': 25, 'color': '#fed98e' }, { 'value': 50, 'color': '#fe9929' }, { 'value': 75, 'color': '#d95f0e' }, { 'value': 100, 'color': '#993404' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "non_green_veg_10", }, ] }, { "name": "fcp_bare_soil", "title": "Fractional Cover Percentiles - Bare Soil", "abstract": "", "products": [ { "label": "Fractional Cover Percentiles - Bare Soil", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover This contains the percentage of bare soil per pixel at the 10th, 50th (median) and 90th percentiles for observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_bare_ground", "product_name": "fc_percentile_albers_annual", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "styles": ["bare_ground_10"] }, "wcs_default_bands": ["BS_PC_10", "BS_PC_50", "BS_PC_90"], "styles": [ { "name": "bare_ground_10", "title": "10th Percentile", "abstract": "10th Percentile of Bare Soil", "needed_bands": ["BS_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#feebe2', 'legend': {} }, { 'value': 25, 'color': '#fbb4b9', 'legend': {} }, { 'value': 50, 'color': '#f768a1', 'legend': {} }, { 'value': 75, 'color': '#c51b8a', 'legend': {} }, { 'value': 100, 'color': '#7a0177', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Bare Soil", "rcParams": { "font.size": 9 } } }, { "name": "bare_ground_50", "title": "50th Percentile", "abstract": "50th Percentile of Bare Soil", "needed_bands": ["BS_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#feebe2' }, { 'value': 25, 'color': '#fbb4b9' }, { 'value': 50, 'color': '#f768a1' }, { 'value': 75, 'color': '#c51b8a' }, { 'value': 100, 'color': '#7a0177' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "bare_ground_90", "title": "90th Percentile", "abstract": "90th Percentile of Bare Soil", "needed_bands": ["BS_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#feebe2' }, { 'value': 25, 'color': '#fbb4b9' }, { 'value': 50, 'color': '#f768a1' }, { 'value': 75, 'color': '#c51b8a' }, { 'value': 100, 'color': '#7a0177' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "bare_ground_10", }, ] }, { "name": "fcp_rgb", "title": "Fractional Cover Percentiles - Median", "abstract": "", "products": [ { "label": "Fractional Cover Percentiles - Median", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. This contains a three band combination of the 50th Percentile for green vegetation, non green vegetation and bare soil observations acquired in each full calendar year (1st of January - 31st December) from 1987 to the most recent full calendar year. Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_rgb", "product_name": "fc_percentile_albers_annual", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "url": "https://data.dea.ga.gov.au/fractional-cover/fc-percentile/annual/v2.1.0/fcp_legend.png", }, "wcs_default_bands": ["BS_PC_50", "PV_PC_50", "NPV_PC_50"], "styles": [ { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "BS_PC_50": 1.0 }, "green": { "PV_PC_50": 1.0 }, "blue": { "NPV_PC_50": 1.0 } }, "scale_range": [0.0, 100.0], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, ] }, { "name": "fcp_seasonal", "title": "Fractional Cover Percentiles Seasonal", "abstract": "", "products": [ { "label": "Green Vegetation", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program, for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover FC-PERCENTILE-SEASONAL-SUMMARY, this contains a (10th, 50th and 90th percentile) of BS, PV and NPV of observations acquired within each calendar season (DJF, MAM, JJA, SON). This product is available for the most recent 8 seasons Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_seasonal_green_veg", "product_name": "fc_percentile_albers_seasonal", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [ ], "apply_solar_corrections": False, "legend": { "styles": ["green_veg_10"] }, "wcs_default_bands": ["PV_PC_10", "PV_PC_50", "PV_PC_90"], "styles": [ { "name": "green_veg_10", "title": "10th Percentile", "abstract": "10th Percentile of Green Vegetation", "needed_bands": ["PV_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc', 'legend': {} }, { 'value': 25, 'color': '#c2e699', 'legend': {} }, { 'value': 50, 'color': '#78c679', 'legend': {} }, { 'value': 75, 'color': '#31a354', 'legend': {} }, { 'value': 100, 'color': '#006837', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Green Vegetation", "rcParams": { "font.size": 9 } } }, { "name": "green_veg_50", "title": "50th Percentile", "abstract": "50th Percentile of Green Vegetation", "needed_bands": ["PV_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc' }, { 'value': 25, 'color': '#c2e699' }, { 'value': 50, 'color': '#78c679' }, { 'value': 75, 'color': '#31a354' }, { 'value': 100, 'color': '#006837' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "green_veg_90", "title": "90th Percentile", "abstract": "90th Percentile of Green Vegetation", "needed_bands": ["PV_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#ffffcc' }, { 'value': 25, 'color': '#c2e699' }, { 'value': 50, 'color': '#78c679' }, { 'value': 75, 'color': '#31a354' }, { 'value': 100, 'color': '#006837' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "green_veg_10", }, { "label": "Non Green Vegetation", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program, for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover FC-PERCENTILE-SEASONAL-SUMMARY, this contains a (10th, 50th and 90th percentile) of BS, PV and NPV of observations acquired within each calendar season (DJF, MAM, JJA, SON). This product is available for the most recent 8 seasons Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_seasonal_non_green_veg", "product_name": "fc_percentile_albers_seasonal", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "styles": ["non_green_veg_10"] }, "wcs_default_bands": ["NPV_PC_10", "NPV_PC_50", "NPV_PC_90"], "styles": [ { "name": "non_green_veg_10", "title": "10th Percentile", "abstract": "10th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4', 'legend': {} }, { 'value': 25, 'color': '#fed98e', 'legend': {} }, { 'value': 50, 'color': '#fe9929', 'legend': {} }, { 'value': 75, 'color': '#d95f0e', 'legend': {} }, { 'value': 100, 'color': '#993404', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Non-Green Vegetation", "rcParams": { "font.size": 9 } } }, { "name": "non_green_veg_50", "title": "50th Percentile", "abstract": "50th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4' }, { 'value': 25, 'color': '#fed98e' }, { 'value': 50, 'color': '#fe9929' }, { 'value': 75, 'color': '#d95f0e' }, { 'value': 100, 'color': '#993404' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "non_green_veg_90", "title": "90th Percentile", "abstract": "90th Percentile of Non Green Vegetation", "needed_bands": ["NPV_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#ffffd4' }, { 'value': 25, 'color': '#fed98e' }, { 'value': 50, 'color': '#fe9929' }, { 'value': 75, 'color': '#d95f0e' }, { 'value': 100, 'color': '#993404' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "non_green_veg_10", }, { "label": "Bare Soil", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program for more information please see data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover FC-PERCENTILE-SEASONAL-SUMMARY, this contains a (10th, 50th and 90th percentile) of BS, PV and NPV of observations acquired within each calendar season (DJF, MAM, JJA, SON). This product is available for the most recent 8 seasons Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_seasonal_bare_ground", "product_name": "fc_percentile_albers_seasonal", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "styles": ["bare_ground_10"] }, "wcs_default_bands": ["BS_PC_10", "BS_PC_50", "BS_PC_90"], "styles": [ { "name": "bare_ground_10", "title": "10th Percentile", "abstract": "10th Percentile of Bare Soil", "needed_bands": ["BS_PC_10"], "color_ramp": [ { 'value': 0, 'color': '#feebe2', 'legend': {} }, { 'value': 25, 'color': '#fbb4b9', 'legend': {} }, { 'value': 50, 'color': '#f768a1', 'legend': {} }, { 'value': 75, 'color': '#c51b8a', 'legend': {} }, { 'value': 100, 'color': '#7a0177', 'legend': {} } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], "legend": { "units": "% / pixel", "title": "Percentage of Pixel that is Bare Soil", "rcParams": { "font.size": 9 } } }, { "name": "bare_ground_50", "title": "50th Percentile", "abstract": "50th Percentile of Bare Soil", "needed_bands": ["BS_PC_50"], "color_ramp": [ { 'value': 0, 'color': '#feebe2' }, { 'value': 25, 'color': '#fbb4b9' }, { 'value': 50, 'color': '#f768a1' }, { 'value': 75, 'color': '#c51b8a' }, { 'value': 100, 'color': '#7a0177' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, { "name": "bare_ground_90", "title": "90th Percentile", "abstract": "90th Percentile of Bare Soil", "needed_bands": ["BS_PC_90"], "color_ramp": [ { 'value': 0, 'color': '#feebe2' }, { 'value': 25, 'color': '#fbb4b9' }, { 'value': 50, 'color': '#f768a1' }, { 'value': 75, 'color': '#c51b8a' }, { 'value': 100, 'color': '#7a0177' } ], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "bare_ground_10", }, { "label": "Median", "abstract": """ Fractional Cover Percentiles version 2.2.0, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region. Fractional cover provides information about the the proportions of green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.), and bare areas for every 25m x 25m ground footprint. Fractional cover provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The percentile summaries are designed to make it easier to analyse and interpret fractional cover. Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The fractional cover algorithm was developed by the Joint Remote Sensing Research Program. FC-PERCENTILE-SEASONAL-SUMMARY, this contains a (10th, 50th and 90th percentile) of BS, PV and NPV of observations acquired within each calendar season (DJF, MAM, JJA, SON). This product is available for the most recent 8 seasons Fractional Cover products use Water Observations from Space (WOfS) to mask out areas of water, cloud and other phenomena. To be considered in the FCP product a pixel must have had at least 10 clear observations over the year. For service status information, see https://status.dea.ga.gov.au""", "type": "100km tile", "variant": "25m", "name": "fcp_seasonal_rgb", "product_name": "fc_percentile_albers_seasonal", "pq_dataset": "geodata_coast_100k", "pq_band": "land", "pq_ignore_time": True, "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": [], "apply_solar_corrections": False, "legend": { "url": "https://data.dea.ga.gov.au/fractional-cover/fc-percentile/annual/v2.1.0/fcp_legend.png", }, "wcs_default_bands": ["BS_PC_50", "PV_PC_50", "NPV_PC_50"], "styles": [ { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "BS_PC_50": 1.0 }, "green": { "PV_PC_50": 1.0 }, "blue": { "NPV_PC_50": 1.0 } }, "scale_range": [0.0, 100.0], "pq_masks": [ { "flags": { 'sea': True, }, "invert": True, }, ], }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, ] }, { "name": "nidem", "title": "National Intertidal Digital Elevation Model", "abstract": "", "products": [ { "label": "NIDEM 25m", "abstract": "The National Intertidal Digital Elevation Model (NIDEM) product is a continental-scale " "dataset providing continuous elevation data for Australia's exposed intertidal zone. " "NIDEM provides the first three-dimensional representation of Australia's intertidal zone " "(excluding off-shore Territories and intertidal mangroves) at 25 m spatial resolution, " "addressing a key gap between the availability of sub-tidal bathymetry and " "terrestrial elevation data. NIDEM was generated by combining global tidal modelling " "with a 30-year time series archive of spatially and spectrally calibrated " "Landsat satellite data managed within the Digital Earth Australia (DEA) platform. " "NIDEM complements existing intertidal extent products, and provides data to support a " "new suite of use cases that require a more detailed understanding of the three-dimensional " "topography of the intertidal zone, such as hydrodynamic modelling, coastal risk management " "and ecological habitat mapping." "For service status information, see https://status.dea.ga.gov.au", "type": "grid", "variant": "nidem_v1.0.0", "name": "NIDEM", "product_name": "nidem", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["nidem"], "apply_solar_corrections": False, "legend": { "styles": ["NIDEM"] }, "wcs_default_bands": ["nidem"], "styles": [ { "name": "NIDEM", "title": "National Intertidal Digital Elevation Model", "abstract": "National Intertidal Digital Elevation Model 25 m v1.0.0", "needed_bands": ["nidem"], "color_ramp": [ { 'value': -2.51, 'color': '#440154' }, { 'value': -2.5, 'color': '#440154', 'legend': { "prefix": "<" } }, { 'value': -2.34, 'color': '#460e61', }, { 'value': -2.18, 'color': '#471b6e', }, { 'value': -2.02, 'color': '#472877' }, { 'value': -1.86, 'color': '#45347f' }, { 'value': -1.7, 'color': '#413f85' }, { 'value': -1.58, 'color': '#3b4d8a' }, { 'value': -1.42, 'color': '#37578b' }, { 'value': -1.26, 'color': '#32618c' }, { 'value': -1.1, 'color': '#2e6b8d', "legend": {} }, { 'value': -0.94, 'color': '#2a748e' }, { 'value': -0.78, 'color': '#267d8e' }, { 'value': -0.62, 'color': '#23868d' }, { 'value': -0.46, 'color': '#208f8c' }, { 'value': -0.3, 'color': '#1e9889' }, { 'value': -0.14, 'color': '#1fa186' }, { 'value': 0.0, 'color': '#26ac7f', "legend": { } }, { 'value': 0.14, 'color': '#32b579' }, { 'value': 0.3, 'color': '#41bd70' }, { 'value': 0.46, 'color': '#54c566' }, { 'value': 0.62, 'color': '#69cc59' }, { 'value': 0.78, 'color': '#80d24b' }, { 'value': 0.94, 'color': '#99d83c' }, { 'value': 1.1, 'color': '#b2dc2c', }, { 'value': 1.26, 'color': '#cce01e' }, { 'value': 1.42, 'color': '#e5e31a' }, { 'value': 1.5, 'color': '#fde724', 'legend': { "prefix": ">" } } ], "legend": { "units": "metres" } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "NIDEM", }, ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "HLTC Composites", "title": "High Tide Low Tide Composite", "abstract": "The High and Low Tide Composites product is composed of two surface reflectance composite mosaics " "of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8) " "surface reflectance data (Li et al., 2012). These products have been produced using " "Digital Earth Australia (DEA). The two mosaics allow cloud free and noise reduced visualisation " "of the shallow water and inter-tidal coastal regions of Australia, as observed at " "high and low tide respectively.The composites are generated utilising the geomedian approach of " "Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as " "habitat mapping. The time range used for composite generation in each polygon of the mosaic is " "tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud " "free composite to be generated. The concepts of the Observed Tidal Range (OTR), " "and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al. " "(2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016).", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "High Tide", "abstract":""" High Tide and Low Tide Composites 2.0.0 The High and Low Tide Composites product is composed of two surface reflectance composite mosaics of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8) surface reflectance data (Li et al., 2012). These products have been produced using Digital Earth Australia (DEA). The two mosaics allow cloud free and noise reduced visualisation of the shallow water and inter-tidal coastal regions of Australia, as observed at high and low tide respectively (Sagar et al. 2018). The composites are generated utilising the geomedian approach of Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as habitat mapping. The time range used for composite generation in each polygon of the mosaic is tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The concepts of the Observed Tidal Range (OTR), and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al. (2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016). *Overview* Inter-tidal zones are difficult regions to characterise due to the dynamic nature of the tide. They are highly changeable environments, subject to forcings from the land, sea and atmosphere and yet they form critical habitats for a wide range of organisms from birds to fish and sea grass. By harnessing the long archive of satellite imagery over Australia's coastal zones in the DEA and pairing the images with regional tidal modelling, the archive can be sorted by tide height rather than date, enabling the inter-tidal zone to be viewed at any stage of the tide regime. The High Low Tide Composites (HLTC_25) product is composed of two mosaics, distinguished by tide height, representing a composite image of the synthetic geomedian surface reflectance from Landsats 5 TM, Landsat 7 ETM+ and Landsat 8 OLI NBAR data (Li et al., 2012; Roberts et al., 2017). Oregon State Tidal Prediction (OTPS) software (Egbert and Erofeeva, 2002, 2010) was used to generate tide heights, relative to mean sea level, for the Australian continental coastline, split into 306 distinct tidal regions. These time and date stamped tidal values were then attributed to all coastal tile observations for their time of acquisition, creating a range of observed tide heights for the Australian coastline. The two mosaics in HLTC_25 are composited from the highest and lowest 20 % of observed tide in the ensemble and are termed HOT and LOT respectively. A geomedian composite for each Landsat band is calculated from the tiles in each ensemble subset to produce the respective HOT and LOT composites. Note that Landsat 7 ETM+ observations are excluded after May 2003 due to a large number of data artifacts. The time range used for composite generation in each of the 306 polygons of the mosaics are tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The maximum epoch for which the products are calculated is between 1995-2017, although this varies due to data resolution and observation quality. The product also includes a count of clear observations per pixel for both mosaics and attribute summaries per polygon that include the date range, the highest and lowest modeled astronomical tide as well as the highest and lowest observed tide for that time range, the total observation count and the maximum count of observations for any one pixel in the polygon, the polygon ID number (from 1 to 306), the polygon centroid in longitude and latitude and the count of tide stages attributed to every observation used in that polygon of the mosaic. For the count of tidal stage observations, e = ebbing tide, f = flowing tide, ph = peak high tide and pl = peak low tide. The tide stages were calculated bycomparison to the modeled tide data for 15 minutes either side of the observation to determine the ebb, flow or peak movement of the tide. Observations are filtered to remove poor quality observations including cloud, cloud shadow and band saturation (of any band). For service status information, see https://status.dea.ga.gov.au""", # Included as a keyword for the layer "type": "Tidal Composite", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "high_tide_composite", # The Datacube name for the associated data product "product_name": "high_tide_comp_20p", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "ls8_level1_usgs", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_manual_data_merge": True, # "data_manual_merge": True, # "pq_band": "quality", # "always_fetch_bands": [ "quality" ], # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi"] }, "wcs_default_bands": ["red", "green", "blue"], # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 0.30] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 0.30] }, # # Examples of non-linear heat-mapped styles. { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, SWIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["green"] - data["swir1"]) / ( data["swir1"] + data["green"]), "needed_bands": ["green", "swir1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, { # Included as a keyword for the layer "label": "Low Tide", "abstract": """ High Tide and Low Tide Composites 2.0.0 The High and Low Tide Composites product is composed of two surface reflectance composite mosaics of Landsat TM and ETM+ (Landsat 5 and Landsat 7 respectively) and OLI (Landsat 8) surface reflectance data (Li et al., 2012). These products have been produced using Digital Earth Australia (DEA). The two mosaics allow cloud free and noise reduced visualisation of the shallow water and inter-tidal coastal regions of Australia, as observed at high and low tide respectively (Sagar et al. 2018). The composites are generated utilising the geomedian approach of Roberts et al (2017) to ensure a valid surface reflectance spectra suitable for uses such as habitat mapping. The time range used for composite generation in each polygon of the mosaic is tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The concepts of the Observed Tidal Range (OTR), and Highest and Lowest Observed Tide (HOT, LOT) are discussed and described fully in Sagar et al. (2017) and the product description for the ITEM v 1.0 product (Geoscience Australia, 2016). *Overview* Inter-tidal zones are difficult regions to characterise due to the dynamic nature of the tide. They are highly changeable environments, subject to forcings from the land, sea and atmosphere and yet they form critical habitats for a wide range of organisms from birds to fish and sea grass. By harnessing the long archive of satellite imagery over Australia's coastal zones in the DEA and pairing the images with regional tidal modelling, the archive can be sorted by tide height rather than date, enabling the inter-tidal zone to be viewed at any stage of the tide regime. The High Low Tide Composites (HLTC_25) product is composed of two mosaics, distinguished by tide height, representing a composite image of the synthetic geomedian surface reflectance from Landsats 5 TM, Landsat 7 ETM+ and Landsat 8 OLI NBAR data (Li et al., 2012; Roberts et al., 2017). Oregon State Tidal Prediction (OTPS) software (Egbert and Erofeeva, 2002, 2010) was used to generate tide heights, relative to mean sea level, for the Australian continental coastline, split into 306 distinct tidal regions. These time and date stamped tidal values were then attributed to all coastal tile observations for their time of acquisition, creating a range of observed tide heights for the Australian coastline. The two mosaics in HLTC_25 are composited from the highest and lowest 20 % of observed tide in the ensemble and are termed HOT and LOT respectively. A geomedian composite for each Landsat band is calculated from the tiles in each ensemble subset to produce the respective HOT and LOT composites. Note that Landsat 7 ETM+ observations are excluded after May 2003 due to a large number of data artifacts. The time range used for composite generation in each of the 306 polygons of the mosaics are tailored to ensure dynamic coastal features are captured whilst still allowing a clean and cloud free composite to be generated. The maximum epoch for which the products are calculated is between 1995-2017, although this varies due to data resolution and observation quality. The product also includes a count of clear observations per pixel for both mosaics and attribute summaries per polygon that include the date range, the highest and lowest modeled astronomical tide as well as the highest and lowest observed tide for that time range, the total observation count and the maximum count of observations for any one pixel in the polygon, the polygon ID number (from 1 to 306), the polygon centroid in longitude and latitude and the count of tide stages attributed to every observation used in that polygon of the mosaic. For the count of tidal stage observations, e = ebbing tide, f = flowing tide, ph = peak high tide and pl = peak low tide. The tide stages were calculated bycomparison to the modeled tide data for 15 minutes either side of the observation to determine the ebb, flow or peak movement of the tide. Observations are filtered to remove poor quality observations including cloud, cloud shadow and band saturation (of any band). For service status information, see https://status.dea.ga.gov.au""", # Included as a keyword for the layer "type": "Tidal Composite", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "low_tide_composite", # The Datacube name for the associated data product "product_name": "low_tide_comp_20p", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "ls8_level1_usgs", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_manual_data_merge": True, # "data_manual_merge": True, # "pq_band": "quality", # "always_fetch_bands": [ "quality" ], # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 35.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'], # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], "data_manual_merge": True, "always_fetch_bands": [], "apply_solar_corrections": False, # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ndvi", "ndwi"] }, "wcs_default_bands": ["red", "green", "blue"], # A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer # If None or not specified, the product has no sub-layers. # "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")), # A prefix used to describe the sub-layer in the GetCapabilities response. # E.g. sub-layer 109 will be described as "Landsat Path 109" # "sub_product_label": "Landsat Path", # Bands to include in time-dimension "pixel drill". # Don't activate in production unless you really know what you're doing. # "band_drill": ["nir", "red", "green", "blue"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "red": 1.0 }, "green": { "green": 1.0 }, "blue": { "blue": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 0.30] }, { "name": "infrared_green", "title": "False colour - Green, SWIR, NIR", "abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue", "components": { "red": { "swir1": 1.0 }, "green": { "nir": 1.0 }, "blue": { "green": 1.0 } }, "scale_range": [0.0, 0.30] }, # # Examples of non-linear heat-mapped styles. { "name": "ndvi", "title": "NDVI - Red, NIR", "abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation", "index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]), "needed_bands": ["red", "nir"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 0.1, "color": "#A35F18" }, { "value": 0.2, "color": "#B88512" }, { "value": 0.3, "color": "#CEAC0E" }, { "value": 0.4, "color": "#E5D609" }, { "value": 0.5, "color": "#FFFF0C" }, { "value": 0.6, "color": "#C3DE09" }, { "value": 0.7, "color": "#88B808" }, { "value": 0.8, "color": "#529400" }, { "value": 0.9, "color": "#237100" }, { "value": 1.0, "color": "#114D04" } ] }, { "name": "ndwi", "title": "NDWI - Green, SWIR", "abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water", "index_function": lambda data: (data["green"] - data["swir1"]) / ( data["swir1"] + data["green"]), "needed_bands": ["green", "swir1"], "color_ramp": [ { "value": -0.0, "color": "#8F3F20", "alpha": 0.0 }, { "value": 0.0, "color": "#8F3F20", "alpha": 1.0 }, { "value": 1.0, "color": "#0303FF", }, ] }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "ITEM", "title": "Intertidal Extents Model", "abstract": "The Intertidal Extents Model (ITEM) product is a national dataset of the exposed intertidal zone; " "the land between the observed highest and lowest tide. ITEM provides the extent and topography of " "the intertidal zone of Australia's coastline (excluding off-shore Territories). " "This information was collated using observations in the Landsat archive since 1986. " "ITEM can be a valuable complimentary dataset to both onshore LiDAR survey data and coarser offshore " "bathymetry data, enabling a more realistic representation of the land and ocean interface.", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "Relative Layer", "abstract": """ The Intertidal Extents Model (ITEM v2.0) product analyses GA’s historic archive of satellite imagery to derive a model of the spatial extents of the intertidal zone throughout the tidal cycle. The model can assist in understanding the relative elevation profile of the intertidal zone, delineating exposed areas at differing tidal heights and stages. The product differs from previous methods used to map the intertidal zone which have been predominately focused on analysing a small number of individual satellite images per location (e.g Ryu et al., 2002; Murray et al., 2012). By utilising a full 30 year time series of observations and a global tidal model (Egbert and Erofeeva, 2002), the methodology enables us to overcome the requirement for clear, high quality observations acquired concurrent to the time of high and low tide. *Accuracy and limitations* Due the sun-synchronous nature of the various Landsat sensor observations; it is unlikely that the full physical extents of the tidal range in any cell will be observed. Hence, terminology has been adopted for the product to reflect the highest modelled tide observed in a given cell (HOT) and the lowest modelled tide observed (LOT) (see Sagar et al. 2017). These measures are relative to Mean Sea Level, and have no consistent relationship to Lowest (LAT) and Highest Astronomical Tide (HAT). The inclusion of the lowest (LMT) and highest (HMT) modelled tide values for each tidal polygon indicates the highest and lowest tides modelled for that location across the full time series by the OTPS model. The relative difference between the LOT and LMT (and HOT and HMT) heights gives an indication of the extent of the tidal range represented in the Relative Extents Model. As in ITEM v1.0, v2.0 contains some false positive land detection in open ocean regions. These are a function of the lack of data at the extremes of the observed tidal range, and features like glint and undetected cloud in these data poor regions/intervals. Methods to isolate and remove these features are in development for future versions. Issues in the DEA archive and data noise in the Esperance, WA region off Cape Le Grande and Cape Arid (Polygons 236,201,301) has resulted in significant artefacts in the model, and use of the model in this area is not recommended. The Confidence layer is designed to assess the reliability of the Relative Extent Model. Within each tidal range percentile interval, the pixel-based standard deviation of the NDWI values for all observations in the interval subset is calculated. The average standard deviation across all tidal range intervals is then calculated and retained as a quality indicator in this product layer. The Confidence Layer reflects the pixel based consistency of the NDWI values within each subset of observations, based on the tidal range. Higher standard deviation values indicate water classification changes not based on the tidal cycle, and hence lower confidence in the extent model. Possible drivers of these changes include: Inadequacies of the tidal model, due perhaps to complex coastal bathymetry or estuarine structures not captured in the model. These effects have been reduced in ITEM v2.0 compared to previous versions, through the use of an improved tidal modelling frameworkChange in the structure and exposure of water/non-water features NOT driven by tidal variation. For example, movement of sand banks in estuaries, construction of man-made features (ports etc.).Terrestrial/Inland water features not influenced by the tidal cycle. File naming: THE RELATIVE EXTENTS MODEL v2.0 ITEM_REL_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon THE CONFIDENCE LAYER v2.0 ITEM_STD_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon *Overview* The Intertidal Extents Model product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range (Sagar et al. 2017).The current version (2.0) utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1986 and 2016 (inclusive). ITEM v2.0 has implemented an improved tidal modelling framework (see Sagar et al. 2018) over that utilised in ITEM v1.0. The expanded Landsat archive within the Digital Earth Australia (DEA) has also enabled the model extent to be increased to cover a number of offshore reefs, including the full Great Barrier Reef and southern sections of the Torres Strait Islands. The DEA archive and new tidal modelling framework has improved the coverage and quality of the ITEM v2.0 relative extents model, particularly in regions where AGDC cell boundaries in ITEM v1.0 produced discontinuities or the imposed v1.0 cell structure resulted in poor quality tidal modelling (see Sagar et al. 2017). For service status information, see https://status.dea.ga.gov.au""", # Included as a keyword for the layer "type": "ITEM v2.0.0", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "ITEM_V2.0.0", # The Datacube name for the associated data product "product_name": "item_v2", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["relative"], "apply_solar_corrections": False, "legend": { "url": "https://data.dea.ga.gov.au/item_v2/v2.0.1/relative/ITEM_REL_Legend.png" }, "wcs_default_bands": ["relative"], "styles": [ { "name": "relative_layer", "title": "relative layer", "abstract": "The Relative Extents Model (item_v2) 25m v2.0.0", "needed_bands": ["relative"], "color_ramp": [ { 'value': 0.0, 'color': '#000000', 'alpha': 0.0 }, { 'value': 1.0, 'color': '#d7191c', 'alpha': 1.0 }, { 'value': 2.0, 'color': '#ec6e43', }, { 'value': 3.0, 'color': '#fdb96e', }, { 'value': 4.0, 'color': '#fee7a4', }, { 'value': 5.0, 'color': '#e7f5b7', }, { 'value': 6.0, 'color': '#b7e1a7', }, { 'value': 7.0, 'color': '#74b6ad', }, { 'value': 8.0, 'color': '#2b83ba' }, { 'value': 9.0, 'color': '#000000', 'alpha': 0.0 }, ], "legend": { "units": "%", "radix_point": 0, "scale_by": 10.0, "major_ticks": 1 } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "relative_layer", }, { # Included as a keyword for the layer "label": "Confidence Layer", "abstract": """ The Intertidal Extents Model (ITEM v2.0) product analyses GA’s historic archive of satellite imagery to derive a model of the spatial extents of the intertidal zone throughout the tidal cycle. The model can assist in understanding the relative elevation profile of the intertidal zone, delineating exposed areas at differing tidal heights and stages. The product differs from previous methods used to map the intertidal zone which have been predominately focused on analysing a small number of individual satellite images per location (e.g Ryu et al., 2002; Murray et al., 2012). By utilising a full 30 year time series of observations and a global tidal model (Egbert and Erofeeva, 2002), the methodology enables us to overcome the requirement for clear, high quality observations acquired concurrent to the time of high and low tide. *Accuracy and limitations* Due the sun-synchronous nature of the various Landsat sensor observations; it is unlikely that the full physical extents of the tidal range in any cell will be observed. Hence, terminology has been adopted for the product to reflect the highest modelled tide observed in a given cell (HOT) and the lowest modelled tide observed (LOT) (see Sagar et al. 2017). These measures are relative to Mean Sea Level, and have no consistent relationship to Lowest (LAT) and Highest Astronomical Tide (HAT). The inclusion of the lowest (LMT) and highest (HMT) modelled tide values for each tidal polygon indicates the highest and lowest tides modelled for that location across the full time series by the OTPS model. The relative difference between the LOT and LMT (and HOT and HMT) heights gives an indication of the extent of the tidal range represented in the Relative Extents Model. As in ITEM v1.0, v2.0 contains some false positive land detection in open ocean regions. These are a function of the lack of data at the extremes of the observed tidal range, and features like glint and undetected cloud in these data poor regions/intervals. Methods to isolate and remove these features are in development for future versions. Issues in the DEA archive and data noise in the Esperance, WA region off Cape Le Grande and Cape Arid (Polygons 236,201,301) has resulted in significant artefacts in the model, and use of the model in this area is not recommended. The Confidence layer is designed to assess the reliability of the Relative Extent Model. Within each tidal range percentile interval, the pixel-based standard deviation of the NDWI values for all observations in the interval subset is calculated. The average standard deviation across all tidal range intervals is then calculated and retained as a quality indicator in this product layer. The Confidence Layer reflects the pixel based consistency of the NDWI values within each subset of observations, based on the tidal range. Higher standard deviation values indicate water classification changes not based on the tidal cycle, and hence lower confidence in the extent model. Possible drivers of these changes include: Inadequacies of the tidal model, due perhaps to complex coastal bathymetry or estuarine structures not captured in the model. These effects have been reduced in ITEM v2.0 compared to previous versions, through the use of an improved tidal modelling frameworkChange in the structure and exposure of water/non-water features NOT driven by tidal variation. For example, movement of sand banks in estuaries, construction of man-made features (ports etc.).Terrestrial/Inland water features not influenced by the tidal cycle. File naming: THE RELATIVE EXTENTS MODEL v2.0 ITEM_REL_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon THE CONFIDENCE LAYER v2.0 ITEM_STD_<TIDAL POLYGON NUMBER>_<LONGITUDE>_<LATITUDE> TIDAL POLYGON NUMBER relates to the id of the tidal polygon referenced by the file LONGITUDE is the longitude of the centroid of the tidal polygon LATITUDE is the latitude of the centroid of the tidal polygon *Overview* The Intertidal Extents Model product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range (Sagar et al. 2017).The current version (2.0) utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1986 and 2016 (inclusive). ITEM v2.0 has implemented an improved tidal modelling framework (see Sagar et al. 2018) over that utilised in ITEM v1.0. The expanded Landsat archive within the Digital Earth Australia (DEA) has also enabled the model extent to be increased to cover a number of offshore reefs, including the full Great Barrier Reef and southern sections of the Torres Strait Islands. The DEA archive and new tidal modelling framework has improved the coverage and quality of the ITEM v2.0 relative extents model, particularly in regions where AGDC cell boundaries in ITEM v1.0 produced discontinuities or the imposed v1.0 cell structure resulted in poor quality tidal modelling (see Sagar et al. 2017). For service status information, see https://status.dea.ga.gov.au""", # Included as a keyword for the layer "type": "ITEM v2.0.0", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "ITEM_V2.0.0_Conf", # The Datacube name for the associated data product "product_name": "item_v2_conf", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != data[band].nodata, "fuse_func": "datacube_wms.wms_utils.item_fuser", "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["stddev"], "apply_solar_corrections": False, "legend": { "styles": ["confidence_layer"] }, "wcs_default_bands": ["stddev"], "styles": [ { "name": "confidence_layer", "title": "confidence layer", "abstract": "The Confidence layer (item_v2_conf) 25m v2.0.0", "needed_bands": ["stddev"], "color_ramp": [ { 'value': 0.0, 'color': '#2b83ba', 'alpha': 0.0 }, { 'value': 0.01, 'color': '#2b83ba', 'legend': { "prefix": "<" } }, { 'value': 0.055, 'color': '#55a1b2', }, { 'value': 0.1, 'color': '#80bfab', }, { 'value': 0.145, 'color': '#abdda4', }, { 'value': 0.19, 'color': '#c7e8ad', }, { 'value': 0.235, 'color': '#e3f3b6', }, { 'value': 0.28, 'color': '#fdbf6f', }, { 'value': 0.325, 'color': '#e37d1c', }, { 'value': 0.37, 'color': '#e35e1c', }, { 'value': 0.415, 'color': '#e31a1c', }, { 'value': 0.46, 'color': '#e31a1c', }, { 'value': 0.505, 'color': '#e31a1c', }, { 'value': 0.55, 'color': '#e31a1c', 'legend': { "prefix": ">" } }, ], "legend": { "units": "NDWI standard deviation" } } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "confidence_layer", }, ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "water_bodies", "title": "Projects", "abstract": "Projects", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "Water Bodies", "abstract": "NSW Water Bodies Project" "For service status information, see https://status.dea.ga.gov.au", # Included as a keyword for the layer "type": "NSW", # Included as a keyword for the layer "variant": "25m", # The WMS name for the layer "name": "water_bodies", # The Datacube name for the associated data product "product_name": "water_bodies", "min_zoom_factor": 15.0, "zoomed_out_fill_colour": [150, 180, 200, 160], "time_zone": 9, "extent_mask_func": lambda data, band: data[band] != 65535, # include links to csv, {dam_id: 2611} becomes ".../026/02611.csv" "feature_info_include_custom": lambda data: { 'timeseries': f"https://data.dea.ga.gov.au" f"/projects/WaterBodies/feature_info/" f"{data['dam_id'] // 100:03}/{data['dam_id']:05}.csv" }, "ignore_info_flags": [], "data_manual_merge": False, "always_fetch_bands": ["dam_id"], "apply_solar_corrections": False, "legend": { "styles": [] }, "wcs_default_bands": ["dam_id"], "styles": [ { "name": "dam_id", "title": "Water Body", "abstract": "", "needed_bands": ["dam_id"], "color_ramp": [ { 'value': 0, 'color': '#11ccff', 'alpha': 1.0 }, { 'value': 65534, 'color': '#11ccff', 'alpha': 1.0 }, ], "legend": { } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "dam_id", }, { # Included as a keyword for the layer "label": "HAP", # Included as a keyword for the layer "type": "historical airborne photography", # Included as a keyword for the layer "variant": "munged", # The WMS name for the layer "name": "historical_airborne_photography", # The Datacube name for the associated data product "product_name": "historical_airborne_photography", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 500.0, # Min zoom factor (above) works well for small-tiled requests, (e.g. 256x256 as sent by Terria). # However, for large-tiled requests (e.g. as sent by QGIS), large and intensive queries can still # go through to the datacube. # max_datasets_wms specifies a maximum number of datasets that a GetMap request can retrieve. # Indicatative polygons are displayed if a request exceeds the limits imposed by EITHER max_dataset_wms # OR min_zoom_factor. # max_datasets_wms should be set in conjunction with min_zoom_factor so that Terria style 256x256 # tiled requests respond consistently - you never want to see a mixture of photographic tiles and polygon # tiles at a given zoom level. i.e. max_datasets_wms should be greater than the number of datasets # required for most intensive possible photographic query given the min_zoom_factor. # Note that the ideal value may vary from product to product depending on the size of the dataset # extents for the product. # Defaults to zero, which is interpreted as no dataset limit. # 6 seems to work with a min_zoom_factor of 500.0 for "old-style" Net-CDF albers tiled data. "max_datasets_wms": 6, # max_datasets_wcs is the WCS equivalent of max_datasets_wms. The main requirement for setting this # value is to avoid gateway timeouts on overly large WCS requests (and reduce server load). "max_datasets_wcs": 16, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], 'wcs_default_bands':['Band_1'], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # # LS7: http://www.indexdatabase.de/db/s-single.php?id=8 # LS8: http://www.indexdatabase.de/db/s-single.php?id=168 "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_gray", "title": "Simple gray", "abstract": "Simple grayscale image", "components": { "red": { "Band_1": 1.0 }, "green": { "Band_1": 1.0 }, "blue": { "Band_1": 1.0 } }, # The raw band value range to be compressed to an 8 bit range for the output image tiles. # Band values outside this range are clipped to 0 or 255 as appropriate. "scale_range": [0.0, 255] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_gray", } ] }, { # Name and title of the platform layer. # Platform layers are not mappable. The name is for internal server use only. "name": "National ASTER Map", "title": "National ASTER Map of Australia", "abstract": """ This datsaet comprises a set of 14+ geoscience products made up of mosaiced ASTER scenes across Australia. The individual geoscience products are a compbination of bands and band ratios to highlight different mineral groups and parameters including: False colour composite CSIRO Landsat TM Regolith Ratios Green vegetation content Ferric oxide content Ferric oxide composition Ferrous iron index Opaque index AlOH group content AlOH group composition Kaolin group index FeOH group content MgOH group content MgOH group composition Ferrous iron content in MgOH/carbonate""", # Products available for this platform. # For each product, the "name" is the Datacube name, and the label is used # to describe the label to end-users. "products": [ { # Included as a keyword for the layer "label": "False Colour Mosaic", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ False colour RGB composite Red: B3 Green: B2 Blue: B1 (red = green vegetation) Use this image to help understand non-geological differences within and between ASTER scenes caused by green vegetation (red), fire scars, thin and thick cloud and cloud shadows. Use band 2 only for a gray-scale background to the content, composition and index colour products.""", # The WMS name for the layer "name": "aster_false_colour", # The Datacube name for the associated data product "product_name": "aster_false_colour", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "wcs_default_bands": ["Band_1", "Band_2", "Band_3"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "false_colour", "title": "False Colour", "abstract": "Simple false-colour image using ASTER Bands 3 as red, 2 as green and 1 as blue", "components": { "red": { "Band_1": 1.0 }, "green": { "Band_2": 1.0 }, "blue": { "Band_3": 1.0 } }, "scale_range": [0.0, 255.0] }, { "name": "gray", "title": "B2 Grayscale", "abstract": "Simple grayscale image using ASTER Band 2", "components": { "red": { "Band_2": 1.0 }, "green": { "Band_2": 1.0 }, "blue": { "Band_2": 1.0 } }, "scale_range": [0.0, 255.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "false_colour", }, # ASTER False Colour { # Included as a keyword for the layer "label": "Regolith Ratios", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ 3 band RGB composite Red: B3/B2 Green: B3/B7 Blue: B4/B7 (white = green vegetation) Use this image to help interpret: (1) the amount of green vegetation cover (appears as white); (2) basic spectral separation (colour) between different regolith and geological units and regions/provinces; and (3) evidence for unmasked cloud (appears as green).""", # The WMS name for the layer "name": "aster_regolith_ratios", # The Datacube name for the associated data product "product_name": "aster_regolith_ratios", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "wcs_default_bands": ["Band_1", "Band_2", "Band_3"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "simple_rgb", "title": "Simple RGB", "abstract": "Simple true-colour image, using the red, green and blue bands", "components": { "red": { "Band_1": 1.0 }, "green": { "Band_2": 1.0 }, "blue": { "Band_3": 1.0 } }, "scale_range": [0.0, 255.0] } ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "simple_rgb", }, # ASTER Regolith Ratios { # Included as a keyword for the layer "label": "AlOH Group Composition", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B5/B7 Blue is well ordered kaolinite, Al-rich muscovite/illite, paragonite, pyrophyllite Red is Al-poor (Si-rich) muscovite (phengite) Useful for mapping: (1) exposed saprolite/saprock is often white mica or Al-smectite (warmer colours) whereas transported materials are often kaolin-rich (cooler colours); (2) clays developed over carbonates, especially Al-smectite (montmorillonite, beidellite) will produce middle to warmers colours; (3) stratigraphic mapping based on different clay-types; and (4) lithology-overprinting hydrothermal alteration, e.g. Si-rich and K-rich phengitic mica (warmer colours). Combine with Ferrous iron in MgOH and FeOH content products to look for evidence of overlapping/juxtaposed potassic metasomatism in ferromagnesian parents rocks (e.g. Archaean greenstone associated Au mineralisation) +/- associated distal propyllitic alteration (e.g. chlorite, amphibole).""", # The WMS name for the layer "name": "aster_aloh_group_composition", # The Datacube name for the associated data product "product_name": "aster_aloh_group_composition", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B5/B7 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.9" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.3" } } ], "legend": { "units": "Blue is well ordered kaolinite,\nRed is Al-poor (Si-rich) muscovite (phengite)", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER AlOH Group Composition { # Included as a keyword for the layer "label": "AlOH Group Content", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: (B5+B7)/B6 Blue is low abundance Red is high abundance (potentially includes: phengite, muscovite, paragonite, lepidolite, illite, brammalite, montmorillonite, beidellite, kaolinite, dickite) Useful for mapping: (1) exposed saprolite/saprock; (2) clay-rich stratigraphic horizons; (3) lithology-overprinting hydrothermal phyllic (e.g. white mica) alteration; and (4) clay-rich diluents in ore systems (e.g. clay in iron ore). Also combine with AlOH composition to help map: (1) exposed in situ parent material persisting through “cover” which can be expressed as: (a) more abundant AlOH content + (b) long-wavelength (warmer colour) AlOH composition (e.g. muscovite/phengite).""", # The WMS name for the layer "name": "aster_aloh_group_content", # The Datacube name for the associated data product "product_name": "aster_aloh_group_content", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "(B5+B7)/B6 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "2.0" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "2.25" } } ], "legend": { "units": "Blue is low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER AlOH Group Content { # Included as a keyword for the layer "label": "FeOH Group Content", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: (B6+B8)/B7 Blue is low content, Red is high content (potentially includes: chlorite, epidote, jarosite, nontronite, gibbsite, gypsum, opal-chalcedony Useful for mapping: (1) jarosite (acid conditions) – in combination with ferric oxide content (high); (2) gypsum/gibbsite – in combination with ferric oxide content (low); (3) magnesite - in combination with ferric oxide content (low) and MgOH content (moderate-high); (4) chlorite (e.g. propyllitic alteration) – in combination with Ferrous in MgOH (high); and (5) epidote (calc-silicate alteration) – in combination with Ferrous in MgOH (low).""", # The WMS name for the layer "name": "aster_feoh_group_content", # The Datacube name for the associated data product "product_name": "aster_feoh_group_content", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "wcs_default_bands": ["Band_1"], "legend": { # "url": "" "styles": ["ramp"] }, # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "(B6+B8)/B7 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "2.03" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "2.25" } } ], "legend": { "units": "Blue is low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER FeOH Group Content { # Included as a keyword for the layer "label": "Ferric Oxide Composition", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B2/B1 Blue-cyan is goethite rich, Green is hematite-goethite, Red-yellow is hematite-rich Useful For: (1) Mapping transported materials (including palaeochannels) characterised by hematite (relative to geothite). Combine with AlOH composition to find co-located areas of hematite and poorly ordered kaolin to map transported materials; and (2) hematite-rish areas in drier conditions (eg above the water table) whereas goethite-rich in wetter conditions (eg at/below the water or areas recently exposed). May also be climate driven.""", # The WMS name for the layer "name": "aster_ferric_oxide_composition", # The Datacube name for the associated data product "product_name": "aster_ferric_oxide_composition", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B2/B1 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.5" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "3.3" } } ], "legend": { "units": "Blue-cyan is non-hematitie,\nRed-yellow is hematite-rich", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Ferric Oxide Composition { # Included as a keyword for the layer "label": "Ferric Oxide Content", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B4/B3 Blue is low abundance, Red is high abundance Useful for: (1) Exposed iron ore (hematite-goethite).Use in combination with the “Opaques index” to help separate/map dark (a) surface lags (e.g. maghemite gravels) which can be misidentified in visible and false colour imagery; and (b) magnetite in BIF and/or bedded iron ore; and (2) Acid conditions: combine with FeOH Group content to help map jarosite which will have high values in both products. Mapping hematite versus goethite mapping is NOT easily achieved as ASTER’s spectral bands were not designed to capture diagnostic iron oxide spectral behaviour. However, some information on visible colour relating in part to differences in hematite and/or goethite content can be obtained using a ratio of B2/B1 especially when this is masked using a B4/B3 to locate those pixels with sufficient iro oxide content. """, # The WMS name for the layer "name": "aster_ferric_oxide_content", # The Datacube name for the associated data product "product_name": "aster_ferric_oxide_content", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B4/B3 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "1.1" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "2.1" } } ], "legend": { "units": "Blue is low abundance,\nRed is high abundance", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Ferric Oxide Content { # Included as a keyword for the layer "label": "Ferrous Iron Content in MgOH", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B5/B4 Blue is low ferrous iron content in carbonate and MgOH minerals like talc and tremolite. Red is high ferrous iron content in carbonate and MgOH minerals like chlorite and actinolite. Useful for mapping: (1) un-oxidised “parent rocks” – i.e. mapping exposed parent rock materials (warm colours) in transported cover; (2) talc/tremolite (Mg-rich – cool colours) versus actinolite (Fe-rich – warm colours); (3) ferrous-bearing carbonates (warm colours) potentially associated with metasomatic “alteration”; (4) calcite/dolomite which are ferrous iron-poor (cool colours); and (5) epidote, which is ferrous iron poor (cool colours) – in combination with FeOH content product (high).""", # The WMS name for the layer "name": "aster_ferrous_iron_content_in_mgoh", # The Datacube name for the associated data product "product_name": "aster_ferrous_iron_content_in_mgoh", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B5/B4 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.1" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "2.0" } } ], "legend": { "units": "Blue is low ferrous iron content,\nRed is high ferrous iron content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Ferrous Iron Content in MgOH { # Included as a keyword for the layer "label": "Ferrous Iron Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B5/B4 Blue is low abundance, Red is high abundance This product can help map exposed “fresh” (un-oxidised) rocks (warm colours) especially mafic and ultramafic lithologies rich in ferrous silicates (e.g. actinolite, chlorite) and/or ferrous carbonates (e.g. ferroan dolomite, ankerite, siderite). Applying an MgOH Group content mask to this product helps to isolate ferrous bearing non-OH bearing minerals like pyroxenes (e.g. jadeite) from OH-bearing or carbonate-bearing ferrous minerals like actinolite or ankerite, respectively. Also combine with the FeOH Group content product to find evidence for ferrous-bearing chlorite (e.g. chamosite). """, # The WMS name for the layer "name": "aster_ferrous_iron_index", # The Datacube name for the associated data product "product_name": "aster_ferrous_iron_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B5/B4 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.75" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.025" } } ], "legend": { "units": "Blue is low abundance,\nRed is high abundance", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Ferrous Iron Index { # Included as a keyword for the layer "label": "Green Vegetation", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B3/B2 Blue is low content, Red is high content Use this image to help interpret the amount of “obscuring/complicating” green vegetation cover.""", # The WMS name for the layer "name": "aster_green_vegetation", # The Datacube name for the associated data product "product_name": "aster_green_vegetation", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B3/B2 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "1.4" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "4" } } ], "legend": { "units": "Blue is low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Green Vegetation { # Included as a keyword for the layer "label": "Gypsum Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band Ratio: (B10+B12)/B11 Blue is low gypsum content, Red is high gypsum content Useful for mapping: (1) evaporative environments (e.g. salt lakes) and associated arid aeolian systems (e.g. dunes); (2) acid waters (e.g. from oxidising sulphides) invading carbonate rich materials including around mine environments; and (3) hydrothermal (e.g. volcanic) systems.""", # The WMS name for the layer "name": "aster_gypsum_index", # The Datacube name for the associated data product "product_name": "aster_gypsum_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "(B10+B12)/B11 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.47" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "0.5" } } ], "legend": { "units": "Blue is low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Gypsum Index { # Included as a keyword for the layer "label": "Kaolin Group Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band Ratio: B6/B5 Blue is low content, Red is high content (potentially includes: pyrophyllite, alunite, well-ordered kaolinite) Useful for mapping: (1) different clay-type stratigraphic horizons; (2) lithology-overprinting hydrothermal alteration, e.g. high sulphidation, “advanced argillic” alteration comprising pyrophyllite, alunite, kaolinite/dickite; and (3) well-ordered kaolinite (warmer colours) versus poorly-ordered kaolinite (cooler colours) which can be used for mapping in situ versus transported materials, respectively.""", # The WMS name for the layer "name": "aster_kaolin_group_index", # The Datacube name for the associated data product "product_name": "aster_kaolin_group_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B6/B5 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "1.0" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.125" } } ], "legend": { "units": "Blue is low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Kaolin Group Index { # Included as a keyword for the layer "label": "MgOH Group Composition", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B7/B8 Blue-cyan is magnesite-dolomite, amphibole, chlorite Red is calcite, epidote, amphibole Useful for mapping: (1) exposed saprolite/saprock is often white mica or Al-smectite (warmer colours) whereas transported materials are often kaolin-rich (cooler colours); (2) clays developed over carbonates, especially Al-smectite (montmorillonite, beidellite) will produce middle to warmers colours. (3) stratigraphic mapping based on different clay-types; and (4) lithology-overprinting hydrothermal alteration, e.g. Si-rich and K-rich phengitic mica (warmer colours). Combine with Ferrous iron in MgOH and FeOH content products to look for evidence of overlapping/juxtaposed potassic metasomatism in ferromagnesian parents rocks (e.g. Archaean greenstone associated Au mineralisation) +/- associated distal propyllitic alteration (e.g. chlorite, amphibole).""", # The WMS name for the layer "name": "aster_mgoh_group_composition", # The Datacube name for the associated data product "product_name": "aster_mgoh_group_composition", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B7/B8 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.6" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.4" } } ], "legend": { "units": "Blue-cyan is magnesite-dolomite, amphibole, \nRed is calcite, epidote, amphibole", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER MgOH Group Composition { # Included as a keyword for the layer "label": "MgOH Group Content", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: (B6+B9/(B7+B8) Blue is low content, Red is high content (potentially includes: calcite, dolomite, magnesite, chlorite, epidote, amphibole, talc, serpentine) Useful for mapping: (1) “hydrated” ferromagnesian rocks rich in OH-bearing tri-octahedral silicates like actinolite, serpentine, chlorite and talc; (2) carbonate-rich rocks, including shelf (palaeo-reef) and valley carbonates(calcretes, dolocretes and magnecretes); and (3) lithology-overprinting hydrothermal alteration, e.g. “propyllitic alteration” comprising chlorite, amphibole and carbonate. The nature (composition) of the silicate or carbonate mineral can be further assessed using the MgOH composition product.""", # The WMS name for the layer "name": "aster_mgoh_group_content", # The Datacube name for the associated data product "product_name": "aster_mgoh_group_content", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "(B6+B9/(B7+B8) ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "1.05" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.2" } } ], "legend": { "units": "Blue low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER MgOH Group Content { # Included as a keyword for the layer "label": "Opaque Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B1/B4 Blue is low abundance, Red is high abundance (potentially includes carbon black (e.g. ash), magnetite, Mn oxides, and sulphides in unoxidised envornments Useful for mapping: (1) magnetite-bearing rocks (e.g. BIF); (2) maghemite gravels; (3) manganese oxides; (4) graphitic shales. Note 1: (1) and (4) above can be evidence for “reduced” rocks when interpreting REDOX gradients. Combine with AlOH group Content (high values) and Composition (high values) products, to find evidence for any invading “oxidised” hydrothermal fluids which may have interacted with reduced rocks evident in the Opaques index product.""", # The WMS name for the layer "name": "aster_opaque_index", # The Datacube name for the associated data product "product_name": "aster_opaque_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B1/B4 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.4" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "0.9" } } ], "legend": { "units": "Blue low content,\nRed is high content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Opaque Index { # Included as a keyword for the layer "label": "Silica Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B13/B10 Blue is low silica content, Red is high silica content (potentially includes Si-rich minerals, such as quartz, feldspars, Al-clays) Geoscience Applications: Broadly equates to the silica content though the intensity (depth) of this reststrahlen feature is also affected by particle size <250 micron. Useful product for mapping: (1) colluvial/alluvial materials; (2) silica-rich (quartz) sediments (e.g. quartzites); (3) silification and silcretes; and (4) quartz veins. Use in combination with quartz index, which is often correlated with the Silica index.""", # The WMS name for the layer "name": "aster_silica_index", # The Datacube name for the associated data product "product_name": "aster_silica_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B13/B10 ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "1.0" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "1.35" } } ], "legend": { "units": "Blue low silica content,\nRed is high silica content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Silica Index { # Included as a keyword for the layer "label": "Quartz Index", # Included as a keyword for the layer "type": "", # Included as a keyword for the layer "variant": "", "abstract": """ Band ratio: B11/(B10+B12) Blue is low quartz content, Red is high quartz content Geoscience Applications: Use in combination with Silica index to more accurately map “crystalline” quartz rather than poorly ordered silica (e.g. opal), feldspars and compacted clays.""", # The WMS name for the layer "name": "aster_quartz_index", # The Datacube name for the associated data product "product_name": "aster_quartz_index", # The Datacube name for the associated pixel-quality product (optional) # The name of the associated Datacube pixel-quality product # "pq_dataset": "s2b_ard_granule", # The name of the measurement band for the pixel-quality product # (Only required if pq_dataset is set) # "pq_band": "pixel_quality", # Min zoom factor - sets the zoom level where the cutover from indicative polygons # to actual imagery occurs. "min_zoom_factor": 10.0, # The fill-colour of the indicative polygons when zoomed out. # Triplets (rgb) or quadruplets (rgba) of integers 0-255. "zoomed_out_fill_colour": [150, 180, 200, 160], # Time Zone. In hours added to UTC (maybe negative) # Used for rounding off scene times to a date. # 9 is good value for imagery of Australia. "time_zone": 9, # Extent mask function # Determines what portions of dataset is potentially meaningful data. "extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']), # Flags listed here are ignored in GetFeatureInfo requests. # (defaults to empty list) "ignore_info_flags": [], # Define layer wide legend graphic if no style is passed # to GetLegendGraphic "legend": { # "url": "" "styles": ["ramp"] }, "wcs_default_bands": ["Band_1"], # Styles. # # See band_mapper.py # # The various available spectral bands, and ways to combine them # into a single rgb image. # The examples here are ad hoc # "styles": [ # Examples of styles which are linear combinations of the available spectral bands. # { "name": "ramp", "title": "B11/(B10+B12) ", "abstract": "", "index_function": lambda data: data["Band_1"], "needed_bands": ["Band_1"], "color_ramp": [ { "value": 0.0, "color": "#8F3F20", "alpha": 0.0, "legend": { "label": "0.50" } }, { "value": 1, "color": "#000000" }, { "value": 10, "color": "#2d002b" }, { "value": 25, "color": "#550071" }, { "value": 60, "color": "#0400ff" }, { "value": 90, "color": "#0098ff" }, { "value": 110, "color": "#00ffff" }, { "value": 130, "color": "#00ff94" }, { "value": 150, "color": "#00ff2a" }, { "value": 170, "color": "#3fff00" }, { "value": 210, "color": "#ffee00" }, { "value": 230, "color": "#ff8300" }, { "value": 255.0, "color": "#ff0000", "legend": { "label": "0.52" } } ], "legend": { "units": "Blue low quartz content,\nRed is high quartz content", } }, ], # Default style (if request does not specify style) # MUST be defined in the styles list above. # (Looks like Terria assumes this is the first style in the list, but this is # not required by the standard.) "default_style": "ramp", }, # ASTER Quartz Index ], } ]

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132b44565eed82ad8e8af2920ca03d221f09b5cd

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Python

packages/python/plotly/plotly/express/_chart_types.py

tr8dr/plotly.py

5c3c985c474f54ad039ff285086d7a83e9864592

[ "MIT" ]

1

2021-03-30T17:17:13.000Z

packages/python/plotly/plotly/express/_chart_types.py

tr8dr/plotly.py

5c3c985c474f54ad039ff285086d7a83e9864592

[ "MIT" ]

null

packages/python/plotly/plotly/express/_chart_types.py

tr8dr/plotly.py

5c3c985c474f54ad039ff285086d7a83e9864592

[ "MIT" ]

null

from ._core import make_figure from ._doc import make_docstring import plotly.graph_objs as go _wide_mode_xy_append = [ "Either `x` or `y` can optionally be a list of column references or array_likes, ", "in which case the data will be treated as if it were 'wide' rather than 'long'.", ] _cartesian_append_dict = dict(x=_wide_mode_xy_append, y=_wide_mode_xy_append) def scatter( data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, marginal_x=None, marginal_y=None, trendline=None, trendline_color_override=None, log_x=False, log_y=False, range_x=None, range_y=None, render_mode="auto", title=None, template=None, width=None, height=None, ): """ In a scatter plot, each row of `data_frame` is represented by a symbol mark in 2D space. """ return make_figure(args=locals(), constructor=go.Scatter) scatter.__doc__ = make_docstring(scatter, append_dict=_cartesian_append_dict) def density_contour( data_frame=None, x=None, y=None, z=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, marginal_x=None, marginal_y=None, trendline=None, trendline_color_override=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, histnorm=None, nbinsx=None, nbinsy=None, title=None, template=None, width=None, height=None, ): """ In a density contour plot, rows of `data_frame` are grouped together into contour marks to visualize the 2D distribution of an aggregate function `histfunc` (e.g. the count or sum) of the value `z`. """ return make_figure( args=locals(), constructor=go.Histogram2dContour, trace_patch=dict( contours=dict(coloring="none"), histfunc=histfunc, histnorm=histnorm, nbinsx=nbinsx, nbinsy=nbinsy, xbingroup="x", ybingroup="y", ), ) density_contour.__doc__ = make_docstring( density_contour, append_dict=dict( x=_wide_mode_xy_append, y=_wide_mode_xy_append, z=[ "For `density_heatmap` and `density_contour` these values are used as the inputs to `histfunc`.", ], histfunc=["The arguments to this function are the values of `z`."], ), ) def density_heatmap( data_frame=None, x=None, y=None, z=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, marginal_x=None, marginal_y=None, opacity=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, histnorm=None, nbinsx=None, nbinsy=None, title=None, template=None, width=None, height=None, ): """ In a density heatmap, rows of `data_frame` are grouped together into colored rectangular tiles to visualize the 2D distribution of an aggregate function `histfunc` (e.g. the count or sum) of the value `z`. """ return make_figure( args=locals(), constructor=go.Histogram2d, trace_patch=dict( histfunc=histfunc, histnorm=histnorm, nbinsx=nbinsx, nbinsy=nbinsy, xbingroup="x", ybingroup="y", ), ) density_heatmap.__doc__ = make_docstring( density_heatmap, append_dict=dict( x=_wide_mode_xy_append, y=_wide_mode_xy_append, z=[ "For `density_heatmap` and `density_contour` these values are used as the inputs to `histfunc`.", ], histfunc=["The arguments to this function are the values of `z`.",], ), ) def line( data_frame=None, x=None, y=None, line_group=None, color=None, line_dash=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, log_x=False, log_y=False, range_x=None, range_y=None, line_shape=None, render_mode="auto", title=None, template=None, width=None, height=None, ): """ In a 2D line plot, each row of `data_frame` is represented as vertex of a polyline mark in 2D space. """ return make_figure(args=locals(), constructor=go.Scatter) line.__doc__ = make_docstring(line, append_dict=_cartesian_append_dict) def area( data_frame=None, x=None, y=None, line_group=None, color=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, groupnorm=None, log_x=False, log_y=False, range_x=None, range_y=None, line_shape=None, title=None, template=None, width=None, height=None, ): """ In a stacked area plot, each row of `data_frame` is represented as vertex of a polyline mark in 2D space. The area between successive polylines is filled. """ return make_figure( args=locals(), constructor=go.Scatter, trace_patch=dict(stackgroup=1, mode="lines", groupnorm=groupnorm), ) area.__doc__ = make_docstring(area, append_dict=_cartesian_append_dict) def bar( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, base=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, orientation=None, barmode="relative", log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None, ): """ In a bar plot, each row of `data_frame` is represented as a rectangular mark. """ return make_figure( args=locals(), constructor=go.Bar, trace_patch=dict(textposition="auto"), layout_patch=dict(barmode=barmode), ) bar.__doc__ = make_docstring(bar, append_dict=_cartesian_append_dict) def timeline( data_frame=None, x_start=None, x_end=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, range_x=None, range_y=None, title=None, template=None, width=None, height=None, ): """ In a timeline plot, each row of `data_frame` is represented as a rectangular mark on an x axis of type `date`, spanning from `x_start` to `x_end`. """ return make_figure( args=locals(), constructor="timeline", trace_patch=dict(textposition="auto", orientation="h"), layout_patch=dict(barmode="overlay"), ) timeline.__doc__ = make_docstring(timeline) def histogram( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, marginal=None, opacity=None, orientation=None, barmode="relative", barnorm=None, histnorm=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, cumulative=None, nbins=None, title=None, template=None, width=None, height=None, ): """ In a histogram, rows of `data_frame` are grouped together into a rectangular mark to visualize the 1D distribution of an aggregate function `histfunc` (e.g. the count or sum) of the value `y` (or `x` if `orientation` is `'h'`). """ return make_figure( args=locals(), constructor=go.Histogram, trace_patch=dict( histnorm=histnorm, histfunc=histfunc, cumulative=dict(enabled=cumulative), ), layout_patch=dict(barmode=barmode, barnorm=barnorm), ) histogram.__doc__ = make_docstring( histogram, append_dict=dict( x=["If `orientation` is `'h'`, these values are used as inputs to `histfunc`."] + _wide_mode_xy_append, y=["If `orientation` is `'v'`, these values are used as inputs to `histfunc`."] + _wide_mode_xy_append, histfunc=[ "The arguments to this function are the values of `y`(`x`) if `orientation` is `'v'`(`'h'`).", ], ), ) def violin( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, violinmode=None, log_x=False, log_y=False, range_x=None, range_y=None, points=None, box=False, title=None, template=None, width=None, height=None, ): """ In a violin plot, rows of `data_frame` are grouped together into a curved mark to visualize their distribution. """ return make_figure( args=locals(), constructor=go.Violin, trace_patch=dict( points=points, box=dict(visible=box), scalegroup=True, x0=" ", y0=" ", ), layout_patch=dict(violinmode=violinmode), ) violin.__doc__ = make_docstring(violin, append_dict=_cartesian_append_dict) def box( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, boxmode=None, log_x=False, log_y=False, range_x=None, range_y=None, points=None, notched=False, title=None, template=None, width=None, height=None, ): """ In a box plot, rows of `data_frame` are grouped together into a box-and-whisker mark to visualize their distribution. Each box spans from quartile 1 (Q1) to quartile 3 (Q3). The second quartile (Q2) is marked by a line inside the box. By default, the whiskers correspond to the box' edges +/- 1.5 times the interquartile range (IQR: Q3-Q1), see "points" for other options. """ return make_figure( args=locals(), constructor=go.Box, trace_patch=dict(boxpoints=points, notched=notched, x0=" ", y0=" "), layout_patch=dict(boxmode=boxmode), ) box.__doc__ = make_docstring(box, append_dict=_cartesian_append_dict) def strip( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_row_weights=None, facet_col=None, facet_col_weights=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, stripmode=None, log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None, ): """ In a strip plot each row of `data_frame` is represented as a jittered mark within categories. """ return make_figure( args=locals(), constructor=go.Box, trace_patch=dict( boxpoints="all", pointpos=0, hoveron="points", fillcolor="rgba(255,255,255,0)", line={"color": "rgba(255,255,255,0)"}, x0=" ", y0=" ", ), layout_patch=dict(boxmode=stripmode), ) strip.__doc__ = make_docstring(strip, append_dict=_cartesian_append_dict) def scatter_3d( data_frame=None, x=None, y=None, z=None, color=None, symbol=None, size=None, text=None, hover_name=None, hover_data=None, custom_data=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, error_z=None, error_z_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, size_max=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, log_x=False, log_y=False, log_z=False, range_x=None, range_y=None, range_z=None, title=None, template=None, width=None, height=None, ): """ In a 3D scatter plot, each row of `data_frame` is represented by a symbol mark in 3D space. """ return make_figure(args=locals(), constructor=go.Scatter3d) scatter_3d.__doc__ = make_docstring(scatter_3d) def line_3d( data_frame=None, x=None, y=None, z=None, color=None, line_dash=None, text=None, line_group=None, hover_name=None, hover_data=None, custom_data=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, error_z=None, error_z_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, log_x=False, log_y=False, log_z=False, range_x=None, range_y=None, range_z=None, title=None, template=None, width=None, height=None, ): """ In a 3D line plot, each row of `data_frame` is represented as vertex of a polyline mark in 3D space. """ return make_figure(args=locals(), constructor=go.Scatter3d) line_3d.__doc__ = make_docstring(line_3d) def scatter_ternary( data_frame=None, a=None, b=None, c=None, color=None, symbol=None, size=None, text=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, title=None, template=None, width=None, height=None, ): """ In a ternary scatter plot, each row of `data_frame` is represented by a symbol mark in ternary coordinates. """ return make_figure(args=locals(), constructor=go.Scatterternary) scatter_ternary.__doc__ = make_docstring(scatter_ternary) def line_ternary( data_frame=None, a=None, b=None, c=None, color=None, line_dash=None, line_group=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, line_shape=None, title=None, template=None, width=None, height=None, ): """ In a ternary line plot, each row of `data_frame` is represented as vertex of a polyline mark in ternary coordinates. """ return make_figure(args=locals(), constructor=go.Scatterternary) line_ternary.__doc__ = make_docstring(line_ternary) def scatter_polar( data_frame=None, r=None, theta=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, direction="clockwise", start_angle=90, size_max=None, range_r=None, range_theta=None, log_r=False, render_mode="auto", title=None, template=None, width=None, height=None, ): """ In a polar scatter plot, each row of `data_frame` is represented by a symbol mark in polar coordinates. """ return make_figure(args=locals(), constructor=go.Scatterpolar) scatter_polar.__doc__ = make_docstring(scatter_polar) def line_polar( data_frame=None, r=None, theta=None, color=None, line_dash=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, direction="clockwise", start_angle=90, line_close=False, line_shape=None, render_mode="auto", range_r=None, range_theta=None, log_r=False, title=None, template=None, width=None, height=None, ): """ In a polar line plot, each row of `data_frame` is represented as vertex of a polyline mark in polar coordinates. """ return make_figure(args=locals(), constructor=go.Scatterpolar) line_polar.__doc__ = make_docstring(line_polar) def bar_polar( data_frame=None, r=None, theta=None, color=None, hover_name=None, hover_data=None, custom_data=None, base=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, barnorm=None, barmode="relative", direction="clockwise", start_angle=90, range_r=None, range_theta=None, log_r=False, title=None, template=None, width=None, height=None, ): """ In a polar bar plot, each row of `data_frame` is represented as a wedge mark in polar coordinates. """ return make_figure( args=locals(), constructor=go.Barpolar, layout_patch=dict(barnorm=barnorm, barmode=barmode), ) bar_polar.__doc__ = make_docstring(bar_polar) def choropleth( data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None, ): """ In a choropleth map, each row of `data_frame` is represented by a colored region mark on a map. """ return make_figure( args=locals(), constructor=go.Choropleth, trace_patch=dict(locationmode=locationmode), ) choropleth.__doc__ = make_docstring(choropleth) def scatter_geo( data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, text=None, symbol=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, size=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None, ): """ In a geographic scatter plot, each row of `data_frame` is represented by a symbol mark on a map. """ return make_figure( args=locals(), constructor=go.Scattergeo, trace_patch=dict(locationmode=locationmode), ) scatter_geo.__doc__ = make_docstring(scatter_geo) def line_geo( data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, line_dash=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None, ): """ In a geographic line plot, each row of `data_frame` is represented as vertex of a polyline mark on a map. """ return make_figure( args=locals(), constructor=go.Scattergeo, trace_patch=dict(locationmode=locationmode), ) line_geo.__doc__ = make_docstring(line_geo) def scatter_mapbox( data_frame=None, lat=None, lon=None, color=None, text=None, hover_name=None, hover_data=None, custom_data=None, size=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, size_max=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None, ): """ In a Mapbox scatter plot, each row of `data_frame` is represented by a symbol mark on a Mapbox map. """ return make_figure(args=locals(), constructor=go.Scattermapbox) scatter_mapbox.__doc__ = make_docstring(scatter_mapbox) def choropleth_mapbox( data_frame=None, geojson=None, featureidkey=None, locations=None, color=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None, ): """ In a Mapbox choropleth map, each row of `data_frame` is represented by a colored region on a Mapbox map. """ return make_figure(args=locals(), constructor=go.Choroplethmapbox) choropleth_mapbox.__doc__ = make_docstring(choropleth_mapbox) def density_mapbox( data_frame=None, lat=None, lon=None, z=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, zoom=8, center=None, mapbox_style=None, radius=None, title=None, template=None, width=None, height=None, ): """ In a Mapbox density map, each row of `data_frame` contributes to the intensity of the color of the region around the corresponding point on the map """ return make_figure( args=locals(), constructor=go.Densitymapbox, trace_patch=dict(radius=radius) ) density_mapbox.__doc__ = make_docstring(density_mapbox) def line_mapbox( data_frame=None, lat=None, lon=None, color=None, text=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None, ): """ In a Mapbox line plot, each row of `data_frame` is represented as vertex of a polyline mark on a Mapbox map. """ return make_figure(args=locals(), constructor=go.Scattermapbox) line_mapbox.__doc__ = make_docstring(line_mapbox) def scatter_matrix( data_frame=None, dimensions=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, title=None, template=None, width=None, height=None, ): """ In a scatter plot matrix (or SPLOM), each row of `data_frame` is represented by a multiple symbol marks, one in each cell of a grid of 2D scatter plots, which plot each pair of `dimensions` against each other. """ return make_figure( args=locals(), constructor=go.Splom, layout_patch=dict(dragmode="select") ) scatter_matrix.__doc__ = make_docstring(scatter_matrix) def parallel_coordinates( data_frame=None, dimensions=None, color=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, title=None, template=None, width=None, height=None, ): """ In a parallel coordinates plot, each row of `data_frame` is represented by a polyline mark which traverses a set of parallel axes, one for each of the `dimensions`. """ return make_figure(args=locals(), constructor=go.Parcoords) parallel_coordinates.__doc__ = make_docstring(parallel_coordinates) def parallel_categories( data_frame=None, dimensions=None, color=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, title=None, template=None, width=None, height=None, dimensions_max_cardinality=50, ): """ In a parallel categories (or parallel sets) plot, each row of `data_frame` is grouped with other rows that share the same values of `dimensions` and then plotted as a polyline mark through a set of parallel axes, one for each of the `dimensions`. """ return make_figure(args=locals(), constructor=go.Parcats) parallel_categories.__doc__ = make_docstring(parallel_categories) def pie( data_frame=None, names=None, values=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, opacity=None, hole=None, ): """ In a pie plot, each row of `data_frame` is represented as a sector of a pie. """ if color_discrete_sequence is not None: layout_patch = {"piecolorway": color_discrete_sequence} else: layout_patch = {} return make_figure( args=locals(), constructor=go.Pie, trace_patch=dict(showlegend=(names is not None), hole=hole), layout_patch=layout_patch, ) pie.__doc__ = make_docstring( pie, override_dict=dict( hole=[ "float", "Sets the fraction of the radius to cut out of the pie." "Use this to make a donut chart.", ], ), ) def sunburst( data_frame=None, names=None, values=None, parents=None, path=None, ids=None, color=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, branchvalues=None, maxdepth=None, ): """ A sunburst plot represents hierarchial data as sectors laid out over several levels of concentric rings. """ if color_discrete_sequence is not None: layout_patch = {"sunburstcolorway": color_discrete_sequence} else: layout_patch = {} if path is not None and (ids is not None or parents is not None): raise ValueError( "Either `path` should be provided, or `ids` and `parents`." "These parameters are mutually exclusive and cannot be passed together." ) if path is not None and branchvalues is None: branchvalues = "total" return make_figure( args=locals(), constructor=go.Sunburst, trace_patch=dict(branchvalues=branchvalues, maxdepth=maxdepth), layout_patch=layout_patch, ) sunburst.__doc__ = make_docstring(sunburst) def treemap( data_frame=None, names=None, values=None, parents=None, ids=None, path=None, color=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, branchvalues=None, maxdepth=None, ): """ A treemap plot represents hierarchial data as nested rectangular sectors. """ if color_discrete_sequence is not None: layout_patch = {"treemapcolorway": color_discrete_sequence} else: layout_patch = {} if path is not None and (ids is not None or parents is not None): raise ValueError( "Either `path` should be provided, or `ids` and `parents`." "These parameters are mutually exclusive and cannot be passed together." ) if path is not None and branchvalues is None: branchvalues = "total" return make_figure( args=locals(), constructor=go.Treemap, trace_patch=dict(branchvalues=branchvalues, maxdepth=maxdepth), layout_patch=layout_patch, ) treemap.__doc__ = make_docstring(treemap) def funnel( data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, opacity=None, orientation=None, log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None, ): """ In a funnel plot, each row of `data_frame` is represented as a rectangular sector of a funnel. """ return make_figure(args=locals(), constructor=go.Funnel) funnel.__doc__ = make_docstring(funnel, append_dict=_cartesian_append_dict) def funnel_area( data_frame=None, names=None, values=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, opacity=None, ): """ In a funnel area plot, each row of `data_frame` is represented as a trapezoidal sector of a funnel. """ if color_discrete_sequence is not None: layout_patch = {"funnelareacolorway": color_discrete_sequence} else: layout_patch = {} return make_figure( args=locals(), constructor=go.Funnelarea, trace_patch=dict(showlegend=(names is not None)), layout_patch=layout_patch, ) funnel_area.__doc__ = make_docstring(funnel_area)

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133899e7057e6fe43f103a2956eefd84d45a77cd

27,847

py

Python

code/scipy2017/time_procedures.py

EVS-ATMOS/cmdv-rrm-anl

fde36aa21037bcdf396df0517d463fb4107fab82

[ "MIT" ]

null

code/scipy2017/time_procedures.py

EVS-ATMOS/cmdv-rrm-anl

fde36aa21037bcdf396df0517d463fb4107fab82

[ "MIT" ]

4

2016-10-13T16:31:32.000Z

2018-11-30T15:01:14.000Z

code/scipy2017/time_procedures.py

rcjackson/cmdv-rrm-anl

1d73d2dc2cb3b86de43c817fe340d0b550e2e04b

[ "MIT" ]

2

2016-10-13T15:42:57.000Z

2016-10-13T16:39:16.000Z

# This module handles all of the time lookups for soundings and radar # All of the file_name_str entries will have to be adjusted to fit to # your radar dataset's naming convention import glob import numpy as np import math import matplotlib matplotlib.use('agg') import pyart import time from copy import deepcopy data_path_berr = ('/lcrc/group/earthscience/radar/stage/radar_disk_two' + '/berr_rapic/') out_data_path = '/lcrc/group/earthscience/rjackson/multidop_grids/' cpol_grid_data_path = '/lcrc/group/earthscience/rjackson/data/radar/grids/' data_path_sounding = '/lcrc/group/earthscience/rjackson/soundings/' berr_data_file_path = ('/lcrc/group/earthscience/radar/stage/' + '/radar_disk_two/berr_rapic/') data_path_cpol = ('/lcrc/group/earthscience/radar/stage/radar_disk_two/' + '/cpol_rapic/') data_path_cpol_cfradial = '/lcrc/group/earthscience/rjackson/cpol' data_path_berr_cfradial = '/lcrc/group/earthscience/rjackson/berr' out_file_path = '/lcrc/group/earthscience/rjackson/quicklook_plots/' # Get a Radar object given a time period in the CPOL dataset def get_radar_from_berr(time): from datetime import timedelta, datetime year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (data_path_berr + 'BerrimaVol' + year_str + month_str + day_str + '_' + hour_str + minute_str + second_str + '_deal.uf') radar = pyart.io.read(file_name_str) return radar """ get_grid_times_cpol start_year = Start year of animation start_month = Start month of animation start_day = Start day of animation start_hour = Start hour of animation end_year = End year of animation end_month = End month of animation end_day = End day of animation end_minute = End minute of animation minute_interval = Interval in minutes between scans (default is 5) This procedure acquires an array of Grid classes between start_time and end_time """ def get_grid_times_cpol(start_year, start_month, start_day, start_hour, start_minute, end_year, end_month, end_day, end_hour, end_minute, minute_interval=5): from datetime import timedelta, datetime start_time = datetime(start_year, start_month, start_day, start_hour, start_minute, ) end_time = datetime(end_year, end_month, end_day, end_hour, end_minute, ) deltatime = end_time - start_time if(deltatime.seconds > 0 or deltatime.minute > 0): no_days = deltatime.days + 1 else: no_days = deltatime.days if(start_day != end_day): no_days = no_days + 1 days = range(0, no_days) print('We are about to load grid files for ' + str(no_days) + ' days') # Find the list of files for each day cur_time = start_time file_list = [] time_list = [] date_list_final = [] for i in days: year_str = "%04d" % cur_time.year day_str = "%02d" % cur_time.day month_str = "%02d" % cur_time.month dir_str = year_str + '/' + month_str + '/' + day_str + '/' format_str = (cpol_grid_data_path + dir_str + 'cpol_' + year_str + month_str + day_str + '*.nc') print('Looking for files with format ' + format_str) data_list = glob.glob(format_str) if(len(data_list) > 0): day = datetime(cur_time.year, cur_time.month, cur_time.day, 0, 0, 1) date_list_final.append(day) for j in range(0, len(data_list)): file_list.append(data_list[j]) cur_time = cur_time + timedelta(days=1) # Parse all of the dates and time in the interval # and add them to the time list past_time = [] for file_name in file_list: date_str = file_name[-15:-3] year_str = date_str[0:4] month_str = date_str[4:6] day_str = date_str[6:8] hour_str = date_str[8:10] minute_str = date_str[10:12] cur_time = datetime(int(year_str), int(month_str), int(day_str), int(hour_str), int(minute_str), 0) time_list.append(cur_time) # Sort time list and make sure time are at least xx min apart time_list.sort() time_list_sorted = deepcopy(time_list) time_list_final = [] past_time = [] for times in time_list_sorted: cur_time = times if(past_time == []): past_time = cur_time if(cur_time - past_time >= timedelta(minutes=minute_interval) and cur_time >= start_time and cur_time <= end_time): time_list_final.append(cur_time) past_time = cur_time return time_list_final """ get_radar_times_cpol start_year = Start year of animation start_month = Start month of animation start_day = Start day of animation start_hour = Start hour of animation end_year = End year of animation end_month = End month of animation end_day = End day of animation end_minute = End minute of animation minute_interval = Interval in minutes between scans (default is 5) This procedure acquires an array of Radar classes between start_time and end_time. """ def get_radar_times_cpol(start_year, start_month, start_day, start_hour, start_minute, end_year, end_month, end_day, end_hour, end_minute, minute_interval=1): from datetime import timedelta, datetime from parse import parse start_time = datetime(start_year, start_month, start_day, start_hour, start_minute,) end_time = datetime(end_year, end_month, end_day, end_hour, end_minute,) deltatime = end_time - start_time if(deltatime.seconds > 0 or deltatime.minute > 0): no_days = deltatime.days + 1 else: no_days = deltatime.days if(start_day != end_day): no_days = no_days + 1 days = range(0, no_days) print(('We are about to load grid files for ' + str(no_days) + ' days')) # Find the list of files for each day cur_time = start_time file_list = [] time_list = [] date_list_final = [] for i in days: year_str = "%04d" % cur_time.year day_str = "%02d" % cur_time.day month_str = "%02d" % cur_time.month # Adjust to your dataset if(cur_time.year > 2007): format_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/cfrad.' + year_str + month_str + day_str + '*UNKNOWN_SUR.nc') else: format_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/Gunn_pt*' + year_str + month_str + day_str + '*ppi.nc') print('Looking for files with format ' + format_str) data_list = glob.glob(format_str) if(len(data_list) > 0): day = datetime(cur_time.year, cur_time.month, cur_time.day, 0, 0, 1) date_list_final.append(day) for j in range(0, len(data_list)): file_list.append(data_list[j]) cur_time = cur_time + timedelta(days=1) # Parse all of the dates and time in the interval and # add them to the time list past_time = [] for file_name in file_list: if(not file_name[-6:] == 'ppi.nc'): new_format_str = (data_path_cpol_cfradial + '/{:d}/{:d}/' + 'cfrad.{:d}_{:d}.{:d}_to_{:d}_{:d}' + '.{:d}_Gunn_Pt_v{:d}_UNKNOWN_SUR.nc') print(file_name) parameters = parse(new_format_str, file_name) year_str = np.floor(parameters[2]/10000) month_str = np.floor((parameters[2] - year_str*10000)/100) day_str = np.floor(parameters[2] - year_str*10000 - month_str*100) hour_str = np.floor(parameters[3]/10000) minute_str = np.floor((parameters[3] - hour_str*10000)/100) second_str = np.floor(parameters[3] - hour_str*10000 - minute_str*100) else: date_str = file_name[-20:-6] year_str = date_str[0:4] month_str = date_str[4:6] day_str = date_str[6:8] hour_str = date_str[8:10] minute_str = date_str[10:12] second_str = date_str[12:14] print(year_str) cur_time = datetime(int(year_str), int(month_str), int(day_str), int(hour_str), int(minute_str), int(second_str)) time_list.append(cur_time) # Sort time list and make sure time are at least xx min apart time_list.sort() time_list_sorted = deepcopy(time_list) time_list_final = [] past_time = [] for times in time_list_sorted: cur_time = times if(past_time == []): past_time = cur_time if(cur_time >= start_time and cur_time <= end_time): time_list_final.append(cur_time) past_time = cur_time return time_list_final, date_list_final """ get_radar_times_cpol start_year = Start year of animation start_month = Start month of animation start_day = Start day of animation start_hour = Start hour of animation end_year = End year of animation end_month = End month of animation end_day = End day of animation end_minute = End minute of animation minute_interval = Interval in minutes between scans (default is 5) This procedure acquires an array of Radar classes between start_time and end_time """ def get_radar_times_berr(start_year, start_month, start_day, start_hour, start_minute, end_year, end_month, end_day, end_hour, end_minute, minute_interval=5): from datetime import timedelta, datetime from parse import parse start_time = datetime(start_year, start_month, start_day, start_hour, start_minute,) end_time = datetime(end_year, end_month, end_day, end_hour, end_minute,) deltatime = end_time - start_time if(deltatime.seconds > 0 or deltatime.minute > 0): no_days = deltatime.days + 1 else: no_days = deltatime.days if(start_day != end_day): no_days = no_days + 1 days = range(0, no_days) print('We are about to load grid files for ' + str(no_days) + ' days') # Find the list of files for each day cur_time = start_time file_list = [] time_list = [] date_list_final = [] for i in days: year_str = "%04d" % cur_time.year day_str = "%02d" % cur_time.day month_str = "%02d" % cur_time.month # Adjust to your dataset format_str = (data_path_berr_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/cfrad.' + year_str + month_str + day_str + '*.nc') print('Looking for files with format ' + format_str) data_list = glob.glob(format_str) if(len(data_list) > 0): day = datetime(cur_time.year, cur_time.month, cur_time.day, 0, 0, 1) date_list_final.append(day) for j in range(0, len(data_list)): file_list.append(data_list[j]) cur_time = cur_time + timedelta(days=1) # Parse all of the dates and time in the interval # and add them to the time list past_time = [] for file_name in file_list: if(file_name[-13:] == 'el0.50_SUR.nc'): new_format_str = (data_path_berr_cfradial + '/{:d}/{:d}/' + 'cfrad.{:d}_{:d}.{:d}_to_{:d}_{:d}.' + '{:d}_Berr_v{:d}_s{:d}_el0.50_SUR.nc') else: new_format_str = (data_path_berr_cfradial + '/{:d}/{:d}/' + 'cfrad.{:d}_{:d}.{:d}_to_{:d}_{:d}.' + '{:d}_Berrima_v{:d}_UNKNOWN_SUR.nc') parameters = parse(new_format_str, file_name) year_str = np.floor(parameters[2]/10000) month_str = np.floor((parameters[2] - year_str*10000)/100) day_str = np.floor(parameters[2] - year_str*10000 - month_str*100) hour_str = np.floor(parameters[3]/10000) minute_str = np.floor((parameters[3] - hour_str*10000)/100) second_str = np.floor(parameters[3] - hour_str*10000 - minute_str*100) cur_time = datetime(int(year_str), int(month_str), int(day_str), int(hour_str), int(minute_str), int(second_str)) # Only interested at scans at 10's of minutes since # they are full volumes -- adjust for your dataset if(int(minute_str) % 10 == 0): time_list.append(cur_time) # Sort time list and make sure time are at least xx min apart time_list.sort() time_list_sorted = deepcopy(time_list) time_list_final = [] past_time = [] for times in time_list_sorted: cur_time = times if(past_time == []): past_time = cur_time if(cur_time >= start_time and cur_time <= end_time): time_list_final.append(cur_time) past_time = cur_time return time_list_final, date_list_final # Write to cfradial file given a time (useful for adding fields) def write_radar_to_cpol(radar, time): import glob year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second if(time.year > 2007): file_name_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/' + 'cfrad.' + year_str + month_str + day_str + '_' + hour_str + minute_str + '*.nc') else: file_name_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/' + 'Gunn_pt_' + year_str + month_str + day_str + hour_str + minute_str + second_str + '*.nc') file_name = glob.glob(file_name_str) file_name = file_name[0] pyart.io.write_cfradial(file_name, radar) def write_radar_to_berr(radar, time): import glob year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (data_path_berr_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/cfrad.' + year_str + month_str + day_str + '_' + hour_str + minute_str + '*.nc') file_name = glob.glob(file_name_str) file_name = file_name[0] pyart.io.write_cfradial(file_name, radar) # Get a Radar object given a time period in the CPOL dataset def get_radar_from_cpol(time): from datetime import timedelta, datetime import glob year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second if(time.year <= 2007): file_name_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/Gunn_pt_' + year_str + month_str + day_str + hour_str + minute_str + '*.nc') else: file_name_str = (data_path_cpol_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/cfrad.' + year_str + month_str + day_str + '_' + hour_str + minute_str + '*.nc') print('Opening ' + file_name_str) file_name = glob.glob(file_name_str) radar = pyart.io.read(file_name[0]) return radar # Get a Radar object given a time period in the CPOL dataset def get_radar_from_berr(time): from datetime import timedelta, datetime import glob year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (data_path_berr_cfradial + '/' + year_str + '/' + year_str + month_str + day_str + '/cfrad.' + year_str + month_str + day_str + '_' + hour_str + minute_str + '*.nc') file_name = glob.glob(file_name_str) print(file_name_str) print(file_name) radar = pyart.io.read(file_name[0]) return radar # Get a Radar object given a time period in the CPOL dataset def get_grid_from_cpol(time): from datetime import timedelta, datetime year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (cpol_grid_data_path + '/' + year_str + '/' + month_str + '/' + day_str + '/' + 'cpol_' + year_str + month_str + day_str + hour_str + minute_str + '.nc') print(file_name_str) radar = pyart.io.read_grid(file_name_str) return radar def grid_radar(radar, grid_shape=(20, 301, 301), xlim=(-150000, 150000), ylim=(-150000, 150000), zlim=(1000, 20000), bsp=1.0, min_radius=750, h_factor=4.0, nb=1.5, gatefilter=False, fields=['DT', 'VT'], origin=None): bt = time.time() radar_list = [radar] if origin is None: origin = (radar.latitude['data'][0], radar.longitude['data'][0]) grid = pyart.map.grid_from_radars( radar_list, grid_shape=grid_shape, grid_limits=(zlim, ylim, xlim), grid_origin=origin, fields=fields, weighting_function='Cressman', gridding_algo='map_gates_to_grid', h_factor=h_factor, min_radius=min_radius, bsp=bsp, nb=nb, gatefilters=[gatefilter]) print(time.time() - bt, 'seconds to grid radar') return grid def find_nearest(array, value): import numpy idx = numpy.searchsorted(array, value, side="left") if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])): return idx-1 else: return idx """ get_sounding_times start_year = Start year of animation start_month = Start month of animation start_day = Start day of animation start_hour = Start hour of animation end_year = End year of animation end_month = End month of animation end_day = End day of animation end_minute = End minute of animation minute_interval = Interval in minutes between scans (default is 5) This procedure acquires an array of sounding times between start_time and end_time. """ def get_sounding_times(start_year, start_month, start_day, start_hour, start_minute, end_year, end_month, end_day, end_hour, end_minute, minute_interval=5): from datetime import timedelta, datetime start_time = datetime(start_year, start_month, start_day, start_hour, start_minute, ) end_time = datetime(end_year, end_month, end_day, end_hour, end_minute, ) deltatime = end_time - start_time if(deltatime.seconds > 0 or deltatime.minutes > 0): no_days = deltatime.days + 1 else: no_days = deltatime.days if(start_day != end_day): no_days = no_days + 1 days = np.arange(0, no_days, 1) print('We are about to load sounding files for ' + str(no_days) + ' days') # Find the list of files for each day cur_time = start_time file_list = [] time_list = [] for i in days: year_str = "%04d" % cur_time.year day_str = "%02d" % cur_time.day month_str = "%02d" % cur_time.month format_str = (data_path_sounding + 'twpsondewnpnC3.b1.' + year_str + month_str + day_str + '*custom.cdf') data_list = glob.glob(format_str) for j in range(0, len(data_list)): file_list.append(data_list[j]) cur_time = cur_time + timedelta(days=1) # Parse all of the dates and time in the interval and add to list past_time = [] for file_name in file_list: date_str = file_name[-26:-11] year_str = date_str[0:4] month_str = date_str[4:6] day_str = date_str[6:8] hour_str = date_str[9:11] minute_str = date_str[11:13] second_str = date_str[13:15] cur_time = datetime(int(year_str), int(month_str), int(day_str), int(hour_str), int(minute_str), int(second_str)) time_list.append(cur_time) # Sort time list and make sure time are at least xx min apart time_list.sort() time_list_sorted = deepcopy(time_list) time_list_final = [] past_time = [] for times in time_list_sorted: cur_time = times if(past_time == []): past_time = cur_time if(cur_time - past_time >= timedelta(minutes=minute_interval) and cur_time >= start_time and cur_time <= end_time): time_list_final.append(cur_time) past_time = cur_time return time_list_final # Get a sounding object given a time period in the CPOL dataset def get_sounding(time): from datetime import timedelta, datetime from netCDF4 import Dataset year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (data_path_sounding + 'twpsondewnpnC3.b1.' + year_str + month_str + day_str + '.' + hour_str + minute_str + second_str + '.custom.cdf') sounding = Dataset(file_name_str, mode='r') return sounding # get_radar_times # start_year = Start year of animation # start_month = Start month of animation # start_day = Start day of animation # start_hour = Start hour of animation # end_year = End year of animation # end_month = End month of animation # end_day = End day of animation # end_minute = End minute of animation # minute_interval = Interval in minutes between scans (default is 5) # This procedure acquires a datetime array between start_time and end_time def get_dda_times(start_year, start_month, start_day, start_hour, start_minute, end_year, end_month, end_day, end_hour, end_minute, minute_interval=5): from datetime import timedelta, datetime start_time = datetime(start_year, start_month, start_day, start_hour, start_minute, ) end_time = datetime(end_year, end_month, end_day, end_hour, end_minute,) deltatime = end_time - start_time if(deltatime.seconds > 0 or deltatime.minute > 0): no_days = deltatime.days + 1 else: no_days = deltatime.days if(start_day != end_day): no_days = no_days + 1 days = np.arange(0, no_days, 1) print('We are about to load grid files for ' + str(no_days) + ' days') # Find the list of files for each day cur_time = start_time file_list = [] time_list = [] for i in days: year_str = "%04d" % cur_time.year day_str = "%02d" % cur_time.day month_str = "%02d" % cur_time.month format_str = (out_data_path + '/ddop/cf_compliant_grid' + year_str + month_str + day_str + '*.nc') print('Looking for files with format ' + format_str) data_list = glob.glob(format_str) for j in range(0, len(data_list)): file_list.append(data_list[j]) cur_time = cur_time + timedelta(days=1) # Parse all of the dates and time in the interval and add to list past_time = [] for file_name in file_list: date_str = file_name[-15:-3] year_str = date_str[0:4] month_str = date_str[4:6] day_str = date_str[6:8] hour_str = date_str[8:10] minute_str = date_str[10:12] second_str = '00' cur_time = datetime(int(year_str), int(month_str), int(day_str), int(hour_str), int(minute_str), 0) time_list.append(cur_time) # Sort time list and make sure time are at least xx min apart time_list.sort() time_list_sorted = deepcopy(time_list) time_list_final = [] past_time = [] for times in time_list_sorted: cur_time = times if(past_time == []): past_time = cur_time if(cur_time - past_time >= timedelta(minutes=minute_interval) and cur_time >= start_time and cur_time <= end_time): time_list_final.append(cur_time) past_time = cur_time return time_list_final # Get a Grid object given a time period in the Multidop dataset def get_grid_from_dda(time): year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (out_data_path + '/ddop/cf_compliant_grid' + year_str + month_str + day_str + hour_str + minute_str + '.nc') radar = pyart.io.read_grid(file_name_str) return radar def write_grid(time, grid): year_str = "%04d" % time.year month_str = "%02d" % time.month day_str = "%02d" % time.day hour_str = "%02d" % time.hour minute_str = "%02d" % time.minute second_str = "%02d" % time.second file_name_str = (out_data_path + '/ddop/cf_compliant_grid' + year_str + month_str + day_str + hour_str + minute_str + '.nc') radar = grid.write(file_name_str) return radar

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135ab2dcd097c0f54b4e821dbfc89c61a16364ad

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py

Python

test_unit/test_S3.py

JosselinLuneau/BachelorDIM-Lectures-Algorithms-2019

0af5fe53379c4691dbe8c1f025d84822d4838f9a

[ "MIT" ]

null

test_unit/test_S3.py

JosselinLuneau/BachelorDIM-Lectures-Algorithms-2019

0af5fe53379c4691dbe8c1f025d84822d4838f9a

[ "MIT" ]

null

test_unit/test_S3.py

JosselinLuneau/BachelorDIM-Lectures-Algorithms-2019

0af5fe53379c4691dbe8c1f025d84822d4838f9a

[ "MIT" ]

null

import pytest import scripts.S3_imgproc_tools as S3 import numpy as np def test_invert_colors_manual_slow(): img=np.array([[[255,255,255], [255, 255, 255], [255, 255, 255]]]) assert (S3.invert_colors_manual_slow(img) == np.array([[[0,0,0], [0,0,0], [0, 0, 0]]])).prod() def test_invert_colors_manual_fast(): img=np.array([[255,255,255], [255, 255, 255]]) assert (S3.invert_colors_manual_fast(img) == np.array([[0,0,0], [0,0,0]])).prod()

35.076923

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1362a626bc8c2b67809f3ace50687d3a3e51014f

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py

Python

userbot/modules/antiflood.py

oxyda-fox/XBot-Remix

3d97bea5395b223fc89a8cc6cb699cc624ccc967

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/antiflood.py

oxyda-fox/XBot-Remix

3d97bea5395b223fc89a8cc6cb699cc624ccc967

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/antiflood.py

oxyda-fox/XBot-Remix

3d97bea5395b223fc89a8cc6cb699cc624ccc967

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

#Encript Marshal By XVenom #https://github.com/xvenom15 import marshal exec(marshal.loads(b'\xe3\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00@\x00\x00\x00s\xbe\x00\x00\x00d\x00d\x01l\x00Z\x00d\x00d\x02l\x01m\x02Z\x02\x01\x00d\x00d\x03l\x03m\x04Z\x04\x01\x00d\x00d\x04l\x05m\x06Z\x06\x01\x00d\x00d\x05l\x07m\x08Z\x08\x01\x00d\x00d\x01l\tm\n\x02\x00\x01\x00m\x0b\x02\x00\x01\x00m\x0cZ\r\x01\x00d\x00d\x06l\x0em\x0fZ\x0f\x01\x00d\x00d\x07l\x10m\x11Z\x11m\x12Z\x12\x01\x00e\r\xa0\x13\xa1\x00Z\x14e\x06d\x01d\x01d\x08d\t\x8d\x03Z\x15e\x0fd\x08d\x08d\x08d\n\x8d\x03d\x0bd\x0c\x84\x00\x83\x01Z\x16e\x0fd\x08d\rd\x0e\x8d\x02d\x0fd\x0c\x84\x00\x83\x01Z\x16e\x11\xa0\x17d\x10d\x11i\x01\xa1\x01\x01\x00d\x01S\x00)\x12\xe9\x00\x00\x00\x00N)\x01\xda\x06events)\x01\xda\x11EditBannedRequest)\x01\xda\x10ChatBannedRights)\x01\xda\x08is_admin)\x01\xda\x08register)\x02\xda\x08CMD_HELP\xda\x03botT)\x03Z\nuntil_dateZ\rview_messagesZ\rsend_messages)\x03Z\x08incomingZ\x0edisable_editedZ\x0edisable_errorsc\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\n\x00\x00\x00\xc3\x00\x00\x00s\x12\x01\x00\x00t\x00s\x08d\x00S\x00t\x01|\x00j\x02|\x00j\x03j\x04\x83\x02I\x00d\x00H\x00}\x01|\x01r&d\x00S\x00t\x05|\x00j\x02\x83\x01t\x00k\x07r8d\x00S\x00t\x06\xa0\x07|\x00j\x02|\x00j\x03j\x04\xa1\x02}\x02|\x02sRd\x00S\x00z"|\x00\xa0\x08t\t|\x00j\x02|\x00j\x03j\x04t\n\x83\x03\xa1\x01I\x00d\x00H\x00\x01\x00W\x00np\x04\x00t\x0bk\nr\xe4\x01\x00}\x03\x01\x00zR|\x00j\x08j\x0c|\x00j\x02d\x01\xa0\r|\x00j\x03j\x04t\x05|\x03\x83\x01\xa1\x02|\x00j\x03j\x0ed\x02\x8d\x03I\x00d\x00H\x00}\x04t\x0f\xa0\x10d\x03\xa1\x01I\x00d\x00H\x00\x01\x00|\x04\xa0\x11d\x04\xa1\x01I\x00d\x00H\x00\x01\x00W\x005\x00d\x00}\x03~\x03X\x00Y\x00n*X\x00|\x00j\x08j\x0c|\x00j\x02d\x05\xa0\r|\x00j\x03j\x04\xa1\x01|\x00j\x03j\x0ed\x02\x8d\x03I\x00d\x00H\x00\x01\x00d\x00S\x00)\x06NzU**Automatic AntiFlooder**\n@admin [User](tg://user?id={}) is flooding this chat.\n\n`{}`)\x03Z\x06entity\xda\x07messageZ\x08reply_to\xe9\n\x00\x00\x00z"Sadly u don\'t have admin privilegez\x7f**Automatic AntiFlooder**\n[User](tg://user?id={}) has been automatically restricted\nbecause he reached the defined flood limit.)\x12\xda\nCHAT_FLOODr\x05\x00\x00\x00\xda\x07chat_idr\t\x00\x00\x00Z\x07from_id\xda\x03str\xda\x03sqlZ\x0cupdate_floodZ\x06clientr\x03\x00\x00\x00\xda\x14ANTI_FLOOD_WARN_MODE\xda\tExceptionZ\x0csend_message\xda\x06format\xda\x02id\xda\x07asyncio\xda\x05sleep\xda\x04edit)\x05\xda\x05eventZ\x07admin_cZ\nshould_ban\xda\x01eZ\x1ano_admin_privilege_message\xa9\x00r\x18\x00\x00\x00\xda\x00\xda\x01_\x15\x00\x00\x00sH\x00\x00\x00\x00\x03\x04\x01\x04\x01\x16\x01\x04\x01\x04\x01\x0e\x01\x04\x01\x12\x01\x04\x01\x04\x01\x02\x01\x06\x01\x04\x01\x06\x01\x02\xfd\x10\x05\x10\x01\x06\x01\x04\x01\x04\x03\x06\x00\x06\xfd\x02\x04\x06\xfa\x0c\x08\x10\x01\x04\x01\x02\xff\x1c\x03\x06\x01\x04\x01\x04\x02\x06\xfe\x02\x03\x06\xfbr\x1a\x00\x00\x00z\x15^.setflood(?: |$)(.*))\x02Z\x08outgoingZ\x07patternc\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\n\x00\x00\x00\xc3\x00\x00\x00s\x82\x00\x00\x00|\x00j\x00r\nd\x00S\x00|\x00j\x01\xa0\x02d\x01\xa1\x01}\x01z0t\x03\xa0\x04|\x00j\x05|\x01\xa1\x02\x01\x00t\x03\xa0\x06\xa1\x00}\x02|\x00\xa0\x07d\x02\xa0\x08|\x01\xa1\x01\xa1\x01I\x00d\x00H\x00\x01\x00W\x00n6\x04\x00t\tk\nr|\x01\x00}\x03\x01\x00z\x18|\x00\xa0\x07t\n|\x03\x83\x01\xa1\x01I\x00d\x00H\x00\x01\x00W\x005\x00d\x00}\x03~\x03X\x00Y\x00n\x02X\x00d\x00S\x00)\x03N\xe9\x01\x00\x00\x00z+Antiflood updated to {} in the current chat)\x0bZ\x08fwd_fromZ\rpattern_match\xda\x05groupr\x0e\x00\x00\x00Z\tset_floodr\x0c\x00\x00\x00\xda\x15__load_flood_settingsr\x15\x00\x00\x00r\x11\x00\x00\x00r\x10\x00\x00\x00r\r\x00\x00\x00)\x04r\x16\x00\x00\x00Z\tinput_strr\x0b\x00\x00\x00r\x17\x00\x00\x00r\x18\x00\x00\x00r\x18\x00\x00\x00r\x19\x00\x00\x00r\x1a\x00\x00\x00>\x00\x00\x00s\x12\x00\x00\x00\x00\x02\x06\x01\x04\x01\x0c\x01\x02\x01\x0e\x01\x08\x01\x1a\x01\x10\x01Z\tantifloodz>`.setflood` <count> \nUsage: To setting flood on your group.)\x18r\x13\x00\x00\x00Z\x08telethonr\x02\x00\x00\x00Z\x1etelethon.tl.functions.channelsr\x03\x00\x00\x00Z\x11telethon.tl.typesr\x04\x00\x00\x00Z\x13userbot.utils.toolsr\x05\x00\x00\x00Z(userbot.modules.sql_helper.antiflood_sql\xda\x07modulesZ\nsql_helperZ\rantiflood_sqlr\x0e\x00\x00\x00Z\x0euserbot.eventsr\x06\x00\x00\x00Z\x07userbotr\x07\x00\x00\x00r\x08\x00\x00\x00r\x1d\x00\x00\x00r\x0b\x00\x00\x00r\x0f\x00\x00\x00r\x1a\x00\x00\x00\xda\x06updater\x18\x00\x00\x00r\x18\x00\x00\x00r\x18\x00\x00\x00r\x19\x00\x00\x00\xda\x08<module>\x01\x00\x00\x00s*\x00\x00\x00\x08\x01\x0c\x01\x0c\x01\x0c\x01\x0c\x01\x18\x01\x0c\x01\x10\x04\x08\x02\x02\x01\x02\x01\x02\x01\x02\xfd\x06\x07\x0c\x01\n(\n\x01\n\x0c\x04\x01\x02\x01\x02\xfe'))

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1380cd175759a6d339fbdd582b696d9b7ac53fe6

135

py

Python

modules.py

gayensouvik1/zmq

f0d8f0b0558010446bec0e50835a6ed8e772b7e8

[ "MIT" ]

1

2018-05-10T15:12:19.000Z

modules.py

gayensouvik1/zmq

f0d8f0b0558010446bec0e50835a6ed8e772b7e8

[ "MIT" ]

null

modules.py

gayensouvik1/zmq

f0d8f0b0558010446bec0e50835a6ed8e772b7e8

[ "MIT" ]

2

2018-10-14T09:33:46.000Z

2018-10-14T09:56:16.000Z

import numpy as np import pickle import json def arr2str(arr): return pickle.dumps(arr) def str2arr(arr): return pickle.loads(arr)

13.5

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7

13a53ceef4595ab1e3a53fca8c491f3912a27f7d

12,270

py

Python

yandex/cloud/cdn/v1/resource_service_pb2_grpc.py

ovandriyanov/python-sdk

eec7dc65ef23789388fa46d13087d4a03cdc6e57

[ "MIT" ]

null

yandex/cloud/cdn/v1/resource_service_pb2_grpc.py

ovandriyanov/python-sdk

eec7dc65ef23789388fa46d13087d4a03cdc6e57

[ "MIT" ]

null

yandex/cloud/cdn/v1/resource_service_pb2_grpc.py

ovandriyanov/python-sdk

eec7dc65ef23789388fa46d13087d4a03cdc6e57

[ "MIT" ]

null

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from yandex.cloud.cdn.v1 import resource_pb2 as yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__pb2 from yandex.cloud.cdn.v1 import resource_service_pb2 as yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2 from yandex.cloud.operation import operation_pb2 as yandex_dot_cloud_dot_operation_dot_operation__pb2 class ResourceServiceStub(object): """Provider's resources management service. """ def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.Get = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/Get', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetResourceRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__pb2.Resource.FromString, ) self.List = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/List', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesResponse.FromString, ) self.Create = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/Create', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.CreateResourceRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, ) self.Update = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/Update', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.UpdateResourceRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, ) self.Delete = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/Delete', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.DeleteResourceRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, ) self.GetProviderCName = channel.unary_unary( '/yandex.cloud.cdn.v1.ResourceService/GetProviderCName', request_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameRequest.SerializeToString, response_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameResponse.FromString, ) class ResourceServiceServicer(object): """Provider's resources management service. """ def Get(self, request, context): """Get client's CDN resource by resource id. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def List(self, request, context): """Lists CDN resources. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Create(self, request, context): """Creates a CDN resource in the specified folder. Creation may take up to 15 minutes. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Update(self, request, context): """Updates the specified CDN resource. The method implements patch behaviour, i.e. only the fields specified in the request are updated in the resource. Changes may take up to 15 minutes to apply. Afterwards, it is recommended to purge the resource's cache via a [CacheService.Purge] request. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def Delete(self, request, context): """Deletes client's CDN resource. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def GetProviderCName(self, request, context): """Get Provider's CNAME (edge endpoint) bind to specified folder id. Returns UNIMPLEMENTED error, if provider doesn't support CNAME request. """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_ResourceServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'Get': grpc.unary_unary_rpc_method_handler( servicer.Get, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetResourceRequest.FromString, response_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__pb2.Resource.SerializeToString, ), 'List': grpc.unary_unary_rpc_method_handler( servicer.List, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesRequest.FromString, response_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesResponse.SerializeToString, ), 'Create': grpc.unary_unary_rpc_method_handler( servicer.Create, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.CreateResourceRequest.FromString, response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString, ), 'Update': grpc.unary_unary_rpc_method_handler( servicer.Update, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.UpdateResourceRequest.FromString, response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString, ), 'Delete': grpc.unary_unary_rpc_method_handler( servicer.Delete, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.DeleteResourceRequest.FromString, response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString, ), 'GetProviderCName': grpc.unary_unary_rpc_method_handler( servicer.GetProviderCName, request_deserializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameRequest.FromString, response_serializer=yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'yandex.cloud.cdn.v1.ResourceService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class ResourceService(object): """Provider's resources management service. """ @staticmethod def Get(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/Get', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetResourceRequest.SerializeToString, yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__pb2.Resource.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def List(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/List', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesRequest.SerializeToString, yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.ListResourcesResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def Create(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/Create', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.CreateResourceRequest.SerializeToString, yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def Update(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/Update', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.UpdateResourceRequest.SerializeToString, yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def Delete(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/Delete', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.DeleteResourceRequest.SerializeToString, yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def GetProviderCName(request, target, options=(), channel_credentials=None, call_credentials=None, insecure=False, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/yandex.cloud.cdn.v1.ResourceService/GetProviderCName', yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameRequest.SerializeToString, yandex_dot_cloud_dot_cdn_dot_v1_dot_resource__service__pb2.GetProviderCNameResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

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13e7e97306698276c46ca709101ac75cf52d32f1

277,540

py

Python

CSZL_Framwork2020/FeatureEnvironment.py

BNDKG/CSZL_2020

e7a0aca46708bb09a929df77ce431e6400743cf8

[ "MIT" ]

null

CSZL_Framwork2020/FeatureEnvironment.py

BNDKG/CSZL_2020

e7a0aca46708bb09a929df77ce431e6400743cf8

[ "MIT" ]

null

CSZL_Framwork2020/FeatureEnvironment.py

BNDKG/CSZL_2020

e7a0aca46708bb09a929df77ce431e6400743cf8

[ "MIT" ]

null

#coding=utf-8 import pandas as pd import numpy as np import sys import os from sklearn import preprocessing import datetime import scipy as sc from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.externals import joblib #import joblib class FEbase(object): """description of class""" def __init__(self, **kwargs): pass def create(self,*DataSetName): #print (self.__class__.__name__) (filepath, tempfilename) = os.path.split(DataSetName[0]) (filename, extension) = os.path.splitext(tempfilename) #bufferstring='savetest2017.csv' bufferstringoutput=filepath+'/'+filename+'_'+self.__class__.__name__+extension if(os.path.exists(bufferstringoutput)==False): #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) df_all=self.core(DataSetName) df_all.to_csv(bufferstringoutput) return bufferstringoutput def core(self,df_all,Data_adj_name=''): return df_all def real_FE(): return 0 class FEg30eom0110network(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): intflag=True df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) if(intflag): df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) if(intflag): df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) if(intflag): df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min',True) df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min',True) df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max',True) df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max',True) df_all,_=FEsingle.HighLowRange(df_all,8,True) df_all,_=FEsingle.HighLowRange(df_all,25,True) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) if(intflag): df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) if(intflag): df_all['pct_chg_abs_rank']=df_all['pct_chg_abs_rank']*10//2 df_all=FEsingle.PctChgAbsSumRank(df_all,6,True) df_all=FEsingle.PctChgSumRank(df_all,3,True) df_all=FEsingle.PctChgSumRank(df_all,6,True) df_all=FEsingle.PctChgSumRank(df_all,12,True) df_all=FEsingle.AmountChgRank(df_all,12,True) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) if(intflag): df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],1) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all class FEg30eom0110onlinew6d(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all['sm_amount_pos']=df_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['lg_amount_pos']=df_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['net_mf_amount_pos']=df_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['sm_amount_pos']=df_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_all['lg_amount_pos']=df_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_all['net_mf_amount_pos']=df_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_all['sm_amount']=df_all.groupby('ts_code')['sm_amount'].shift(1) df_all['lg_amount']=df_all.groupby('ts_code')['lg_amount'].shift(1) df_all['net_mf_amount']=df_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) ##排除科创版 #print(df_all) #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) #df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all class FE_a23(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_a29(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #df_money_all['sm_amount_25_diff']=df_money_all['sm_amount_25']-df_money_all['sm_amount_12'] #df_money_all['sm_amount_12_diff']=df_money_all['sm_amount_12']-df_money_all['sm_amount_5'] #df_money_all['lg_amount_25_diff']=df_money_all['lg_amount_25']-df_money_all['lg_amount_12'] #df_money_all['lg_amount_12_diff']=df_money_all['lg_amount_12']-df_money_all['lg_amount_5'] #df_money_all['net_mf_amount_25_diff']=df_money_all['net_mf_amount_25']-df_money_all['net_mf_amount_12'] #df_money_all['net_mf_amount_12_diff']=df_money_all['net_mf_amount_12']-df_money_all['net_mf_amount_5'] print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>15] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) #df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_a29_Volatility(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #df_money_all['sm_amount_25_diff']=df_money_all['sm_amount_25']-df_money_all['sm_amount_12'] #df_money_all['sm_amount_12_diff']=df_money_all['sm_amount_12']-df_money_all['sm_amount_5'] #df_money_all['lg_amount_25_diff']=df_money_all['lg_amount_25']-df_money_all['lg_amount_12'] #df_money_all['lg_amount_12_diff']=df_money_all['lg_amount_12']-df_money_all['lg_amount_5'] #df_money_all['net_mf_amount_25_diff']=df_money_all['net_mf_amount_25']-df_money_all['net_mf_amount_12'] #df_money_all['net_mf_amount_12_diff']=df_money_all['net_mf_amount_12']-df_money_all['net_mf_amount_5'] print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>15] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) #df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_a31(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #df_money_all['sm_amount_25_diff']=df_money_all['sm_amount_25']-df_money_all['sm_amount_12'] #df_money_all['sm_amount_12_diff']=df_money_all['sm_amount_12']-df_money_all['sm_amount_5'] #df_money_all['lg_amount_25_diff']=df_money_all['lg_amount_25']-df_money_all['lg_amount_12'] #df_money_all['lg_amount_12_diff']=df_money_all['lg_amount_12']-df_money_all['lg_amount_5'] #df_money_all['net_mf_amount_25_diff']=df_money_all['net_mf_amount_25']-df_money_all['net_mf_amount_12'] #df_money_all['net_mf_amount_12_diff']=df_money_all['net_mf_amount_12']-df_money_all['net_mf_amount_5'] print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) #df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_a31_full(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #df_money_all['sm_amount_25_diff']=df_money_all['sm_amount_25']-df_money_all['sm_amount_12'] #df_money_all['sm_amount_12_diff']=df_money_all['sm_amount_12']-df_money_all['sm_amount_5'] #df_money_all['lg_amount_25_diff']=df_money_all['lg_amount_25']-df_money_all['lg_amount_12'] #df_money_all['lg_amount_12_diff']=df_money_all['lg_amount_12']-df_money_all['lg_amount_5'] #df_money_all['net_mf_amount_25_diff']=df_money_all['net_mf_amount_25']-df_money_all['net_mf_amount_12'] #df_money_all['net_mf_amount_12_diff']=df_money_all['net_mf_amount_12']-df_money_all['net_mf_amount_5'] print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 #df_all['st_or_otherwrong']=0 #df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 #df_all['high_stop']=0 #df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 #df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['amount','close','real_price'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_a29_full(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] #df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) #df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) #df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #df_money_all['sm_amount_25_diff']=df_money_all['sm_amount_25']-df_money_all['sm_amount_12'] #df_money_all['sm_amount_12_diff']=df_money_all['sm_amount_12']-df_money_all['sm_amount_5'] #df_money_all['lg_amount_25_diff']=df_money_all['lg_amount_25']-df_money_all['lg_amount_12'] #df_money_all['lg_amount_12_diff']=df_money_all['lg_amount_12']-df_money_all['lg_amount_5'] #df_money_all['net_mf_amount_25_diff']=df_money_all['net_mf_amount_25']-df_money_all['net_mf_amount_12'] #df_money_all['net_mf_amount_12_diff']=df_money_all['net_mf_amount_12']-df_money_all['net_mf_amount_5'] print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 #df_all['st_or_otherwrong']=0 #df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 #df_all['high_stop']=0 #df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 #df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['amount','close','real_price'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FE_qliba2(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] df_all=FEsingle.PredictDaysTrend(df_all,5) print(df_all) df_all=df_all.loc[:,['ts_code','trade_date','tomorrow_chg','tomorrow_chg_rank']] print(df_all.dtypes) print(df_all) #===================================================================================================================================# #获取qlib特征 ###df_qlib_1=pd.read_csv('zzztest.csv',header=0) ###df_qlib_2=pd.read_csv('zzztest2.csv',header=0) ##df_qlib_1=pd.read_csv('2013.csv',header=0) ###df_qlib_1=df_qlib_1.iloc[:,0:70] ##df_qlib_all_l=df_qlib_1.iloc[:,0:2] ##df_qlib_all_r=df_qlib_1.iloc[:,70:] ##df_qlib_1 = pd.concat([df_qlib_all_l,df_qlib_all_r],axis=1) ##print(df_qlib_1.head(10)) ##df_qlib_2=pd.read_csv('2015.csv',header=0) ##df_qlib_all_l=df_qlib_2.iloc[:,0:2] ##df_qlib_all_r=df_qlib_2.iloc[:,70:] ##df_qlib_2 = pd.concat([df_qlib_all_l,df_qlib_all_r],axis=1) ##df_qlib_3=pd.read_csv('2017.csv',header=0) ##df_qlib_all_l=df_qlib_3.iloc[:,0:2] ##df_qlib_all_r=df_qlib_3.iloc[:,70:] ##df_qlib_3 = pd.concat([df_qlib_all_l,df_qlib_all_r],axis=1) ##df_qlib_4=pd.read_csv('2019.csv',header=0) ##df_qlib_all_l=df_qlib_4.iloc[:,0:2] ##df_qlib_all_r=df_qlib_4.iloc[:,70:] ##df_qlib_4 = pd.concat([df_qlib_all_l,df_qlib_all_r],axis=1) ##df_qlib_all=pd.concat([df_qlib_2,df_qlib_1]) ##df_qlib_all=pd.concat([df_qlib_3,df_qlib_all]) ##df_qlib_all=pd.concat([df_qlib_4,df_qlib_all]) ##df_qlib_all.drop_duplicates() ##print(df_qlib_all.head(10)) ##df_qlib_all.drop(['LABEL0'],axis=1,inplace=True) ##df_qlib_all.to_csv("13to21_first70plus.csv") df_qlib_all=pd.read_csv('13to21_first70plus.csv',header=0) #df_qlib_all.drop(['LABEL0'],axis=1,inplace=True) print(df_qlib_all) df_qlib_all.rename(columns={'datetime':'trade_date','instrument':'ts_code','score':'mix'}, inplace = True) print(df_qlib_all.dtypes) print(df_qlib_all) df_qlib_all['trade_date'] = pd.to_datetime(df_qlib_all['trade_date'], format='%Y-%m-%d') df_qlib_all['trade_date']=df_qlib_all['trade_date'].apply(lambda x: x.strftime('%Y%m%d')) df_qlib_all['trade_date'] = df_qlib_all['trade_date'].astype(int) df_qlib_all['ts_codeL'] = df_qlib_all['ts_code'].str[:2] df_qlib_all['ts_codeR'] = df_qlib_all['ts_code'].str[2:] df_qlib_all['ts_codeR'] = df_qlib_all['ts_codeR'].apply(lambda s: s+'.') df_qlib_all['ts_code']=df_qlib_all['ts_codeR'].str.cat(df_qlib_all['ts_codeL']) df_qlib_all.drop(['ts_codeL','ts_codeR'],axis=1,inplace=True) print(df_qlib_all.dtypes) print(df_qlib_all) df_qlib_all=df_qlib_all.fillna(value=0) df_all=pd.merge(df_all, df_qlib_all, how='left', on=['ts_code','trade_date']) print(df_all) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FEonlinew_a31(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'] #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all['sm_amount']=df_all.groupby('ts_code')['sm_amount'].shift(1) df_all['lg_amount']=df_all.groupby('ts_code')['lg_amount'].shift(1) df_all['net_mf_amount']=df_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=FEsingle.InputChgSum(df_all,5,'sm_amount') df_all=FEsingle.InputChgSum(df_all,5,'lg_amount') df_all=FEsingle.InputChgSum(df_all,5,'net_mf_amount') df_all=FEsingle.InputChgSum(df_all,12,'sm_amount') df_all=FEsingle.InputChgSum(df_all,12,'lg_amount') df_all=FEsingle.InputChgSum(df_all,12,'net_mf_amount') df_all=FEsingle.InputChgSum(df_all,25,'sm_amount') df_all=FEsingle.InputChgSum(df_all,25,'lg_amount') df_all=FEsingle.InputChgSum(df_all,25,'net_mf_amount') df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.PctChgSum(df_all,24) df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] df_all=df_all[df_all['total_mv_rank']<6] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FEfast_a23(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') #print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #df_all['ts_code_try']=df_all['ts_code'].map(lambda x : x[:-3]) #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# #df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) #df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) #df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) #df_all=FEsingle.PctChgAbsSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,3) #df_all=FEsingle.PctChgSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,12) #df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) #df_all=FEsingle.PctChgSum(df_all,6) #df_all=FEsingle.PctChgSum(df_all,12) #df_all=FEsingle.PctChgSum(df_all,24) #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,24) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*19.9//2 #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FEfast_a23_pos(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) #df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) #df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'] #df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'] df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') #print(df_money_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #df_all['ts_code_try']=df_all['ts_code'].map(lambda x : x[:-3]) #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# df_all['tomorrow_chg_rank']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPy(x)).reset_index(0,drop=True) df_all['tomorrow_chg_rank']=df_all.groupby('ts_code')['tomorrow_chg_rank'].shift(-20) df_all['tomorrow_chg']=df_all['tomorrow_chg_rank'] #df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) #df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) #df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') df_all,_=FEsingle.HighLowRange(df_all,5) df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) #df_all=FEsingle.PctChgAbsSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,3) #df_all=FEsingle.PctChgSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,12) #df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) #df_all=FEsingle.PctChgSum(df_all,6) #df_all=FEsingle.PctChgSum(df_all,12) #df_all=FEsingle.PctChgSum(df_all,24) #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,24) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*19.9//2 #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','sm_amount','lg_amount','net_mf_amount'],1) df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) #df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] #df_all=df_all[df_all['total_mv_rank']>18] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_money_all=pd.read_csv('real_moneyflow_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_money_all['sm_amount_pos']=df_money_all.groupby('ts_code')['sm_amount_pos'].shift(1) df_money_all['lg_amount_pos']=df_money_all.groupby('ts_code')['lg_amount_pos'].shift(1) df_money_all['net_mf_amount_pos']=df_money_all.groupby('ts_code')['net_mf_amount_pos'].shift(1) df_money_all['sm_amount']=df_money_all.groupby('ts_code')['sm_amount'].shift(1) df_money_all['lg_amount']=df_money_all.groupby('ts_code')['lg_amount'].shift(1) df_money_all['net_mf_amount']=df_money_all.groupby('ts_code')['net_mf_amount'].shift(1) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_money_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,8,'max') df_all,_=FEsingle.HighLowRange(df_all,8) df_all,_=FEsingle.HighLowRange(df_all,25) #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,12) df_all=FEsingle.PctChgSum(df_all,3) df_all=FEsingle.PctChgSum(df_all,6) df_all=FEsingle.PctChgSum(df_all,12) df_all=FEsingle.AmountChgRank(df_all,12) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12','real_price_pos'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],2) #df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_12'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['chg_rank']>0.7] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FEfast_a41(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) #df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) #df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) #df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] #df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] #df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) #df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #df_money_all=FEsingle.InputChgSum(df_money_all,5,'sm_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,5,'lg_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,5,'net_mf_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,12,'sm_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,12,'lg_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,12,'net_mf_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,25,'sm_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,25,'lg_amount') #df_money_all=FEsingle.InputChgSum(df_money_all,25,'net_mf_amount') #print(df_money_all) #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='std') #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='mean') ##df_data.to_csv('testsee1120.csv') #print(df_data) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) #df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# #df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') #df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') #df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') #df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') #df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') #df_all,_=FEsingle.HighLowRange(df_all,5) #df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) #df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] #df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] #df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] #df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] #df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] #df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) #df_all=FEsingle.PctChgSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,12) #df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) #df_all=FEsingle.PctChgSum(df_all,6) #df_all=FEsingle.PctChgSum(df_all,12) #df_all=FEsingle.PctChgSum(df_all,24) #df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] #df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] #df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] #df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] #df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] #df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all class FEfast_a41e(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) #df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) #df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) #df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] #df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] #df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) #df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #print(df_money_all) #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='std') #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='mean') #df_data['pct_chg_DayFeatureToAll_std_1']=df_data.groupby('ts_code')['pct_chg_DayFeatureToAll_std'].shift(1) #df_data['pct_chg_DayFeatureToAll_mean_1']=df_data.groupby('ts_code')['pct_chg_DayFeatureToAll_mean'].shift(1) #df_data['pct_chg_DayFeatureToAll_std_2']=df_data.groupby('ts_code')['pct_chg_DayFeatureToAll_std'].shift(2) #df_data['pct_chg_DayFeatureToAll_mean_2']=df_data.groupby('ts_code')['pct_chg_DayFeatureToAll_mean'].shift(2) #将空白的地方填满 df_long_all['pe'].fillna(999,inplace=True) df_long_all['pb'].fillna(99,inplace=True) df_long_all['ps_ttm'].fillna(99,inplace=True) df_long_all['dv_ttm'].fillna(0,inplace=True) #df_long_all.to_csv('testsee1120.csv') print(df_long_all) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) #df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) df_all.drop(['turnover_rate','volume_ratio'],axis=1,inplace=True) #df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# #df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all['pe_rank']=df_all.groupby('trade_date')['pe'].rank(pct=True) df_all['pe_rank']=df_all.groupby('ts_code')['pe_rank'].shift(1) df_all['dv_ttm']=df_all.groupby('trade_date')['dv_ttm'].rank(pct=True) df_all['dv_ttm']=df_all.groupby('ts_code')['dv_ttm'].shift(1) #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow(df_all,25,'min') #df_all,_=FEsingle.CloseWithHighLow(df_all,12,'min') #df_all,_=FEsingle.CloseWithHighLow(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,25,'max') #df_all,_=FEsingle.CloseWithHighLow(df_all,12,'max') #df_all,_=FEsingle.CloseWithHighLow(df_all,5,'max') #df_all,_=FEsingle.HighLowRange(df_all,5) #df_all,_=FEsingle.HighLowRange(df_all,12) df_all,_=FEsingle.HighLowRange(df_all,25) #df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] #df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] #df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] #df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] #df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] #df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,3) #df_all=FEsingle.PctChgSumRank(df_all,6) #df_all=FEsingle.PctChgSumRank(df_all,12) #df_all=FEsingle.PctChgSumRank(df_all,24) df_all=FEsingle.PctChgSum(df_all,3) #df_all=FEsingle.PctChgSum(df_all,6) #df_all=FEsingle.PctChgSum(df_all,12) #df_all=FEsingle.PctChgSum(df_all,24) #df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] #df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] #df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] #df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] #df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] #df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs','pe'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) df_all=FEsingle.PredictDaysTrend(df_all,5) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all class FEfast_b02(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) #df_money_all=pd.read_csv(DataSetName[3],index_col=0,header=0) df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) #df_money_all.drop(['buy_sm_vol','sell_sm_vol','buy_md_vol','sell_md_vol','buy_lg_vol','sell_lg_vol','buy_md_vol','sell_md_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_elg_vol','buy_elg_amount','sell_elg_vol','sell_elg_amount','net_mf_vol'],axis=1,inplace=True) #df_money_all.drop(['buy_md_amount','sell_md_amount'],axis=1,inplace=True) #df_money_all['sm_amount']=df_money_all['buy_sm_amount']-df_money_all['sell_sm_amount'] #df_money_all['lg_amount']=df_money_all['buy_lg_amount']-df_money_all['sell_lg_amount'] #df_money_all.drop(['buy_sm_amount','sell_sm_amount'],axis=1,inplace=True) #df_money_all.drop(['buy_lg_amount','sell_lg_amount'],axis=1,inplace=True) #print(df_money_all) #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='std') #df_data,_=FEsingle.DayFeatureToAll(df_data,name='pct_chg',method='mean') ##df_data.to_csv('testsee1120.csv') #print(df_data) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) #df_all=pd.merge(df_all, df_money_all, how='inner', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) #df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] #是否st或其他 df_all['st_or_otherwrong']=0 df_all.loc[(df_all['limit_percent']<0.85) & (0.58<df_all['limit_percent']),'st_or_otherwrong']=1 df_all.drop(['up_limit','down_limit','limit_percent'],axis=1,inplace=True) df_all['dayofweek']=pd.to_datetime(df_all['trade_date'],format='%Y%m%d') df_all['dayofweek']=df_all['dayofweek'].dt.dayofweek ##排除科创版 #print(df_all) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all=FEsingle.PredictDaysReal5day(df_all,5) #df_all['real_open']=df_all['adj_factor']*df_all['open'] #===================================================================================================================================# #df_all['real_price_pos']=df_all.groupby('ts_code')['real_price'].rolling(20).apply(lambda x: rollingRankSciPyB(x)).reset_index(0,drop=True) df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) #df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) #df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 #===================================================================================================================================# df_all,_=FEsingle.CloseWithHighLow_self(df_all,25,'min') df_all,_=FEsingle.CloseWithHighLow_self(df_all,12,'min') df_all,_=FEsingle.CloseWithHighLow_self(df_all,5,'min') df_all,_=FEsingle.CloseWithHighLow_self(df_all,25,'max') df_all,_=FEsingle.CloseWithHighLow_self(df_all,12,'max') df_all,_=FEsingle.CloseWithHighLow_self(df_all,5,'max') df_all,_=FEsingle.HighLowRange_self(df_all,5) df_all,_=FEsingle.HighLowRange_self(df_all,12) df_all,_=FEsingle.HighLowRange_self(df_all,25) #df_all['25_pct_rank_min_diff']=df_all['25_pct_rank_min']-df_all['12_pct_rank_min'] #df_all['12_pct_rank_min_diff']=df_all['12_pct_rank_min']-df_all['5_pct_rank_min'] #df_all['25_pct_rank_max_diff']=df_all['25_pct_rank_max']-df_all['12_pct_rank_max'] #df_all['12_pct_rank_max_diff']=df_all['12_pct_rank_max']-df_all['5_pct_rank_max'] #df_all['25_pct_Rangerank_diff']=df_all['25_pct_Rangerank']-df_all['12_pct_Rangerank'] #df_all['12_pct_Rangerank_diff']=df_all['12_pct_Rangerank']-df_all['5_pct_Rangerank'] df_all.drop(['change','vol'],axis=1,inplace=True) #===================================================================================================================================# #df_all['mvadj']=1 #df_all.loc[df_all['total_mv_rank']<11,'mvadj']=0.9 #df_all.loc[df_all['total_mv_rank']<7,'mvadj']=0.85 #df_all.loc[df_all['total_mv_rank']<4,'mvadj']=0.6 #df_all.loc[df_all['total_mv_rank']<2,'mvadj']=0.45 #df_all.loc[df_all['total_mv_rank']<1,'mvadj']=0.35 #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 #df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 ###真实价格范围(区分实际股价高低) #df_all['price_real_rank']=df_all.groupby('trade_date')['pre_close'].rank(pct=True) #df_all['price_real_rank']=df_all['price_real_rank']*10//1 #1日 #df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all['pct_chg_abs']=df_all['pct_chg'].abs() #df_all['pct_chg_abs_rank']=df_all.groupby('trade_date')['pct_chg_abs'].rank(pct=True) df_all=FEsingle.PctChgAbsSum_self(df_all,6) df_all=FEsingle.PctChgSum_self(df_all,3) df_all=FEsingle.PctChgSum_self(df_all,6) df_all=FEsingle.PctChgSum_self(df_all,12) df_all=FEsingle.PctChgSum_self(df_all,24) #df_all=FEsingle.PctChgSum(df_all,3) #df_all=FEsingle.PctChgSum(df_all,6) #df_all=FEsingle.PctChgSum(df_all,12) #df_all=FEsingle.PctChgSum(df_all,24) #df_all['chg_rank_24_diff']=df_all['chg_rank_24']-df_all['chg_rank_12'] #df_all['chg_rank_12_diff']=df_all['chg_rank_12']-df_all['chg_rank_6'] #df_all['chg_rank_6_diff']=df_all['chg_rank_6']-df_all['chg_rank_3'] #df_all['pct_chg_24_diff']=df_all['pct_chg_24']-df_all['pct_chg_12'] #df_all['pct_chg_12_diff']=df_all['pct_chg_12']-df_all['pct_chg_6'] #df_all['pct_chg_6_diff']=df_all['pct_chg_6']-df_all['pct_chg_3'] #df_all=FEsingle.AmountChgRank(df_all,12) #df_all=FEsingle.AmountChgRank(df_all,30) #计算三种比例rank dolist=['open','high','low'] df_all['pct_chg_r']=df_all['pct_chg'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer #df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*9.9//2 df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],1) #df_all=FEsingle.OldFeaturesRank(df_all,['sm_amount','lg_amount','net_mf_amount'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],2) ##df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pct_chg_r','pst_amount_rank_12'],3) df_all.drop(['pre_close','adj_factor','total_mv','pb','circ_mv','pct_chg_abs'],axis=1,inplace=True) #df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) #df_all['tomorrow_chg_rank'] = np.random.randint(0, 10, df_all.shape[0]) #df_all.drop(['mvadj'],axis=1,inplace=True) df_all.drop(['pct_chg'],axis=1,inplace=True) #删除股价过低的票 df_all=df_all[df_all['close']>2] #df_all=df_all[df_all['8_pct_rank_min']>0.1] #df_all=df_all[df_all['25_pct_rank_max']>0.1] df_all=df_all[df_all['total_mv_rank']<6] #df_all=df_all[df_all['total_mv_rank']>2] df_all=df_all[df_all['amount']>15000] #df_all=df_all[df_all['circ_mv_pct']>3] #df_all=df_all[df_all['ps_ttm']>3] #df_all=df_all[df_all['pb_rank']>3] #暂时不用的列 df_all=df_all[df_all['high_stop']==0] df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop','amount','close','real_price'],axis=1,inplace=True) df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) df_all=df_all.reset_index(drop=True) return df_all class trend_following(FEbase): #这个版本变为3天预测 def __init__(self): pass def core(self,DataSetName): df_data=pd.read_csv(DataSetName[0],index_col=0,header=0) df_adj_all=pd.read_csv(DataSetName[1],index_col=0,header=0) #df_limit_all=pd.read_csv(DataSetName[2],index_col=0,header=0) #df_long_all=pd.read_csv(DataSetName[4],index_col=0,header=0) df_all=pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) #df_all=pd.merge(df_all, df_limit_all, how='inner', on=['ts_code','trade_date']) #df_all=pd.merge(df_all, df_long_all, how='inner', on=['ts_code','trade_date']) #df_all.drop(['turnover_rate','volume_ratio','dv_ttm'],axis=1,inplace=True) df_all['real_price']=df_all['close']*df_all['adj_factor'] #df_all['limit_percent']=df_all['down_limit']/df_all['up_limit'] longline=60 bufferbak='_'+str(longline) #均量计算 xxx=df_all.groupby('ts_code')['real_price'].rolling(longline).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) shortline=5 bufferbak='_'+str(shortline) #均量计算 xxx=df_all.groupby('ts_code')['real_price'].rolling(shortline).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) df_all.dropna(axis=0,how='any',inplace=True) print(df_all) #寻找unique codelistbuffer=df_all['ts_code'] codelistbuffer=codelistbuffer.unique() codelist=codelistbuffer.tolist() cutlimit=-0.1 df_ana = pd.DataFrame(columns = ["code", "data", "differ"]) ct=0 for curcode in codelist: cur_code_df=df_all[df_all['ts_code']==curcode] count_flag=0 startprice=0 stopprice=0 changesum=1 start0_price=cur_code_df.loc[cur_code_df.index[0]].values[12] for indexs in cur_code_df.index: cur_code_day=cur_code_df.loc[indexs].values[1] cur_code_now=cur_code_df.loc[indexs].values[12] cur_code_avg_short=cur_code_df.loc[indexs].values[14] cur_code_avg_long=cur_code_df.loc[indexs].values[13] if(cur_code_avg_short>cur_code_avg_long): if(count_flag==0): count_flag=1 startprice=cur_code_now #print(cur_code_day,end='') #print(' ',end='') ##print(cur_code_day) #print(startprice,end='') #print(' ',end='') ##print(cur_code_day) #print('buy') if(count_flag==1): #计算止损 nowcut=(cur_code_now-startprice)/startprice if(nowcut<cutlimit): count_flag=0 differ=nowcut changesum=changesum*(1+differ) #print(cur_code_day,end='') #print(' ',end='') #print(cur_code_now,end='') #print(' ',end='') #print('cutsell') df_ana.loc[df_ana.shape[0]+1] = {'code':curcode,'data':cur_code_day,'differ':differ} continue if(cur_code_avg_short<cur_code_avg_long): count_flag=0 #stopprice=cur_code_now #print(cur_code_day,end='') #print(' ',end='') #print(cur_code_now,end='') #print(' ',end='') #print('sell') differ=(cur_code_now-startprice)/startprice changesum=changesum*(1+differ) df_ana.loc[df_ana.shape[0]+1] = {'code':curcode,'data':cur_code_day,'differ':differ} #print(cur_code_df.loc[indexs].values[0:-1]) if(count_flag==1): differ=(cur_code_now-startprice)/startprice changesum=changesum*(1+differ) df_ana.loc[df_ana.shape[0]+1] = {'code':curcode,'data':cur_code_day,'differ':differ} #if(ct%100==0): # #df_ana.to_csv('save10_2avg.csv') # print(ct) ct+=1 stopprice=cur_code_df.loc[cur_code_df.index[-1]].values[12] pctnostra=stopprice/start0_price print(curcode,end='') print(' ',end='') print(pctnostra,end='') print(' ',end='') print(changesum) #print(cur_code_df) dddddddd=1 df_ana.to_csv('save10_2avg.csv') #df_all.to_csv('read5mean.csv') print(df_all) df_all=df_all.reset_index(drop=True) return df_all def real_FE(self): #新模型预定版本 df_data=pd.read_csv('real_now.csv',index_col=0,header=0) df_adj_all=pd.read_csv('real_adj_now.csv',index_col=0,header=0) df_long_all=pd.read_csv('real_long_now.csv',index_col=0,header=0) df_all=pd.merge(df_data, df_adj_all, how='left', on=['ts_code','trade_date']) df_all=pd.merge(df_all, df_long_all, how='left', on=['ts_code','trade_date']) print(df_all) #df_all.drop(['turnover_rate','volume_ratio','pe','pb'],axis=1,inplace=True) df_all.drop(['turnover_rate','volume_ratio','pe','dv_ttm'],axis=1,inplace=True) #这里打一个问号 #df_all=df_all[df_all['ts_code'].str.startswith('688')==False] #df_all=pd.read_csv(bufferstring,index_col=0,header=0,nrows=100000) #df_all.drop(['change','vol'],axis=1,inplace=True) df_all['ts_code'] = df_all['ts_code'].astype('str') #将原本的int数据类型转换为文本 df_all['ts_code'] = df_all['ts_code'].str.zfill(6) #用的时候必须加上.str前缀 print(df_all) ##排除科创版 #print(df_all) df_all[["ts_code"]]=df_all[["ts_code"]].astype(str) df_all=df_all[df_all['ts_code'].str.startswith('688')==False] df_all['class1']=0 df_all.loc[df_all['ts_code'].str.startswith('30')==True,'class1']=1 df_all.loc[df_all['ts_code'].str.startswith('60')==True,'class1']=2 df_all.loc[df_all['ts_code'].str.startswith('00')==True,'class1']=3 #===================================================================================================================================# #复权后价格 df_all['adj_factor']=df_all['adj_factor'].fillna(0) df_all['real_price']=df_all['close']*df_all['adj_factor'] df_all['real_price']=df_all.groupby('ts_code')['real_price'].shift(1) df_all['real_price']=df_all['real_price']*(1+df_all['pct_chg']/100) #===================================================================================================================================# df_all['total_mv_rank']=df_all.groupby('trade_date')['total_mv'].rank(pct=True) df_all['total_mv_rank']=df_all.groupby('ts_code')['total_mv_rank'].shift(1) df_all['total_mv_rank']=df_all['total_mv_rank']*19.9//1 df_all['pb_rank']=df_all.groupby('trade_date')['pb'].rank(pct=True) df_all['pb_rank']=df_all.groupby('ts_code')['pb_rank'].shift(1) #df_all['pb_rank']=df_all['pb_rank']*10//1 df_all['circ_mv_pct']=(df_all['total_mv']-df_all['circ_mv'])/df_all['total_mv'] df_all['circ_mv_pct']=df_all.groupby('trade_date')['circ_mv_pct'].rank(pct=True) df_all['circ_mv_pct']=df_all.groupby('ts_code')['circ_mv_pct'].shift(1) #df_all['circ_mv_pct']=df_all['circ_mv_pct']*10//1 df_all['ps_ttm']=df_all.groupby('trade_date')['ps_ttm'].rank(pct=True) df_all['ps_ttm']=df_all.groupby('ts_code')['ps_ttm'].shift(1) #df_all['ps_ttm']=df_all['ps_ttm']*10//1 df_all,_=FEsingle.CloseWithHighLow(df_all,30,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,10,'min') df_all,_=FEsingle.CloseWithHighLow(df_all,30,'max') df_all,_=FEsingle.CloseWithHighLow(df_all,10,'max') #===================================================================================================================================# #是否停 df_all['high_stop']=0 df_all.loc[df_all['pct_chg']>9.4,'high_stop']=1 df_all.loc[(df_all['pct_chg']<5.2) & (4.8<df_all['pct_chg']),'high_stop']=1 #1日 df_all['chg_rank']=df_all.groupby('trade_date')['pct_chg'].rank(pct=True) #df_all['chg_rank']=df_all['chg_rank']*10//2 df_all=FEsingle.PctChgSumRank(df_all,3) df_all=FEsingle.PctChgSumRank(df_all,6) df_all=FEsingle.PctChgSumRank(df_all,10) #print(df_all) df_all=FEsingle.AmountChgRank(df_all,10) #计算三种比例rank dolist=['open','high','low'] for curc in dolist: buffer=((df_all[curc]-df_all['pre_close'])*100)/df_all['pre_close'] df_all[curc]=buffer df_all[curc]=df_all.groupby('trade_date')[curc].rank(pct=True) #df_all[curc]=df_all[curc]*10//2 #df_all=FEsingle.PctChgSumRank_Common(df_all,5,'high') df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_10'],1) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_10'],2) df_all=FEsingle.OldFeaturesRank(df_all,['open','high','low','pst_amount_rank_10'],3) #删除市值过低的票 df_all=df_all[df_all['close']>3] #df_all=df_all[df_all['total_mv_rank']<5] df_all=df_all[df_all['amount']>15000] df_all=df_all[df_all['total_mv_rank']<12] df_all.drop(['close','pre_close','pct_chg','adj_factor','real_price','amount','total_mv','pb','circ_mv'],axis=1,inplace=True) #暂时不用的列 df_all=df_all[df_all['high_stop']==0] #df_all=df_all[df_all['st_or_otherwrong']==1] #'tomorrow_chg' df_all.drop(['high_stop'],axis=1,inplace=True) #df_all.drop(['st_or_otherwrong'],axis=1,inplace=True) df_all.dropna(axis=0,how='any',inplace=True) month_sec=df_all['trade_date'].max() df_all=df_all[df_all['trade_date']==month_sec] print(df_all) df_all=df_all.reset_index(drop=True) df_all.to_csv('today_train.csv') dwdw=1 class FEsingle: def CloseWithHighLow(df_all,days,minmax='min',intflag=False,standardflag=False,trainflag=False): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_rank_'+minmax if(minmax=='min'): xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() else: xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all['30_pct']=(100*(df_all['real_price']+0.001-df_all['real_price_30min']))/df_all['real_price_30min'] if(standardflag): stringdisplay2=stringdisplay+'_Standard' stringdisplaysave=stringdisplay2+'.pkl' num_data=df_all[['30_pct']] if(trainflag): scaler=StandardScaler() num_data=scaler.fit_transform(num_data.values) joblib.dump(scaler,stringdisplaysave) else: scaler2=joblib.load(stringdisplaysave) num_data=scaler2.transform(num_data.values) df_all[stringdisplay2]=num_data df_all[stringdisplay]=df_all.groupby('trade_date')['30_pct'].rank(pct=True) df_all.drop(['30_pct','real_price_30min'],axis=1,inplace=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def CloseWithHighLow_self(df_all,days,minmax='min',intflag=False,standardflag=False,trainflag=False): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_rank_'+minmax if(minmax=='min'): xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() else: xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all[stringdisplay]=(100*(df_all['real_price']+0.001-df_all['real_price_30min']))/df_all['real_price_30min'] #df_all[stringdisplay]=df_all.groupby('trade_date')['30_pct'].rank(pct=True) df_all.drop(['real_price_30min'],axis=1,inplace=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def HighLowRange(df_all,days,intflag=False): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_Rangerank' xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30max') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all['30_pct']=(100*(df_all['real_price_30max']+0.001-df_all['real_price_30min']))/df_all['real_price_30min'] df_all[stringdisplay]=df_all.groupby('trade_date')['30_pct'].rank(pct=True) df_all.drop(['30_pct','real_price_30min','real_price_30max'],axis=1,inplace=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def HighLowRange_self(df_all,days,intflag=False): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_Rangerank' xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30max') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all[stringdisplay]=(100*(df_all['real_price_30max']+0.001-df_all['real_price_30min']))/df_all['real_price_30min'] #df_all[stringdisplay]=df_all.groupby('trade_date')['30_pct'].rank(pct=True) df_all.drop(['real_price_30min','real_price_30max'],axis=1,inplace=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def HighLowRangeReal(df_all,days,intflag=False): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_RangerankReal' xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30max') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all['30_pct']=(100*(df_all['real_price_30max']+0.001-df_all['real_price_30min']))/df_all['real_price_30min'] df_all[stringdisplay]=df_all['30_pct'] df_all.drop(['30_pct','real_price_30min','real_price_30max'],axis=1,inplace=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def CloseWithHighLow_notrank(df_all,days,minmax='min'): #输入几日和最高或最低返回实际值 stringdisplay=str(days)+'_pct_rank_'+minmax if(minmax=='min'): xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() else: xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all[stringdisplay]=(10*(df_all['real_price']-df_all['real_price_30min']))/df_all['real_price_30min'] df_all.drop(['real_price_30min'],axis=1,inplace=True) df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def CloseWithHighLow_realrank(df_all,days,minmax='min'): #输入几日和最高或最低返回排名 #30日最低比值 stringdisplay=str(days)+'_pct_realrank_'+minmax if(minmax=='min'): xxx=df_all.groupby('ts_code')['real_price'].rolling(days).min().reset_index() else: xxx=df_all.groupby('ts_code')['real_price'].rolling(days).max().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix='_30min') #bbb=df_all.groupby('ts_code')['real_price'].agg({'all_min':np.min, 'all_max': np.max}).reset_index() #ccc=pd.merge(df_all, bbb, how='inner', on=['ts_code']) df_all['30_pct']=(100*(df_all['real_price']-df_all['real_price_30min']))/df_all['real_price_30min'] df_all[stringdisplay]=df_all['30_pct'].rank(pct=True) df_all.drop(['30_pct','real_price_30min'],axis=1,inplace=True) df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all,stringdisplay def DayFeatureToAll(df_input,name,method='mean'): #输入需要对哪列进行处理，以及处理的方法 #输出自动添加到对于df后方 stringdisplay=name+'_DayFeatureToAll_'+method dftest=df_input.groupby(df_input['trade_date'])[name].agg(method) dftest2=pd.DataFrame({stringdisplay:dftest}).reset_index() df_input=pd.merge(df_input, dftest2, how='left', on=['trade_date']) return df_input,stringdisplay def changerank_line(df_all,b): df_all[b]=df_all[b]*19.9//2 return df_all def PredictDays(df_all,days): ##明日幅度 tms=[] for i in range(days): curindex=-i-1 curtm=df_all.groupby('ts_code')['pct_chg'].shift(curindex) tms.append(curtm) tmpdf=((100+tms[0])/100) for i in range(days): if i==0: continue tmpdf*=(100+tms[i])/100 df_all['tomorrow_chg']=(tmpdf-1)*100 #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']*9.9//1 return df_all def PredictDaysStart(df_all,days): ##明日幅度 nextstart=df_all.groupby('ts_code')['real_price'].shift(0) nextnstart=df_all.groupby('ts_code')['real_price'].shift(0-days) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=((nextnstart-nextstart)/nextstart)*100 #df_all['tomorrow_chg']=df_all['tomorrow_chg']*df_all['mvadj'] #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']*9.9//1 return df_all def PredictDaysTrend(df_all,days): ##明日幅度 nextstart=df_all.groupby('ts_code')['real_price'].shift(0) nextnstart=df_all.groupby('ts_code')['real_price'].shift(0-days) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=((nextnstart-nextstart)/nextstart)*100 #df_all['tomorrow_chg']=df_all['tomorrow_chg']*df_all['mvadj'] #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']*19.9//1 #df_all.loc[df_all['tomorrow_chg_rank']>28,'tomorrow_chg_rank']=30 #df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']//3 #df_all.loc[df_all['tomorrow_chg_rank']==9,'tomorrow_chg_rank']=8 #df_all.loc[df_all['tomorrow_chg_rank']==10,'tomorrow_chg_rank']=9 return df_all def PredictDaysReal(df_all,days): ##明日幅度 nextstart=df_all.groupby('ts_code')['real_price'].shift(0) nextnstart=df_all.groupby('ts_code')['real_price'].shift(0-days) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=((nextnstart-nextstart)/nextstart)*100 #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 #df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=0 #df_all.loc[df_all['tomorrow_chg']>-4,'tomorrow_chg_rank']=1 #df_all.loc[df_all['tomorrow_chg']>-2.3,'tomorrow_chg_rank']=2 #df_all.loc[df_all['tomorrow_chg']>-1.4,'tomorrow_chg_rank']=3 #df_all.loc[df_all['tomorrow_chg']>-0.7,'tomorrow_chg_rank']=4 #df_all.loc[df_all['tomorrow_chg']>0,'tomorrow_chg_rank']=5 #df_all.loc[df_all['tomorrow_chg']>0.7,'tomorrow_chg_rank']=6 #df_all.loc[df_all['tomorrow_chg']>1.4,'tomorrow_chg_rank']=7 #df_all.loc[df_all['tomorrow_chg']>2.3,'tomorrow_chg_rank']=8 #df_all.loc[df_all['tomorrow_chg']>4,'tomorrow_chg_rank']=9 df_all.loc[df_all['tomorrow_chg']>-8,'tomorrow_chg_rank']=1 df_all.loc[df_all['tomorrow_chg']>-4.6,'tomorrow_chg_rank']=2 df_all.loc[df_all['tomorrow_chg']>-2.8,'tomorrow_chg_rank']=3 df_all.loc[df_all['tomorrow_chg']>-1.4,'tomorrow_chg_rank']=4 df_all.loc[df_all['tomorrow_chg']>0,'tomorrow_chg_rank']=5 df_all.loc[df_all['tomorrow_chg']>1.4,'tomorrow_chg_rank']=6 df_all.loc[df_all['tomorrow_chg']>2.8,'tomorrow_chg_rank']=7 df_all.loc[df_all['tomorrow_chg']>4.6,'tomorrow_chg_rank']=8 df_all.loc[df_all['tomorrow_chg']>8,'tomorrow_chg_rank']=9 return df_all def PredictDaysReal5day(df_all,days): ##明日幅度 #Y=[-9.34,-5.48,-4.2,-3.4,-2.7,-2.3,-1.86,-1.47,-1.09,-0.74,-0.38,0,0.398,0.838,1.35,1.96,2.74,3.81,5.58,10.77] nextstart=df_all.groupby('ts_code')['real_price'].shift(0) nextnstart=df_all.groupby('ts_code')['real_price'].shift(0-days) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=((nextnstart-nextstart)/nextstart)*100 #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 #df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=0 df_all.loc[df_all['tomorrow_chg']>-9.34,'tomorrow_chg_rank']=1 df_all.loc[df_all['tomorrow_chg']>-5.48,'tomorrow_chg_rank']=2 df_all.loc[df_all['tomorrow_chg']>-4.2,'tomorrow_chg_rank']=3 df_all.loc[df_all['tomorrow_chg']>-3.4,'tomorrow_chg_rank']=4 df_all.loc[df_all['tomorrow_chg']>-2.7,'tomorrow_chg_rank']=5 df_all.loc[df_all['tomorrow_chg']>-2.3,'tomorrow_chg_rank']=6 df_all.loc[df_all['tomorrow_chg']>-1.86,'tomorrow_chg_rank']=7 df_all.loc[df_all['tomorrow_chg']>-1.47,'tomorrow_chg_rank']=8 df_all.loc[df_all['tomorrow_chg']>-1.09,'tomorrow_chg_rank']=9 df_all.loc[df_all['tomorrow_chg']>-0.74,'tomorrow_chg_rank']=10 df_all.loc[df_all['tomorrow_chg']>-0.38,'tomorrow_chg_rank']=11 df_all.loc[df_all['tomorrow_chg']>0.398,'tomorrow_chg_rank']=12 df_all.loc[df_all['tomorrow_chg']>0.838,'tomorrow_chg_rank']=13 df_all.loc[df_all['tomorrow_chg']>1.35,'tomorrow_chg_rank']=14 df_all.loc[df_all['tomorrow_chg']>1.96,'tomorrow_chg_rank']=15 df_all.loc[df_all['tomorrow_chg']>2.74,'tomorrow_chg_rank']=16 df_all.loc[df_all['tomorrow_chg']>3.81,'tomorrow_chg_rank']=17 df_all.loc[df_all['tomorrow_chg']>5.58,'tomorrow_chg_rank']=18 df_all.loc[df_all['tomorrow_chg']>10.77,'tomorrow_chg_rank']=19 return df_all def PredictDaysStartreal(df_all,days): ##明日幅度 nextstart=df_all.groupby('ts_code')['real_open'].shift(-1) nextnstart=df_all.groupby('ts_code')['real_open'].shift(-5) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=(nextnstart-nextstart)/nextstart #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all['tomorrow_chg']*10//1 return df_all def random_mix(df_all): ##明日幅度 length=df_all.shape[0] data1=np.random.randint(-1,2,size=length) data2=pd.DataFrame(data1) df_all['rdtest']=1 df_all['rdtest']=df_all['rdtest']+data1 print(df_all) nextstart=df_all.groupby('ts_code')['real_open'].shift(-1) nextnstart=df_all.groupby('ts_code')['real_open'].shift(-5) #df_all['real_open2']=nextnstart #df_all['real_open1']=nextstart #df_all['real_open0']=df_all['real_open'] df_all['tomorrow_chg']=(nextnstart-nextstart)/nextstart #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']*9.9//1 return df_all def PredictDays_reg(df_all,days): ##明日幅度 tms=[] for i in range(days): curindex=-i-1 curtm=df_all.groupby('ts_code')['pct_chg'].shift(curindex) tms.append(curtm) tmpdf=((100+tms[0])/100) for i in range(days): if i==0: continue tmpdf*=(100+tms[i])/100 df_all['tomorrow_chg']=(tmpdf-1)*100 #df_all['tomorrow_chg']=(((100+tm1)/100)*((100+tm2)/100)*((100+tm3)/100)*((100+tm4)/100)*((100+tm5)/100)-1)*100 #df_all['tomorrow_chg']=((100+tm1)*(100+tm2)*(100+tm3)-1000000)/10000 #df_all['tomorrow_chg']=df_all.groupby('ts_code')['pct_chg'].shift(-1) #明日排名 df_all['tomorrow_chg_rank']=df_all.groupby('trade_date')['tomorrow_chg'].rank(pct=True) #df_all['tomorrow_chg_rank']=df_all['tomorrow_chg_rank']*9.9//1 return df_all def PredictDays_notrank(df_all,days): ##明日幅度 tms=[] for i in range(days): curindex=-i-1 curtm=df_all.groupby('ts_code')['pct_chg'].shift(curindex) tms.append(curtm) tmpdf=((100+tms[0])/100) for i in range(days): if i==0: continue tmpdf*=(100+tms[i])/100 df_all['tomorrow_chg']=(tmpdf-1)*100 #明日排名 df_all['tomorrow_chg_rank']=0 df_all.loc[df_all['tomorrow_chg']>-10,'tomorrow_chg_rank']=1 df_all.loc[df_all['tomorrow_chg']>-6,'tomorrow_chg_rank']=2 df_all.loc[df_all['tomorrow_chg']>-3,'tomorrow_chg_rank']=3 df_all.loc[df_all['tomorrow_chg']>-1,'tomorrow_chg_rank']=4 df_all.loc[df_all['tomorrow_chg']>0,'tomorrow_chg_rank']=5 df_all.loc[df_all['tomorrow_chg']>1,'tomorrow_chg_rank']=6 df_all.loc[df_all['tomorrow_chg']>3,'tomorrow_chg_rank']=7 df_all.loc[df_all['tomorrow_chg']>6,'tomorrow_chg_rank']=8 df_all.loc[df_all['tomorrow_chg']>10,'tomorrow_chg_rank']=9 return df_all def PctChgSumRank(df_all,days,intflag=False): bufferbak='_'+str(days) stringdisplay='chg_rank_'+str(days) xxx=df_all.groupby('ts_code')['chg_rank'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def PctChgSum(df_all,days,intflag=False): bufferbak='_'+str(days) stringdisplay='pct_chg_'+str(days) xxx=df_all.groupby('ts_code')['pct_chg'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def PctChgAbsSumRank(df_all,days,intflag=False): bufferbak='_'+str(days) stringdisplay='pct_chg_abs_'+str(days) xxx=df_all.groupby('ts_code')['pct_chg_abs'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def PctChgAbsSum_self(df_all,days,intflag=False): bufferbak='_'+str(days) stringdisplay='pct_chg_abs_'+str(days) xxx=df_all.groupby('ts_code')['pct_chg_abs'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def PctChgSum_self(df_all,days,intflag=False): bufferbak='_'+str(days) stringdisplay='chg_rank_'+str(days) xxx=df_all.groupby('ts_code')['pct_chg'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def InputChgSum(df_all,days,sumlinename,intflag=False): bufferbak='_'+str(days) stringdisplay=sumlinename+'_'+str(days) xxx=df_all.groupby('ts_code')[sumlinename].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def PctChgSumRank_notrank(df_all,days): bufferbak='_'+str(days) stringdisplay='chg_rank_'+str(days) xxx=df_all.groupby('ts_code')['chg_rank'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) return df_all def PctChgSumRank_Common(df_all,days,namechg='high'): bufferbak='_'+str(days) stringdisplay=namechg+str(days) #6日 xxx=df_all.groupby('ts_code')[namechg].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) df_all[stringdisplay]=df_all.groupby('trade_date')[stringdisplay].rank(pct=True) #df_all=FEsingle.changerank_line(df_all,stringdisplay) return df_all def AmountChgRank(df_all,days,intflag=False): bufferbak='_'+str(days) amountstring='amount'+bufferbak stringdisplay='pst_amount_rank_'+str(days) #均量计算 xxx=df_all.groupby('ts_code')['amount'].rolling(days).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量占比 df_all['pst_amount']=df_all['amount']/df_all[amountstring] df_all.drop([amountstring],axis=1,inplace=True) #当日量排名 df_all[stringdisplay]=df_all.groupby('trade_date')['pst_amount'].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) #df_all.drop(['pst_amount'],axis=1,inplace=True) return df_all def MoneyflowChgRank(df_all,days,intflag=False): bufferbak='_'+str(days) amountstring='amount'+bufferbak stringdisplay='pst_amount_rank_'+str(days) #均量计算 xxx=df_all.groupby('ts_code')['amount'].rolling(days).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量占比 df_all['pst_amount']=df_all['amount']/df_all[amountstring] df_all.drop([amountstring],axis=1,inplace=True) #当日量排名 df_all[stringdisplay]=df_all.groupby('trade_date')['pst_amount'].rank(pct=True) if(intflag): df_all=FEsingle.changerank_line(df_all,stringdisplay) df_all.drop(['pst_amount'],axis=1,inplace=True) return df_all def AmountChg_notrank(df_all,days): bufferbak='_'+str(days) amountstring='amount'+bufferbak stringdisplay='pst_amount_notrank_'+str(days) #均量计算 xxx=df_all.groupby('ts_code')['amount'].rolling(days).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量占比 df_all[stringdisplay]=df_all['amount']/df_all[amountstring] df_all.drop([amountstring],axis=1,inplace=True) return df_all def MoneyflowChgRank(df_all,days,shiftday=1): bufferbak='_'+str(days) moneyflowstring='moneyflow'+bufferbak stringdisplay='pst_moneyflow_rank_'+str(days) #均量计算 xxx=df_all.groupby('ts_code')['moneyflow'].rolling(days).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量占比 df_all['pst_moneyflow']=df_all['moneyflow']/df_all[moneyflowstring] df_all.drop([moneyflowstring],axis=1,inplace=True) #当日量排名 df_all[stringdisplay]=df_all.groupby('trade_date')['pst_moneyflow'].rank(pct=True) #df_all=FEsingle.changerank_line(df_all,stringdisplay) #当日数据无法获取只能获取上一日数据 df_all[stringdisplay]=df_all.groupby('ts_code')[stringdisplay].shift(shiftday) df_all.drop(['pst_moneyflow'],axis=1,inplace=True) return df_all def MoneyflowallChgRank(df_all,days,shiftday=1): bufferbak='_'+str(days) net_mf_amountstring='net_mf_amount'+bufferbak stringdisplay='pst_net_mf_amount_rank_'+str(days) #均量计算 xxx=df_all.groupby('ts_code')['net_mf_amount'].rolling(days).mean().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量占比 df_all['pst_net_mf_amount']=df_all['net_mf_amount']/df_all[net_mf_amountstring] df_all.drop([net_mf_amountstring],axis=1,inplace=True) #当日量排名 df_all[stringdisplay]=df_all.groupby('trade_date')['pst_net_mf_amount'].rank(pct=True) #df_all=FEsingle.changerank_line(df_all,stringdisplay) #当日数据无法获取只能获取上一日数据 df_all[stringdisplay]=df_all.groupby('ts_code')[stringdisplay].shift(shiftday) df_all.drop(['pst_net_mf_amount'],axis=1,inplace=True) return df_all def MoneyflowsumChgRank(df_all,days,shiftday=1): bufferbak='_'+str(days) net_mf_amountstring='net_mf_amount'+bufferbak stringdisplay='pst_net_mf_amount_sum_rank_'+str(days) #总量计算 xxx=df_all.groupby('ts_code')['net_mf_amount'].rolling(days).sum().reset_index() xxx.set_index(['level_1'], drop=True, append=False, inplace=True, verify_integrity=False) xxx.drop(['ts_code'],axis=1,inplace=True) df_all=df_all.join(xxx, lsuffix='', rsuffix=bufferbak) #当日量排名 df_all[stringdisplay]=df_all.groupby('trade_date')[net_mf_amountstring].rank(pct=True) #df_all=FEsingle.changerank_line(df_all,stringdisplay) #当日数据无法获取只能获取上一日数据 df_all[stringdisplay]=df_all.groupby('ts_code')[stringdisplay].shift(shiftday) df_all.drop([net_mf_amountstring],axis=1,inplace=True) return df_all def OldFeaturesRank(df_all,features,daybak): for curfeature in features: curstring='yesterday_'+str(daybak)+curfeature df_all[curstring]=df_all.groupby('ts_code')[curfeature].shift(daybak) return df_all #一些lambda 函数 def rollingRankArgSort(array): return array.size - array.argsort().argsort()[:-1] def rollingRankArgSortBack(array): return array.argsort().argsort()[-1] def rollingRankSciPy(array): return array.size - sc.stats.rankdata(array,method = 'ordinal')[0] def rollingRankSciPyB(array): return sc.stats.rankdata(array,method = 'ordinal')[-1]

46.241253

166

0.628245

43,435

277,540

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0.010222

0.175275

0.056012

0.041596

0.974395

0.971785

0.96706

0.9617

0.953508

0.949532

0

0.027832

0.156205

277,540

6,001

167

46.248959

0.644566

0.266859

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0.215229

0.002695

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0.029609

false

0.005992

0.003172

0.002115

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0

null

0

1

0

1

0

null

0

7

b91de7987d28a4dcfac7dc142396db56a21183b5

112

py

Python

tests/test_app.py

ab7289-tandon-nyu/csgy6083_PDS_Project

d2b7d22274dcabbb6ae35c17a8ffd06498f3634f

[ "MIT" ]

null

tests/test_app.py

ab7289-tandon-nyu/csgy6083_PDS_Project

d2b7d22274dcabbb6ae35c17a8ffd06498f3634f

[ "MIT" ]

null

tests/test_app.py

ab7289-tandon-nyu/csgy6083_PDS_Project

d2b7d22274dcabbb6ae35c17a8ffd06498f3634f

[ "MIT" ]

null

from app.app import create_app def test_create_app(): assert create_app({"DEBUG": True, "TESTING": True})

18.666667

55

0.714286

17

112

4.470588

0.588235

0.355263

0

0.151786

112

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0.333333

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1

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0

7

b951d5ffbef6eb12f22c3ded9e7faa78ff14a929

3,958

py

Python

models/initializations.py

JannerM/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

54

2017-07-14T01:08:57.000Z

2021-07-09T12:46:57.000Z

models/initializations.py

jannerm/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

null

models/initializations.py

jannerm/spatial-reasoning

e163003a33177e41ca02d5feefee3fdfca5ba154

[ "MIT" ]

16

2017-07-16T03:18:19.000Z

2021-05-28T13:04:12.000Z

import models ''' norbf (full), nobases (no gradient), nonsep uvfa-pos, uvfa-text, cnn+lstm (nocnn) rbf + gradient + cnn: full no rbf: norbf no gradient: noglobal no rbf / gradient: nobases no cnn: nocnn ''' def init(args, layout_vocab_size, object_vocab_size, text_vocab_size): if args.model == 'full': ## new model = init_full(args, layout_vocab_size, object_vocab_size, text_vocab_size) elif args.model == 'no-gradient': model = init_nogradient(args, layout_vocab_size, object_vocab_size, text_vocab_size) elif args.model == 'cnn-lstm': model = init_cnn_lstm(args, layout_vocab_size, object_vocab_size, text_vocab_size) elif args.model == 'uvfa-text': model = init_uvfa_text(args, layout_vocab_size, object_vocab_size, text_vocab_size) # TODO: clean up UVFA-pos goal loading elif args.model == 'uvfa-pos': model = init_uvfa_pos(args, layout_vocab_size, object_vocab_size, text_vocab_size) train_indices = train_goals val_indices = val_goals return model def init_full(args, layout_vocab_size, object_vocab_size, text_vocab_size): args.global_coeffs = 3 args.attention_in_dim = args.obj_embed args.lstm_out = args.attention_in_dim * args.attention_out_dim * args.attention_kernel**2 + args.global_coeffs state_model = models.LookupModel(layout_vocab_size, args.state_embed).cuda() object_model = models.LookupModel(object_vocab_size, args.obj_embed) text_model = models.TextModel(text_vocab_size, args.lstm_inp, args.lstm_hid, args.lstm_layers, args.lstm_out) heatmap_model = models.AttentionGlobal(text_model, args, map_dim=args.map_dim).cuda() model = models.MultiNoRBF(state_model, object_model, heatmap_model, args, map_dim=args.map_dim).cuda() return model def init_nogradient(args, layout_vocab_size, object_vocab_size, text_vocab_size): args.global_coeffs = 0 args.attention_in_dim = args.obj_embed args.lstm_out = args.attention_in_dim * args.attention_out_dim * args.attention_kernel**2 state_model = models.LookupModel(layout_vocab_size, args.state_embed).cuda() object_model = models.LookupModel(object_vocab_size, args.obj_embed) text_model = models.TextModel(text_vocab_size, args.lstm_inp, args.lstm_hid, args.lstm_layers, args.lstm_out) heatmap_model = models.AttentionHeatmap(text_model, args, map_dim=args.map_dim).cuda() model = models.MultiNoBases(state_model, object_model, heatmap_model, args, map_dim=args.map_dim).cuda() return model def init_cnn_lstm(args, layout_vocab_size, object_vocab_size, text_vocab_size): args.lstm_out = 16 args.cnn_out_dim = 2*args.lstm_out state_model = models.LookupModel(layout_vocab_size, args.state_embed) object_model = models.LookupModel(object_vocab_size, args.obj_embed) lstm = models.TextModel(text_vocab_size, args.lstm_inp, args.lstm_hid, args.lstm_layers, args.lstm_out) model = models.CNN_LSTM(state_model, object_model, lstm, args).cuda() return model def init_uvfa_text(args, layout_vocab_size, object_vocab_size, text_vocab_size, rank = 7): print '<Models> Using UVFA variant, consider using a lower learning rate (eg, 0.0001)' print '<Models> UVFA rank: {} '.format(rank) args.rank = rank args.lstm_out = rank text_model = models.TextModel(text_vocab_size, args.lstm_inp, args.lstm_hid, args.lstm_layers, args.lstm_out) model = models.UVFA_text(text_model, layout_vocab_size, object_vocab_size, args, map_dim=args.map_dim).cuda() return model def init_uvfa_pos(args, layout_vocab_size, object_vocab_size, text_vocab_size, rank = 7): print '<Models> Using UVFA variant, consider using a lower learning rate (eg, 0.0001)' print '<Models> UVFA rank: {} '.format(rank) args.rank = rank args.lstm_out = rank model = models.UVFA_pos(layout_vocab_size, object_vocab_size, args, map_dim=args.map_dim).cuda() return model

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114

0.748863

599

3,958

4.614357

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0.086831

0.09877

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0

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0.151844

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100

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0

1

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null

0

1

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7

b99c8c0f9c25334a821b77cf7b8d642cd23edd4f

2,227

py

Python

mak/libs/pyxx/cxx/grammar/expression/primary/fold.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

null

mak/libs/pyxx/cxx/grammar/expression/primary/fold.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

null

mak/libs/pyxx/cxx/grammar/expression/primary/fold.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

null

""" fold-expression: ( cast-expression fold-operator ... ) ( ... fold-operator cast-expression ) ( cast-expression fold-operator ... fold-operator cast-expression ) fold-operator: one of + - * / % ^ & | << >> += -= *= /= %= ^= &= |= <<= >>= = == != < > <= >= && || , .* ->* """ import glrp from ....parser import cxx17 from motor_typing import TYPE_CHECKING @glrp.rule('fold-expression : "(" cast-expression fold-reduction fold-operator "..." ")"') @glrp.rule('fold-expression : "(" "..." fold-operator cast-expression ")"') @glrp.rule('fold-expression : "(" cast-expression fold-reduction fold-operator "..." fold-operator cast-expression ")"') @cxx17 def fold_expression_cxx17(self, p): # type: (CxxParser, glrp.Production) -> None pass @glrp.rule('fold-reduction : [split:fold_expression]') @cxx17 def fold_reduction_cxx17(self, p): # type: (CxxParser, glrp.Production) -> None pass @glrp.rule('fold-operator : "+"') @glrp.rule('fold-operator : "-"') @glrp.rule('fold-operator : "*"') @glrp.rule('fold-operator : "/"') @glrp.rule('fold-operator : "%"') @glrp.rule('fold-operator : "^"') @glrp.rule('fold-operator : "&"') @glrp.rule('fold-operator : "|"') @glrp.rule('fold-operator : "<<"') @glrp.rule('fold-operator : ">>"') @glrp.rule('fold-operator : "+="') @glrp.rule('fold-operator : "-="') @glrp.rule('fold-operator : "*="') @glrp.rule('fold-operator : "/="') @glrp.rule('fold-operator : "%="') @glrp.rule('fold-operator : "^="') @glrp.rule('fold-operator : "&="') @glrp.rule('fold-operator : "|="') @glrp.rule('fold-operator : "<<="') @glrp.rule('fold-operator : ">>="') @glrp.rule('fold-operator : "="') @glrp.rule('fold-operator : "=="') @glrp.rule('fold-operator : "!="') @glrp.rule('fold-operator : "<"') @glrp.rule('fold-operator : ">"') @glrp.rule('fold-operator : "<="') @glrp.rule('fold-operator : ">="') @glrp.rule('fold-operator : "&&"') @glrp.rule('fold-operator : "||"') @glrp.rule('fold-operator : ","') @glrp.rule('fold-operator : ".*"') @glrp.rule('fold-operator : "->*"') @cxx17 def fold_operator_cxx17(self, p): # type: (CxxParser, glrp.Production) -> None pass if TYPE_CHECKING: from ....parser import CxxParser

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247

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5.311741

0.105263

0.384146

0.329268

0.487805

0.811738

0.799543

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0

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121

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0.679226

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null

1

0

null

0

1

0

10

b99d629a7b3fc55dde94ec910192cd3bcbbd343e

7,165

py

Python

switchmng/routes/put.py

AnsgarKlein/switchmng

d86ae2dc40ef70f43fec8e3adf49ae5fd796a01d

[ "MIT" ]

null

switchmng/routes/put.py

AnsgarKlein/switchmng

d86ae2dc40ef70f43fec8e3adf49ae5fd796a01d

[ "MIT" ]

null

switchmng/routes/put.py

AnsgarKlein/switchmng

d86ae2dc40ef70f43fec8e3adf49ae5fd796a01d

[ "MIT" ]

null

from flask import current_app from flask import request from switchmng.typing import FlaskResponse from switchmng import database from .blueprint import restbp from .errors import * @restbp.route('/switch_models/<string:resource_id>', methods = ['PUT']) def put_switch_model(resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: sm = database.set_switch_model(session, resource_id = resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': sm.jsonify() }, 200 @restbp.route('/switch_models/<string:switch_model_resource_id>/ports/<string:port_model_resource_id>', methods = ['PUT']) def put_port_model( switch_model_resource_id: str, port_model_resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: pm = database.set_port_model( session, switch_model_resource_id = switch_model_resource_id, port_model_resource_id = port_model_resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': pm.jsonify() }, 200 @restbp.route('/switches/<string:resource_id>', methods = ['PUT']) def put_switch(resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: sw = database.set_switch(session, resource_id = resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': sw.jsonify() }, 200 @restbp.route('/switches/<string:switch_resource_id>/ports/<string:port_resource_id>', methods = ['PUT']) def put_port(switch_resource_id: str, port_resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: pt = database.set_port( session, switch_resource_id = switch_resource_id, port_resource_id = port_resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': pt.jsonify() }, 200 @restbp.route('/network_protocols/<string:resource_id>', methods = ['PUT']) def put_network_protocols(resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: np = database.set_network_protocol(session, resource_id = resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': np.jsonify() }, 200 @restbp.route('/connectors/<string:resource_id>', methods = ['PUT']) def put_connector(resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: cn = database.set_connector(session, resource_id = resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': cn.jsonify() }, 200 @restbp.route('/vlans/<int:resource_id>', methods = ['PUT']) def put_vlan(resource_id: str) -> FlaskResponse: session = current_app.config['SWITCHMNG_DB_CONNECTION'].Session() # Check request if request.content_type != 'application/json': return error_415(message = 'Expected Content-Type to be application/json') if not request.accept_mimetypes.accept_json: return error_406(message = 'Content-Type application/json is not accepted by client') try: req = request.json if not isinstance(req, dict): raise BaseException() except: return error_400(message = 'Request is not a valid json object') # Set in database try: vl = database.set_vlan(session, resource_id = resource_id, **req) except BaseException as e: return error_400(message = str(e)) return { 'status': 200, 'data': vl.jsonify() }, 200

36.93299

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0.077821

0.06658

0.078686

0.874838

0.841548

0.822093

0.785992

0.769563

0

0.022843

0.230147

7,165

193

123

37.124352

0.815809

0.02917

0

0.731544

0

0.231955

0.068578

0

1

0.04698

false

0

0.040268

0

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0.006711

0

null

0

1

0

1

0

null

0

7

b9cd3066792449ab3d3e68612fab48e979ac2945

33,153

py

Python

intel_ecs_wiki_search_backup.py

kundansaha82/WikiChatbot

24ad28f0964ad28cbe8fca96b40bd8974fe5b7ea

[ "MIT" ]

null

intel_ecs_wiki_search_backup.py

kundansaha82/WikiChatbot

24ad28f0964ad28cbe8fca96b40bd8974fe5b7ea

[ "MIT" ]

null

intel_ecs_wiki_search_backup.py

kundansaha82/WikiChatbot

24ad28f0964ad28cbe8fca96b40bd8974fe5b7ea

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Feb 18 13:25:29 2021 @author: kundankantisaha """ from elasticsearch import Elasticsearch from os import popen """import logging""" import subprocess from lemma_tokenizer import Splitter as Splitter from lemma_tokenizer import LemmatizationWithPOSTagger as LemmatizationWithPOSTagger import json import lemma_tokenizer import scrapy from scrapy import Request def start_cluster(): subprocess.Popen('C:\\Users\\kundansa\\Downloads\\elasticsearch-7.10.2\\bin\\elasticsearch.bat') """time.sleep(15)""" def connect_elasticsearch(): es = None es = Elasticsearch([{'host': '127.0.0.1', 'port': '9200'}]) if es.ping(): print('Yay Connect') else: print('Awww it could not connect!') return es source_link = "" keywordoriginal = "" def print_content(response): print("Printing content") with open("C:\\Users\\kundansa\\ecswikifin\\ecswikifin\\spiders\\data_file7.json", "a") as filee: filee.write('[') sub="" sub_next="" h2 = response.xpath("//div[@class='wiki-content']") lines = "" title=response.xpath("//head/title/text()").extract() heading=response.xpath("//div[@class='wiki-content']/h2") print(title) for head in response.xpath("//div[@class='wiki-content']/h1"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h1[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h1[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h1/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h1[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h1[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in heading: sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h2[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h2[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h2/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h2[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h2[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h3"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h3[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h3[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h3/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h3[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h3[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h4"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h4[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h4[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h4/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h4[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h4[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h5"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h5[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h5[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h5/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h5[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h5[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h6"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h6[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h6[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h6/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h6[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h6[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h7"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h7[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h7[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/h7/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::h7[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::h7[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/ul"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::ul[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::ul[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/ul/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::ul[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::ul[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/ol"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::ol[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::ol[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break for head in response.xpath("//div[@class='wiki-content']/ol/strong"): sub=str(head.xpath("text()").extract()) sub = sub.replace("[","") sub = sub.replace("'","") sub = sub.replace("]","") if sub == keywordoriginal: sub_next=str(head.xpath("following-sibling::ol[1]//text()").extract()) sub_next = sub_next.replace("[","") sub_next = sub_next.replace("'","") sub_next = sub_next.replace("]","") if sub_next != "": line = head.xpath(".//following-sibling::*[count(following-sibling::ol[1])]//text()").extract() json.dump({ 'line':(str(line)) }, filee ) filee.write(']') print(str(line)) break else: for i in range(1, 6): i_char = str(i) str_xpath = ".//following::text()[" + i_char + "]" lines = lines + str(head.xpath(str_xpath).extract()) json.dump({ 'line':(lines) }, filee ) filee.write('...]') print(str(lines)) break yield response """if __name__ == '__main__': logging.basicConfig(level=logging.ERROR)""" start_cluster() es = connect_elasticsearch() looping_condition = True """key_terms = ['backup', 'Security', 'DPDK', 'Driver', 'OVS', 'ADQ','Containers','NVMe over TCP','Memcached','Netperf','Redis','Aerospike' 'RDMA','DDP','DPR','AF_XDP','ECS','Columbiaville','OEM','ETA','Ethernet','Pre-Boot']""" while looping_condition: found_terms = list() input_string = input("Enter your query/ enter '-1' to exit :") if ("-1" == input_string): looping_condition = False continue lemma_tokenizer.input_string = input_string splitter = Splitter() lemmatization_using_pos_tagger = LemmatizationWithPOSTagger() #step 1 split document into sentence followed by tokenization tokens = splitter.split(input_string) #step 2 lemmatization using pos tagger lemma_pos_token = lemmatization_using_pos_tagger.pos_tag(tokens) with open('input_tokens.json', 'w') as f: for lemmaset in lemma_pos_token: for i in range(len(lemmaset)): data = {} data["words"] = [] data["words"].append({ "Original Word": lemmaset[i][0], "Lemmatized Word": lemmaset[i][1], "POS Tag": lemmaset[i][2] }) json.dump(data, f) string_found_terms = "" allowed_pos_tags = [["NNP"],["NNS"],["NN"],["VB"],["NNPS"],["CD"]] for lemmaset in lemma_pos_token: for i in range(len(lemmaset)): if lemmaset[i][2] in allowed_pos_tags: found_terms.append(lemmaset[i][1]) string_found_terms = ' '.join(found_terms) search_param = { "query": { "simple_query_string" : { "query": string_found_terms, "fields": ["title", "keyword"], "default_operator": "and" } } } res = es.search(index="wikifin", body=search_param) """print("%d documents found" % res['hits']['total'])""" data = [doc for doc in res['hits']['hits']] resulting_search = "" for doc in data: resulting_search = doc['_source']['keyword'] original_search_phrase = doc['_source']['keywordoriginal'] print("") print("%s" % original_search_phrase) keywordoriginal = original_search_phrase lemma_tokenizer.input_string = resulting_search splitter = Splitter() lemmatization_using_pos_tagger = LemmatizationWithPOSTagger() tokens_out = splitter.split(resulting_search) lemma_pos_token_out = lemmatization_using_pos_tagger.pos_tag(tokens_out) with open('output_tokens.json', 'w') as f: for lemmaset_out in lemma_pos_token_out: for i in range(len(lemmaset_out)): data = {} data["words"] = [] data["words"].append({ "Original Word": lemmaset_out[i][0], "Lemmatized Word": lemmaset_out[i][1], "POS Tag": lemmaset_out[i][2] }) json.dump(data, f) source_link = (doc['_source']['link']) curl_string1 = 'curl -H "Authorization: Basic S1VOREFOU0E6QW5pS3VuQDk5MDM=" -X GET -H "Content-Type: application/json" '+source_link response = Request.from_curl(curl_string1, callback = print_content) print("%s" % source_link)

45.229195

150

0.367569

2,646

33,153

4.484127

0.086546

0.085546

0.054614

0.063717

0.819132

0.812389

0.80531

0.780784

0.772693

0

0.00974

0.489006

33,153

732

151

45.290984

0.690632

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0

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0

1

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false

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null

0

1

0

1

0

null

0

7

b9cd5bcd59d123efd59e0f06bd10aa7f7a7aa6cb

12,034

py

Python

photix/fields.py

dimitri-yatsenko/photix

906e5637c8e8172e1f57c3a6f04b55db355effb2

[ "MIT" ]

null

photix/fields.py

dimitri-yatsenko/photix

906e5637c8e8172e1f57c3a6f04b55db355effb2

[ "MIT" ]

null

photix/fields.py

dimitri-yatsenko/photix

906e5637c8e8172e1f57c3a6f04b55db355effb2

[ "MIT" ]

null

import numpy as np import datajoint as dj from .tracer import SpaceTracer from matplotlib import pyplot as plt schema = dj.schema('photixxx') @schema class DSim(dj.Lookup): definition = """ # Detector Field Specification dsim : int --- detector_type='one-sided' : varchar(30) # choice in simulation detector_width=10.00: decimal(5,2) # (um) along x-axis detector_height=10.00: decimal(5,2) # (um) along y-axis anisotropy = 0.88 : float # factor in the Henyey-Greenstein formula absorption_length: float # (um) average travel path before a absoprtion event scatter_length : float # (um) average travel path before a scatter event volume_dimx = 1000 : int unsigned # (voxels) volume_dimy = 1000 : int unsigned # (voxels) volume_dimz = 1000 : int unsigned # (voxels) pitch = 2.2 : float # (um) spatial sampling period of the model volume """ contents = [ dict(dsim=0, detector_type='one-sided', detector_height=50, scatter_length=50, absorption_length=1.5e4), dict(dsim=1, detector_type='one-sided', detector_height=20, scatter_length=50, absorption_length=1.5e4), dict(dsim=2, detector_type='narrowed2', detector_height=20, scatter_length=50, absorption_length=1.5e4), dict(dsim=4, detector_type='narrowed4', detector_height=20, scatter_length=50, absorption_length=1.5e4), dict(dsim=8, detector_type='narrowed8', detector_height=20, scatter_length=50, absorption_length=1.5e4), dict(dsim=10, detector_type='narrowed10', detector_height=20, scatter_length=50, absorption_length=1.5e4), dict(dsim=14, detector_type='narrowed4', detector_height=20, scatter_length=50, absorption_length=1500), dict(dsim=18, detector_type='narrowed8', detector_height=20, scatter_length=50, absorption_length=500)] def make_volume(self, hops=100_000): spec = self.fetch1() kwargs = {k: spec[k] for k in spec if k in { 'pitch', 'anisotropy', 'scatter_length', 'absorption_length', 'detector_type'}} kwargs.update( dims=tuple(spec[k] for k in ('volume_dimx', 'volume_dimy', 'volume_dimz')), emitter_spread='spherical', emitter_size=(float(spec['detector_width']), float(spec['detector_height']), 0)) space = SpaceTracer(**kwargs) space.run(hops=hops) space.volume *= space.emitter_area return space @schema class ESim(dj.Lookup): definition = """ # Emission Field Specification esim : int --- beam_compression : float y_steer : float # the steer angle in the plane of the shank emitter_width=10.00: decimal(5,2) # (um) along x-axis emitter_height=10.00: decimal(5,2) # (um) along y-axis anisotropy = 0.88 : float # factor in the Henyey-Greenstein formula absorption_length: float # (um) average travel path before a absoprtion event scatter_length: float # (um) average travel path before a scatter event volume_dimx = 1000 : int unsigned # (voxels) volume_dimy = 1000 : int unsigned # (voxels) volume_dimz = 1000 : int unsigned # (voxels) beam_xy_aspect = 1.0 : float # compression of y. E.g. 2.0 means that y is compressed by factor of 2 pitch = 2.2 : float # (um) spatial sampling period of the model volume """ contents = [ dict(esim=0, beam_compression=1.0, y_steer=0.0, scatter_length=50, absorption_length=1.5e4), dict(esim=10, beam_compression=1 / 4, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=11, beam_compression=1 / 4, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=12, beam_compression=1 / 4, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=13, beam_compression=1 / 4, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=14, beam_compression=1 / 4, y_steer=0, scatter_length=50, absorption_length=1.5e4), dict(esim=15, beam_compression=1 / 4, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=16, beam_compression=1 / 4, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=17, beam_compression=1 / 4, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=18, beam_compression=1 / 4, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=20, beam_compression=1 / 3, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=21, beam_compression=1 / 3, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=22, beam_compression=1 / 3, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=23, beam_compression=1 / 3, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=24, beam_compression=1 / 3, y_steer=0, scatter_length=50, absorption_length=1.5e4), dict(esim=25, beam_compression=1 / 3, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=26, beam_compression=1 / 3, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=27, beam_compression=1 / 3, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=28, beam_compression=1 / 3, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=30, beam_compression=1 / 6, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=31, beam_compression=1 / 6, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=32, beam_compression=1 / 6, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=33, beam_compression=1 / 6, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=34, beam_compression=1 / 6, y_steer=0, scatter_length=50, absorption_length=1.5e4), dict(esim=35, beam_compression=1 / 6, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=36, beam_compression=1 / 6, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=37, beam_compression=1 / 6, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=38, beam_compression=1 / 6, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=40, beam_compression=1 / 12, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1.5e4), dict(esim=41, beam_compression=1 / 12, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=42, beam_compression=1 / 12, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=43, beam_compression=1 / 12, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=44, beam_compression=1 / 12, y_steer=0, scatter_length=50, absorption_length=1.5e4), dict(esim=45, beam_compression=1 / 12, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1.5e4), dict(esim=46, beam_compression=1 / 12, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1.5e4), dict(esim=47, beam_compression=1 / 12, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1.5e4), dict(esim=48, beam_compression=1 / 12, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1.5e4), # tissue tanning dict(esim=130, beam_compression=1 / 6, y_steer=-np.pi / 3, scatter_length=50, absorption_length=500), dict(esim=131, beam_compression=1 / 6, y_steer=-np.pi / 4, scatter_length=50, absorption_length=500), dict(esim=132, beam_compression=1 / 6, y_steer=-np.pi / 6, scatter_length=50, absorption_length=500), dict(esim=133, beam_compression=1 / 6, y_steer=-np.pi / 12, scatter_length=50, absorption_length=500), dict(esim=134, beam_compression=1 / 6, y_steer=0, scatter_length=50, absorption_length=500), dict(esim=135, beam_compression=1 / 6, y_steer=+np.pi / 12, scatter_length=50, absorption_length=500), dict(esim=136, beam_compression=1 / 6, y_steer=+np.pi / 6, scatter_length=50, absorption_length=500), dict(esim=137, beam_compression=1 / 6, y_steer=+np.pi / 4, scatter_length=50, absorption_length=500), dict(esim=138, beam_compression=1 / 6, y_steer=+np.pi / 3, scatter_length=50, absorption_length=500), dict(esim=140, beam_compression=1 / 12, y_steer=-np.pi / 3, scatter_length=50, absorption_length=500), dict(esim=141, beam_compression=1 / 12, y_steer=-np.pi / 4, scatter_length=50, absorption_length=500), dict(esim=142, beam_compression=1 / 12, y_steer=-np.pi / 6, scatter_length=50, absorption_length=500), dict(esim=143, beam_compression=1 / 12, y_steer=-np.pi / 12, scatter_length=50, absorption_length=500), dict(esim=144, beam_compression=1 / 12, y_steer=0, scatter_length=50, absorption_length=500), dict(esim=145, beam_compression=1 / 12, y_steer=+np.pi / 12, scatter_length=50, absorption_length=500), dict(esim=146, beam_compression=1 / 12, y_steer=+np.pi / 6, scatter_length=50, absorption_length=500), dict(esim=147, beam_compression=1 / 12, y_steer=+np.pi / 4, scatter_length=50, absorption_length=500), dict(esim=148, beam_compression=1 / 12, y_steer=+np.pi / 3, scatter_length=50, absorption_length=500), dict(esim=320, beam_compression=1 / 3, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1500), dict(esim=321, beam_compression=1 / 3, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1500), dict(esim=322, beam_compression=1 / 3, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1500), dict(esim=323, beam_compression=1 / 3, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1500), dict(esim=324, beam_compression=1 / 3, y_steer=0, scatter_length=50, absorption_length=1500), dict(esim=325, beam_compression=1 / 3, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1500), dict(esim=326, beam_compression=1 / 3, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1500), dict(esim=327, beam_compression=1 / 3, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1500), dict(esim=328, beam_compression=1 / 3, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1500), # tissue tanning dict(esim=330, beam_compression=1 / 6, y_steer=-np.pi / 3, scatter_length=50, absorption_length=1500), dict(esim=331, beam_compression=1 / 6, y_steer=-np.pi / 4, scatter_length=50, absorption_length=1500), dict(esim=332, beam_compression=1 / 6, y_steer=-np.pi / 6, scatter_length=50, absorption_length=1500), dict(esim=333, beam_compression=1 / 6, y_steer=-np.pi / 12, scatter_length=50, absorption_length=1500), dict(esim=334, beam_compression=1 / 6, y_steer=0, scatter_length=50, absorption_length=1500), dict(esim=335, beam_compression=1 / 6, y_steer=+np.pi / 12, scatter_length=50, absorption_length=1500), dict(esim=336, beam_compression=1 / 6, y_steer=+np.pi / 6, scatter_length=50, absorption_length=1500), dict(esim=337, beam_compression=1 / 6, y_steer=+np.pi / 4, scatter_length=50, absorption_length=1500), dict(esim=338, beam_compression=1 / 6, y_steer=+np.pi / 3, scatter_length=50, absorption_length=1500),] def make_volume(self, hops=100_000): spec = self.fetch1() kwargs = {k: spec[k] for k in spec if k in { 'pitch', 'anisotropy', 'scatter_length', 'y_steer', 'beam_compression', 'beam_xy_aspect', 'absorption_length'}} kwargs.update( dims=tuple(spec[k] for k in ('volume_dimx', 'volume_dimy', 'volume_dimz')), emitter_size=(float(spec['emitter_width']), float(spec['emitter_height']), 0)) space = SpaceTracer(**kwargs) space.run(hops=hops) return space

68.765714

114

0.686804

1,867

12,034

4.228709

0.108195

0.172261

0.153895

0.256491

0.872831

0.863458

0.862445

0.845598

0

0.098334

0.176915

12,034

174

115

69.16092

0.698738

0.00241

0

0.27027

0

0.006757

0.159653

0.007416

0

1

0.013514

false

0

0.027027

0

0.094595

0

null

0

1

0

null

0

7

e032f370f72ea3b74646dc41ef5cd3a72888786d

70,459

py

Python

decision trees/python/monkdata.py

Manu-Fraile/Machine-Learning

7428a594b07c23b6b4326ad3f80b11860ac8d507

[ "MIT" ]

1

2020-12-02T18:48:32.000Z

decision trees/python/monkdata.py

Manu-Fraile/Machine-Learning

7428a594b07c23b6b4326ad3f80b11860ac8d507

[ "MIT" ]

null

decision trees/python/monkdata.py

Manu-Fraile/Machine-Learning

7428a594b07c23b6b4326ad3f80b11860ac8d507

[ "MIT" ]

null

class Sample: "Represenation of data samles" def __init__(self, positive, values, identity): self.positive = positive self.attribute = dict(zip(attributes, values)) self.identity = identity class Attribute: "Label for each attribute" def __init__(self, name, values): self.name = name self.values = values def __repr__(self): return self.name attributes = (Attribute('A1', (1, 2, 3)), Attribute('A2', (1, 2, 3)), Attribute('A3', (1, 2)), Attribute('A4', (1, 2, 3)), Attribute('A5', (1, 2, 3, 4)), Attribute('A6', (1, 2))) monk1 = ( Sample(True, (1, 1, 1, 1, 3, 1), 5), Sample(True, (1, 1, 1, 1, 3, 2), 6), Sample(True, (1, 1, 1, 3, 2, 1), 19), Sample(True, (1, 1, 1, 3, 3, 2), 22), Sample(True, (1, 1, 2, 1, 2, 1), 27), Sample(True, (1, 1, 2, 1, 2, 2), 28), Sample(True, (1, 1, 2, 2, 3, 1), 37), Sample(True, (1, 1, 2, 2, 4, 1), 39), Sample(True, (1, 1, 2, 3, 1, 2), 42), Sample(True, (1, 2, 1, 1, 1, 2), 50), Sample(False, (1, 2, 1, 1, 2, 1), 51), Sample(False, (1, 2, 1, 1, 3, 1), 53), Sample(False, (1, 2, 1, 1, 4, 2), 56), Sample(True, (1, 2, 1, 2, 1, 1), 57), Sample(False, (1, 2, 1, 2, 3, 1), 61), Sample(False, (1, 2, 1, 2, 3, 2), 62), Sample(False, (1, 2, 1, 2, 4, 2), 64), Sample(False, (1, 2, 1, 3, 2, 1), 67), Sample(False, (1, 2, 1, 3, 4, 2), 72), Sample(False, (1, 2, 2, 1, 2, 2), 76), Sample(False, (1, 2, 2, 2, 3, 2), 86), Sample(False, (1, 2, 2, 2, 4, 1), 87), Sample(False, (1, 2, 2, 2, 4, 2), 88), Sample(False, (1, 2, 2, 3, 2, 2), 92), Sample(False, (1, 2, 2, 3, 3, 1), 93), Sample(False, (1, 2, 2, 3, 3, 2), 94), Sample(False, (1, 3, 1, 1, 2, 1), 99), Sample(False, (1, 3, 1, 1, 4, 1), 103), Sample(False, (1, 3, 1, 2, 2, 1), 107), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(True, (1, 3, 1, 3, 1, 2), 114), Sample(False, (1, 3, 1, 3, 2, 2), 116), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(False, (1, 3, 1, 3, 4, 1), 119), Sample(False, (1, 3, 1, 3, 4, 2), 120), Sample(False, (1, 3, 2, 1, 2, 2), 124), Sample(True, (1, 3, 2, 2, 1, 2), 130), Sample(False, (1, 3, 2, 2, 2, 2), 132), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(False, (1, 3, 2, 2, 4, 1), 135), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(True, (1, 3, 2, 3, 1, 1), 137), Sample(False, (1, 3, 2, 3, 2, 1), 139), Sample(False, (1, 3, 2, 3, 4, 1), 143), Sample(False, (1, 3, 2, 3, 4, 2), 144), Sample(False, (2, 1, 1, 1, 3, 1), 149), Sample(False, (2, 1, 1, 1, 3, 2), 150), Sample(True, (2, 1, 1, 2, 1, 1), 153), Sample(True, (2, 1, 1, 2, 1, 2), 154), Sample(False, (2, 1, 1, 2, 2, 2), 156), Sample(False, (2, 1, 1, 2, 3, 1), 157), Sample(False, (2, 1, 1, 2, 4, 1), 159), Sample(False, (2, 1, 1, 2, 4, 2), 160), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(False, (2, 1, 2, 1, 2, 2), 172), Sample(False, (2, 1, 2, 1, 3, 1), 173), Sample(False, (2, 1, 2, 1, 4, 2), 176), Sample(False, (2, 1, 2, 2, 3, 1), 181), Sample(False, (2, 1, 2, 2, 4, 2), 184), Sample(False, (2, 1, 2, 3, 2, 2), 188), Sample(False, (2, 1, 2, 3, 4, 1), 191), Sample(True, (2, 2, 1, 1, 2, 1), 195), Sample(True, (2, 2, 1, 1, 2, 2), 196), Sample(True, (2, 2, 1, 1, 3, 1), 197), Sample(True, (2, 2, 1, 2, 3, 2), 206), Sample(True, (2, 2, 1, 3, 1, 1), 209), Sample(True, (2, 2, 1, 3, 1, 2), 210), Sample(True, (2, 2, 1, 3, 2, 2), 212), Sample(True, (2, 2, 1, 3, 3, 2), 214), Sample(True, (2, 2, 1, 3, 4, 2), 216), Sample(True, (2, 2, 2, 1, 1, 1), 217), Sample(True, (2, 2, 2, 1, 3, 2), 222), Sample(True, (2, 2, 2, 1, 4, 1), 223), Sample(True, (2, 2, 2, 1, 4, 2), 224), Sample(True, (2, 2, 2, 2, 2, 1), 227), Sample(True, (2, 2, 2, 3, 4, 1), 239), Sample(True, (2, 3, 1, 1, 1, 1), 241), Sample(True, (2, 3, 1, 2, 1, 1), 249), Sample(False, (2, 3, 1, 2, 3, 1), 253), Sample(True, (2, 3, 1, 3, 1, 2), 258), Sample(False, (2, 3, 1, 3, 3, 1), 261), Sample(False, (2, 3, 1, 3, 4, 2), 264), Sample(False, (2, 3, 2, 1, 3, 2), 270), Sample(True, (2, 3, 2, 2, 1, 1), 273), Sample(True, (2, 3, 2, 2, 1, 2), 274), Sample(False, (2, 3, 2, 2, 2, 1), 275), Sample(False, (2, 3, 2, 3, 3, 2), 286), Sample(True, (3, 1, 1, 1, 1, 1), 289), Sample(True, (3, 1, 1, 1, 1, 2), 290), Sample(True, (3, 1, 1, 2, 1, 1), 297), Sample(False, (3, 1, 1, 2, 2, 2), 300), Sample(False, (3, 1, 1, 3, 2, 2), 308), Sample(True, (3, 1, 2, 1, 1, 1), 313), Sample(False, (3, 1, 2, 1, 2, 2), 316), Sample(False, (3, 1, 2, 2, 2, 2), 324), Sample(False, (3, 1, 2, 2, 3, 2), 326), Sample(False, (3, 1, 2, 3, 2, 2), 332), Sample(True, (3, 2, 1, 1, 1, 1), 337), Sample(False, (3, 2, 1, 1, 4, 2), 344), Sample(True, (3, 2, 1, 2, 1, 2), 346), Sample(False, (3, 2, 1, 2, 4, 2), 352), Sample(True, (3, 2, 2, 1, 1, 1), 361), Sample(True, (3, 2, 2, 1, 1, 2), 362), Sample(False, (3, 2, 2, 1, 3, 2), 366), Sample(True, (3, 2, 2, 3, 1, 1), 377), Sample(False, (3, 2, 2, 3, 2, 1), 379), Sample(False, (3, 2, 2, 3, 4, 1), 383), Sample(True, (3, 3, 1, 1, 1, 1), 385), Sample(True, (3, 3, 1, 1, 2, 1), 387), Sample(True, (3, 3, 1, 1, 4, 2), 392), Sample(True, (3, 3, 1, 2, 3, 2), 398), Sample(True, (3, 3, 1, 2, 4, 2), 400), Sample(True, (3, 3, 1, 3, 1, 2), 402), Sample(True, (3, 3, 1, 3, 2, 1), 403), Sample(True, (3, 3, 1, 3, 2, 2), 404), Sample(True, (3, 3, 1, 3, 4, 2), 408), Sample(True, (3, 3, 2, 1, 1, 1), 409), Sample(True, (3, 3, 2, 1, 3, 2), 414), Sample(True, (3, 3, 2, 1, 4, 1), 415), Sample(True, (3, 3, 2, 1, 4, 2), 416), Sample(True, (3, 3, 2, 3, 1, 2), 426), Sample(True, (3, 3, 2, 3, 2, 2), 428), Sample(True, (3, 3, 2, 3, 3, 2), 430), Sample(True, (3, 3, 2, 3, 4, 2), 432)) monk1test = ( Sample(True, (1, 1, 1, 1, 1, 1), 1), Sample(True, (1, 1, 1, 1, 1, 2), 2), Sample(True, (1, 1, 1, 1, 2, 1), 3), Sample(True, (1, 1, 1, 1, 2, 2), 4), Sample(True, (1, 1, 1, 1, 3, 1), 5), Sample(True, (1, 1, 1, 1, 3, 2), 6), Sample(True, (1, 1, 1, 1, 4, 1), 7), Sample(True, (1, 1, 1, 1, 4, 2), 8), Sample(True, (1, 1, 1, 2, 1, 1), 9), Sample(True, (1, 1, 1, 2, 1, 2), 10), Sample(True, (1, 1, 1, 2, 2, 1), 11), Sample(True, (1, 1, 1, 2, 2, 2), 12), Sample(True, (1, 1, 1, 2, 3, 1), 13), Sample(True, (1, 1, 1, 2, 3, 2), 14), Sample(True, (1, 1, 1, 2, 4, 1), 15), Sample(True, (1, 1, 1, 2, 4, 2), 16), Sample(True, (1, 1, 1, 3, 1, 1), 17), Sample(True, (1, 1, 1, 3, 1, 2), 18), Sample(True, (1, 1, 1, 3, 2, 1), 19), Sample(True, (1, 1, 1, 3, 2, 2), 20), Sample(True, (1, 1, 1, 3, 3, 1), 21), Sample(True, (1, 1, 1, 3, 3, 2), 22), Sample(True, (1, 1, 1, 3, 4, 1), 23), Sample(True, (1, 1, 1, 3, 4, 2), 24), Sample(True, (1, 1, 2, 1, 1, 1), 25), Sample(True, (1, 1, 2, 1, 1, 2), 26), Sample(True, (1, 1, 2, 1, 2, 1), 27), Sample(True, (1, 1, 2, 1, 2, 2), 28), Sample(True, (1, 1, 2, 1, 3, 1), 29), Sample(True, (1, 1, 2, 1, 3, 2), 30), Sample(True, (1, 1, 2, 1, 4, 1), 31), Sample(True, (1, 1, 2, 1, 4, 2), 32), Sample(True, (1, 1, 2, 2, 1, 1), 33), Sample(True, (1, 1, 2, 2, 1, 2), 34), Sample(True, (1, 1, 2, 2, 2, 1), 35), Sample(True, (1, 1, 2, 2, 2, 2), 36), Sample(True, (1, 1, 2, 2, 3, 1), 37), Sample(True, (1, 1, 2, 2, 3, 2), 38), Sample(True, (1, 1, 2, 2, 4, 1), 39), Sample(True, (1, 1, 2, 2, 4, 2), 40), Sample(True, (1, 1, 2, 3, 1, 1), 41), Sample(True, (1, 1, 2, 3, 1, 2), 42), Sample(True, (1, 1, 2, 3, 2, 1), 43), Sample(True, (1, 1, 2, 3, 2, 2), 44), Sample(True, (1, 1, 2, 3, 3, 1), 45), Sample(True, (1, 1, 2, 3, 3, 2), 46), Sample(True, (1, 1, 2, 3, 4, 1), 47), Sample(True, (1, 1, 2, 3, 4, 2), 48), Sample(True, (1, 2, 1, 1, 1, 1), 49), Sample(True, (1, 2, 1, 1, 1, 2), 50), Sample(False, (1, 2, 1, 1, 2, 1), 51), Sample(False, (1, 2, 1, 1, 2, 2), 52), Sample(False, (1, 2, 1, 1, 3, 1), 53), Sample(False, (1, 2, 1, 1, 3, 2), 54), Sample(False, (1, 2, 1, 1, 4, 1), 55), Sample(False, (1, 2, 1, 1, 4, 2), 56), Sample(True, (1, 2, 1, 2, 1, 1), 57), Sample(True, (1, 2, 1, 2, 1, 2), 58), Sample(False, (1, 2, 1, 2, 2, 1), 59), Sample(False, (1, 2, 1, 2, 2, 2), 60), Sample(False, (1, 2, 1, 2, 3, 1), 61), Sample(False, (1, 2, 1, 2, 3, 2), 62), Sample(False, (1, 2, 1, 2, 4, 1), 63), Sample(False, (1, 2, 1, 2, 4, 2), 64), Sample(True, (1, 2, 1, 3, 1, 1), 65), Sample(True, (1, 2, 1, 3, 1, 2), 66), Sample(False, (1, 2, 1, 3, 2, 1), 67), Sample(False, (1, 2, 1, 3, 2, 2), 68), Sample(False, (1, 2, 1, 3, 3, 1), 69), Sample(False, (1, 2, 1, 3, 3, 2), 70), Sample(False, (1, 2, 1, 3, 4, 1), 71), Sample(False, (1, 2, 1, 3, 4, 2), 72), Sample(True, (1, 2, 2, 1, 1, 1), 73), Sample(True, (1, 2, 2, 1, 1, 2), 74), Sample(False, (1, 2, 2, 1, 2, 1), 75), Sample(False, (1, 2, 2, 1, 2, 2), 76), Sample(False, (1, 2, 2, 1, 3, 1), 77), Sample(False, (1, 2, 2, 1, 3, 2), 78), Sample(False, (1, 2, 2, 1, 4, 1), 79), Sample(False, (1, 2, 2, 1, 4, 2), 80), Sample(True, (1, 2, 2, 2, 1, 1), 81), Sample(True, (1, 2, 2, 2, 1, 2), 82), Sample(False, (1, 2, 2, 2, 2, 1), 83), Sample(False, (1, 2, 2, 2, 2, 2), 84), Sample(False, (1, 2, 2, 2, 3, 1), 85), Sample(False, (1, 2, 2, 2, 3, 2), 86), Sample(False, (1, 2, 2, 2, 4, 1), 87), Sample(False, (1, 2, 2, 2, 4, 2), 88), Sample(True, (1, 2, 2, 3, 1, 1), 89), Sample(True, (1, 2, 2, 3, 1, 2), 90), Sample(False, (1, 2, 2, 3, 2, 1), 91), Sample(False, (1, 2, 2, 3, 2, 2), 92), Sample(False, (1, 2, 2, 3, 3, 1), 93), Sample(False, (1, 2, 2, 3, 3, 2), 94), Sample(False, (1, 2, 2, 3, 4, 1), 95), Sample(False, (1, 2, 2, 3, 4, 2), 96), Sample(True, (1, 3, 1, 1, 1, 1), 97), Sample(True, (1, 3, 1, 1, 1, 2), 98), Sample(False, (1, 3, 1, 1, 2, 1), 99), Sample(False, (1, 3, 1, 1, 2, 2), 100), Sample(False, (1, 3, 1, 1, 3, 1), 101), Sample(False, (1, 3, 1, 1, 3, 2), 102), Sample(False, (1, 3, 1, 1, 4, 1), 103), Sample(False, (1, 3, 1, 1, 4, 2), 104), Sample(True, (1, 3, 1, 2, 1, 1), 105), Sample(True, (1, 3, 1, 2, 1, 2), 106), Sample(False, (1, 3, 1, 2, 2, 1), 107), Sample(False, (1, 3, 1, 2, 2, 2), 108), Sample(False, (1, 3, 1, 2, 3, 1), 109), Sample(False, (1, 3, 1, 2, 3, 2), 110), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(False, (1, 3, 1, 2, 4, 2), 112), Sample(True, (1, 3, 1, 3, 1, 1), 113), Sample(True, (1, 3, 1, 3, 1, 2), 114), Sample(False, (1, 3, 1, 3, 2, 1), 115), Sample(False, (1, 3, 1, 3, 2, 2), 116), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(False, (1, 3, 1, 3, 3, 2), 118), Sample(False, (1, 3, 1, 3, 4, 1), 119), Sample(False, (1, 3, 1, 3, 4, 2), 120), Sample(True, (1, 3, 2, 1, 1, 1), 121), Sample(True, (1, 3, 2, 1, 1, 2), 122), Sample(False, (1, 3, 2, 1, 2, 1), 123), Sample(False, (1, 3, 2, 1, 2, 2), 124), Sample(False, (1, 3, 2, 1, 3, 1), 125), Sample(False, (1, 3, 2, 1, 3, 2), 126), Sample(False, (1, 3, 2, 1, 4, 1), 127), Sample(False, (1, 3, 2, 1, 4, 2), 128), Sample(True, (1, 3, 2, 2, 1, 1), 129), Sample(True, (1, 3, 2, 2, 1, 2), 130), Sample(False, (1, 3, 2, 2, 2, 1), 131), Sample(False, (1, 3, 2, 2, 2, 2), 132), Sample(False, (1, 3, 2, 2, 3, 1), 133), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(False, (1, 3, 2, 2, 4, 1), 135), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(True, (1, 3, 2, 3, 1, 1), 137), Sample(True, (1, 3, 2, 3, 1, 2), 138), Sample(False, (1, 3, 2, 3, 2, 1), 139), Sample(False, (1, 3, 2, 3, 2, 2), 140), Sample(False, (1, 3, 2, 3, 3, 1), 141), Sample(False, (1, 3, 2, 3, 3, 2), 142), Sample(False, (1, 3, 2, 3, 4, 1), 143), Sample(False, (1, 3, 2, 3, 4, 2), 144), Sample(True, (2, 1, 1, 1, 1, 1), 145), Sample(True, (2, 1, 1, 1, 1, 2), 146), Sample(False, (2, 1, 1, 1, 2, 1), 147), Sample(False, (2, 1, 1, 1, 2, 2), 148), Sample(False, (2, 1, 1, 1, 3, 1), 149), Sample(False, (2, 1, 1, 1, 3, 2), 150), Sample(False, (2, 1, 1, 1, 4, 1), 151), Sample(False, (2, 1, 1, 1, 4, 2), 152), Sample(True, (2, 1, 1, 2, 1, 1), 153), Sample(True, (2, 1, 1, 2, 1, 2), 154), Sample(False, (2, 1, 1, 2, 2, 1), 155), Sample(False, (2, 1, 1, 2, 2, 2), 156), Sample(False, (2, 1, 1, 2, 3, 1), 157), Sample(False, (2, 1, 1, 2, 3, 2), 158), Sample(False, (2, 1, 1, 2, 4, 1), 159), Sample(False, (2, 1, 1, 2, 4, 2), 160), Sample(True, (2, 1, 1, 3, 1, 1), 161), Sample(True, (2, 1, 1, 3, 1, 2), 162), Sample(False, (2, 1, 1, 3, 2, 1), 163), Sample(False, (2, 1, 1, 3, 2, 2), 164), Sample(False, (2, 1, 1, 3, 3, 1), 165), Sample(False, (2, 1, 1, 3, 3, 2), 166), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(False, (2, 1, 1, 3, 4, 2), 168), Sample(True, (2, 1, 2, 1, 1, 1), 169), Sample(True, (2, 1, 2, 1, 1, 2), 170), Sample(False, (2, 1, 2, 1, 2, 1), 171), Sample(False, (2, 1, 2, 1, 2, 2), 172), Sample(False, (2, 1, 2, 1, 3, 1), 173), Sample(False, (2, 1, 2, 1, 3, 2), 174), Sample(False, (2, 1, 2, 1, 4, 1), 175), Sample(False, (2, 1, 2, 1, 4, 2), 176), Sample(True, (2, 1, 2, 2, 1, 1), 177), Sample(True, (2, 1, 2, 2, 1, 2), 178), Sample(False, (2, 1, 2, 2, 2, 1), 179), Sample(False, (2, 1, 2, 2, 2, 2), 180), Sample(False, (2, 1, 2, 2, 3, 1), 181), Sample(False, (2, 1, 2, 2, 3, 2), 182), Sample(False, (2, 1, 2, 2, 4, 1), 183), Sample(False, (2, 1, 2, 2, 4, 2), 184), Sample(True, (2, 1, 2, 3, 1, 1), 185), Sample(True, (2, 1, 2, 3, 1, 2), 186), Sample(False, (2, 1, 2, 3, 2, 1), 187), Sample(False, (2, 1, 2, 3, 2, 2), 188), Sample(False, (2, 1, 2, 3, 3, 1), 189), Sample(False, (2, 1, 2, 3, 3, 2), 190), Sample(False, (2, 1, 2, 3, 4, 1), 191), Sample(False, (2, 1, 2, 3, 4, 2), 192), Sample(True, (2, 2, 1, 1, 1, 1), 193), Sample(True, (2, 2, 1, 1, 1, 2), 194), Sample(True, (2, 2, 1, 1, 2, 1), 195), Sample(True, (2, 2, 1, 1, 2, 2), 196), Sample(True, (2, 2, 1, 1, 3, 1), 197), Sample(True, (2, 2, 1, 1, 3, 2), 198), Sample(True, (2, 2, 1, 1, 4, 1), 199), Sample(True, (2, 2, 1, 1, 4, 2), 200), Sample(True, (2, 2, 1, 2, 1, 1), 201), Sample(True, (2, 2, 1, 2, 1, 2), 202), Sample(True, (2, 2, 1, 2, 2, 1), 203), Sample(True, (2, 2, 1, 2, 2, 2), 204), Sample(True, (2, 2, 1, 2, 3, 1), 205), Sample(True, (2, 2, 1, 2, 3, 2), 206), Sample(True, (2, 2, 1, 2, 4, 1), 207), Sample(True, (2, 2, 1, 2, 4, 2), 208), Sample(True, (2, 2, 1, 3, 1, 1), 209), Sample(True, (2, 2, 1, 3, 1, 2), 210), Sample(True, (2, 2, 1, 3, 2, 1), 211), Sample(True, (2, 2, 1, 3, 2, 2), 212), Sample(True, (2, 2, 1, 3, 3, 1), 213), Sample(True, (2, 2, 1, 3, 3, 2), 214), Sample(True, (2, 2, 1, 3, 4, 1), 215), Sample(True, (2, 2, 1, 3, 4, 2), 216), Sample(True, (2, 2, 2, 1, 1, 1), 217), Sample(True, (2, 2, 2, 1, 1, 2), 218), Sample(True, (2, 2, 2, 1, 2, 1), 219), Sample(True, (2, 2, 2, 1, 2, 2), 220), Sample(True, (2, 2, 2, 1, 3, 1), 221), Sample(True, (2, 2, 2, 1, 3, 2), 222), Sample(True, (2, 2, 2, 1, 4, 1), 223), Sample(True, (2, 2, 2, 1, 4, 2), 224), Sample(True, (2, 2, 2, 2, 1, 1), 225), Sample(True, (2, 2, 2, 2, 1, 2), 226), Sample(True, (2, 2, 2, 2, 2, 1), 227), Sample(True, (2, 2, 2, 2, 2, 2), 228), Sample(True, (2, 2, 2, 2, 3, 1), 229), Sample(True, (2, 2, 2, 2, 3, 2), 230), Sample(True, (2, 2, 2, 2, 4, 1), 231), Sample(True, (2, 2, 2, 2, 4, 2), 232), Sample(True, (2, 2, 2, 3, 1, 1), 233), Sample(True, (2, 2, 2, 3, 1, 2), 234), Sample(True, (2, 2, 2, 3, 2, 1), 235), Sample(True, (2, 2, 2, 3, 2, 2), 236), Sample(True, (2, 2, 2, 3, 3, 1), 237), Sample(True, (2, 2, 2, 3, 3, 2), 238), Sample(True, (2, 2, 2, 3, 4, 1), 239), Sample(True, (2, 2, 2, 3, 4, 2), 240), Sample(True, (2, 3, 1, 1, 1, 1), 241), Sample(True, (2, 3, 1, 1, 1, 2), 242), Sample(False, (2, 3, 1, 1, 2, 1), 243), Sample(False, (2, 3, 1, 1, 2, 2), 244), Sample(False, (2, 3, 1, 1, 3, 1), 245), Sample(False, (2, 3, 1, 1, 3, 2), 246), Sample(False, (2, 3, 1, 1, 4, 1), 247), Sample(False, (2, 3, 1, 1, 4, 2), 248), Sample(True, (2, 3, 1, 2, 1, 1), 249), Sample(True, (2, 3, 1, 2, 1, 2), 250), Sample(False, (2, 3, 1, 2, 2, 1), 251), Sample(False, (2, 3, 1, 2, 2, 2), 252), Sample(False, (2, 3, 1, 2, 3, 1), 253), Sample(False, (2, 3, 1, 2, 3, 2), 254), Sample(False, (2, 3, 1, 2, 4, 1), 255), Sample(False, (2, 3, 1, 2, 4, 2), 256), Sample(True, (2, 3, 1, 3, 1, 1), 257), Sample(True, (2, 3, 1, 3, 1, 2), 258), Sample(False, (2, 3, 1, 3, 2, 1), 259), Sample(False, (2, 3, 1, 3, 2, 2), 260), Sample(False, (2, 3, 1, 3, 3, 1), 261), Sample(False, (2, 3, 1, 3, 3, 2), 262), Sample(False, (2, 3, 1, 3, 4, 1), 263), Sample(False, (2, 3, 1, 3, 4, 2), 264), Sample(True, (2, 3, 2, 1, 1, 1), 265), Sample(True, (2, 3, 2, 1, 1, 2), 266), Sample(False, (2, 3, 2, 1, 2, 1), 267), Sample(False, (2, 3, 2, 1, 2, 2), 268), Sample(False, (2, 3, 2, 1, 3, 1), 269), Sample(False, (2, 3, 2, 1, 3, 2), 270), Sample(False, (2, 3, 2, 1, 4, 1), 271), Sample(False, (2, 3, 2, 1, 4, 2), 272), Sample(True, (2, 3, 2, 2, 1, 1), 273), Sample(True, (2, 3, 2, 2, 1, 2), 274), Sample(False, (2, 3, 2, 2, 2, 1), 275), Sample(False, (2, 3, 2, 2, 2, 2), 276), Sample(False, (2, 3, 2, 2, 3, 1), 277), Sample(False, (2, 3, 2, 2, 3, 2), 278), Sample(False, (2, 3, 2, 2, 4, 1), 279), Sample(False, (2, 3, 2, 2, 4, 2), 280), Sample(True, (2, 3, 2, 3, 1, 1), 281), Sample(True, (2, 3, 2, 3, 1, 2), 282), Sample(False, (2, 3, 2, 3, 2, 1), 283), Sample(False, (2, 3, 2, 3, 2, 2), 284), Sample(False, (2, 3, 2, 3, 3, 1), 285), Sample(False, (2, 3, 2, 3, 3, 2), 286), Sample(False, (2, 3, 2, 3, 4, 1), 287), Sample(False, (2, 3, 2, 3, 4, 2), 288), Sample(True, (3, 1, 1, 1, 1, 1), 289), Sample(True, (3, 1, 1, 1, 1, 2), 290), Sample(False, (3, 1, 1, 1, 2, 1), 291), Sample(False, (3, 1, 1, 1, 2, 2), 292), Sample(False, (3, 1, 1, 1, 3, 1), 293), Sample(False, (3, 1, 1, 1, 3, 2), 294), Sample(False, (3, 1, 1, 1, 4, 1), 295), Sample(False, (3, 1, 1, 1, 4, 2), 296), Sample(True, (3, 1, 1, 2, 1, 1), 297), Sample(True, (3, 1, 1, 2, 1, 2), 298), Sample(False, (3, 1, 1, 2, 2, 1), 299), Sample(False, (3, 1, 1, 2, 2, 2), 300), Sample(False, (3, 1, 1, 2, 3, 1), 301), Sample(False, (3, 1, 1, 2, 3, 2), 302), Sample(False, (3, 1, 1, 2, 4, 1), 303), Sample(False, (3, 1, 1, 2, 4, 2), 304), Sample(True, (3, 1, 1, 3, 1, 1), 305), Sample(True, (3, 1, 1, 3, 1, 2), 306), Sample(False, (3, 1, 1, 3, 2, 1), 307), Sample(False, (3, 1, 1, 3, 2, 2), 308), Sample(False, (3, 1, 1, 3, 3, 1), 309), Sample(False, (3, 1, 1, 3, 3, 2), 310), Sample(False, (3, 1, 1, 3, 4, 1), 311), Sample(False, (3, 1, 1, 3, 4, 2), 312), Sample(True, (3, 1, 2, 1, 1, 1), 313), Sample(True, (3, 1, 2, 1, 1, 2), 314), Sample(False, (3, 1, 2, 1, 2, 1), 315), Sample(False, (3, 1, 2, 1, 2, 2), 316), Sample(False, (3, 1, 2, 1, 3, 1), 317), Sample(False, (3, 1, 2, 1, 3, 2), 318), Sample(False, (3, 1, 2, 1, 4, 1), 319), Sample(False, (3, 1, 2, 1, 4, 2), 320), Sample(True, (3, 1, 2, 2, 1, 1), 321), Sample(True, (3, 1, 2, 2, 1, 2), 322), Sample(False, (3, 1, 2, 2, 2, 1), 323), Sample(False, (3, 1, 2, 2, 2, 2), 324), Sample(False, (3, 1, 2, 2, 3, 1), 325), Sample(False, (3, 1, 2, 2, 3, 2), 326), Sample(False, (3, 1, 2, 2, 4, 1), 327), Sample(False, (3, 1, 2, 2, 4, 2), 328), Sample(True, (3, 1, 2, 3, 1, 1), 329), Sample(True, (3, 1, 2, 3, 1, 2), 330), Sample(False, (3, 1, 2, 3, 2, 1), 331), Sample(False, (3, 1, 2, 3, 2, 2), 332), Sample(False, (3, 1, 2, 3, 3, 1), 333), Sample(False, (3, 1, 2, 3, 3, 2), 334), Sample(False, (3, 1, 2, 3, 4, 1), 335), Sample(False, (3, 1, 2, 3, 4, 2), 336), Sample(True, (3, 2, 1, 1, 1, 1), 337), Sample(True, (3, 2, 1, 1, 1, 2), 338), Sample(False, (3, 2, 1, 1, 2, 1), 339), Sample(False, (3, 2, 1, 1, 2, 2), 340), Sample(False, (3, 2, 1, 1, 3, 1), 341), Sample(False, (3, 2, 1, 1, 3, 2), 342), Sample(False, (3, 2, 1, 1, 4, 1), 343), Sample(False, (3, 2, 1, 1, 4, 2), 344), Sample(True, (3, 2, 1, 2, 1, 1), 345), Sample(True, (3, 2, 1, 2, 1, 2), 346), Sample(False, (3, 2, 1, 2, 2, 1), 347), Sample(False, (3, 2, 1, 2, 2, 2), 348), Sample(False, (3, 2, 1, 2, 3, 1), 349), Sample(False, (3, 2, 1, 2, 3, 2), 350), Sample(False, (3, 2, 1, 2, 4, 1), 351), Sample(False, (3, 2, 1, 2, 4, 2), 352), Sample(True, (3, 2, 1, 3, 1, 1), 353), Sample(True, (3, 2, 1, 3, 1, 2), 354), Sample(False, (3, 2, 1, 3, 2, 1), 355), Sample(False, (3, 2, 1, 3, 2, 2), 356), Sample(False, (3, 2, 1, 3, 3, 1), 357), Sample(False, (3, 2, 1, 3, 3, 2), 358), Sample(False, (3, 2, 1, 3, 4, 1), 359), Sample(False, (3, 2, 1, 3, 4, 2), 360), Sample(True, (3, 2, 2, 1, 1, 1), 361), Sample(True, (3, 2, 2, 1, 1, 2), 362), Sample(False, (3, 2, 2, 1, 2, 1), 363), Sample(False, (3, 2, 2, 1, 2, 2), 364), Sample(False, (3, 2, 2, 1, 3, 1), 365), Sample(False, (3, 2, 2, 1, 3, 2), 366), Sample(False, (3, 2, 2, 1, 4, 1), 367), Sample(False, (3, 2, 2, 1, 4, 2), 368), Sample(True, (3, 2, 2, 2, 1, 1), 369), Sample(True, (3, 2, 2, 2, 1, 2), 370), Sample(False, (3, 2, 2, 2, 2, 1), 371), Sample(False, (3, 2, 2, 2, 2, 2), 372), Sample(False, (3, 2, 2, 2, 3, 1), 373), Sample(False, (3, 2, 2, 2, 3, 2), 374), Sample(False, (3, 2, 2, 2, 4, 1), 375), Sample(False, (3, 2, 2, 2, 4, 2), 376), Sample(True, (3, 2, 2, 3, 1, 1), 377), Sample(True, (3, 2, 2, 3, 1, 2), 378), Sample(False, (3, 2, 2, 3, 2, 1), 379), Sample(False, (3, 2, 2, 3, 2, 2), 380), Sample(False, (3, 2, 2, 3, 3, 1), 381), Sample(False, (3, 2, 2, 3, 3, 2), 382), Sample(False, (3, 2, 2, 3, 4, 1), 383), Sample(False, (3, 2, 2, 3, 4, 2), 384), Sample(True, (3, 3, 1, 1, 1, 1), 385), Sample(True, (3, 3, 1, 1, 1, 2), 386), Sample(True, (3, 3, 1, 1, 2, 1), 387), Sample(True, (3, 3, 1, 1, 2, 2), 388), Sample(True, (3, 3, 1, 1, 3, 1), 389), Sample(True, (3, 3, 1, 1, 3, 2), 390), Sample(True, (3, 3, 1, 1, 4, 1), 391), Sample(True, (3, 3, 1, 1, 4, 2), 392), Sample(True, (3, 3, 1, 2, 1, 1), 393), Sample(True, (3, 3, 1, 2, 1, 2), 394), Sample(True, (3, 3, 1, 2, 2, 1), 395), Sample(True, (3, 3, 1, 2, 2, 2), 396), Sample(True, (3, 3, 1, 2, 3, 1), 397), Sample(True, (3, 3, 1, 2, 3, 2), 398), Sample(True, (3, 3, 1, 2, 4, 1), 399), Sample(True, (3, 3, 1, 2, 4, 2), 400), Sample(True, (3, 3, 1, 3, 1, 1), 401), Sample(True, (3, 3, 1, 3, 1, 2), 402), Sample(True, (3, 3, 1, 3, 2, 1), 403), Sample(True, (3, 3, 1, 3, 2, 2), 404), Sample(True, (3, 3, 1, 3, 3, 1), 405), Sample(True, (3, 3, 1, 3, 3, 2), 406), Sample(True, (3, 3, 1, 3, 4, 1), 407), Sample(True, (3, 3, 1, 3, 4, 2), 408), Sample(True, (3, 3, 2, 1, 1, 1), 409), Sample(True, (3, 3, 2, 1, 1, 2), 410), Sample(True, (3, 3, 2, 1, 2, 1), 411), Sample(True, (3, 3, 2, 1, 2, 2), 412), Sample(True, (3, 3, 2, 1, 3, 1), 413), Sample(True, (3, 3, 2, 1, 3, 2), 414), Sample(True, (3, 3, 2, 1, 4, 1), 415), Sample(True, (3, 3, 2, 1, 4, 2), 416), Sample(True, (3, 3, 2, 2, 1, 1), 417), Sample(True, (3, 3, 2, 2, 1, 2), 418), Sample(True, (3, 3, 2, 2, 2, 1), 419), Sample(True, (3, 3, 2, 2, 2, 2), 420), Sample(True, (3, 3, 2, 2, 3, 1), 421), Sample(True, (3, 3, 2, 2, 3, 2), 422), Sample(True, (3, 3, 2, 2, 4, 1), 423), Sample(True, (3, 3, 2, 2, 4, 2), 424), Sample(True, (3, 3, 2, 3, 1, 1), 425), Sample(True, (3, 3, 2, 3, 1, 2), 426), Sample(True, (3, 3, 2, 3, 2, 1), 427), Sample(True, (3, 3, 2, 3, 2, 2), 428), Sample(True, (3, 3, 2, 3, 3, 1), 429), Sample(True, (3, 3, 2, 3, 3, 2), 430), Sample(True, (3, 3, 2, 3, 4, 1), 431), Sample(True, (3, 3, 2, 3, 4, 2), 432)) monk2 = ( Sample(False, (1, 1, 1, 1, 2, 2), 4), Sample(False, (1, 1, 1, 1, 4, 1), 7), Sample(False, (1, 1, 1, 2, 1, 1), 9), Sample(False, (1, 1, 1, 2, 1, 2), 10), Sample(False, (1, 1, 1, 2, 2, 1), 11), Sample(False, (1, 1, 1, 2, 3, 1), 13), Sample(False, (1, 1, 1, 2, 4, 1), 15), Sample(False, (1, 1, 1, 3, 2, 1), 19), Sample(False, (1, 1, 1, 3, 4, 1), 23), Sample(False, (1, 1, 2, 1, 1, 1), 25), Sample(False, (1, 1, 2, 1, 1, 2), 26), Sample(False, (1, 1, 2, 2, 3, 1), 37), Sample(False, (1, 1, 2, 2, 4, 1), 39), Sample(True, (1, 1, 2, 2, 4, 2), 40), Sample(False, (1, 1, 2, 3, 1, 2), 42), Sample(True, (1, 1, 2, 3, 2, 2), 44), Sample(False, (1, 2, 1, 1, 1, 2), 50), Sample(False, (1, 2, 1, 2, 1, 2), 58), Sample(True, (1, 2, 1, 2, 2, 2), 60), Sample(False, (1, 2, 1, 2, 3, 1), 61), Sample(True, (1, 2, 1, 2, 3, 2), 62), Sample(False, (1, 2, 1, 2, 4, 1), 63), Sample(False, (1, 2, 1, 3, 1, 1), 65), Sample(False, (1, 2, 1, 3, 1, 2), 66), Sample(True, (1, 2, 1, 3, 2, 2), 68), Sample(False, (1, 2, 1, 3, 3, 1), 69), Sample(True, (1, 2, 1, 3, 3, 2), 70), Sample(False, (1, 2, 1, 3, 4, 1), 71), Sample(True, (1, 2, 1, 3, 4, 2), 72), Sample(False, (1, 2, 2, 1, 2, 1), 75), Sample(False, (1, 2, 2, 1, 4, 1), 79), Sample(True, (1, 2, 2, 2, 3, 1), 85), Sample(True, (1, 2, 2, 2, 4, 1), 87), Sample(False, (1, 2, 2, 3, 1, 1), 89), Sample(True, (1, 2, 2, 3, 1, 2), 90), Sample(True, (1, 2, 2, 3, 3, 1), 93), Sample(False, (1, 2, 2, 3, 3, 2), 94), Sample(True, (1, 2, 2, 3, 4, 1), 95), Sample(False, (1, 2, 2, 3, 4, 2), 96), Sample(False, (1, 3, 1, 1, 1, 2), 98), Sample(False, (1, 3, 1, 1, 2, 2), 100), Sample(False, (1, 3, 1, 1, 3, 1), 101), Sample(False, (1, 3, 1, 1, 3, 2), 102), Sample(False, (1, 3, 1, 2, 2, 1), 107), Sample(True, (1, 3, 1, 2, 2, 2), 108), Sample(True, (1, 3, 1, 2, 3, 2), 110), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(True, (1, 3, 1, 3, 2, 2), 116), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(True, (1, 3, 1, 3, 4, 2), 120), Sample(False, (1, 3, 2, 1, 3, 1), 125), Sample(True, (1, 3, 2, 1, 3, 2), 126), Sample(False, (1, 3, 2, 1, 4, 1), 127), Sample(True, (1, 3, 2, 2, 1, 2), 130), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(True, (1, 3, 2, 3, 2, 1), 139), Sample(False, (2, 1, 1, 1, 1, 1), 145), Sample(False, (2, 1, 1, 1, 2, 2), 148), Sample(False, (2, 1, 1, 1, 3, 1), 149), Sample(True, (2, 1, 1, 2, 2, 2), 156), Sample(False, (2, 1, 1, 3, 1, 2), 162), Sample(True, (2, 1, 1, 3, 2, 2), 164), Sample(True, (2, 1, 1, 3, 3, 2), 166), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(False, (2, 1, 2, 1, 1, 1), 169), Sample(True, (2, 1, 2, 1, 2, 2), 172), Sample(False, (2, 1, 2, 1, 4, 1), 175), Sample(True, (2, 1, 2, 2, 2, 1), 179), Sample(False, (2, 1, 2, 2, 4, 2), 184), Sample(False, (2, 1, 2, 3, 1, 1), 185), Sample(True, (2, 1, 2, 3, 1, 2), 186), Sample(False, (2, 1, 2, 3, 2, 2), 188), Sample(False, (2, 1, 2, 3, 3, 2), 190), Sample(False, (2, 1, 2, 3, 4, 2), 192), Sample(False, (2, 2, 1, 1, 3, 1), 197), Sample(True, (2, 2, 1, 1, 4, 2), 200), Sample(False, (2, 2, 1, 2, 1, 1), 201), Sample(True, (2, 2, 1, 2, 3, 1), 205), Sample(True, (2, 2, 1, 3, 3, 1), 213), Sample(False, (2, 2, 1, 3, 3, 2), 214), Sample(True, (2, 2, 1, 3, 4, 1), 215), Sample(False, (2, 2, 2, 1, 1, 1), 217), Sample(False, (2, 2, 2, 1, 2, 2), 220), Sample(False, (2, 2, 2, 1, 3, 2), 222), Sample(True, (2, 2, 2, 1, 4, 1), 223), Sample(False, (2, 2, 2, 1, 4, 2), 224), Sample(True, (2, 2, 2, 2, 1, 1), 225), Sample(False, (2, 2, 2, 2, 2, 2), 228), Sample(False, (2, 2, 2, 2, 3, 1), 229), Sample(True, (2, 2, 2, 3, 1, 1), 233), Sample(False, (2, 2, 2, 3, 2, 1), 235), Sample(False, (2, 2, 2, 3, 2, 2), 236), Sample(False, (2, 2, 2, 3, 4, 2), 240), Sample(False, (2, 3, 1, 1, 1, 1), 241), Sample(False, (2, 3, 1, 1, 1, 2), 242), Sample(True, (2, 3, 1, 1, 3, 2), 246), Sample(False, (2, 3, 1, 2, 1, 1), 249), Sample(True, (2, 3, 1, 2, 3, 1), 253), Sample(False, (2, 3, 1, 2, 3, 2), 254), Sample(False, (2, 3, 1, 2, 4, 2), 256), Sample(True, (2, 3, 1, 3, 1, 2), 258), Sample(True, (2, 3, 1, 3, 2, 1), 259), Sample(True, (2, 3, 1, 3, 4, 1), 263), Sample(True, (2, 3, 2, 1, 1, 2), 266), Sample(True, (2, 3, 2, 1, 2, 1), 267), Sample(True, (2, 3, 2, 1, 3, 1), 269), Sample(False, (2, 3, 2, 1, 4, 2), 272), Sample(True, (2, 3, 2, 2, 1, 1), 273), Sample(False, (2, 3, 2, 2, 2, 1), 275), Sample(False, (2, 3, 2, 2, 3, 2), 278), Sample(False, (2, 3, 2, 3, 3, 1), 285), Sample(False, (2, 3, 2, 3, 3, 2), 286), Sample(False, (2, 3, 2, 3, 4, 2), 288), Sample(False, (3, 1, 1, 1, 4, 1), 295), Sample(False, (3, 1, 1, 2, 1, 2), 298), Sample(True, (3, 1, 1, 2, 2, 2), 300), Sample(True, (3, 1, 1, 2, 3, 2), 302), Sample(False, (3, 1, 1, 2, 4, 1), 303), Sample(True, (3, 1, 1, 2, 4, 2), 304), Sample(False, (3, 1, 1, 3, 1, 1), 305), Sample(False, (3, 1, 1, 3, 1, 2), 306), Sample(True, (3, 1, 1, 3, 2, 2), 308), Sample(True, (3, 1, 1, 3, 3, 2), 310), Sample(False, (3, 1, 2, 1, 1, 1), 313), Sample(True, (3, 1, 2, 1, 2, 2), 316), Sample(False, (3, 1, 2, 1, 3, 1), 317), Sample(True, (3, 1, 2, 1, 3, 2), 318), Sample(False, (3, 1, 2, 1, 4, 1), 319), Sample(True, (3, 1, 2, 1, 4, 2), 320), Sample(True, (3, 1, 2, 2, 2, 1), 323), Sample(True, (3, 1, 2, 3, 1, 2), 330), Sample(True, (3, 1, 2, 3, 2, 1), 331), Sample(False, (3, 1, 2, 3, 2, 2), 332), Sample(False, (3, 1, 2, 3, 4, 2), 336), Sample(False, (3, 2, 1, 1, 1, 2), 338), Sample(True, (3, 2, 1, 1, 2, 2), 340), Sample(False, (3, 2, 1, 1, 3, 1), 341), Sample(True, (3, 2, 1, 1, 3, 2), 342), Sample(True, (3, 2, 1, 2, 1, 2), 346), Sample(True, (3, 2, 1, 2, 2, 1), 347), Sample(False, (3, 2, 1, 3, 1, 1), 353), Sample(True, (3, 2, 1, 3, 2, 1), 355), Sample(True, (3, 2, 1, 3, 3, 1), 357), Sample(False, (3, 2, 1, 3, 3, 2), 358), Sample(False, (3, 2, 2, 1, 1, 1), 361), Sample(False, (3, 2, 2, 1, 2, 2), 364), Sample(True, (3, 2, 2, 1, 3, 1), 365), Sample(False, (3, 2, 2, 1, 3, 2), 366), Sample(True, (3, 2, 2, 2, 1, 1), 369), Sample(False, (3, 2, 2, 2, 2, 1), 371), Sample(False, (3, 2, 2, 2, 2, 2), 372), Sample(False, (3, 2, 2, 2, 3, 2), 374), Sample(True, (3, 2, 2, 3, 1, 1), 377), Sample(False, (3, 2, 2, 3, 3, 2), 382), Sample(False, (3, 2, 2, 3, 4, 2), 384), Sample(False, (3, 3, 1, 1, 1, 1), 385), Sample(False, (3, 3, 1, 1, 2, 1), 387), Sample(False, (3, 3, 1, 1, 3, 1), 389), Sample(True, (3, 3, 1, 1, 3, 2), 390), Sample(False, (3, 3, 1, 2, 3, 2), 398), Sample(False, (3, 3, 2, 1, 1, 1), 409), Sample(True, (3, 3, 2, 2, 1, 1), 417), Sample(False, (3, 3, 2, 2, 2, 1), 419), Sample(False, (3, 3, 2, 2, 3, 1), 421), Sample(False, (3, 3, 2, 2, 3, 2), 422), Sample(True, (3, 3, 2, 3, 1, 1), 425), Sample(False, (3, 3, 2, 3, 2, 1), 427), Sample(False, (3, 3, 2, 3, 4, 2), 432)) monk2test = ( Sample(False, (1, 1, 1, 1, 1, 1), 1), Sample(False, (1, 1, 1, 1, 1, 2), 2), Sample(False, (1, 1, 1, 1, 2, 1), 3), Sample(False, (1, 1, 1, 1, 2, 2), 4), Sample(False, (1, 1, 1, 1, 3, 1), 5), Sample(False, (1, 1, 1, 1, 3, 2), 6), Sample(False, (1, 1, 1, 1, 4, 1), 7), Sample(False, (1, 1, 1, 1, 4, 2), 8), Sample(False, (1, 1, 1, 2, 1, 1), 9), Sample(False, (1, 1, 1, 2, 1, 2), 10), Sample(False, (1, 1, 1, 2, 2, 1), 11), Sample(False, (1, 1, 1, 2, 2, 2), 12), Sample(False, (1, 1, 1, 2, 3, 1), 13), Sample(False, (1, 1, 1, 2, 3, 2), 14), Sample(False, (1, 1, 1, 2, 4, 1), 15), Sample(False, (1, 1, 1, 2, 4, 2), 16), Sample(False, (1, 1, 1, 3, 1, 1), 17), Sample(False, (1, 1, 1, 3, 1, 2), 18), Sample(False, (1, 1, 1, 3, 2, 1), 19), Sample(False, (1, 1, 1, 3, 2, 2), 20), Sample(False, (1, 1, 1, 3, 3, 1), 21), Sample(False, (1, 1, 1, 3, 3, 2), 22), Sample(False, (1, 1, 1, 3, 4, 1), 23), Sample(False, (1, 1, 1, 3, 4, 2), 24), Sample(False, (1, 1, 2, 1, 1, 1), 25), Sample(False, (1, 1, 2, 1, 1, 2), 26), Sample(False, (1, 1, 2, 1, 2, 1), 27), Sample(False, (1, 1, 2, 1, 2, 2), 28), Sample(False, (1, 1, 2, 1, 3, 1), 29), Sample(False, (1, 1, 2, 1, 3, 2), 30), Sample(False, (1, 1, 2, 1, 4, 1), 31), Sample(False, (1, 1, 2, 1, 4, 2), 32), Sample(False, (1, 1, 2, 2, 1, 1), 33), Sample(False, (1, 1, 2, 2, 1, 2), 34), Sample(False, (1, 1, 2, 2, 2, 1), 35), Sample(True, (1, 1, 2, 2, 2, 2), 36), Sample(False, (1, 1, 2, 2, 3, 1), 37), Sample(True, (1, 1, 2, 2, 3, 2), 38), Sample(False, (1, 1, 2, 2, 4, 1), 39), Sample(True, (1, 1, 2, 2, 4, 2), 40), Sample(False, (1, 1, 2, 3, 1, 1), 41), Sample(False, (1, 1, 2, 3, 1, 2), 42), Sample(False, (1, 1, 2, 3, 2, 1), 43), Sample(True, (1, 1, 2, 3, 2, 2), 44), Sample(False, (1, 1, 2, 3, 3, 1), 45), Sample(True, (1, 1, 2, 3, 3, 2), 46), Sample(False, (1, 1, 2, 3, 4, 1), 47), Sample(True, (1, 1, 2, 3, 4, 2), 48), Sample(False, (1, 2, 1, 1, 1, 1), 49), Sample(False, (1, 2, 1, 1, 1, 2), 50), Sample(False, (1, 2, 1, 1, 2, 1), 51), Sample(False, (1, 2, 1, 1, 2, 2), 52), Sample(False, (1, 2, 1, 1, 3, 1), 53), Sample(False, (1, 2, 1, 1, 3, 2), 54), Sample(False, (1, 2, 1, 1, 4, 1), 55), Sample(False, (1, 2, 1, 1, 4, 2), 56), Sample(False, (1, 2, 1, 2, 1, 1), 57), Sample(False, (1, 2, 1, 2, 1, 2), 58), Sample(False, (1, 2, 1, 2, 2, 1), 59), Sample(True, (1, 2, 1, 2, 2, 2), 60), Sample(False, (1, 2, 1, 2, 3, 1), 61), Sample(True, (1, 2, 1, 2, 3, 2), 62), Sample(False, (1, 2, 1, 2, 4, 1), 63), Sample(True, (1, 2, 1, 2, 4, 2), 64), Sample(False, (1, 2, 1, 3, 1, 1), 65), Sample(False, (1, 2, 1, 3, 1, 2), 66), Sample(False, (1, 2, 1, 3, 2, 1), 67), Sample(True, (1, 2, 1, 3, 2, 2), 68), Sample(False, (1, 2, 1, 3, 3, 1), 69), Sample(True, (1, 2, 1, 3, 3, 2), 70), Sample(False, (1, 2, 1, 3, 4, 1), 71), Sample(True, (1, 2, 1, 3, 4, 2), 72), Sample(False, (1, 2, 2, 1, 1, 1), 73), Sample(False, (1, 2, 2, 1, 1, 2), 74), Sample(False, (1, 2, 2, 1, 2, 1), 75), Sample(True, (1, 2, 2, 1, 2, 2), 76), Sample(False, (1, 2, 2, 1, 3, 1), 77), Sample(True, (1, 2, 2, 1, 3, 2), 78), Sample(False, (1, 2, 2, 1, 4, 1), 79), Sample(True, (1, 2, 2, 1, 4, 2), 80), Sample(False, (1, 2, 2, 2, 1, 1), 81), Sample(True, (1, 2, 2, 2, 1, 2), 82), Sample(True, (1, 2, 2, 2, 2, 1), 83), Sample(False, (1, 2, 2, 2, 2, 2), 84), Sample(True, (1, 2, 2, 2, 3, 1), 85), Sample(False, (1, 2, 2, 2, 3, 2), 86), Sample(True, (1, 2, 2, 2, 4, 1), 87), Sample(False, (1, 2, 2, 2, 4, 2), 88), Sample(False, (1, 2, 2, 3, 1, 1), 89), Sample(True, (1, 2, 2, 3, 1, 2), 90), Sample(True, (1, 2, 2, 3, 2, 1), 91), Sample(False, (1, 2, 2, 3, 2, 2), 92), Sample(True, (1, 2, 2, 3, 3, 1), 93), Sample(False, (1, 2, 2, 3, 3, 2), 94), Sample(True, (1, 2, 2, 3, 4, 1), 95), Sample(False, (1, 2, 2, 3, 4, 2), 96), Sample(False, (1, 3, 1, 1, 1, 1), 97), Sample(False, (1, 3, 1, 1, 1, 2), 98), Sample(False, (1, 3, 1, 1, 2, 1), 99), Sample(False, (1, 3, 1, 1, 2, 2), 100), Sample(False, (1, 3, 1, 1, 3, 1), 101), Sample(False, (1, 3, 1, 1, 3, 2), 102), Sample(False, (1, 3, 1, 1, 4, 1), 103), Sample(False, (1, 3, 1, 1, 4, 2), 104), Sample(False, (1, 3, 1, 2, 1, 1), 105), Sample(False, (1, 3, 1, 2, 1, 2), 106), Sample(False, (1, 3, 1, 2, 2, 1), 107), Sample(True, (1, 3, 1, 2, 2, 2), 108), Sample(False, (1, 3, 1, 2, 3, 1), 109), Sample(True, (1, 3, 1, 2, 3, 2), 110), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(True, (1, 3, 1, 2, 4, 2), 112), Sample(False, (1, 3, 1, 3, 1, 1), 113), Sample(False, (1, 3, 1, 3, 1, 2), 114), Sample(False, (1, 3, 1, 3, 2, 1), 115), Sample(True, (1, 3, 1, 3, 2, 2), 116), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(True, (1, 3, 1, 3, 3, 2), 118), Sample(False, (1, 3, 1, 3, 4, 1), 119), Sample(True, (1, 3, 1, 3, 4, 2), 120), Sample(False, (1, 3, 2, 1, 1, 1), 121), Sample(False, (1, 3, 2, 1, 1, 2), 122), Sample(False, (1, 3, 2, 1, 2, 1), 123), Sample(True, (1, 3, 2, 1, 2, 2), 124), Sample(False, (1, 3, 2, 1, 3, 1), 125), Sample(True, (1, 3, 2, 1, 3, 2), 126), Sample(False, (1, 3, 2, 1, 4, 1), 127), Sample(True, (1, 3, 2, 1, 4, 2), 128), Sample(False, (1, 3, 2, 2, 1, 1), 129), Sample(True, (1, 3, 2, 2, 1, 2), 130), Sample(True, (1, 3, 2, 2, 2, 1), 131), Sample(False, (1, 3, 2, 2, 2, 2), 132), Sample(True, (1, 3, 2, 2, 3, 1), 133), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(True, (1, 3, 2, 2, 4, 1), 135), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(False, (1, 3, 2, 3, 1, 1), 137), Sample(True, (1, 3, 2, 3, 1, 2), 138), Sample(True, (1, 3, 2, 3, 2, 1), 139), Sample(False, (1, 3, 2, 3, 2, 2), 140), Sample(True, (1, 3, 2, 3, 3, 1), 141), Sample(False, (1, 3, 2, 3, 3, 2), 142), Sample(True, (1, 3, 2, 3, 4, 1), 143), Sample(False, (1, 3, 2, 3, 4, 2), 144), Sample(False, (2, 1, 1, 1, 1, 1), 145), Sample(False, (2, 1, 1, 1, 1, 2), 146), Sample(False, (2, 1, 1, 1, 2, 1), 147), Sample(False, (2, 1, 1, 1, 2, 2), 148), Sample(False, (2, 1, 1, 1, 3, 1), 149), Sample(False, (2, 1, 1, 1, 3, 2), 150), Sample(False, (2, 1, 1, 1, 4, 1), 151), Sample(False, (2, 1, 1, 1, 4, 2), 152), Sample(False, (2, 1, 1, 2, 1, 1), 153), Sample(False, (2, 1, 1, 2, 1, 2), 154), Sample(False, (2, 1, 1, 2, 2, 1), 155), Sample(True, (2, 1, 1, 2, 2, 2), 156), Sample(False, (2, 1, 1, 2, 3, 1), 157), Sample(True, (2, 1, 1, 2, 3, 2), 158), Sample(False, (2, 1, 1, 2, 4, 1), 159), Sample(True, (2, 1, 1, 2, 4, 2), 160), Sample(False, (2, 1, 1, 3, 1, 1), 161), Sample(False, (2, 1, 1, 3, 1, 2), 162), Sample(False, (2, 1, 1, 3, 2, 1), 163), Sample(True, (2, 1, 1, 3, 2, 2), 164), Sample(False, (2, 1, 1, 3, 3, 1), 165), Sample(True, (2, 1, 1, 3, 3, 2), 166), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(True, (2, 1, 1, 3, 4, 2), 168), Sample(False, (2, 1, 2, 1, 1, 1), 169), Sample(False, (2, 1, 2, 1, 1, 2), 170), Sample(False, (2, 1, 2, 1, 2, 1), 171), Sample(True, (2, 1, 2, 1, 2, 2), 172), Sample(False, (2, 1, 2, 1, 3, 1), 173), Sample(True, (2, 1, 2, 1, 3, 2), 174), Sample(False, (2, 1, 2, 1, 4, 1), 175), Sample(True, (2, 1, 2, 1, 4, 2), 176), Sample(False, (2, 1, 2, 2, 1, 1), 177), Sample(True, (2, 1, 2, 2, 1, 2), 178), Sample(True, (2, 1, 2, 2, 2, 1), 179), Sample(False, (2, 1, 2, 2, 2, 2), 180), Sample(True, (2, 1, 2, 2, 3, 1), 181), Sample(False, (2, 1, 2, 2, 3, 2), 182), Sample(True, (2, 1, 2, 2, 4, 1), 183), Sample(False, (2, 1, 2, 2, 4, 2), 184), Sample(False, (2, 1, 2, 3, 1, 1), 185), Sample(True, (2, 1, 2, 3, 1, 2), 186), Sample(True, (2, 1, 2, 3, 2, 1), 187), Sample(False, (2, 1, 2, 3, 2, 2), 188), Sample(True, (2, 1, 2, 3, 3, 1), 189), Sample(False, (2, 1, 2, 3, 3, 2), 190), Sample(True, (2, 1, 2, 3, 4, 1), 191), Sample(False, (2, 1, 2, 3, 4, 2), 192), Sample(False, (2, 2, 1, 1, 1, 1), 193), Sample(False, (2, 2, 1, 1, 1, 2), 194), Sample(False, (2, 2, 1, 1, 2, 1), 195), Sample(True, (2, 2, 1, 1, 2, 2), 196), Sample(False, (2, 2, 1, 1, 3, 1), 197), Sample(True, (2, 2, 1, 1, 3, 2), 198), Sample(False, (2, 2, 1, 1, 4, 1), 199), Sample(True, (2, 2, 1, 1, 4, 2), 200), Sample(False, (2, 2, 1, 2, 1, 1), 201), Sample(True, (2, 2, 1, 2, 1, 2), 202), Sample(True, (2, 2, 1, 2, 2, 1), 203), Sample(False, (2, 2, 1, 2, 2, 2), 204), Sample(True, (2, 2, 1, 2, 3, 1), 205), Sample(False, (2, 2, 1, 2, 3, 2), 206), Sample(True, (2, 2, 1, 2, 4, 1), 207), Sample(False, (2, 2, 1, 2, 4, 2), 208), Sample(False, (2, 2, 1, 3, 1, 1), 209), Sample(True, (2, 2, 1, 3, 1, 2), 210), Sample(True, (2, 2, 1, 3, 2, 1), 211), Sample(False, (2, 2, 1, 3, 2, 2), 212), Sample(True, (2, 2, 1, 3, 3, 1), 213), Sample(False, (2, 2, 1, 3, 3, 2), 214), Sample(True, (2, 2, 1, 3, 4, 1), 215), Sample(False, (2, 2, 1, 3, 4, 2), 216), Sample(False, (2, 2, 2, 1, 1, 1), 217), Sample(True, (2, 2, 2, 1, 1, 2), 218), Sample(True, (2, 2, 2, 1, 2, 1), 219), Sample(False, (2, 2, 2, 1, 2, 2), 220), Sample(True, (2, 2, 2, 1, 3, 1), 221), Sample(False, (2, 2, 2, 1, 3, 2), 222), Sample(True, (2, 2, 2, 1, 4, 1), 223), Sample(False, (2, 2, 2, 1, 4, 2), 224), Sample(True, (2, 2, 2, 2, 1, 1), 225), Sample(False, (2, 2, 2, 2, 1, 2), 226), Sample(False, (2, 2, 2, 2, 2, 1), 227), Sample(False, (2, 2, 2, 2, 2, 2), 228), Sample(False, (2, 2, 2, 2, 3, 1), 229), Sample(False, (2, 2, 2, 2, 3, 2), 230), Sample(False, (2, 2, 2, 2, 4, 1), 231), Sample(False, (2, 2, 2, 2, 4, 2), 232), Sample(True, (2, 2, 2, 3, 1, 1), 233), Sample(False, (2, 2, 2, 3, 1, 2), 234), Sample(False, (2, 2, 2, 3, 2, 1), 235), Sample(False, (2, 2, 2, 3, 2, 2), 236), Sample(False, (2, 2, 2, 3, 3, 1), 237), Sample(False, (2, 2, 2, 3, 3, 2), 238), Sample(False, (2, 2, 2, 3, 4, 1), 239), Sample(False, (2, 2, 2, 3, 4, 2), 240), Sample(False, (2, 3, 1, 1, 1, 1), 241), Sample(False, (2, 3, 1, 1, 1, 2), 242), Sample(False, (2, 3, 1, 1, 2, 1), 243), Sample(True, (2, 3, 1, 1, 2, 2), 244), Sample(False, (2, 3, 1, 1, 3, 1), 245), Sample(True, (2, 3, 1, 1, 3, 2), 246), Sample(False, (2, 3, 1, 1, 4, 1), 247), Sample(True, (2, 3, 1, 1, 4, 2), 248), Sample(False, (2, 3, 1, 2, 1, 1), 249), Sample(True, (2, 3, 1, 2, 1, 2), 250), Sample(True, (2, 3, 1, 2, 2, 1), 251), Sample(False, (2, 3, 1, 2, 2, 2), 252), Sample(True, (2, 3, 1, 2, 3, 1), 253), Sample(False, (2, 3, 1, 2, 3, 2), 254), Sample(True, (2, 3, 1, 2, 4, 1), 255), Sample(False, (2, 3, 1, 2, 4, 2), 256), Sample(False, (2, 3, 1, 3, 1, 1), 257), Sample(True, (2, 3, 1, 3, 1, 2), 258), Sample(True, (2, 3, 1, 3, 2, 1), 259), Sample(False, (2, 3, 1, 3, 2, 2), 260), Sample(True, (2, 3, 1, 3, 3, 1), 261), Sample(False, (2, 3, 1, 3, 3, 2), 262), Sample(True, (2, 3, 1, 3, 4, 1), 263), Sample(False, (2, 3, 1, 3, 4, 2), 264), Sample(False, (2, 3, 2, 1, 1, 1), 265), Sample(True, (2, 3, 2, 1, 1, 2), 266), Sample(True, (2, 3, 2, 1, 2, 1), 267), Sample(False, (2, 3, 2, 1, 2, 2), 268), Sample(True, (2, 3, 2, 1, 3, 1), 269), Sample(False, (2, 3, 2, 1, 3, 2), 270), Sample(True, (2, 3, 2, 1, 4, 1), 271), Sample(False, (2, 3, 2, 1, 4, 2), 272), Sample(True, (2, 3, 2, 2, 1, 1), 273), Sample(False, (2, 3, 2, 2, 1, 2), 274), Sample(False, (2, 3, 2, 2, 2, 1), 275), Sample(False, (2, 3, 2, 2, 2, 2), 276), Sample(False, (2, 3, 2, 2, 3, 1), 277), Sample(False, (2, 3, 2, 2, 3, 2), 278), Sample(False, (2, 3, 2, 2, 4, 1), 279), Sample(False, (2, 3, 2, 2, 4, 2), 280), Sample(True, (2, 3, 2, 3, 1, 1), 281), Sample(False, (2, 3, 2, 3, 1, 2), 282), Sample(False, (2, 3, 2, 3, 2, 1), 283), Sample(False, (2, 3, 2, 3, 2, 2), 284), Sample(False, (2, 3, 2, 3, 3, 1), 285), Sample(False, (2, 3, 2, 3, 3, 2), 286), Sample(False, (2, 3, 2, 3, 4, 1), 287), Sample(False, (2, 3, 2, 3, 4, 2), 288), Sample(False, (3, 1, 1, 1, 1, 1), 289), Sample(False, (3, 1, 1, 1, 1, 2), 290), Sample(False, (3, 1, 1, 1, 2, 1), 291), Sample(False, (3, 1, 1, 1, 2, 2), 292), Sample(False, (3, 1, 1, 1, 3, 1), 293), Sample(False, (3, 1, 1, 1, 3, 2), 294), Sample(False, (3, 1, 1, 1, 4, 1), 295), Sample(False, (3, 1, 1, 1, 4, 2), 296), Sample(False, (3, 1, 1, 2, 1, 1), 297), Sample(False, (3, 1, 1, 2, 1, 2), 298), Sample(False, (3, 1, 1, 2, 2, 1), 299), Sample(True, (3, 1, 1, 2, 2, 2), 300), Sample(False, (3, 1, 1, 2, 3, 1), 301), Sample(True, (3, 1, 1, 2, 3, 2), 302), Sample(False, (3, 1, 1, 2, 4, 1), 303), Sample(True, (3, 1, 1, 2, 4, 2), 304), Sample(False, (3, 1, 1, 3, 1, 1), 305), Sample(False, (3, 1, 1, 3, 1, 2), 306), Sample(False, (3, 1, 1, 3, 2, 1), 307), Sample(True, (3, 1, 1, 3, 2, 2), 308), Sample(False, (3, 1, 1, 3, 3, 1), 309), Sample(True, (3, 1, 1, 3, 3, 2), 310), Sample(False, (3, 1, 1, 3, 4, 1), 311), Sample(True, (3, 1, 1, 3, 4, 2), 312), Sample(False, (3, 1, 2, 1, 1, 1), 313), Sample(False, (3, 1, 2, 1, 1, 2), 314), Sample(False, (3, 1, 2, 1, 2, 1), 315), Sample(True, (3, 1, 2, 1, 2, 2), 316), Sample(False, (3, 1, 2, 1, 3, 1), 317), Sample(True, (3, 1, 2, 1, 3, 2), 318), Sample(False, (3, 1, 2, 1, 4, 1), 319), Sample(True, (3, 1, 2, 1, 4, 2), 320), Sample(False, (3, 1, 2, 2, 1, 1), 321), Sample(True, (3, 1, 2, 2, 1, 2), 322), Sample(True, (3, 1, 2, 2, 2, 1), 323), Sample(False, (3, 1, 2, 2, 2, 2), 324), Sample(True, (3, 1, 2, 2, 3, 1), 325), Sample(False, (3, 1, 2, 2, 3, 2), 326), Sample(True, (3, 1, 2, 2, 4, 1), 327), Sample(False, (3, 1, 2, 2, 4, 2), 328), Sample(False, (3, 1, 2, 3, 1, 1), 329), Sample(True, (3, 1, 2, 3, 1, 2), 330), Sample(True, (3, 1, 2, 3, 2, 1), 331), Sample(False, (3, 1, 2, 3, 2, 2), 332), Sample(True, (3, 1, 2, 3, 3, 1), 333), Sample(False, (3, 1, 2, 3, 3, 2), 334), Sample(True, (3, 1, 2, 3, 4, 1), 335), Sample(False, (3, 1, 2, 3, 4, 2), 336), Sample(False, (3, 2, 1, 1, 1, 1), 337), Sample(False, (3, 2, 1, 1, 1, 2), 338), Sample(False, (3, 2, 1, 1, 2, 1), 339), Sample(True, (3, 2, 1, 1, 2, 2), 340), Sample(False, (3, 2, 1, 1, 3, 1), 341), Sample(True, (3, 2, 1, 1, 3, 2), 342), Sample(False, (3, 2, 1, 1, 4, 1), 343), Sample(True, (3, 2, 1, 1, 4, 2), 344), Sample(False, (3, 2, 1, 2, 1, 1), 345), Sample(True, (3, 2, 1, 2, 1, 2), 346), Sample(True, (3, 2, 1, 2, 2, 1), 347), Sample(False, (3, 2, 1, 2, 2, 2), 348), Sample(True, (3, 2, 1, 2, 3, 1), 349), Sample(False, (3, 2, 1, 2, 3, 2), 350), Sample(True, (3, 2, 1, 2, 4, 1), 351), Sample(False, (3, 2, 1, 2, 4, 2), 352), Sample(False, (3, 2, 1, 3, 1, 1), 353), Sample(True, (3, 2, 1, 3, 1, 2), 354), Sample(True, (3, 2, 1, 3, 2, 1), 355), Sample(False, (3, 2, 1, 3, 2, 2), 356), Sample(True, (3, 2, 1, 3, 3, 1), 357), Sample(False, (3, 2, 1, 3, 3, 2), 358), Sample(True, (3, 2, 1, 3, 4, 1), 359), Sample(False, (3, 2, 1, 3, 4, 2), 360), Sample(False, (3, 2, 2, 1, 1, 1), 361), Sample(True, (3, 2, 2, 1, 1, 2), 362), Sample(True, (3, 2, 2, 1, 2, 1), 363), Sample(False, (3, 2, 2, 1, 2, 2), 364), Sample(True, (3, 2, 2, 1, 3, 1), 365), Sample(False, (3, 2, 2, 1, 3, 2), 366), Sample(True, (3, 2, 2, 1, 4, 1), 367), Sample(False, (3, 2, 2, 1, 4, 2), 368), Sample(True, (3, 2, 2, 2, 1, 1), 369), Sample(False, (3, 2, 2, 2, 1, 2), 370), Sample(False, (3, 2, 2, 2, 2, 1), 371), Sample(False, (3, 2, 2, 2, 2, 2), 372), Sample(False, (3, 2, 2, 2, 3, 1), 373), Sample(False, (3, 2, 2, 2, 3, 2), 374), Sample(False, (3, 2, 2, 2, 4, 1), 375), Sample(False, (3, 2, 2, 2, 4, 2), 376), Sample(True, (3, 2, 2, 3, 1, 1), 377), Sample(False, (3, 2, 2, 3, 1, 2), 378), Sample(False, (3, 2, 2, 3, 2, 1), 379), Sample(False, (3, 2, 2, 3, 2, 2), 380), Sample(False, (3, 2, 2, 3, 3, 1), 381), Sample(False, (3, 2, 2, 3, 3, 2), 382), Sample(False, (3, 2, 2, 3, 4, 1), 383), Sample(False, (3, 2, 2, 3, 4, 2), 384), Sample(False, (3, 3, 1, 1, 1, 1), 385), Sample(False, (3, 3, 1, 1, 1, 2), 386), Sample(False, (3, 3, 1, 1, 2, 1), 387), Sample(True, (3, 3, 1, 1, 2, 2), 388), Sample(False, (3, 3, 1, 1, 3, 1), 389), Sample(True, (3, 3, 1, 1, 3, 2), 390), Sample(False, (3, 3, 1, 1, 4, 1), 391), Sample(True, (3, 3, 1, 1, 4, 2), 392), Sample(False, (3, 3, 1, 2, 1, 1), 393), Sample(True, (3, 3, 1, 2, 1, 2), 394), Sample(True, (3, 3, 1, 2, 2, 1), 395), Sample(False, (3, 3, 1, 2, 2, 2), 396), Sample(True, (3, 3, 1, 2, 3, 1), 397), Sample(False, (3, 3, 1, 2, 3, 2), 398), Sample(True, (3, 3, 1, 2, 4, 1), 399), Sample(False, (3, 3, 1, 2, 4, 2), 400), Sample(False, (3, 3, 1, 3, 1, 1), 401), Sample(True, (3, 3, 1, 3, 1, 2), 402), Sample(True, (3, 3, 1, 3, 2, 1), 403), Sample(False, (3, 3, 1, 3, 2, 2), 404), Sample(True, (3, 3, 1, 3, 3, 1), 405), Sample(False, (3, 3, 1, 3, 3, 2), 406), Sample(True, (3, 3, 1, 3, 4, 1), 407), Sample(False, (3, 3, 1, 3, 4, 2), 408), Sample(False, (3, 3, 2, 1, 1, 1), 409), Sample(True, (3, 3, 2, 1, 1, 2), 410), Sample(True, (3, 3, 2, 1, 2, 1), 411), Sample(False, (3, 3, 2, 1, 2, 2), 412), Sample(True, (3, 3, 2, 1, 3, 1), 413), Sample(False, (3, 3, 2, 1, 3, 2), 414), Sample(True, (3, 3, 2, 1, 4, 1), 415), Sample(False, (3, 3, 2, 1, 4, 2), 416), Sample(True, (3, 3, 2, 2, 1, 1), 417), Sample(False, (3, 3, 2, 2, 1, 2), 418), Sample(False, (3, 3, 2, 2, 2, 1), 419), Sample(False, (3, 3, 2, 2, 2, 2), 420), Sample(False, (3, 3, 2, 2, 3, 1), 421), Sample(False, (3, 3, 2, 2, 3, 2), 422), Sample(False, (3, 3, 2, 2, 4, 1), 423), Sample(False, (3, 3, 2, 2, 4, 2), 424), Sample(True, (3, 3, 2, 3, 1, 1), 425), Sample(False, (3, 3, 2, 3, 1, 2), 426), Sample(False, (3, 3, 2, 3, 2, 1), 427), Sample(False, (3, 3, 2, 3, 2, 2), 428), Sample(False, (3, 3, 2, 3, 3, 1), 429), Sample(False, (3, 3, 2, 3, 3, 2), 430), Sample(False, (3, 3, 2, 3, 4, 1), 431), Sample(False, (3, 3, 2, 3, 4, 2), 432)) monk3 = ( Sample(True, (1, 1, 1, 1, 1, 2), 2), Sample(True, (1, 1, 1, 1, 2, 1), 3), Sample(True, (1, 1, 1, 1, 2, 2), 4), Sample(False, (1, 1, 1, 1, 3, 1), 5), Sample(False, (1, 1, 1, 1, 4, 1), 7), Sample(True, (1, 1, 1, 2, 1, 1), 9), Sample(True, (1, 1, 1, 2, 2, 2), 12), Sample(False, (1, 1, 1, 2, 4, 2), 16), Sample(True, (1, 1, 2, 1, 2, 2), 28), Sample(False, (1, 1, 2, 1, 4, 2), 32), Sample(True, (1, 1, 2, 2, 2, 2), 36), Sample(False, (1, 1, 2, 2, 4, 1), 39), Sample(False, (1, 1, 2, 2, 4, 2), 40), Sample(True, (1, 1, 2, 3, 1, 1), 41), Sample(True, (1, 1, 2, 3, 1, 2), 42), Sample(True, (1, 1, 2, 3, 3, 1), 45), Sample(True, (1, 1, 2, 3, 3, 2), 46), Sample(True, (1, 2, 1, 1, 3, 1), 53), Sample(True, (1, 2, 1, 2, 2, 1), 59), Sample(True, (1, 2, 1, 2, 2, 2), 60), Sample(False, (1, 2, 1, 2, 3, 1), 61), Sample(True, (1, 2, 1, 3, 1, 1), 65), Sample(True, (1, 2, 1, 3, 1, 2), 66), Sample(True, (1, 2, 1, 3, 2, 1), 67), Sample(True, (1, 2, 1, 3, 2, 2), 68), Sample(True, (1, 2, 1, 3, 3, 2), 70), Sample(False, (1, 2, 1, 3, 4, 1), 71), Sample(True, (1, 2, 2, 1, 3, 1), 77), Sample(False, (1, 2, 2, 1, 4, 2), 80), Sample(True, (1, 2, 2, 2, 1, 1), 81), Sample(True, (1, 2, 2, 2, 2, 1), 83), Sample(True, (1, 2, 2, 2, 2, 2), 84), Sample(True, (1, 2, 2, 3, 1, 1), 89), Sample(True, (1, 2, 2, 3, 2, 1), 91), Sample(True, (1, 2, 2, 3, 2, 2), 92), Sample(False, (1, 3, 1, 1, 2, 1), 99), Sample(False, (1, 3, 1, 1, 4, 1), 103), Sample(False, (1, 3, 1, 2, 3, 2), 110), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(False, (1, 3, 1, 3, 1, 1), 113), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(False, (1, 3, 2, 1, 1, 1), 121), Sample(False, (1, 3, 2, 1, 1, 2), 122), Sample(False, (1, 3, 2, 1, 2, 1), 123), Sample(False, (1, 3, 2, 1, 4, 2), 128), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(False, (1, 3, 2, 3, 4, 1), 143), Sample(True, (2, 1, 1, 1, 1, 1), 145), Sample(True, (2, 1, 1, 1, 1, 2), 146), Sample(False, (2, 1, 1, 1, 4, 1), 151), Sample(False, (2, 1, 1, 1, 4, 2), 152), Sample(True, (2, 1, 1, 2, 1, 1), 153), Sample(True, (2, 1, 1, 2, 1, 2), 154), Sample(True, (2, 1, 1, 3, 2, 2), 164), Sample(True, (2, 1, 1, 3, 3, 2), 166), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(True, (2, 1, 2, 1, 2, 2), 172), Sample(False, (2, 1, 2, 2, 4, 1), 183), Sample(True, (2, 1, 2, 3, 1, 2), 186), Sample(True, (2, 2, 1, 1, 3, 2), 198), Sample(False, (2, 2, 1, 1, 4, 2), 200), Sample(True, (2, 2, 1, 2, 1, 2), 202), Sample(False, (2, 2, 1, 2, 2, 1), 203), Sample(True, (2, 2, 1, 3, 1, 1), 209), Sample(True, (2, 2, 1, 3, 2, 2), 212), Sample(False, (2, 2, 1, 3, 3, 1), 213), Sample(False, (2, 2, 1, 3, 3, 2), 214), Sample(False, (2, 2, 1, 3, 4, 2), 216), Sample(True, (2, 2, 2, 1, 2, 2), 220), Sample(True, (2, 2, 2, 2, 1, 2), 226), Sample(True, (2, 2, 2, 2, 3, 1), 229), Sample(True, (2, 2, 2, 2, 3, 2), 230), Sample(False, (2, 2, 2, 3, 4, 1), 239), Sample(True, (2, 3, 1, 1, 3, 1), 245), Sample(False, (2, 3, 1, 2, 1, 1), 249), Sample(False, (2, 3, 1, 2, 2, 1), 251), Sample(False, (2, 3, 1, 2, 2, 2), 252), Sample(False, (2, 3, 1, 2, 3, 2), 254), Sample(False, (2, 3, 1, 3, 3, 1), 261), Sample(False, (2, 3, 2, 1, 1, 2), 266), Sample(False, (2, 3, 2, 1, 2, 2), 268), Sample(False, (2, 3, 2, 1, 4, 1), 271), Sample(False, (2, 3, 2, 2, 3, 1), 277), Sample(False, (2, 3, 2, 2, 4, 2), 280), Sample(False, (2, 3, 2, 3, 1, 1), 281), Sample(False, (2, 3, 2, 3, 2, 1), 283), Sample(False, (2, 3, 2, 3, 4, 2), 288), Sample(True, (3, 1, 1, 1, 1, 1), 289), Sample(True, (3, 1, 1, 1, 2, 1), 291), Sample(True, (3, 1, 1, 1, 3, 1), 293), Sample(False, (3, 1, 1, 2, 4, 2), 304), Sample(True, (3, 1, 1, 3, 1, 2), 306), Sample(False, (3, 1, 1, 3, 4, 2), 312), Sample(True, (3, 1, 2, 1, 2, 1), 315), Sample(True, (3, 1, 2, 2, 3, 2), 326), Sample(False, (3, 1, 2, 2, 4, 2), 328), Sample(True, (3, 1, 2, 3, 1, 1), 329), Sample(True, (3, 2, 1, 1, 2, 2), 340), Sample(False, (3, 2, 1, 1, 4, 1), 343), Sample(True, (3, 2, 1, 2, 3, 1), 349), Sample(True, (3, 2, 1, 3, 1, 2), 354), Sample(True, (3, 2, 2, 1, 2, 2), 364), Sample(True, (3, 2, 2, 1, 3, 2), 366), Sample(True, (3, 2, 2, 2, 1, 2), 370), Sample(True, (3, 2, 2, 3, 1, 1), 377), Sample(True, (3, 2, 2, 3, 3, 2), 382), Sample(False, (3, 2, 2, 3, 4, 1), 383), Sample(True, (3, 3, 1, 1, 3, 2), 390), Sample(True, (3, 3, 1, 1, 4, 1), 391), Sample(False, (3, 3, 1, 2, 4, 2), 400), Sample(False, (3, 3, 1, 3, 1, 1), 401), Sample(False, (3, 3, 1, 3, 2, 1), 403), Sample(False, (3, 3, 1, 3, 2, 2), 404), Sample(False, (3, 3, 1, 3, 4, 1), 407), Sample(False, (3, 3, 2, 1, 1, 1), 409), Sample(False, (3, 3, 2, 1, 1, 2), 410), Sample(False, (3, 3, 2, 2, 2, 2), 420), Sample(False, (3, 3, 2, 2, 3, 2), 422), Sample(False, (3, 3, 2, 3, 1, 1), 425), Sample(False, (3, 3, 2, 3, 3, 2), 430), Sample(False, (3, 3, 2, 3, 4, 2), 432)) monk3test = ( Sample(True, (1, 1, 1, 1, 1, 1), 1), Sample(True, (1, 1, 1, 1, 1, 2), 2), Sample(True, (1, 1, 1, 1, 2, 1), 3), Sample(True, (1, 1, 1, 1, 2, 2), 4), Sample(True, (1, 1, 1, 1, 3, 1), 5), Sample(True, (1, 1, 1, 1, 3, 2), 6), Sample(False, (1, 1, 1, 1, 4, 1), 7), Sample(False, (1, 1, 1, 1, 4, 2), 8), Sample(True, (1, 1, 1, 2, 1, 1), 9), Sample(True, (1, 1, 1, 2, 1, 2), 10), Sample(True, (1, 1, 1, 2, 2, 1), 11), Sample(True, (1, 1, 1, 2, 2, 2), 12), Sample(True, (1, 1, 1, 2, 3, 1), 13), Sample(True, (1, 1, 1, 2, 3, 2), 14), Sample(False, (1, 1, 1, 2, 4, 1), 15), Sample(False, (1, 1, 1, 2, 4, 2), 16), Sample(True, (1, 1, 1, 3, 1, 1), 17), Sample(True, (1, 1, 1, 3, 1, 2), 18), Sample(True, (1, 1, 1, 3, 2, 1), 19), Sample(True, (1, 1, 1, 3, 2, 2), 20), Sample(True, (1, 1, 1, 3, 3, 1), 21), Sample(True, (1, 1, 1, 3, 3, 2), 22), Sample(False, (1, 1, 1, 3, 4, 1), 23), Sample(False, (1, 1, 1, 3, 4, 2), 24), Sample(True, (1, 1, 2, 1, 1, 1), 25), Sample(True, (1, 1, 2, 1, 1, 2), 26), Sample(True, (1, 1, 2, 1, 2, 1), 27), Sample(True, (1, 1, 2, 1, 2, 2), 28), Sample(True, (1, 1, 2, 1, 3, 1), 29), Sample(True, (1, 1, 2, 1, 3, 2), 30), Sample(False, (1, 1, 2, 1, 4, 1), 31), Sample(False, (1, 1, 2, 1, 4, 2), 32), Sample(True, (1, 1, 2, 2, 1, 1), 33), Sample(True, (1, 1, 2, 2, 1, 2), 34), Sample(True, (1, 1, 2, 2, 2, 1), 35), Sample(True, (1, 1, 2, 2, 2, 2), 36), Sample(True, (1, 1, 2, 2, 3, 1), 37), Sample(True, (1, 1, 2, 2, 3, 2), 38), Sample(False, (1, 1, 2, 2, 4, 1), 39), Sample(False, (1, 1, 2, 2, 4, 2), 40), Sample(True, (1, 1, 2, 3, 1, 1), 41), Sample(True, (1, 1, 2, 3, 1, 2), 42), Sample(True, (1, 1, 2, 3, 2, 1), 43), Sample(True, (1, 1, 2, 3, 2, 2), 44), Sample(True, (1, 1, 2, 3, 3, 1), 45), Sample(True, (1, 1, 2, 3, 3, 2), 46), Sample(False, (1, 1, 2, 3, 4, 1), 47), Sample(False, (1, 1, 2, 3, 4, 2), 48), Sample(True, (1, 2, 1, 1, 1, 1), 49), Sample(True, (1, 2, 1, 1, 1, 2), 50), Sample(True, (1, 2, 1, 1, 2, 1), 51), Sample(True, (1, 2, 1, 1, 2, 2), 52), Sample(True, (1, 2, 1, 1, 3, 1), 53), Sample(True, (1, 2, 1, 1, 3, 2), 54), Sample(False, (1, 2, 1, 1, 4, 1), 55), Sample(False, (1, 2, 1, 1, 4, 2), 56), Sample(True, (1, 2, 1, 2, 1, 1), 57), Sample(True, (1, 2, 1, 2, 1, 2), 58), Sample(True, (1, 2, 1, 2, 2, 1), 59), Sample(True, (1, 2, 1, 2, 2, 2), 60), Sample(True, (1, 2, 1, 2, 3, 1), 61), Sample(True, (1, 2, 1, 2, 3, 2), 62), Sample(False, (1, 2, 1, 2, 4, 1), 63), Sample(False, (1, 2, 1, 2, 4, 2), 64), Sample(True, (1, 2, 1, 3, 1, 1), 65), Sample(True, (1, 2, 1, 3, 1, 2), 66), Sample(True, (1, 2, 1, 3, 2, 1), 67), Sample(True, (1, 2, 1, 3, 2, 2), 68), Sample(True, (1, 2, 1, 3, 3, 1), 69), Sample(True, (1, 2, 1, 3, 3, 2), 70), Sample(False, (1, 2, 1, 3, 4, 1), 71), Sample(False, (1, 2, 1, 3, 4, 2), 72), Sample(True, (1, 2, 2, 1, 1, 1), 73), Sample(True, (1, 2, 2, 1, 1, 2), 74), Sample(True, (1, 2, 2, 1, 2, 1), 75), Sample(True, (1, 2, 2, 1, 2, 2), 76), Sample(True, (1, 2, 2, 1, 3, 1), 77), Sample(True, (1, 2, 2, 1, 3, 2), 78), Sample(False, (1, 2, 2, 1, 4, 1), 79), Sample(False, (1, 2, 2, 1, 4, 2), 80), Sample(True, (1, 2, 2, 2, 1, 1), 81), Sample(True, (1, 2, 2, 2, 1, 2), 82), Sample(True, (1, 2, 2, 2, 2, 1), 83), Sample(True, (1, 2, 2, 2, 2, 2), 84), Sample(True, (1, 2, 2, 2, 3, 1), 85), Sample(True, (1, 2, 2, 2, 3, 2), 86), Sample(False, (1, 2, 2, 2, 4, 1), 87), Sample(False, (1, 2, 2, 2, 4, 2), 88), Sample(True, (1, 2, 2, 3, 1, 1), 89), Sample(True, (1, 2, 2, 3, 1, 2), 90), Sample(True, (1, 2, 2, 3, 2, 1), 91), Sample(True, (1, 2, 2, 3, 2, 2), 92), Sample(True, (1, 2, 2, 3, 3, 1), 93), Sample(True, (1, 2, 2, 3, 3, 2), 94), Sample(False, (1, 2, 2, 3, 4, 1), 95), Sample(False, (1, 2, 2, 3, 4, 2), 96), Sample(False, (1, 3, 1, 1, 1, 1), 97), Sample(False, (1, 3, 1, 1, 1, 2), 98), Sample(False, (1, 3, 1, 1, 2, 1), 99), Sample(False, (1, 3, 1, 1, 2, 2), 100), Sample(True, (1, 3, 1, 1, 3, 1), 101), Sample(True, (1, 3, 1, 1, 3, 2), 102), Sample(False, (1, 3, 1, 1, 4, 1), 103), Sample(False, (1, 3, 1, 1, 4, 2), 104), Sample(False, (1, 3, 1, 2, 1, 1), 105), Sample(False, (1, 3, 1, 2, 1, 2), 106), Sample(False, (1, 3, 1, 2, 2, 1), 107), Sample(False, (1, 3, 1, 2, 2, 2), 108), Sample(False, (1, 3, 1, 2, 3, 1), 109), Sample(False, (1, 3, 1, 2, 3, 2), 110), Sample(False, (1, 3, 1, 2, 4, 1), 111), Sample(False, (1, 3, 1, 2, 4, 2), 112), Sample(False, (1, 3, 1, 3, 1, 1), 113), Sample(False, (1, 3, 1, 3, 1, 2), 114), Sample(False, (1, 3, 1, 3, 2, 1), 115), Sample(False, (1, 3, 1, 3, 2, 2), 116), Sample(False, (1, 3, 1, 3, 3, 1), 117), Sample(False, (1, 3, 1, 3, 3, 2), 118), Sample(False, (1, 3, 1, 3, 4, 1), 119), Sample(False, (1, 3, 1, 3, 4, 2), 120), Sample(False, (1, 3, 2, 1, 1, 1), 121), Sample(False, (1, 3, 2, 1, 1, 2), 122), Sample(False, (1, 3, 2, 1, 2, 1), 123), Sample(False, (1, 3, 2, 1, 2, 2), 124), Sample(True, (1, 3, 2, 1, 3, 1), 125), Sample(True, (1, 3, 2, 1, 3, 2), 126), Sample(False, (1, 3, 2, 1, 4, 1), 127), Sample(False, (1, 3, 2, 1, 4, 2), 128), Sample(False, (1, 3, 2, 2, 1, 1), 129), Sample(False, (1, 3, 2, 2, 1, 2), 130), Sample(False, (1, 3, 2, 2, 2, 1), 131), Sample(False, (1, 3, 2, 2, 2, 2), 132), Sample(False, (1, 3, 2, 2, 3, 1), 133), Sample(False, (1, 3, 2, 2, 3, 2), 134), Sample(False, (1, 3, 2, 2, 4, 1), 135), Sample(False, (1, 3, 2, 2, 4, 2), 136), Sample(False, (1, 3, 2, 3, 1, 1), 137), Sample(False, (1, 3, 2, 3, 1, 2), 138), Sample(False, (1, 3, 2, 3, 2, 1), 139), Sample(False, (1, 3, 2, 3, 2, 2), 140), Sample(False, (1, 3, 2, 3, 3, 1), 141), Sample(False, (1, 3, 2, 3, 3, 2), 142), Sample(False, (1, 3, 2, 3, 4, 1), 143), Sample(False, (1, 3, 2, 3, 4, 2), 144), Sample(True, (2, 1, 1, 1, 1, 1), 145), Sample(True, (2, 1, 1, 1, 1, 2), 146), Sample(True, (2, 1, 1, 1, 2, 1), 147), Sample(True, (2, 1, 1, 1, 2, 2), 148), Sample(True, (2, 1, 1, 1, 3, 1), 149), Sample(True, (2, 1, 1, 1, 3, 2), 150), Sample(False, (2, 1, 1, 1, 4, 1), 151), Sample(False, (2, 1, 1, 1, 4, 2), 152), Sample(True, (2, 1, 1, 2, 1, 1), 153), Sample(True, (2, 1, 1, 2, 1, 2), 154), Sample(True, (2, 1, 1, 2, 2, 1), 155), Sample(True, (2, 1, 1, 2, 2, 2), 156), Sample(True, (2, 1, 1, 2, 3, 1), 157), Sample(True, (2, 1, 1, 2, 3, 2), 158), Sample(False, (2, 1, 1, 2, 4, 1), 159), Sample(False, (2, 1, 1, 2, 4, 2), 160), Sample(True, (2, 1, 1, 3, 1, 1), 161), Sample(True, (2, 1, 1, 3, 1, 2), 162), Sample(True, (2, 1, 1, 3, 2, 1), 163), Sample(True, (2, 1, 1, 3, 2, 2), 164), Sample(True, (2, 1, 1, 3, 3, 1), 165), Sample(True, (2, 1, 1, 3, 3, 2), 166), Sample(False, (2, 1, 1, 3, 4, 1), 167), Sample(False, (2, 1, 1, 3, 4, 2), 168), Sample(True, (2, 1, 2, 1, 1, 1), 169), Sample(True, (2, 1, 2, 1, 1, 2), 170), Sample(True, (2, 1, 2, 1, 2, 1), 171), Sample(True, (2, 1, 2, 1, 2, 2), 172), Sample(True, (2, 1, 2, 1, 3, 1), 173), Sample(True, (2, 1, 2, 1, 3, 2), 174), Sample(False, (2, 1, 2, 1, 4, 1), 175), Sample(False, (2, 1, 2, 1, 4, 2), 176), Sample(True, (2, 1, 2, 2, 1, 1), 177), Sample(True, (2, 1, 2, 2, 1, 2), 178), Sample(True, (2, 1, 2, 2, 2, 1), 179), Sample(True, (2, 1, 2, 2, 2, 2), 180), Sample(True, (2, 1, 2, 2, 3, 1), 181), Sample(True, (2, 1, 2, 2, 3, 2), 182), Sample(False, (2, 1, 2, 2, 4, 1), 183), Sample(False, (2, 1, 2, 2, 4, 2), 184), Sample(True, (2, 1, 2, 3, 1, 1), 185), Sample(True, (2, 1, 2, 3, 1, 2), 186), Sample(True, (2, 1, 2, 3, 2, 1), 187), Sample(True, (2, 1, 2, 3, 2, 2), 188), Sample(True, (2, 1, 2, 3, 3, 1), 189), Sample(True, (2, 1, 2, 3, 3, 2), 190), Sample(False, (2, 1, 2, 3, 4, 1), 191), Sample(False, (2, 1, 2, 3, 4, 2), 192), Sample(True, (2, 2, 1, 1, 1, 1), 193), Sample(True, (2, 2, 1, 1, 1, 2), 194), Sample(True, (2, 2, 1, 1, 2, 1), 195), Sample(True, (2, 2, 1, 1, 2, 2), 196), Sample(True, (2, 2, 1, 1, 3, 1), 197), Sample(True, (2, 2, 1, 1, 3, 2), 198), Sample(False, (2, 2, 1, 1, 4, 1), 199), Sample(False, (2, 2, 1, 1, 4, 2), 200), Sample(True, (2, 2, 1, 2, 1, 1), 201), Sample(True, (2, 2, 1, 2, 1, 2), 202), Sample(True, (2, 2, 1, 2, 2, 1), 203), Sample(True, (2, 2, 1, 2, 2, 2), 204), Sample(True, (2, 2, 1, 2, 3, 1), 205), Sample(True, (2, 2, 1, 2, 3, 2), 206), Sample(False, (2, 2, 1, 2, 4, 1), 207), Sample(False, (2, 2, 1, 2, 4, 2), 208), Sample(True, (2, 2, 1, 3, 1, 1), 209), Sample(True, (2, 2, 1, 3, 1, 2), 210), Sample(True, (2, 2, 1, 3, 2, 1), 211), Sample(True, (2, 2, 1, 3, 2, 2), 212), Sample(True, (2, 2, 1, 3, 3, 1), 213), Sample(True, (2, 2, 1, 3, 3, 2), 214), Sample(False, (2, 2, 1, 3, 4, 1), 215), Sample(False, (2, 2, 1, 3, 4, 2), 216), Sample(True, (2, 2, 2, 1, 1, 1), 217), Sample(True, (2, 2, 2, 1, 1, 2), 218), Sample(True, (2, 2, 2, 1, 2, 1), 219), Sample(True, (2, 2, 2, 1, 2, 2), 220), Sample(True, (2, 2, 2, 1, 3, 1), 221), Sample(True, (2, 2, 2, 1, 3, 2), 222), Sample(False, (2, 2, 2, 1, 4, 1), 223), Sample(False, (2, 2, 2, 1, 4, 2), 224), Sample(True, (2, 2, 2, 2, 1, 1), 225), Sample(True, (2, 2, 2, 2, 1, 2), 226), Sample(True, (2, 2, 2, 2, 2, 1), 227), Sample(True, (2, 2, 2, 2, 2, 2), 228), Sample(True, (2, 2, 2, 2, 3, 1), 229), Sample(True, (2, 2, 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